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Cattarinussi G, Heidari-Foroozan M, Jafary H, Mohammadi E, Sambataro F, Ferro A, Barone Y, Delvecchio G. Resting-state functional magnetic resonance imaging alterations in first-degree relatives of individuals with bipolar disorder: A systematic review. J Affect Disord 2024; 365:321-331. [PMID: 39142577 DOI: 10.1016/j.jad.2024.08.040] [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: 09/27/2023] [Revised: 07/25/2024] [Accepted: 08/11/2024] [Indexed: 08/16/2024]
Abstract
BACKGROUND Relatives of individuals with bipolar disorder (BD) are at higher risk of developing the disorder. Identifying brain alterations associated with familial vulnerability in BD can help discover endophenotypes, which are quantifiable biological traits more prevalent in unaffected relatives of BD (BD-RELs) than the general population. This review aimed at expanding our knowledge on endophenotypes of BD by providing an overview of resting-state functional magnetic resonance imaging (rs-fMRI) alterations in BD-RELs. METHODS A systematic search of PubMed, Scopus, and Web of Science was performed to identify all available rs-fMRI studies conducted in BD-RELs up to January 2024. A total of 18 studies were selected. Six included BD-RELs with no history of psychiatric disorders and 10 included BD-RELs that presented psychiatric disorders. Two investigations examined rs-fMRI alterations in BD-RELs with and without subthreshold symptoms for BD. RESULTS BD-RELs presented rs-fMRI alterations in the cortico-limbic network, fronto-thalamic-striatal circuit, fronto-occipital network, and, to a lesser extent, in the default mode network. This was true both for BD-RELs with no history of psychopathology and for BD-RELs that presented psychiatric disorders. The direct comparison of rs-fMRI alterations in BD-RELs with and without psychiatric symptoms displayed largely non-overlapping patterns of rs-fMRI abnormalities. LIMITATIONS Small sample sizes and the clinical heterogeneity of BD-RELs limit the generalizability of our findings. CONCLUSIONS The current literature suggests that first-degree BD-RELs exhibit rs-fMRI alterations in brain circuits involved in emotion regulation, cognition, reward processing, and psychosis susceptibility. Future studies are needed to validate these findings and to explore their potential as biomarkers for early detection and intervention.
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Affiliation(s)
- Giulia Cattarinussi
- Department of Neuroscience (DNS), Padua Neuroscience Center, University of Padova, Padua, Italy; Padua Neuroscience Center, University of Padova, Padua, Italy
| | - Mahsa Heidari-Foroozan
- Student Research Committee, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hosein Jafary
- Student Research Committee, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Esmaeil Mohammadi
- Department of Neurological Surgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Fabio Sambataro
- Department of Neuroscience (DNS), Padua Neuroscience Center, University of Padova, Padua, Italy; Padua Neuroscience Center, University of Padova, Padua, Italy
| | - Adele Ferro
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Ylenia Barone
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
| | - Giuseppe Delvecchio
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
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Iraji A, Chen J, Lewis N, Faghiri A, Fu Z, Agcaoglu O, Kochunov P, Adhikari BM, Mathalon DH, Pearlson GD, Macciardi F, Preda A, van Erp TGM, Bustillo JR, Díaz-Caneja CM, Andrés-Camazón P, Dhamala M, Adali T, Calhoun VD. Spatial Dynamic Subspaces Encode Sex-Specific Schizophrenia Disruptions in Transient Network Overlap and Their Links to Genetic Risk. Biol Psychiatry 2024; 96:188-197. [PMID: 38070846 PMCID: PMC11156799 DOI: 10.1016/j.biopsych.2023.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 11/15/2023] [Accepted: 12/01/2023] [Indexed: 12/19/2023]
Abstract
BACKGROUND Schizophrenia research reveals sex differences in incidence, symptoms, genetic risk factors, and brain function. However, a knowledge gap remains regarding sex-specific schizophrenia alterations in brain function. Schizophrenia is considered a dysconnectivity syndrome, but the dynamic integration and segregation of brain networks are poorly understood. Recent advances in resting-state functional magnetic resonance imaging allow us to study spatial dynamics, the phenomenon of brain networks spatially evolving over time. Nevertheless, estimating time-resolved networks remains challenging due to low signal-to-noise ratio, limited short-time information, and uncertain network identification. METHODS We adapted a reference-informed network estimation technique to capture time-resolved networks and their dynamic spatial integration and segregation for 193 individuals with schizophrenia and 315 control participants. We focused on time-resolved spatial functional network connectivity, an estimate of network spatial coupling, to study sex-specific alterations in schizophrenia and their links to