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Du Y, Niu J, Xing Y, Li B, Calhoun VD. Neuroimage Analysis Methods and Artificial Intelligence Techniques for Reliable Biomarkers and Accurate Diagnosis of Schizophrenia: Achievements Made by Chinese Scholars Around the Past Decade. Schizophr Bull 2024:sbae110. [PMID: 38982882 DOI: 10.1093/schbul/sbae110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/11/2024]
Abstract
BACKGROUND AND HYPOTHESIS Schizophrenia (SZ) is characterized by significant cognitive and behavioral disruptions. Neuroimaging techniques, particularly magnetic resonance imaging (MRI), have been widely utilized to investigate biomarkers of SZ, distinguish SZ from healthy conditions or other mental disorders, and explore biotypes within SZ or across SZ and other mental disorders, which aim to promote the accurate diagnosis of SZ. In China, research on SZ using MRI has grown considerably in recent years. STUDY DESIGN The article reviews advanced neuroimaging and artificial intelligence (AI) methods using single-modal or multimodal MRI to reveal the mechanism of SZ and promote accurate diagnosis of SZ, with a particular emphasis on the achievements made by Chinese scholars around the past decade. STUDY RESULTS Our article focuses on the methods for capturing subtle brain functional and structural properties from the high-dimensional MRI data, the multimodal fusion and feature selection methods for obtaining important and sparse neuroimaging features, the supervised statistical analysis and classification for distinguishing disorders, and the unsupervised clustering and semi-supervised learning methods for identifying neuroimage-based biotypes. Crucially, our article highlights the characteristics of each method and underscores the interconnections among various approaches regarding biomarker extraction and neuroimage-based diagnosis, which is beneficial not only for comprehending SZ but also for exploring other mental disorders. CONCLUSIONS We offer a valuable review of advanced neuroimage analysis and AI methods primarily focused on SZ research by Chinese scholars, aiming to promote the diagnosis, treatment, and prevention of SZ, as well as other mental disorders, both within China and internationally.
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Affiliation(s)
- Yuhui Du
- School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China
| | - Ju Niu
- School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China
| | - Ying Xing
- School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China
| | - Bang Li
- School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China
| | - Vince D Calhoun
- The Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, 30303, GA, USA
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2
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Colombo F, Calesella F, Bravi B, Fortaner-Uyà L, Monopoli C, Tassi E, Carminati M, Zanardi R, Bollettini I, Poletti S, Lorenzi C, Spadini S, Brambilla P, Serretti A, Maggioni E, Fabbri C, Benedetti F, Vai B. Multimodal brain-derived subtypes of Major depressive disorder differentiate patients for anergic symptoms, immune-inflammatory markers, history of childhood trauma and treatment-resistance. Eur Neuropsychopharmacol 2024; 85:45-57. [PMID: 38936143 DOI: 10.1016/j.euroneuro.2024.05.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 05/20/2024] [Accepted: 05/27/2024] [Indexed: 06/29/2024]
Abstract
An estimated 30 % of Major Depressive Disorder (MDD) patients exhibit resistance to conventional antidepressant treatments. Identifying reliable biomarkers of treatment-resistant depression (TRD) represents a major goal of precision psychiatry, which is hampered by the clinical and biological heterogeneity. To uncover biologically-driven subtypes of MDD, we applied an unsupervised data-driven framework to stratify 102 MDD patients on their neuroimaging signature, including extracted measures of cortical thickness, grey matter volumes, and white matter fractional anisotropy. Our novel analytical pipeline integrated different machine learning algorithms to harmonize data, perform data dimensionality reduction, and provide a stability-based relative clustering validation. The obtained clusters were characterized for immune-inflammatory peripheral biomarkers, TRD, history of childhood trauma and depressive symptoms. Our results indicated two different clusters of patients, differentiable with 67 % of accuracy: one cluster (n = 59) was associated with a higher proportion of TRD, and higher scores of energy-related depressive symptoms, history of childhood abuse and emotional neglect; this cluster showed a widespread reduction in cortical thickness (d = 0.43-1.80) and volumes (d = 0.45-1.05), along with fractional anisotropy in the fronto-occipital fasciculus, stria terminalis, and corpus callosum (d = 0.46-0.52); the second cluster (n = 43) was associated with cognitive and affective depressive symptoms, thicker cortices and wider volumes. Multivariate analyses revealed distinct brain-inflammation relationships between the two clusters, with increase in pro-inflammatory markers being associated with decreased cortical thickness and volumes. Our stratification of MDD patients based on structural neuroimaging identified clinically-relevant subgroups of MDD with specific symptomatic and immune-inflammatory profiles, which can contribute to the development of tailored personalized interventions for MDD.
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Affiliation(s)
- Federica Colombo
- University Vita-Salute San Raffaele, Milano, Italy; Psychiatry and Clinical Psychobiology Unit, Division of Neuroscience, IRCCS San Raffaele Hospital, Milano, Italy.
| | - Federico Calesella
- Psychiatry and Clinical Psychobiology Unit, Division of Neuroscience, IRCCS San Raffaele Hospital, Milano, Italy
| | - Beatrice Bravi
- University Vita-Salute San Raffaele, Milano, Italy; Psychiatry and Clinical Psychobiology Unit, Division of Neuroscience, IRCCS San Raffaele Hospital, Milano, Italy
| | - Lidia Fortaner-Uyà
- University Vita-Salute San Raffaele, Milano, Italy; Psychiatry and Clinical Psychobiology Unit, Division of Neuroscience, IRCCS San Raffaele Hospital, Milano, Italy
| | - Camilla Monopoli
- Psychiatry and Clinical Psychobiology Unit, Division of Neuroscience, IRCCS San Raffaele Hospital, Milano, Italy
| | - Emma Tassi
- Department of Neurosciences and Mental Health, IRCCS Fondazione Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy; Politecnico di Milano, Department of Electronics, Information and Bioengineering, Milan, Italy
| | | | - Raffaella Zanardi
- University Vita-Salute San Raffaele, Milano, Italy; Mood Disorders Unit, Scientific Institute IRCCS San Raffaele Hospital, Milan, Italy
| | - Irene Bollettini
- Psychiatry and Clinical Psychobiology Unit, Division of Neuroscience, IRCCS San Raffaele Hospital, Milano, Italy
| | - Sara Poletti
- University Vita-Salute San Raffaele, Milano, Italy; Psychiatry and Clinical Psychobiology Unit, Division of Neuroscience, IRCCS San Raffaele Hospital, Milano, Italy
| | - Cristina Lorenzi
- Psychiatry and Clinical Psychobiology Unit, Division of Neuroscience, IRCCS San Raffaele Hospital, Milano, Italy
| | - Sara Spadini
- Psychiatry and Clinical Psychobiology Unit, Division of Neuroscience, IRCCS San Raffaele Hospital, Milano, Italy
| | - Paolo Brambilla
- Department of Neurosciences and Mental Health, IRCCS Fondazione Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | | | - Eleonora Maggioni
- Politecnico di Milano, Department of Electronics, Information and Bioengineering, Milan, Italy
| | - Chiara Fabbri
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Francesco Benedetti
- University Vita-Salute San Raffaele, Milano, Italy; Psychiatry and Clinical Psychobiology Unit, Division of Neuroscience, IRCCS San Raffaele Hospital, Milano, Italy
| | - Benedetta Vai
- University Vita-Salute San Raffaele, Milano, Italy; Psychiatry and Clinical Psychobiology Unit, Division of Neuroscience, IRCCS San Raffaele Hospital, Milano, Italy
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3
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Tozzi L, Zhang X, Pines A, Olmsted AM, Zhai ES, Anene ET, Chesnut M, Holt-Gosselin B, Chang S, Stetz PC, Ramirez CA, Hack LM, Korgaonkar MS, Wintermark M, Gotlib IH, Ma J, Williams LM. Personalized brain circuit scores identify clinically distinct biotypes in depression and anxiety. Nat Med 2024:10.1038/s41591-024-03057-9. [PMID: 38886626 DOI: 10.1038/s41591-024-03057-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 05/09/2024] [Indexed: 06/20/2024]
Abstract
There is an urgent need to derive quantitative measures based on coherent neurobiological dysfunctions or 'biotypes' to enable stratification of patients with depression and anxiety. We used task-free and task-evoked data from a standardized functional magnetic resonance imaging protocol conducted across multiple studies in patients with depression and anxiety when treatment free (n = 801) and after randomization to pharmacotherapy or behavioral therapy (n = 250). From these patients, we derived personalized and interpretable scores of brain circuit dysfunction grounded in a theoretical taxonomy. Participants were subdivided into six biotypes defined by distinct profiles of intrinsic task-free functional connectivity within the default mode, salience and frontoparietal attention circuits, and of activation and connectivity within frontal and subcortical regions elicited by emotional and cognitive tasks. The six biotypes showed consistency with our theoretical taxonomy and were distinguished by symptoms, behavioral performance on general and emotional cognitive computerized tests, and response to pharmacotherapy as well as behavioral therapy. Our results provide a new, theory-driven, clinically validated and interpretable quantitative method to parse the biological heterogeneity of depression and anxiety. Thus, they represent a promising approach to advance precision clinical care in psychiatry.
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Affiliation(s)
- Leonardo Tozzi
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Xue Zhang
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Adam Pines
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Alisa M Olmsted
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Sierra-Pacific Mental Illness Research, Education and Clinical Center, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - Emily S Zhai
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Esther T Anene
- Department of Counseling and Clinical Psychology, Teacher's College, Columbia University, New York, NY, USA
| | - Megan Chesnut
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Bailey Holt-Gosselin
- Interdepartmental Neuroscience Graduate Program, Yale University School of Medicine, New Haven, CT, USA
| | - Sarah Chang
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Patrick C Stetz
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Sierra-Pacific Mental Illness Research, Education and Clinical Center, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - Carolina A Ramirez
- Center for Intelligent Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Laura M Hack
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Sierra-Pacific Mental Illness Research, Education and Clinical Center, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - Mayuresh S Korgaonkar
- Brain Dynamics Centre, Westmead Institute for Medical Research, University of Sydney, Westmead, New South Wales, Australia
- Department of Radiology, Westmead Hospital, Western Sydney Local Health District, Westmead, New South Wales, Australia
| | - Max Wintermark
- Department of Neuroradiology, the University of Texas MD Anderson Center, Houston, TX, USA
| | - Ian H Gotlib
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Jun Ma
- Department of Medicine, College of Medicine, University of Illinois Chicago, Chicago, IL, USA
| | - Leanne M Williams
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA.
- Sierra-Pacific Mental Illness Research, Education and Clinical Center, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA.
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4
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Yang C, Zhou Z, Bao W, Zhong R, Tang M, Wang Y, Gao Y, Hu X, Zhang L, Qiu L, Kuang W, Huang X, Gong Q. Sex differences in aberrant functional connectivity of three core networks and subcortical networks in medication-free adolescent-onset major depressive disorder. Cereb Cortex 2024; 34:bhae225. [PMID: 38836288 DOI: 10.1093/cercor/bhae225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Revised: 05/02/2024] [Accepted: 05/04/2024] [Indexed: 06/06/2024] Open
Abstract
Major depressive disorder demonstrated sex differences in prevalence and symptoms, which were more pronounced during adolescence. Yet, research on sex-specific brain network characteristics in adolescent-onset major depressive disorder remains limited. This study investigated sex-specific and nonspecific alterations in resting-state functional connectivity of three core networks (frontoparietal network, salience network, and default mode network) and subcortical networks in adolescent-onset major depressive disorder, using seed-based resting-state functional connectivity in 50 medication-free patients with adolescent-onset major depressive disorder and 56 healthy controls. Irrespective of sex, compared with healthy controls, adolescent-onset major depressive disorder patients showed hypoconnectivity between bilateral hippocampus and right superior temporal gyrus (default mode network). More importantly, we further found that females with adolescent-onset major depressive disorder exhibited hypoconnectivity within the default mode network (medial prefrontal cortex), and between the subcortical regions (i.e. amygdala, striatum, and thalamus) with the default mode network (angular gyrus and posterior cingulate cortex) and the frontoparietal network (dorsal prefrontal cortex), while the opposite patterns of resting-state functional connectivity alterations were observed in males with adolescent-onset major depressive disorder, relative to their sex-matched healthy controls. Moreover, several sex-specific resting-state functional connectivity changes were correlated with age of onset, sleep disturbance, and anxiety in adolescent-onset major depressive disorder with different sex. These findings suggested that these sex-specific resting-state functional connectivity alterations may reflect the differences in brain development or processes related to early illness onset, underscoring the necessity for sex-tailored diagnostic and therapeutic approaches in adolescent-onset major depressive disorder.
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Affiliation(s)
- Chunyu Yang
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, China
- Department of Radiology, The Second People's Hospital of Yibin, Yibin, 644000, China
| | - Zilin Zhou
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Weijie Bao
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Ruihan Zhong
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Mengyue Tang
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Yidan Wang
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Yingxue Gao
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Xinyue Hu
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Lianqing Zhang
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Lihua Qiu
- Department of Radiology, The Second People's Hospital of Yibin, Yibin, 644000, China
| | - Weihong Kuang
- Department of Psychiatry, West China Hospital of Sichuan University, Chengdu, 610041, China
| | - Xiaoqi Huang
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, China
- The Xiamen Key Lab of Psychoradiology and Neuromodulation, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, 361022, China
| | - Qiyong Gong
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, China
- The Xiamen Key Lab of Psychoradiology and Neuromodulation, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, 361022, China
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5
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Wen J, Antoniades M, Yang Z, Hwang G, Skampardoni I, Wang R, Davatzikos C. Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning. Biol Psychiatry 2024:S0006-3223(24)01286-1. [PMID: 38718880 DOI: 10.1016/j.biopsych.2024.04.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 03/29/2024] [Accepted: 04/22/2024] [Indexed: 05/21/2024]
Abstract
Machine learning has been increasingly used to obtain individualized neuroimaging signatures for disease diagnosis, prognosis, and response to treatment in neuropsychiatric and neurodegenerative disorders. Therefore, it has contributed to a better understanding of disease heterogeneity by identifying disease subtypes with different brain phenotypic measures. In this review, we first present a systematic literature overview of studies using machine learning and multimodal magnetic resonance imaging to unravel disease heterogeneity in various neuropsychiatric and neurodegenerative disorders, including Alzheimer's disease, schizophrenia, major depressive disorder, autism spectrum disorder, and multiple sclerosis, as well as their potential in a transdiagnostic framework, where neuroanatomical and neurobiological commonalities were assessed across diagnostic boundaries. Subsequently, we summarize relevant machine learning methodologies and their clinical interpretability. We discuss the potential clinical implications of the current findings and envision future research avenues. Finally, we discuss an emerging paradigm called dimensional neuroimaging endophenotypes. Dimensional neuroimaging endophenotypes dissects the neurobiological heterogeneity of neuropsychiatric and neurodegenerative disorders into low-dimensional yet informative, quantitative brain phenotypic representations, serving as robust intermediate phenotypes (i.e., endophenotypes), presumably reflecting the interplay of underlying genetic, lifestyle, and environmental processes associated with disease etiology.
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Affiliation(s)
- Junhao Wen
- Laboratory of AI and Biomedical Science, University of Southern California, Los Angeles, California.
| | - Mathilde Antoniades
- Artificial Intelligence in Biomedical Imaging Laboratory, Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory, Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Gyujoon Hwang
- Psychiatry and Behavioral Medicine, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Ioanna Skampardoni
- Artificial Intelligence in Biomedical Imaging Laboratory, Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Rongguang Wang
- Artificial Intelligence in Biomedical Imaging Laboratory, Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory, Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
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6
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Fermin ASR, Sasaoka T, Maekawa T, Ono K, Chan HL, Yamawaki S. Insula-cortico-subcortical networks predict interoceptive awareness and stress resilience. Asian J Psychiatr 2024; 95:103991. [PMID: 38484483 DOI: 10.1016/j.ajp.2024.103991] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 02/25/2024] [Accepted: 02/28/2024] [Indexed: 05/13/2024]
Abstract
BACKGROUND Interoception, the neural sensing of visceral signals, and interoceptive awareness (IA), the conscious perception of interoception, are crucial for life survival functions and mental health. Resilience, the capacity to overcome adversity, has been associated with reduced interoceptive disturbances. Here, we sought evidence for our Insula Modular Active Control (IMAC) model that suggest that the insula, a brain region specialized in the processing of interoceptive information, realizes IA and contributes to resilience and mental health via cortico-subcortical connections. METHODS 64 healthy participants (32 females; ages 18-34 years) answered questionnaires that assess IA and resilience. Mental health was evaluated with the Beck Depression Inventory II that assesses depressive mood. Participants also underwent a 15 minute resting-state functional resonance imaging session. Pearson correlations and mediation analyses were used to investigate the relationship between IA and resilience and their contributions to depressive mood. We then performed insula seed-based functional connectivity analyzes to identify insula networks involved in IA, resilience and depressive mood. RESULTS We first demonstrated that resilience mediates the relationship between IA and depressive mood. Second, shared and distinct intra-insula, insula-cortical and insula-subcortical networks were associated with IA, resilience and also predicted the degree of experienced depressive mood. Third, while resilience was associated with stronger insula-precuneus, insula-cerebellum and insula-prefrontal networks, IA was linked with stronger intra-insula, insula-striatum and insula-motor networks. CONCLUSIONS Our findings help understand the roles of insula-cortico-subcortical networks in IA and resilience. These results also highlight the potential use of insula networks as biomarkers for depression prediction.
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Affiliation(s)
- Alan S R Fermin
- Center for Brain, Mind and Kansei Sciences Research, Hiroshima University, Hiroshima, Japan.
| | - Takafumi Sasaoka
- Center for Brain, Mind and Kansei Sciences Research, Hiroshima University, Hiroshima, Japan
| | - Toru Maekawa
- Center for Brain, Mind and Kansei Sciences Research, Hiroshima University, Hiroshima, Japan
| | - Kentaro Ono
- Center for Brain, Mind and Kansei Sciences Research, Hiroshima University, Hiroshima, Japan
| | - Hui-Ling Chan
- Center for Brain, Mind and Kansei Sciences Research, Hiroshima University, Hiroshima, Japan
| | - Shigeto Yamawaki
- Center for Brain, Mind and Kansei Sciences Research, Hiroshima University, Hiroshima, Japan
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7
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Chen Y, Chen Y, Zheng R, Xue K, Li S, Pang J, Li H, Zhang Y, Cheng J, Han S. Identifying two distinct neuroanatomical subtypes of first-episode depression using heterogeneity through discriminative analysis. J Affect Disord 2024; 349:479-485. [PMID: 38218252 DOI: 10.1016/j.jad.2024.01.091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 12/06/2023] [Accepted: 01/07/2024] [Indexed: 01/15/2024]
Abstract
BACKGROUND Neurobiological heterogeneity in depression remains largely unknown, leading to inconsistent neuroimaging findings. METHODS Here, we adopted a novel proposed machine learning method ground on gray matter volumes (GMVs) to investigate neuroanatomical subtypes of first-episode treatment-naïve depression. GMVs were obtained from high-resolution T1-weighted images of 195 patients with first-episode, treatment-naïve depression and 78 matched healthy controls (HCs). Then we explored distinct subtypes of depression by employing heterogeneity through discriminative analysis (HYDRA) with regional GMVs as features. RESULTS Two prominently divergent subtypes of first-episode depression were identified, exhibiting opposite structural alterations compared with HCs but no different demographic features. Subtype 1 presented widespread increased GMVs mainly located in frontal, parietal, temporal cortex and partially located in limbic system. Subtype 2 presented widespread decreased GMVs mainly located in thalamus, cerebellum, limbic system and partially located in frontal, parietal, temporal cortex. Subtype 2 had smaller TIV and longer illness duration than Subtype 1. And TIV in Subtype 1 was positively correlated with age of onset while not in Subtype 2, probably implying the different potential neuropathological mechanisms. LIMITATIONS Despite results obtained in this study were validated by employing another brain atlas, the conclusions were acquired from a single dataset. CONCLUSIONS This study revealed two distinguishing neuroanatomical subtypes of first-episode depression, which provides new insights into underlying biological mechanisms of the heterogeneity in depression and might be helpful for accurate clinical diagnosis and future treatment.
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Affiliation(s)
- Yuan Chen
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450000, China; Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, Henan 450000, China; Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, Henan 450000, China; Engineering Research Center of Brain Function Development and Application of Henan Province, Zhengzhou, Henan 450000, China; Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, Henan 450000, China; Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, Henan 450000, China
| | - Yi Chen
- Clinical Research Service Center, Henan Provincial People's Hospital, Zhengzhou University People's Hospital, Zhengzhou, Henan 450000, China
| | - Ruiping Zheng
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450000, China; Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, Henan 450000, China; Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, Henan 450000, China; Engineering Research Center of Brain Function Development and Application of Henan Province, Zhengzhou, Henan 450000, China; Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, Henan 450000, China; Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, Henan 450000, China
| | - Kangkang Xue
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450000, China; Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, Henan 450000, China; Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, Henan 450000, China; Engineering Research Center of Brain Function Development and Application of Henan Province, Zhengzhou, Henan 450000, China; Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, Henan 450000, China; Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, Henan 450000, China
| | - Shuying Li
- Department of Psychiatry, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450000, China
| | - Jianyue Pang
- Department of Psychiatry, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450000, China
| | - Hengfen Li
- Department of Psychiatry, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450000, China
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450000, China; Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, Henan 450000, China; Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, Henan 450000, China; Engineering Research Center of Brain Function Development and Application of Henan Province, Zhengzhou, Henan 450000, China; Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, Henan 450000, China; Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, Henan 450000, China.
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450000, China; Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, Henan 450000, China; Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, Henan 450000, China; Engineering Research Center of Brain Function Development and Application of Henan Province, Zhengzhou, Henan 450000, China; Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, Henan 450000, China; Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, Henan 450000, China.
| | - Shaoqiang Han
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450000, China; Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, Henan 450000, China; Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, Henan 450000, China; Engineering Research Center of Brain Function Development and Application of Henan Province, Zhengzhou, Henan 450000, China; Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, Henan 450000, China; Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, Henan 450000, China.
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8
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Dunlop K, Grosenick L, Downar J, Vila-Rodriguez F, Gunning FM, Daskalakis ZJ, Blumberger DM, Liston C. Dimensional and Categorical Solutions to Parsing Depression Heterogeneity in a Large Single-Site Sample. Biol Psychiatry 2024:S0006-3223(24)00055-6. [PMID: 38280408 DOI: 10.1016/j.biopsych.2024.01.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 12/21/2023] [Accepted: 01/13/2024] [Indexed: 01/29/2024]
Abstract
BACKGROUND Recent studies have reported significant advances in modeling the biological basis of heterogeneity in major depressive disorder, but investigators have also identified important technical challenges, including scanner-related artifacts, a propensity for multivariate models to overfit, and a need for larger samples with more extensive clinical phenotyping. The goals of the current study were to evaluate dimensional and categorical solutions to parsing heterogeneity in depression that are stable and generalizable in a large, single-site sample. METHODS We used regularized canonical correlation analysis to identify data-driven brain-behavior dimensions that explain individual differences in depression symptom domains in a large, single-site dataset comprising clinical assessments and resting-state functional magnetic resonance imaging data for 328 patients with major depressive disorder and 461 healthy control participants. We examined the stability of clinical loadings and model performance in held-out data. Finally, hierarchical clustering on these dimensions was used to identify categorical depression subtypes. RESULTS The optimal regularized canonical correlation analysis model yielded 3 robust and generalizable brain-behavior dimensions that explained individual differences in depressed mood and anxiety, anhedonia, and insomnia. Hierarchical clustering identified 4 depression subtypes, each with distinct clinical symptom profiles, abnormal resting-state functional connectivity patterns, and antidepressant responsiveness to repetitive transcranial magnetic stimulation. CONCLUSIONS Our results define dimensional and categorical solutions to parsing neurobiological heterogeneity in major depressive disorder that are stable, generalizable, and capable of predicting treatment outcomes, each with distinct advantages in different contexts. They also provide additional evidence that regularized canonical correlation analysis and hierarchical clustering are effective tools for investigating associations between functional connectivity and clinical symptoms.
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Affiliation(s)
- Katharine Dunlop
- Centre for Depression and Suicide Studies, St Michael's Hospital, Toronto, Ontario, Canada; Keenan Research Centre for Biomedical Science, St. Michael's Hospital, Toronto, Ontario, Canada; Department of Psychiatry and Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Logan Grosenick
- Department of Psychiatry, Weill Cornell Medicine, New York, New York
| | - Jonathan Downar
- Department of Psychiatry and Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Fidel Vila-Rodriguez
- Non-Invasive Neurostimulation Therapies Laboratory, Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
| | - Faith M Gunning
- Institute of Geriatric Psychiatry, Weill Cornell Medicine, White Plains, New York
| | - Zafiris J Daskalakis
- Department of Psychiatry, University of California San Diego, San Diego, California
| | - Daniel M Blumberger
- Department of Psychiatry and Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada; Department of Psychiatry, Weill Cornell Medicine, New York, New York; Temerty Centre for Therapeutic Brain Intervention and Campbell Family Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Conor Liston
- Department of Psychiatry, Weill Cornell Medicine, New York, New York; Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, New York.
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9
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Wen J, Antoniades M, Yang Z, Hwang G, Skampardoni I, Wang R, Davatzikos C. Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning. ARXIV 2024:arXiv:2401.09517v1. [PMID: 38313197 PMCID: PMC10836087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 02/06/2024]
Abstract
Machine learning has been increasingly used to obtain individualized neuroimaging signatures for disease diagnosis, prognosis, and response to treatment in neuropsychiatric and neurodegenerative disorders. Therefore, it has contributed to a better understanding of disease heterogeneity by identifying disease subtypes that present significant differences in various brain phenotypic measures. In this review, we first present a systematic literature overview of studies using machine learning and multimodal MRI to unravel disease heterogeneity in various neuropsychiatric and neurodegenerative disorders, including Alzheimer's disease, schizophrenia, major depressive disorder, autism spectrum disorder, multiple sclerosis, as well as their potential in transdiagnostic settings. Subsequently, we summarize relevant machine learning methodologies and discuss an emerging paradigm which we call dimensional neuroimaging endophenotype (DNE). DNE dissects the neurobiological heterogeneity of neuropsychiatric and neurodegenerative disorders into a low-dimensional yet informative, quantitative brain phenotypic representation, serving as a robust intermediate phenotype (i.e., endophenotype) largely reflecting underlying genetics and etiology. Finally, we discuss the potential clinical implications of the current findings and envision future research avenues.
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Affiliation(s)
- Junhao Wen
- Laboratory of AI and Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Mathilde Antoniades
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Gyujoon Hwang
- Psychiatry and Behavioral Medicine, Medical College of Wisconsin, Watertown Plank Rd, Milwaukee, WI, USA
| | - Ioanna Skampardoni
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Rongguang Wang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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10
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Fu CHY, Antoniades M, Erus G, Garcia JA, Fan Y, Arnone D, Arnott SR, Chen T, Choi KS, Fatt CC, Frey BN, Frokjaer VG, Ganz M, Godlewska BR, Hassel S, Ho K, McIntosh AM, Qin K, Rotzinger S, Sacchet MD, Savitz J, Shou H, Singh A, Stolicyn A, Strigo I, Strother SC, Tosun D, Victor TA, Wei D, Wise T, Zahn R, Anderson IM, Craighead WE, Deakin JFW, Dunlop BW, Elliott R, Gong Q, Gotlib IH, Harmer CJ, Kennedy SH, Knudsen GM, Mayberg HS, Paulus MP, Qiu J, Trivedi MH, Whalley HC, Yan CG, Young AH, Davatzikos C. Neuroanatomical dimensions in medication-free individuals with major depressive disorder and treatment response to SSRI antidepressant medications or placebo. NATURE. MENTAL HEALTH 2024; 2:164-176. [PMID: 38948238 PMCID: PMC11211072 DOI: 10.1038/s44220-023-00187-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 11/17/2023] [Indexed: 07/02/2024]
Abstract
Major depressive disorder (MDD) is a heterogeneous clinical syndrome with widespread subtle neuroanatomical correlates. Our objective was to identify the neuroanatomical dimensions that characterize MDD and predict treatment response to selective serotonin reuptake inhibitor (SSRI) antidepressants or placebo. In the COORDINATE-MDD consortium, raw MRI data were shared from international samples (N = 1,384) of medication-free individuals with first-episode and recurrent MDD (N = 685) in a current depressive episode of at least moderate severity, but not treatment-resistant depression, as well as healthy controls (N = 699). Prospective longitudinal data on treatment response were available for a subset of MDD individuals (N = 359). Treatments were either SSRI antidepressant medication (escitalopram, citalopram, sertraline) or placebo. Multi-center MRI data were harmonized, and HYDRA, a semi-supervised machine-learning clustering algorithm, was utilized to identify patterns in regional brain volumes that are associated with disease. MDD was optimally characterized by two neuroanatomical dimensions that exhibited distinct treatment responses to placebo and SSRI antidepressant medications. Dimension 1 was characterized by preserved gray and white matter (N = 290 MDD), whereas Dimension 2 was characterized by widespread subtle reductions in gray and white matter (N = 395 MDD) relative to healthy controls. Although there were no significant differences in age of onset, years of illness, number of episodes, or duration of current episode between dimensions, there was a significant interaction effect between dimensions and treatment response. Dimension 1 showed a significant improvement in depressive symptoms following treatment with SSRI medication (51.1%) but limited changes following placebo (28.6%). By contrast, Dimension 2 showed comparable improvements to either SSRI (46.9%) or placebo (42.2%) (β = -18.3, 95% CI (-34.3 to -2.3), P = 0.03). Findings from this case-control study indicate that neuroimaging-based markers can help identify the disease-based dimensions that constitute MDD and predict treatment response.
