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Sun S, Yan C, Qu S, Luo G, Liu X, Tian F, Dong Q, Li X, Hu B. Resting-state dynamic functional connectivity in major depressive disorder: A systematic review. Prog Neuropsychopharmacol Biol Psychiatry 2024; 135:111076. [PMID: 38972502 DOI: 10.1016/j.pnpbp.2024.111076] [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: 03/05/2024] [Revised: 06/02/2024] [Accepted: 06/26/2024] [Indexed: 07/09/2024]
Abstract
As a novel measure, dynamic functional connectivity (dFC) provides insight into the dynamic nature of brain networks and their interactions in resting-state, surpassing traditional static functional connectivity in pathological conditions such as depression. Since a comprehensive review is still lacking, we then reviewed forty-five eligible papers to explore pathological mechanisms of major depressive disorder (MDD) from perspectives including abnormal brain regions and functional networks, brain state, topological properties, relevant recognition, along with longitudinal studies. Though inconsistencies could be found, common findings are: (1) From different perspectives based on dFC, default-mode network (DMN) with its subregions exhibited a close relation to the pathological mechanism of MDD. (2) With a corrupted integrity within large-scale functional networks and imbalance between them, longer fraction time in a relatively weakly-connected state may be a possible property of MDD concerning its relation with DMN. Abnormal transition frequencies between states were correlated to the severity of MDD. (3) Including dynamic properties in topological network metrics enhanced recognition effect. In all, this review summarized its use for clinical diagnosis and treatment, elucidating the non-stationary of MDD patients' aberrant brain activity in the absence of stimuli and bringing new views into its underlying neuro mechanism.
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Affiliation(s)
- Shuting Sun
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, China; Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China
| | - Chang Yan
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, China
| | - Shanshan Qu
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, China
| | - Gang Luo
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, China
| | - Xuesong Liu
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, China
| | - Fuze Tian
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, China; Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China
| | - Qunxi Dong
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, China
| | - Xiaowei Li
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China
| | - Bin Hu
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, China; Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China.
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2
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Liang Q, Xu Z, Chen S, Lin S, Lin X, Li Y, Zhang Y, Peng B, Hou G, Qiu Y. Spatiotemporal discoordination of brain spontaneous activity in major depressive disorder. J Affect Disord 2024; 365:134-143. [PMID: 39154985 DOI: 10.1016/j.jad.2024.08.030] [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: 02/23/2024] [Revised: 06/03/2024] [Accepted: 08/10/2024] [Indexed: 08/20/2024]
Abstract
BACKGROUND Major depressive disorder (MDD) is a widespread mental health issue, impacting spatial and temporal aspects of brain activity. The neural mechanisms behind MDD remain unclear. To address this gap, we introduce a novel measure, spatiotemporal topology (SPT), capturing both the hierarchy and dynamic attributes of brain activity in depressive disorder patients. METHODS We analyzed fMRI data from 285 MDD inpatients and 141 healthy controls (HC). SPT was assessed by coupling brain gradient measurement and time delay estimation. A nested machine learning process distinguished between MDD and HC using SPT. Person's correlation tested the link between SPT's and symptom severity, and another machine learning method predicted the gap between patients' chronological and brain age. RESULTS SPT demonstrated significant differences between patients and healthy controls (F = 2.944, p < 0.001). Machine learning approaches revealed SPT's ability to discriminate between patients and healthy controls (Accuracy = 0.65, Sensitivity = 0.67, Specificity = 0.64). Moreover, SPT correlated with the severity of depression symptom (r = 0.32. pFDR = 0.045) and predicted the gap between patients' chronological age and brain age (r = 0.756, p < 0.001). LIMITATIONS Evaluation of brain dynamics was constrained by MRI temporal resolution. CONCLUSIONS Our study introduces SPT as a promising metric to characterize the spatiotemporal signature of brain function, providing insights into deviant brain activity associated with depressive disorders and advancing our understanding of their psychopathological mechanisms.
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Affiliation(s)
- Qunjun Liang
- Department of Medical Imaging, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan AVE 89, Nanshan District, Shenzhen 518000, People's Republic of China; Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen 518060, People's Republic of China
| | - Ziyun Xu
- Neuropsychiatry Imaging Center, Department of Radiology, Shenzhen Mental Health Center, Shenzhen Kangning Hospital, Shenzhen 518020, People's Republic of China
| | - Shengli Chen
- Department of Medical Imaging, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan AVE 89, Nanshan District, Shenzhen 518000, People's Republic of China
| | - Shiwei Lin
- Department of Medical Imaging, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan AVE 89, Nanshan District, Shenzhen 518000, People's Republic of China
| | - Xiaoshan Lin
- Department of Medical Imaging, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan AVE 89, Nanshan District, Shenzhen 518000, People's Republic of China
| | - Ying Li
- Department of Medical Imaging, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan AVE 89, Nanshan District, Shenzhen 518000, People's Republic of China
| | - Yingli Zhang
- Department of Depressive Disorder, Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, Guangdong 518020, People's Republic of China
| | - Bo Peng
- Department of Depressive Disorder, Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, Guangdong 518020, People's Republic of China
| | - Gangqiang Hou
- Neuropsychiatry Imaging Center, Department of Radiology, Shenzhen Mental Health Center, Shenzhen Kangning Hospital, Shenzhen 518020, People's Republic of China.
| | - Yingwei Qiu
- Department of Medical Imaging, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan AVE 89, Nanshan District, Shenzhen 518000, People's Republic of China.
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3
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Koutsouleris N, Fusar-Poli P. From Heterogeneity to Precision: Redefining Diagnosis, Prognosis, and Treatment of Mental Disorders. Biol Psychiatry 2024; 96:508-510. [PMID: 39232589 DOI: 10.1016/j.biopsych.2024.07.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2024] [Accepted: 07/17/2024] [Indexed: 09/06/2024]
Affiliation(s)
- Nikolaos Koutsouleris
- Section for Precision Psychiatry, Department of Psychiatry and Psychotherapy, University Medical Centre, Ludwig-Maximilians-University, Munich, Germany; Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom; Max Planck Institute of Psychiatry, Munich, Germany.
| | - Paolo Fusar-Poli
- Section for Precision Psychiatry, Department of Psychiatry and Psychotherapy, University Medical Centre, Ludwig-Maximilians-University, Munich, Germany; Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom; Department of Brain and Behavioural Sciences, University of Pavia, Italy
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4
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Guo J, He C, Song H, Gao H, Yao S, Dong SS, Yang TL. Unveiling Promising Neuroimaging Biomarkers for Schizophrenia Through Clinical and Genetic Perspectives. Neurosci Bull 2024; 40:1333-1352. [PMID: 38703276 PMCID: PMC11365900 DOI: 10.1007/s12264-024-01214-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 01/08/2024] [Indexed: 05/06/2024] Open
Abstract
Schizophrenia is a complex and serious brain disorder. Neuroscientists have become increasingly interested in using magnetic resonance-based brain imaging-derived phenotypes (IDPs) to investigate the etiology of psychiatric disorders. IDPs capture valuable clinical advantages and hold biological significance in identifying brain abnormalities. In this review, we aim to discuss current and prospective approaches to identify potential biomarkers for schizophrenia using clinical multimodal neuroimaging and imaging genetics. We first described IDPs through their phenotypic classification and neuroimaging genomics. Secondly, we discussed the applications of multimodal neuroimaging by clinical evidence in observational studies and randomized controlled trials. Thirdly, considering the genetic evidence of IDPs, we discussed how can utilize neuroimaging data as an intermediate phenotype to make association inferences by polygenic risk scores and Mendelian randomization. Finally, we discussed machine learning as an optimum approach for validating biomarkers. Together, future research efforts focused on neuroimaging biomarkers aim to enhance our understanding of schizophrenia.
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Affiliation(s)
- Jing Guo
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Changyi He
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Huimiao Song
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Huiwu Gao
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Shi Yao
- Guangdong Key Laboratory of Age-Related Cardiac and Cerebral Diseases, Affiliated Hospital of Guangdong Medical University, Zhanjiang, 524000, China
| | - Shan-Shan Dong
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China.
| | - Tie-Lin Yang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China.
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5
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Freichel R, Lenartowicz A, Douw L, Kruschwitz JD, Banaschewski T, Barker GJ, Bokde ALW, Desrivières S, Flor H, Grigis A, Garavan H, Heinz A, Brühl R, Martinot JL, Martinot MLP, Artiges E, Nees F, Orfanos DP, Paus T, Poustka L, Holz N, Baeuchl C, Smolka MN, Vaidya N, Whelan R, Frouin V, Schumann G, Walter H, Blanken TF. Unraveling robust brain-behavior links of depressive complaints through granular network models for understanding heterogeneity. J Affect Disord 2024; 359:140-144. [PMID: 38754596 DOI: 10.1016/j.jad.2024.05.060] [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: 11/21/2023] [Revised: 04/12/2024] [Accepted: 05/12/2024] [Indexed: 05/18/2024]
Abstract
BACKGROUND Depressive symptoms are highly prevalent, present in heterogeneous symptom patterns, and share diverse neurobiological underpinnings. Understanding the links between psychopathological symptoms and biological factors is critical in elucidating its etiology and persistence. We aimed to evaluate the utility of using symptom-brain network models to parse the heterogeneity of depressive complaints in a large adolescent sample. METHODS We used data from the third wave of the IMAGEN study, a multi-center panel cohort study involving 1317 adolescents (52.49 % female, mean ± SD age = 18.5 ± 0.7). Two network models were estimated: one including an overall depressive symptom severity sum score based on the Adolescent Depression Rating Scale (ADRS), and one incorporating individual ADRS item scores. Both networks included measures of cortical thickness in several regions (insula, cingulate, mOFC, fusiform gyrus) and hippocampal volume derived from neuroimaging. RESULTS The network based on individual item scores revealed associations between cortical thickness measures and specific depressive complaints, obscured when using an aggregate depression severity score. Notably, the insula's cortical thickness showed negative associations with cognitive dysfunction (partial cor. = -0.15); the cingulate's cortical thickness showed negative associations with feelings of worthlessness (partial cor. = -0.10), and mOFC was negatively associated with anhedonia (partial cor. = -0.05). LIMITATIONS This cross-sectional study relied on the self-reported assessment of depression complaints and used a non-clinical sample with predominantly healthy participants (19 % with depression or sub-threshold depression). CONCLUSIONS This study showcases the utility of network models in parsing heterogeneity in depressive complaints, linking individual complaints to specific neural substrates. We outline the next steps to integrate neurobiological and cognitive markers to unravel MDD's phenotypic heterogeneity.
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Affiliation(s)
- René Freichel
- Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands.
| | - Agatha Lenartowicz
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, United States
| | - Linda Douw
- Department of Anatomy and Neurosciences, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Johann D Kruschwitz
- Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Gareth J Barker
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, United Kingdom
| | - Arun L W Bokde
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Sylvane Desrivières
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, SGDP Centre, King's College London, UK
| | - Herta Flor
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Department of Psychology, School of Social Sciences, University of Mannheim, Mannheim, Germany
| | - Antoine Grigis
- NeuroSpin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Hugh Garavan
- Departments of Psychiatry and Psychology, University of Vermont, Burlington, VT, USA
| | - Andreas Heinz
- Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Rüdiger Brühl
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Jean-Luc Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U A10 'Trajectoires développementales en psychiatrie', Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli, Gif-sur-Yvette, France
| | - Marie-Laure Paillère Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U A10 'Trajectoires développementales en psychiatrie', Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli, Gif-sur-Yvette, France; AP-HP, Sorbonne Université, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Paris, France
| | - Eric Artiges
- Institut National de la Santé et de la Recherche Médicale, INSERM U A10 'Trajectoires développementales en psychiatrie', Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli, Gif-sur-Yvette, France; Psychiatry Department, EPS Barthélémy Durand, Etampes, France
| | - Frauke Nees
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Institute of Medical Psychology and Medical Sociology, Kiel University, Kiel, Germany
| | | | - Tomáš Paus
- Departments of Psychiatry and Psychology, University of Toronto, Toronto, Ontario, Canada; Department of Psychiatry, Faculty of Medicine and Centre Hospitalier Universitaire Sainte-Justine, University of Montreal, Montreal, Quebec, Canada
| | - Luise Poustka
- Department of Child and Adolescent Psychiatry, Center for Psychosocial Medicine, University Hospital Heidelberg, Heidelberg, Germany
| | - Nathalie Holz
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Christian Baeuchl
- Faculty of Psychology, Technische Universität Dresden, Dresden, Germany
| | - Michael N Smolka
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Nilakshi Vaidya
- Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Germany
| | - Robert Whelan
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
| | - Vincent Frouin
- NeuroSpin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Gunter Schumann
- Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Germany; Centre for Population Neuroscience and Precision Medicine (PONS), Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, China
| | - Henrik Walter
- Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Tessa F Blanken
- Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands
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6
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Jahanshad N, Lenzini P, Bijsterbosch J. Current best practices and future opportunities for reproducible findings using large-scale neuroimaging in psychiatry. Neuropsychopharmacology 2024:10.1038/s41386-024-01938-8. [PMID: 39117903 DOI: 10.1038/s41386-024-01938-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 06/05/2024] [Accepted: 07/09/2024] [Indexed: 08/10/2024]
Abstract
Research into the brain basis of psychopathology is challenging due to the heterogeneity of psychiatric disorders, extensive comorbidities, underdiagnosis or overdiagnosis, multifaceted interactions with genetics and life experiences, and the highly multivariate nature of neural correlates. Therefore, increasingly larger datasets that measure more variables in larger cohorts are needed to gain insights. In this review, we present current "best practice" approaches for using existing databases, collecting and sharing new repositories for big data analyses, and future directions for big data in neuroimaging and psychiatry with an emphasis on contributing to collaborative efforts and the challenges of multi-study data analysis.
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Affiliation(s)
- Neda Jahanshad
- Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, 90292, USA.
| | - Petra Lenzini
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO, 63110, USA
| | - Janine Bijsterbosch
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO, 63110, USA.
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7
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Briley PM, Webster L, Boutry C, Oh H, Auer DP, Liddle PF, Morriss R. Magnetic resonance imaging connectivity features associated with response to transcranial magnetic stimulation in major depressive disorder. Psychiatry Res Neuroimaging 2024; 342:111846. [PMID: 38908353 DOI: 10.1016/j.pscychresns.2024.111846] [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: 07/24/2023] [Revised: 03/23/2024] [Accepted: 06/11/2024] [Indexed: 06/24/2024]
Abstract
Transcranial magnetic stimulation (TMS) is an FDA-approved neuromodulation treatment for major depressive disorder (MDD), thought to work by altering dysfunctional brain connectivity pathways, or by indirectly modulating the activity of subcortical brain regions. Clinical response to TMS remains highly variable, highlighting the need for baseline predictors of response and for understanding brain changes associated with response. This systematic review examined brain connectivity features, and changes in connectivity features, associated with clinical improvement following TMS in MDD. Forty-one studies met inclusion criteria, including 1097 people with MDD. Most studies delivered one of two types of TMS to left dorsolateral prefrontal cortex and measured connectivity using resting-state functional MRI. The subgenual anterior cingulate cortex was the most well-studied brain region, particularly its connectivity with the TMS target or with the "executive control network" of brain regions. There was marked heterogeneity in findings. There is a need for greater understanding of how cortical TMS modulates connectivity with, and the activity of, subcortical regions, and how these effects change within and across treatment sessions.
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Affiliation(s)
- P M Briley
- Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom; Nottingham National Institute for Health and Care Research (NIHR) Biomedical Research Centre, Nottingham, United Kingdom; Institute of Mental Health, Nottinghamshire Healthcare NHS Foundation Trust, Nottingham, United Kingdom.