genomic data. RESULTS Our findings are consistent with the dysconnectivity and neurodevelopment hypotheses and with the cerebello-thalamo-cortical, triple-network, and frontoparietal dysconnectivity models, helping to unify them. The potential unification offers a new understanding of the underlying mechanisms. Notably, the posterior default mode/salience spatial functional network connectivity exhibits sex-specific schizophrenia alteration during the state with the highest global network integration and is correlated with genetic risk for schizophrenia. This dysfunction is reflected in regions with weak functional connectivity to corresponding networks. CONCLUSIONS Our method can effectively capture spatially dynamic networks, detect nuanced schizophrenia effects including sex-specific ones, and reveal the intricate relationship of dynamic information to genomic data. The results also underscore the clinical potential of dynamic spatial dependence and weak connectivity.
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Affiliation(s)
- Armin Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Atlanta, Georgia; Department of Computer Science, Georgia State University, Atlanta, Georgia.
| | - Jiayu Chen
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Atlanta, Georgia
| | - Noah Lewis
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Atlanta, Georgia; Department of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia
| | - Ashkan Faghiri
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Atlanta, Georgia
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Atlanta, Georgia
| | - Oktay Agcaoglu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Atlanta, Georgia
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland
| | - Bhim M Adhikari
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland
| | - Daniel H Mathalon
- Department of Psychiatry, University of California San Francisco, San Francisco, California; San Francisco Veteran Affairs Medical Center, San Francisco, California
| | - Godfrey D Pearlson
- Departments of Psychiatry and Neuroscience, Yale University School of Medicine, New Haven, Connecticut
| | - Fabio Macciardi
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California
| | - Theo G M van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California
| | - Juan R Bustillo
- Department of Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque, New Mexico
| | - Covadonga M Díaz-Caneja
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain
| | - Pablo Andrés-Camazón
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain
| | - Mukesh Dhamala
- Department of Physics and Astronomy, Georgia State University, Atlanta, Georgia
| | - Tulay Adali
- Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, Maryland
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Atlanta, Georgia; Department of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia.
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3
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Dini H, Bruni LE, Ramsøy TZ, Calhoun VD, Sendi MSE. The overlap across psychotic disorders: A functional network connectivity analysis. Int J Psychophysiol 2024; 201:112354. [PMID: 38670348 PMCID: PMC11163820 DOI: 10.1016/j.ijpsycho.2024.112354] [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: 07/24/2023] [Revised: 03/20/2024] [Accepted: 04/20/2024] [Indexed: 04/28/2024]
Abstract
Functional network connectivity (FNC) has previously been shown to distinguish patient groups from healthy controls (HC). However, the overlap across psychiatric disorders such as schizophrenia (SZ), bipolar (BP), and schizoaffective disorder (SAD) is not evident yet. This study focuses on studying the overlap across these three psychotic disorders in both dynamic and static FNC (dFNC/sFNC). We used resting-state fMRI, demographics, and clinical information from the Bipolar-Schizophrenia Network on Intermediate Phenotypes cohort (BSNIP). The data includes three groups of patients with schizophrenia (SZ, N = 181), bipolar (BP, N = 163), and schizoaffective (SAD, N = 130) and HC (N = 238) groups. After estimating each individual's dFNC, we group them into three distinct states. We evaluated two dFNC features, including occupancy rate (OCR) and distance travelled over time. Finally, the extracted features, including both sFNC and dFNC, are tested statistically across patients and HC groups. In addition, we explored the link between the clinical scores and the extracted features. We evaluated the connectivity patterns and their overlap among SZ, BP, and SAD disorders (false discovery rate or FDR corrected p < 0.05). Results showed dFNC captured unique information about overlap across disorders where all disorder groups showed similar pattern of activity in state 2. Moreover, the results showed similar patterns between SZ and SAD in state 1 which was different than BP. Finally, the distance travelled feature of SZ (average R = 0.245, p < 0.01) and combined distance travelled from all disorders was predictive of the PANSS symptoms scores (average R = 0.147, p < 0.01).