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Affiliation(s)
- Cynthia H. Y. Fu
- School of Psychology, University of East London, London, UK
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, UK
| | - Mathilde Antoniades
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | - Jose A. Garcia
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | - Danilo Arnone
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, UK
| | | | - Taolin Chen
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Ki Sueng Choi
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Cherise Chin Fatt
- Department of Psychiatry, Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, TX USA
| | - Benicio N. Frey
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario Canada
- Mood Disorders Treatment and Research Centre and Women’s Health Concerns Clinic, St Joseph’s Healthcare Hamilton, Hamilton, Ontario Canada
| | - Vibe G. Frokjaer
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Psychiatry, Psychiatric Centre Copenhagen, Copenhagen, Denmark
| | - Melanie Ganz
- School of Psychology, University of East London, London, UK
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Beata R. Godlewska
- Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, UK
| | - Stefanie Hassel
- Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, Alberta Canada
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, Alberta Canada
| | - Keith Ho
- Department of Psychiatry, University Health Network, Toronto, Ontario Canada
| | - Andrew M. McIntosh
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Kun Qin
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Susan Rotzinger
- Department of Psychiatry, University Health Network, Toronto, Ontario Canada
- Centre for Depression and Suicide Studies, Unity Health Toronto, Toronto, Ontario Canada
| | - Matthew D. Sacchet
- Meditation Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA USA
| | | | - Haochang Shou
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE) Center, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA USA
| | - Ashish Singh
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | - Aleks Stolicyn
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Irina Strigo
- Department of Psychiatry, University of California San Francisco, San Francisco, USA
| | - Stephen C. Strother
- Rotman Research Institute, Baycrest Centre, Toronto, Ontario Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario Canada
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA USA
| | | | - Dongtao Wei
- School of Psychology, Southwest University, Chongqing, China
| | - Toby Wise
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Roland Zahn
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, UK
| | - Ian M. Anderson
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK
| | - W. Edward Craighead
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA USA
- Department of Psychology, Emory University, Atlanta, GA USA
| | - J. F. William Deakin
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK
| | - Boadie W. Dunlop
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA USA
| | - Rebecca Elliott
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK
| | - Qiyong Gong
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Ian H. Gotlib
- Department of Psychology, Stanford University, Stanford, CA USA
| | | | - Sidney H. Kennedy
- Department of Psychiatry, University Health Network, Toronto, Ontario Canada
- Centre for Depression and Suicide Studies, Unity Health Toronto, Toronto, Ontario Canada
| | - Gitte M. Knudsen
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Helen S. Mayberg
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | | | - Jiang Qiu
- School of Psychology, Southwest University, Chongqing, China
| | - Madhukar H. Trivedi
- Department of Psychiatry, Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, TX USA
| | - Heather C. Whalley
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Chao-Gan Yan
- Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Allan H. Young
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, UK
- South London and Maudsley NHS Foundation Trust, Bethlem Royal Hospital, London, UK
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
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11
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Sun X, Sun J, Lu X, Dong Q, Zhang L, Wang W, Liu J, Ma Q, Wang X, Wei D, Chen Y, Liu B, Huang CC, Zheng Y, Wu Y, Chen T, Cheng Y, Xu X, Gong Q, Si T, Qiu S, Lin CP, Cheng J, Tang Y, Wang F, Qiu J, Xie P, Li L, He Y, Xia M. Mapping Neurophysiological Subtypes of Major Depressive Disorder Using Normative Models of the Functional Connectome. Biol Psychiatry 2023; 94:936-947. [PMID: 37295543 DOI: 10.1016/j.biopsych.2023.05.021] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 05/15/2023] [Accepted: 05/29/2023] [Indexed: 06/12/2023]
Abstract
BACKGROUND Major depressive disorder (MDD) is a highly heterogeneous disorder that typically emerges in adolescence and can occur throughout adulthood. Studies aimed at quantitatively uncovering the heterogeneity of individual functional connectome abnormalities in MDD and identifying reproducibly distinct neurophysiological MDD subtypes across the lifespan, which could provide promising insights for precise diagnosis and treatment prediction, are still lacking. METHODS Leveraging resting-state functional magnetic resonance imaging data from 1148 patients with MDD and 1079 healthy control participants (ages 11-93), we conducted the largest multisite analysis to date for neurophysiological MDD subtyping. First, we characterized typical lifespan trajectories of functional connectivity strength based on the normative model and quantitatively mapped the heterogeneous individual deviations among patients with MDD. Then, we identified neurobiological MDD subtypes using an unsupervised clustering algorithm and evaluated intersite reproducibility. Finally, we validated the subtype differences in baseline clinical variables and longitudinal treatment predictive capacity. RESULTS Our findings indicated great intersubject heterogeneity in the spatial distribution and severity of functional connectome deviations among patients with MDD, which inspired the identification of 2 reproducible neurophysiological subtypes. Subtype 1 showed severe deviations, with positive deviations in the default mode, limbic, and subcortical areas and negative deviations in the sensorimotor and attention areas. Subtype 2 showed a moderate but converse deviation pattern. More importantly, subtype differences were observed in depressive item scores and the predictive ability of baseline deviations for antidepressant treatment outcomes. CONCLUSIONS These findings shed light on our understanding of different neurobiological mechanisms underlying the clinical heterogeneity of MDD and are essential for developing personalized treatments for this disorder.
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Affiliation(s)
- Xiaoyi Sun
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; School of Systems Science, Beijing Normal University, Beijing, China
| | - Jinrong Sun
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China; Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Changsha, Hunan, China; Affiliated WuTaiShan Hospital of Medical College of Yangzhou University, Yangzhou Mental Health Centre, Yangzhou, China
| | - Xiaowen Lu
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China; Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Changsha, Hunan, China; Affiliated Wuhan Mental Health Center, Huazhong University of Science and Technology, Wuhan, China
| | - Qiangli Dong
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China; Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Changsha, Hunan, China; Department of Psychiatry, Lanzhou University Second Hospital, Lanzhou, China
| | - Liang Zhang
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China; Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Changsha, Hunan, China; Mental Health Education and Counseling Center, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Wenxu Wang
- School of Systems Science, Beijing Normal University, Beijing, China
| | - Jin Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Qing Ma
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Xiaoqin Wang
- Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing, China; Department of Psychology, Southwest University, Chongqing, China
| | - Dongtao Wei
- Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing, China; Department of Psychology, Southwest University, Chongqing, China
| | - Yuan Chen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Bangshan Liu
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China; Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Changsha, Hunan, China
| | - Chu-Chung Huang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Health Center, School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Yanting Zheng
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yankun Wu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, National Health Commission Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China
| | - Taolin Chen
- Huaxi Magnetic Resonance Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Yuqi Cheng
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Xiufeng Xu
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Qiyong Gong
- Huaxi Magnetic Resonance Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
| | - Tianmei Si
- Peking University Sixth Hospital, Peking University Institute of Mental Health, National Health Commission Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China
| | - Shijun Qiu
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Education and Research, Taipei City Hospital, Taipei, Taiwan
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yanqing Tang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Fei Wang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing, China; Department of Psychology, Southwest University, Chongqing, China
| | - Peng Xie
- Chongqing Key Laboratory of Neurobiology, Chongqing, China; Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Lingjiang Li
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China; Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Changsha, Hunan, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Chinese Institute for Brain Research, Beijing, China
| | - Mingrui Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
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12
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Zhang B, Li Y, Shen Y, Zhao W, Yu Y, Tang J. Dimensional subtyping of first-episode drug-naïve major depressive disorder: A multisite resting-state fMRI study. Psychiatry Res 2023; 330:115598. [PMID: 37979320 DOI: 10.1016/j.psychres.2023.115598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 11/01/2023] [Accepted: 11/06/2023] [Indexed: 11/20/2023]
Abstract
Major depressive disorder (MDD) is a heterogeneous syndrome, and understanding its neural mechanisms is crucial for the advancement of personalized medicine. However, conventional subtyping studies often categorize MDD patients into a single subgroup, neglecting the continuous interindividual variations. This implies a pressing need for a dimensional approach. 230 first-episode drug-naïve MDD patients and 395 healthy controls were obtained from 5 sites via the Rest-meta-MDD project. A Bayesian model was used to decompose the resting-state functional connectivity (RSFC) into multiple distinct RSFC patterns (refer to as "factors"), and each individual was allowed to express multiple factors to varying degrees (dimensional subtyping). The associations between demographic and clinical variables with the identified factors were calculated. We identified three latent factors with distinct but partially overlapping hypo- and hyper-RSFC patterns. Most participants co-expressed multiple latent factors. All factors shared abnormal RSFC involving the default mode network and frontoparietal network, but the directionality partially differed across factors. All factors were not significantly associated with demographic and clinical variables. These findings shed light on the interindividual variability in MDD and could form the basis for developing novel therapeutic approaches that capitalize on the heterogeneity of MDD.
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Affiliation(s)
- Biao Zhang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Research Center of Clinical Medical Imaging, Anhui, Hefei 230032, China; Anhui Provincial Institute of Translational Medicine, Hefei 230032, China; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230026, China
| | - Yating Li
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Research Center of Clinical Medical Imaging, Anhui, Hefei 230032, China; Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Yuhao Shen
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Research Center of Clinical Medical Imaging, Anhui, Hefei 230032, China; Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Wenming Zhao
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Research Center of Clinical Medical Imaging, Anhui, Hefei 230032, China; Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Research Center of Clinical Medical Imaging, Anhui, Hefei 230032, China; Anhui Provincial Institute of Translational Medicine, Hefei 230032, China.
| | - Jin Tang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230026, China.
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13
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Gao CX, Dwyer D, Zhu Y, Smith CL, Du L, Filia KM, Bayer J, Menssink JM, Wang T, Bergmeir C, Wood S, Cotton SM. An overview of clustering methods with guidelines for application in mental health research. Psychiatry Res 2023; 327:115265. [PMID: 37348404 DOI: 10.1016/j.psychres.2023.115265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 05/20/2023] [Accepted: 05/21/2023] [Indexed: 06/24/2023]
Abstract
Cluster analyzes have been widely used in mental health research to decompose inter-individual heterogeneity by identifying more homogeneous subgroups of individuals. However, despite advances in new algorithms and increasing popularity, there is little guidance on model choice, analytical framework and reporting requirements. In this paper, we aimed to address this gap by introducing the philosophy, design, advantages/disadvantages and implementation of major algorithms that are particularly relevant in mental health research. Extensions of basic models, such as kernel methods, deep learning, semi-supervised clustering, and clustering ensembles are subsequently introduced. How to choose algorithms to address common issues as well as methods for pre-clustering data processing, clustering evaluation and validation are then discussed. Importantly, we also provide general guidance on clustering workflow and reporting requirements. To facilitate the implementation of different algorithms, we provide information on R functions and libraries.
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Affiliation(s)
- Caroline X Gao
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia; Orygen, Parkville, VIC, Australia; Department of Epidemiology and Preventative Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia.
| | - Dominic Dwyer
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia; Orygen, Parkville, VIC, Australia
| | - Ye Zhu
- School of Information Technology, Deakin University, Geelong, VIC, Australia
| | - Catherine L Smith
- Department of Epidemiology and Preventative Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Lan Du
- Faculty of Information Technology, Monash University, Clayton, VIC, Australia
| | - Kate M Filia
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia; Orygen, Parkville, VIC, Australia
| | - Johanna Bayer
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia; Orygen, Parkville, VIC, Australia
| | - Jana M Menssink
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia; Orygen, Parkville, VIC, Australia
| | - Teresa Wang
- Faculty of Information Technology, Monash University, Clayton, VIC, Australia
| | - Christoph Bergmeir
- Faculty of Information Technology, Monash University, Clayton, VIC, Australia; Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain
| | - Stephen Wood
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia; Orygen, Parkville, VIC, Australia
| | - Sue M Cotton
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia; Orygen, Parkville, VIC, Australia
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14
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Bellini H, Cretaz E, Carneiro AM, da Silva PHR, dos Santos LA, Gallucci-Neto J, Brunoni AR. Magnetic Waves vs. Electric Shocks: A Non-Inferiority Study of Magnetic Seizure Therapy and Electroconvulsive Therapy in Treatment-Resistant Depression. Biomedicines 2023; 11:2150. [PMID: 37626647 PMCID: PMC10452083 DOI: 10.3390/biomedicines11082150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 07/22/2023] [Accepted: 07/26/2023] [Indexed: 08/27/2023] Open
Abstract
Treatment-resistant depression (TRD), characterized by the failure to achieve symptomatic remission despite multiple pharmacotherapeutic treatments, poses a significant challenge for clinicians. Electroconvulsive therapy (ECT) is an effective but limited option due to its cognitive side effects. In this context, magnetic seizure therapy (MST) has emerged as a promising alternative, offering comparable antidepressant efficacy with better cognitive outcomes. However, the clinical outcomes and cognitive effects of MST require further investigation. This double-blinded, randomized, non-inferiority study aims to compare the efficacy, tolerability, cognitive adverse effects, and neurophysiological biomarkers of MST with bilateral ECT (BT ECT) in patients with TRD. This study will employ multimodal nuclear magnetic resonance imaging (MRI) and serum neurotrophic markers to gain insight into the neurobiological basis of seizure therapy. Additionally, neurophysiological biomarkers will be evaluated as secondary outcomes to predict the antidepressant and cognitive effects of both techniques. The study design, recruitment methods, ethical considerations, eligibility criteria, interventions, and blinding procedures are described. The expected outcomes will advance the field by offering a potential alternative to ECT with improved cognitive outcomes and a better understanding of the underlying pathophysiology of depression and antidepressant therapies.
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Affiliation(s)
- Helena Bellini
- Service of Interdisciplinary Neuromodulation, Laboratory of Neurosciences (LIM-27), Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo 05403-010, Brazil; (H.B.); (E.C.); (A.M.C.); (P.H.R.d.S.); (L.A.d.S.); (J.G.-N.)
- Service of Electroconvulsive Therapy, Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo 05403-010, Brazil
| | - Eric Cretaz
- Service of Interdisciplinary Neuromodulation, Laboratory of Neurosciences (LIM-27), Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo 05403-010, Brazil; (H.B.); (E.C.); (A.M.C.); (P.H.R.d.S.); (L.A.d.S.); (J.G.-N.)
- Service of Electroconvulsive Therapy, Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo 05403-010, Brazil
| | - Adriana Munhoz Carneiro
- Service of Interdisciplinary Neuromodulation, Laboratory of Neurosciences (LIM-27), Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo 05403-010, Brazil; (H.B.); (E.C.); (A.M.C.); (P.H.R.d.S.); (L.A.d.S.); (J.G.-N.)
- Service of Electroconvulsive Therapy, Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo 05403-010, Brazil
| | - Pedro Henrique Rodrigues da Silva
- Service of Interdisciplinary Neuromodulation, Laboratory of Neurosciences (LIM-27), Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo 05403-010, Brazil; (H.B.); (E.C.); (A.M.C.); (P.H.R.d.S.); (L.A.d.S.); (J.G.-N.)
- Service of Electroconvulsive Therapy, Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo 05403-010, Brazil
| | - Leonardo Afonso dos Santos
- Service of Interdisciplinary Neuromodulation, Laboratory of Neurosciences (LIM-27), Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo 05403-010, Brazil; (H.B.); (E.C.); (A.M.C.); (P.H.R.d.S.); (L.A.d.S.); (J.G.-N.)
- Service of Electroconvulsive Therapy, Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo 05403-010, Brazil
| | - José Gallucci-Neto
- Service of Interdisciplinary Neuromodulation, Laboratory of Neurosciences (LIM-27), Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo 05403-010, Brazil; (H.B.); (E.C.); (A.M.C.); (P.H.R.d.S.); (L.A.d.S.); (J.G.-N.)
- Service of Electroconvulsive Therapy, Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo 05403-010, Brazil
| | - André Russowsky Brunoni
- Service of Interdisciplinary Neuromodulation, Laboratory of Neurosciences (LIM-27), Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo 05403-010, Brazil; (H.B.); (E.C.); (A.M.C.); (P.H.R.d.S.); (L.A.d.S.); (J.G.-N.)
- Service of Electroconvulsive Therapy, Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo 05403-010, Brazil
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15
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Fisher ZF, Parsons J, Gates KM, Hopfinger JB. Blind Subgrouping of Task-based fMRI. PSYCHOMETRIKA 2023; 88:434-455. [PMID: 36892726 DOI: 10.1007/s11336-023-09907-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Indexed: 05/17/2023]
Abstract
Significant heterogeneity in network structures reflecting individuals' dynamic processes can exist within subgroups of people (e.g., diagnostic category, gender). This makes it difficult to make inferences regarding these predefined subgroups. For this reason, researchers sometimes wish to identify subsets of individuals who have similarities in their dynamic processes regardless of any predefined category. This requires unsupervised classification of individuals based on similarities in their dynamic processes, or equivalently, in this case, similarities in their network structures of edges. The present paper tests a recently developed algorithm, S-GIMME, that takes into account heterogeneity across individuals with the aim of providing subgroup membership and precise information about the specific network structures that differentiate subgroups. The algorithm has previously provided robust and accurate classification when evaluated with large-scale simulation studies but has not yet been validated on empirical data. Here, we investigate S-GIMME's ability to differentiate, in a purely data-driven manner, between brain states explicitly induced through different tasks in a new fMRI dataset. The results provide new evidence that the algorithm was able to resolve, in an unsupervised data-driven manner, the differences between different active brain states in empirical fMRI data to segregate individuals and arrive at subgroup-specific network structures of edges. The ability to arrive at subgroups that correspond to empirically designed fMRI task conditions, with no biasing or priors, suggests this data-driven approach can be a powerful addition to existing methods for unsupervised classification of individuals based on their dynamic processes.
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Affiliation(s)
- Zachary F Fisher
- Quantitative Developmental Systems Methodology Core, Department of Human Development and Family Studies, The Pennsylvania State University, Health and Human Development Building, University Park, PA, 16802, USA.
| | | | - Kathleen M Gates
- The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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16
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Chen X, Dai Z, Lin Y. Biotypes of major depressive disorder identified by a multiview clustering framework. J Affect Disord 2023; 329:257-272. [PMID: 36863463 DOI: 10.1016/j.jad.2023.02.118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 02/11/2023] [Accepted: 02/22/2023] [Indexed: 03/04/2023]
Abstract
BACKGROUND The advances in resting-state functional magnetic resonance imaging techniques motivate parsing heterogeneity in major depressive disorder (MDD) through neurophysiological subtypes (i.e., biotypes). Based on graph theories, researchers have observed the functional organization of the human brain as a complex system with modular structures and have found wide-spread but variable MDD-related abnormality regarding the modules. The evidence implies the possibility of identifying biotypes using high-dimensional functional connectivity (FC) data in ways that suit the potentially multifaceted biotypes taxonomy. METHODS We proposed a multiview biotype discovery framework that involves theory-driven feature subspace partition (i.e., "view") and independent subspace clustering. Six views were defined using intra- and intermodule FC regarding three MDD focal modules (i.e., the sensory-motor system, default mode network, and subcortical network). For robust biotypes, the framework was applied to a large multisite sample (805 MDD participants and 738 healthy controls). RESULTS Two biotypes were stably obtained in each view, respectively characterized by significantly increased and decreased FC compared to healthy controls. These view-specific biotypes promoted the diagnosis of MDD and showed different symptom profiles. By integrating the view-specific biotypes into biotype profiles, a broad spectrum in the neural heterogeneity of MDD and its separation from symptom-based subtypes was further revealed. LIMITATIONS The power of clinical effects is limited and the cross-sectional nature cannot predict the treatment effects of the biotypes. CONCLUSIONS Our findings not only contribute to the understanding of heterogeneity in MDD, but also provide a novel subtyping framework that could transcend current diagnostic boundaries and data modality.
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Affiliation(s)
- Xitian Chen
- Department of Psychology, Sun Yat-sen University, Guangzhou 510006, China
| | - Zhengjia Dai
- Department of Psychology, Sun Yat-sen University, Guangzhou 510006, China.
| | - Ying Lin
- Department of Psychology, Sun Yat-sen University, Guangzhou 510006, China.
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17
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Ge R, Sassi R, Yatham LN, Frangou S. Neuroimaging profiling identifies distinct brain maturational subtypes of youth with mood and anxiety disorders. Mol Psychiatry 2023; 28:1072-1078. [PMID: 36577839 PMCID: PMC10005933 DOI: 10.1038/s41380-022-01925-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 12/07/2022] [Accepted: 12/13/2022] [Indexed: 12/29/2022]
Abstract
Mood and anxiety disorders typically begin in adolescence and have overlapping clinical features but marked inter-individual variation in clinical presentation. The use of multimodal neuroimaging data may offer novel insights into the underlying brain mechanisms. We applied Heterogeneity Through Discriminative Analysis (HYDRA) to measures of regional brain morphometry, neurite density, and intracortical myelination to identify subtypes of youth, aged 9-10 years, with mood and anxiety disorders (N = 1931) compared to typically developing youth (N = 2823). We identified three subtypes that were robust to permutation testing and sample composition. Subtype 1 evidenced a pattern of imbalanced cortical-subcortical maturation compared to the typically developing group, with subcortical regions lagging behind prefrontal cortical thinning and myelination and greater cortical surface expansion globally. Subtype 2 displayed a pattern of delayed cortical maturation indicated by higher cortical thickness and lower cortical surface area expansion and myelination compared to the typically developing group. Subtype 3 showed evidence of atypical brain maturation involving globally lower cortical thickness and surface coupled with higher myelination and neural density. Subtype 1 had superior cognitive function in contrast to the other two subtypes that underperformed compared to the typically developing group. Higher levels of parental psychopathology, family conflict, and social adversity were common to all subtypes, with subtype 3 having the highest burden of adverse exposures. These analyses comprehensively characterize pre-adolescent mood and anxiety disorders, the biopsychosocial context in which they arise, and lay the foundation for the examination of the longitudinal evolution of the subtypes identified as the study sample transitions through adolescence.
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Affiliation(s)
- Ruiyang Ge
- Djavad Mowafaghian Centre for Brain Health, Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada.,Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Roberto Sassi
- Djavad Mowafaghian Centre for Brain Health, Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada.,Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada.,BC Children's Hospital, Vancouver, BC, Canada
| | - Lakshmi N Yatham
- Djavad Mowafaghian Centre for Brain Health, Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada.,Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Sophia Frangou
- Djavad Mowafaghian Centre for Brain Health, Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada. .,Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada. .,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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18
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Okada G, Sakai Y, Shibakawa M, Yoshioka T, Itai E, Shinzato H, Yamamoto O, Kurata K, Tamura T, Jitsuiki H, Yamashita H, Mantani A, Yokota N, Kawato M, Okamoto Y. Examining the usefulness of the brain network marker program using fMRI for the diagnosis and stratification of major depressive disorder: a non-randomized study protocol. BMC Psychiatry 2023; 23:63. [PMID: 36694153 PMCID: PMC9875439 DOI: 10.1186/s12888-023-04560-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 01/19/2023] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND Although many studies have reported the biological basis of major depressive disorder (MDD), none have been put into practical use. Recently, we developed a generalizable brain network marker for MDD diagnoses (diagnostic marker) across multiple imaging sites using resting-state functional magnetic resonance imaging (rs-fMRI). We have planned this clinical trial to establish evidence for the practical applicability of this diagnostic marker as a medical device. In addition, we have developed generalizable brain network markers for MDD stratification (stratification markers), and the verification of these brain network markers is a secondary endpoint of this study. METHODS This is a non-randomized, open-label study involving patients with MDD and healthy controls (HCs). We will prospectively acquire rs-fMRI data from 50 patients with MDD and 50 HCs and anterogradely verify whether our diagnostic marker can distinguish between patients with MDD and HCs. Furthermore, we will longitudinally obtain rs-fMRI and clinical data at baseline and 6 weeks later in 80 patients with MDD treated with escitalopram and verify whether it is possible to prospectively distinguish MDD subtypes that are expected to be effectively responsive to escitalopram using our stratification markers. DISCUSSION In this study, we will confirm that sufficient accuracy of the diagnostic marker could be reproduced for data from a prospective clinical study. Using longitudinally obtained data, we will also examine whether the "brain network marker for MDD diagnosis" reflects treatment effects in patients with MDD and whether treatment effects can be predicted by "brain network markers for MDD stratification". Data collected in this study will be extremely important for the clinical application of the brain network markers for MDD diagnosis and stratification. TRIAL REGISTRATION Japan Registry of Clinical Trials ( jRCTs062220063 ). Registered 12/10/2022.
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Affiliation(s)
- Go Okada
- grid.257022.00000 0000 8711 3200Department of Psychiatry and Neurosciences, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Yuki Sakai
- grid.418163.90000 0001 2291 1583Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan ,XNef, Inc., Kyoto, Japan
| | | | - Toshinori Yoshioka
- grid.418163.90000 0001 2291 1583Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan ,XNef, Inc., Kyoto, Japan
| | - Eri Itai
- grid.257022.00000 0000 8711 3200Department of Psychiatry and Neurosciences, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Hotaka Shinzato
- grid.257022.00000 0000 8711 3200Department of Psychiatry and Neurosciences, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | | | | | | | | | | | | | | | - Mitsuo Kawato
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan. .,XNef, Inc., Kyoto, Japan.
| | - Yasumasa Okamoto
- Department of Psychiatry and Neurosciences, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
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19
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Tang J, Wang J, Pei Y, Dereje SB, Chen Q, Yan N, Luo Y, Wang Y, Wang W. How adverse and benevolent childhood experiences influence depression and suicidal ideation in Chinese undergraduates: a latent class analysis. Environ Health Prev Med 2023; 28:17. [PMID: 36823044 PMCID: PMC9989774 DOI: 10.1265/ehpm.22-00242] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2023] Open
Abstract
BACKGROUND There has been minimal research on the role of benevolent childhood experiences (BCEs) and how such events may offer protection from the insidious effects of adverse childhood experiences (ACEs) or later in life. OBJECTIVES This research aims to learn how BCEs and ACEs interact to affect adolescents' psychological distress. METHODS Cross-sectional survey was conducted in three cities (Xuzhou, Nanjing, and Wuhan) in China from March 2021 to May 2021. Latent class analysis (LCA) was used to classify the patterns of ACEs and BCEs. We adopted hierarchical multivariable regression to examine the influences of ACEs and BCEs on depression and suicidal ideation. RESULTS To explore the relationship between childhood experience and suicidal ideation and depression, LCA revealed three patterns of ACEs: (1) emotional abuse (10.57%); (2) high ACEs (0.55%); and (3) low ACEs classes (88.88%). Adolescents with emotional abuse (depression: OR = 3.82, 95%CI = 2.80-5.22, P < 0.001; suicidal ideation: OR = 5.766, 95%CI = 3.97-8.38, P < 0.001) and high ACEs class (suicidal ideation: OR = 5.93, 95%CI = 1.19-29.66, P < 0.05) had an increased risk of psychological distress (reference: low ACEs). LCA revealed four patterns of BCEs: (1) relationship support (14.54%); (2) low BCEs (4.85%); (3) high BCEs (55.34%); and (4) high quality of life classes (25.28%). Adolescents with a high quality of life (depression: OR = 0.09, 95%CI = 0.05-0.16, P < 0.001; suicidal ideation: OR = 0.22, 95%CI = 0.12-0.40, P < 0.001) and high BCEs (depression: OR = 0.05, 95%CI = 0.03-0.09, P < 0.001; suicidal ideation: OR = 0.15, 95%CI = 0.09-0.26, P < 0.001) protected the mental health of adolescents (reference: low BCEs). CONCLUSIONS High ACEs and emotional abuse classes were significantly associated with poorer mental health symptoms, including suicidal ideation and depression. In contrast, high BCEs and high quality of life classes were associated with better mental health. These findings point out that it is more necessary to identify and support victims of ACEs, and it is urgent to increase BCEs in early childhood.
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Affiliation(s)
- Jie Tang
- School of Public Health, Xuzhou Medical University
| | | | - Yifei Pei
- School of Public Health, Xuzhou Medical University
| | | | - Qian Chen
- School of Public Health, Xuzhou Medical University
| | - Na Yan
- School of Public Health, Xuzhou Medical University
| | - Yunjiao Luo
- School of Public Health, Xuzhou Medical University
| | - Yuhao Wang
- School of Public Health, Xuzhou Medical University
| | - Wei Wang
- School of Public Health, Xuzhou Medical University.,Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University.,Engineering Research Innovation Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University
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20
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Chen J, Patil KR, Yeo BTT, Eickhoff SB. Leveraging Machine Learning for Gaining Neurobiological and Nosological Insights in Psychiatric Research. Biol Psychiatry 2023; 93:18-28. [PMID: 36307328 DOI: 10.1016/j.biopsych.2022.07.025] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 07/06/2022] [Accepted: 07/28/2022] [Indexed: 11/18/2022]
Abstract
Much attention is currently devoted to developing diagnostic classifiers for mental disorders. Complementing these efforts, we highlight the potential of machine learning to gain biological insights into the psychopathology and nosology of mental disorders. Studies to this end have mainly used brain imaging data, which can be obtained noninvasively from large cohorts and have repeatedly been argued to reveal potentially intermediate phenotypes. This may become particularly relevant in light of recent efforts to identify magnetic resonance imaging-derived biomarkers that yield insight into pathophysiological processes as well as to refine the taxonomy of mental illness. In particular, the accuracy of machine learning models may be used as dependent variables to identify features relevant to pathophysiology. Moreover, such approaches may help disentangle the dimensional (within diagnosis) and often overlapping (across diagnoses) symptomatology of psychiatric illness. We also point out a multiview perspective that combines data from different sources, bridging molecular and system-level information. Finally, we summarize recent efforts toward a data-driven definition of subtypes or disease entities through unsupervised and semisupervised approaches. The latter, blending unsupervised and supervised concepts, may represent a particularly promising avenue toward dissecting heterogeneous categories. Finally, we raise several technical and conceptual aspects related to the reviewed approaches. In particular, we discuss common pitfalls pertaining to flawed input data or analytic procedures that would likely lead to unreliable outputs.
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Affiliation(s)
- Ji Chen
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China; Department of Psychiatry, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany.
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-universität Düsseldorf, Düsseldorf, Germany
| | - B T Thomas Yeo
- Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; Integrative Sciences & Engineering Programme, National University of Singapore, Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-universität Düsseldorf, Düsseldorf, Germany
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21
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Kelly JR, Baker A, Babiker M, Burke L, Brennan C, O'Keane V. The psychedelic renaissance: the next trip for psychiatry? Ir J Psychol Med 2022; 39:335-339. [PMID: 31543078 DOI: 10.1017/ipm.2019.39] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The psychedelic research renaissance is gaining traction. Preliminary clinical studies of the hallucinogenic fungi, psilocybin, with psychological support, have indicated improvements in mood, anxiety and quality of life. A seminal, open-label study demonstrated marked reductions in depression symptoms in participants with treatment-resistant depression (TRD). The associated neurobiological processes involve alterations in brain connectivity, together with altered amygdala and default mode network activity. At the cellular level, psychedelics promote synaptogenesis and neural plasticity. Prompted by the promising preliminary studies, a randomized, double-blind trial has recently been launched across Europe and North America to investigate the efficacy of psilocybin in TRD. One of these centres is based in Ireland - CHO Area 7 and Tallaght University Hospital. The outcome of this trial will determine whether psilocybin with psychological support will successfully translate into the psychiatric clinic for the benefit of patients.