| | - L Webster
- Nottingham National Institute for Health and Care Research (NIHR) Biomedical Research Centre, Nottingham, United Kingdom; Institute of Mental Health, Nottinghamshire Healthcare NHS Foundation Trust, Nottingham, United Kingdom
| | - C Boutry
- Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom; Institute of Mental Health, Nottinghamshire Healthcare NHS Foundation Trust, Nottingham, United Kingdom; NIHR Applied Research Collaboration East Midlands, University of Nottingham, Nottingham, United Kingdom
| | - H Oh
- Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom; Nottingham National Institute for Health and Care Research (NIHR) Biomedical Research Centre, Nottingham, United Kingdom; Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom
| | - D P Auer
- Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom; Nottingham National Institute for Health and Care Research (NIHR) Biomedical Research Centre, Nottingham, United Kingdom; Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom
| | - P F Liddle
- Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom; Institute of Mental Health, Nottinghamshire Healthcare NHS Foundation Trust, Nottingham, United Kingdom
| | - R Morriss
- Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom; Nottingham National Institute for Health and Care Research (NIHR) Biomedical Research Centre, Nottingham, United Kingdom; Institute of Mental Health, Nottinghamshire Healthcare NHS Foundation Trust, Nottingham, United Kingdom; NIHR Applied Research Collaboration East Midlands, University of Nottingham, Nottingham, United Kingdom; NIHR Mental Health (MindTech) Health Technology Collaboration, University of Nottingham, Nottingham, United Kingdom
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8
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Quidé Y, Jahanshad N, Andoh J, Antoniou G, Apkarian AV, Ashar YK, Badran BW, Baird CL, Baxter L, Bell TR, Blanco-Hinojo L, Borckardt J, Cheung CL, Ciampi de Andrade D, Couto BA, Cox SR, Cruz-Almeida Y, Dannlowski U, De Martino E, de Tommaso M, Deus J, Domin M, Egorova-Brumley N, Elliott J, Fanton S, Fauchon C, Flor H, Franz CE, Gatt JM, Gerdhem P, Gilman JM, Gollub RL, Govind V, Graven-Nielsen T, Håkansson G, Hales T, Haswell C, Heukamp NJ, Hu L, Huang L, Hussain A, Jensen K, Kircher T, Kremen WS, Leehr EJ, Lindquist M, Loggia ML, Lotze M, Martucci KT, Meeker TJ, Meinert S, Millard SK, Morey RA, Murillo C, Nees F, Nenadic I, Park HRP, Peng X, Ploner M, Pujol J, Robayo LE, Salan T, Seminowicz DA, Serian A, Slater R, Stein F, Stevens J, Strauss S, Sun D, Vachon-Presseau E, Valdes-Hernandez PA, Vanneste S, Vernon M, Verriotis M, Wager TD, Widerstrom-Noga E, Woodbury A, Zeidan F, Bhatt RR, Ching CRK, Haddad E, Thomopoulos SI, Thompson PM, Gustin SM. ENIGMA-Chronic Pain: a worldwide initiative to identify brain correlates of chronic pain. Pain 2024:00006396-990000000-00664. [PMID: 39058957 DOI: 10.1097/j.pain.0000000000003317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2024] [Accepted: 05/20/2024] [Indexed: 07/28/2024]
Affiliation(s)
- Yann Quidé
- School of Psychology, The University of New South Wales (UNSW) Sydney, Sydney, NSW, Australia
- Centre for Pain IMPACT, Neuroscience Research Australia, Randwick, NSW, Australia
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Jamila Andoh
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Georgia Antoniou
- Division of Population Health and Genomics, Medical Research Institute, University of Dundee, Dundee, Scotland, United Kingdom
| | - Apkar Vania Apkarian
- Center for Translational Pain Research, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
- Department of Neuroscience, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
- Department of Anesthesiology, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Yoni K Ashar
- Department of General Internal Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Bashar W Badran
- Department of Psychiatry and Behavioral Sciences, Neuro-X Lab, Medical University of South Carolina, Charleston, SC, United States
| | - C Lexi Baird
- Duke-UNC Brain Imaging and Analysis Center, Duke University, Durham, NC, United States
- VA Mid-Atlantic MIRECC, Durham VA Medical Center, Durham VA, Durham, NC, United States
| | - Luke Baxter
- Department of Paediatrics, University of Oxford, Oxford, United Kingdom
| | - Tyler R Bell
- Department of Psychiatry, University of California, San Diego, CA, United States
- Center for Behavior Genetics of Aging, University of California, San Diego, CA, United States
| | - Laura Blanco-Hinojo
- MRI Research Unit, Department of Radiology, Hospital del Mar, Barcelona, Spain
- IsGlobal, Barcelona, Spain
| | - Jeffrey Borckardt
- Department of Psychiatry and Behavioral Sciences, Neuro-X Lab, Medical University of South Carolina, Charleston, SC, United States
- Medical University of South Carolina, Charleston, SC, United States
- Ralph H. Johnson VAMC, Charleston, SC, United States
| | - Chloe L Cheung
- Neuroscience Graduate Program, Schulich School of Medicine & Dentistry, University of Western Ontario, London, ON, Canada
| | - Daniel Ciampi de Andrade
- Center for Neuroplasticity and Pain (CNAP), Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Bruno A Couto
- Center for Neuroplasticity and Pain (CNAP), Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Simon R Cox
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, Scotland, United Kingdom
| | - Yenisel Cruz-Almeida
- Pain Research and Intervention Center of Excellence, University of Florida, Gainesville, FL, United States
- Department of Community Dentistry and Behavioral Sciences, College of Dentistry, University of Florida, Gainesville, FL, United States
- Department of Neuroscience, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Udo Dannlowski
- Institute of Translational Psychiatry, University of Münster, Münster, Germany
| | - Enrico De Martino
- Center for Neuroplasticity and Pain (CNAP), Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Marina de Tommaso
- Neurophysiopathology Unit, DiBrain Department, Bari Aldo Moro University, Bari, Italy
| | - Joan Deus
- MRI Research Unit, Department of Radiology, Hospital del Mar, Barcelona, Spain
- Department of Clinical and Health Psychology, Autonomous University of Barcelona, Barcelona, Spain
| | - Martin Domin
- Functional Imaging Unit, Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany
| | - Natalia Egorova-Brumley
- Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, VIC, Australia
| | - James Elliott
- Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
- Northern Sydney Local Health District, Sydney, NSW, Australia
- The Kolling Institute, St Leonards, NSW, Australia
| | - Silvia Fanton
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Camille Fauchon
- Neuro-Dol, Inserm, University Hospital of Clermont-Ferrand, University of Clermont-Auvergne, Clermont-Ferrand, France
- NEUROPAIN Team, CRNL, CNRS, Inserm, University of Saint-Etienne, Saint-Etienne, France
| | - Herta Flor
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Heidelberg University, Mannheim, Germany
| | - Carol E Franz
- Department of Psychiatry, University of California, San Diego, CA, United States
- Center for Behavior Genetics of Aging, University of California, San Diego, CA, United States
| | - Justine M Gatt
- School of Psychology, The University of New South Wales (UNSW) Sydney, Sydney, NSW, Australia
- Centre for Wellbeing, Resilience and Recovery, Neuroscience Research Australia, Randwick, NSW, Australia
- Black Dog Institute, Randwick, NSW, Australia
| | - Paul Gerdhem
- Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Stockholm, Sweden
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
- Department of Orthopaedics and Hand Surgery, Uppsala University Hospital, Uppsala, Sweden
| | - Jodi M Gilman
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
- Center for Addiction Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Randy L Gollub
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Varan Govind
- Department of Radiology, University of Miami, Miller School of Medicine, Miami, FL, United States
| | - Thomas Graven-Nielsen
- Center for Neuroplasticity and Pain (CNAP), Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Gustaf Håkansson
- Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Stockholm, Sweden
| | - Tim Hales
- Consortium Against Pain Inequality, University of Dundee, Dundee, Scotland, United Kingdom
| | - Courtney Haswell
- Duke-UNC Brain Imaging and Analysis Center, Duke University, Durham, NC, United States
- VA Mid-Atlantic MIRECC, Durham VA Medical Center, Durham VA, Durham, NC, United States
| | - Nils Jannik Heukamp
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig-Holstein, Kiel University, Kiel, Germany
| | - Li Hu
- CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Lejian Huang
- Center for Translational Pain Research, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
- Department of Neuroscience, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Ahmed Hussain
- Duke-UNC Brain Imaging and Analysis Center, Duke University, Durham, NC, United States
- VA Mid-Atlantic MIRECC, Durham VA Medical Center, Durham VA, Durham, NC, United States
| | - Karin Jensen
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - William S Kremen
- Department of Psychiatry, University of California, San Diego, CA, United States
- Center for Behavior Genetics of Aging, University of California, San Diego, CA, United States
| | - Elisabeth J Leehr
- Institute of Translational Psychiatry, University of Münster, Münster, Germany
| | - Martin Lindquist
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD, United States
| | - Marco L Loggia
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
- Department of Anesthesia, Clinical Care, and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Martin Lotze
- Functional Imaging Unit, Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany
| | - Katherine T Martucci
- Department of Anesthesiology, Center for Translational Pain Medicine, Duke University School of Medicine, Durham, NC, United States
| | - Timothy J Meeker
- Department of Biology, Morgan State University, Baltimore, MD, United States
| | - Susanne Meinert
- Institute of Translational Psychiatry, University of Münster, Münster, Germany
- Institute for Translational Neuroscience, University of Münster, Münster, Germany
| | - Samantha K Millard
- Center for Neuroplasticity and Pain (CNAP), Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Rajendra A Morey
- Duke-UNC Brain Imaging and Analysis Center, Duke University, Durham, NC, United States
- VA Mid-Atlantic MIRECC, Durham VA Medical Center, Durham VA, Durham, NC, United States
| | - Carlos Murillo
- Department of General Internal Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
- Department of Rehabilitation Sciences, Ghent University, Ghent, Belgium
| | - Frauke Nees
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig-Holstein, Kiel University, Kiel, Germany
| | - Igor Nenadic
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Haeme R P Park
- School of Psychology, The University of New South Wales (UNSW) Sydney, Sydney, NSW, Australia
- Centre for Wellbeing, Resilience and Recovery, Neuroscience Research Australia, Randwick, NSW, Australia
| | - Xiaolong Peng
- Department of Psychiatry and Behavioral Sciences, Neuro-X Lab, Medical University of South Carolina, Charleston, SC, United States
| | - Markus Ploner
- Department of Neurology, Center for Interdisciplinary Pain Medicine and TUM-Neuroimaging Center, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Jesus Pujol
- MRI Research Unit, Department of Radiology, Hospital del Mar, Barcelona, Spain
| | - Linda E Robayo
- The Miami Project to Cure Paralysis, Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Teddy Salan
- Department of Radiology, University of Miami, Miller School of Medicine, Miami, FL, United States
| | - David A Seminowicz
- Schulich School of Medicine and Dentistry, University of Western Ontario, London, ON, Canada
| | - Angela Serian
- Department of Neurology, University Hospital Greifswald, Greifswald, Germany
| | - Rebeccah Slater
- Department of Paediatrics, University of Oxford, Oxford, United Kingdom
| | - Frederike Stein
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Jennifer Stevens
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States
- Atlanta Veterans Affairs Healthcare System, Atlanta, GA, United States
| | - Sebastian Strauss
- Department of Neurology, University Hospital Greifswald, Greifswald, Germany
| | - Delin Sun
- Duke-UNC Brain Imaging and Analysis Center, Duke University, Durham, NC, United States
- VA Mid-Atlantic MIRECC, Durham VA Medical Center, Durham VA, Durham, NC, United States
- Department of Psychiatry, School of Medicine, Duke University, Durham, NC, United States
| | - Etienne Vachon-Presseau
- Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, QC, Canada
- Department of Anesthesia, Faculty of Medicine, McGill University, Montreal, QC, Canada
- Alan Edwards Centre for Research on Pain (AECRP), McGill University, Montreal, QC, Canada
| | - Pedro A Valdes-Hernandez
- Department of Community Dentistry and Behavioral Sciences, College of Dentistry, University of Florida, Gainesville, FL, United States
| | - Sven Vanneste
- School of Psychology, Trinity College Dublin, Dublin, Ireland
- Trinity Institute for Neuroscience, Trinity College Dublin, Dublin, Ireland
- Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
| | - Mark Vernon
- Atlanta Veterans Affairs Healthcare System, Atlanta, GA, United States
| | - Madeleine Verriotis
- Developmental Neurosciences Department, UCL Great Ormond Street Institute of Child Health, London, United Kingdom
- Department of Anaesthesia and Pain Medicine, Great Ormond Street Hospital NHS Foundation Trust, London, United Kingdom
| | - Tor D Wager
- Dartmouth College, Hanover, NH, United States
| | - Eva Widerstrom-Noga
- The Miami Project to Cure Paralysis, Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Anna Woodbury
- Atlanta Veterans Affairs Healthcare System, Atlanta, GA, United States
- Division of Pain Medicine, Department of Anesthesiology, Emory University School of Medicine, Atlanta, GA, United States
| | - Fadel Zeidan
- Center for Pain Medicine, Department of Anesthesiology, University of California San Diego, La Jolla, CA, United States
| | - Ravi R Bhatt
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Christopher R K Ching
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Elizabeth Haddad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Sylvia M Gustin
- School of Psychology, The University of New South Wales (UNSW) Sydney, Sydney, NSW, Australia
- Centre for Pain IMPACT, Neuroscience Research Australia, Randwick, NSW, Australia
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9
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Kalisch R, Russo SJ, Müller MB. Neurobiology and systems biology of stress resilience. Physiol Rev 2024; 104:1205-1263. [PMID: 38483288 PMCID: PMC11381009 DOI: 10.1152/physrev.00042.2023] [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: 11/01/2023] [Revised: 03/06/2024] [Accepted: 03/12/2024] [Indexed: 05/16/2024] Open
Abstract
Stress resilience is the phenomenon that some people maintain their mental health despite exposure to adversity or show only temporary impairments followed by quick recovery. Resilience research attempts to unravel the factors and mechanisms that make resilience possible and to harness its insights for the development of preventative interventions in individuals at risk for acquiring stress-related dysfunctions. Biological resilience research has been lagging behind the psychological and social sciences but has seen a massive surge in recent years. At the same time, progress in this field has been hampered by methodological challenges related to finding suitable operationalizations and study designs, replicating findings, and modeling resilience in animals. We embed a review of behavioral, neuroimaging, neurobiological, and systems biological findings in adults in a critical methods discussion. We find preliminary evidence that hippocampus-based pattern separation and prefrontal-based cognitive control functions protect against the development of pathological fears in the aftermath of singular, event-type stressors [as found in fear-related disorders, including simpler forms of posttraumatic stress disorder (PTSD)] by facilitating the perception of safety. Reward system-based pursuit and savoring of positive reinforcers appear to protect against the development of more generalized dysfunctions of the anxious-depressive spectrum resulting from more severe or longer-lasting stressors (as in depression, generalized or comorbid anxiety, or severe PTSD). Links between preserved functioning of these neural systems under stress and neuroplasticity, immunoregulation, gut microbiome composition, and integrity of the gut barrier and the blood-brain barrier are beginning to emerge. On this basis, avenues for biological interventions are pointed out.
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Affiliation(s)
- Raffael Kalisch
- Leibniz Institute for Resilience Research (LIR), Mainz, Germany
- Neuroimaging Center (NIC), Focus Program Translational Neuroscience (FTN), Johannes Gutenberg University Medical Center, Mainz, Germany
| | - Scott J Russo
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, United States
- Brain and Body Research Center, Icahn School of Medicine at Mount Sinai, New York, New York, United States
| | - Marianne B Müller
- Leibniz Institute for Resilience Research (LIR), Mainz, Germany
- Translational Psychiatry, Department of Psychiatry and Psychotherapy, Johannes Gutenberg University Medical Center, Mainz, Germany
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10
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Prompiengchai S, Dunlop K. Breakthroughs and challenges for generating brain network-based biomarkers of treatment response in depression. Neuropsychopharmacology 2024:10.1038/s41386-024-01907-1. [PMID: 38951585 DOI: 10.1038/s41386-024-01907-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 05/17/2024] [Accepted: 06/13/2024] [Indexed: 07/03/2024]
Abstract
Treatment outcomes widely vary for individuals diagnosed with major depressive disorder, implicating a need for deeper understanding of the biological mechanisms conferring a greater likelihood of response to a particular treatment. Our improved understanding of intrinsic brain networks underlying depression psychopathology via magnetic resonance imaging and other neuroimaging modalities has helped reveal novel and potentially clinically meaningful biological markers of response. And while we have made considerable progress in identifying such biomarkers over the last decade, particularly with larger, multisite trials, there are significant methodological and practical obstacles that need to be overcome to translate these markers into the clinic. The aim of this review is to review current literature on brain network structural and functional biomarkers of treatment response or selection in depression, with a specific focus on recent large, multisite trials reporting predictive accuracy of candidate biomarkers. Regarding pharmaco- and psychotherapy, we discuss candidate biomarkers, reporting that while we have identified candidate biomarkers of response to a single intervention, we need more trials that distinguish biomarkers between first-line treatments. Further, we discuss the ways prognostic neuroimaging may help to improve treatment outcomes to neuromodulation-based therapies, such as transcranial magnetic stimulation and deep brain stimulation. Lastly, we highlight obstacles and technical developments that may help to address the knowledge gaps in this area of research. Ultimately, integrating neuroimaging-derived biomarkers into clinical practice holds promise for enhancing treatment outcomes and advancing precision psychiatry strategies for depression management. By elucidating the neural predictors of treatment response and selection, we can move towards more individualized and effective depression interventions, ultimately improving patient outcomes and quality of life.
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Affiliation(s)
| | - Katharine Dunlop
- Centre for Depression and Suicide Studies, Unity Health Toronto, Toronto, ON, Canada.
- Keenan Research Centre for Biomedical Science, Unity Health Toronto, Toronto, ON, Canada.
- Department of Psychiatry and Institute of Medical Science, University of Toronto, Toronto, ON, Canada.
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11
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Jung M, Han KM. Behavioral Activation and Brain Network Changes in Depression. J Clin Neurol 2024; 20:362-377. [PMID: 38951971 PMCID: PMC11220350 DOI: 10.3988/jcn.2024.0148] [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: 03/27/2024] [Revised: 04/19/2024] [Accepted: 04/22/2024] [Indexed: 07/03/2024] Open
Abstract
Behavioral activation (BA) is a well-established method of evidence-based treatment for depression. There are clear links between the neural mechanisms underlying reward processing and BA treatment for depressive symptoms, including anhedonia; however, integrated interpretations of these two domains are lacking. Here we examine brain imaging studies involving BA treatments to investigate how changes in brain networks, including the reward networks, mediate the therapeutic effects of BA, and whether brain circuits are predictors of BA treatment responses. Increased activation of the prefrontal and subcortical regions associated with reward processing has been reported after BA treatment. Activation of these regions improves anhedonia. Conversely, some studies have found decreased activation of prefrontal regions after BA treatment in response to cognitive control stimuli in sad contexts, which indicates that the therapeutic mechanism of BA may involve disengagement from negative or sad contexts. Furthermore, the decrease in resting-state functional connectivity of the default-mode network after BA treatment appears to facilitate the ability to counteract depressive rumination, thereby promoting enjoyable and valuable activities. Conflicting results suggest that an intact neural response to rewards or defective reward functioning is predictive of the efficacy of BA treatments. Increasing the benefits of BA treatments requires identification of the unique individual characteristics determining which of these conflicting findings are relevant for the personalized treatment of each individual with depression.
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Affiliation(s)
- Minjee Jung
- Department of Biomedical Sciences, Korea University College of Medicine, Seoul, Korea
| | - Kyu-Man Han
- Department of Psychiatry, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea.
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12
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Küppers V, Bi H, Nicolaisen-Sobesky E, Hoffstaedter F, Yeo BT, Drzezga A, Eickhoff SB, Tahmasian M. Lower motor performance is linked with poor sleep quality, depressive symptoms, and grey matter volume alterations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.07.597666. [PMID: 38895316 PMCID: PMC11185664 DOI: 10.1101/2024.06.07.597666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Motor performance (MP) is essential for functional independence and well-being, particularly in later life. However, the relationship between behavioural aspects such as sleep quality and depressive symptoms, which contribute to MP, and the underlying structural brain substrates of their interplay remains unclear. This study used three population-based cohorts of younger and older adults (n=1,950) from the Human Connectome Project-Young Adult (HCP-YA), HCP-Aging (HCP-A), and enhanced Nathan Kline Institute-Rockland sample (eNKI-RS). Several canonical correlation analyses were computed within a machine learning framework to assess the associations between each of the three domains (sleep quality, depressive symptoms, grey matter volume (GMV)) and MP. The HCP-YA analyses showed progressively stronger associations between MP and each domain: depressive symptoms (unexpectedly positive, r=0.13, SD=0.06), sleep quality (r=0.17, SD=0.05), and GMV (r=0.19, SD=0.06). Combining sleep and depressive symptoms significantly improved the canonical correlations (r=0.25, SD=0.05), while the addition of GMV exhibited no further increase (r=0.23, SD=0.06). In young adults, better sleep quality, mild depressive symptoms, and GMV of several brain regions were associated with better MP. This was conceptually replicated in young adults from the eNKI-RS cohort. In HCP-Aging, better sleep quality, fewer depressive symptoms, and increased GMV were associated with MP. Robust multivariate associations were observed between sleep quality, depressive symptoms and GMV with MP, as well as age-related variations in these factors. Future studies should further explore these associations and consider interventions targeting sleep and mental health to test the potential effects on MP across the lifespan.