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Affiliation(s)
- Hossein Dini
- Augmented Cognition Lab, Department of Architecture, Design and Media Technology, Aalborg University, Copenhagen, Denmark
| | - Luis E Bruni
- Augmented Cognition Lab, Department of Architecture, Design and Media Technology, Aalborg University, Copenhagen, Denmark
| | - Thomas Z Ramsøy
- Department of Applied Neuroscience, Neurons Inc., Taastrup, Denmark; Faculty of Neuroscience, Singularity University, Santa Clara, CA, United States
| | - Vince D Calhoun
- Wallace H. Coulter Department of Biomedical Engineering at, Georgia Institute of Technology and Emory University, Atlanta, GA, United States; Department of Electrical and Computer Engineering at, Georgia Institute of Technology, Atlanta, GA, United States; Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Mohammad S E Sendi
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States; McLean Hospital and Harvard Medical School, Boston, MA, USA.
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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 2024:10.1007/s12264-024-01214-1. [PMID: 38703276 DOI: 10.1007/s12264-024-01214-1] [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: 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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Kotov R, Carpenter WT, Cicero DC, Correll CU, Martin EA, Young JW, Zald DH, Jonas KG. Psychosis superspectrum II: neurobiology, treatment, and implications. Mol Psychiatry 2024; 29:1293-1309. [PMID: 38351173 DOI: 10.1038/s41380-024-02410-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 12/24/2023] [Accepted: 01/04/2024] [Indexed: 02/16/2024]
Abstract
Alternatives to traditional categorical diagnoses have been proposed to improve the validity and utility of psychiatric nosology. This paper continues the companion review of an alternative model, the psychosis superspectrum of the Hierarchical Taxonomy of Psychopathology (HiTOP). The superspectrum model aims to describe psychosis-related psychopathology according to data on distributions and associations among signs and symptoms. The superspectrum includes psychoticism and detachment spectra as well as narrow subdimensions within them. Auxiliary domains of cognitive deficit and functional impairment complete the psychopathology profile. The current paper reviews evidence on this model from neurobiology, treatment response, clinical utility, and measure development. Neurobiology research suggests that psychopathology included in the superspectrum shows similar patterns of neural alterations. Treatment response often mirrors the hierarchy of the superspectrum with some treatments being efficacious for psychoticism, others for detachment, and others for a specific subdimension. Compared to traditional diagnostic systems, the quantitative nosology shows an approximately 2-fold increase in reliability, explanatory power, and prognostic accuracy. Clinicians consistently report that the quantitative nosology has more utility than traditional diagnoses, but studies of patients with frank psychosis are currently lacking. Validated measures are available to implement the superspectrum model in practice. The dimensional conceptualization of psychosis-related psychopathology has implications for research, clinical practice, and public health programs. For example, it encourages use of the cohort study design (rather than case-control), transdiagnostic treatment strategies, and selective prevention based on subclinical symptoms. These approaches are already used in the field, and the superspectrum provides further impetus and guidance for their implementation. Existing knowledge on this model is substantial, but significant gaps remain. We identify outstanding questions and propose testable hypotheses to guide further research. Overall, we predict that the more informative, reliable, and valid characterization of psychopathology offered by the superspectrum model will facilitate progress in research and clinical care.