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Affiliation(s)
- J R Kelly
- Department of Psychiatry, Trinity College Dublin & Tallaght Hospital, Trinity Centre for Health Sciences, Tallaght University Hospital, Tallaght, Dublin, Ireland
- Tallaght University Hospital, Tallaght, Dublin, Ireland
| | - A Baker
- Sheaf House, Exchange Hall, Tallaght, Dublin, Ireland
| | - M Babiker
- Tallaght University Hospital, Tallaght, Dublin, Ireland
| | - L Burke
- Sheaf House, Exchange Hall, Tallaght, Dublin, Ireland
| | - C Brennan
- Sheaf House, Exchange Hall, Tallaght, Dublin, Ireland
| | - V O'Keane
- Department of Psychiatry, Trinity College Dublin & Tallaght Hospital, Trinity Centre for Health Sciences, Tallaght University Hospital, Tallaght, Dublin, Ireland
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22
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Talishinsky A, Downar J, Vértes PE, Seidlitz J, Dunlop K, Lynch CJ, Whalley H, McIntosh A, Vila-Rodriguez F, Daskalakis ZJ, Blumberger DM, Liston C. Regional gene expression signatures are associated with sex-specific functional connectivity changes in depression. Nat Commun 2022; 13:5692. [PMID: 36171190 PMCID: PMC9519925 DOI: 10.1038/s41467-022-32617-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Accepted: 08/09/2022] [Indexed: 12/02/2022] Open
Abstract
The neural substrates of depression may differ in men and women, but the underlying mechanisms are incompletely understood. Here, we show that depression is associated with sex-specific patterns of abnormal functional connectivity in the default mode network and in five regions of interest with sexually dimorphic transcriptional effects. Regional differences in gene expression in two independent datasets explained the neuroanatomical distribution of abnormal connectivity. These gene sets varied by sex and were strongly enriched for genes implicated in depression, synapse function, immune signaling, and neurodevelopment. In an independent sample, we confirmed the prediction that individual differences in default mode network connectivity are explained by inferred brain expression levels for six depression-related genes, including PCDH8, a brain-specific protocadherin integral membrane protein implicated in activity-related synaptic reorganization. Together, our results delineate both shared and sex-specific changes in the organization of depression-related functional networks, with implications for biomarker development and fMRI-guided therapeutic neuromodulation.
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Affiliation(s)
- Aleksandr Talishinsky
- Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - Jonathan Downar
- Krembil Research Institute and Centre for Mental Health, University Health Network, Toronto, ON, USA.
- Department of Psychiatry, University of Toronto, Toronto, ON, USA.
| | - Petra E Vértes
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Jakob Seidlitz
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Katharine Dunlop
- Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - Charles J Lynch
- Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - Heather Whalley
- Center for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Andrew McIntosh
- Center for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Fidel Vila-Rodriguez
- Non-Invasive Neurostimulation Therapies Lab and Department of Psychiatry, University of British Columbia, Vancouver, BC, USA
| | | | - Daniel M Blumberger
- Department of Psychiatry, University of Toronto, Toronto, ON, USA
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, USA
| | - Conor Liston
- Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA.
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA.
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23
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Zhu T, Becquey C, Chen Y, Lejuez CW, Li CSR, Bi J. Identifying alcohol misuse biotypes from neural connectivity markers and concurrent genetic associations. Transl Psychiatry 2022; 12:253. [PMID: 35710901 PMCID: PMC9203552 DOI: 10.1038/s41398-022-01983-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 05/18/2022] [Accepted: 05/25/2022] [Indexed: 11/08/2022] Open
Abstract
Alcohol use behaviors are highly heterogeneous, posing significant challenges to etiologic research of alcohol use disorder (AUD). Magnetic resonance imaging (MRI) provides intermediate endophenotypes in characterizing problem alcohol use and assessing the genetic architecture of addictive behavior. We used connectivity features derived from resting state functional MRI to subtype alcohol misuse (AM) behavior. With a machine learning pipeline of feature selection, dimension reduction, clustering, and classification we identified three AM biotypes-mild, comorbid, and moderate AM biotypes (MIA, COA, and MOA)-from a Human Connectome Project (HCP) discovery sample (194 drinkers). The three groups and controls (397 non-drinkers) demonstrated significant differences in alcohol use frequency during the heaviest 12-month drinking period (MOA > MIA; COA > non-drinkers) and were distinguished by connectivity features involving the frontal, parietal, subcortical and default mode networks. Further, COA relative to MIA, MOA and controls endorsed significantly higher scores in antisocial personality. A genetic association study identified that an alcohol use and antisocial behavior related variant rs16930842 from LINC01414 was significantly associated with COA. Using a replication HCP sample (28 drinkers and 46 non-drinkers), we found that subtyping helped in classifying AM from controls (area under the curve or AUC = 0.70, P < 0.005) in comparison to classifiers without subtyping (AUC = 0.60, not significant) and successfully reproduced the genetic association. Together, the results suggest functional connectivities as important features in classifying AM subgroups and the utility of reducing the heterogeneity in connectivity features among AM subgroups in advancing the research of etiological neural markers of AUD.
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Affiliation(s)
- Tan Zhu
- Department of Computer Science and Engineering, School of Engineering, University of Connecticut, Storrs, CT, USA
| | - Chloe Becquey
- Department of Computer Science and Engineering, School of Engineering, University of Connecticut, Storrs, CT, USA
| | - Yu Chen
- Department of Psychiatry, School of Medicine, Yale University, New Haven, CT, USA
| | - Carl W Lejuez
- Department of Psychological Sciences, College of Liberal Arts and Sciences, University of Connecticut, Storrs, CT, USA
| | - Chiang-Shan R Li
- Department of Psychiatry, School of Medicine, Yale University, New Haven, CT, USA
- Department of Neuroscience, School of Medicine, Yale University, New Haven, CT, USA
- Wu Tsai Institute, Yale University, New Haven, CT, USA
| | - Jinbo Bi
- Department of Computer Science and Engineering, School of Engineering, University of Connecticut, Storrs, CT, USA.
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24
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Gool JK, Zhang Z, Oei MSSL, Mathias S, Dauvilliers Y, Mayer G, Plazzi G, Del Rio-Villegas R, Cano JS, Šonka K, Partinen M, Overeem S, Peraita-Adrados R, Heinzer R, Martins da Silva A, Högl B, Wierzbicka A, Heidbreder A, Feketeova E, Manconi M, Bušková J, Canellas F, Bassetti CL, Barateau L, Pizza F, Schmidt MH, Fronczek R, Khatami R, Lammers GJ. Data-Driven Phenotyping of Central Disorders of Hypersomnolence With Unsupervised Clustering. Neurology 2022; 98:e2387-e2400. [PMID: 35437263 PMCID: PMC9202524 DOI: 10.1212/wnl.0000000000200519] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Accepted: 02/28/2022] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Recent studies fueled doubts as to whether all currently defined central disorders of hypersomnolence are stable entities, especially narcolepsy type 2 and idiopathic hypersomnia. New reliable biomarkers are needed, and the question arises of whether current diagnostic criteria of hypersomnolence disorders should be reassessed. The main aim of this data-driven observational study was to see whether data-driven algorithms would segregate narcolepsy type 1 and identify more reliable subgrouping of individuals without cataplexy with new clinical biomarkers. METHODS We used agglomerative hierarchical clustering, an unsupervised machine learning algorithm, to identify distinct hypersomnolence clusters in the large-scale European Narcolepsy Network database. We included 97 variables, covering all aspects of central hypersomnolence disorders such as symptoms, demographics, objective and subjective sleep measures, and laboratory biomarkers. We specifically focused on subgrouping of patients without cataplexy. The number of clusters was chosen to be the minimal number for which patients without cataplexy were put in distinct groups. RESULTS We included 1,078 unmedicated adolescents and adults. Seven clusters were identified, of which 4 clusters included predominantly individuals with cataplexy. The 2 most distinct clusters consisted of 158 and 157 patients, were dominated by those without cataplexy, and among other variables, significantly differed in presence of sleep drunkenness, subjective difficulty awakening, and weekend-week sleep length difference. Patients formally diagnosed as having narcolepsy type 2 and idiopathic hypersomnia were evenly mixed in these 2 clusters. DISCUSSION Using a data-driven approach in the largest study on central disorders of hypersomnolence to date, our study identified distinct patient subgroups within the central disorders of hypersomnolence population. Our results contest inclusion of sleep-onset REM periods in diagnostic criteria for people without cataplexy and provide promising new variables for reliable diagnostic categories that better resemble different patient phenotypes. Cluster-guided classification will result in a more solid hypersomnolence classification system that is less vulnerable to instability of single features.
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Affiliation(s)
- Jari K Gool
- From the Sleep Wake Center SEIN Heemstede (J.K.G., R.F., G.J.L.), Stichting Epilepsie Instellingen Nederland, Heemstede; Department of Neurology and Clinical Neurophysiology (J.K.G., R.F., G.J.L.), Leiden University Medical Center; Department of Anatomy and Neurosciences (J.K.G., S.M.), Amsterdam UMC (Location VUmc), the Netherlands; Center for Sleep Medicine, Sleep Research and Epileptology (Z.Z., R.K.), Klinik Barmelweid AG, Barmelweid, Switzerland; Leiden Observatory (M.S.S.L.O.), Leiden University, the Netherlands; Sleep-Wake Disorders Unit (Y.D., L.B.), Department of Neurology, Gui-de-Chauliac Hospital, CHU Montpellier; National Reference Centre for Orphan Diseases, Narcolepsy, Idiopathic Hypersomnia, and Kleine-Levin Syndrome (Y.D., L.B.); Institute for Neurosciences of Montpellier INM (Y.D., L.B.), Univ Montpellier, INSERM, France; Neurology Department (G.M.), Hephata Klinik, Schwalmstadt, Germany; Department of Biomedical, Metabolic and Neural Sciences (G.P.), University of Modena and Reggio Emilia; IRCCS Istituto delle Scienze Neurologiche di Bologna (G.P, F.P.), Bologna, Italy; Neurophysiology and Sleep Disorders Unit (R.d.R.-V.), Hospital Vithas Nuestra Señora de América, Madrid; Neurology Service (J.S.C.), Institut de Neurociències Hospital Clínic, University of Barcelona, Spain; Neurology Department and Centre of Clinical Neurosciences (K.S.), First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic; Helsinki Sleep Clinic (M.P.), Vitalmed Research Center, Finland; Sleep Medicine Center Kempenhaeghe (S.O.), Heeze; Eindhoven University of Technology (S.O.), the Netherlands; Sleep and Epilepsy Unit-Clinical Neurophysiology Service (R.P.-A.), University General Hospital Gregorio Marañón, Research Institute Gregorio Marañón; University Complutense of Madrid (R.P.-A.), Spain; Center for Investigation and Research in Sleep (R.H.), Lausanne University Hospital, Switzerland; Serviço de Neurofisiologia (A.M.d.S.), Hospital Santo António/Centro Hospitalar Universitário do Porto and UMIB-Instituto Ciências Biomédicas Abel Salazar, Universidade do Porto, Portugal; Neurology Department (B.H., A.H.), Sleep Disorders Clinic, Innsbruck Medical University, Austria; Department of Clinical Neurophysiology (A.W.), Institute of Psychiatry and Neurology, Warsaw, Poland; Department of Sleep Medicine and Neuromuscular Disorders (A.H.), University of Münster, Germany; Neurology Department (E.F.), Medical Faculty of P.J. Safarik University, University Hospital of L. Pasteur Kosice, Kosice, Slovak Republic; Neurology Department (M.M.), EOC, Ospedale Regionale di Lugano, Ticino, Switzerland; Department of Sleep Medicine (J.B.), National Institute of Mental Health, Klecany, Czech Republic; Fundacio d`Investigacio Sanitaria de les illes balears (F.C.), Hospital Universitari Son Espases, Palma de Mallorca, Spain; Department of Neurology (C.L.B., M.H.S., R.K.), Inselspital, Bern University Hospital, University of Bern, Switzerland; and Department of Biomedical and Neuromotor Sciences (F.P.), University of Bologna, Italy.
| | - Zhongxing Zhang
- From the Sleep Wake Center SEIN Heemstede (J.K.G., R.F., G.J.L.), Stichting Epilepsie Instellingen Nederland, Heemstede; Department of Neurology and Clinical Neurophysiology (J.K.G., R.F., G.J.L.), Leiden University Medical Center; Department of Anatomy and Neurosciences (J.K.G., S.M.), Amsterdam UMC (Location VUmc), the Netherlands; Center for Sleep Medicine, Sleep Research and Epileptology (Z.Z., R.K.), Klinik Barmelweid AG, Barmelweid, Switzerland; Leiden Observatory (M.S.S.L.O.), Leiden University, the Netherlands; Sleep-Wake Disorders Unit (Y.D., L.B.), Department of Neurology, Gui-de-Chauliac Hospital, CHU Montpellier; National Reference Centre for Orphan Diseases, Narcolepsy, Idiopathic Hypersomnia, and Kleine-Levin Syndrome (Y.D., L.B.); Institute for Neurosciences of Montpellier INM (Y.D., L.B.), Univ Montpellier, INSERM, France; Neurology Department (G.M.), Hephata Klinik, Schwalmstadt, Germany; Department of Biomedical, Metabolic and Neural Sciences (G.P.), University of Modena and Reggio Emilia; IRCCS Istituto delle Scienze Neurologiche di Bologna (G.P, F.P.), Bologna, Italy; Neurophysiology and Sleep Disorders Unit (R.d.R.-V.), Hospital Vithas Nuestra Señora de América, Madrid; Neurology Service (J.S.C.), Institut de Neurociències Hospital Clínic, University of Barcelona, Spain; Neurology Department and Centre of Clinical Neurosciences (K.S.), First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic; Helsinki Sleep Clinic (M.P.), Vitalmed Research Center, Finland; Sleep Medicine Center Kempenhaeghe (S.O.), Heeze; Eindhoven University of Technology (S.O.), the Netherlands; Sleep and Epilepsy Unit-Clinical Neurophysiology Service (R.P.-A.), University General Hospital Gregorio Marañón, Research Institute Gregorio Marañón; University Complutense of Madrid (R.P.-A.), Spain; Center for Investigation and Research in Sleep (R.H.), Lausanne University Hospital, Switzerland; Serviço de Neurofisiologia (A.M.d.S.), Hospital Santo António/Centro Hospitalar Universitário do Porto and UMIB-Instituto Ciências Biomédicas Abel Salazar, Universidade do Porto, Portugal; Neurology Department (B.H., A.H.), Sleep Disorders Clinic, Innsbruck Medical University, Austria; Department of Clinical Neurophysiology (A.W.), Institute of Psychiatry and Neurology, Warsaw, Poland; Department of Sleep Medicine and Neuromuscular Disorders (A.H.), University of Münster, Germany; Neurology Department (E.F.), Medical Faculty of P.J. Safarik University, University Hospital of L. Pasteur Kosice, Kosice, Slovak Republic; Neurology Department (M.M.), EOC, Ospedale Regionale di Lugano, Ticino, Switzerland; Department of Sleep Medicine (J.B.), National Institute of Mental Health, Klecany, Czech Republic; Fundacio d`Investigacio Sanitaria de les illes balears (F.C.), Hospital Universitari Son Espases, Palma de Mallorca, Spain; Department of Neurology (C.L.B., M.H.S., R.K.), Inselspital, Bern University Hospital, University of Bern, Switzerland; and Department of Biomedical and Neuromotor Sciences (F.P.), University of Bologna, Italy
| | - Martijn S S L Oei
- From the Sleep Wake Center SEIN Heemstede (J.K.G., R.F., G.J.L.), Stichting Epilepsie Instellingen Nederland, Heemstede; Department of Neurology and Clinical Neurophysiology (J.K.G., R.F., G.J.L.), Leiden University Medical Center; Department of Anatomy and Neurosciences (J.K.G., S.M.), Amsterdam UMC (Location VUmc), the Netherlands; Center for Sleep Medicine, Sleep Research and Epileptology (Z.Z., R.K.), Klinik Barmelweid AG, Barmelweid, Switzerland; Leiden Observatory (M.S.S.L.O.), Leiden University, the Netherlands; Sleep-Wake Disorders Unit (Y.D., L.B.), Department of Neurology, Gui-de-Chauliac Hospital, CHU Montpellier; National Reference Centre for Orphan Diseases, Narcolepsy, Idiopathic Hypersomnia, and Kleine-Levin Syndrome (Y.D., L.B.); Institute for Neurosciences of Montpellier INM (Y.D., L.B.), Univ Montpellier, INSERM, France; Neurology Department (G.M.), Hephata Klinik, Schwalmstadt, Germany; Department of Biomedical, Metabolic and Neural Sciences (G.P.), University of Modena and Reggio Emilia; IRCCS Istituto delle Scienze Neurologiche di Bologna (G.P, F.P.), Bologna, Italy; Neurophysiology and Sleep Disorders Unit (R.d.R.-V.), Hospital Vithas Nuestra Señora de América, Madrid; Neurology Service (J.S.C.), Institut de Neurociències Hospital Clínic, University of Barcelona, Spain; Neurology Department and Centre of Clinical Neurosciences (K.S.), First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic; Helsinki Sleep Clinic (M.P.), Vitalmed Research Center, Finland; Sleep Medicine Center Kempenhaeghe (S.O.), Heeze; Eindhoven University of Technology (S.O.), the Netherlands; Sleep and Epilepsy Unit-Clinical Neurophysiology Service (R.P.-A.), University General Hospital Gregorio Marañón, Research Institute Gregorio Marañón; University Complutense of Madrid (R.P.-A.), Spain; Center for Investigation and Research in Sleep (R.H.), Lausanne University Hospital, Switzerland; Serviço de Neurofisiologia (A.M.d.S.), Hospital Santo António/Centro Hospitalar Universitário do Porto and UMIB-Instituto Ciências Biomédicas Abel Salazar, Universidade do Porto, Portugal; Neurology Department (B.H., A.H.), Sleep Disorders Clinic, Innsbruck Medical University, Austria; Department of Clinical Neurophysiology (A.W.), Institute of Psychiatry and Neurology, Warsaw, Poland; Department of Sleep Medicine and Neuromuscular Disorders (A.H.), University of Münster, Germany; Neurology Department (E.F.), Medical Faculty of P.J. Safarik University, University Hospital of L. Pasteur Kosice, Kosice, Slovak Republic; Neurology Department (M.M.), EOC, Ospedale Regionale di Lugano, Ticino, Switzerland; Department of Sleep Medicine (J.B.), National Institute of Mental Health, Klecany, Czech Republic; Fundacio d`Investigacio Sanitaria de les illes balears (F.C.), Hospital Universitari Son Espases, Palma de Mallorca, Spain; Department of Neurology (C.L.B., M.H.S., R.K.), Inselspital, Bern University Hospital, University of Bern, Switzerland; and Department of Biomedical and Neuromotor Sciences (F.P.), University of Bologna, Italy
| | - Stephanie Mathias
- From the Sleep Wake Center SEIN Heemstede (J.K.G., R.F., G.J.L.), Stichting Epilepsie Instellingen Nederland, Heemstede; Department of Neurology and Clinical Neurophysiology (J.K.G., R.F., G.J.L.), Leiden University Medical Center; Department of Anatomy and Neurosciences (J.K.G., S.M.), Amsterdam UMC (Location VUmc), the Netherlands; Center for Sleep Medicine, Sleep Research and Epileptology (Z.Z., R.K.), Klinik Barmelweid AG, Barmelweid, Switzerland; Leiden Observatory (M.S.S.L.O.), Leiden University, the Netherlands; Sleep-Wake Disorders Unit (Y.D., L.B.), Department of Neurology, Gui-de-Chauliac Hospital, CHU Montpellier; National Reference Centre for Orphan Diseases, Narcolepsy, Idiopathic Hypersomnia, and Kleine-Levin Syndrome (Y.D., L.B.); Institute for Neurosciences of Montpellier INM (Y.D., L.B.), Univ Montpellier, INSERM, France; Neurology Department (G.M.), Hephata Klinik, Schwalmstadt, Germany; Department of Biomedical, Metabolic and Neural Sciences (G.P.), University of Modena and Reggio Emilia; IRCCS Istituto delle Scienze Neurologiche di Bologna (G.P, F.P.), Bologna, Italy; Neurophysiology and Sleep Disorders Unit (R.d.R.-V.), Hospital Vithas Nuestra Señora de América, Madrid; Neurology Service (J.S.C.), Institut de Neurociències Hospital Clínic, University of Barcelona, Spain; Neurology Department and Centre of Clinical Neurosciences (K.S.), First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic; Helsinki Sleep Clinic (M.P.), Vitalmed Research Center, Finland; Sleep Medicine Center Kempenhaeghe (S.O.), Heeze; Eindhoven University of Technology (S.O.), the Netherlands; Sleep and Epilepsy Unit-Clinical Neurophysiology Service (R.P.-A.), University General Hospital Gregorio Marañón, Research Institute Gregorio Marañón; University Complutense of Madrid (R.P.-A.), Spain; Center for Investigation and Research in Sleep (R.H.), Lausanne University Hospital, Switzerland; Serviço de Neurofisiologia (A.M.d.S.), Hospital Santo António/Centro Hospitalar Universitário do Porto and UMIB-Instituto Ciências Biomédicas Abel Salazar, Universidade do Porto, Portugal; Neurology Department (B.H., A.H.), Sleep Disorders Clinic, Innsbruck Medical University, Austria; Department of Clinical Neurophysiology (A.W.), Institute of Psychiatry and Neurology, Warsaw, Poland; Department of Sleep Medicine and Neuromuscular Disorders (A.H.), University of Münster, Germany; Neurology Department (E.F.), Medical Faculty of P.J. Safarik University, University Hospital of L. Pasteur Kosice, Kosice, Slovak Republic; Neurology Department (M.M.), EOC, Ospedale Regionale di Lugano, Ticino, Switzerland; Department of Sleep Medicine (J.B.), National Institute of Mental Health, Klecany, Czech Republic; Fundacio d`Investigacio Sanitaria de les illes balears (F.C.), Hospital Universitari Son Espases, Palma de Mallorca, Spain; Department of Neurology (C.L.B., M.H.S., R.K.), Inselspital, Bern University Hospital, University of Bern, Switzerland; and Department of Biomedical and Neuromotor Sciences (F.P.), University of Bologna, Italy
| | - Yves Dauvilliers
- From the Sleep Wake Center SEIN Heemstede (J.K.G., R.F., G.J.L.), Stichting Epilepsie Instellingen Nederland, Heemstede; Department of Neurology and Clinical Neurophysiology (J.K.G., R.F., G.J.L.), Leiden University Medical Center; Department of Anatomy and Neurosciences (J.K.G., S.M.), Amsterdam UMC (Location VUmc), the Netherlands; Center for Sleep Medicine, Sleep Research and Epileptology (Z.Z., R.K.), Klinik Barmelweid AG, Barmelweid, Switzerland; Leiden Observatory (M.S.S.L.O.), Leiden University, the Netherlands; Sleep-Wake Disorders Unit (Y.D., L.B.), Department of Neurology, Gui-de-Chauliac Hospital, CHU Montpellier; National Reference Centre for Orphan Diseases, Narcolepsy, Idiopathic Hypersomnia, and Kleine-Levin Syndrome (Y.D., L.B.); Institute for Neurosciences of Montpellier INM (Y.D., L.B.), Univ Montpellier, INSERM, France; Neurology Department (G.M.), Hephata Klinik, Schwalmstadt, Germany; Department of Biomedical, Metabolic and Neural Sciences (G.P.), University of Modena and Reggio Emilia; IRCCS Istituto delle Scienze Neurologiche di Bologna (G.P, F.P.), Bologna, Italy; Neurophysiology and Sleep Disorders Unit (R.d.R.-V.), Hospital Vithas Nuestra Señora de América, Madrid; Neurology Service (J.S.C.), Institut de Neurociències Hospital Clínic, University of Barcelona, Spain; Neurology Department and Centre of Clinical Neurosciences (K.S.), First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic; Helsinki Sleep Clinic (M.P.), Vitalmed Research Center, Finland; Sleep Medicine Center Kempenhaeghe (S.O.), Heeze; Eindhoven University of Technology (S.O.), the Netherlands; Sleep and Epilepsy Unit-Clinical Neurophysiology Service (R.P.-A.), University General Hospital Gregorio Marañón, Research Institute Gregorio Marañón; University Complutense of Madrid (R.P.-A.), Spain; Center for Investigation and Research in Sleep (R.H.), Lausanne University Hospital, Switzerland; Serviço de Neurofisiologia (A.M.d.S.), Hospital Santo António/Centro Hospitalar Universitário do Porto and UMIB-Instituto Ciências Biomédicas Abel Salazar, Universidade do Porto, Portugal; Neurology Department (B.H., A.H.), Sleep Disorders Clinic, Innsbruck Medical University, Austria; Department of Clinical Neurophysiology (A.W.), Institute of Psychiatry and Neurology, Warsaw, Poland; Department of Sleep Medicine and Neuromuscular Disorders (A.H.), University of Münster, Germany; Neurology Department (E.F.), Medical Faculty of P.J. Safarik University, University Hospital of L. Pasteur Kosice, Kosice, Slovak Republic; Neurology Department (M.M.), EOC, Ospedale Regionale di Lugano, Ticino, Switzerland; Department of Sleep Medicine (J.B.), National Institute of Mental Health, Klecany, Czech Republic; Fundacio d`Investigacio Sanitaria de les illes balears (F.C.), Hospital Universitari Son Espases, Palma de Mallorca, Spain; Department of Neurology (C.L.B., M.H.S., R.K.), Inselspital, Bern University Hospital, University of Bern, Switzerland; and Department of Biomedical and Neuromotor Sciences (F.P.), University of Bologna, Italy
| | - Geert Mayer
- From the Sleep Wake Center SEIN Heemstede (J.K.G., R.F., G.J.L.), Stichting Epilepsie Instellingen Nederland, Heemstede; Department of Neurology and Clinical Neurophysiology (J.K.G., R.F., G.J.L.), Leiden University Medical Center; Department of Anatomy and Neurosciences (J.K.G., S.M.), Amsterdam UMC (Location VUmc), the Netherlands; Center for Sleep Medicine, Sleep Research and Epileptology (Z.Z., R.K.), Klinik Barmelweid AG, Barmelweid, Switzerland; Leiden Observatory (M.S.S.L.O.), Leiden University, the Netherlands; Sleep-Wake Disorders Unit (Y.D., L.B.), Department of Neurology, Gui-de-Chauliac Hospital, CHU Montpellier; National Reference Centre for Orphan Diseases, Narcolepsy, Idiopathic Hypersomnia, and Kleine-Levin Syndrome (Y.D., L.B.); Institute for Neurosciences of Montpellier INM (Y.D., L.B.), Univ Montpellier, INSERM, France; Neurology Department (G.M.), Hephata Klinik, Schwalmstadt, Germany; Department of Biomedical, Metabolic and Neural Sciences (G.P.), University of Modena and Reggio Emilia; IRCCS Istituto delle Scienze Neurologiche di Bologna (G.P, F.P.), Bologna, Italy; Neurophysiology and Sleep Disorders Unit (R.d.R.-V.), Hospital Vithas Nuestra Señora de América, Madrid; Neurology Service (J.S.C.), Institut de Neurociències Hospital Clínic, University of Barcelona, Spain; Neurology Department and Centre of Clinical Neurosciences (K.S.), First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic; Helsinki Sleep Clinic (M.P.), Vitalmed Research Center, Finland; Sleep Medicine Center Kempenhaeghe (S.O.), Heeze; Eindhoven University of Technology (S.O.), the Netherlands; Sleep and Epilepsy Unit-Clinical Neurophysiology Service (R.P.-A.), University General Hospital Gregorio Marañón, Research Institute Gregorio Marañón; University Complutense of Madrid (R.P.-A.), Spain; Center for Investigation and Research in Sleep (R.H.), Lausanne University Hospital, Switzerland; Serviço de Neurofisiologia (A.M.d.S.), Hospital Santo António/Centro Hospitalar Universitário do Porto and UMIB-Instituto Ciências Biomédicas Abel Salazar, Universidade do Porto, Portugal; Neurology Department (B.H., A.H.), Sleep Disorders Clinic, Innsbruck Medical University, Austria; Department of Clinical Neurophysiology (A.W.), Institute of Psychiatry and Neurology, Warsaw, Poland; Department of Sleep Medicine and Neuromuscular Disorders (A.H.), University of Münster, Germany; Neurology Department (E.F.), Medical Faculty of P.J. Safarik University, University Hospital of L. Pasteur Kosice, Kosice, Slovak Republic; Neurology Department (M.M.), EOC, Ospedale Regionale di Lugano, Ticino, Switzerland; Department of Sleep Medicine (J.B.), National Institute of Mental Health, Klecany, Czech Republic; Fundacio d`Investigacio Sanitaria de les illes balears (F.C.), Hospital Universitari Son Espases, Palma de Mallorca, Spain; Department of Neurology (C.L.B., M.H.S., R.K.), Inselspital, Bern University Hospital, University of Bern, Switzerland; and Department of Biomedical and Neuromotor Sciences (F.P.), University of Bologna, Italy
| | - Giuseppe Plazzi