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Affiliation(s)
- Vincent Küppers
- Department of Nuclear Medicine, University Hospital and Medical Faculty, University of Cologne, Cologne, Germany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
| | - Hanwen Bi
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
| | - Eliana Nicolaisen-Sobesky
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University, 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
- N.1 Institute for Health, National University of Singapore, Singapore
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore
- Department of Medicine, Human Potential Translational Research Programme & Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Alexander Drzezga
- Department of Nuclear Medicine, University Hospital and Medical Faculty, University of Cologne, Cologne, Germany
- German Center for Neurodegenerative Diseases (DZNE), Bonn-Cologne, Germany
- Institute of Neuroscience and Medicine, Molecular Organization of the Brain (INM-2), Research Centre Jülich, Jülich, Germany
| | - Simon B. Eickhoff
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
| | - Masoud Tahmasian
- Department of Nuclear Medicine, University Hospital and Medical Faculty, University of Cologne, Cologne, Germany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
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13
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Lu B, Chen X, Xavier Castellanos F, Thompson PM, Zuo XN, Zang YF, Yan CG. The power of many brains: Catalyzing neuropsychiatric discovery through open neuroimaging data and large-scale collaboration. Sci Bull (Beijing) 2024; 69:1536-1555. [PMID: 38519398 DOI: 10.1016/j.scib.2024.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 12/12/2023] [Accepted: 02/27/2024] [Indexed: 03/24/2024]
Abstract
Recent advances in open neuroimaging data are enhancing our comprehension of neuropsychiatric disorders. By pooling images from various cohorts, statistical power has increased, enabling the detection of subtle abnormalities and robust associations, and fostering new research methods. Global collaborations in imaging have furthered our knowledge of the neurobiological foundations of brain disorders and aided in imaging-based prediction for more targeted treatment. Large-scale magnetic resonance imaging initiatives are driving innovation in analytics and supporting generalizable psychiatric studies. We also emphasize the significant role of big data in understanding neural mechanisms and in the early identification and precise treatment of neuropsychiatric disorders. However, challenges such as data harmonization across different sites, privacy protection, and effective data sharing must be addressed. With proper governance and open science practices, we conclude with a projection of how large-scale imaging resources and collaborations could revolutionize diagnosis, treatment selection, and outcome prediction, contributing to optimal brain health.
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Affiliation(s)
- Bin Lu
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100101, China
| | - Xiao Chen
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100101, China
| | - Francisco Xavier Castellanos
- Department of Child and Adolescent Psychiatry, NYU Grossman School of Medicine, New York 10016, USA; Nathan Kline Institute for Psychiatric Research, Orangeburg 10962, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles 90033, USA
| | - Xi-Nian Zuo
- Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; National Basic Science Data Center, Beijing 100190, China
| | - Yu-Feng Zang
- Centre for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou 310004, China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou 310030, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairment, Hangzhou 311121, China
| | - Chao-Gan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100101, China; International Big-Data Center for Depression Research, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China.
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14
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Viejo-Romero M, Whalley HC, Shen X, Stolicyn A, Smith DJ, Howard DM. An epidemiological study of season of birth, mental health, and neuroimaging in the UK Biobank. PLoS One 2024; 19:e0300449. [PMID: 38776272 PMCID: PMC11111058 DOI: 10.1371/journal.pone.0300449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 02/27/2024] [Indexed: 05/24/2024] Open
Abstract
Environmental exposures during the perinatal period are known to have a long-term effect on adult physical and mental health. One such influential environmental exposure is the time of year of birth which affects the amount of daylight, nutrients, and viral load that an individual is exposed to within this key developmental period. Here, we investigate associations between season of birth (seasonality), four mental health traits (n = 137,588) and multi-modal neuroimaging measures (n = 33,212) within the UK Biobank. Summer births were associated with probable recurrent Major Depressive Disorder (β = 0.026, pcorr = 0.028) and greater mean cortical thickness in temporal and occipital lobes (β = 0.013 to 0.014, pcorr<0.05). Winter births were associated with greater white matter integrity globally, in the association fibers, thalamic radiations, and six individual tracts (β = -0.013 to -0.022, pcorr<0.05). Results of sensitivity analyses adjusting for birth weight were similar, with an additional association between winter birth and white matter microstructure in the forceps minor and between summer births, greater cingulate thickness and amygdala volume. Further analyses revealed associations between probable depressive phenotypes and a range of neuroimaging measures but a paucity of interactions with seasonality. Our results suggest that seasonality of birth may affect later-life brain structure and play a role in lifetime recurrent Major Depressive Disorder. Due to the small effect sizes observed, and the lack of associations with other mental health traits, further research is required to validate birth season effects in the context of different latitudes, and by co-examining genetic and epigenetic measures to reveal informative biological pathways.
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Affiliation(s)
- Maria Viejo-Romero
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, United Kingdom
| | - Heather C. Whalley
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, United Kingdom
| | - Xueyi Shen
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, United Kingdom
| | - Aleks Stolicyn
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, United Kingdom
| | - Daniel J. Smith
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, United Kingdom
| | - David M. Howard
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, United Kingdom
- Institute of Psychiatry, Social, Genetic and Developmental Psychiatry Centre, Psychology & Neuroscience, King’s College London, London, United Kingdom
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15
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Miquel-Rio L, Sarriés-Serrano U, Sancho-Alonso M, Florensa-Zanuy E, Paz V, Ruiz-Bronchal E, Manashirov S, Campa L, Pilar-Cuéllar F, Bortolozzi A. ER stress in mouse serotonin neurons triggers a depressive phenotype alleviated by ketamine targeting eIF2α signaling. iScience 2024; 27:109787. [PMID: 38711453 PMCID: PMC11070602 DOI: 10.1016/j.isci.2024.109787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 02/19/2024] [Accepted: 04/16/2024] [Indexed: 05/08/2024] Open
Abstract
Depression is a devastating mood disorder that causes significant disability worldwide. Current knowledge of its pathophysiology remains modest and clear biological markers are lacking. Emerging evidence from human and animal models reveals persistent alterations in endoplasmic reticulum (ER) homeostasis, suggesting that ER stress-related signaling pathways may be targets for prevention and treatment. However, the neurobiological basis linking the pathways involved in depression-related ER stress remains unknown. Here, we report that an induced model of ER stress in mouse serotonin (5-HT) neurons is associated with reduced Egr1-dependent 5-HT cellular activity and 5-HT neurotransmission, resulting in neuroplasticity deficits in forebrain regions and a depressive-like phenotype. Ketamine administration engages downstream eIF2α signaling to trigger rapid neuroplasticity events that rescue the depressive-like effects. Collectively, these data identify ER stress in 5-HT neurons as a cellular pathway involved in the pathophysiology of depression and show that eIF2α is critical in eliciting ketamine's fast antidepressant effects.
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Affiliation(s)
- Lluis Miquel-Rio
- Institute of Biomedical Research of Barcelona (IIBB), Spanish National Research Council (CSIC), 08036 Barcelona, Spain
- Systems Neuropharmacology Research Group, August Pi i Sunyer Biomedical Research Institute (IDIBAPS), 08036 Barcelona, Spain
- Biomedical Research Networking Center for Mental Health (CIBERSAM), Institute of Health Carlos III (ISCIII), 28029 Madrid, Spain
- University of Barcelona (UB), 08036 Barcelona, Spain
| | - Unai Sarriés-Serrano
- Institute of Biomedical Research of Barcelona (IIBB), Spanish National Research Council (CSIC), 08036 Barcelona, Spain
- Systems Neuropharmacology Research Group, August Pi i Sunyer Biomedical Research Institute (IDIBAPS), 08036 Barcelona, Spain
- Biomedical Research Networking Center for Mental Health (CIBERSAM), Institute of Health Carlos III (ISCIII), 28029 Madrid, Spain
- University of the Basque Country UPV/EHU, E-48940 Leioa, Bizkaia, Spain
| | - María Sancho-Alonso
- Institute of Biomedical Research of Barcelona (IIBB), Spanish National Research Council (CSIC), 08036 Barcelona, Spain
- Systems Neuropharmacology Research Group, August Pi i Sunyer Biomedical Research Institute (IDIBAPS), 08036 Barcelona, Spain
- Biomedical Research Networking Center for Mental Health (CIBERSAM), Institute of Health Carlos III (ISCIII), 28029 Madrid, Spain
| | - Eva Florensa-Zanuy
- Biomedical Research Networking Center for Mental Health (CIBERSAM), Institute of Health Carlos III (ISCIII), 28029 Madrid, Spain
- Department of Molecular and Cellular Signaling, Institute of Biomedicine and Biotechnology of Cantabria (IBBTEC), University of Cantabria-CSIC, 39011 Santander, Spain
| | - Verónica Paz
- Institute of Biomedical Research of Barcelona (IIBB), Spanish National Research Council (CSIC), 08036 Barcelona, Spain
- Systems Neuropharmacology Research Group, August Pi i Sunyer Biomedical Research Institute (IDIBAPS), 08036 Barcelona, Spain
- Biomedical Research Networking Center for Mental Health (CIBERSAM), Institute of Health Carlos III (ISCIII), 28029 Madrid, Spain
| | - Esther Ruiz-Bronchal
- Institute of Biomedical Research of Barcelona (IIBB), Spanish National Research Council (CSIC), 08036 Barcelona, Spain
- Systems Neuropharmacology Research Group, August Pi i Sunyer Biomedical Research Institute (IDIBAPS), 08036 Barcelona, Spain
- Biomedical Research Networking Center for Mental Health (CIBERSAM), Institute of Health Carlos III (ISCIII), 28029 Madrid, Spain
| | - Sharon Manashirov
- Institute of Biomedical Research of Barcelona (IIBB), Spanish National Research Council (CSIC), 08036 Barcelona, Spain
- Biomedical Research Networking Center for Mental Health (CIBERSAM), Institute of Health Carlos III (ISCIII), 28029 Madrid, Spain
- miCure Therapeutics LTD., Tel-Aviv 6423902, Israel
| | - Leticia Campa
- Institute of Biomedical Research of Barcelona (IIBB), Spanish National Research Council (CSIC), 08036 Barcelona, Spain
- Systems Neuropharmacology Research Group, August Pi i Sunyer Biomedical Research Institute (IDIBAPS), 08036 Barcelona, Spain
- Biomedical Research Networking Center for Mental Health (CIBERSAM), Institute of Health Carlos III (ISCIII), 28029 Madrid, Spain
| | - Fuencisla Pilar-Cuéllar
- Biomedical Research Networking Center for Mental Health (CIBERSAM), Institute of Health Carlos III (ISCIII), 28029 Madrid, Spain
- Department of Molecular and Cellular Signaling, Institute of Biomedicine and Biotechnology of Cantabria (IBBTEC), University of Cantabria-CSIC, 39011 Santander, Spain
| | - Analia Bortolozzi
- Institute of Biomedical Research of Barcelona (IIBB), Spanish National Research Council (CSIC), 08036 Barcelona, Spain
- Systems Neuropharmacology Research Group, August Pi i Sunyer Biomedical Research Institute (IDIBAPS), 08036 Barcelona, Spain
- Biomedical Research Networking Center for Mental Health (CIBERSAM), Institute of Health Carlos III (ISCIII), 28029 Madrid, Spain
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Liu N, Sun H, Yang C, Li X, Gao Z, Gong Q, Zhang W, Lui S. The difference in volumetric alternations of the orbitofrontal-limbic-striatal system between major depressive disorder and anxiety disorders: A systematic review and voxel-based meta-analysis. J Affect Disord 2024; 350:65-77. [PMID: 38199394 DOI: 10.1016/j.jad.2024.01.043] [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: 08/09/2023] [Revised: 12/12/2023] [Accepted: 01/03/2024] [Indexed: 01/12/2024]
Abstract
BACKGROUND Major depressive disorder (MDD) and anxiety disorders (ANX) are psychiatric disorders with high mutual comorbidity rates that might indicate some shared neurobiological pathways between them, but they retain diverse phenotypes that characterize themselves specifically. However, no consistent evidence exists for common and disorder-specific gray matter volume (GMV) alternations between them. METHODS A systematic review and meta-analysis on voxel-based morphometry studies of patients with MDD and ANX were performed. The effect of comorbidity was explicitly controlled during disorder-specific analysis and particularly investigated in patient with comorbidity. RESULTS A total of 45 studies with 54 datasets comprising 2196 patients and 2055 healthy participants met the inclusion criteria. Deficits in the orbitofrontal cortex, striatum, and limbic regions were found in MDD and ANX. The disorder-specific analyses showed decreased GMV in the bilateral anterior cingulate cortex, right striatum, hippocampus, and cerebellum in MDD, while decreased GMV in the left striatum, amygdala, insula, and increased cerebellar volume in ANX. A totally different GMV alternation pattern was shown involving bilateral temporal and parietal gyri and left fusiform gyrus in patients with comorbidity. LIMITATIONS Owing to the design of included studies, only partial patients in the comorbid group had a secondary comorbidity diagnosis. CONCLUSION Patients with MDD and ANX shared a structural disruption in the orbitofrontal-limbic-striatal system. The disorder-specific effects manifested their greatest severity in distinct lateralization and directionality of these changes that differentiate MDD from ANX. The comorbid group showed a totally different GMV alternation pattern, possibly suggesting another illness subtype that requires further investigation.
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Affiliation(s)
- Naici Liu
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Hui Sun
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Chengmin Yang
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Xing Li
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Ziyang Gao
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Qiyong Gong
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China; Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, China
| | - Wenjing Zhang
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China.
| | - Su Lui
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China.
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17
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Ching CRK, Kang MJY, Thompson PM. Large-Scale Neuroimaging of Mental Illness. Curr Top Behav Neurosci 2024. [PMID: 38554248 DOI: 10.1007/7854_2024_462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/01/2024]
Abstract
Neuroimaging has provided important insights into the brain variations related to mental illness. Inconsistencies in prior studies, however, call for methods that lead to more replicable and generalizable brain markers that can reliably predict illness severity, treatment course, and prognosis. A paradigm shift is underway with large-scale international research teams actively pooling data and resources to drive consensus findings and test emerging methods aimed at achieving the goals of precision psychiatry. In parallel with large-scale psychiatric genomics studies, international consortia combining neuroimaging data are mapping the transdiagnostic brain signatures of mental illness on an unprecedented scale. This chapter discusses the major challenges, recent findings, and a roadmap for developing better neuroimaging-based tools and markers for mental illness.
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Affiliation(s)
- Christopher R K Ching
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Melody J Y Kang
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
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Backhausen LL, Granzow J, Fröhner JH, Artiges E, Paillère‐Martinot M, Lemaître H, Sticca F, Banaschewski T, Desrivières S, Grigis A, Heinz A, Brühl R, Papadopoulos‐Orfanos D, Poustka L, Hohmann S, Robinson L, Walter H, Winterer J, Schumann G, Martinot J, Smolka MN, Vetter NC. Interplay of early negative life events, development of orbitofrontal cortical thickness and depression in young adulthood. JCPP ADVANCES 2024; 4:e12210. [PMID: 38486954 PMCID: PMC10933677 DOI: 10.1002/jcv2.12210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 09/26/2023] [Indexed: 03/17/2024] Open
Abstract
Background Early negative life events (NLE) have long-lasting influences on neurodevelopment and psychopathology. Reduced orbitofrontal cortex (OFC) thickness was frequently associated with NLE and depressive symptoms. OFC thinning might mediate the effect of NLE on depressive symptoms, although few longitudinal studies exist. Using a complete longitudinal design with four time points, we examined whether NLE during childhood and early adolescence predict depressive symptoms in young adulthood through accelerated OFC thinning across adolescence. Methods We acquired structural MRI from 321 participants at two sites across four time points from ages 14 to 22. We measured NLE with the Life Events Questionnaire at the first time point and depressive symptoms with the Center for Epidemiologic Studies Depression Scale at the fourth time point. Modeling latent growth curves, we tested whether OFC thinning mediates the effect of NLE on depressive symptoms. Results A higher burden of NLE, a thicker OFC at the age of 14, and an accelerated OFC thinning across adolescence predicted young adults' depressive symptoms. We did not identify an effect of NLE on OFC thickness nor OFC thickness mediating effects of NLE on depressive symptoms. Conclusions Using a complete longitudinal design with four waves, we show that NLE in childhood and early adolescence predict depressive symptoms in the long term. Results indicate that an accelerated OFC thinning may precede depressive symptoms. Assessment of early additionally to acute NLEs and neurodevelopment may be warranted in clinical settings to identify risk factors for depression.