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Affiliation(s)
- Roman Kotov
- Department of Psychiatry and Behavioral Health, Stony Brook University, Stony Brook, NY, USA.
| | | | - David C Cicero
- Department of Psychology, University of North Texas, Denton, TX, USA
| | - Christoph U Correll
- Department of Psychiatry, The Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, USA
- Department of Psychiatry and Molecular Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Child and Adolescent Psychiatry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Elizabeth A Martin
- Department of Psychological Science, University of California, Irvine, Irvine, CA, USA
| | - Jared W Young
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Research Service, VA San Diego Healthcare System, San Diego, CA, USA
| | - David H Zald
- Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA
| | - Katherine G Jonas
- Department of Psychiatry and Behavioral Health, Stony Brook University, Stony Brook, NY, USA
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6
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Alho J, Lahnakoski JM, Panula JM, Rikandi E, Mäntylä T, Lindgren M, Kieseppä T, Suvisaari J, Sams M, Raij TT. Hippocampus-Centered Network Is Associated With Positive Symptom Alleviation in Patients With First-Episode Psychosis. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:1197-1206. [PMID: 37336263 DOI: 10.1016/j.bpsc.2023.06.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 05/17/2023] [Accepted: 06/07/2023] [Indexed: 06/21/2023]
Abstract
BACKGROUND Previous functional magnetic resonance imaging studies have reported widespread brain functional connectivity alterations in patients with psychosis. These studies have mostly used either resting-state or simple-task paradigms, thereby compromising experimental control or ecological validity, respectively. Additionally, in a conventional functional magnetic resonance imaging intrasubject functional connectivity analysis, it is difficult to identify which connections relate to extrinsic (stimulus-induced) and which connections relate to intrinsic (non-stimulus-related) neural processes. METHODS To mitigate these limitations, we used intersubject functional connectivity (ISFC) to analyze longitudinal functional magnetic resonance imaging data collected while 36 individuals with first-episode psychosis (FEP) and 29 age- and sex-matched population control participants watched scenes from the fantasy movie Alice in Wonderland at baseline and again at 1-year follow-up. Furthermore, to allow unconfounded comparison and to overcome possible circularity of ISFC, we introduced a novel approach wherein ISFC in both the FEP and population control groups was calculated with respect to an independent group of participants (not included in the analyses). RESULTS Using this independent-reference ISFC approach, we found an interaction effect wherein the independent-reference ISFC in individuals with FEP, but not in the control group participants, was significantly stronger at baseline than at follow-up in a network centered in the hippocampus and involving thalamic, striatal, and cortical regions, such as the orbitofrontal cortex. Alleviation of positive symptoms, particularly delusions, from baseline to follow-up was correlated with decreased network connectivity in patients with FEP. CONCLUSIONS These findings link deviation of naturalistic information processing in the hippocampus-centered network to positive symptoms.
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Affiliation(s)
- Jussi Alho
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland; Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts.
| | - Juha M Lahnakoski
- Institute of Neuroscience and Medicine, Brain, & Behaviour (INM-7), Research Center Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-universität Düsseldorf, Düsseldorf, Germany; Independent Max Planck Research Group for Social Neuroscience, Max Planck Institute of Psychiatry, Munich, Germany
| | - Jonatan M Panula
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
| | - Eva Rikandi
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland; Mental Health Team, Finnish Institute for Health and Welfare, Helsinki, Finland; Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland
| | - Teemu Mäntylä
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland; Mental Health Team, Finnish Institute for Health and Welfare, Helsinki, Finland; Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland
| | - Maija Lindgren
- Mental Health Team, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Tuula Kieseppä
- Mental Health Team, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Jaana Suvisaari
- Mental Health Team, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Mikko Sams
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
| | - Tuukka T Raij
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland; Department of Psychiatry, Helsinki University and Helsinki University Hospital, Helsinki, Finland; Advanced Magnetic Imaging Centre, Aalto NeuroImaging, Aalto University School of Science, Espoo, Finland.