- From the Sleep Wake Center SEIN Heemstede (J.K.G., R.F., G.J.L.), Stichting Epilepsie Instellingen Nederland, Heemstede; Department of Neurology and Clinical Neurophysiology (J.K.G., R.F., G.J.L.), Leiden University Medical Center; Department of Anatomy and Neurosciences (J.K.G., S.M.), Amsterdam UMC (Location VUmc), the Netherlands; Center for Sleep Medicine, Sleep Research and Epileptology (Z.Z., R.K.), Klinik Barmelweid AG, Barmelweid, Switzerland; Leiden Observatory (M.S.S.L.O.), Leiden University, the Netherlands; Sleep-Wake Disorders Unit (Y.D., L.B.), Department of Neurology, Gui-de-Chauliac Hospital, CHU Montpellier; National Reference Centre for Orphan Diseases, Narcolepsy, Idiopathic Hypersomnia, and Kleine-Levin Syndrome (Y.D., L.B.); Institute for Neurosciences of Montpellier INM (Y.D., L.B.), Univ Montpellier, INSERM, France; Neurology Department (G.M.), Hephata Klinik, Schwalmstadt, Germany; Department of Biomedical, Metabolic and Neural Sciences (G.P.), University of Modena and Reggio Emilia; IRCCS Istituto delle Scienze Neurologiche di Bologna (G.P, F.P.), Bologna, Italy; Neurophysiology and Sleep Disorders Unit (R.d.R.-V.), Hospital Vithas Nuestra Señora de América, Madrid; Neurology Service (J.S.C.), Institut de Neurociències Hospital Clínic, University of Barcelona, Spain; Neurology Department and Centre of Clinical Neurosciences (K.S.), First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic; Helsinki Sleep Clinic (M.P.), Vitalmed Research Center, Finland; Sleep Medicine Center Kempenhaeghe (S.O.), Heeze; Eindhoven University of Technology (S.O.), the Netherlands; Sleep and Epilepsy Unit-Clinical Neurophysiology Service (R.P.-A.), University General Hospital Gregorio Marañón, Research Institute Gregorio Marañón; University Complutense of Madrid (R.P.-A.), Spain; Center for Investigation and Research in Sleep (R.H.), Lausanne University Hospital, Switzerland; Serviço de Neurofisiologia (A.M.d.S.), Hospital Santo António/Centro Hospitalar Universitário do Porto and UMIB-Instituto Ciências Biomédicas Abel Salazar, Universidade do Porto, Portugal; Neurology Department (B.H., A.H.), Sleep Disorders Clinic, Innsbruck Medical University, Austria; Department of Clinical Neurophysiology (A.W.), Institute of Psychiatry and Neurology, Warsaw, Poland; Department of Sleep Medicine and Neuromuscular Disorders (A.H.), University of Münster, Germany; Neurology Department (E.F.), Medical Faculty of P.J. Safarik University, University Hospital of L. Pasteur Kosice, Kosice, Slovak Republic; Neurology Department (M.M.), EOC, Ospedale Regionale di Lugano, Ticino, Switzerland; Department of Sleep Medicine (J.B.), National Institute of Mental Health, Klecany, Czech Republic; Fundacio d`Investigacio Sanitaria de les illes balears (F.C.), Hospital Universitari Son Espases, Palma de Mallorca, Spain; Department of Neurology (C.L.B., M.H.S., R.K.), Inselspital, Bern University Hospital, University of Bern, Switzerland; and Department of Biomedical and Neuromotor Sciences (F.P.), University of Bologna, Italy
| | - Rafael Del Rio-Villegas
- From the Sleep Wake Center SEIN Heemstede (J.K.G., R.F., G.J.L.), Stichting Epilepsie Instellingen Nederland, Heemstede; Department of Neurology and Clinical Neurophysiology (J.K.G., R.F., G.J.L.), Leiden University Medical Center; Department of Anatomy and Neurosciences (J.K.G., S.M.), Amsterdam UMC (Location VUmc), the Netherlands; Center for Sleep Medicine, Sleep Research and Epileptology (Z.Z., R.K.), Klinik Barmelweid AG, Barmelweid, Switzerland; Leiden Observatory (M.S.S.L.O.), Leiden University, the Netherlands; Sleep-Wake Disorders Unit (Y.D., L.B.), Department of Neurology, Gui-de-Chauliac Hospital, CHU Montpellier; National Reference Centre for Orphan Diseases, Narcolepsy, Idiopathic Hypersomnia, and Kleine-Levin Syndrome (Y.D., L.B.); Institute for Neurosciences of Montpellier INM (Y.D., L.B.), Univ Montpellier, INSERM, France; Neurology Department (G.M.), Hephata Klinik, Schwalmstadt, Germany; Department of Biomedical, Metabolic and Neural Sciences (G.P.), University of Modena and Reggio Emilia; IRCCS Istituto delle Scienze Neurologiche di Bologna (G.P, F.P.), Bologna, Italy; Neurophysiology and Sleep Disorders Unit (R.d.R.-V.), Hospital Vithas Nuestra Señora de América, Madrid; Neurology Service (J.S.C.), Institut de Neurociències Hospital Clínic, University of Barcelona, Spain; Neurology Department and Centre of Clinical Neurosciences (K.S.), First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic; Helsinki Sleep Clinic (M.P.), Vitalmed Research Center, Finland; Sleep Medicine Center Kempenhaeghe (S.O.), Heeze; Eindhoven University of Technology (S.O.), the Netherlands; Sleep and Epilepsy Unit-Clinical Neurophysiology Service (R.P.-A.), University General Hospital Gregorio Marañón, Research Institute Gregorio Marañón; University Complutense of Madrid (R.P.-A.), Spain; Center for Investigation and Research in Sleep (R.H.), Lausanne University Hospital, Switzerland; Serviço de Neurofisiologia (A.M.d.S.), Hospital Santo António/Centro Hospitalar Universitário do Porto and UMIB-Instituto Ciências Biomédicas Abel Salazar, Universidade do Porto, Portugal; Neurology Department (B.H., A.H.), Sleep Disorders Clinic, Innsbruck Medical University, Austria; Department of Clinical Neurophysiology (A.W.), Institute of Psychiatry and Neurology, Warsaw, Poland; Department of Sleep Medicine and Neuromuscular Disorders (A.H.), University of Münster, Germany; Neurology Department (E.F.), Medical Faculty of P.J. Safarik University, University Hospital of L. Pasteur Kosice, Kosice, Slovak Republic; Neurology Department (M.M.), EOC, Ospedale Regionale di Lugano, Ticino, Switzerland; Department of Sleep Medicine (J.B.), National Institute of Mental Health, Klecany, Czech Republic; Fundacio d`Investigacio Sanitaria de les illes balears (F.C.), Hospital Universitari Son Espases, Palma de Mallorca, Spain; Department of Neurology (C.L.B., M.H.S., R.K.), Inselspital, Bern University Hospital, University of Bern, Switzerland; and Department of Biomedical and Neuromotor Sciences (F.P.), University of Bologna, Italy
| | - Joan Santamaria Cano
- From the Sleep Wake Center SEIN Heemstede (J.K.G., R.F., G.J.L.), Stichting Epilepsie Instellingen Nederland, Heemstede; Department of Neurology and Clinical Neurophysiology (J.K.G., R.F., G.J.L.), Leiden University Medical Center; Department of Anatomy and Neurosciences (J.K.G., S.M.), Amsterdam UMC (Location VUmc), the Netherlands; Center for Sleep Medicine, Sleep Research and Epileptology (Z.Z., R.K.), Klinik Barmelweid AG, Barmelweid, Switzerland; Leiden Observatory (M.S.S.L.O.), Leiden University, the Netherlands; Sleep-Wake Disorders Unit (Y.D., L.B.), Department of Neurology, Gui-de-Chauliac Hospital, CHU Montpellier; National Reference Centre for Orphan Diseases, Narcolepsy, Idiopathic Hypersomnia, and Kleine-Levin Syndrome (Y.D., L.B.); Institute for Neurosciences of Montpellier INM (Y.D., L.B.), Univ Montpellier, INSERM, France; Neurology Department (G.M.), Hephata Klinik, Schwalmstadt, Germany; Department of Biomedical, Metabolic and Neural Sciences (G.P.), University of Modena and Reggio Emilia; IRCCS Istituto delle Scienze Neurologiche di Bologna (G.P, F.P.), Bologna, Italy; Neurophysiology and Sleep Disorders Unit (R.d.R.-V.), Hospital Vithas Nuestra Señora de América, Madrid; Neurology Service (J.S.C.), Institut de Neurociències Hospital Clínic, University of Barcelona, Spain; Neurology Department and Centre of Clinical Neurosciences (K.S.), First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic; Helsinki Sleep Clinic (M.P.), Vitalmed Research Center, Finland; Sleep Medicine Center Kempenhaeghe (S.O.), Heeze; Eindhoven University of Technology (S.O.), the Netherlands; Sleep and Epilepsy Unit-Clinical Neurophysiology Service (R.P.-A.), University General Hospital Gregorio Marañón, Research Institute Gregorio Marañón; University Complutense of Madrid (R.P.-A.), Spain; Center for Investigation and Research in Sleep (R.H.), Lausanne University Hospital, Switzerland; Serviço de Neurofisiologia (A.M.d.S.), Hospital Santo António/Centro Hospitalar Universitário do Porto and UMIB-Instituto Ciências Biomédicas Abel Salazar, Universidade do Porto, Portugal; Neurology Department (B.H., A.H.), Sleep Disorders Clinic, Innsbruck Medical University, Austria; Department of Clinical Neurophysiology (A.W.), Institute of Psychiatry and Neurology, Warsaw, Poland; Department of Sleep Medicine and Neuromuscular Disorders (A.H.), University of Münster, Germany; Neurology Department (E.F.), Medical Faculty of P.J. Safarik University, University Hospital of L. Pasteur Kosice, Kosice, Slovak Republic; Neurology Department (M.M.), EOC, Ospedale Regionale di Lugano, Ticino, Switzerland; Department of Sleep Medicine (J.B.), National Institute of Mental Health, Klecany, Czech Republic; Fundacio d`Investigacio Sanitaria de les illes balears (F.C.), Hospital Universitari Son Espases, Palma de Mallorca, Spain; Department of Neurology (C.L.B., M.H.S., R.K.), Inselspital, Bern University Hospital, University of Bern, Switzerland; and Department of Biomedical and Neuromotor Sciences (F.P.), University of Bologna, Italy
| | - Karel Šonka
- From the Sleep Wake Center SEIN Heemstede (J.K.G., R.F., G.J.L.), Stichting Epilepsie Instellingen Nederland, Heemstede; Department of Neurology and Clinical Neurophysiology (J.K.G., R.F., G.J.L.), Leiden University Medical Center; Department of Anatomy and Neurosciences (J.K.G., S.M.), Amsterdam UMC (Location VUmc), the Netherlands; Center for Sleep Medicine, Sleep Research and Epileptology (Z.Z., R.K.), Klinik Barmelweid AG, Barmelweid, Switzerland; Leiden Observatory (M.S.S.L.O.), Leiden University, the Netherlands; Sleep-Wake Disorders Unit (Y.D., L.B.), Department of Neurology, Gui-de-Chauliac Hospital, CHU Montpellier; National Reference Centre for Orphan Diseases, Narcolepsy, Idiopathic Hypersomnia, and Kleine-Levin Syndrome (Y.D., L.B.); Institute for Neurosciences of Montpellier INM (Y.D., L.B.), Univ Montpellier, INSERM, France; Neurology Department (G.M.), Hephata Klinik, Schwalmstadt, Germany; Department of Biomedical, Metabolic and Neural Sciences (G.P.), University of Modena and Reggio Emilia; IRCCS Istituto delle Scienze Neurologiche di Bologna (G.P, F.P.), Bologna, Italy; Neurophysiology and Sleep Disorders Unit (R.d.R.-V.), Hospital Vithas Nuestra Señora de América, Madrid; Neurology Service (J.S.C.), Institut de Neurociències Hospital Clínic, University of Barcelona, Spain; Neurology Department and Centre of Clinical Neurosciences (K.S.), First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic; Helsinki Sleep Clinic (M.P.), Vitalmed Research Center, Finland; Sleep Medicine Center Kempenhaeghe (S.O.), Heeze; Eindhoven University of Technology (S.O.), the Netherlands; Sleep and Epilepsy Unit-Clinical Neurophysiology Service (R.P.-A.), University General Hospital Gregorio Marañón, Research Institute Gregorio Marañón; University Complutense of Madrid (R.P.-A.), Spain; Center for Investigation and Research in Sleep (R.H.), Lausanne University Hospital, Switzerland; Serviço de Neurofisiologia (A.M.d.S.), Hospital Santo António/Centro Hospitalar Universitário do Porto and UMIB-Instituto Ciências Biomédicas Abel Salazar, Universidade do Porto, Portugal; Neurology Department (B.H., A.H.), Sleep Disorders Clinic, Innsbruck Medical University, Austria; Department of Clinical Neurophysiology (A.W.), Institute of Psychiatry and Neurology, Warsaw, Poland; Department of Sleep Medicine and Neuromuscular Disorders (A.H.), University of Münster, Germany; Neurology Department (E.F.), Medical Faculty of P.J. Safarik University, University Hospital of L. Pasteur Kosice, Kosice, Slovak Republic; Neurology Department (M.M.), EOC, Ospedale Regionale di Lugano, Ticino, Switzerland; Department of Sleep Medicine (J.B.), National Institute of Mental Health, Klecany, Czech Republic; Fundacio d`Investigacio Sanitaria de les illes balears (F.C.), Hospital Universitari Son Espases, Palma de Mallorca, Spain; Department of Neurology (C.L.B., M.H.S., R.K.), Inselspital, Bern University Hospital, University of Bern, Switzerland; and Department of Biomedical and Neuromotor Sciences (F.P.), University of Bologna, Italy
| | - Markku Partinen
- From the Sleep Wake Center SEIN Heemstede (J.K.G., R.F., G.J.L.), Stichting Epilepsie Instellingen Nederland, Heemstede; Department of Neurology and Clinical Neurophysiology (J.K.G., R.F., G.J.L.), Leiden University Medical Center; Department of Anatomy and Neurosciences (J.K.G., S.M.), Amsterdam UMC (Location VUmc), the Netherlands; Center for Sleep Medicine, Sleep Research and Epileptology (Z.Z., R.K.), Klinik Barmelweid AG, Barmelweid, Switzerland; Leiden Observatory (M.S.S.L.O.), Leiden University, the Netherlands; Sleep-Wake Disorders Unit (Y.D., L.B.), Department of Neurology, Gui-de-Chauliac Hospital, CHU Montpellier; National Reference Centre for Orphan Diseases, Narcolepsy, Idiopathic Hypersomnia, and Kleine-Levin Syndrome (Y.D., L.B.); Institute for Neurosciences of Montpellier INM (Y.D., L.B.), Univ Montpellier, INSERM, France; Neurology Department (G.M.), Hephata Klinik, Schwalmstadt, Germany; Department of Biomedical, Metabolic and Neural Sciences (G.P.), University of Modena and Reggio Emilia; IRCCS Istituto delle Scienze Neurologiche di Bologna (G.P, F.P.), Bologna, Italy; Neurophysiology and Sleep Disorders Unit (R.d.R.-V.), Hospital Vithas Nuestra Señora de América, Madrid; Neurology Service (J.S.C.), Institut de Neurociències Hospital Clínic, University of Barcelona, Spain; Neurology Department and Centre of Clinical Neurosciences (K.S.), First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic; Helsinki Sleep Clinic (M.P.), Vitalmed Research Center, Finland; Sleep Medicine Center Kempenhaeghe (S.O.), Heeze; Eindhoven University of Technology (S.O.), the Netherlands; Sleep and Epilepsy Unit-Clinical Neurophysiology Service (R.P.-A.), University General Hospital Gregorio Marañón, Research Institute Gregorio Marañón; University Complutense of Madrid (R.P.-A.), Spain; Center for Investigation and Research in Sleep (R.H.), Lausanne University Hospital, Switzerland; Serviço de Neurofisiologia (A.M.d.S.), Hospital Santo António/Centro Hospitalar Universitário do Porto and UMIB-Instituto Ciências Biomédicas Abel Salazar, Universidade do Porto, Portugal; Neurology Department (B.H., A.H.), Sleep Disorders Clinic, Innsbruck Medical University, Austria; Department of Clinical Neurophysiology (A.W.), Institute of Psychiatry and Neurology, Warsaw, Poland; Department of Sleep Medicine and Neuromuscular Disorders (A.H.), University of Münster, Germany; Neurology Department (E.F.), Medical Faculty of P.J. Safarik University, University Hospital of L. Pasteur Kosice, Kosice, Slovak Republic; Neurology Department (M.M.), EOC, Ospedale Regionale di Lugano, Ticino, Switzerland; Department of Sleep Medicine (J.B.), National Institute of Mental Health, Klecany, Czech Republic; Fundacio d`Investigacio Sanitaria de les illes balears (F.C.), Hospital Universitari Son Espases, Palma de Mallorca, Spain; Department of Neurology (C.L.B., M.H.S., R.K.), Inselspital, Bern University Hospital, University of Bern, Switzerland; and Department of Biomedical and Neuromotor Sciences (F.P.), University of Bologna, Italy
| | - Sebastiaan Overeem
- From the Sleep Wake Center SEIN Heemstede (J.K.G., R.F., G.J.L.), Stichting Epilepsie Instellingen Nederland, Heemstede; Department of Neurology and Clinical Neurophysiology (J.K.G., R.F., G.J.L.), Leiden University Medical Center; Department of Anatomy and Neurosciences (J.K.G., S.M.), Amsterdam UMC (Location VUmc), the Netherlands; Center for Sleep Medicine, Sleep Research and Epileptology (Z.Z., R.K.), Klinik Barmelweid AG, Barmelweid, Switzerland; Leiden Observatory (M.S.S.L.O.), Leiden University, the Netherlands; Sleep-Wake Disorders Unit (Y.D., L.B.), Department of Neurology, Gui-de-Chauliac Hospital, CHU Montpellier; National Reference Centre for Orphan Diseases, Narcolepsy, Idiopathic Hypersomnia, and Kleine-Levin Syndrome (Y.D., L.B.); Institute for Neurosciences of Montpellier INM (Y.D., L.B.), Univ Montpellier, INSERM, France; Neurology Department (G.M.), Hephata Klinik, Schwalmstadt, Germany; Department of Biomedical, Metabolic and Neural Sciences (G.P.), University of Modena and Reggio Emilia; IRCCS Istituto delle Scienze Neurologiche di Bologna (G.P, F.P.), Bologna, Italy; Neurophysiology and Sleep Disorders Unit (R.d.R.-V.), Hospital Vithas Nuestra Señora de América, Madrid; Neurology Service (J.S.C.), Institut de Neurociències Hospital Clínic, University of Barcelona, Spain; Neurology Department and Centre of Clinical Neurosciences (K.S.), First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic; Helsinki Sleep Clinic (M.P.), Vitalmed Research Center, Finland; Sleep Medicine Center Kempenhaeghe (S.O.), Heeze; Eindhoven University of Technology (S.O.), the Netherlands; Sleep and Epilepsy Unit-Clinical Neurophysiology Service (R.P.-A.), University General Hospital Gregorio Marañón, Research Institute Gregorio Marañón; University Complutense of Madrid (R.P.-A.), Spain; Center for Investigation and Research in Sleep (R.H.), Lausanne University Hospital, Switzerland; Serviço de Neurofisiologia (A.M.d.S.), Hospital Santo António/Centro Hospitalar Universitário do Porto and UMIB-Instituto Ciências Biomédicas Abel Salazar, Universidade do Porto, Portugal; Neurology Department (B.H., A.H.), Sleep Disorders Clinic, Innsbruck Medical University, Austria; Department of Clinical Neurophysiology (A.W.), Institute of Psychiatry and Neurology, Warsaw, Poland; Department of Sleep Medicine and Neuromuscular Disorders (A.H.), University of Münster, Germany; Neurology Department (E.F.), Medical Faculty of P.J. Safarik University, University Hospital of L. Pasteur Kosice, Kosice, Slovak Republic; Neurology Department (M.M.), EOC, Ospedale Regionale di Lugano, Ticino, Switzerland; Department of Sleep Medicine (J.B.), National Institute of Mental Health, Klecany, Czech Republic; Fundacio d`Investigacio Sanitaria de les illes balears (F.C.), Hospital Universitari Son Espases, Palma de Mallorca, Spain; Department of Neurology (C.L.B., M.H.S., R.K.), Inselspital, Bern University Hospital, University of Bern, Switzerland; and Department of Biomedical and Neuromotor Sciences (F.P.), University of Bologna, Italy
| | - Rosa Peraita-Adrados
- From the Sleep Wake Center SEIN Heemstede (J.K.G., R.F., G.J.L.), Stichting Epilepsie Instellingen Nederland, Heemstede; Department of Neurology and Clinical Neurophysiology (J.K.G., R.F., G.J.L.), Leiden University Medical Center; Department of Anatomy and Neurosciences (J.K.G., S.M.), Amsterdam UMC (Location VUmc), the Netherlands; Center for Sleep Medicine, Sleep Research and Epileptology (Z.Z., R.K.), Klinik Barmelweid AG, Barmelweid, Switzerland; Leiden Observatory (M.S.S.L.O.), Leiden University, the Netherlands; Sleep-Wake Disorders Unit (Y.D., L.B.), Department of Neurology, Gui-de-Chauliac Hospital, CHU Montpellier; National Reference Centre for Orphan Diseases, Narcolepsy, Idiopathic Hypersomnia, and Kleine-Levin Syndrome (Y.D., L.B.); Institute for Neurosciences of Montpellier INM (Y.D., L.B.), Univ Montpellier, INSERM, France; Neurology Department (G.M.), Hephata Klinik, Schwalmstadt, Germany; Department of Biomedical, Metabolic and Neural Sciences (G.P.), University of Modena and Reggio Emilia; IRCCS Istituto delle Scienze Neurologiche di Bologna (G.P, F.P.), Bologna, Italy; Neurophysiology and Sleep Disorders Unit (R.d.R.-V.), Hospital Vithas Nuestra Señora de América, Madrid; Neurology Service (J.S.C.), Institut de Neurociències Hospital Clínic, University of Barcelona, Spain; Neurology Department and Centre of Clinical Neurosciences (K.S.), First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic; Helsinki Sleep Clinic (M.P.), Vitalmed Research Center, Finland; Sleep Medicine Center Kempenhaeghe (S.O.), Heeze; Eindhoven University of Technology (S.O.), the Netherlands; Sleep and Epilepsy Unit-Clinical Neurophysiology Service (R.P.-A.), University General Hospital Gregorio Marañón, Research Institute Gregorio Marañón; University Complutense of Madrid (R.P.-A.), Spain; Center for Investigation and Research in Sleep (R.H.), Lausanne University Hospital, Switzerland; Serviço de Neurofisiologia (A.M.d.S.), Hospital Santo António/Centro Hospitalar Universitário do Porto and UMIB-Instituto Ciências Biomédicas Abel Salazar, Universidade do Porto, Portugal; Neurology Department (B.H., A.H.), Sleep Disorders Clinic, Innsbruck Medical University, Austria; Department of Clinical Neurophysiology (A.W.), Institute of Psychiatry and Neurology, Warsaw, Poland; Department of Sleep Medicine and Neuromuscular Disorders (A.H.), University of Münster, Germany; Neurology Department (E.F.), Medical Faculty of P.J. Safarik University, University Hospital of L. Pasteur Kosice, Kosice, Slovak Republic; Neurology Department (M.M.), EOC, Ospedale Regionale di Lugano, Ticino, Switzerland; Department of Sleep Medicine (J.B.), National Institute of Mental Health, Klecany, Czech Republic; Fundacio d`Investigacio Sanitaria de les illes balears (F.C.), Hospital Universitari Son Espases, Palma de Mallorca, Spain; Department of Neurology (C.L.B., M.H.S., R.K.), Inselspital, Bern University Hospital, University of Bern, Switzerland; and Department of Biomedical and Neuromotor Sciences (F.P.), University of Bologna, Italy
| | - Raphael Heinzer
- From the Sleep Wake Center SEIN Heemstede (J.K.G., R.F., G.J.L.), Stichting Epilepsie Instellingen Nederland, Heemstede; Department of Neurology and Clinical Neurophysiology (J.K.G., R.F., G.J.L.), Leiden University Medical Center; Department of Anatomy and Neurosciences (J.K.G., S.M.), Amsterdam UMC (Location VUmc), the Netherlands; Center for Sleep Medicine, Sleep Research and Epileptology (Z.Z., R.K.), Klinik Barmelweid AG, Barmelweid, Switzerland; Leiden Observatory (M.S.S.L.O.), Leiden University, the Netherlands; Sleep-Wake Disorders Unit (Y.D., L.B.), Department of Neurology, Gui-de-Chauliac Hospital, CHU Montpellier; National Reference Centre for Orphan Diseases, Narcolepsy, Idiopathic Hypersomnia, and Kleine-Levin Syndrome (Y.D., L.B.); Institute for Neurosciences of Montpellier INM (Y.D., L.B.), Univ Montpellier, INSERM, France; Neurology Department (G.M.), Hephata Klinik, Schwalmstadt, Germany; Department of Biomedical, Metabolic and Neural Sciences (G.P.), University of Modena and Reggio Emilia; IRCCS Istituto delle Scienze Neurologiche di Bologna (G.P, F.P.), Bologna, Italy; Neurophysiology and Sleep Disorders Unit (R.d.R.-V.), Hospital Vithas Nuestra Señora de América, Madrid; Neurology Service (J.S.C.), Institut de Neurociències Hospital Clínic, University of Barcelona, Spain; Neurology Department and Centre of Clinical Neurosciences (K.S.), First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic; Helsinki Sleep Clinic (M.P.), Vitalmed Research Center, Finland; Sleep Medicine Center Kempenhaeghe (S.O.), Heeze; Eindhoven University of Technology (S.O.), the Netherlands; Sleep and Epilepsy Unit-Clinical Neurophysiology Service (R.P.-A.), University General Hospital Gregorio Marañón, Research Institute Gregorio Marañón; University Complutense of Madrid (R.P.-A.), Spain; Center for Investigation and Research in Sleep (R.H.), Lausanne University Hospital, Switzerland; Serviço de Neurofisiologia (A.M.d.S.), Hospital Santo António/Centro Hospitalar Universitário do Porto and UMIB-Instituto Ciências Biomédicas Abel Salazar, Universidade do Porto, Portugal; Neurology Department (B.H., A.H.), Sleep Disorders Clinic, Innsbruck Medical University, Austria; Department of Clinical Neurophysiology (A.W.), Institute of Psychiatry and Neurology, Warsaw, Poland; Department of Sleep Medicine and Neuromuscular Disorders (A.H.), University of Münster, Germany; Neurology Department (E.F.), Medical Faculty of P.J. Safarik University, University Hospital of L. Pasteur Kosice, Kosice, Slovak Republic; Neurology Department (M.M.), EOC, Ospedale Regionale di Lugano, Ticino, Switzerland; Department of Sleep Medicine (J.B.), National Institute of Mental Health, Klecany, Czech Republic; Fundacio d`Investigacio Sanitaria de les illes balears (F.C.), Hospital Universitari Son Espases, Palma de Mallorca, Spain; Department of Neurology (C.L.B., M.H.S., R.K.), Inselspital, Bern University Hospital, University of Bern, Switzerland; and Department of Biomedical and Neuromotor Sciences (F.P.), University of Bologna, Italy
| | - Antonio Martins da Silva
- From the Sleep Wake Center SEIN Heemstede (J.K.G., R.F., G.J.L.), Stichting Epilepsie Instellingen Nederland, Heemstede; Department of Neurology and Clinical Neurophysiology (J.K.G., R.F., G.J.L.), Leiden University Medical Center; Department of Anatomy and Neurosciences (J.K.G., S.M.), Amsterdam UMC (Location VUmc), the Netherlands; Center for Sleep Medicine, Sleep Research and Epileptology (Z.Z., R.K.), Klinik Barmelweid AG, Barmelweid, Switzerland; Leiden Observatory (M.S.S.L.O.), Leiden University, the Netherlands; Sleep-Wake Disorders Unit (Y.D., L.B.), Department of Neurology, Gui-de-Chauliac Hospital, CHU Montpellier; National Reference Centre for Orphan Diseases, Narcolepsy, Idiopathic Hypersomnia, and Kleine-Levin Syndrome (Y.D., L.B.); Institute for Neurosciences of Montpellier INM (Y.D., L.B.), Univ Montpellier, INSERM, France; Neurology Department (G.M.), Hephata Klinik, Schwalmstadt, Germany; Department of Biomedical, Metabolic and Neural Sciences (G.P.), University of Modena and Reggio Emilia; IRCCS Istituto delle Scienze Neurologiche di Bologna (G.P, F.P.), Bologna, Italy; Neurophysiology and Sleep Disorders Unit (R.d.R.-V.), Hospital Vithas Nuestra Señora de América, Madrid; Neurology Service (J.S.C.), Institut de Neurociències Hospital Clínic, University of Barcelona, Spain; Neurology Department and Centre of Clinical Neurosciences (K.S.), First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic; Helsinki Sleep Clinic (M.P.), Vitalmed Research Center, Finland; Sleep Medicine Center Kempenhaeghe (S.O.), Heeze; Eindhoven University of Technology (S.O.), the Netherlands; Sleep and Epilepsy Unit-Clinical Neurophysiology Service (R.P.-A.), University General Hospital Gregorio Marañón, Research Institute Gregorio Marañón; University Complutense of Madrid (R.P.-A.), Spain; Center for Investigation and Research in Sleep (R.H.), Lausanne University Hospital, Switzerland; Serviço de Neurofisiologia (A.M.d.S.), Hospital Santo António/Centro Hospitalar Universitário do Porto and UMIB-Instituto Ciências Biomédicas Abel Salazar, Universidade do Porto, Portugal; Neurology Department (B.H., A.H.), Sleep Disorders Clinic, Innsbruck Medical University, Austria; Department of Clinical Neurophysiology (A.W.), Institute of Psychiatry and Neurology, Warsaw, Poland; Department of Sleep Medicine and Neuromuscular Disorders (A.H.), University of Münster, Germany; Neurology Department (E.F.), Medical Faculty of P.J. Safarik University, University Hospital of L. Pasteur Kosice, Kosice, Slovak Republic; Neurology Department (M.M.), EOC, Ospedale Regionale di Lugano, Ticino, Switzerland; Department of Sleep Medicine (J.B.), National Institute of Mental Health, Klecany, Czech Republic; Fundacio d`Investigacio Sanitaria de les illes balears (F.C.), Hospital Universitari Son Espases, Palma de Mallorca, Spain; Department of Neurology (C.L.B., M.H.S., R.K.), Inselspital, Bern University Hospital, University of Bern, Switzerland; and Department of Biomedical and Neuromotor Sciences (F.P.), University of Bologna, Italy
| | - Birgit Högl
- From the Sleep Wake Center SEIN Heemstede (J.K.G., R.F., G.J.L.), Stichting Epilepsie Instellingen Nederland, Heemstede; Department of Neurology and Clinical Neurophysiology (J.K.G., R.F., G.J.L.), Leiden University Medical Center; Department of Anatomy and Neurosciences (J.K.G., S.M.), Amsterdam UMC (Location VUmc), the Netherlands; Center for Sleep Medicine, Sleep Research and Epileptology (Z.Z., R.K.), Klinik Barmelweid AG, Barmelweid, Switzerland; Leiden Observatory (M.S.S.L.O.), Leiden University, the Netherlands; Sleep-Wake Disorders Unit (Y.D., L.B.), Department of Neurology, Gui-de-Chauliac Hospital, CHU Montpellier; National Reference Centre for Orphan Diseases, Narcolepsy, Idiopathic Hypersomnia, and Kleine-Levin Syndrome (Y.D., L.B.); Institute for Neurosciences of Montpellier INM (Y.D., L.B.), Univ Montpellier, INSERM, France; Neurology Department (G.M.), Hephata Klinik, Schwalmstadt, Germany; Department of Biomedical, Metabolic and Neural Sciences (G.P.), University of Modena and Reggio Emilia; IRCCS Istituto delle Scienze Neurologiche di Bologna (G.P, F.P.), Bologna, Italy; Neurophysiology and Sleep Disorders Unit (R.d.R.-V.), Hospital Vithas Nuestra Señora de América, Madrid; Neurology Service (J.S.C.), Institut de Neurociències Hospital Clínic, University of Barcelona, Spain; Neurology Department and Centre of Clinical Neurosciences (K.S.), First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic; Helsinki Sleep Clinic (M.P.), Vitalmed Research Center, Finland; Sleep Medicine Center Kempenhaeghe (S.O.), Heeze; Eindhoven University of Technology (S.O.), the Netherlands; Sleep and Epilepsy Unit-Clinical Neurophysiology Service (R.P.-A.), University General Hospital Gregorio Marañón, Research Institute Gregorio Marañón; University Complutense of Madrid (R.P.-A.), Spain; Center for Investigation and Research in Sleep (R.H.), Lausanne University Hospital, Switzerland; Serviço de Neurofisiologia (A.M.d.S.), Hospital Santo António/Centro Hospitalar Universitário do Porto and UMIB-Instituto Ciências Biomédicas Abel Salazar, Universidade do Porto, Portugal; Neurology Department (B.H., A.H.), Sleep Disorders Clinic, Innsbruck Medical University, Austria; Department of Clinical Neurophysiology (A.W.), Institute of Psychiatry and Neurology, Warsaw, Poland; Department of Sleep Medicine and Neuromuscular Disorders (A.H.), University of Münster, Germany; Neurology Department (E.F.), Medical Faculty of P.J. Safarik University, University Hospital of L. Pasteur Kosice, Kosice, Slovak Republic; Neurology Department (M.M.), EOC, Ospedale Regionale di Lugano, Ticino, Switzerland; Department of Sleep Medicine (J.B.), National Institute of Mental Health, Klecany, Czech Republic; Fundacio d`Investigacio Sanitaria de les illes balears (F.C.), Hospital Universitari Son Espases, Palma de Mallorca, Spain; Department of Neurology (C.L.B., M.H.S., R.K.), Inselspital, Bern University Hospital, University of Bern, Switzerland; and Department of Biomedical and Neuromotor Sciences (F.P.), University of Bologna, Italy