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Affiliation(s)
- Lea L. Backhausen
- Department of Psychiatry and PsychotherapyTUD Dresden University of TechnologyDresdenGermany
- Department of Child and Adolescent PsychiatryMedical Faculty and University Hospital Carl Gustav Carus, TUD Dresden University of TechnologyDresdenGermany
| | - Jonas Granzow
- Department of Child and Adolescent PsychiatryMedical Faculty and University Hospital Carl Gustav Carus, TUD Dresden University of TechnologyDresdenGermany
| | - Juliane H. Fröhner
- Department of Psychiatry and PsychotherapyTUD Dresden University of TechnologyDresdenGermany
| | - Eric Artiges
- Institut National de la Santé et de la Recherche MédicaleINSERM U1299 “Trajectoires développementales en psychiatrie”Université Paris‐SaclayEcole Normale supérieure Paris‐SaclayCNRSCentre BorelliGif‐sur‐YvetteFrance
- Department of PsychiatryLab‐D‐PsyEPS Barthélémy DurandEtampesFrance
| | - Marie‐Laure Paillère‐Martinot
- Institut National de la Santé et de la Recherche MédicaleINSERM U1299 “Trajectoires développementales en psychiatrie”Université Paris‐SaclayEcole Normale supérieure Paris‐SaclayCNRSCentre BorelliGif‐sur‐YvetteFrance
- Department of Child and Adolescent PsychiatryPitié‐Salpêtrière HospitalParisFrance
| | | | - Fabio Sticca
- Institute for Educational Support for Behaviour, Social‐Emotional, and Psychomotor DevelopmentUniversity of Teacher Education in Special NeedsZurichSwitzerland
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and PsychotherapyCentral Institute of Mental HealthMedical Faculty MannheimHeidelberg UniversityMannheimGermany
| | - Sylvane Desrivières
- Centre for Population Neuroscience and Precision Medicine (PONS)Institute of Psychiatry, Psychology & NeuroscienceSGDP CentreKing's College LondonLondonUK
| | | | - Andreas Heinz
- Department of Psychiatry and NeurosciencesCharité – Universitätsmedizin BerlinCorporate Member of Freie Universität BerlinHumboldt‐Universität zu Berlin, and Berlin Institute of HealthBerlinGermany
| | - Rüdiger Brühl
- Physikalisch‐Technische Bundesanstalt (PTB)Braunschweig and BerlinBerlinGermany
| | | | - Luise Poustka
- Department of Child and Adolescent Psychiatry and PsychotherapyUniversity Medical Centre GöttingenGöttingenGermany
| | - Sarah Hohmann
- Department of Child and Adolescent Psychiatry and PsychotherapyCentral Institute of Mental HealthMedical Faculty MannheimHeidelberg UniversityMannheimGermany
- Department of Child and Adolescent PsychiatryPsychotherapy and PsychosomaticsUniversity Medical Center Hamburg EppendorfHamburgGermany
| | - Lauren Robinson
- Department of Psychological MedicineSection for Eating DisordersInstitute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUK
| | - Henrik Walter
- Department of Psychiatry and NeurosciencesCharité – Universitätsmedizin BerlinCorporate Member of Freie Universität BerlinHumboldt‐Universität zu Berlin, and Berlin Institute of HealthBerlinGermany
| | - Jeanne Winterer
- Department of Psychiatry and NeurosciencesCharité – Universitätsmedizin BerlinCorporate Member of Freie Universität BerlinHumboldt‐Universität zu Berlin, and Berlin Institute of HealthBerlinGermany
- Department of Education and PsychologyFreie Universität BerlinBerlinGermany
| | - Gunter Schumann
- Centre for Population Neuroscience and Precision Medicine (PONS)Institute of Psychiatry, Psychology & NeuroscienceSGDP CentreKing's College LondonLondonUK
- Department of Psychiatry and PsychotherapyPONS Research GroupCampus Charite MitteHumboldt UniversityBerlin and Leibniz Institute for NeurobiologyMagdeburgGermany
- Institute for Science and Technology of Brain‐inspired Intelligence (ISTBI)Fudan UniversityShanghaiChina
| | - Jean‐Luc Martinot
- Institut National de la Santé et de la Recherche MédicaleINSERM U1299 “Trajectoires développementales en psychiatrie”Université Paris‐SaclayEcole Normale supérieure Paris‐SaclayCNRSCentre BorelliGif‐sur‐YvetteFrance
| | - Michael N. Smolka
- Department of Psychiatry and PsychotherapyTUD Dresden University of TechnologyDresdenGermany
| | - Nora C. Vetter
- Department of Psychiatry and PsychotherapyTUD Dresden University of TechnologyDresdenGermany
- Department of Child and Adolescent PsychiatryMedical Faculty and University Hospital Carl Gustav Carus, TUD Dresden University of TechnologyDresdenGermany
- Department of PsychologyMSB Medical School BerlinBerlinGermany
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Nazarova A, Drobinin V, Helmick CA, Schmidt MH, Cookey J, Uher R. Intracortical Myelin in Youths at Risk for Depression. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2024; 4:100285. [PMID: 38323155 PMCID: PMC10844807 DOI: 10.1016/j.bpsgos.2023.100285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 11/28/2023] [Accepted: 12/02/2023] [Indexed: 02/08/2024] Open
Abstract
Background Major depressive disorder (MDD) is a leading cause of disability. To understand why depression develops, it is important to distinguish between early neural markers of vulnerability that precede the onset of MDD and features that develop during depression. Recent neuroimaging findings suggest that reduced global and regional intracortical myelination (ICM), especially in the lateral prefrontal cortex, may be associated with depression, but it is unknown whether it is a precursor or a consequence of MDD. The study of offspring of affected parents offers the opportunity to distinguish between precursors and consequences by examining individuals who carry high risk at a time when they have not experienced depression. Methods We acquired 129 T1-weighted and T2-weighted scans from 56 (25 female) unaffected offspring of parents with depression and 114 scans from 63 (34 female) unaffected offspring of parents without a history of depression (ages 9 to 16 years). To assess scan quality, we calculated test-retest reliability. We used the scan ratios to calculate myelin maps for 68 cortical regions. We analyzed data using mixed-effects modeling. Results ICM did not differ between high and low familial risk youths in global (B = 0.06, SE = 0.03, p = .06) or regional (B = 0.05, SE = 0.03, p = .08) analyses. Our pediatric sample had high ICM reliability (intraclass correlation coefficient = 0.79; 95% CI, 0.55-0.88). Conclusions Based on our results, reduced ICM does not appear to be a precursor of MDD. Future studies should examine ICM in familial high-risk youths across a broad developmental period.
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Affiliation(s)
- Anna Nazarova
- Department of Psychiatry, Dalhousie University, Abbie J. Lane Memorial Building Queen Elizabeth II Health Sciences Centre, Halifax, Nova Scotia, Canada
- Nova Scotia Health Authority, Halifax, Nova Scotia, Canada
| | - Vladislav Drobinin
- Department of Psychiatry, Dalhousie University, Abbie J. Lane Memorial Building Queen Elizabeth II Health Sciences Centre, Halifax, Nova Scotia, Canada
| | - Carl A. Helmick
- Department of Psychiatry, Dalhousie University, Abbie J. Lane Memorial Building Queen Elizabeth II Health Sciences Centre, Halifax, Nova Scotia, Canada
| | - Matthias H. Schmidt
- Department of Diagnostic Radiology, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Jacob Cookey
- Department of Psychiatry, Dalhousie University, Abbie J. Lane Memorial Building Queen Elizabeth II Health Sciences Centre, Halifax, Nova Scotia, Canada
- Nova Scotia Health Authority, Halifax, Nova Scotia, Canada
| | - Rudolf Uher
- Department of Psychiatry, Dalhousie University, Abbie J. Lane Memorial Building Queen Elizabeth II Health Sciences Centre, Halifax, Nova Scotia, Canada
- Nova Scotia Health Authority, Halifax, Nova Scotia, Canada
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Wang X, Hoffstaedter F, Kasper J, Eickhoff SB, Patil KR, Dukart J. Lifetime Exposure to Depression and Neuroimaging Measures of Brain Structure and Function. JAMA Netw Open 2024; 7:e2356787. [PMID: 38372997 PMCID: PMC10877455 DOI: 10.1001/jamanetworkopen.2023.56787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 12/28/2023] [Indexed: 02/20/2024] Open
Abstract
Importance Despite decades of neuroimaging studies reporting brain structural and functional alterations in depression, discrepancies in findings across studies and limited convergence across meta-analyses have raised questions about the consistency and robustness of the observed brain phenotypes. Objective To investigate the associations between 6 operational criteria of lifetime exposure to depression and functional and structural neuroimaging measures. Design, Setting, and Participants This cross-sectional study analyzed data from a UK Biobank cohort of individuals aged 45 to 80 years who were enrolled between January 1, 2014, and December 31, 2018. Participants included individuals with a lifetime exposure to depression and matched healthy controls without indications of psychosis, mental illness, behavior disorder, and disease of the nervous system. Six operational criteria of lifetime exposure to depression were evaluated: help seeking for depression; self-reported depression; antidepressant use; depression definition by Smith et al; hospital International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) diagnosis codes F32 and F33; and Composite International Diagnostic Interview Short Form score. Six increasingly restrictive depression definitions and groups were defined based on the 6 depression criteria, ranging from meeting only 1 criterion to meeting all 6 criteria. Data were analyzed between January and October 2022. Main Outcomes and Measures Functional measures were calculated using voxel-wise fractional amplitude of low-frequency fluctuation (fALFF), global correlation (GCOR), and local correlation (LCOR). Structural measures were calculated using gray matter volume (GMV). Results The study included 20 484 individuals with lifetime depression (12 645 females [61.7%]; mean [SD] age, 63.91 [7.60] years) and 25 462 healthy controls (14 078 males [55.3%]; mean [SD] age, 65.05 [7.8] years). Across all depression criteria, individuals with lifetime depression displayed regionally consistent decreases in fALFF, LCOR, and GCOR (Cohen d range, -0.53 [95% CI, -0.88 to -0.15] to -0.04 [95% CI, -0.07 to -0.01]) but not in GMV (Cohen d range, -0.47 [95 % CI, -0.75 to -0.12] to 0.26 [95% CI, 0.15-0.37]). Hospital ICD-10 diagnosis codes F32 and F33 (median [IQR] difference in effect sizes, -0.14 [-0.17 to -0.11]) and antidepressant use (median [IQR] difference in effect sizes, -0.12 [-0.16 to -0.10]) were criteria associated with the most pronounced alterations. Conclusions and Relevance Results of this cross-sectional study indicate that lifetime exposure to depression was associated with robust functional changes, with a more restrictive depression definition revealing more pronounced alterations. Different inclusion criteria for depression may be associated with the substantial variation in imaging findings reported in the literature.
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Affiliation(s)
- Xinyi Wang
- School of Biological Sciences and Medical Engineering, Child Development, and Learning Science, Key Laboratory of Ministry of Education, Southeast University, Nanjing, China
- Institute of Neuroscience and Medicine, INM-7: Brain and Behaviour, Research Centre Jülich, Jülich, Germany
| | - Felix Hoffstaedter
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, INM-7: Brain and Behaviour, Research Centre Jülich, Jülich, Germany
| | - Jan Kasper
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, INM-7: Brain and Behaviour, Research Centre Jülich, Jülich, Germany
| | - Simon B. Eickhoff
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, INM-7: Brain and Behaviour, Research Centre Jülich, Jülich, Germany
| | - Kaustubh R. Patil
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, INM-7: Brain and Behaviour, Research Centre Jülich, Jülich, Germany
| | - Juergen Dukart
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, INM-7: Brain and Behaviour, Research Centre Jülich, Jülich, Germany
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Kong H, Xu T, Wang S, Zhang Z, Li M, Qu S, Li Q, Gao P, Cong Z. The molecular mechanism of polysaccharides in combating major depressive disorder: A comprehensive review. Int J Biol Macromol 2024; 259:129067. [PMID: 38163510 DOI: 10.1016/j.ijbiomac.2023.129067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 12/10/2023] [Accepted: 12/25/2023] [Indexed: 01/03/2024]
Abstract
Major depressive disorder (MDD) is a complex psychiatric condition with diverse etiological factors. Typical pathological features include decreased cerebral cortex, subcortical structures, and grey matter volumes, as well as monoamine transmitter dysregulation. Although medications exist to treat MDD, unmet needs persist due to limited efficacy, induced side effects, and relapse upon drug withdrawal. Polysaccharides offer promising new therapies for MDD, demonstrating antidepressant effects with minimal side effects and multiple targets. These include neurotransmitter, neurotrophin, neuroinflammation, hypothalamic-pituitary-adrenal axis, mitochondrial function, oxidative stress, and intestinal flora regulation. This review explores the latest advancements in understanding the pharmacological actions and mechanisms of polysaccharides in treating major depression. We discuss the impact of polysaccharides' diverse structures and properties on their pharmacological actions, aiming to inspire new research directions and facilitate the discovery of novel anti-depressive drugs.
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Affiliation(s)
- Hongwei Kong
- College of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan 250355, China
| | - Tianren Xu
- College of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan 250355, China
| | - Shengguang Wang
- College of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan 250355, China
| | - Zhiyuan Zhang
- College of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan 250355, China
| | - Min Li
- Institute of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan 250355, China
| | - Suyan Qu
- Tai 'an Taishan District People's Hospital, China
| | - Qinqing Li
- Shanxi University of Chinese Medicine, China
| | - Peng Gao
- Institute of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan 250355, China.
| | - Zhufeng Cong
- College of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan 250355, China; Affiliated Cancer Hospital of Shandong First Medical University, China.
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Sisay T, Mulate M, Hailu T, Belete TM. The prevalence of depression and anxiety among cardiovascular patients at University of Gondar specialized hospital using beck's depression inventory II and beck anxiety inventory: A cross-sectional study. Heliyon 2024; 10:e24079. [PMID: 38293464 PMCID: PMC10827446 DOI: 10.1016/j.heliyon.2024.e24079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 12/22/2023] [Accepted: 01/03/2024] [Indexed: 02/01/2024] Open
Abstract
Psychosocial issues are leading factor as well as consequences of cardiovascular disease. Identifying factors associated with depression facilitate service quality improvement for inpatients. This study assessed the prevalence and identified associated factors with depression and anxiety among patients with cardio vascular disease. Method An institution-based cross-sectional study was conducted with a convenience sample of 370 stable adult patients from June 1 to July 30, 2020 among cardiovascular disease patients at the University of Gondar Specialized Hospital Ethiopia. Data were collected by using structured questionnaires. Data analyses were conducted using SPSS version 21. The statistical significance declared at p-value <0.05. Result In this study, among 370 Cardiovascular diseases patients, 228 (61.6 %) suffer from anxiety, and 53.51 % (198) suffer with depression. There was a significant mean difference in the level of depression and anxiety between male and female Cardiovascular diseases patients. The females' scores of depression (mean = 28, p < 0.01) and anxiety (mean = 25.3, p < 0.01) were more than that of males 'scores of depression (mean = 15.1, p < 0.01) and anxiety (mean = 12.3, p < 0.01). Cardiovascular diseases patients aged greater than 60 years have the highest rate of prevalence of depression in all age group. Being in the age category of greater than 60 years was 1.16 (0.57-2.32) times more likely to have depression than the age category of 18-24 years. Depression and anxiety were significantly associated with being woman, widowed, being single, unable to read and write, and possess mental disorders history.
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Affiliation(s)
- Tihitina Sisay
- Department of Psychology, College of Social Science and Humanities, University of Gondar, Gondar, Ethiopia
| | - Missaye Mulate
- Department of Psychology, College of Social Science and Humanities, University of Gondar, Gondar, Ethiopia
| | - Tewodrose Hailu
- Department of Psychology, College of Social Science and Humanities, University of Gondar, Gondar, Ethiopia
| | - Tafere Mulaw Belete
- Department of Pharmacology, College of Medicine and Health Sciences, University of Gondar, P.o.box 196, Gondar, Ethiopia
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23
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Hannon K, Bijsterbosch J. Challenges in Identifying Individualized Brain Biomarkers of Late Life Depression. ADVANCES IN GERIATRIC MEDICINE AND RESEARCH 2024; 5:e230010. [PMID: 38348374 PMCID: PMC10861244 DOI: 10.20900/agmr20230010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/15/2024]
Abstract
Research into neuroimaging biomarkers for Late Life Depression (LLD) has identified neural correlates of LLD including increased white matter hyperintensities and reduced hippocampal volume. However, studies into neuroimaging biomarkers for LLD largely fail to converge. This lack of replicability is potentially due to challenges linked to construct variability, etiological heterogeneity, and experimental rigor. We discuss suggestions to help address these challenges, including improved construct standardization, increased sample sizes, multimodal approaches to parse heterogeneity, and the use of individualized analytical models.
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Affiliation(s)
- Kayla Hannon
- Department of Radiology, Washington University in St Louis, St Louis MO, 63110, USA
| | - Janine Bijsterbosch
- Department of Radiology, Washington University in St Louis, St Louis MO, 63110, USA
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24
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Belov V, Erwin-Grabner T, Aghajani M, Aleman A, Amod AR, Basgoze Z, Benedetti F, Besteher B, Bülow R, Ching CRK, Connolly CG, Cullen K, Davey CG, Dima D, Dols A, Evans JW, Fu CHY, Gonul AS, Gotlib IH, Grabe HJ, Groenewold N, Hamilton JP, Harrison BJ, Ho TC, Mwangi B, Jaworska N, Jahanshad N, Klimes-Dougan B, Koopowitz SM, Lancaster T, Li M, Linden DEJ, MacMaster FP, Mehler DMA, Melloni E, Mueller BA, Ojha A, Oudega ML, Penninx BWJH, Poletti S, Pomarol-Clotet E, Portella MJ, Pozzi E, Reneman L, Sacchet MD, Sämann PG, Schrantee A, Sim K, Soares JC, Stein DJ, Thomopoulos SI, Uyar-Demir A, van der Wee NJA, van der Werff SJA, Völzke H, Whittle S, Wittfeld K, Wright MJ, Wu MJ, Yang TT, Zarate C, Veltman DJ, Schmaal L, Thompson PM, Goya-Maldonado R. Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures. Sci Rep 2024; 14:1084. [PMID: 38212349 PMCID: PMC10784593 DOI: 10.1038/s41598-023-47934-8] [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: 01/23/2023] [Accepted: 11/19/2023] [Indexed: 01/13/2024] Open
Abstract
Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects.