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7
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Zhu Z, Lei D, Qin K, Tallman MJ, Patino LR, Fleck DE, Gong Q, Sweeney JA, DelBello MP, McNamara RK. Cortical and subcortical structural differences in psychostimulant-free ADHD youth with and without a family history of bipolar I disorder: a cross-sectional morphometric comparison. Transl Psychiatry 2023; 13:368. [PMID: 38036505 PMCID: PMC10689449 DOI: 10.1038/s41398-023-02667-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 10/31/2023] [Accepted: 11/13/2023] [Indexed: 12/02/2023] Open
Abstract
Although attention-deficit/hyperactivity disorder (ADHD) and a family history of bipolar I disorder (BD) are associated with increased risk for developing BD, their neuroanatomical substrates remain poorly understood. This study compared cortical and subcortical gray matter morphology in psychostimulant-free ADHD youth with and without a first-degree relative with BD and typically developing healthy controls. ADHD youth (ages 10-18 years) with ('high-risk', HR) or without ('low-risk', LR) a first-degree relative with BD and healthy comparison youth (HC) were enrolled. High-resolution 3D T1-weighted images were acquired using a Philips 3.0 T MR scanner. The FreeSurfer image analysis suite was used to measure cortical thickness, surface area, and subcortical volumes. A general linear model evaluated group differences in MRI features with age and sex as covariates, and exploratory correlational analyses evaluated associations with symptom ratings. A total of n = 142 youth (mean age: 14.16 ± 2.54 years, 35.9% female) were included in the analysis (HC, n = 48; LR, n = 49; HR, n = 45). The HR group exhibited a more severe symptom profile, including higher mania and dysregulation scores, compared to the LR group. For subcortical volumes, the HR group exhibited smaller bilateral thalamic, hippocampal, and left caudate nucleus volumes compared to both LR and HC, and smaller right caudate nucleus compared with LR. No differences were found between LR and HC groups. For cortical surface area, the HR group exhibited lower parietal and temporal surface area compared with HC and LR, and lower orbitofrontal and superior frontal surface area compared to LR. The HR group exhibited lower left anterior cingulate surface area compared with HC. LR participants exhibited greater right pars opercularis surface area compared with the HC. Some cortical alterations correlated with symptom severity ratings. These findings suggest that ADHD in youth with a BD family history is associated with a more a severe symptom profile and a neuroanatomical phenotype that distinguishes it from ADHD without a BD family history.
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Affiliation(s)
- Ziyu Zhu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, PR China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, 45219, OH, USA
| | - Du Lei
- College of Medical Informatics, Chongqing Medical University, Chongqing, 400016, PR China.
| | - Kun Qin
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, PR China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, 45219, OH, USA
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, PR China
- Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan, 442012, PR China
| | - Maxwell J Tallman
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, 45219, OH, USA
| | - L Rodrigo Patino
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, 45219, OH, USA
| | - David E Fleck
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, 45219, OH, USA
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, PR China.
- Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, 361021, Fujian, PR China.
| | - John A Sweeney
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, PR China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, 45219, OH, USA
| | - Melissa P DelBello
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, 45219, OH, USA
| | - Robert K McNamara
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, 45219, OH, USA
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8
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Lizano P, Kiely C, Mijalkov M, Meda SA, Keedy SK, Hoang D, Zeng V, Lutz O, Pereira JB, Ivleva EI, Volpe G, Xu Y, Lee AM, Rubin LH, Kristian Hill S, Clementz BA, Tamminga CA, Pearlson GD, Sweeney JA, Gershon ES, Keshavan MS, Bishop JR. Peripheral inflammatory subgroup differences in anterior Default Mode network and multiplex functional network topology are associated with cognition in psychosis. Brain Behav Immun 2023; 114:3-15. [PMID: 37506949 PMCID: PMC10592140 DOI: 10.1016/j.bbi.2023.07.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 07/17/2023] [Accepted: 07/22/2023] [Indexed: 07/30/2023] Open
Abstract