| | - Aleksandra Wierzbicka
- From the Sleep Wake Center SEIN Heemstede (J.K.G., R.F., G.J.L.), Stichting Epilepsie Instellingen Nederland, Heemstede; Department of Neurology and Clinical Neurophysiology (J.K.G., R.F., G.J.L.), Leiden University Medical Center; Department of Anatomy and Neurosciences (J.K.G., S.M.), Amsterdam UMC (Location VUmc), the Netherlands; Center for Sleep Medicine, Sleep Research and Epileptology (Z.Z., R.K.), Klinik Barmelweid AG, Barmelweid, Switzerland; Leiden Observatory (M.S.S.L.O.), Leiden University, the Netherlands; Sleep-Wake Disorders Unit (Y.D., L.B.), Department of Neurology, Gui-de-Chauliac Hospital, CHU Montpellier; National Reference Centre for Orphan Diseases, Narcolepsy, Idiopathic Hypersomnia, and Kleine-Levin Syndrome (Y.D., L.B.); Institute for Neurosciences of Montpellier INM (Y.D., L.B.), Univ Montpellier, INSERM, France; Neurology Department (G.M.), Hephata Klinik, Schwalmstadt, Germany; Department of Biomedical, Metabolic and Neural Sciences (G.P.), University of Modena and Reggio Emilia; IRCCS Istituto delle Scienze Neurologiche di Bologna (G.P, F.P.), Bologna, Italy; Neurophysiology and Sleep Disorders Unit (R.d.R.-V.), Hospital Vithas Nuestra Señora de América, Madrid; Neurology Service (J.S.C.), Institut de Neurociències Hospital Clínic, University of Barcelona, Spain; Neurology Department and Centre of Clinical Neurosciences (K.S.), First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic; Helsinki Sleep Clinic (M.P.), Vitalmed Research Center, Finland; Sleep Medicine Center Kempenhaeghe (S.O.), Heeze; Eindhoven University of Technology (S.O.), the Netherlands; Sleep and Epilepsy Unit-Clinical Neurophysiology Service (R.P.-A.), University General Hospital Gregorio Marañón, Research Institute Gregorio Marañón; University Complutense of Madrid (R.P.-A.), Spain; Center for Investigation and Research in Sleep (R.H.), Lausanne University Hospital, Switzerland; Serviço de Neurofisiologia (A.M.d.S.), Hospital Santo António/Centro Hospitalar Universitário do Porto and UMIB-Instituto Ciências Biomédicas Abel Salazar, Universidade do Porto, Portugal; Neurology Department (B.H., A.H.), Sleep Disorders Clinic, Innsbruck Medical University, Austria; Department of Clinical Neurophysiology (A.W.), Institute of Psychiatry and Neurology, Warsaw, Poland; Department of Sleep Medicine and Neuromuscular Disorders (A.H.), University of Münster, Germany; Neurology Department (E.F.), Medical Faculty of P.J. Safarik University, University Hospital of L. Pasteur Kosice, Kosice, Slovak Republic; Neurology Department (M.M.), EOC, Ospedale Regionale di Lugano, Ticino, Switzerland; Department of Sleep Medicine (J.B.), National Institute of Mental Health, Klecany, Czech Republic; Fundacio d`Investigacio Sanitaria de les illes balears (F.C.), Hospital Universitari Son Espases, Palma de Mallorca, Spain; Department of Neurology (C.L.B., M.H.S., R.K.), Inselspital, Bern University Hospital, University of Bern, Switzerland; and Department of Biomedical and Neuromotor Sciences (F.P.), University of Bologna, Italy
| | - Anna Heidbreder
- From the Sleep Wake Center SEIN Heemstede (J.K.G., R.F., G.J.L.), Stichting Epilepsie Instellingen Nederland, Heemstede; Department of Neurology and Clinical Neurophysiology (J.K.G., R.F., G.J.L.), Leiden University Medical Center; Department of Anatomy and Neurosciences (J.K.G., S.M.), Amsterdam UMC (Location VUmc), the Netherlands; Center for Sleep Medicine, Sleep Research and Epileptology (Z.Z., R.K.), Klinik Barmelweid AG, Barmelweid, Switzerland; Leiden Observatory (M.S.S.L.O.), Leiden University, the Netherlands; Sleep-Wake Disorders Unit (Y.D., L.B.), Department of Neurology, Gui-de-Chauliac Hospital, CHU Montpellier; National Reference Centre for Orphan Diseases, Narcolepsy, Idiopathic Hypersomnia, and Kleine-Levin Syndrome (Y.D., L.B.); Institute for Neurosciences of Montpellier INM (Y.D., L.B.), Univ Montpellier, INSERM, France; Neurology Department (G.M.), Hephata Klinik, Schwalmstadt, Germany; Department of Biomedical, Metabolic and Neural Sciences (G.P.), University of Modena and Reggio Emilia; IRCCS Istituto delle Scienze Neurologiche di Bologna (G.P, F.P.), Bologna, Italy; Neurophysiology and Sleep Disorders Unit (R.d.R.-V.), Hospital Vithas Nuestra Señora de América, Madrid; Neurology Service (J.S.C.), Institut de Neurociències Hospital Clínic, University of Barcelona, Spain; Neurology Department and Centre of Clinical Neurosciences (K.S.), First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic; Helsinki Sleep Clinic (M.P.), Vitalmed Research Center, Finland; Sleep Medicine Center Kempenhaeghe (S.O.), Heeze; Eindhoven University of Technology (S.O.), the Netherlands; Sleep and Epilepsy Unit-Clinical Neurophysiology Service (R.P.-A.), University General Hospital Gregorio Marañón, Research Institute Gregorio Marañón; University Complutense of Madrid (R.P.-A.), Spain; Center for Investigation and Research in Sleep (R.H.), Lausanne University Hospital, Switzerland; Serviço de Neurofisiologia (A.M.d.S.), Hospital Santo António/Centro Hospitalar Universitário do Porto and UMIB-Instituto Ciências Biomédicas Abel Salazar, Universidade do Porto, Portugal; Neurology Department (B.H., A.H.), Sleep Disorders Clinic, Innsbruck Medical University, Austria; Department of Clinical Neurophysiology (A.W.), Institute of Psychiatry and Neurology, Warsaw, Poland; Department of Sleep Medicine and Neuromuscular Disorders (A.H.), University of Münster, Germany; Neurology Department (E.F.), Medical Faculty of P.J. Safarik University, University Hospital of L. Pasteur Kosice, Kosice, Slovak Republic; Neurology Department (M.M.), EOC, Ospedale Regionale di Lugano, Ticino, Switzerland; Department of Sleep Medicine (J.B.), National Institute of Mental Health, Klecany, Czech Republic; Fundacio d`Investigacio Sanitaria de les illes balears (F.C.), Hospital Universitari Son Espases, Palma de Mallorca, Spain; Department of Neurology (C.L.B., M.H.S., R.K.), Inselspital, Bern University Hospital, University of Bern, Switzerland; and Department of Biomedical and Neuromotor Sciences (F.P.), University of Bologna, Italy
| | - Eva Feketeova
- From the Sleep Wake Center SEIN Heemstede (J.K.G., R.F., G.J.L.), Stichting Epilepsie Instellingen Nederland, Heemstede; Department of Neurology and Clinical Neurophysiology (J.K.G., R.F., G.J.L.), Leiden University Medical Center; Department of Anatomy and Neurosciences (J.K.G., S.M.), Amsterdam UMC (Location VUmc), the Netherlands; Center for Sleep Medicine, Sleep Research and Epileptology (Z.Z., R.K.), Klinik Barmelweid AG, Barmelweid, Switzerland; Leiden Observatory (M.S.S.L.O.), Leiden University, the Netherlands; Sleep-Wake Disorders Unit (Y.D., L.B.), Department of Neurology, Gui-de-Chauliac Hospital, CHU Montpellier; National Reference Centre for Orphan Diseases, Narcolepsy, Idiopathic Hypersomnia, and Kleine-Levin Syndrome (Y.D., L.B.); Institute for Neurosciences of Montpellier INM (Y.D., L.B.), Univ Montpellier, INSERM, France; Neurology Department (G.M.), Hephata Klinik, Schwalmstadt, Germany; Department of Biomedical, Metabolic and Neural Sciences (G.P.), University of Modena and Reggio Emilia; IRCCS Istituto delle Scienze Neurologiche di Bologna (G.P, F.P.), Bologna, Italy; Neurophysiology and Sleep Disorders Unit (R.d.R.-V.), Hospital Vithas Nuestra Señora de América, Madrid; Neurology Service (J.S.C.), Institut de Neurociències Hospital Clínic, University of Barcelona, Spain; Neurology Department and Centre of Clinical Neurosciences (K.S.), First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic; Helsinki Sleep Clinic (M.P.), Vitalmed Research Center, Finland; Sleep Medicine Center Kempenhaeghe (S.O.), Heeze; Eindhoven University of Technology (S.O.), the Netherlands; Sleep and Epilepsy Unit-Clinical Neurophysiology Service (R.P.-A.), University General Hospital Gregorio Marañón, Research Institute Gregorio Marañón; University Complutense of Madrid (R.P.-A.), Spain; Center for Investigation and Research in Sleep (R.H.), Lausanne University Hospital, Switzerland; Serviço de Neurofisiologia (A.M.d.S.), Hospital Santo António/Centro Hospitalar Universitário do Porto and UMIB-Instituto Ciências Biomédicas Abel Salazar, Universidade do Porto, Portugal; Neurology Department (B.H., A.H.), Sleep Disorders Clinic, Innsbruck Medical University, Austria; Department of Clinical Neurophysiology (A.W.), Institute of Psychiatry and Neurology, Warsaw, Poland; Department of Sleep Medicine and Neuromuscular Disorders (A.H.), University of Münster, Germany; Neurology Department (E.F.), Medical Faculty of P.J. Safarik University, University Hospital of L. Pasteur Kosice, Kosice, Slovak Republic; Neurology Department (M.M.), EOC, Ospedale Regionale di Lugano, Ticino, Switzerland; Department of Sleep Medicine (J.B.), National Institute of Mental Health, Klecany, Czech Republic; Fundacio d`Investigacio Sanitaria de les illes balears (F.C.), Hospital Universitari Son Espases, Palma de Mallorca, Spain; Department of Neurology (C.L.B., M.H.S., R.K.), Inselspital, Bern University Hospital, University of Bern, Switzerland; and Department of Biomedical and Neuromotor Sciences (F.P.), University of Bologna, Italy
| | - Mauro Manconi
- From the Sleep Wake Center SEIN Heemstede (J.K.G., R.F., G.J.L.), Stichting Epilepsie Instellingen Nederland, Heemstede; Department of Neurology and Clinical Neurophysiology (J.K.G., R.F., G.J.L.), Leiden University Medical Center; Department of Anatomy and Neurosciences (J.K.G., S.M.), Amsterdam UMC (Location VUmc), the Netherlands; Center for Sleep Medicine, Sleep Research and Epileptology (Z.Z., R.K.), Klinik Barmelweid AG, Barmelweid, Switzerland; Leiden Observatory (M.S.S.L.O.), Leiden University, the Netherlands; Sleep-Wake Disorders Unit (Y.D., L.B.), Department of Neurology, Gui-de-Chauliac Hospital, CHU Montpellier; National Reference Centre for Orphan Diseases, Narcolepsy, Idiopathic Hypersomnia, and Kleine-Levin Syndrome (Y.D., L.B.); Institute for Neurosciences of Montpellier INM (Y.D., L.B.), Univ Montpellier, INSERM, France; Neurology Department (G.M.), Hephata Klinik, Schwalmstadt, Germany; Department of Biomedical, Metabolic and Neural Sciences (G.P.), University of Modena and Reggio Emilia; IRCCS Istituto delle Scienze Neurologiche di Bologna (G.P, F.P.), Bologna, Italy; Neurophysiology and Sleep Disorders Unit (R.d.R.-V.), Hospital Vithas Nuestra Señora de América, Madrid; Neurology Service (J.S.C.), Institut de Neurociències Hospital Clínic, University of Barcelona, Spain; Neurology Department and Centre of Clinical Neurosciences (K.S.), First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic; Helsinki Sleep Clinic (M.P.), Vitalmed Research Center, Finland; Sleep Medicine Center Kempenhaeghe (S.O.), Heeze; Eindhoven University of Technology (S.O.), the Netherlands; Sleep and Epilepsy Unit-Clinical Neurophysiology Service (R.P.-A.), University General Hospital Gregorio Marañón, Research Institute Gregorio Marañón; University Complutense of Madrid (R.P.-A.), Spain; Center for Investigation and Research in Sleep (R.H.), Lausanne University Hospital, Switzerland; Serviço de Neurofisiologia (A.M.d.S.), Hospital Santo António/Centro Hospitalar Universitário do Porto and UMIB-Instituto Ciências Biomédicas Abel Salazar, Universidade do Porto, Portugal; Neurology Department (B.H., A.H.), Sleep Disorders Clinic, Innsbruck Medical University, Austria; Department of Clinical Neurophysiology (A.W.), Institute of Psychiatry and Neurology, Warsaw, Poland; Department of Sleep Medicine and Neuromuscular Disorders (A.H.), University of Münster, Germany; Neurology Department (E.F.), Medical Faculty of P.J. Safarik University, University Hospital of L. Pasteur Kosice, Kosice, Slovak Republic; Neurology Department (M.M.), EOC, Ospedale Regionale di Lugano, Ticino, Switzerland; Department of Sleep Medicine (J.B.), National Institute of Mental Health, Klecany, Czech Republic; Fundacio d`Investigacio Sanitaria de les illes balears (F.C.), Hospital Universitari Son Espases, Palma de Mallorca, Spain; Department of Neurology (C.L.B., M.H.S., R.K.), Inselspital, Bern University Hospital, University of Bern, Switzerland; and Department of Biomedical and Neuromotor Sciences (F.P.), University of Bologna, Italy
| | - Jitka Bušková
- From the Sleep Wake Center SEIN Heemstede (J.K.G., R.F., G.J.L.), Stichting Epilepsie Instellingen Nederland, Heemstede; Department of Neurology and Clinical Neurophysiology (J.K.G., R.F., G.J.L.), Leiden University Medical Center; Department of Anatomy and Neurosciences (J.K.G., S.M.), Amsterdam UMC (Location VUmc), the Netherlands; Center for Sleep Medicine, Sleep Research and Epileptology (Z.Z., R.K.), Klinik Barmelweid AG, Barmelweid, Switzerland; Leiden Observatory (M.S.S.L.O.), Leiden University, the Netherlands; Sleep-Wake Disorders Unit (Y.D., L.B.), Department of Neurology, Gui-de-Chauliac Hospital, CHU Montpellier; National Reference Centre for Orphan Diseases, Narcolepsy, Idiopathic Hypersomnia, and Kleine-Levin Syndrome (Y.D., L.B.); Institute for Neurosciences of Montpellier INM (Y.D., L.B.), Univ Montpellier, INSERM, France; Neurology Department (G.M.), Hephata Klinik, Schwalmstadt, Germany; Department of Biomedical, Metabolic and Neural Sciences (G.P.), University of Modena and Reggio Emilia; IRCCS Istituto delle Scienze Neurologiche di Bologna (G.P, F.P.), Bologna, Italy; Neurophysiology and Sleep Disorders Unit (R.d.R.-V.), Hospital Vithas Nuestra Señora de América, Madrid; Neurology Service (J.S.C.), Institut de Neurociències Hospital Clínic, University of Barcelona, Spain; Neurology Department and Centre of Clinical Neurosciences (K.S.), First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic; Helsinki Sleep Clinic (M.P.), Vitalmed Research Center, Finland; Sleep Medicine Center Kempenhaeghe (S.O.), Heeze; Eindhoven University of Technology (S.O.), the Netherlands; Sleep and Epilepsy Unit-Clinical Neurophysiology Service (R.P.-A.), University General Hospital Gregorio Marañón, Research Institute Gregorio Marañón; University Complutense of Madrid (R.P.-A.), Spain; Center for Investigation and Research in Sleep (R.H.), Lausanne University Hospital, Switzerland; Serviço de Neurofisiologia (A.M.d.S.), Hospital Santo António/Centro Hospitalar Universitário do Porto and UMIB-Instituto Ciências Biomédicas Abel Salazar, Universidade do Porto, Portugal; Neurology Department (B.H., A.H.), Sleep Disorders Clinic, Innsbruck Medical University, Austria; Department of Clinical Neurophysiology (A.W.), Institute of Psychiatry and Neurology, Warsaw, Poland; Department of Sleep Medicine and Neuromuscular Disorders (A.H.), University of Münster, Germany; Neurology Department (E.F.), Medical Faculty of P.J. Safarik University, University Hospital of L. Pasteur Kosice, Kosice, Slovak Republic; Neurology Department (M.M.), EOC, Ospedale Regionale di Lugano, Ticino, Switzerland; Department of Sleep Medicine (J.B.), National Institute of Mental Health, Klecany, Czech Republic; Fundacio d`Investigacio Sanitaria de les illes balears (F.C.), Hospital Universitari Son Espases, Palma de Mallorca, Spain; Department of Neurology (C.L.B., M.H.S., R.K.), Inselspital, Bern University Hospital, University of Bern, Switzerland; and Department of Biomedical and Neuromotor Sciences (F.P.), University of Bologna, Italy
| | - Francesca Canellas
- From the Sleep Wake Center SEIN Heemstede (J.K.G., R.F., G.J.L.), Stichting Epilepsie Instellingen Nederland, Heemstede; Department of Neurology and Clinical Neurophysiology (J.K.G., R.F., G.J.L.), Leiden University Medical Center; Department of Anatomy and Neurosciences (J.K.G., S.M.), Amsterdam UMC (Location VUmc), the Netherlands; Center for Sleep Medicine, Sleep Research and Epileptology (Z.Z., R.K.), Klinik Barmelweid AG, Barmelweid, Switzerland; Leiden Observatory (M.S.S.L.O.), Leiden University, the Netherlands; Sleep-Wake Disorders Unit (Y.D., L.B.), Department of Neurology, Gui-de-Chauliac Hospital, CHU Montpellier; National Reference Centre for Orphan Diseases, Narcolepsy, Idiopathic Hypersomnia, and Kleine-Levin Syndrome (Y.D., L.B.); Institute for Neurosciences of Montpellier INM (Y.D., L.B.), Univ Montpellier, INSERM, France; Neurology Department (G.M.), Hephata Klinik, Schwalmstadt, Germany; Department of Biomedical, Metabolic and Neural Sciences (G.P.), University of Modena and Reggio Emilia; IRCCS Istituto delle Scienze Neurologiche di Bologna (G.P, F.P.), Bologna, Italy; Neurophysiology and Sleep Disorders Unit (R.d.R.-V.), Hospital Vithas Nuestra Señora de América, Madrid; Neurology Service (J.S.C.), Institut de Neurociències Hospital Clínic, University of Barcelona, Spain; Neurology Department and Centre of Clinical Neurosciences (K.S.), First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic; Helsinki Sleep Clinic (M.P.), Vitalmed Research Center, Finland; Sleep Medicine Center Kempenhaeghe (S.O.), Heeze; Eindhoven University of Technology (S.O.), the Netherlands; Sleep and Epilepsy Unit-Clinical Neurophysiology Service (R.P.-A.), University General Hospital Gregorio Marañón, Research Institute Gregorio Marañón; University Complutense of Madrid (R.P.-A.), Spain; Center for Investigation and Research in Sleep (R.H.), Lausanne University Hospital, Switzerland; Serviço de Neurofisiologia (A.M.d.S.), Hospital Santo António/Centro Hospitalar Universitário do Porto and UMIB-Instituto Ciências Biomédicas Abel Salazar, Universidade do Porto, Portugal; Neurology Department (B.H., A.H.), Sleep Disorders Clinic, Innsbruck Medical University, Austria; Department of Clinical Neurophysiology (A.W.), Institute of Psychiatry and Neurology, Warsaw, Poland; Department of Sleep Medicine and Neuromuscular Disorders (A.H.), University of Münster, Germany; Neurology Department (E.F.), Medical Faculty of P.J. Safarik University, University Hospital of L. Pasteur Kosice, Kosice, Slovak Republic; Neurology Department (M.M.), EOC, Ospedale Regionale di Lugano, Ticino, Switzerland; Department of Sleep Medicine (J.B.), National Institute of Mental Health, Klecany, Czech Republic; Fundacio d`Investigacio Sanitaria de les illes balears (F.C.), Hospital Universitari Son Espases, Palma de Mallorca, Spain; Department of Neurology (C.L.B., M.H.S., R.K.), Inselspital, Bern University Hospital, University of Bern, Switzerland; and Department of Biomedical and Neuromotor Sciences (F.P.), University of Bologna, Italy
| | - Claudio L Bassetti
- From the Sleep Wake Center SEIN Heemstede (J.K.G., R.F., G.J.L.), Stichting Epilepsie Instellingen Nederland, Heemstede; Department of Neurology and Clinical Neurophysiology (J.K.G., R.F., G.J.L.), Leiden University Medical Center; Department of Anatomy and Neurosciences (J.K.G., S.M.), Amsterdam UMC (Location VUmc), the Netherlands; Center for Sleep Medicine, Sleep Research and Epileptology (Z.Z., R.K.), Klinik Barmelweid AG, Barmelweid, Switzerland; Leiden Observatory (M.S.S.L.O.), Leiden University, the Netherlands; Sleep-Wake Disorders Unit (Y.D., L.B.), Department of Neurology, Gui-de-Chauliac Hospital, CHU Montpellier; National Reference Centre for Orphan Diseases, Narcolepsy, Idiopathic Hypersomnia, and Kleine-Levin Syndrome (Y.D., L.B.); Institute for Neurosciences of Montpellier INM (Y.D., L.B.), Univ Montpellier, INSERM, France; Neurology Department (G.M.), Hephata Klinik, Schwalmstadt, Germany; Department of Biomedical, Metabolic and Neural Sciences (G.P.), University of Modena and Reggio Emilia; IRCCS Istituto delle Scienze Neurologiche di Bologna (G.P, F.P.), Bologna, Italy; Neurophysiology and Sleep Disorders Unit (R.d.R.-V.), Hospital Vithas Nuestra Señora de América, Madrid; Neurology Service (J.S.C.), Institut de Neurociències Hospital Clínic, University of Barcelona, Spain; Neurology Department and Centre of Clinical Neurosciences (K.S.), First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic; Helsinki Sleep Clinic (M.P.), Vitalmed Research Center, Finland; Sleep Medicine Center Kempenhaeghe (S.O.), Heeze; Eindhoven University of Technology (S.O.), the Netherlands; Sleep and Epilepsy Unit-Clinical Neurophysiology Service (R.P.-A.), University General Hospital Gregorio Marañón, Research Institute Gregorio Marañón; University Complutense of Madrid (R.P.-A.), Spain; Center for Investigation and Research in Sleep (R.H.), Lausanne University Hospital, Switzerland; Serviço de Neurofisiologia (A.M.d.S.), Hospital Santo António/Centro Hospitalar Universitário do Porto and UMIB-Instituto Ciências Biomédicas Abel Salazar, Universidade do Porto, Portugal; Neurology Department (B.H., A.H.), Sleep Disorders Clinic, Innsbruck Medical University, Austria; Department of Clinical Neurophysiology (A.W.), Institute of Psychiatry and Neurology, Warsaw, Poland; Department of Sleep Medicine and Neuromuscular Disorders (A.H.), University of Münster, Germany; Neurology Department (E.F.), Medical Faculty of P.J. Safarik University, University Hospital of L. Pasteur Kosice, Kosice, Slovak Republic; Neurology Department (M.M.), EOC, Ospedale Regionale di Lugano, Ticino, Switzerland; Department of Sleep Medicine (J.B.), National Institute of Mental Health, Klecany, Czech Republic; Fundacio d`Investigacio Sanitaria de les illes balears (F.C.), Hospital Universitari Son Espases, Palma de Mallorca, Spain; Department of Neurology (C.L.B., M.H.S., R.K.), Inselspital, Bern University Hospital, University of Bern, Switzerland; and Department of Biomedical and Neuromotor Sciences (F.P.), University of Bologna, Italy
| | - Lucie Barateau
- From the Sleep Wake Center SEIN Heemstede (J.K.G., R.F., G.J.L.), Stichting Epilepsie Instellingen Nederland, Heemstede; Department of Neurology and Clinical Neurophysiology (J.K.G., R.F., G.J.L.), Leiden University Medical Center; Department of Anatomy and Neurosciences (J.K.G., S.M.), Amsterdam UMC (Location VUmc), the Netherlands; Center for Sleep Medicine, Sleep Research and Epileptology (Z.Z., R.K.), Klinik Barmelweid AG, Barmelweid, Switzerland; Leiden Observatory (M.S.S.L.O.), Leiden University, the Netherlands; Sleep-Wake Disorders Unit (Y.D., L.B.), Department of Neurology, Gui-de-Chauliac Hospital, CHU Montpellier; National Reference Centre for Orphan Diseases, Narcolepsy, Idiopathic Hypersomnia, and Kleine-Levin Syndrome (Y.D., L.B.); Institute for Neurosciences of Montpellier INM (Y.D., L.B.), Univ Montpellier, INSERM, France; Neurology Department (G.M.), Hephata Klinik, Schwalmstadt, Germany; Department of Biomedical, Metabolic and Neural Sciences (G.P.), University of Modena and Reggio Emilia; IRCCS Istituto delle Scienze Neurologiche di Bologna (G.P, F.P.), Bologna, Italy; Neurophysiology and Sleep Disorders Unit (R.d.R.-V.), Hospital Vithas Nuestra Señora de América, Madrid; Neurology Service (J.S.C.), Institut de Neurociències Hospital Clínic, University of Barcelona, Spain; Neurology Department and Centre of Clinical Neurosciences (K.S.), First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic; Helsinki Sleep Clinic (M.P.), Vitalmed Research Center, Finland; Sleep Medicine Center Kempenhaeghe (S.O.), Heeze; Eindhoven University of Technology (S.O.), the Netherlands; Sleep and Epilepsy Unit-Clinical Neurophysiology Service (R.P.-A.), University General Hospital Gregorio Marañón, Research Institute Gregorio Marañón; University Complutense of Madrid (R.P.-A.), Spain; Center for Investigation and Research in Sleep (R.H.), Lausanne University Hospital, Switzerland; Serviço de Neurofisiologia (A.M.d.S.), Hospital Santo António/Centro Hospitalar Universitário do Porto and UMIB-Instituto Ciências Biomédicas Abel Salazar, Universidade do Porto, Portugal; Neurology Department (B.H., A.H.), Sleep Disorders Clinic, Innsbruck Medical University, Austria; Department of Clinical Neurophysiology (A.W.), Institute of Psychiatry and Neurology, Warsaw, Poland; Department of Sleep Medicine and Neuromuscular Disorders (A.H.), University of Münster, Germany; Neurology Department (E.F.), Medical Faculty of P.J. Safarik University, University Hospital of L. Pasteur Kosice, Kosice, Slovak Republic; Neurology Department (M.M.), EOC, Ospedale Regionale di Lugano, Ticino, Switzerland; Department of Sleep Medicine (J.B.), National Institute of Mental Health, Klecany, Czech Republic; Fundacio d`Investigacio Sanitaria de les illes balears (F.C.), Hospital Universitari Son Espases, Palma de Mallorca, Spain; Department of Neurology (C.L.B., M.H.S., R.K.), Inselspital, Bern University Hospital, University of Bern, Switzerland; and Department of Biomedical and Neuromotor Sciences (F.P.), University of Bologna, Italy
| | - Fabio Pizza
- From the Sleep Wake Center SEIN Heemstede (J.K.G., R.F., G.J.L.), Stichting Epilepsie Instellingen Nederland, Heemstede; Department of Neurology and Clinical Neurophysiology (J.K.G., R.F., G.J.L.), Leiden University Medical Center; Department of Anatomy and Neurosciences (J.K.G., S.M.), Amsterdam UMC (Location VUmc), the Netherlands; Center for Sleep Medicine, Sleep Research and Epileptology (Z.Z., R.K.), Klinik Barmelweid AG, Barmelweid, Switzerland; Leiden Observatory (M.S.S.L.O.), Leiden University, the Netherlands; Sleep-Wake Disorders Unit (Y.D., L.B.), Department of Neurology, Gui-de-Chauliac Hospital, CHU Montpellier; National Reference Centre for Orphan Diseases, Narcolepsy, Idiopathic Hypersomnia, and Kleine-Levin Syndrome (Y.D., L.B.); Institute for Neurosciences of Montpellier INM (Y.D., L.B.), Univ Montpellier, INSERM, France; Neurology Department (G.M.), Hephata Klinik, Schwalmstadt, Germany; Department of Biomedical, Metabolic and Neural Sciences (G.P.), University of Modena and Reggio Emilia; IRCCS Istituto delle Scienze Neurologiche di Bologna (G.P, F.P.), Bologna, Italy; Neurophysiology and Sleep Disorders Unit (R.d.R.-V.), Hospital Vithas Nuestra Señora de América, Madrid; Neurology Service (J.S.C.), Institut de Neurociències Hospital Clínic, University of Barcelona, Spain; Neurology Department and Centre of Clinical Neurosciences (K.S.), First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic; Helsinki Sleep Clinic (M.P.), Vitalmed Research Center, Finland; Sleep Medicine Center Kempenhaeghe (S.O.), Heeze; Eindhoven University of Technology (S.O.), the Netherlands; Sleep and Epilepsy Unit-Clinical Neurophysiology Service (R.P.-A.), University General Hospital Gregorio Marañón, Research Institute Gregorio Marañón; University Complutense of Madrid (R.P.-A.), Spain; Center for Investigation and Research in Sleep (R.H.), Lausanne University Hospital, Switzerland; Serviço de Neurofisiologia (A.M.d.S.), Hospital Santo António/Centro Hospitalar Universitário do Porto and UMIB-Instituto Ciências Biomédicas Abel Salazar, Universidade do Porto, Portugal; Neurology Department (B.H., A.H.), Sleep Disorders Clinic, Innsbruck Medical University, Austria; Department of Clinical Neurophysiology (A.W.), Institute of Psychiatry and Neurology, Warsaw, Poland; Department of Sleep Medicine and Neuromuscular Disorders (A.H.), University of Münster, Germany; Neurology Department (E.F.), Medical Faculty of P.J. Safarik University, University Hospital of L. Pasteur Kosice, Kosice, Slovak Republic; Neurology Department (M.M.), EOC, Ospedale Regionale di Lugano, Ticino, Switzerland; Department of Sleep Medicine (J.B.), National Institute of Mental Health, Klecany, Czech Republic; Fundacio d`Investigacio Sanitaria de les illes balears (F.C.), Hospital Universitari Son Espases, Palma de Mallorca, Spain; Department of Neurology (C.L.B., M.H.S., R.K.), Inselspital, Bern University Hospital, University of Bern, Switzerland; and Department of Biomedical and Neuromotor Sciences (F.P.), University of Bologna, Italy
| | - Markus H Schmidt