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Affiliation(s)
- Vladimir Belov
- Laboratory of Systems Neuroscience and Imaging in Psychiatry (SNIP-Lab), Department of Psychiatry and Psychotherapy, University Medical Center Göttingen (UMG), Georg-August University, Von-Siebold-Str. 5, 37075, Göttingen, Germany
| | - Tracy Erwin-Grabner
- Laboratory of Systems Neuroscience and Imaging in Psychiatry (SNIP-Lab), Department of Psychiatry and Psychotherapy, University Medical Center Göttingen (UMG), Georg-August University, Von-Siebold-Str. 5, 37075, Göttingen, Germany
| | - Moji Aghajani
- Department of Psychiatry, Amsterdam UMC, Amsterdam Neuroscience, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Institute of Education and Child Studies, Section Forensic Family and Youth Care, Leiden University, Leiden, The Netherlands
| | - Andre Aleman
- Department of Biomedical Sciences of Cells and Systems, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Alyssa R Amod
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - Zeynep Basgoze
- Department of Psychiatry and Behavioral Science, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Francesco Benedetti
- Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Bianca Besteher
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Robin Bülow
- Institute for Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany
| | - Christopher R K Ching
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Colm G Connolly
- Department of Biomedical Sciences, Florida State University, Tallahassee, FL, USA
| | - Kathryn Cullen
- Department of Psychiatry and Behavioral Science, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Christopher G Davey
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Parkville, VIC, Australia
| | - Danai Dima
- Department of Psychology, School of Arts and Social Sciences, City, University of London, London, UK
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Annemiek Dols
- Department of Psychiatry, Amsterdam UMC, Amsterdam Neuroscience, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Jennifer W Evans
- Experimental Therapeutics and Pathophysiology Branch, National Institute for Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Cynthia H Y Fu
- School of Psychology, University of East London, London, UK
- Centre for Affective Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Ali Saffet Gonul
- SoCAT Lab, Department of Psychiatry, School of Medicine, Ege University, Izmir, Turkey
| | - Ian H Gotlib
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Hans J Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Nynke Groenewold
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - J Paul Hamilton
- Center for Social and Affective Neuroscience, Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
- Center for Medical Imaging and Visualization, Linköping University, Linköping, Sweden
| | - Ben J Harrison
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Parkville, VIC, Australia
| | - Tiffany C Ho
- Department of Psychiatry and Behavioral Sciences, Division of Child and Adolescent Psychiatry, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Benson Mwangi
- Louis A. Faillace, MD, Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Center Of Excellence On Mood Disorders, Louis A. Faillace, MD, Department of Psychiatry and Behavioral Sciences at McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Natalia Jaworska
- Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | | | | | - Thomas Lancaster
- Cardiff University Brain Research Imaging Center, Cardiff University, Cardiff, UK
- MRC Center for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
| | - Meng Li
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - David E J Linden
- Cardiff University Brain Research Imaging Center, Cardiff University, Cardiff, UK
- MRC Center for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
- School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Frank P MacMaster
- Departments of Psychiatry and Pediatrics, University of Calgary, Calgary, AB, Canada
| | - David M A Mehler
- Cardiff University Brain Research Imaging Center, Cardiff University, Cardiff, UK
- MRC Center for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical School, RWTH Aachen University, Aachen, Germany
| | - Elisa Melloni
- Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Bryon A Mueller
- Department of Psychiatry and Behavioral Science, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Amar Ojha
- Center for Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA
- Center for Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA
| | - Mardien L Oudega
- Department of Psychiatry, Amsterdam UMC, Amsterdam Neuroscience, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam UMC, Amsterdam Neuroscience, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Sara Poletti
- Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Edith Pomarol-Clotet
- FIDMAG Germanes Hospitalàries Research Foundation, Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Barcelona, Catalonia, Spain
| | - Maria J Portella
- Sant Pau Mental Health Research Group, Institut de Recerca de L'Hospital de La Santa Creu I Sant Pau, Barcelona, Catalonia, Spain
| | - Elena Pozzi
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Liesbeth Reneman
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Matthew D Sacchet
- Meditation Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Anouk Schrantee
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Kang Sim
- West Region, Institute of Mental Health, Singapore, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Jair C Soares
- Center Of Excellence On Mood Disorders, Louis A. Faillace, MD, Department of Psychiatry and Behavioral Sciences at McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Dan J Stein
- SA MRC Research Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Aslihan Uyar-Demir
- SoCAT Lab, Department of Psychiatry, School of Medicine, Ege University, Izmir, Turkey
| | - Nic J A van der Wee
- Leiden Institute for Brain and Cognition, Leiden University Medical Center, Leiden, The Netherlands
| | - Steven J A van der Werff
- Leiden Institute for Brain and Cognition, Leiden University Medical Center, Leiden, The Netherlands
- Department of Psychiatry, Leiden University Medical Center, Leiden, The Netherlands
| | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Sarah Whittle
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Melbourne, VIC, Australia
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
- German Center for Neurodegenerative Diseases (DZNE), Site Rostock/ Greifswald, Greifswald, Germany
| | - Margaret J Wright
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
- Centre for Advanced Imaging, The University of Queensland, Brisbane, QLD, Australia
| | - Mon-Ju Wu
- Louis A. Faillace, MD, Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Center Of Excellence On Mood Disorders, Louis A. Faillace, MD, Department of Psychiatry and Behavioral Sciences at McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Tony T Yang
- Department of Psychiatry and Behavioral Sciences, Division of Child and Adolescent Psychiatry, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Carlos Zarate
- Section on the Neurobiology and Treatment of Mood Disorders, National Institute of Mental Health, Bethesda, MD, USA
| | - Dick J Veltman
- Department of Psychiatry, Amsterdam UMC, Amsterdam Neuroscience, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Lianne Schmaal
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Roberto Goya-Maldonado
- Laboratory of Systems Neuroscience and Imaging in Psychiatry (SNIP-Lab), Department of Psychiatry and Psychotherapy, University Medical Center Göttingen (UMG), Georg-August University, Von-Siebold-Str. 5, 37075, Göttingen, Germany.
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25
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Kokkosis AG, Madeira MM, Hage Z, Valais K, Koliatsis D, Resutov E, Tsirka SE. Chronic psychosocial stress triggers microglial-/macrophage-induced inflammatory responses leading to neuronal dysfunction and depressive-related behavior. Glia 2024; 72:111-132. [PMID: 37675659 PMCID: PMC10842267 DOI: 10.1002/glia.24464] [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: 12/18/2022] [Revised: 08/14/2023] [Accepted: 08/21/2023] [Indexed: 09/08/2023]
Abstract
Chronic environmental stress and traumatic social experiences induce maladaptive behavioral changes and is a risk factor for major depressive disorder (MDD) and various anxiety-related psychiatric disorders. Clinical studies and animal models of chronic stress have reported that symptom severity is correlated with innate immune responses and upregulation of neuroinflammatory cytokine signaling in brain areas implicated in mood regulation (mPFC; medial Prefrontal Cortex). Despite increasing evidence implicating impairments of neuroplasticity and synaptic signaling deficits into the pathophysiology of stress-related mental disorders, how microglia may modulate neuronal homeostasis in response to chronic stress has not been defined. Here, using the repeated social defeat stress (RSDS) mouse model we demonstrate that microglial-induced inflammatory responses are regulating neuronal plasticity associated with psychosocial stress. Specifically, we show that chronic stress induces a rapid activation and proliferation of microglia as well as macrophage infiltration in the mPFC, and these processes are spatially related to neuronal activation. Moreover, we report a significant association of microglial inflammatory responses with susceptibility or resilience to chronic stress. In addition, we find that exposure to chronic stress exacerbates phagocytosis of synaptic elements and deficits in neuronal plasticity. Importantly, by utilizing two different CSF1R inhibitors (the brain penetrant PLX5622 and the non-penetrant PLX73086) we highlight a crucial role for microglia (and secondarily macrophages) in catalyzing the pathological manifestations linked to psychosocial stress in the mPFC and the resulting behavioral deficits usually associated with depression.
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Affiliation(s)
- Alexandros G. Kokkosis
- Department of Pharmacological Sciences, Renaissance School of Medicine at Stony Brook University, Stony Brook, New York
| | - Miguel M. Madeira
- Department of Pharmacological Sciences, Renaissance School of Medicine at Stony Brook University, Stony Brook, New York
| | - Zachary Hage
- Department of Pharmacological Sciences, Renaissance School of Medicine at Stony Brook University, Stony Brook, New York
| | - Kimonas Valais
- Department of Pharmacological Sciences, Renaissance School of Medicine at Stony Brook University, Stony Brook, New York
| | - Dimitris Koliatsis
- Department of Pharmacological Sciences, Renaissance School of Medicine at Stony Brook University, Stony Brook, New York
| | - Emran Resutov
- Department of Pharmacological Sciences, Renaissance School of Medicine at Stony Brook University, Stony Brook, New York
| | - Stella E. Tsirka
- Department of Pharmacological Sciences, Renaissance School of Medicine at Stony Brook University, Stony Brook, New York
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26
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Blöchl M, Schaare HL, Kumral D, Gaebler M, Nestler S, Villringer A. Vascular risk factors, white matter microstructure, and depressive symptoms: a longitudinal analysis in the UK Biobank. Psychol Med 2024; 54:125-135. [PMID: 37016768 DOI: 10.1017/s0033291723000697] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/06/2023]
Abstract
BACKGROUND Cumulative burden from vascular risk factors (VRFs) has been associated with an increased risk of depressive symptoms in mid- and later life. It has been hypothesised that this association arises because VRFs disconnect fronto-subcortical white matter tracts involved in mood regulation, which puts older adults at higher risk of developing depressive symptoms. However, evidence for the hypothesis that disconnection of white matter tracts underlies the association between VRF burden and depressive symptoms from longitudinal studies is scarce. METHODS This preregistered study analysed longitudinal data from 6,964 middle-aged and older adults from the UK Biobank who participated in consecutive assessments of VRFs, brain imaging, and depressive symptoms. Using mediation modelling, we directly tested to what extend white matter microstructure mediates the longitudinal association between VRF burden and depressive symptoms. RESULTS VRF burden showed a small association with depressive symptoms at follow-up. However, there was no evidence that fractional anisotropy (FA) of white matter tracts mediated this association. Additional analyses also yielded no mediating effects using alternative operationalisations of VRF burden, mean diffusivity (MD) of single tracts, or overall average of tract-based white matter microstructure (global FA, global MD, white matter hyperintensity volume). CONCLUSIONS Our results lend no support to the hypothesis that disconnection of white matter tracts underlies the association between VRF burden and depressive symptoms, while highlighting the relevance of using longitudinal data to directly test pathways linking vascular and mental health.
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Affiliation(s)
- Maria Blöchl
- Department for Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- International Max Planck Research School: Neuroscience of Communication: Structure, Function, and Plasticity, Leipzig, Germany
- Department of Psychology, University of Münster, Münster, Germany
| | - H Lina Schaare
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour) Research Centre Jülich, Germany
| | - Deniz Kumral
- Institute of Psychology, Neuropsychology, University of Freiburg, Freiburg, Germany
- Clinical Psychology and Psychotherapy Unit, Institute of Psychology, University of Freiburg, Freiburg, Germany
| | - Michael Gaebler
- Department for Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Faculty of Philosophy, Humboldt-Universität zu Berlin, Berlin School of Mind and Brain, MindBrainBody Institute
- Max Planck Dahlem Campus of Cognition, Berlin, Germany
| | - Steffen Nestler
- Department of Psychology, University of Münster, Münster, Germany
| | - Arno Villringer
- Department for Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Clinic for Cognitive Neurology, University Clinic Leipzig, Leipzig, Germany
- Center for Stroke Research Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany
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27
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崔 兴, 秦 泽, 高 之, 万 旺, 顾 忠. [Applications and challenges of wearable electroencephalogram signals in depression recognition and personalized music intervention]. SHENG WU YI XUE GONG CHENG XUE ZA ZHI = JOURNAL OF BIOMEDICAL ENGINEERING = SHENGWU YIXUE GONGCHENGXUE ZAZHI 2023; 40:1093-1101. [PMID: 38151931 PMCID: PMC10753324 DOI: 10.7507/1001-5515.202210065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 05/09/2023] [Indexed: 12/29/2023]
Abstract
Rapid and accurate identification and effective non-drug intervention are the worldwide challenges in the field of depression. Electroencephalogram (EEG) signals contain rich quantitative markers of depression, but whole-brain EEG signals acquisition process is too complicated to be applied on a large-scale population. Based on the wearable frontal lobe EEG monitoring device developed by the authors' laboratory, this study discussed the application of wearable EEG signal in depression recognition and intervention. The technical principle of wearable EEG signals monitoring device and the commonly used wearable EEG devices were introduced. Key technologies for wearable EEG signals-based depression recognition and the existing technical limitations were reviewed and discussed. Finally, a closed-loop brain-computer music interface system for personalized depression intervention was proposed, and the technical challenges were further discussed. This review paper may contribute to the transformation of relevant theories and technologies from basic research to application, and further advance the process of depression screening and personalized intervention.
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Affiliation(s)
- 兴然 崔
- 东南大学 生物科学与医学工程学院(南京 210096)School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, P. R. China
| | - 泽光 秦
- 东南大学 生物科学与医学工程学院(南京 210096)School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, P. R. China
| | - 之琳 高
- 东南大学 生物科学与医学工程学院(南京 210096)School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, P. R. China
| | - 旺 万
- 东南大学 生物科学与医学工程学院(南京 210096)School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, P. R. China
| | - 忠泽 顾
- 东南大学 生物科学与医学工程学院(南京 210096)School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, P. R. China
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28
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Krystal JH, Kaye AP, Jefferson S, Girgenti MJ, Wilkinson ST, Sanacora G, Esterlis I. Ketamine and the neurobiology of depression: Toward next-generation rapid-acting antidepressant treatments. Proc Natl Acad Sci U S A 2023; 120:e2305772120. [PMID: 38011560 DOI: 10.1073/pnas.2305772120] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2023] Open
Abstract
Ketamine has emerged as a transformative and mechanistically novel pharmacotherapy for depression. Its rapid onset of action, efficacy for treatment-resistant symptoms, and protection against relapse distinguish it from prior antidepressants. Its discovery emerged from a reconceptualization of the neurobiology of depression and, in turn, insights from the elaboration of its mechanisms of action inform studies of the pathophysiology of depression and related disorders. It has been 25 y since we first presented our ketamine findings in depression. Thus, it is timely for this review to consider what we have learned from studies of ketamine and to suggest future directions for the optimization of rapid-acting antidepressant treatment.
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Affiliation(s)
- John H Krystal
- Department of Psychiatry, Yale School of Medicine, New Haven, CT 06511
- Psychiatry and Behavioral Health Services, Yale-New Haven Hospital, New Haven, CT 06510
- Clinical Neuroscience Division, National Center for Posttraumatic Stress Disorder, Veterans Affairs Connecticut Healthcare System, West Haven, CT 06516
| | - Alfred P Kaye
- Department of Psychiatry, Yale School of Medicine, New Haven, CT 06511
- Clinical Neuroscience Division, National Center for Posttraumatic Stress Disorder, Veterans Affairs Connecticut Healthcare System, West Haven, CT 06516
| | - Sarah Jefferson
- Department of Psychiatry, Yale School of Medicine, New Haven, CT 06511
- Clinical Neuroscience Division, National Center for Posttraumatic Stress Disorder, Veterans Affairs Connecticut Healthcare System, West Haven, CT 06516
| | - Matthew J Girgenti
- Department of Psychiatry, Yale School of Medicine, New Haven, CT 06511
- Clinical Neuroscience Division, National Center for Posttraumatic Stress Disorder, Veterans Affairs Connecticut Healthcare System, West Haven, CT 06516
| | - Samuel T Wilkinson
- Department of Psychiatry, Yale School of Medicine, New Haven, CT 06511
- Psychiatry and Behavioral Health Services, Yale-New Haven Hospital, New Haven, CT 06510
| | - Gerard Sanacora
- Department of Psychiatry, Yale School of Medicine, New Haven, CT 06511
- Psychiatry and Behavioral Health Services, Yale-New Haven Hospital, New Haven, CT 06510
| | - Irina Esterlis
- Department of Psychiatry, Yale School of Medicine, New Haven, CT 06511
- Clinical Neuroscience Division, National Center for Posttraumatic Stress Disorder, Veterans Affairs Connecticut Healthcare System, West Haven, CT 06516
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29
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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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30
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Jia F, Chen X, Du X, Tang Z, Ma X, Ning T, Zou S, Zuo S, Li H, Cui S, Deng Z, Fu J, Fu X, Huang Y, Li X, Lian T, Liao Y, Liu L, Lu B, Wang Y, Wang Y, Wang Z, Ye G, Zhang X, Zhu H, Quan C, Sun H, Yan C, Liu Y. Aberrant degree centrality profiles during rumination in major depressive disorder. Hum Brain Mapp 2023; 44:6245-6257. [PMID: 37837649 PMCID: PMC10619375 DOI: 10.1002/hbm.26510] [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: 04/11/2023] [Revised: 09/14/2023] [Accepted: 09/25/2023] [Indexed: 10/16/2023] Open
Abstract
Rumination is closely linked to the onset and maintenance of major depressive disorder (MDD). Prior neuroimaging studies have identified the association between self-reported rumination trait and the functional coupling among a network of brain regions using resting-state functional magnetic resonance imaging (MRI). However, little is known about the underlying neural circuitry mechanism during active rumination in MDD. Degree centrality (DC) is a simple metric to denote network integration, which is critical for higher-order psychological processes such as rumination. During an MRI scan, individuals with MDD (N = 45) and healthy controls (HC, N = 46) completed a rumination state task. We examined the interaction effect between the group (MDD vs. HC) and condition (rumination vs. distraction) on vertex-wise DC. We further characterized the identified brain region's functional involvement with Neurosynth and BrainMap. Network-wise seed-based functional connectivity (FC) analysis was also conducted for the identified region of interest. Finally, exploratory correlation analysis was conducted between the identified region of interest's network FCs and self-reported in-scanner affect levels. We found that a left superior frontal gyrus (SFG) region, generally overlapped with the frontal eye field, showed a significant interaction effect. Further analysis revealed its involvement with executive functions. FCs between this region, the frontoparietal, and the dorsal attention network (DAN) also showed significant interaction effects. Furthermore, its FC to DAN during distraction showed a marginally significant negative association with in-scanner affect level at the baseline. Our results implicated an essential role of the left SFG in the rumination's underlying neural circuitry mechanism in MDD and provided novel evidence for the conceptualization of rumination in terms of impaired executive control.