INTRODUCTION High-inflammation subgroups of patients with psychosis demonstrate cognitive deficits and neuroanatomical alterations. Systemic inflammation assessed using IL-6 and C-reactive protein may alter functional connectivity within and between resting-state networks, but the cognitive and clinical implications of these alterations remain unknown. We aim to determine the relationships of elevated peripheral inflammation subgroups with resting-state functional networks and cognition in psychosis spectrum disorders. METHODS Serum and resting-state fMRI were collected from psychosis probands (schizophrenia, schizoaffective, psychotic bipolar disorder) and healthy controls (HC) from the B-SNIP1 (Chicago site) study who were stratified into inflammatory subgroups based on factor and cluster analyses of 13 cytokines (HC Low n = 32, Proband Low n = 65, Proband High n = 29). Nine resting-state networks derived from independent component analysis were used to assess functional and multilayer connectivity. Inter-network connectivity was measured using Fisher z-transformation of correlation coefficients. Network organization was assessed by investigating networks of positive and negative connections separately, as well as investigating multilayer networks using both positive and negative connections. Cognition was assessed using the Brief Assessment of Cognition in Schizophrenia. Linear regressions, Spearman correlations, permutations tests and multiple comparison corrections were used for analyses in R. RESULTS Anterior default mode network (DMNa) connectivity was significantly reduced in the Proband High compared to Proband Low (Cohen's d = -0.74, p = 0.002) and HC Low (d = -0.85, p = 0.0008) groups. Inter-network connectivity between the DMNa and the right-frontoparietal networks was lower in Proband High compared to Proband Low (d = -0.66, p = 0.004) group. Compared to Proband Low, the Proband High group had lower negative (d = 0.54, p = 0.021) and positive network (d = 0.49, p = 0.042) clustering coefficient, and lower multiplex network participation coefficient (d = -0.57, p = 0.014). Network findings in high inflammation subgroups correlate with worse verbal fluency, verbal memory, symbol coding, and overall cognition. CONCLUSION These results expand on our understanding of the potential effects of peripheral inflammatory signatures and/or subgroups on network dysfunction in psychosis and how they relate to worse cognitive performance. Additionally, the novel multiplex approach taken in this study demonstrated how inflammation may disrupt the brain's ability to maintain healthy co-activation patterns between the resting-state networks while inhibiting certain connections between them.
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Affiliation(s)
- Paulo Lizano
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA; Division of Translational Neuroscience, Beth Israel Deaconess Medical Center, Boston, MA, USA.
| | - Chelsea Kiely
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Mite Mijalkov
- Neuro Division, Department of Clinical Neurosciences, Karolinska Institutet, Stockholm, Sweden
| | - Shashwath A Meda
- Department of Psychiatry, Yale University, New Haven, Connecticut, USA
| | - Sarah K Keedy
- Department of Psychiatry and Behavioral Neurosciences, University of Chicago, Chicago, IL, USA
| | - Dung Hoang
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Victor Zeng
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Olivia Lutz
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Joana B Pereira
- Neuro Division, Department of Clinical Neurosciences, Karolinska Institutet, Stockholm, Sweden; Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Sweden
| | - Elena I Ivleva
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX
| | - Giovanni Volpe
- Physics Department, University of Gothenburg, Gothenburg, Sweden
| | - Yanxun Xu
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA
| | - Adam M Lee
- Department of Experimental and Clinical Pharmacology and Psychiatry, University of Minnesota, Minneapolis, MN, USA
| | - Leah H Rubin
- Department of Neurology, Psychiatry and Behavioral Sciences, Molecular and Comparative Pathobiology, and Epidemiology, Johns Hopkins University, Baltimore, MD, USA
| | - S Kristian Hill
- Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago, IL, USA
| | - Brett A Clementz
- Department of Psychology, University of Georgia, Athens, Georgia
| | - Carol A Tamminga
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX
| | | | - John A Sweeney
- Department of Psychiatry, University of Cincinnati Medical Center, Cincinnati, OH, USA
| | - Elliot S Gershon
- Department of Psychiatry and Behavioral Neurosciences, University of Chicago, Chicago, IL, USA
| | - Matcheri S Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Jeffrey R Bishop
- Department of Experimental and Clinical Pharmacology and Psychiatry, University of Minnesota, Minneapolis, MN, USA
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Fekih-Romdhane F, Hajje R, Haddad C, Hallit S, Azar J. Exploring negative symptoms heterogeneity in patients diagnosed with schizophrenia and schizoaffective disorder using cluster analysis. BMC Psychiatry 2023; 23:595. [PMID: 37582728 PMCID: PMC10428523 DOI: 10.1186/s12888-023-05101-3] [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: 06/16/2023] [Accepted: 08/10/2023] [Indexed: 08/17/2023] Open