- From the Sleep Wake Center SEIN Heemstede (J.K.G., R.F., G.J.L.), Stichting Epilepsie Instellingen Nederland, Heemstede; Department of Neurology and Clinical Neurophysiology (J.K.G., R.F., G.J.L.), Leiden University Medical Center; Department of Anatomy and Neurosciences (J.K.G., S.M.), Amsterdam UMC (Location VUmc), the Netherlands; Center for Sleep Medicine, Sleep Research and Epileptology (Z.Z., R.K.), Klinik Barmelweid AG, Barmelweid, Switzerland; Leiden Observatory (M.S.S.L.O.), Leiden University, the Netherlands; Sleep-Wake Disorders Unit (Y.D., L.B.), Department of Neurology, Gui-de-Chauliac Hospital, CHU Montpellier; National Reference Centre for Orphan Diseases, Narcolepsy, Idiopathic Hypersomnia, and Kleine-Levin Syndrome (Y.D., L.B.); Institute for Neurosciences of Montpellier INM (Y.D., L.B.), Univ Montpellier, INSERM, France; Neurology Department (G.M.), Hephata Klinik, Schwalmstadt, Germany; Department of Biomedical, Metabolic and Neural Sciences (G.P.), University of Modena and Reggio Emilia; IRCCS Istituto delle Scienze Neurologiche di Bologna (G.P, F.P.), Bologna, Italy; Neurophysiology and Sleep Disorders Unit (R.d.R.-V.), Hospital Vithas Nuestra Señora de América, Madrid; Neurology Service (J.S.C.), Institut de Neurociències Hospital Clínic, University of Barcelona, Spain; Neurology Department and Centre of Clinical Neurosciences (K.S.), First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic; Helsinki Sleep Clinic (M.P.), Vitalmed Research Center, Finland; Sleep Medicine Center Kempenhaeghe (S.O.), Heeze; Eindhoven University of Technology (S.O.), the Netherlands; Sleep and Epilepsy Unit-Clinical Neurophysiology Service (R.P.-A.), University General Hospital Gregorio Marañón, Research Institute Gregorio Marañón; University Complutense of Madrid (R.P.-A.), Spain; Center for Investigation and Research in Sleep (R.H.), Lausanne University Hospital, Switzerland; Serviço de Neurofisiologia (A.M.d.S.), Hospital Santo António/Centro Hospitalar Universitário do Porto and UMIB-Instituto Ciências Biomédicas Abel Salazar, Universidade do Porto, Portugal; Neurology Department (B.H., A.H.), Sleep Disorders Clinic, Innsbruck Medical University, Austria; Department of Clinical Neurophysiology (A.W.), Institute of Psychiatry and Neurology, Warsaw, Poland; Department of Sleep Medicine and Neuromuscular Disorders (A.H.), University of Münster, Germany; Neurology Department (E.F.), Medical Faculty of P.J. Safarik University, University Hospital of L. Pasteur Kosice, Kosice, Slovak Republic; Neurology Department (M.M.), EOC, Ospedale Regionale di Lugano, Ticino, Switzerland; Department of Sleep Medicine (J.B.), National Institute of Mental Health, Klecany, Czech Republic; Fundacio d`Investigacio Sanitaria de les illes balears (F.C.), Hospital Universitari Son Espases, Palma de Mallorca, Spain; Department of Neurology (C.L.B., M.H.S., R.K.), Inselspital, Bern University Hospital, University of Bern, Switzerland; and Department of Biomedical and Neuromotor Sciences (F.P.), University of Bologna, Italy
| | - Rolf Fronczek
- From the Sleep Wake Center SEIN Heemstede (J.K.G., R.F., G.J.L.), Stichting Epilepsie Instellingen Nederland, Heemstede; Department of Neurology and Clinical Neurophysiology (J.K.G., R.F., G.J.L.), Leiden University Medical Center; Department of Anatomy and Neurosciences (J.K.G., S.M.), Amsterdam UMC (Location VUmc), the Netherlands; Center for Sleep Medicine, Sleep Research and Epileptology (Z.Z., R.K.), Klinik Barmelweid AG, Barmelweid, Switzerland; Leiden Observatory (M.S.S.L.O.), Leiden University, the Netherlands; Sleep-Wake Disorders Unit (Y.D., L.B.), Department of Neurology, Gui-de-Chauliac Hospital, CHU Montpellier; National Reference Centre for Orphan Diseases, Narcolepsy, Idiopathic Hypersomnia, and Kleine-Levin Syndrome (Y.D., L.B.); Institute for Neurosciences of Montpellier INM (Y.D., L.B.), Univ Montpellier, INSERM, France; Neurology Department (G.M.), Hephata Klinik, Schwalmstadt, Germany; Department of Biomedical, Metabolic and Neural Sciences (G.P.), University of Modena and Reggio Emilia; IRCCS Istituto delle Scienze Neurologiche di Bologna (G.P, F.P.), Bologna, Italy; Neurophysiology and Sleep Disorders Unit (R.d.R.-V.), Hospital Vithas Nuestra Señora de América, Madrid; Neurology Service (J.S.C.), Institut de Neurociències Hospital Clínic, University of Barcelona, Spain; Neurology Department and Centre of Clinical Neurosciences (K.S.), First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic; Helsinki Sleep Clinic (M.P.), Vitalmed Research Center, Finland; Sleep Medicine Center Kempenhaeghe (S.O.), Heeze; Eindhoven University of Technology (S.O.), the Netherlands; Sleep and Epilepsy Unit-Clinical Neurophysiology Service (R.P.-A.), University General Hospital Gregorio Marañón, Research Institute Gregorio Marañón; University Complutense of Madrid (R.P.-A.), Spain; Center for Investigation and Research in Sleep (R.H.), Lausanne University Hospital, Switzerland; Serviço de Neurofisiologia (A.M.d.S.), Hospital Santo António/Centro Hospitalar Universitário do Porto and UMIB-Instituto Ciências Biomédicas Abel Salazar, Universidade do Porto, Portugal; Neurology Department (B.H., A.H.), Sleep Disorders Clinic, Innsbruck Medical University, Austria; Department of Clinical Neurophysiology (A.W.), Institute of Psychiatry and Neurology, Warsaw, Poland; Department of Sleep Medicine and Neuromuscular Disorders (A.H.), University of Münster, Germany; Neurology Department (E.F.), Medical Faculty of P.J. Safarik University, University Hospital of L. Pasteur Kosice, Kosice, Slovak Republic; Neurology Department (M.M.), EOC, Ospedale Regionale di Lugano, Ticino, Switzerland; Department of Sleep Medicine (J.B.), National Institute of Mental Health, Klecany, Czech Republic; Fundacio d`Investigacio Sanitaria de les illes balears (F.C.), Hospital Universitari Son Espases, Palma de Mallorca, Spain; Department of Neurology (C.L.B., M.H.S., R.K.), Inselspital, Bern University Hospital, University of Bern, Switzerland; and Department of Biomedical and Neuromotor Sciences (F.P.), University of Bologna, Italy
| | - Ramin Khatami
- From the Sleep Wake Center SEIN Heemstede (J.K.G., R.F., G.J.L.), Stichting Epilepsie Instellingen Nederland, Heemstede; Department of Neurology and Clinical Neurophysiology (J.K.G., R.F., G.J.L.), Leiden University Medical Center; Department of Anatomy and Neurosciences (J.K.G., S.M.), Amsterdam UMC (Location VUmc), the Netherlands; Center for Sleep Medicine, Sleep Research and Epileptology (Z.Z., R.K.), Klinik Barmelweid AG, Barmelweid, Switzerland; Leiden Observatory (M.S.S.L.O.), Leiden University, the Netherlands; Sleep-Wake Disorders Unit (Y.D., L.B.), Department of Neurology, Gui-de-Chauliac Hospital, CHU Montpellier; National Reference Centre for Orphan Diseases, Narcolepsy, Idiopathic Hypersomnia, and Kleine-Levin Syndrome (Y.D., L.B.); Institute for Neurosciences of Montpellier INM (Y.D., L.B.), Univ Montpellier, INSERM, France; Neurology Department (G.M.), Hephata Klinik, Schwalmstadt, Germany; Department of Biomedical, Metabolic and Neural Sciences (G.P.), University of Modena and Reggio Emilia; IRCCS Istituto delle Scienze Neurologiche di Bologna (G.P, F.P.), Bologna, Italy; Neurophysiology and Sleep Disorders Unit (R.d.R.-V.), Hospital Vithas Nuestra Señora de América, Madrid; Neurology Service (J.S.C.), Institut de Neurociències Hospital Clínic, University of Barcelona, Spain; Neurology Department and Centre of Clinical Neurosciences (K.S.), First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic; Helsinki Sleep Clinic (M.P.), Vitalmed Research Center, Finland; Sleep Medicine Center Kempenhaeghe (S.O.), Heeze; Eindhoven University of Technology (S.O.), the Netherlands; Sleep and Epilepsy Unit-Clinical Neurophysiology Service (R.P.-A.), University General Hospital Gregorio Marañón, Research Institute Gregorio Marañón; University Complutense of Madrid (R.P.-A.), Spain; Center for Investigation and Research in Sleep (R.H.), Lausanne University Hospital, Switzerland; Serviço de Neurofisiologia (A.M.d.S.), Hospital Santo António/Centro Hospitalar Universitário do Porto and UMIB-Instituto Ciências Biomédicas Abel Salazar, Universidade do Porto, Portugal; Neurology Department (B.H., A.H.), Sleep Disorders Clinic, Innsbruck Medical University, Austria; Department of Clinical Neurophysiology (A.W.), Institute of Psychiatry and Neurology, Warsaw, Poland; Department of Sleep Medicine and Neuromuscular Disorders (A.H.), University of Münster, Germany; Neurology Department (E.F.), Medical Faculty of P.J. Safarik University, University Hospital of L. Pasteur Kosice, Kosice, Slovak Republic; Neurology Department (M.M.), EOC, Ospedale Regionale di Lugano, Ticino, Switzerland; Department of Sleep Medicine (J.B.), National Institute of Mental Health, Klecany, Czech Republic; Fundacio d`Investigacio Sanitaria de les illes balears (F.C.), Hospital Universitari Son Espases, Palma de Mallorca, Spain; Department of Neurology (C.L.B., M.H.S., R.K.), Inselspital, Bern University Hospital, University of Bern, Switzerland; and Department of Biomedical and Neuromotor Sciences (F.P.), University of Bologna, Italy
| | - Gert Jan Lammers
- From the Sleep Wake Center SEIN Heemstede (J.K.G., R.F., G.J.L.), Stichting Epilepsie Instellingen Nederland, Heemstede; Department of Neurology and Clinical Neurophysiology (J.K.G., R.F., G.J.L.), Leiden University Medical Center; Department of Anatomy and Neurosciences (J.K.G., S.M.), Amsterdam UMC (Location VUmc), the Netherlands; Center for Sleep Medicine, Sleep Research and Epileptology (Z.Z., R.K.), Klinik Barmelweid AG, Barmelweid, Switzerland; Leiden Observatory (M.S.S.L.O.), Leiden University, the Netherlands; Sleep-Wake Disorders Unit (Y.D., L.B.), Department of Neurology, Gui-de-Chauliac Hospital, CHU Montpellier; National Reference Centre for Orphan Diseases, Narcolepsy, Idiopathic Hypersomnia, and Kleine-Levin Syndrome (Y.D., L.B.); Institute for Neurosciences of Montpellier INM (Y.D., L.B.), Univ Montpellier, INSERM, France; Neurology Department (G.M.), Hephata Klinik, Schwalmstadt, Germany; Department of Biomedical, Metabolic and Neural Sciences (G.P.), University of Modena and Reggio Emilia; IRCCS Istituto delle Scienze Neurologiche di Bologna (G.P, F.P.), Bologna, Italy; Neurophysiology and Sleep Disorders Unit (R.d.R.-V.), Hospital Vithas Nuestra Señora de América, Madrid; Neurology Service (J.S.C.), Institut de Neurociències Hospital Clínic, University of Barcelona, Spain; Neurology Department and Centre of Clinical Neurosciences (K.S.), First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic; Helsinki Sleep Clinic (M.P.), Vitalmed Research Center, Finland; Sleep Medicine Center Kempenhaeghe (S.O.), Heeze; Eindhoven University of Technology (S.O.), the Netherlands; Sleep and Epilepsy Unit-Clinical Neurophysiology Service (R.P.-A.), University General Hospital Gregorio Marañón, Research Institute Gregorio Marañón; University Complutense of Madrid (R.P.-A.), Spain; Center for Investigation and Research in Sleep (R.H.), Lausanne University Hospital, Switzerland; Serviço de Neurofisiologia (A.M.d.S.), Hospital Santo António/Centro Hospitalar Universitário do Porto and UMIB-Instituto Ciências Biomédicas Abel Salazar, Universidade do Porto, Portugal; Neurology Department (B.H., A.H.), Sleep Disorders Clinic, Innsbruck Medical University, Austria; Department of Clinical Neurophysiology (A.W.), Institute of Psychiatry and Neurology, Warsaw, Poland; Department of Sleep Medicine and Neuromuscular Disorders (A.H.), University of Münster, Germany; Neurology Department (E.F.), Medical Faculty of P.J. Safarik University, University Hospital of L. Pasteur Kosice, Kosice, Slovak Republic; Neurology Department (M.M.), EOC, Ospedale Regionale di Lugano, Ticino, Switzerland; Department of Sleep Medicine (J.B.), National Institute of Mental Health, Klecany, Czech Republic; Fundacio d`Investigacio Sanitaria de les illes balears (F.C.), Hospital Universitari Son Espases, Palma de Mallorca, Spain; Department of Neurology (C.L.B., M.H.S., R.K.), Inselspital, Bern University Hospital, University of Bern, Switzerland; and Department of Biomedical and Neuromotor Sciences (F.P.), University of Bologna, Italy
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Fermin ASR, Friston K, Yamawaki S. An insula hierarchical network architecture for active interoceptive inference. ROYAL SOCIETY OPEN SCIENCE 2022; 9:220226. [PMID: 35774133 PMCID: PMC9240682 DOI: 10.1098/rsos.220226] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 06/09/2022] [Indexed: 05/05/2023]
Abstract
In the brain, the insular cortex receives a vast amount of interoceptive information, ascending through deep brain structures, from multiple visceral organs. The unique hierarchical and modular architecture of the insula suggests specialization for processing interoceptive afferents. Yet, the biological significance of the insula's neuroanatomical architecture, in relation to deep brain structures, remains obscure. In this opinion piece, we propose the Insula Hierarchical Modular Adaptive Interoception Control (IMAC) model to suggest that insula modules (granular, dysgranular and agranular), forming parallel networks with the prefrontal cortex and striatum, are specialized to form higher order interoceptive representations. These interoceptive representations are recruited in a context-dependent manner to support habitual, model-based and exploratory control of visceral organs and physiological processes. We discuss how insula interoceptive representations may give rise to conscious feelings that best explain lower order deep brain interoceptive representations, and how the insula may serve to defend the body and mind against pathological depression.
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Affiliation(s)
- Alan S. R. Fermin
- Center for Brain, Mind and Kansei Sciences Research, Hiroshima University, Hiroshima, Japan
| | - Karl Friston
- The Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, England
| | - Shigeto Yamawaki
- Center for Brain, Mind and Kansei Sciences Research, Hiroshima University, Hiroshima, Japan
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26
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Pilmeyer J, Huijbers W, Lamerichs R, Jansen JFA, Breeuwer M, Zinger S. Functional MRI in major depressive disorder: A review of findings, limitations, and future prospects. J Neuroimaging 2022; 32:582-595. [PMID: 35598083 PMCID: PMC9540243 DOI: 10.1111/jon.13011] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 05/04/2022] [Accepted: 05/04/2022] [Indexed: 02/02/2023] Open
Abstract
Objective diagnosis and prognosis in major depressive disorder (MDD) remains a challenge due to the absence of biomarkers based on physiological parameters or medical tests. Numerous studies have been conducted to identify functional magnetic resonance imaging‐based biomarkers of depression that either objectively differentiate patients with depression from healthy subjects, predict personalized treatment outcome, or characterize biological subtypes of depression. While there are some findings of consistent functional biomarkers, there is still lack of robust data acquisition and analysis methodology. According to current findings, primarily, the anterior cingulate cortex, prefrontal cortex, and default mode network play a crucial role in MDD. Yet, there are also less consistent results and the involvement of other regions or networks remains ambiguous. We further discuss image acquisition, processing, and analysis limitations that might underlie these inconsistencies. Finally, the current review aims to address and discuss possible remedies and future opportunities that could improve the search for consistent functional imaging biomarkers of depression. Novel acquisition techniques, such as multiband and multiecho imaging, and neural network‐based cleaning approaches can enhance the signal quality in limbic and frontal regions. More comprehensive analyses, such as directed or dynamic functional features or the identification of biological depression subtypes, can improve objective diagnosis or treatment outcome prediction and mitigate the heterogeneity of MDD. Overall, these improvements in functional MRI imaging techniques, processing, and analysis could advance the search for biomarkers and ultimately aid patients with MDD and their treatment course.
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Affiliation(s)
- Jesper Pilmeyer
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Department of Research and Development, Epilepsy Centre Kempenhaeghe, Heeze, The Netherlands
| | - Willem Huijbers
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Philips Research, Eindhoven, The Netherlands
| | - Rolf Lamerichs
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Department of Research and Development, Epilepsy Centre Kempenhaeghe, Heeze, The Netherlands.,Philips Research, Eindhoven, The Netherlands
| | - Jacobus F A Jansen
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, Maastricht, The Netherlands.,School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Marcel Breeuwer
- Philips Healthcare, Best, The Netherlands.,Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Svitlana Zinger
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Department of Research and Development, Epilepsy Centre Kempenhaeghe, Heeze, The Netherlands
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27
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Li Y, Zhou Z, Li Q, Li T, Julian IN, Guo H, Chen J. Depression Classification Using Frequent Subgraph Mining Based on Pattern Growth of Frequent Edge in Functional Magnetic Resonance Imaging Uncertain Network. Front Neurosci 2022; 16:889105. [PMID: 35578623 PMCID: PMC9106560 DOI: 10.3389/fnins.2022.889105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 04/01/2022] [Indexed: 11/13/2022] Open
Abstract
The brain network structure is highly uncertain due to the noise in imaging signals and evaluation methods. Recent works have shown that uncertain brain networks could capture uncertain information with regards to functional connections. Most of the existing research studies covering uncertain brain networks used graph mining methods for analysis; for example, the mining uncertain subgraph patterns (MUSE) method was used to mine frequent subgraphs and the discriminative feature selection for uncertain graph classification (DUG) method was used to select discriminant subgraphs. However, these methods led to a lack of effective discriminative information; this reduced the classification accuracy for brain diseases. Therefore, considering these problems, we propose an approximate frequent subgraph mining algorithm based on pattern growth of frequent edge (unFEPG) for uncertain brain networks and a novel discriminative feature selection method based on statistical index (dfsSI) to perform graph mining and selection. Results showed that compared with the conventional methods, the unFEPG and dfsSI methods achieved a higher classification accuracy. Furthermore, to demonstrate the efficacy of the proposed method, we used consistent discriminative subgraph patterns based on thresholding and weighting approaches to compare the classification performance of uncertain networks and certain networks in a bidirectional manner. Results showed that classification performance of the uncertain network was superior to that of the certain network within a defined sparsity range. This indicated that if a better classification performance is to be achieved, it is necessary to select a certain brain network with a higher threshold or an uncertain brain network model. Moreover, if the uncertain brain network model was selected, it is necessary to make full use of the uncertain information of its functional connection.
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Affiliation(s)
- Yao Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Zihao Zhou
- College of Mathematics, Taiyuan University of Technology, Taiyuan, China
| | - Qifan Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Tao Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Ibegbu Nnamdi Julian
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Hao Guo
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Junjie Chen
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
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28
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Ceolin G, Breda V, Koning E, Meyyappan AC, Gomes FA, Moreira JD, Gerchman F, Brietzke E. A Possible Antidepressive Effect of Dietary Interventions: Emergent Findings and Research Challenges. CURRENT TREATMENT OPTIONS IN PSYCHIATRY 2022; 9:151-162. [PMID: 35496470 PMCID: PMC9034261 DOI: 10.1007/s40501-022-00259-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Accepted: 04/01/2022] [Indexed: 02/07/2023]
Abstract
Purpose Despite recent advancements in the treatment of depression, the prevalence of affected individuals continues to grow. The development of new strategies has been required and emerging evidence has linked a possible antidepressant effect with dietary interventions. In this review, we discuss recent findings about the possible antidepressant effect of dietary interventions with an emphasis on the results of randomized controlled trials. Recent findings A high consumption of refined sugars and saturated fat and a low dietary content of fruits and vegetables has been associated with the development of depression. There is evidence supporting a small to moderate beneficial effect of a Mediterranean-type diet in depression. In addition, new dietary protocols are being studied for their use as possible interventions, such as the ketogenic diet, Nordic diet, and plant-based diet. Summary Lifestyle interventions surrounding diet and nutrition are a relatively affordable way to enhance response to treatment and to be employed as an adjunct in mental health care. Most studies, however, are limited by the difficulty in controlling for the placebo effect. Mediterranean-style diets seem to be the most promising as an adjunctive treatment for mood disorders. Larger randomized controlled trials that could assess predictors of response to dietary interventions are needed to establish a clear positive effect of diet and guide clinical care and nutritional recommendations concerning mental health care.
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Affiliation(s)
- Gilciane Ceolin
- Centre for Neuroscience Studies (CNS), Queen's University, Kingston, 752 King Street West, Kingston, ON K7L 7X3 Canada.,Postgraduate Program in Nutrition, Universidade Federal de Santa Catarina, Florianópolis, SC Brazil
| | - Vitor Breda
- Centre for Neuroscience Studies (CNS), Queen's University, Kingston, 752 King Street West, Kingston, ON K7L 7X3 Canada.,Department of Psychiatry, Queen's University School of Medicine, Kingston, ON Canada
| | - Elena Koning
- Centre for Neuroscience Studies (CNS), Queen's University, Kingston, 752 King Street West, Kingston, ON K7L 7X3 Canada
| | - Arun Chinna Meyyappan
- Centre for Neuroscience Studies (CNS), Queen's University, Kingston, 752 King Street West, Kingston, ON K7L 7X3 Canada
| | - Fabiano A Gomes
- Centre for Neuroscience Studies (CNS), Queen's University, Kingston, 752 King Street West, Kingston, ON K7L 7X3 Canada.,Department of Psychiatry, Queen's University School of Medicine, Kingston, ON Canada
| | - Júlia Dubois Moreira
- Department of Nutrition, Universidade Federal de Santa Catarina (UFSC), Florianópolis, SC Brazil
| | - Fernando Gerchman
- Endocrinology and Metabolism, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, RS Brazil.,Postgraduate Program in Medical Sciences: Endocrinology, Department of Internal Medicine, Faculty of Medicine, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS Brazil
| | - Elisa Brietzke
- Centre for Neuroscience Studies (CNS), Queen's University, Kingston, 752 King Street West, Kingston, ON K7L 7X3 Canada.,Department of Psychiatry, Queen's University School of Medicine, Kingston, ON Canada
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29
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Urbina-Treviño L, von Mücke-Heim IA, Deussing JM. P2X7 Receptor-Related Genetic Mouse Models – Tools for Translational Research in Psychiatry. Front Neural Circuits 2022; 16:876304. [PMID: 35422688 PMCID: PMC9001905 DOI: 10.3389/fncir.2022.876304] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 03/07/2022] [Indexed: 11/20/2022] Open
Abstract
Depression is a common psychiatric disorder and the leading cause of disability worldwide. Although treatments are available, only about 60% of treated patients experience a significant improvement in disease symptoms. Numerous clinical and rodent studies have identified the purinergic P2X7 receptor (P2X7R) as one of the genetic factors potentially contributing to the disease risk. In this respect, genetically engineered mouse models targeting the P2X7R have become increasingly important in studying designated immunological features and subtypes of depression in vivo. This review provides an overview of the P2X7R -related mouse lines currently available for translational psychiatric research and discusses their strengths, weaknesses, and potentials.
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Affiliation(s)
- Lidia Urbina-Treviño
- Max Planck Institute of Psychiatry, Molecular Neurogenetics, Munich, Germany
- Graduate School of Systemic Neurosciences, Ludwig Maximilian University of Munich, Munich, Germany
| | - Iven-Alex von Mücke-Heim
- Max Planck Institute of Psychiatry, Molecular Neurogenetics, Munich, Germany
- International Max Planck Research School for Translational Psychiatry, Munich, Germany
| | - Jan M. Deussing
- Max Planck Institute of Psychiatry, Molecular Neurogenetics, Munich, Germany
- *Correspondence: Jan M. Deussing,
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30
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Taylor JE, Yamada T, Kawashima T, Kobayashi Y, Yoshihara Y, Miyata J, Murai T, Kawato M, Motegi T. Depressive symptoms reduce when dorsolateral prefrontal cortex-precuneus connectivity normalizes after functional connectivity neurofeedback. Sci Rep 2022; 12:2581. [PMID: 35173179 PMCID: PMC8850610 DOI: 10.1038/s41598-022-05860-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 01/18/2022] [Indexed: 11/09/2022] Open
Abstract
Depressive disorders contribute heavily to global disease burden; This is possibly because patients are often treated homogeneously, despite having heterogeneous symptoms with differing underlying neural mechanisms. A novel treatment that can directly influence the neural circuit relevant to an individual patient's subset of symptoms might more precisely and thus effectively aid in the alleviation of their specific symptoms. We tested this hypothesis in a proof-of-concept study using fMRI functional connectivity neurofeedback. We targeted connectivity between the left dorsolateral prefrontal cortex/middle frontal gyrus and the left precuneus/posterior cingulate cortex, because this connection has been well-established as relating to a specific subset of depressive symptoms. Specifically, this connectivity has been shown in a data-driven manner to be less anticorrelated in patients with melancholic depression than in healthy controls. Furthermore, a posterior cingulate dominant state-which results in a loss of this anticorrelation-is expected to specifically relate to an increase in rumination symptoms such as brooding. In line with predictions, we found that, with neurofeedback training, the more a participant normalized this connectivity (restored the anticorrelation), the more related (depressive and brooding symptoms), but not unrelated (trait anxiety), symptoms were reduced. Because these results look promising, this paradigm next needs to be examined with a greater sample size and with better controls. Nonetheless, here we provide preliminary evidence for a correlation between the normalization of a neural network and a reduction in related symptoms. Showing their reproducibility, these results were found in two experiments that took place several years apart by different experimenters. Indicative of its potential clinical utility, effects of this treatment remained one-two months later.Clinical trial registration: Both experiments reported here were registered clinical trials (UMIN000015249, jRCTs052180169).
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Affiliation(s)
- Jessica Elizabeth Taylor
- Department of Decoded Neurofeedback (DecNef), Computational Neuroscience Laboratories, Advanced Telecommunications Research Institute International (ATR), Hikaridai 2-2-2. Seika-cho, Soraku, Kyoto, 619-0237, Japan
| | - Takashi Yamada
- Department of Decoded Neurofeedback (DecNef), Computational Neuroscience Laboratories, Advanced Telecommunications Research Institute International (ATR), Hikaridai 2-2-2. Seika-cho, Soraku, Kyoto, 619-0237, Japan.,Department of Cognitive, Linguistic and Psychological Sciences, Brown University, Providence, USA.,Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
| | - Takahiko Kawashima
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yuko Kobayashi
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yujiro Yoshihara
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Jun Miyata
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Toshiya Murai
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Mitsuo Kawato
- Department of Decoded Neurofeedback (DecNef), Computational Neuroscience Laboratories, Advanced Telecommunications Research Institute International (ATR), Hikaridai 2-2-2. Seika-cho, Soraku, Kyoto, 619-0237, Japan
| | - Tomokazu Motegi
- Department of Decoded Neurofeedback (DecNef), Computational Neuroscience Laboratories, Advanced Telecommunications Research Institute International (ATR), Hikaridai 2-2-2. Seika-cho, Soraku, Kyoto, 619-0237, Japan. .,Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Gunma, Japan.