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Affiliation(s)
- Feng‐Nan Jia
- Soochow UniversitySuzhouJiangsuChina
- Suzhou Guangji HospitalThe Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Xiao Chen
- CAS Key Laboratory of Behavioral ScienceInstitute of PsychologyBeijingChina
- Temerty Centre for Therapeutic Brain Intervention, Campbell Family Research InstituteCentre for Addiction and Mental HealthTorontoOntarioCanada
- Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
- Magnetic Resonance Imaging Research CenterInstitute of Psychology, Chinese Academy of SciencesBeijingChina
- International Big‐Data Center for Depression ResearchChinese Academy of SciencesBeijingChina
| | - Xiang‐Dong Du
- Soochow UniversitySuzhouJiangsuChina
- Suzhou Guangji HospitalThe Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Zhen Tang
- Suzhou Guangji HospitalThe Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Xiao‐Yun Ma
- Suzhou Guangji HospitalThe Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Tian‐Tian Ning
- Suzhou Guangji HospitalThe Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Si‐Yun Zou
- Suzhou Guangji HospitalThe Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Shang‐Fu Zuo
- Boston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
| | - Hui‐Xian Li
- The Third Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Shi‐Xian Cui
- CAS Key Laboratory of Behavioral ScienceInstitute of PsychologyBeijingChina
- Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
- Magnetic Resonance Imaging Research CenterInstitute of Psychology, Chinese Academy of SciencesBeijingChina
- International Big‐Data Center for Depression ResearchChinese Academy of SciencesBeijingChina
- Sino‐Danish CollegeUniversity of Chinese Academy of SciencesBeijingChina
- Sino‐Danish Center for Education and ResearchBeijingChina
| | - Zhao‐Yu Deng
- CAS Key Laboratory of Behavioral ScienceInstitute of PsychologyBeijingChina
- Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
- Magnetic Resonance Imaging Research CenterInstitute of Psychology, Chinese Academy of SciencesBeijingChina
- International Big‐Data Center for Depression ResearchChinese Academy of SciencesBeijingChina
| | - Jia‐Lin Fu
- Suzhou Guangji HospitalThe Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Xiao‐Qian Fu
- Suzhou Guangji HospitalThe Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Yue‐Xiang Huang
- Suzhou Guangji HospitalThe Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Xue‐Ying Li
- CAS Key Laboratory of Behavioral ScienceInstitute of PsychologyBeijingChina
- Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
- Magnetic Resonance Imaging Research CenterInstitute of Psychology, Chinese Academy of SciencesBeijingChina
- International Big‐Data Center for Depression ResearchChinese Academy of SciencesBeijingChina
| | - Tao Lian
- CAS Key Laboratory of Behavioral ScienceInstitute of PsychologyBeijingChina
- Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
- Magnetic Resonance Imaging Research CenterInstitute of Psychology, Chinese Academy of SciencesBeijingChina
- International Big‐Data Center for Depression ResearchChinese Academy of SciencesBeijingChina
| | - Yi‐Fan Liao
- CAS Key Laboratory of Behavioral ScienceInstitute of PsychologyBeijingChina
- Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
- Magnetic Resonance Imaging Research CenterInstitute of Psychology, Chinese Academy of SciencesBeijingChina
- International Big‐Data Center for Depression ResearchChinese Academy of SciencesBeijingChina
| | - Li‐Li Liu
- Suzhou Guangji HospitalThe Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Bin Lu
- CAS Key Laboratory of Behavioral ScienceInstitute of PsychologyBeijingChina
- Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
- Magnetic Resonance Imaging Research CenterInstitute of Psychology, Chinese Academy of SciencesBeijingChina
- International Big‐Data Center for Depression ResearchChinese Academy of SciencesBeijingChina
| | - Yan Wang
- Suzhou Guangji HospitalThe Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Yu‐Wei Wang
- CAS Key Laboratory of Behavioral ScienceInstitute of PsychologyBeijingChina
- Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
- Magnetic Resonance Imaging Research CenterInstitute of Psychology, Chinese Academy of SciencesBeijingChina
- International Big‐Data Center for Depression ResearchChinese Academy of SciencesBeijingChina
| | - Zi‐Han Wang
- CAS Key Laboratory of Behavioral ScienceInstitute of PsychologyBeijingChina
- Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
- Magnetic Resonance Imaging Research CenterInstitute of Psychology, Chinese Academy of SciencesBeijingChina
- International Big‐Data Center for Depression ResearchChinese Academy of SciencesBeijingChina
| | - Gang Ye
- Suzhou Guangji HospitalThe Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Xin‐Zhu Zhang
- Suzhou Guangji HospitalThe Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Hong‐Liang Zhu
- Suzhou Guangji HospitalThe Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Chuan‐Sheng Quan
- Department of PsychologyZhangjiagang Fourth People's HospitalZhangjiagangJiangsuChina
| | - Hong‐Yan Sun
- Department of RadiologySuzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Chao‐Gan Yan
- CAS Key Laboratory of Behavioral ScienceInstitute of PsychologyBeijingChina
- Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
- Magnetic Resonance Imaging Research CenterInstitute of Psychology, Chinese Academy of SciencesBeijingChina
- International Big‐Data Center for Depression ResearchChinese Academy of SciencesBeijingChina
- Sino‐Danish CollegeUniversity of Chinese Academy of SciencesBeijingChina
- Sino‐Danish Center for Education and ResearchBeijingChina
| | - Yan‐Song Liu
- Soochow UniversitySuzhouJiangsuChina
- Suzhou Guangji HospitalThe Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
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Hao X, Jia Y, Chen J, Zou C, Jiang C. Subthreshold Depression: A Systematic Review and Network Meta-Analysis of Non-Pharmacological Interventions. Neuropsychiatr Dis Treat 2023; 19:2149-2169. [PMID: 37867932 PMCID: PMC10588757 DOI: 10.2147/ndt.s425509] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 09/22/2023] [Indexed: 10/24/2023] Open
Abstract
Background Subthreshold depression (StD) is considered to be the "precursor" stage of major depressive disorder (MDD), which could cause higher risk of suicide, disease burden and functional impairment. There have been various non-pharmacological interventions for StD. However, the comparison of their effectiveness still lacks sufficient evidence. We performed a systematic review and network meta-analysis to evaluate and rank the efficacy of multiple non-pharmacological interventions targeting StD. Methods We conducted a thorough search across various databases including PubMed, Medline, Embase, Web of Science and PsycINFO from inception to December 2022. All included studies were randomized controlled trials (RCTs) of non-pharmacological interventions for patients with StD compared with control group (CG). Several universal scales for measuring depression severity were used as efficacy outcomes. The surface under the cumulative ranking curve (SUCRA) was used to separately rank each intervention using the "Stata 17.0" software. Results A total of thirty-six trials were included, involving twenty-eight interventions and 7417 participants. The research found that most non-pharmacological interventions were superior to controls for StD. In each outcome evaluation by different scales for measuring depression, psychotherapy always ranked first in terms of treatment effectiveness, especially Problem-solving Therapy (PST), Behavioral Activation Therapy (BAT), Cognitive Behavioral Therapy (CBT)/Internet-based CBT (I-CBT)/Telephone-based CBT (T-CBT). Since different groups could not be directly compared, the total optimal intervention could not be determined. Conclusion Here, we show that psychotherapy may be the better choice for the treatment of StD. This study provides some evidence on StD management selection for clinical workers. However, to establish its intervention effect more conclusively, the content, format and operators of psychotherapy still require extensive exploration to conduct more effective, convenient and cost-effective implementation in primary healthcare. Notably, further research is also urgently needed to find the biological and neural mechanisms of StD by examining whether psychotherapy alters neuroplasticity in patients with StD.
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Affiliation(s)
- Xiaofei Hao
- Department of General Medicine, Chengdu Fifth People’s Hospital, Chengdu, Sichuan, 611130, People’s Republic of China
| | - Yuying Jia
- Department of Outpatient, The General Hospital of Western Theater Command, Chengdu, Sichuan, 610083, People’s Republic of China
| | - Jie Chen
- Department of Outpatient, The General Hospital of Western Theater Command, Chengdu, Sichuan, 610083, People’s Republic of China
| | - Chuan Zou
- Department of General Medicine, Chengdu Fifth People’s Hospital, Chengdu, Sichuan, 611130, People’s Republic of China
| | - Cuinan Jiang
- Department of General Surgery, The Third People’s Hospital of Chengdu & The Affiliated Hospital of Southwest Jiaotong University, Chengdu, Sichuan, 610031, People’s Republic of China
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Jiang C, Jiang W, Chen G, Xu W, Sun T, You L, Chen S, Yin Y, Liu X, Hou Z, Qing Z, Xie C, Zhang Z, Turner JA, Yuan Y. Childhood trauma and social support affect symptom profiles through cortical thickness abnormalities in major depressive disorder: A structural equation modeling analysis. Asian J Psychiatr 2023; 88:103744. [PMID: 37619416 DOI: 10.1016/j.ajp.2023.103744] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 08/10/2023] [Accepted: 08/17/2023] [Indexed: 08/26/2023]
Abstract
BACKGROUND Childhood trauma, low social support, and alexithymia are recognized as risk factors for major depressive disorder (MDD). However, the mechanisms of risk factors, symptoms, and corresponding structural brain abnormalities in MDD are not fully understood. Structural equation modeling (SEM) has advantages in studying multivariate interrelationships. We aim to illustrate their relationships using SEM. METHODS 313 MDD patients (213 female; mean age 42.49 years) underwent magnetic resonance imaging and completed assessments. We integrated childhood trauma, alexithymia, social support, anhedonia, depression, anxiety, suicidal ideation and cortical thickness into a multivariate SEM. RESULTS We first established the risk factors-clinical phenotype SEM with an adequate fit. Cortical thickness results show a negative correlation of childhood trauma with the left middle temporal gyrus (MTG) (p = 0.012), and social support was negatively correlated with the left posterior cingulate cortex (PCC) (p < 0.001). The final good fit SEM (χ2 = 32.92, df = 21, χ2/df = 1.57, CFI = 0.962, GFI = 0.978, RMSEA = 0.043) suggested two pathways, with left PCC thickness mediating the relationship between social support and suicidal ideation, and left MTG thickness mediating between childhood trauma and anhedonia/anxiety. CONCLUSION Our findings provide evidence for the impact of risk factor variables on the brain structure and clinical phenotype of MDD patients. Insufficient social support and childhood trauma might lead to corresponding cortical abnormalities in PCC and MTG, affecting the patient's mood and suicidal ideation. Future interventions should aim at these nodes.
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Affiliation(s)
- Chenguang Jiang
- Department of Psychosomatics and Psychiatry, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Wenhao Jiang
- Department of Psychosomatics and Psychiatry, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Gang Chen
- Department of Psychosomatics and Psychiatry, ZhongDa Hospital, School of Medicine, Southeast University; Department of Medical Psychology, Huai'an No.3 People's Hospital, Huaian, China
| | - Wei Xu
- Department of Psychosomatics and Psychiatry, ZhongDa Hospital, School of Medicine, Southeast University; Department of Clinical Psychology, Northern Jiangsu People's Hospital, Yangzhou, China
| | - Taipeng Sun
- Department of Psychosomatics and Psychiatry, ZhongDa Hospital, School of Medicine, Southeast University; Department of Medical Psychology, Huai'an No.3 People's Hospital, Huaian, China
| | - Linlin You
- Department of Psychosomatics and Psychiatry, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Suzhen Chen
- Department of Psychosomatics and Psychiatry, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Yingying Yin
- Department of Psychosomatics and Psychiatry, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Xiaoyun Liu
- Department of Psychosomatics and Psychiatry, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Zhenghua Hou
- Department of Psychosomatics and Psychiatry, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Zhao Qing
- Shing-Tung Yau Center; School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Chunming Xie
- Department of Neurology, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Zhijun Zhang
- Department of Neurology, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Jessica A Turner
- Department of Psychiatry and Behavioral Health, Wexner Medical Center, Ohio State University, OH, United States.
| | - Yonggui Yuan
- Department of Psychosomatics and Psychiatry, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China.
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Samiotis A, Hicks AJ, Ponsford J, Spitz G. Transdiagnostic MRI markers of psychopathology following traumatic brain injury: a systematic review and network meta-analysis protocol. BMJ Open 2023; 13:e072075. [PMID: 37730404 PMCID: PMC10510890 DOI: 10.1136/bmjopen-2023-072075] [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: 01/24/2023] [Accepted: 08/09/2023] [Indexed: 09/22/2023] Open
Abstract
INTRODUCTION Psychopathology following traumatic brain injury (TBI) is a common and debilitating consequence that is often associated with reduced functional and psychosocial outcomes. There is a lack of evidence regarding the neural underpinnings of psychopathology following TBI, and whether there may be transdiagnostic neural markers that are shared across traditional psychiatric diagnoses. The aim of this systematic review and meta-analysis is to examine the association of MRI-derived markers of brain structure and function with both transdiagnostic and specific psychopathology following moderate-severe TBI. METHODS AND ANALYSIS A systematic literature search of Embase (1974-2022), Ovid MEDLINE (1946-2022) and PsycINFO (1806-2022) will be conducted. Publications in English that investigate MRI correlates of psychopathology characterised by formal diagnoses or symptoms of psychopathology in closed moderate-severe TBI populations over 16 years of age will be included. Publications will be excluded that: (a) evaluate non-MRI neuroimaging techniques (CT, positron emission tomography, magnetoencephalography, electroencephalogram); (b) comprise primarily a paediatric cohort; (c) comprise primarily penetrating TBI. Eligible studies will be assessed against a modified Joanna Briggs Institute Critical Appraisal Instrument and data will be extracted by two independent reviewers. A descriptive analysis of MRI findings will be provided based on qualitative synthesis of data extracted. Quantitative analyses will include a meta-analysis and a network meta-analysis where there are sufficient data available. ETHICS AND DISSEMINATION Ethics approval is not required for the present study as there will be no original data collected. We intend to disseminate the results through publication to a high-quality peer-reviewed journal and conference presentations on completion. PROSPERO REGISTRATION NUMBER CRD42022358358.
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Affiliation(s)
- Alexia Samiotis
- Translational Neuroscience, Monash Epworth Rehabilitation Research Centre, Richmond, Victoria, Australia
- School of Psychological Sciences, Monash University, Clayton, Victoria, Australia
| | - Amelia J Hicks
- Translational Neuroscience, Monash Epworth Rehabilitation Research Centre, Richmond, Victoria, Australia
- School of Psychological Sciences, Monash University, Clayton, Victoria, Australia
| | - Jennie Ponsford
- Translational Neuroscience, Monash Epworth Rehabilitation Research Centre, Richmond, Victoria, Australia
| | - Gershon Spitz
- Translational Neuroscience, Monash Epworth Rehabilitation Research Centre, Richmond, Victoria, Australia
- School of Psychological Sciences, Monash University, Clayton, Victoria, Australia
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Zhang W, Rutlin J, Eisenstein SA, Wang Y, Barch DM, Hershey T, Bogdan R, Bijsterbosch JD. Neuroinflammation in the Amygdala Is Associated With Recent Depressive Symptoms. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:967-975. [PMID: 37164312 DOI: 10.1016/j.bpsc.2023.04.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 04/03/2023] [Accepted: 04/29/2023] [Indexed: 05/12/2023]
Abstract
BACKGROUND Converging evidence suggests that elevated inflammation may contribute to depression. Yet, the link between peripheral inflammation and neuroinflammation in depression is unclear. Here, using data from the UK Biobank, we estimated associations among depression, C-reactive protein (CRP) as a measure of peripheral inflammation, and neuroinflammation as indexed by diffusion basis spectral imaging-based restricted fraction (DBSI-RF). METHODS DBSI-RF was derived from diffusion-weighted imaging data (N = 11,512) for whole-brain gray matter (global-RF), and regions of interest in the bilateral amygdala (amygdala-RF) and hippocampus (hippocampus-RF), and CRP was estimated from blood (serum) samples. Self-reported recent depression symptoms were measured using a 4-item assessment. Linear regressions were used to estimate associations between CRP and DBSI-RFs with depression while adjusting for the following covariates: age, sex, body mass index, smoking, drinking, and medical conditions. RESULTS Elevated CRP was associated with higher depression symptoms (β = 0.04, false discovery rate-corrected p < .005) and reduced global-RF (β = -0.03, false discovery rate-corrected p < .001). Higher amygdala-RF was associated with elevated depression-an effect resilient to added covariates and CRP (β = 0.02, false discovery rate-corrected p < .05). Interestingly, this association was stronger in individuals with a lifetime history of depression (β = 0.07, p < .005) than in those without (β = 0.03, p < .05). Associations between global-RF or hippocampus-RF with depression were not significant, and no DBSI-RF indices indirectly linked CRP with depression (i.e., mediation effect). CONCLUSIONS Peripheral inflammation and DBSI-RF neuroinflammation in the amygdala are independently associated with depression, consistent with animal studies suggesting distinct pathways of peripheral inflammation and neuroinflammation in the pathophysiology of depression and with investigations highlighting the role of the amygdala in stress-induced inflammation and depression.
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Affiliation(s)
- Wei Zhang
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri.
| | - Jerrel Rutlin
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri
| | - Sarah A Eisenstein
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri; Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri
| | - Yong Wang
- Department of Obstetrics and Gynecology, Washington University School of Medicine, St. Louis, Missouri; Department of Electrical and Systems Engineering, Washington University, St. Louis, Missouri
| | - Deanna M Barch
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri; Department of Psychological & Brain Sciences, Washington University, St. Louis, Missouri
| | - Tamara Hershey
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri; Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri; Department of Psychological & Brain Sciences, Washington University, St. Louis, Missouri; Department of Neurology, Washington University School of Medicine, St. Louis, Missouri
| | - Ryan Bogdan
- Department of Psychological & Brain Sciences, Washington University, St. Louis, Missouri.
| | - Janine D Bijsterbosch
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri
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Wang K, Li X, Wang X, Hommel B, Xia X, Qiu J, Fu Y, Zhou Z. In vivo analyses reveal hippocampal subfield volume reductions in adolescents with schizophrenia, but not with major depressive disorder. J Psychiatr Res 2023; 165:56-63. [PMID: 37459779 DOI: 10.1016/j.jpsychires.2023.07.012] [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: 02/28/2023] [Revised: 05/25/2023] [Accepted: 07/10/2023] [Indexed: 09/03/2023]
Abstract
BACKGROUND Adult studies have reported atypicalities in the hippocampus and subfields in patients with schizophrenia (SCZ) and major depressive disorder (MDD). Both affective and psychotic disorders typically onset in adolescence, when human brain develops rapidly and shows increased susceptibility to adverse environments. However, few in vivo studies have investigated whether hippocampus subfield abnormalities occur in adolescence and whether they differ between SCZ and MDD cases. METHODS We recruited 150 adolescents (49 SCZ patients, 67 MDD patients, and 34 healthy controls) and obtained their structural images. We used FreeSurfer to automatically segment hippocampus into 12 subfields and analyzed subfield volumetric differences between groups by analysis of covariance, covarying for age, sex, and intracranial volume. Composite measures by summing subfield volumes were further compared across groups and analyzed in relation to clinical characteristic. RESULTS SCZ adolescents showed significant volume reductions in subfields of CA1, molecular layer, subiculum, parasubiculum, dentate gyrus and CA4 than healthy controls, and almost significant reductions, as compared to the MDD group, in left molecular layer, dentate gyrus, CA2/3 and CA4. Composite analyses showed smaller volumes in SCZ group than in healthy controls in all bilateral composite measures, and reduced volumes in comparison to MDD group in all left composite measures only. CONCLUSIONS SCZ adolescents exhibited both hippocampal subfield and composite volumes reduction, and also showed greater magnitude of deviance than those diagnosed with MDD, particularly in core CA regions. These results indicate a hippocampal disease process, suggesting a potential intervention marker of early psychotic patients and risk youths.