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31
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Pinto B, Conde T, Domingues I, Domingues MR. Adaptation of Lipid Profiling in Depression Disease and Treatment: A Critical Review. Int J Mol Sci 2022; 23:ijms23042032. [PMID: 35216147 PMCID: PMC8874755 DOI: 10.3390/ijms23042032] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 02/09/2022] [Accepted: 02/10/2022] [Indexed: 11/30/2022] Open
Abstract
Major depressive disorder (MDD), also called depression, is a serious disease that impairs the quality of life of patients and has a high incidence, affecting approximately 3.8% of the world population. Its diagnosis is very subjective and is not supported by measurable biomarkers mainly due to the lack of biochemical markers. Recently, disturbance of lipid profiling has been recognized in MDD, in animal models of MDD or in depressed patients, which may contribute to unravel the etiology of the disease and find putative new biomarkers, for a diagnosis or for monitoring the disease and therapeutics outcomes. In this review, we provide an overview of current knowledge of lipidomics analysis, both in animal models of MDD (at the brain and plasma level) and in humans (in plasma and serum). Furthermore, studies of lipidomics analyses after antidepressant treatment in rodents (in brain, plasma, and serum), in primates (in the brain) and in humans (in plasma) were reviewed and give evidence that antidepressants seem to counteract the modification seen in lipids in MDD, giving some evidence that certain altered lipid profiles could be useful MDD biomarkers for future precision medicine.
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Affiliation(s)
- Bruno Pinto
- Centre for Environmental and Marine Studies, CESAM, Department of Chemistry, Santiago University Campus, University of Aveiro, 3810-193 Aveiro, Portugal; (B.P.); (T.C.)
- Mass Spectrometry Centre, LAQV-REQUIMTE, Department of Chemistry, Santiago University Campus, University of Aveiro, 3810-193 Aveiro, Portugal
| | - Tiago Conde
- Centre for Environmental and Marine Studies, CESAM, Department of Chemistry, Santiago University Campus, University of Aveiro, 3810-193 Aveiro, Portugal; (B.P.); (T.C.)
- Mass Spectrometry Centre, LAQV-REQUIMTE, Department of Chemistry, Santiago University Campus, University of Aveiro, 3810-193 Aveiro, Portugal
- Institute of Biomedicine—iBiMED, Department of Medical Sciences, University of Aveiro, 3810-193 Aveiro, Portugal
| | - Inês Domingues
- Centre for Environmental and Marine Studies, CESAM, Department of Biology, Santiago University Campus, University of Aveiro, 3810-193 Aveiro, Portugal;
| | - M. Rosário Domingues
- Centre for Environmental and Marine Studies, CESAM, Department of Chemistry, Santiago University Campus, University of Aveiro, 3810-193 Aveiro, Portugal; (B.P.); (T.C.)
- Mass Spectrometry Centre, LAQV-REQUIMTE, Department of Chemistry, Santiago University Campus, University of Aveiro, 3810-193 Aveiro, Portugal
- Correspondence:
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32
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Den Ouden L, Suo C, Albertella L, Greenwood LM, Lee RSC, Fontenelle LF, Parkes L, Tiego J, Chamberlain SR, Richardson K, Segrave R, Yücel M. Transdiagnostic phenotypes of compulsive behavior and associations with psychological, cognitive, and neurobiological affective processing. Transl Psychiatry 2022; 12:10. [PMID: 35013101 PMCID: PMC8748429 DOI: 10.1038/s41398-021-01773-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 12/02/2021] [Accepted: 12/16/2021] [Indexed: 01/10/2023] Open
Abstract
Compulsivity is a poorly understood transdiagnostic construct thought to underlie multiple disorders, including obsessive-compulsive disorder, addictions, and binge eating. Our current understanding of the causes of compulsive behavior remains primarily based on investigations into specific diagnostic categories or findings relying on one or two laboratory measures to explain complex phenotypic variance. This proof-of-concept study drew on a heterogeneous sample of community-based individuals (N = 45; 18-45 years; 25 female) exhibiting compulsive behavioral patterns in alcohol use, eating, cleaning, checking, or symmetry. Data-driven statistical modeling of multidimensional markers was utilized to identify homogeneous subtypes that were independent of traditional clinical phenomenology. Markers were based on well-defined measures of affective processing and included psychological assessment of compulsivity, behavioral avoidance, and stress, neurocognitive assessment of reward vs. punishment learning, and biological assessment of the cortisol awakening response. The neurobiological validity of the subtypes was assessed using functional magnetic resonance imaging. Statistical modeling identified three stable, distinct subtypes of compulsivity and affective processing, which we labeled "Compulsive Non-Avoidant", "Compulsive Reactive" and "Compulsive Stressed". They differed meaningfully on validation measures of mood, intolerance of uncertainty, and urgency. Most importantly, subtypes captured neurobiological variance on amygdala-based resting-state functional connectivity, suggesting they were valid representations of underlying neurobiology and highlighting the relevance of emotion-related brain networks in compulsive behavior. Although independent larger samples are needed to confirm the stability of subtypes, these data offer an integrated understanding of how different systems may interact in compulsive behavior and provide new considerations for guiding tailored intervention decisions.
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Affiliation(s)
- Lauren Den Ouden
- BrainPark, The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, Australia.
| | - Chao Suo
- BrainPark, The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, Australia
| | - Lucy Albertella
- BrainPark, The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, Australia
| | - Lisa-Marie Greenwood
- BrainPark, The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, Australia
- Research School of Psychology, ANU College of Health and Medicine, The Australian National University, Canberra, Australia
| | - Rico S C Lee
- BrainPark, The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, Australia
| | - Leonardo F Fontenelle
- BrainPark, The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, Australia
- D'Or Institute for Research and Education and Anxiety, Obsessive, Compulsive Research Program, Institute of Psychiatry, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Linden Parkes
- BrainPark, The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, Australia
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Jeggan Tiego
- Neural Systems and Behaviour Lab, The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, Australia
| | - Samuel R Chamberlain
- Department of Psychiatry, University of Southampton, Southampton, UK
- Southern Health NHS Foundation Trust, Southampton, UK
| | - Karyn Richardson
- BrainPark, The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, Australia
| | - Rebecca Segrave
- BrainPark, The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, Australia
| | - Murat Yücel
- BrainPark, The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, Australia
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33
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Wu X, Zhu Y, Wu Z, Huang J, Cao L, Wang Y, Su Y, Liu H, Fang M, Yao Z, Wang Z, Wang F, Wang Y, Peng D, Chen J, Fang Y. Identifying the Subtypes of Major Depressive Disorder Based on Somatic Symptoms: A Longitudinal Study Using Latent Profile Analysis. Front Psychiatry 2022; 13:759334. [PMID: 35903631 PMCID: PMC9314656 DOI: 10.3389/fpsyt.2022.759334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 06/06/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Two-thirds of major depressive disorder (MDD) patients initially present with somatic symptoms, yet no study has used approaches based on somatic symptoms to subtype MDD. This study aimed to classify MDD via somatic symptoms and tracked the prognosis of each subtype. METHODS Data were obtained from the study of Algorithm Guided Treatment Strategies for Major Depressive Disorder (AGTs-MDD). We recruited 395 subjects who received monotherapy of mirtazapine or escitalopram and conducted 2-, 4-, 6-, 8-, and 12-week follow-up assessments (n = 311, 278, 251, 199, and 178, respectively). Latent profile analysis (LPA) was performed on somatic symptom items of the depression and somatic symptoms scale (DSSS). Generalized linear mixed models (GLMM) were used to study the longitudinal prognosis of the subtypes classed by LPA. Primary outcome measures were the Hamilton Depression Rating Scale (HAMD), HAMD score reduction rate, as well as somatic and depressive items of DSSS. RESULTS Three subtypes of MDD were found, namely, depression with mild somatic symptoms (68.9%), depression with moderate somatic symptoms (19.2%), and depression with severe somatic symptoms (11.9%). Scores of HAMD (F = 3.175, p = 0.001), somatic (F = 23.594, p < 0.001), and depressive (F = 4.163, p < 0.001) DSSS items throughout the 12-week follow-up showed statistical difference among the three subtypes. The moderate group displayed a higher HAMD-17 score and a lower reduction rate at the 6th week, and more severe depressive symptoms both at the 4th and 6th weeks. CONCLUSION The results indicate that somatic symptoms should be emphasized in patients with MDD, and more attention is needed for those with moderate somatic symptoms, which may be relevant to a worse prognosis.
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Affiliation(s)
- Xiaohui Wu
- Clinical Research Center and Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yuncheng Zhu
- Clinical Research Center and Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhiguo Wu
- Clinical Research Center and Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jia Huang
- Clinical Research Center and Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lan Cao
- Clinical Research Center and Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yun Wang
- Clinical Research Center and Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yousong Su
- Clinical Research Center and Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hongmei Liu
- Clinical Research Center and Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | | | - Zhijian Yao
- Nanjing Medical University Affiliated Brain Hospital, Nanjing, China
| | - Zuowei Wang
- Department of Psychiatry, Hongkou District Mental Health Center of Shanghai, Shanghai, China
| | - Fan Wang
- Clinical Research Center and Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yong Wang
- Clinical Research Center and Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Daihui Peng
- Clinical Research Center and Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jun Chen
- Clinical Research Center and Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yiru Fang
- Clinical Research Center and Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai, China.,Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China
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34
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Wang X, Qin J, Zhu R, Zhang S, Tian S, Sun Y, Wang Q, Zhao P, Tang H, Wang L, Si T, Yao Z, Lu Q. Predicting treatment selections for individuals with major depressive disorder according to functional connectivity subgroups. Brain Connect 2021; 12:699-710. [PMID: 34913731 DOI: 10.1089/brain.2021.0153] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Major depressive disorder (MDD) is a highly prevalent and disabling disease. Currently, patients' treatment choices depend on their clinical symptoms observed by clinicians, which are subjective. Rich evidence suggests that different functional networks' dysfunctions correspond to different intervention preferences. Here, we aimed to develop a prediction model based on data-driven subgroups to provide treatment recommendations. METHODS All 630 participants enrolled from four sites underwent functional magnetic resonances imaging at baseline. In the discovery dataset (n=228), we firstly identified MDD subgroups by the hierarchical clustering method using the canonical variates of resting-state functional connectivity (FC) through canonical correlation analyses. The demographic, symptom improvement and FC were compared among subgroups. The preference intervention for each subgroup was also determined. Next, we predicted the individual treatment strategy. Specifically, a patient was assigned into predefined subgroups based on FC similarities and then his/her treatment strategy was determined by the subgroups' preferred interventions. RESULTS Three subgroups with specific treatment recommendations were emerged including: (1) a selective serotonin reuptake inhibitors-oriented subgroup with early improvements in working and activities. (2) a stimulation-oriented subgroup with more alleviation in suicide. (3) a selective serotonin noradrenaline reuptake inhibitors-oriented subgroup with more alleviation in hypochondriasis. Through cross-dataset testing respectively conducted on three testing datasets, results showed an overall accuracy of 72.83%. CONCLUSIONS Our works revealed the correspondences between subgroups and their treatment preferences and predicted individual treatment strategy based on such correspondences. Our model has the potential to support psychiatrists in early clinical decision making for better treatment outcomes.
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Affiliation(s)
- Xinyi Wang
- Southeast University, 12579, School of Biological Sciences & Medical Engineering, Nanjing, Jiangsu, China.,Southeast University, 12579, Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, Jiangsu, China;
| | - Jiaolong Qin
- Nanjing University of Science and Technology, 12436, The Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, School of Computer Science and Engineering, Nanjing, Jiangsu, China;
| | - Rongxin Zhu
- Nanjing Medical University Affiliated Brain Hospital, 56647, Department of Psychiatry, Nanjing, Jiangsu, China;
| | - Siqi Zhang
- Southeast University, 12579, School of Biological Sciences & Medical Engineering, Nanjing, China.,Southeast University, 12579, Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, Jiangsu, China;
| | - Shui Tian
- Southeast University, 12579, School of Biological Sciences & Medical Engineering, Nanjing, Jiangsu, China.,Southeast University, 12579, Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, Jiangsu, China;
| | - Yurong Sun
- Southeast University, 12579, School of Biological Sciences & Medical Engineering, Nanjing, Jiangsu, China.,Southeast University, 12579, Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, Jiangsu, China;
| | - Qiang Wang
- Nanjing Drum Tower Hospital, 66506, Nanjing, Jiangsu, China;
| | - Peng Zhao
- Nanjing Drum Tower Hospital, 66506, Nanjing, Jiangsu, China;
| | - Hao Tang
- Nanjing Medical University Affiliated Brain Hospital, 56647, Department of Psychiatry, Nanjing, Jiangsu, China;
| | - Li Wang
- Peking University Institute of Mental Health, 74577, Beijing, Beijing, China;
| | - Tianmei Si
- Peking University Institute of Mental Health, 74577, Beijing, Beijing, China;
| | - Zhijian Yao
- Nanjing Medical University Affiliated Brain Hospital, 56647, Department of psychiatry, Nanjing, Jiangsu, China.,Nanjing Brain Hospital, 56647, Medical School of Nanjing University, Nanjing, Nanjing, China;
| | - Qing Lu
- Southeast University, 12579, School of Biological Sciences & Medical Engineering, Nanjing, China.,Southeast University, 12579, Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, Jiangsu, China;
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35
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McPartland JC, Lerner MD, Bhat A, Clarkson T, Jack A, Koohsari S, Matuskey D, McQuaid GA, Su WC, Trevisan DA. Looking Back at the Next 40 Years of ASD Neuroscience Research. J Autism Dev Disord 2021; 51:4333-4353. [PMID: 34043128 PMCID: PMC8542594 DOI: 10.1007/s10803-021-05095-5] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/14/2021] [Indexed: 12/18/2022]
Abstract
During the last 40 years, neuroscience has become one of the most central and most productive approaches to investigating autism. In this commentary, we assemble a group of established investigators and trainees to review key advances and anticipated developments in neuroscience research across five modalities most commonly employed in autism research: magnetic resonance imaging, functional near infrared spectroscopy, positron emission tomography, electroencephalography, and transcranial magnetic stimulation. Broadly, neuroscience research has provided important insights into brain systems involved in autism but not yet mechanistic understanding. Methodological advancements are expected to proffer deeper understanding of neural circuitry associated with function and dysfunction during the next 40 years.
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Affiliation(s)
| | - Matthew D Lerner
- Department of Psychology, Stony Brook University, Stony Brook, NY, USA
| | - Anjana Bhat
- Department of Physical Therapy, University of Delaware, Newark, DE, USA
| | - Tessa Clarkson
- Department of Psychology, Temple University, Philadelphia, PA, USA
| | - Allison Jack
- Department of Psychology, George Mason University, Fairfax, VA, USA
| | - Sheida Koohsari
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - David Matuskey
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Goldie A McQuaid
- Department of Psychology, George Mason University, Fairfax, VA, USA
| | - Wan-Chun Su
- Department of Physical Therapy, University of Delaware, Newark, DE, USA
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36
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Khodadadifar T, Soltaninejad Z, Ebneabbasi A, Eickhoff CR, Sorg C, Van Eimeren T, Vogeley K, Zarei M, Eickhoff SB, Tahmasian M. In search of convergent regional brain abnormality in cognitive emotion regulation: A transdiagnostic neuroimaging meta-analysis. Hum Brain Mapp 2021; 43:1309-1325. [PMID: 34826162 PMCID: PMC8837597 DOI: 10.1002/hbm.25722] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 10/29/2021] [Accepted: 11/01/2021] [Indexed: 01/28/2023] Open
Abstract
Ineffective use of adaptive cognitive strategies (e.g., reappraisal) to regulate emotional states is often reported in a wide variety of psychiatric disorders, suggesting a common characteristic across different diagnostic categories. However, the extent of shared neurobiological impairments is incompletely understood. This study, therefore, aimed to identify the transdiagnostic neural signature of disturbed reappraisal using the coordinate‐based meta‐analysis (CBMA) approach. Following the best‐practice guidelines for conducting neuroimaging meta‐analyses, we systematically searched PubMed, ScienceDirect, and Web of Science databases and tracked the references. Out of 1,608 identified publications, 32 whole‐brain neuroimaging studies were retrieved that compared brain activation in patients with psychiatric disorders and healthy controls during a reappraisal task. Then, the reported peak coordinates of group comparisons were extracted and several activation likelihood estimation (ALE) analyses were performed at three hierarchical levels to identify the potential spatial convergence: the global level (i.e., the pooled analysis and the analyses of increased/decreased activations), the experimental‐contrast level (i.e., the analyses of grouped data based on the regulation goal, stimulus valence, and instruction rule) and the disorder‐group level (i.e., the analyses across the experimental‐contrast level focused on increasing homogeneity of disorders). Surprisingly, none of our analyses provided significant convergent findings. This CBMA indicates a lack of transdiagnostic convergent regional abnormality related to reappraisal task, probably due to the complex nature of cognitive emotion regulation, heterogeneity of clinical populations, and/or experimental and statistical flexibility of individual studies.
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Affiliation(s)
- Tina Khodadadifar
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences, Tehran, Iran
| | - Zahra Soltaninejad
- Institute of Medical Science and Technology, Shahid Beheshti University, Tehran, Iran.,Cognitive and Brain Science Institute, Shahid Beheshti University, Tehran, Iran
| | - Amir Ebneabbasi
- Institute of Medical Science and Technology, Shahid Beheshti University, Tehran, Iran
| | - Claudia R Eickhoff
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, Jülich, Germany.,Institute of Clinical Neuroscience and Medical Psychology, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.,Institute of Neuroscience and Medicine, Structural and functional organization of the brain (INM-1), Research Center Jülich, Jülich, Germany
| | - Christian Sorg
- TUM-Neuroimaging Center (TUM-NIC), Klinikum Rechts der Isar, Technische Universität München, Munich, Germany.,Department of Neuroradiology, Klinikum Rechts der Isar, Technische Universität München, Munich, Germany.,Department of Psychiatry and Psychotherapy, Klinikum Rechts der Isar, Technische Universität München, Munich, Germany
| | - Thilo Van Eimeren
- Multimodal Neuroimaging Group, Department of Nuclear Medicine, Faculty of Medicine and University Hospital of Cologne, University of Cologne, Cologne, Germany.,Department of Neurology, Faculty of Medicine and University Hospital of Cologne, University of Cologne, Cologne, Germany
| | - Kai Vogeley
- Department of Psychiatry and Psychotherapy, University Hospital Cologne, Cologne, Germany.,Cognitive Neuroscience (INM-3), Institute of Neuroscience and Medicine, Research Center Jülich, Jülich, Germany
| | - Mojtaba Zarei
- Institute of Medical Science and Technology, Shahid Beheshti University, Tehran, Iran
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, Jülich, Germany.,Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Masoud Tahmasian
- Institute of Medical Science and Technology, Shahid Beheshti University, Tehran, Iran.,Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, Jülich, Germany.,Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
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Tarchi L, Damiani S, La Torraca Vittori P, Marini S, Nazzicari N, Castellini G, Pisano T, Politi P, Ricca V. The colors of our brain: an integrated approach for dimensionality reduction and explainability in fMRI through color coding (i-ECO). Brain Imaging Behav 2021; 16:977-990. [PMID: 34689318 PMCID: PMC9107439 DOI: 10.1007/s11682-021-00584-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/06/2021] [Indexed: 11/29/2022]
Abstract
Several systematic reviews have highlighted the role of multiple sources in the investigation of psychiatric illness. For what concerns fMRI, the focus of recent literature preferentially lies on three lines of research, namely: functional connectivity, network analysis and spectral analysis. Data was gathered from the UCLA Consortium for Neuropsychiatric Phenomics. The sample was composed by 130 neurotypicals, 50 participants diagnosed with Schizophrenia, 49 with Bipolar disorder and 43 with ADHD. Single fMRI scans were reduced in their dimensionality by a novel method (i-ECO) averaging results per Region of Interest and through an additive color method (RGB): local connectivity values (Regional Homogeneity), network centrality measures (Eigenvector Centrality), spectral dimensions (fractional Amplitude of Low-Frequency Fluctuations). Average images per diagnostic group were plotted and described. The discriminative power of this novel method for visualizing and analyzing fMRI results in an integrative manner was explored through the usage of convolutional neural networks. The new methodology of i-ECO showed between-groups differences that could be easily appreciated by the human eye. The precision-recall Area Under the Curve (PR-AUC) of our models was > 84.5% for each diagnostic group as evaluated on the test-set – 80/20 split. In conclusion, this study provides evidence for an integrative and easy-to-understand approach in the analysis and visualization of fMRI results. A high discriminative power for psychiatric conditions was reached. This proof-of-work study may serve to investigate further developments over more extensive datasets covering a wider range of psychiatric diagnoses.
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Affiliation(s)
- Livio Tarchi
- Psychiatry Unit, Department of Health Sciences, University of Florence, viale della Maternità, Padiglione 8b, AOU Careggi, Firenze, Florence, FI, 50134, Italy.
| | - Stefano Damiani
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, PV, Italy
| | | | - Simone Marini
- Department of Epidemiology, University of Florida, Gainesville, FL, USA
| | - Nelson Nazzicari
- Council for Agricultural Research and Economics (CREA), Research Centre for Fodder Crops and Dairy Productions, Lodi, LO, Italy
| | - Giovanni Castellini
- Psychiatry Unit, Department of Health Sciences, University of Florence, viale della Maternità, Padiglione 8b, AOU Careggi, Firenze, Florence, FI, 50134, Italy
| | - Tiziana Pisano
- Pediatric Neurology, Neurogenetics and Neurobiology Unit and Laboratories, Neuroscience Department, Meyer Children's Hospital, University of Florence, Florence, Italy
| | - Pierluigi Politi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, PV, Italy
| | - Valdo Ricca
- Psychiatry Unit, Department of Health Sciences, University of Florence, viale della Maternità, Padiglione 8b, AOU Careggi, Firenze, Florence, FI, 50134, Italy
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38
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Miranda L, Paul R, Pütz B, Koutsouleris N, Müller-Myhsok B. Systematic Review of Functional MRI Applications for Psychiatric Disease Subtyping. Front Psychiatry 2021; 12:665536. [PMID: 34744805 PMCID: PMC8569315 DOI: 10.3389/fpsyt.2021.665536] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 09/07/2021] [Indexed: 12/12/2022] Open
Abstract
Background: Psychiatric disorders have been historically classified using symptom information alone. Recently, there has been a dramatic increase in research interest not only in identifying the mechanisms underlying defined pathologies but also in redefining their etiology. This is particularly relevant for the field of personalized medicine, which searches for data-driven approaches to improve diagnosis, prognosis, and treatment selection for individual patients. Methods: This review aims to provide a high-level overview of the rapidly growing field of functional magnetic resonance imaging (fMRI) from the perspective of unsupervised machine learning applications for disease subtyping. Following the PRISMA guidelines for protocol reproducibility, we searched the PubMed database for articles describing functional MRI applications used to obtain, interpret, or validate psychiatric disease subtypes. We also employed the active learning framework ASReview to prioritize publications in a machine learning-guided way. Results: From the 20 studies that met the inclusion criteria, five used functional MRI data to interpret symptom-derived disease clusters, four used it to interpret clusters derived from biomarker data other than fMRI itself, and 11 applied clustering techniques involving fMRI directly. Major depression disorder and schizophrenia were the two most frequently studied pathologies (35% and 30% of the retrieved studies, respectively), followed by ADHD (15%), psychosis as a whole (10%), autism disorder (5%), and the consequences of early exposure to violence (5%). Conclusions: The increased interest in personalized medicine and data-driven disease subtyping also extends to psychiatric disorders. However, to date, this subfield is at an incipient exploratory stage, and all retrieved studies were mostly proofs of principle where further validation and increased sample sizes are craved for. Whereas results for all explored diseases are inconsistent, we believe this reflects the need for concerted, multisite data collection efforts with a strong focus on measuring the generalizability of results. Finally, whereas functional MRI is the best way of measuring brain function available to date, its low signal-to-noise ratio and elevated monetary cost make it a poor clinical alternative. Even with technology progressing and costs decreasing, this might incentivize the search for more accessible, clinically ready functional proxies in the future.
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Affiliation(s)
- Lucas Miranda
- Department of Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany
| | - Riya Paul
- Department of Precision Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
- Department of Psychiatry and Psychotherapy, Section for Neurodiagnostic Applications, Ludwig-Maximilian University, Munich, Germany
| | - Benno Pütz
- Department of Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany
| | - Nikolaos Koutsouleris
- Department of Precision Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
- Department of Psychiatry and Psychotherapy, Section for Neurodiagnostic Applications, Ludwig-Maximilian University, Munich, Germany
| | - Bertram Müller-Myhsok
- Department of Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany
- Department of Health Data Science, Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom
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Pelin H, Ising M, Stein F, Meinert S, Meller T, Brosch K, Winter NR, Krug A, Leenings R, Lemke H, Nenadić I, Heilmann-Heimbach S, Forstner AJ, Nöthen MM, Opel N, Repple J, Pfarr J, Ringwald K, Schmitt S, Thiel K, Waltemate L, Winter A, Streit F, Witt S, Rietschel M, Dannlowski U, Kircher T, Hahn T, Müller-Myhsok B, Andlauer TFM. Identification of transdiagnostic psychiatric disorder subtypes using unsupervised learning. Neuropsychopharmacology 2021; 46:1895-1905. [PMID: 34127797 PMCID: PMC8429672 DOI: 10.1038/s41386-021-01051-0] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 05/24/2021] [Accepted: 05/28/2021] [Indexed: 02/07/2023]
Abstract
Psychiatric disorders show heterogeneous symptoms and trajectories, with current nosology not accurately reflecting their molecular etiology and the variability and symptomatic overlap within and between diagnostic classes. This heterogeneity impedes timely and targeted treatment. Our study aimed to identify psychiatric patient clusters that share clinical and genetic features and may profit from similar therapies. We used high-dimensional data clustering on deep clinical data to identify transdiagnostic groups in a discovery sample (N = 1250) of healthy controls and patients diagnosed with depression, bipolar disorder, schizophrenia, schizoaffective disorder, and other psychiatric disorders. We observed five diagnostically mixed clusters and ordered them based on severity. The least impaired cluster 0, containing most healthy controls, showed general well-being. Clusters 1-3 differed predominantly regarding levels of maltreatment, depression, daily functioning, and parental bonding. Cluster 4 contained most patients diagnosed with psychotic disorders and exhibited the highest severity in many dimensions, including medication load. Depressed patients were present in all clusters, indicating that we captured different disease stages or subtypes. We replicated all but the smallest cluster 1 in an independent sample (N = 622). Next, we analyzed genetic differences between clusters using polygenic scores (PGS) and the psychiatric family history. These genetic variables differed mainly between clusters 0 and 4 (prediction area under the receiver operating characteristic curve (AUC) = 81%; significant PGS: cross-disorder psychiatric risk, schizophrenia, and educational attainment). Our results confirm that psychiatric disorders consist of heterogeneous subtypes sharing molecular factors and symptoms. The identification of transdiagnostic clusters advances our understanding of the heterogeneity of psychiatric disorders and may support the development of personalized treatments.
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Affiliation(s)
- Helena Pelin
- Max Planck Institute of Psychiatry, Munich, Germany.
- International Max Planck Research School for Translational Psychiatry, Munich, Germany.
| | - Marcus Ising
- Max Planck Institute of Psychiatry, Munich, Germany
| | - Frederike Stein
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Marburg, Germany
| | - Susanne Meinert
- Institute for Translational Psychiatry, Westfälische Wilhelms-Universität Münster, Münster, Germany
| | - Tina Meller
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Marburg, Germany
| | - Katharina Brosch
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Marburg, Germany
| | - Nils R Winter
- Institute for Translational Psychiatry, Westfälische Wilhelms-Universität Münster, Münster, Germany
| | - Axel Krug
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany
| | - Ramona Leenings
- Institute for Translational Psychiatry, Westfälische Wilhelms-Universität Münster, Münster, Germany
| | - Hannah Lemke
- Institute for Translational Psychiatry, Westfälische Wilhelms-Universität Münster, Münster, Germany
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Marburg, Germany
| | - Stefanie Heilmann-Heimbach
- Institute of Human Genetics, University of Bonn School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Andreas J Forstner
- Institute of Human Genetics, University of Bonn School of Medicine & University Hospital Bonn, Bonn, Germany
- Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Jülich, Germany
- Centre for Human Genetics, University of Marburg, Marburg, Germany
| | - Markus M Nöthen
- Institute of Human Genetics, University of Bonn School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Nils Opel
- Institute for Translational Psychiatry, Westfälische Wilhelms-Universität Münster, Münster, Germany
| | - Jonathan Repple
- Institute for Translational Psychiatry, Westfälische Wilhelms-Universität Münster, Münster, Germany
| | - Julia Pfarr
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
| | - Kai Ringwald
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Marburg, Germany
| | - Simon Schmitt
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Marburg, Germany
| | - Katharina Thiel
- Institute for Translational Psychiatry, Westfälische Wilhelms-Universität Münster, Münster, Germany
| | - Lena Waltemate
- Institute for Translational Psychiatry, Westfälische Wilhelms-Universität Münster, Münster, Germany
| | - Alexandra Winter
- Institute for Translational Psychiatry, Westfälische Wilhelms-Universität Münster, Münster, Germany
| | - Fabian Streit
- Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Stephanie Witt
- Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Marcella Rietschel
- Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, Westfälische Wilhelms-Universität Münster, Münster, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Marburg, Germany
| | - Tim Hahn
- Institute for Translational Psychiatry, Westfälische Wilhelms-Universität Münster, Münster, Germany
| | - Bertram Müller-Myhsok
- Max Planck Institute of Psychiatry, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Institute of Translational Medicine, University of Liverpool, Liverpool, UK
| | - Till F M Andlauer
- Max Planck Institute of Psychiatry, Munich, Germany.
- Department of Neurology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany.
- Global Computational Biology and Data Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riß, Germany.