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Affiliation(s)
- Kangcheng Wang
- School of Psychology, Shandong Normal University, Jinan, 250358, China; Shandong Mental Health Center, Shandong University, Jinan, 250014, China
| | - Xingyan Li
- School of Psychology, Shandong Normal University, Jinan, 250358, China
| | - Xiaotong Wang
- School of Psychology, Shandong Normal University, Jinan, 250358, China
| | - Bernhard Hommel
- School of Psychology, Shandong Normal University, Jinan, 250358, China
| | - Xiaodi Xia
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Jiang Qiu
- Faculty of Psychology, Southwest University, Chongqing, 400715, China
| | - Yixiao Fu
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
| | - Zheyi Zhou
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.
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Marx W, Penninx BWJH, Solmi M, Furukawa TA, Firth J, Carvalho AF, Berk M. Major depressive disorder. Nat Rev Dis Primers 2023; 9:44. [PMID: 37620370 DOI: 10.1038/s41572-023-00454-1] [Citation(s) in RCA: 52] [Impact Index Per Article: 52.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/11/2023] [Indexed: 08/26/2023]
Abstract
Major depressive disorder (MDD) is characterized by persistent depressed mood, loss of interest or pleasure in previously enjoyable activities, recurrent thoughts of death, and physical and cognitive symptoms. People with MDD can have reduced quality of life owing to the disorder itself as well as related medical comorbidities, social factors, and impaired functional outcomes. MDD is a complex disorder that cannot be fully explained by any one single established biological or environmental pathway. Instead, MDD seems to be caused by a combination of genetic, environmental, psychological and biological factors. Treatment for MDD commonly involves pharmacological therapy with antidepressant medications, psychotherapy or a combination of both. In people with severe and/or treatment-resistant MDD, other biological therapies, such as electroconvulsive therapy, may also be offered.
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Affiliation(s)
- Wolfgang Marx
- Deakin University, IMPACT - the Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Barwon Health, Geelong, Victoria, Australia.
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam Public Health and Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - Marco Solmi
- Department of Psychiatry, University of Ottawa, Ottawa, Ontario, Canada
- On Track: The Champlain First Episode Psychosis Program, Department of Mental Health, The Ottawa Hospital, Ottawa, Ontario, Canada
- Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin, Berlin, Germany
| | - Toshi A Furukawa
- Department of Health Promotion and Human Behavior, Kyoto University Graduate School of Medicine/School of Public Health, Kyoto, Japan
| | - Joseph Firth
- Division of Psychology and Mental Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
- Greater Manchester Mental Health NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Andre F Carvalho
- Deakin University, IMPACT - the Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Barwon Health, Geelong, Victoria, Australia
| | - Michael Berk
- Deakin University, IMPACT - the Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Barwon Health, Geelong, Victoria, Australia
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Botvinik-Nezer R, Wager TD. Reproducibility in Neuroimaging Analysis: Challenges and Solutions. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:780-788. [PMID: 36906444 DOI: 10.1016/j.bpsc.2022.12.006] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 11/27/2022] [Accepted: 12/11/2022] [Indexed: 12/23/2022]
Abstract
Recent years have marked a renaissance in efforts to increase research reproducibility in psychology, neuroscience, and related fields. Reproducibility is the cornerstone of a solid foundation of fundamental research-one that will support new theories built on valid findings and technological innovation that works. The increased focus on reproducibility has made the barriers to it increasingly apparent, along with the development of new tools and practices to overcome these barriers. Here, we review challenges, solutions, and emerging best practices with a particular emphasis on neuroimaging studies. We distinguish 3 main types of reproducibility, discussing each in turn. Analytical reproducibility is the ability to reproduce findings using the same data and methods. Replicability is the ability to find an effect in new datasets, using the same or similar methods. Finally, robustness to analytical variability refers to the ability to identify a finding consistently across variation in methods. The incorporation of these tools and practices will result in more reproducible, replicable, and robust psychological and brain research and a stronger scientific foundation across fields of inquiry.
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Affiliation(s)
- Rotem Botvinik-Nezer
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, New Hampshire.
| | - Tor D Wager
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, New Hampshire
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Ping L, Sun S, Zhou C, Que J, You Z, Xu X, Cheng Y. Altered topology of individual brain structural covariance networks in major depressive disorder. Psychol Med 2023:1-12. [PMID: 37427670 DOI: 10.1017/s003329172300168x] [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: 07/11/2023]
Abstract
BACKGROUND The neurobiological pathogenesis of major depression disorder (MDD) remains largely controversial. Previous literatures with limited sample size utilizing group-level structural covariance networks (SCN) commonly generated mixed findings regarding the topology of brain networks. METHODS We analyzed T1 images from a high-powered multisite sample including 1173 patients with MDD and 1019 healthy controls (HCs). We used regional gray matter volume to construct individual SCN by utilizing a novel approach based on the interregional effect size difference. We further investigated MDD-related structural connectivity alterations using topological metrics. RESULTS Compared to HCs, the MDD patients showed a shift toward randomization characterized by increased integration. Further subgroup analysis of patients in different stages revealed this randomization pattern was also observed in patients with recurrent MDD, while the first-episode drug naïve patients exhibited decreased segregation. Altered nodal properties in several brain regions which have a key role in both emotion regulation and executive control were also found in MDD patients compared with HCs. The abnormalities in inferior temporal gyrus were not influenced by any specific site. Moreover, antidepressants increased nodal efficiency in the anterior ventromedial prefrontal cortex. CONCLUSIONS The MDD patients at different stages exhibit distinct patterns of randomization in their brain networks, with increased integration during illness progression. These findings provide valuable insights into the disruption in structural brain networks that occurs in patients with MDD and might be useful to guide future therapeutic interventions.
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Affiliation(s)
- Liangliang Ping
- Department of Psychiatry, Xiamen Xianyue Hospital, Xiamen, China
| | - Shan Sun
- Department of Psychiatry, Xiamen Xianyue Hospital, Xiamen, China
| | - Cong Zhou
- School of Mental Health, Jining Medical University, Jining, China
| | - Jianyu Que
- Department of Psychiatry, Xiamen Xianyue Hospital, Xiamen, China
| | - Zhiyi You
- Department of Psychiatry, Xiamen Xianyue Hospital, Xiamen, China
| | - Xiufeng Xu
- Department of Psychiatry, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yuqi Cheng
- Department of Psychiatry, The First Affiliated Hospital of Kunming Medical University, Kunming, China
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Wang K, Hu Y, Yan C, Li M, Wu Y, Qiu J, Zhu X. Brain structural abnormalities in adult major depressive disorder revealed by voxel- and source-based morphometry: evidence from the REST-meta-MDD Consortium. Psychol Med 2023; 53:3672-3682. [PMID: 35166200 DOI: 10.1017/s0033291722000320] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
BACKGROUND Neuroimaging studies on major depressive disorder (MDD) have identified an extensive range of brain structural abnormalities, but the exact neural mechanisms associated with MDD remain elusive. Most previous studies were performed with voxel- or surface-based morphometry which were univariate methods without considering spatial information across voxels/vertices. METHODS Brain morphology was investigated using voxel-based morphometry (VBM) and source-based morphometry (SBM) in 1082 MDD patients and 990 healthy controls (HCs) from the REST-meta-MDD Consortium. We first examined group differences in regional grey matter (GM) volumes and structural covariance networks between patients and HCs. We then compared first-episode, drug-naïve (FEDN) patients, and recurrent patients. Additionally, we assessed the effects of symptom severity and illness duration on brain alterations. RESULTS VBM showed decreased GM volume in various regions in MDD patients including the superior temporal cortex, anterior and middle cingulate cortex, inferior frontal cortex, and precuneus. SBM returned differences only in the prefrontal network. Comparisons between FEDN and recurrent MDD patients showed no significant differences by VBM, but SBM showed greater decreases in prefrontal, basal ganglia, visual, and cerebellar networks in the recurrent group. Moreover, depression severity was associated with volumes in the inferior frontal gyrus and precuneus, as well as the prefrontal network. CONCLUSIONS Simultaneous application of VBM and SBM methods revealed brain alterations in MDD patients and specified differences between recurrent and FEDN patients, which tentatively provide an effective multivariate method to identify potential neurobiological markers for depression.
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Affiliation(s)
- KangCheng Wang
- School of Psychology, Shandong Normal University, Jinan, Shandong, China
| | - YuFei Hu
- School of Psychology, Shandong Normal University, Jinan, Shandong, China
| | - ChaoGan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
- Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- International Big-Data Center for Depression Research, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - MeiLing Li
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA
| | - YanJing Wu
- Faculty of Foreign Languages, Ningbo University, Ningbo, Zhejiang, China
| | - Jiang Qiu
- Faculty of Psychology, Southwest University, Chongqing 400716, China
| | - XingXing Zhu
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
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Abi-Dargham A, Moeller SJ, Ali F, DeLorenzo C, Domschke K, Horga G, Jutla A, Kotov R, Paulus MP, Rubio JM, Sanacora G, Veenstra-VanderWeele J, Krystal JH. Candidate biomarkers in psychiatric disorders: state of the field. World Psychiatry 2023; 22:236-262. [PMID: 37159365 PMCID: PMC10168176 DOI: 10.1002/wps.21078] [Citation(s) in RCA: 67] [Impact Index Per Article: 67.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/08/2023] [Indexed: 05/11/2023] Open
Abstract
The field of psychiatry is hampered by a lack of robust, reliable and valid biomarkers that can aid in objectively diagnosing patients and providing individualized treatment recommendations. Here we review and critically evaluate the evidence for the most promising biomarkers in the psychiatric neuroscience literature for autism spectrum disorder, schizophrenia, anxiety disorders and post-traumatic stress disorder, major depression and bipolar disorder, and substance use disorders. Candidate biomarkers reviewed include various neuroimaging, genetic, molecular and peripheral assays, for the purposes of determining susceptibility or presence of illness, and predicting treatment response or safety. This review highlights a critical gap in the biomarker validation process. An enormous societal investment over the past 50 years has identified numerous candidate biomarkers. However, to date, the overwhelming majority of these measures have not been proven sufficiently reliable, valid and useful to be adopted clinically. It is time to consider whether strategic investments might break this impasse, focusing on a limited number of promising candidates to advance through a process of definitive testing for a specific indication. Some promising candidates for definitive testing include the N170 signal, an event-related brain potential measured using electroencephalography, for subgroup identification within autism spectrum disorder; striatal resting-state functional magnetic resonance imaging (fMRI) measures, such as the striatal connectivity index (SCI) and the functional striatal abnormalities (FSA) index, for prediction of treatment response in schizophrenia; error-related negativity (ERN), an electrophysiological index, for prediction of first onset of generalized anxiety disorder, and resting-state and structural brain connectomic measures for prediction of treatment response in social anxiety disorder. Alternate forms of classification may be useful for conceptualizing and testing potential biomarkers. Collaborative efforts allowing the inclusion of biosystems beyond genetics and neuroimaging are needed, and online remote acquisition of selected measures in a naturalistic setting using mobile health tools may significantly advance the field. Setting specific benchmarks for well-defined target application, along with development of appropriate funding and partnership mechanisms, would also be crucial. Finally, it should never be forgotten that, for a biomarker to be actionable, it will need to be clinically predictive at the individual level and viable in clinical settings.
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Affiliation(s)
- Anissa Abi-Dargham
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Scott J Moeller
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Farzana Ali
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Christine DeLorenzo
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Katharina Domschke
- Department of Psychiatry and Psychotherapy, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Centre for Basics in Neuromodulation, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Guillermo Horga
- Department of Psychiatry, Columbia University, New York, NY, USA
- New York State Psychiatric Institute, New York, NY, USA
| | - Amandeep Jutla
- Department of Psychiatry, Columbia University, New York, NY, USA
- New York State Psychiatric Institute, New York, NY, USA
| | - Roman Kotov
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | | | - Jose M Rubio
- Zucker School of Medicine at Hofstra-Northwell, Hempstead, NY, USA
- Feinstein Institute for Medical Research - Northwell, Manhasset, NY, USA
- Zucker Hillside Hospital - Northwell Health, Glen Oaks, NY, USA
| | - Gerard Sanacora
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Jeremy Veenstra-VanderWeele
- Department of Psychiatry, Columbia University, New York, NY, USA
- New York State Psychiatric Institute, New York, NY, USA
| | - John H Krystal
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
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Brasanac J, Chien C. A review on multiple sclerosis prognostic findings from imaging, inflammation, and mental health studies. Front Hum Neurosci 2023; 17:1151531. [PMID: 37250694 PMCID: PMC10213782 DOI: 10.3389/fnhum.2023.1151531] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 04/21/2023] [Indexed: 05/31/2023] Open
Abstract
Magnetic resonance imaging (MRI) of the brain is commonly used to detect where chronic and active lesions are in multiple sclerosis (MS). MRI is also extensively used as a tool to calculate and extrapolate brain health by way of volumetric analysis or advanced imaging techniques. In MS patients, psychiatric symptoms are common comorbidities, with depression being the main one. Even though these symptoms are a major determinant of quality of life in MS, they are often overlooked and undertreated. There has been evidence of bidirectional interactions between the course of MS and comorbid psychiatric symptoms. In order to mitigate disability progression in MS, treating psychiatric comorbidities should be investigated and optimized. New research for the prediction of disease states or phenotypes of disability have advanced, primarily due to new technologies and a better understanding of the aging brain.
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Affiliation(s)
- Jelena Brasanac
- Charité – Universitätsmedizin Berlin, Klinik für Psychiatrie und Psychotherapie, Berlin, Germany
- Charité – Universitätsmedizin Berlin, Medizinische Klinik m.S. Psychosomatik, Berlin, Germany
| | - Claudia Chien
- Charité – Universitätsmedizin Berlin, Klinik für Psychiatrie und Psychotherapie, Berlin, Germany
- Charité – Universitätsmedizin Berlin, Medizinische Klinik m.S. Psychosomatik, Berlin, Germany
- Charité – Universitätsmedizin Berlin, Experimental and Clinical Research Center, Berlin, Germany
- Charité – Universitätsmedizin Berlin, Neuroscience Clinical Research Center, Berlin, Germany
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Wang L, Ma Q, Sun X, Xu Z, Zhang J, Liao X, 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, Zhang Y, Li L, Cheng J, Gong Q, Li L, Lin CP, Qiu J, Qiu S, Si T, Tang Y, Wang F, Xie P, Xu X, Xia M. Frequency-resolved connectome alterations in major depressive disorder: A multisite resting fMRI study. J Affect Disord 2023; 328:47-57. [PMID: 36781144 DOI: 10.1016/j.jad.2023.01.104] [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: 08/22/2022] [Revised: 01/24/2023] [Accepted: 01/27/2023] [Indexed: 02/13/2023]
Abstract
BACKGROUND Functional connectome studies have revealed widespread connectivity alterations in major depressive disorder (MDD). However, the low frequency bandpass filtering (0.01-0.08 Hz or 0.01-0.1 Hz) in most studies have impeded our understanding on whether and how these alterations are affected by frequency of interest. METHODS Here, we performed frequency-resolved (0.01-0.06 Hz, 0.06-0.16 Hz and 0.16-0.24 Hz) connectome analyses using a large-sample resting-state functional MRI dataset of 1002 MDD patients and 924 healthy controls from seven independent centers. RESULTS We reported significant frequency-dependent connectome alterations in MDD in left inferior parietal, inferior temporal, precentral, and fusiform cortices and bilateral precuneus. These frequency-dependent connectome alterations are mainly derived by abnormalities of medium- and long-distance connections and are brain network-dependent. Moreover, the connectome alteration of left precuneus in high frequency band (0.16-0.24 Hz) is significantly associated with illness duration. LIMITATIONS Multisite harmonization model only removed linear site effects. Neurobiological underpinning of alterations in higher frequency (0.16-0.24 Hz) should be further examined by combining fMRI data with respiration, heartbeat and blood flow recordings in future studies. CONCLUSIONS These results highlight the frequency-dependency of connectome alterations in MDD and the benefit of examining connectome alteration in MDD under a wider frequency band.
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Affiliation(s)
- Lei Wang
- 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
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; State Key Laboratory of Medical Neurobiology, Fudan University, Shanghai, China; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - 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
| | - Zhilei Xu
- 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
| | - Jiaying Zhang
- 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
| | - Xuhong Liao
- School of Systems Science, Beijing Normal University, Beijing, China
| | - Xiaoqin Wang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China; Department of Psychology, Southwest University, Chongqing, China
| | - Dongtao Wei
- Key Laboratory of Cognition and Personality (SWU), 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, 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 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), Institute of Cognitive Neuroscience, 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, NHC 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 MR Research Center (HMRRC), 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
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), 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, NHC 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
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London, UK; Institute of Neuroscience, National Yang-Ming Chiao-Tung University, 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 (SWU), 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, 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 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.
| | - Yihe Zhang
- 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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Pine JG, Paul SE, Johnson E, Bogdan R, Kandala S, Barch DM. Polygenic Risk for Schizophrenia, Major Depression, and Post-traumatic Stress Disorder and Hippocampal Subregion Volumes in Middle Childhood. Behav Genet 2023; 53:279-291. [PMID: 36720770 PMCID: PMC10875985 DOI: 10.1007/s10519-023-10134-1] [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: 11/02/2022] [Accepted: 01/17/2023] [Indexed: 02/02/2023]
Abstract
Studies demonstrate that individuals with diagnoses for Major Depressive Disorder (MDD), Post-traumatic Stress Disorder (PTSD), and Schizophrenia (SCZ) may exhibit smaller hippocampal gray matter relative to otherwise healthy controls, although the effect sizes vary in each disorder. Existing work suggests that hippocampal abnormalities in each disorder may be attributable to genetic liability and/or environmental variables. The following study uses baseline data from the Adolescent Brain and Cognitive Development[Formula: see text] Study (ABCD Study[Formula: see text]) to address three open questions regarding the relationship between genetic risk for each disorder and hippocampal volume reductions: (a) whether polygenic risk scores (PGRS) for MDD, PTSD, and SCZ are related to hippocampal volume; (b) whether PGRS for MDD, PTSD, and SCZ are differentially related to specific hippocampal subregions along the longitudinal axis; and (c) whether the association between PGRS for MDD, PTSD, and SCZ and hippocampal volume is moderated by sex and/or environmental adversity. In short, we did not find associations between PGRS for MDD, PTSD, and SCZ to be significantly related to any hippocampal subregion volumes. Furthermore, neither sex nor enviornmental adversity significantly moderated these associations. Our study provides an important null finding on the relationship genetic risk for MDD, PTSD, and SCZ to measures of hippocampal volume.
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Affiliation(s)
- Jacob G Pine
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, 63130, USA.
| | - Sarah E Paul
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, 63130, USA
| | - Emma Johnson
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Ryan Bogdan
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, 63130, USA
| | - Sridhar Kandala
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Deanna M Barch
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, 63130, USA
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Department of Radiology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, 63110, USA
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Olubodun-Obadun TG, Ishola IO, Adesokan TP, Anih BO, Adeyemi OO. Antidepressant- and anxiolytic-like actions of Cajanus cajan seed extract mediated through monoaminergic, nitric oxide-cyclic GMP and GABAergic pathways. JOURNAL OF ETHNOPHARMACOLOGY 2023; 306:116142. [PMID: 36638856 DOI: 10.1016/j.jep.2023.116142] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 12/31/2022] [Accepted: 01/01/2023] [Indexed: 06/17/2023]
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE The seeds of Cajanus cajan (L) Millsp, are used in Traditional medicine for the treatment of anxiety and other neurological disorders. Hence, this study is designed to investigate the antidepressant- and anxiolytic-like properties of ethanol seed extract of Cajanus cajan (CC) in mice. MATERIALS AND METHODS CC (50, 100 or 200 mg/kg, p.o.) was administered 1h before subjecting the animals to different behavioral models: forced swim test (FST) and tail suspension test (TST) (depressive-like behaviour), open field test (OFT), elevated plus maze (EPM), light-dark test (LDT) and hole-board test (HBT) for anxiety-like behaviour. To ascertain the pharmacodynamic of CC mice were pretreated with monoaminergic, nitrergic and GABAergic receptors antagonists. As well as molecular docking analysis of about 19 flavonoids present in CC on GABAA, α2 adrenoceptors and 5-HT2A receptors. RESULTS CC (50, 100 or 200 mg/kg, p.o.) treatment significantly reduced immobile time in both FST and TST when compared with vehicle-treated control. However, the pretreatment of mice with prazosin/yohimbine (α1/2 adrenoceptor antagonists, respectively), WAY100635 (5-HT1A receptor antagonist), ketanserin (5-HT2A receptor antagonist), sulpiride (dopamine D2 receptor antagonist), L-NG-Nitro arginine methyl ester (L-NAME), or methylene blue reversed the antidepressant-like effect of CC. In anxiety model, CC produced significant (p < 0.05) increase in open arms exploration and head dipping behavior which was reversed by flumazenil (benzodiazepine receptor antagonist) in the EPM. Docking analysis showed significant binding affinity of orientin, vitexin, pinostrobin and quercetin with 5HT2A, α2-adrenoceptor and GABAA receptors. CONCLUSION Findings from this study showed that C.cajan seeds extract exerts antidepressant-like effect through participation of monoaminergic systems (5-HT2 receptor, α1/α2-adrenoceptors, and dopamine D2-receptors), nitric oxide-cyclic GMP pathway and anxiolytic-like effect via GABAA benzodiazepine receptors. Moreso, presence of flavonoids with significant binding energies with monoaminergic and GABAergic systems support the potential of the extract in the management of mixed anxiety-depressive illness.
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Affiliation(s)
- Taiwo G Olubodun-Obadun
- African Center of Excellence for Drug Research, Herbal Medicine Development and Regulatory Science (ACEDHARS), University of Lagos (UNILAG), Lagos, Nigeria; Department of Pharmacology, Therapeutics and Toxicology, Faculty of Basic Medical Sciences, College of Medicine, University of Lagos, Lagos, Nigeria
| | - Ismail O Ishola
- African Center of Excellence for Drug Research, Herbal Medicine Development and Regulatory Science (ACEDHARS), University of Lagos (UNILAG), Lagos, Nigeria; Department of Pharmacology, Therapeutics and Toxicology, Faculty of Basic Medical Sciences, College of Medicine, University of Lagos, Lagos, Nigeria.
| | - Timisola P Adesokan
- Department of Pharmacology, Therapeutics and Toxicology, Faculty of Basic Medical Sciences, College of Medicine, University of Lagos, Lagos, Nigeria
| | - Blessing O Anih
- African Center of Excellence for Drug Research, Herbal Medicine Development and Regulatory Science (ACEDHARS), University of Lagos (UNILAG), Lagos, Nigeria
| | - Olufunmilayo O Adeyemi
- African Center of Excellence for Drug Research, Herbal Medicine Development and Regulatory Science (ACEDHARS), University of Lagos (UNILAG), Lagos, Nigeria; Department of Pharmacology, Therapeutics and Toxicology, Faculty of Basic Medical Sciences, College of Medicine, University of Lagos, Lagos, Nigeria
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Hicks EM, Seah C, Cote A, Marchese S, Brennand KJ, Nestler EJ, Girgenti MJ, Huckins LM. Integrating genetics and transcriptomics to study major depressive disorder: a conceptual framework, bioinformatic approaches, and recent findings. Transl Psychiatry 2023; 13:129. [PMID: 37076454 PMCID: PMC10115809 DOI: 10.1038/s41398-023-02412-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 03/17/2023] [Accepted: 03/24/2023] [Indexed: 04/21/2023] Open
Abstract
Major depressive disorder (MDD) is a complex and heterogeneous psychiatric syndrome with genetic and environmental influences. In addition to neuroanatomical and circuit-level disturbances, dysregulation of the brain transcriptome is a key phenotypic signature of MDD. Postmortem brain gene expression data are uniquely valuable resources for identifying this signature and key genomic drivers in human depression; however, the scarcity of brain tissue limits our capacity to observe the dynamic transcriptional landscape of MDD. It is therefore crucial to explore and integrate depression and stress transcriptomic data from numerous, complementary perspectives to construct a richer understanding of the pathophysiology of depression. In this review, we discuss multiple approaches for exploring the brain transcriptome reflecting dynamic stages of MDD: predisposition, onset, and illness. We next highlight bioinformatic approaches for hypothesis-free, genome-wide analyses of genomic and transcriptomic data and their integration. Last, we summarize the findings of recent genetic and transcriptomic studies within this conceptual framework.
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Affiliation(s)
- Emily M Hicks
- Pamela Sklar Division of Psychiatric Genomics, Departments of Psychiatry and of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Carina Seah
- Pamela Sklar Division of Psychiatric Genomics, Departments of Psychiatry and of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Alanna Cote
- Pamela Sklar Division of Psychiatric Genomics, Departments of Psychiatry and of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Shelby Marchese
- Pamela Sklar Division of Psychiatric Genomics, Departments of Psychiatry and of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Kristen J Brennand
- Pamela Sklar Division of Psychiatric Genomics, Departments of Psychiatry and of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
- Department of Genetics, Yale University School of Medicine, New Haven, CT, 06511, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06511, USA
| | - Eric J Nestler
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Matthew J Girgenti
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06511, USA.
| | - Laura M Huckins
- Pamela Sklar Division of Psychiatric Genomics, Departments of Psychiatry and of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA.
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06511, USA.
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Hao Y, Farah M. Heterogeneity of depression across the socioeconomic spectrum. Proc Natl Acad Sci U S A 2023; 120:e2222069120. [PMID: 37036974 PMCID: PMC10119997 DOI: 10.1073/pnas.2222069120] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 02/22/2023] [Indexed: 04/12/2023] Open
Abstract
Why is lower socioeconomic status associated with higher rates of depression? And, is the surplus of depression at lower SES just more of the same type as depression found at higher levels, or is it distinctive? We addressed these questions by examining the relations among SES, amygdala volume, and symptoms of depression in healthy young adults. Amygdala volume, a risk factor for depression, does not synergize with SES in a diathesis-stress relation, nor does it mediate the relation of SES to depression. Rather, SES and amygdala volume are independent, additive risk factors. They are also associated with different depression symptoms and, whereas perceived stress fully mediates the relation of SES to depression, it has no relation to amygdala volume. These findings suggest heterogeneity of depression across the socioeconomic spectrum, with implications for treatment selection as well as for future genetic and brain studies.
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Affiliation(s)
- Yu Hao
- Center for Neuroscience & Society, The Department of Psychology, University of Pennsylvania, Philadelphia, PA19104
| | - Martha J. Farah
- Center for Neuroscience & Society, The Department of Psychology, University of Pennsylvania, Philadelphia, PA19104
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Sadeh Y, Denejkina A, Karyotaki E, Lenferink LIM, Kassam-Adams N. Opportunities for improving data sharing and FAIR data practices to advance global mental health. Glob Ment Health (Camb) 2023; 10:e14. [PMID: 37860102 PMCID: PMC10581864 DOI: 10.1017/gmh.2023.7] [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: 08/10/2022] [Revised: 01/24/2023] [Accepted: 02/23/2023] [Indexed: 03/06/2023] Open
Abstract
It is crucial to optimize global mental health research to address the high burden of mental health challenges and mental illness for individuals and societies. Data sharing and reuse have demonstrated value for advancing science and accelerating knowledge development. The FAIR (Findable, Accessible, Interoperable, and Reusable) Guiding Principles for scientific data provide a framework to improve the transparency, efficiency, and impact of research. In this review, we describe ethical and equity considerations in data sharing and reuse, delineate the FAIR principles as they apply to mental health research, and consider the current state of FAIR data practices in global mental health research, identifying challenges and opportunities. We describe noteworthy examples of collaborative efforts, often across disciplinary and national boundaries, to improve Findability and Accessibility of global mental health data, as well as efforts to create integrated data resources and tools that improve Interoperability and Reusability. Based on this review, we suggest a vision for the future of FAIR global mental health research and suggest practical steps for researchers with regard to study planning, data preservation and indexing, machine-actionable metadata, data reuse to advance science and improve equity, metrics and recognition.
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Affiliation(s)
- Yaara Sadeh
- Center for Injury Research and Prevention, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Trauma Data Institute, Lovingston, VA, USA
| | - Anna Denejkina
- Graduate Research School, Western Sydney University, Penrith, NSW, Australia
- Translational Health Research Institute, Sydney, Australia
- Young and Resilient Research Centre, Sydney, Australia
| | - Eirini Karyotaki
- Department of Clinical, Neuro- and Developmental Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Amsterdam Public Health Institute, Amsterdam, Netherlands
| | - Lonneke I. M. Lenferink
- Department of Psychology, Health & Technology, University of Twente, Enschede, Netherlands
- Department of Clinical Psychology, Utrecht University, Utrecht, Netherlands
- Department of Clinical Psychology and Experimental Psychopathology, University of Groningen, Groningen, Netherlands
| | - Nancy Kassam-Adams
- Center for Injury Research and Prevention, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Trauma Data Institute, Lovingston, VA, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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The role of brain structure in the association between pubertal timing and depression risk in an early adolescent sample (the ABCD Study®): A registered report. Dev Cogn Neurosci 2023; 60:101223. [PMID: 36870214 PMCID: PMC10009199 DOI: 10.1016/j.dcn.2023.101223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 02/03/2023] [Accepted: 02/21/2023] [Indexed: 02/26/2023] Open
Abstract
BACKGROUND Earlier pubertal timing is associated with higher rates of depressive disorders in adolescence. Neuroimaging studies report brain structural associations with both pubertal timing and depression. However, whether brain structure mediates the relationship between pubertal timing and depression remains unclear. METHODS The current registered report examined associations between pubertal timing (indexed via perceived pubertal development), brain structure (cortical and subcortical metrics, and white matter microstructure) and depressive symptoms in a large sample (N = ∼5000) of adolescents (aged 9-13 years) from the Adolescent Brain Cognitive Development (ABCD) Study. We used three waves of follow-up data when the youth were aged 10-11 years, 11-12 years, and 12-13 years, respectively. We used generalised linear-mixed models (H1) and structural equation modelling (H2 & H3) to test our hypotheses. HYPOTHESES We hypothesised that earlier pubertal timing at Year 1 would be associated with increased depressive symptoms at Year 3 (H1), and that this relationship would be mediated by global (H2a-b) and regional (H3a-g) brain structural measures at Year 2. Global measures included reduced cortical volume, thickness, surface area and sulcal depth. Regional measures included reduced cortical thickness and volume in temporal and fronto-parietal areas, increased cortical volume in the ventral diencephalon, increased sulcal depth in the pars orbitalis, and reduced fractional anisotropy in the cortico-striatal tract and corpus callosum. These regions of interest were informed by our pilot analyses using baseline ABCD data when the youth were aged 9-10 years. RESULTS Earlier pubertal timing was associated with increased depressive symptoms two years later. The magnitude of effect was stronger in female youth and the association remained significant when controlling for parental depression, family income, and BMI in females but not in male youth. Our hypothesised brain structural measures did not however mediate the association between earlier pubertal timing and later depressive symptoms. CONCLUSION The present results demonstrate that youth, particularly females, who begin puberty ahead of their peers are at an increased risk for adolescent-onset depression. Future work should explore additional biological and socio-environmental factors that may affect this association so that we can identify targets for intervention to help these at-risk youth.
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Wu H, Wang J, Teng T, Yin B, He Y, Jiang Y, Liu X, Yu Y, Li X, Zhou X. Biomarkers of intestinal permeability and blood-brain barrier permeability in adolescents with major depressive disorder. J Affect Disord 2023; 323:659-666. [PMID: 36493942 DOI: 10.1016/j.jad.2022.11.058] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 10/28/2022] [Accepted: 11/20/2022] [Indexed: 12/13/2022]
Abstract
BACKGROUND The etiology in major depressive disorder (MDD) has not been fully understood. Accumulating evidence suggests an association between altered intestinal and blood-brain barrier (BBB) permeability and psychiatric disorders, while its changes in adolescent MDD populations have been received less attention. In this study, our aim was to explore the differences in plasma levels of intestinal and blood-brain barrier permeability markers in adolescents with MDD compared with healthy controls (HCs). METHODS We enrolled MDD (n = 50), and HCs (n = 40) with the age of 13-18 years old. The plasma level of zonulin, I-FABP, LPS, and claudin-5 were quantified. The Hamilton Depression Scale 17 items (HAMD-17) and Hamilton Anxiety Scale 14 items (HAMA-14) were used for symptom assessments. RESULTS The plasma levels of zonulin, I-FABP, LPS, and claudin-5 in the MDD group were significantly higher than those in the HCs. Plasma I-FABP levels in MDD with moderate to severe anxiety were significantly higher than those in MDD without moderate to severe anxiety and HCs. In addition, these four biomarkers (alone or combined) can be used as diagnostic markers for MDD in adolescents. LIMITATIONS The key limitation of this study is the blood measurements at a single time point with a relatively small sample size. CONCLUSIONS These findings advance our understanding of the pathophysiology of intestinal barrier injury, bacterial translocation, and blood-brain barrier injury involved in adolescents with MDD.
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Affiliation(s)
- Hongyan Wu
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China; NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jie Wang
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China; NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Teng Teng
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China; NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Bangmin Yin
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China; NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yuqian He
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China; Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yuanliang Jiang
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China; NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xueer Liu
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China; NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Ying Yu
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China; Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xuemei Li
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China; NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xinyu Zhou
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China; NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
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Schuurmans IK, Lamballais S, Zou R, Muetzel RL, Hillegers MHJ, Cecil CAM, Luik AI. 10-Year trajectories of depressive symptoms and subsequent brain health in middle-aged adults. J Psychiatr Res 2023; 158:126-133. [PMID: 36584490 DOI: 10.1016/j.jpsychires.2022.12.018] [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: 10/07/2022] [Revised: 12/09/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022]
Abstract
Depressive symptoms differ in severity and stability over time. Trajectories depicting these changes, particularly those with high late-life depressive symptoms, have been associated with poor brain health at old age. To better understand these associations across the lifespan, we examined depressive symptoms trajectories in relation to brain health in middle age. We included 1676 participants from the ORACLE Study, all were expecting a child at baseline (mean age 32.8, 66.6% women). Depressive symptoms were assessed at baseline, 3 years and 10 years after baseline. Brain health (global brain volume, subcortical structures volume, white matter lesions, cerebral microbleeds, cortical thickness, cortical surface area) was assessed 15 years after baseline. Using k-means clustering, four depressive symptoms trajectories were identified: low, low increasing, decreasing, and high increasing symptoms. The high increasing trajectory was associated with smaller brain volume compared to low symptoms, not surviving multiple testing correction. The low increasing trajectory was associated with more cortical thickness in a small region encompassing the right lateral occipital cortex compared to low symptoms. These findings show that longitudinal depressive symptoms trajectories are only minimally associated with brain health in middle age, suggesting that associations may only emerge later in life.
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Affiliation(s)
- Isabel K Schuurmans
- Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands; The Generation R Study Group, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Sander Lamballais
- Department of Clinical Genetics, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Runyu Zou
- Department of Child and Adolescent Psychiatry and Psychology, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Ryan L Muetzel
- Department of Child and Adolescent Psychiatry and Psychology, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Manon H J Hillegers
- Department of Child and Adolescent Psychiatry and Psychology, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Charlotte A M Cecil
- Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands; Department of Child and Adolescent Psychiatry and Psychology, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands; Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Annemarie I Luik
- Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands; Department of Child and Adolescent Psychiatry and Psychology, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands.
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