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40
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Dadario NB, Brahimaj B, Yeung J, Sughrue ME. Reducing the Cognitive Footprint of Brain Tumor Surgery. Front Neurol 2021; 12:711646. [PMID: 34484105 PMCID: PMC8415405 DOI: 10.3389/fneur.2021.711646] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 07/12/2021] [Indexed: 12/03/2022] Open
Abstract
The surgical management of brain tumors is based on the principle that the extent of resection improves patient outcomes. Traditionally, neurosurgeons have considered that lesions in “non-eloquent” cerebrum can be more aggressively surgically managed compared to lesions in “eloquent” regions with more known functional relevance. Furthermore, advancements in multimodal imaging technologies have improved our ability to extend the rate of resection while minimizing the risk of inducing new neurologic deficits, together referred to as the “onco-functional balance.” However, despite the common utilization of invasive techniques such as cortical mapping to identify eloquent tissue responsible for language and motor functions, glioma patients continue to present post-operatively with poor cognitive morbidity in higher-order functions. Such observations are likely related to the difficulty in interpreting the highly-dimensional information these technologies present to us regarding cognition in addition to our classically poor understanding of the functional and structural neuroanatomy underlying complex higher-order cognitive functions. Furthermore, reduction of the brain into isolated cortical regions without consideration of the complex, interacting brain networks which these regions function within to subserve higher-order cognition inherently prevents our successful navigation of true eloquent and non-eloquent cerebrum. Fortunately, recent large-scale movements in the neuroscience community, such as the Human Connectome Project (HCP), have provided updated neural data detailing the many intricate macroscopic connections between cortical regions which integrate and process the information underlying complex human behavior within a brain “connectome.” Connectomic data can provide us better maps on how to understand convoluted cortical and subcortical relationships between tumor and human cerebrum such that neurosurgeons can begin to make more informed decisions during surgery to maximize the onco-functional balance. However, connectome-based neurosurgery and related applications for neurorehabilitation are relatively nascent and require further work moving forward to optimize our ability to add highly valuable connectomic data to our surgical armamentarium. In this manuscript, we review four concepts with detailed examples which will help us better understand post-operative cognitive outcomes and provide a guide for how to utilize connectomics to reduce cognitive morbidity following cerebral surgery.
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Affiliation(s)
- Nicholas B Dadario
- Robert Wood Johnson School of Medicine, Rutgers University, New Brunswick, NJ, United States
| | - Bledi Brahimaj
- Department of Neurosurgery, Rush University Medical Center, Chicago, IL, United States
| | - Jacky Yeung
- Centre for Minimally Invasive Neurosurgery, Prince of Wales Private Hospital, Sydney, NSW, Australia
| | - Michael E Sughrue
- Centre for Minimally Invasive Neurosurgery, Prince of Wales Private Hospital, Sydney, NSW, Australia
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Yeung HW, Shen X, Stolicyn A, de Nooij L, Harris MA, Romaniuk L, Buchanan CR, Waiter GD, Sandu AL, McNeil CJ, Murray A, Steele JD, Campbell A, Porteous D, Lawrie SM, McIntosh AM, Cox SR, Smith KM, Whalley HC. Spectral clustering based on structural magnetic resonance imaging and its relationship with major depressive disorder and cognitive ability. Eur J Neurosci 2021; 54:6281-6303. [PMID: 34390586 DOI: 10.1111/ejn.15423] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 08/09/2021] [Indexed: 11/29/2022]
Abstract
There is increasing interest in using data-driven unsupervised methods to identify structural underpinnings of common mental illnesses, including major depressive disorder (MDD) and associated traits such as cognition. However, studies are often limited to severe clinical cases with small sample sizes and most do not include replication. Here, we examine two relatively large samples with structural magnetic resonance imaging (MRI), measures of lifetime MDD and cognitive variables: Generation Scotland (GS subsample, N = 980) and UK Biobank (UKB, N = 8,900), for discovery and replication, using an exploratory approach. Regional measures of FreeSurfer derived cortical thickness (CT), cortical surface area (CSA), cortical volume (CV) and subcortical volume (subCV) were input into a clustering process, controlling for common covariates. The main analysis steps involved constructing participant K-nearest neighbour graphs and graph partitioning with Markov stability to determine optimal clustering of participants. Resultant clusters were (1) checked whether they were replicated in an independent cohort and (2) tested for associations with depression status and cognitive measures. Participants separated into two clusters based on structural brain measurements in GS subsample, with large Cohen's d effect sizes between clusters in higher order cortical regions, commonly associated with executive function and decision making. Clustering was replicated in the UKB sample, with high correlations of cluster effect sizes for CT, CSA, CV and subCV between cohorts across regions. The identified clusters were not significantly different with respect to MDD case-control status in either cohort (GS subsample: pFDR = .2239-.6585; UKB: pFDR = .2003-.7690). Significant differences in general cognitive ability were, however, found between the clusters for both datasets, for CSA, CV and subCV (GS subsample: d = 0.2529-.3490, pFDR < .005; UKB: d = 0.0868-0.1070, pFDR < .005). Our results suggest that there are replicable natural groupings of participants based on cortical and subcortical brain measures, which may be related to differences in cognitive performance, but not to the MDD case-control status.
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Affiliation(s)
- Hon Wah Yeung
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Xueyi Shen
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Aleks Stolicyn
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Laura de Nooij
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Mathew A Harris
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Liana Romaniuk
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Colin R Buchanan
- Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Gordon D Waiter
- Aberdeen Biomedical Imaging Centre, Institute of Medical Sciences, University of Aberdeen, Aberdeen, UK
| | - Anca-Larisa Sandu
- Aberdeen Biomedical Imaging Centre, Institute of Medical Sciences, University of Aberdeen, Aberdeen, UK
| | - Christopher J McNeil
- Aberdeen Biomedical Imaging Centre, Institute of Medical Sciences, University of Aberdeen, Aberdeen, UK
| | - Alison Murray
- Aberdeen Biomedical Imaging Centre, Institute of Medical Sciences, University of Aberdeen, Aberdeen, UK
| | - J Douglas Steele
- School of Medicine, University of Dundee, Dundee, UK.,Department of Neurology, NHS Tayside, Ninewells Hospital and Medical School, Dundee, UK
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - David Porteous
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.,Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | | | - Andrew M McIntosh
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK.,Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Simon R Cox
- Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Keith M Smith
- Usher Institute, University of Edinburgh, Edinburgh, UK.,Health Data Research UK, London, UK
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42
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Omran M, Belcher EK, Mohile NA, Kesler SR, Janelsins MC, Hohmann AG, Kleckner IR. Review of the Role of the Brain in Chemotherapy-Induced Peripheral Neuropathy. Front Mol Biosci 2021; 8:693133. [PMID: 34179101 PMCID: PMC8226121 DOI: 10.3389/fmolb.2021.693133] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 05/24/2021] [Indexed: 12/18/2022] Open
Abstract
Chemotherapy-induced peripheral neuropathy (CIPN) is a common, debilitating, and dose-limiting side effect of many chemotherapy regimens yet has limited treatments due to incomplete knowledge of its pathophysiology. Research on the pathophysiology of CIPN has focused on peripheral nerves because CIPN symptoms are felt in the hands and feet. However, better understanding the role of the brain in CIPN may accelerate understanding, diagnosing, and treating CIPN. The goals of this review are to (1) investigate the role of the brain in CIPN, and (2) use this knowledge to inform future research and treatment of CIPN. We identified 16 papers using brain interventions in animal models of CIPN and five papers using brain imaging in humans or monkeys with CIPN. These studies suggest that CIPN is partly caused by (1) brain hyperactivity, (2) reduced GABAergic inhibition, (3) neuroinflammation, and (4) overactivation of GPCR/MAPK pathways. These four features were observed in several brain regions including the thalamus, periaqueductal gray, anterior cingulate cortex, somatosensory cortex, and insula. We discuss how to leverage this knowledge for future preclinical research, clinical research, and brain-based treatments for CIPN.
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Affiliation(s)
- Maryam Omran
- University of Rochester Medical Center, Rochester, NY, United States
| | | | - Nimish A Mohile
- University of Rochester Medical Center, Rochester, NY, United States
| | - Shelli R Kesler
- The University of Texas at Austin, Austin, TX, United States
| | | | - Andrea G Hohmann
- Psychological and Brain Sciences, Program in Neuroscience and Gill Center for Biomolecular Science, Indiana University Bloomington, Bloomington, IN, United States
| | - Ian R Kleckner
- University of Rochester Medical Center, Rochester, NY, United States
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43
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Tokuda T, Yamashita O, Yoshimoto J. Multiple clustering for identifying subject clusters and brain sub-networks using functional connectivity matrices without vectorization. Neural Netw 2021; 142:269-287. [PMID: 34052471 DOI: 10.1016/j.neunet.2021.05.016] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 04/21/2021] [Accepted: 05/12/2021] [Indexed: 12/21/2022]
Abstract
In neuroscience, the functional magnetic resonance imaging (fMRI) is a vital tool to non-invasively access brain activity. Using fMRI, the functional connectivity (FC) between brain regions can be inferred, which has contributed to a number of findings of the fundamental properties of the brain. As an important clinical application of FC, clustering of subjects based on FC recently draws much attention, which can potentially reveal important heterogeneity in subjects such as subtypes of psychiatric disorders. In particular, a multiple clustering method is a powerful analytical tool, which identifies clustering patterns of subjects depending on their FC in specific brain areas. However, when one applies an existing multiple clustering method to fMRI data, there is a need to simplify the data structure, independently dealing with elements in a FC matrix, i.e., vectorizing a correlation matrix. Such a simplification may distort the clustering results. To overcome this problem, we propose a novel multiple clustering method based on Wishart mixture models, which preserves the correlation matrix structure without vectorization. The uniqueness of this method is that the multiple clustering of subjects is based on particular networks of nodes (or regions of interest, ROIs), optimized in a data-driven manner. Hence, it can identify multiple underlying pairs of associations between a subject cluster solution and a ROI sub-network. The key assumption of the method is independence among sub-networks, which is effectively addressed by whitening correlation matrices. We applied the proposed method to synthetic and fMRI data, demonstrating the usefulness and power of the proposed method.
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Affiliation(s)
- Tomoki Tokuda
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0288, Japan; Okinawa Institute of Science and Technology Graduate University, 1919-1 Tancha, Okinawa 904-0495, Japan.
| | - Okito Yamashita
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0288, Japan; Center for Advanced Intelligence Project, RIKEN, Nihonbashi 1-chome Mitsui Building, 15th floor, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Junichiro Yoshimoto
- Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara 630-0192, Japan; Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0288, Japan
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44
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Sharma RK, Chern A, Golub JS. Age-Related Hearing Loss and the Development of Cognitive Impairment and Late-Life Depression: A Scoping Overview. Semin Hear 2021; 42:10-25. [PMID: 33883788 DOI: 10.1055/s-0041-1725997] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Age-related hearing loss (ARHL) has been connected to both cognitive decline and late-life depression. Several mechanisms have been offered to explain both individual links. Causal and common mechanisms have been theorized for the relationship between ARHL and impaired cognition, including dementia. The causal mechanisms include increased cognitive load, social isolation, and structural brain changes. Common mechanisms include neurovascular disease as well as other known or as-yet undiscovered neuropathologic processes. Behavioral mechanisms have been used to explain the potentially causal association of ARHL with depression. Behavioral mechanisms include social isolation, loneliness, as well as decreased mobility and impairments of activities of daily living, all of which can increase the risk of depression. The mechanisms underlying the associations between hearing loss and impaired cognition, as well as hearing loss and depression, are likely not mutually exclusive. ARHL may contribute to both impaired cognition and depression through overlapping mechanisms. Furthermore, ARHL may contribute to impaired cognition which may, in turn, contribute to depression. Because ARHL is highly prevalent and greatly undertreated, targeting this condition is an appealing and potentially influential strategy to reduce the risk of developing two potentially devastating diseases of later life. However, further studies are necessary to elucidate the mechanistic relationship between ARHL, depression, and impaired cognition.
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Affiliation(s)
- Rahul K Sharma
- Department of Otolaryngology-Head and Neck Surgery, Columbia University Irving Medical Center, New York, New York.,Columbia University Vagelos College of Physicians and Surgeons, New York, New York
| | - Alexander Chern
- Department of Otolaryngology-Head and Neck Surgery, Columbia University Irving Medical Center, New York, New York
| | - Justin S Golub
- Department of Otolaryngology-Head and Neck Surgery, Columbia University Irving Medical Center, New York, New York
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45
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Gloger S, Vöhringer PA, Martínez P, Chacón MV, Cáceres C, Diez de Medina D, Cottin M, Behn A. The contribution of early adverse stress to complex and severe depression in depressed outpatients. Depress Anxiety 2021; 38:431-438. [PMID: 33621410 DOI: 10.1002/da.23144] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 12/16/2020] [Accepted: 02/12/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND To assess whether linear effects or threshold effects best describe the association between early adverse stress (EAS) and complex and severe depression (i.e., depression with treatment resistance, psychotic symptoms, and/or suicidal ideation), and to examine the attributable risk of complex and severe depression associated with EAS. METHODS A cross-sectional study was conducted using deidentified clinical data (on demographics, presence of complex and severe depression, and exposure to seven types of EAS) from 1,013 adults who were seen in an outpatient mental health clinic in Santiago, Chile, for a major depressive episode. Multivariate logistic regressions were fitted to estimate odds ratios (ORs), using a bootstrap approach to compute 95% bias-corrected confidence intervals (95% BC CIs). A detailed examination of the cumulative risk score and calculations of the attributable risk was conducted. RESULTS Exposure to at least five EASs was reported by 3.6% of the sample. In the multivariate logistic regression models, there was a marked increase in the odds of having complex and severe depression associated with exposure to at least five EASs (OR = 4.24; 95% BC CI: 1.25 to 9.09), according to a threshold effect. The attributable risk of complex and severe depression associated with exposure to at least one EAS was 36.8% (95% BC CI: 17.7 to 55.9). CONCLUSIONS High levels of EAS distinctively contribute to complex clinical presentations of depression in adulthood. Patients with complex clinical presentations of depression and history of EAS should need a differentiated treatment approach, particularly those having high levels of EAS.
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Affiliation(s)
- Sergio Gloger
- Psicomedica, Clinical & Research Group, Santiago, Chile.,Departamento de Psiquiatría y Salud Mental Campus Oriente, Facultad de Medicina, Universidad de Chile, Santiago, Chile
| | - Paul A Vöhringer
- Psicomedica, Clinical & Research Group, Santiago, Chile.,Millennium Institute for Depression and Personality Research (MIDAP), Santiago, Chile.,Departamento de Psiquiatría y Salud Mental, Hospital Clínico Universidad de Chile, Santiago, Chile.,Mood Disorders Program, Tufts Medical Center, Boston, Massachusetts, USA.,Department of Psychiatry, Tufts University School of Medicine, Boston, Massachusetts, USA
| | - Pablo Martínez
- Psicomedica, Clinical & Research Group, Santiago, Chile.,Millennium Institute for Depression and Personality Research (MIDAP), Santiago, Chile.,Departamento de Psiquiatría y Salud Mental, Hospital Clínico Universidad de Chile, Santiago, Chile.,Núcleo Milenio para Mejorar la Salud Mental de Adolescentes y Jóvenes (Imhay), Santiago, Chile
| | - M Victoria Chacón
- Psicomedica, Clinical & Research Group, Santiago, Chile.,Millennium Institute for Depression and Personality Research (MIDAP), Santiago, Chile
| | - Cristian Cáceres
- Psicomedica, Clinical & Research Group, Santiago, Chile.,Millennium Institute for Depression and Personality Research (MIDAP), Santiago, Chile
| | | | - Marianne Cottin
- Millennium Institute for Depression and Personality Research (MIDAP), Santiago, Chile.,Escuela de Psicología, Pontificia Universidad Católica de Chile, Santiago, Chile.,Facultad de Medicina, Universidad de Chile, Santiago, Chile.,Escuela de Psicología, Universidad Finis Terrae, Santiago, Chile
| | - Alex Behn
- Millennium Institute for Depression and Personality Research (MIDAP), Santiago, Chile.,Escuela de Psicología, Pontificia Universidad Católica de Chile, Santiago, Chile
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Gloger S, Martínez P, Behn A, Chacón MV, Cottin M, Diez de Medina D, Vöhringer PA. Population-attributable risk of adverse childhood experiences for high suicide risk, psychiatric admissions, and recurrent depression, in depressed outpatients. Eur J Psychotraumatol 2021; 12:1874600. [PMID: 34025917 PMCID: PMC8118528 DOI: 10.1080/20008198.2021.1874600] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Background: Population-attributable risk (PAR) may help estimate the potential contribution of adverse childhood experiences (ACEs) to serious clinical presentations of depression, characterized by suicidality, previous psychiatric admissions, and episode recurrence. Objective: To determine the PAR of ACEs for serious clinical presentations of depression (high suicide risk, previous psychiatric admissions, and recurrent depression) in outpatients with ICD-10 clinical depression. Method: Systematic chart review of 1,013 adults who were assessed and/or treated in a mental health clinic in Santiago, Chile for a major depressive episode. Data were collected on demographics and clinical characteristics of depression. Exposure to ACEs was determined with the Brief Physical and Sexual Abuse Questionnaire, assessing seven types of ACEs. Multivariable logistic regression analysis was used to assess the association between exposure to ACEs and suicidality, previous psychiatric admissions, and recurrence. Predicted probabilities were used for calculations of PAR. Results: Of the 1,001 study participants with complete data, 53.3% had recurrent depression, 13.5% had high suicide risk, and 5.0% had previous psychiatric admissions. Exposure to at least one ACE was recorded for 69.0% of the sample. Exposure to at least one ACE and specific types of ACEs (i.e. childhood sexual abuse and traumatic separation from caregiver) were associated with serious clinical presentations of depression. A dose-response relationship was observed between cumulative exposure to ACEs and the most serious clinical presentations of depression. ACEs were attributed to a significant proportion of disease: 61.6% of previous psychiatric admissions, 45.0% of high suicide risk, and 14.5% of recurrent depression. Conclusions: A substantial proportion of serious clinical presentations of depression among outpatients are associated with ACEs. Early detection of depressive episodes associated with ACEs, and tailored treatment for these patients, may potentially reduce the incidence of serious complications in this population.
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Affiliation(s)
- Sergio Gloger
- Psicomedica, Clinical & Research Group, Santiago, Chile.,Departamento de Psiquiatría y Salud Mental Campus Oriente, Facultad de Medicina, Universidad de Chile, Santiago, Chile.,ANID, Millennium Science Initiative Program, Millennium Institute for Depression and Personality Research (MIDAP), Santiago, Chile
| | - Pablo Martínez
- Psicomedica, Clinical & Research Group, Santiago, Chile.,ANID, Millennium Science Initiative Program, Millennium Institute for Depression and Personality Research (MIDAP), Santiago, Chile.,Departamento de Psiquiatría y Salud Mental, Hospital Clínico Universidad de Chile, Santiago, Chile.,ANID, Millennium Science Initiative Program, Millennium Nucleus to Improve the Mental Health of Adolescents and Youths, Imhay, Santiago, Chile.,Escuela de Psicología, Facultad de Humanidades, Universidad de Santiago de Chile, Santiago, Chile.,CITIAPS, Universidad de Santiago de Chile, Santiago, Chile
| | - Alex Behn
- ANID, Millennium Science Initiative Program, Millennium Institute for Depression and Personality Research (MIDAP), Santiago, Chile.,Escuela de Psicología, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - M Victoria Chacón
- Psicomedica, Clinical & Research Group, Santiago, Chile.,ANID, Millennium Science Initiative Program, Millennium Institute for Depression and Personality Research (MIDAP), Santiago, Chile
| | - Marianne Cottin
- ANID, Millennium Science Initiative Program, Millennium Institute for Depression and Personality Research (MIDAP), Santiago, Chile.,Escuela de Psicología, Pontificia Universidad Católica de Chile, Santiago, Chile.,Facultad de Medicina, Universidad de Chile, Santiago, Chile.,Escuela de Psicología, Universidad Finis Terrae, Santiago, Chile
| | | | - Paul A Vöhringer
- Psicomedica, Clinical & Research Group, Santiago, Chile.,ANID, Millennium Science Initiative Program, Millennium Institute for Depression and Personality Research (MIDAP), Santiago, Chile.,Departamento de Psiquiatría y Salud Mental, Hospital Clínico Universidad de Chile, Santiago, Chile.,Mood Disorders Program, Tufts Medical Center, Boston, MA, USA.,Department of Psychiatry, Tufts University School of Medicine, Boston, MA, USA
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47
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Setoyama D, Yoshino A, Takamura M, Okada G, Iwata M, Tsunetomi K, Ohgidani M, Kuwano N, Yoshimoto J, Okamoto Y, Yamawaki S, Kanba S, Kang D, Kato TA. Personality classification enhances blood metabolome analysis and biotyping for major depressive disorders: two-species investigation. J Affect Disord 2021; 279:20-30. [PMID: 33038697 DOI: 10.1016/j.jad.2020.09.118] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 08/11/2020] [Accepted: 09/27/2020] [Indexed: 12/27/2022]
Abstract
BACKGROUND The relationship between depression and personality has long been suggested, however, biomarker investigations for depression have mostly overlooked this connection. METHODS We collected personality traits from 100 drug-free patients with major depressive disorders (MDD) and 100 healthy controls based on the Five-Factor Model (FFM) such as Neuroticism (N) and Extraversion (E), and also obtained 63 plasma metabolites profiles by LCMS-based metabolome analysis. RESULTS Partitional clustering analysis using the NEO-FFI data classified all subjects into three major clusters. Eighty-six subjects belonging to Cluster 1 (C1: less personality-biased group) constituted half of MDD patients and half of healthy controls. C2 constituted 50 subjects mainly MDD patients (N high + E low), and C3 constituted 64 subjects mainly healthy subjects (N low + E high). Using metabolome information, the machine learning model was optimized to discriminate MDD patients from healthy controls among all subjects and C1, respectively. The performance of the model for all subjects was moderate (AUC = 0. 715), while the performance was extremely improved when limited to C1 (AUC = 0. 907). Tryptophan-pathway plasma metabolites including tryptophan, serotonin and kynurenine were significantly lower in MDD patients especially among C1. We also validated metabolomic findings using a social-defeat mice model of stress-induced depression. LIMITATIONS A case-control study design and sample size is not large. CONCLUSIONS Our results suggest that personality classification enhances blood biomarker analysis for MDD patients and further translational investigations should be conducted to clarify the biological relationship between personality traits, stress and depression.
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Affiliation(s)
- Daiki Setoyama
- Department of Clinical Chemistry and Laboratory Medicine, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi Higashi-Ku, Fukuoka 812-8582, Japan
| | - Atsuo Yoshino
- Department of Psychiatry and Neurosciences, Hiroshima University, 1-2-3 Kasumi, Minami-Ku, Hiroshima 734-8553, Japan
| | - Masahiro Takamura
- Department of Psychiatry and Neurosciences, Hiroshima University, 1-2-3 Kasumi, Minami-Ku, Hiroshima 734-8553, Japan
| | - Go Okada
- Department of Psychiatry and Neurosciences, Hiroshima University, 1-2-3 Kasumi, Minami-Ku, Hiroshima 734-8553, Japan
| | - Masaaki Iwata
- Department of Neuropsychiatry, Faculty of Medicine, Tottori University, 86 Nishi-Cho, Yonago 683-8503, Japan
| | - Kyohei Tsunetomi
- Department of Neuropsychiatry, Faculty of Medicine, Tottori University, 86 Nishi-Cho, Yonago 683-8503, Japan
| | - Masahiro Ohgidani
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi Higashi-Ku, Fukuoka 812-8582, Japan
| | - Nobuki Kuwano
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi Higashi-Ku, Fukuoka 812-8582, Japan
| | - Junichiro Yoshimoto
- Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara 630-0192, Japan
| | - Yasumasa Okamoto
- Department of Psychiatry and Neurosciences, Hiroshima University, 1-2-3 Kasumi, Minami-Ku, Hiroshima 734-8553, Japan
| | - Shigeto Yamawaki
- Department of Psychiatry and Neurosciences, Hiroshima University, 1-2-3 Kasumi, Minami-Ku, Hiroshima 734-8553, Japan
| | - Shigenobu Kanba
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi Higashi-Ku, Fukuoka 812-8582, Japan
| | - Dongchon Kang
- Department of Clinical Chemistry and Laboratory Medicine, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi Higashi-Ku, Fukuoka 812-8582, Japan
| | - Takahiro A Kato
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi Higashi-Ku, Fukuoka 812-8582, Japan.
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48
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Buch AM, Liston C. Dissecting diagnostic heterogeneity in depression by integrating neuroimaging and genetics. Neuropsychopharmacology 2021; 46:156-175. [PMID: 32781460 PMCID: PMC7688954 DOI: 10.1038/s41386-020-00789-3] [Citation(s) in RCA: 98] [Impact Index Per Article: 32.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 07/07/2020] [Accepted: 07/16/2020] [Indexed: 12/12/2022]
Abstract
Depression is a heterogeneous and etiologically complex psychiatric syndrome, not a unitary disease entity, encompassing a broad spectrum of psychopathology arising from distinct pathophysiological mechanisms. Motivated by a need to advance our understanding of these mechanisms and develop new treatment strategies, there is a renewed interest in investigating the neurobiological basis of heterogeneity in depression and rethinking our approach to diagnosis for research purposes. Large-scale genome-wide association studies have now identified multiple genetic risk variants implicating excitatory neurotransmission and synapse function and underscoring a highly polygenic inheritance pattern that may be another important contributor to heterogeneity in depression. Here, we review various sources of phenotypic heterogeneity and approaches to defining and studying depression subtypes, including symptom-based subtypes and biology-based approaches to decomposing the depression syndrome. We review "dimensional," "categorical," and "hybrid" approaches to parsing phenotypic heterogeneity in depression and defining subtypes using functional neuroimaging. Next, we review recent progress in neuroimaging genetics (correlating neuroimaging patterns of brain function with genetic data) and its potential utility for generating testable hypotheses concerning molecular and circuit-level mechanisms. We discuss how genetic variants and transcriptomic profiles may confer risk for depression by modulating brain structure and function. We conclude by highlighting several promising areas for future research into the neurobiological underpinnings of heterogeneity, including efforts to understand sexually dimorphic mechanisms, the longitudinal dynamics of depressive episodes, and strategies for developing personalized treatments and facilitating clinical decision-making.
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Affiliation(s)
- Amanda M Buch
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, 413 East 69th Street, Box 240, New York, NY, 10021, USA
| | - Conor Liston
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, 413 East 69th Street, Box 240, New York, NY, 10021, USA.
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49
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Regonia PR, Takamura M, Nakano T, Ichikawa N, Fermin A, Okada G, Okamoto Y, Yamawaki S, Ikeda K, Yoshimoto J. Modeling Heterogeneous Brain Dynamics of Depression and Melancholia Using Energy Landscape Analysis. Front Psychiatry 2021; 12:780997. [PMID: 34899435 PMCID: PMC8656401 DOI: 10.3389/fpsyt.2021.780997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 11/01/2021] [Indexed: 11/13/2022] Open
Abstract
Our current understanding of melancholic depression is shaped by its position in the depression spectrum. The lack of consensus on how it should be treated-whether as a subtype of depression, or as a distinct disorder altogethe-interferes with the recovery of suffering patients. In this study, we analyzed brain state energy landscape models of melancholic depression, in contrast to healthy and non-melancholic energy landscapes. Our analyses showed significant group differences on basin energy, basin frequency, and transition dynamics in several functional brain networks such as basal ganglia, dorsal default mode, and left executive control networks. Furthermore, we found evidences suggesting the connection between energy landscape characteristics (basin characteristics) and depressive symptom scores (BDI-II and SHAPS). These results indicate that melancholic depression is distinguishable from its non-melancholic counterpart, not only in terms of depression severity, but also in brain dynamics.
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Affiliation(s)
- Paul Rossener Regonia
- Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan.,Department of Computer Science, College of Engineering, University of the Philippines Diliman, Quezon City, Philippines
| | - Masahiro Takamura
- Center for Brain, Mind and KANSEI Research Sciences, Hiroshima University, Hiroshima, Japan.,Department of Neurology, Faculty of Medicine, Shimane University, Izumo, Japan
| | - Takashi Nakano
- Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan.,School of Medicine, Fujita Health University, Toyoake, Japan
| | - Naho Ichikawa
- Center for Brain, Mind and KANSEI Research Sciences, Hiroshima University, Hiroshima, Japan
| | - Alan Fermin
- Center for Brain, Mind and KANSEI Research Sciences, Hiroshima University, Hiroshima, Japan
| | - Go Okada
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
| | - Yasumasa Okamoto
- Center for Brain, Mind and KANSEI Research Sciences, Hiroshima University, Hiroshima, Japan.,Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
| | - Shigeto Yamawaki
- Center for Brain, Mind and KANSEI Research Sciences, Hiroshima University, Hiroshima, Japan
| | - Kazushi Ikeda
- Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan
| | - Junichiro Yoshimoto
- Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan
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50
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Tokuda T, Yamashita O, Sakai Y, Yoshimoto J. Clustering of Multiple Psychiatric Disorders Using Functional Connectivity in the Data-Driven Brain Subnetwork. Front Psychiatry 2021; 12:683280. [PMID: 34483983 PMCID: PMC8416352 DOI: 10.3389/fpsyt.2021.683280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Accepted: 07/26/2021] [Indexed: 12/04/2022] Open
Abstract
Recently, the dimensional approach has attracted much attention, bringing a paradigm shift to a continuum of understanding of different psychiatric disorders. In line with this new paradigm, we examined whether there was common functional connectivity related to various psychiatric disorders in an unsupervised manner without explicitly using diagnostic label information. To this end, we uniquely applied a newly developed network-based multiple clustering method to resting-state functional connectivity data, which allowed us to identify pairs of relevant brain subnetworks and subject cluster solutions accordingly. Thus, we identified four subject clusters, which were characterized as major depressive disorder (MDD), young healthy control (young HC), schizophrenia (SCZ)/bipolar disorder (BD), and autism spectrum disorder (ASD), respectively, with the relevant brain subnetwork represented by the cerebellum-thalamus-pallidum-temporal circuit. The clustering results were validated using independent datasets. This study is the first cross-disorder analysis in the framework of unsupervised learning of functional connectivity based on a data-driven brain subnetwork.
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Affiliation(s)
- Tomoki Tokuda
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan.,Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan
| | - Okito Yamashita
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan.,Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan
| | - Yuki Sakai
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan
| | - Junichiro Yoshimoto
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan.,Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Nara, Japan
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