Abstract
BACKGROUND Dissecting the heterogeneity of schizophrenia may help foster progress in understanding its etiology and lay the groundwork for the development of new treatment options for primary or enduring negative symptoms (NS). In this regard, the present study aimed to: (1) to use cluster analysis to identify subgroups of Lebanese patients diagnosed with either schizophrenia or schizoaffective disorder based on NS clusters, and (2) to relate the statistically-derived subgroups to clinically relevant external validators (including measures if state and trait depression, stigma, insight, loneliness, social support). METHOD A total of 202 adult long-stay, chronic, and clinically remitted patients (166 diagnosed with schizophrenia and 36 with schizoaffective disorder) were enrolled. A cluster analysis approach was adopted to classify patients based on the five NS domains social withdrawal, emotional withdrawal, alogia, avolition and anhedonia. RESULTS A three-cluster solution was obtained based on unique NS profiles, and divided patients into (1) low NS (LNS; 42.6%) which characterized by the lowest mean scores in all NS domains, (2) moderate NS (MNS; 25.7%), and (3) high NS (HNS; 31.7%). Post-hoc comparisons showed that depression (state and trait), loneliness and social support could accurately distinguish the schizophrenia subgroups. Additionally, individuals in the HNS cluster had longer duration of illness, longer duration of hospitalization, and were given higher dosages of antipsychotic medication compared to those in the other clusters, but these differences did not achieve the statistical significance. CONCLUSION Findings provide additional support to the categorical model of schizophrenia by confirming the existence of three alternate subtypes based on NS. The determination of distinct NS subgroups within the broad heterogeneous population of people diagnosed with schizophrenia may imply that each subgroup possibly has unique underlying mechanisms and necessitates different treatment approaches.
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Affiliation(s)
- Feten Fekih-Romdhane
- The Tunisian Center of Early Intervention in Psychosis, Department of Psychiatry “Ibn Omrane”, Razi hospital, Manouba, 2010 Tunisia
- Faculty of Medicine of Tunis, Tunis El Manar University, Tunis, Tunisia
| | - Romy Hajje
- Faculty of Science, Lebanese University, Fanar, Lebanon
| | - Chadia Haddad
- Research Department, Psychiatric Hospital of the Cross, Jal Eddib, Lebanon
- INSPECT-LB (Institut National de Santé Publique, d’Épidémiologie Clinique et de Toxicologie-Liban), Beirut, Lebanon
- School of Health Sciences, Modern University for Business and Science, Beirut, Lebanon
| | - Souheil Hallit
- Research Department, Psychiatric Hospital of the Cross, Jal Eddib, Lebanon
- School of Medicine and Medical Sciences, Holy Spirit University of Kaslik, P.O. Box 446, Jounieh, Lebanon
- Applied Science Research Center, Applied Science Private University, Amman, Jordan
| | - Jocelyne Azar
- School of Medicine, Lebanese American University, Byblos, Lebanon
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10
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Iraji A, Chen J, Lewis N, Faghiri A, Fu Z, Agcaoglu O, Kochunov P, Adhikari BM, Mathalon D, Pearlson G, Macciardi F, Preda A, van Erp T, Bustillo JR, Díaz-Caneja CM, Andrés-Camazón P, Dhamala M, Adali T, Calhoun V. Spatial Dynamic Subspaces Encode Sex-Specific Schizophrenia Disruptions in Transient Network Overlap and its Links to Genetic Risk. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.18.548880. [PMID: 37503085 PMCID: PMC10370141 DOI: 10.1101/2023.07.18.548880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Background Recent advances in resting-state fMRI allow us to study spatial dynamics, the phenomenon of brain networks spatially evolving over time. However, most dynamic studies still use subject-specific, spatially-static nodes. As recent studies have demonstrated, incorporating time-resolved spatial properties is crucial for precise functional connectivity estimation and gaining unique insights into brain function. Nevertheless, estimating time-resolved networks poses challenges due to the low signal-to-noise ratio, limited information in short time segments, and uncertain identification of corresponding networks within and between subjects. Methods We adapt a reference-informed network estimation technique to capture time-resolved spatial networks and their dynamic spatial integration and segregation. We focus on time-resolved spatial functional network connectivity (spFNC), an estimate of network spatial coupling, to study sex-specific alterations in schizophrenia and their links to multi-factorial genomic data. Results Our findings are consistent with the dysconnectivity and neurodevelopment hypotheses and align with the cerebello-thalamo-cortical, triple-network, and frontoparietal dysconnectivity models, helping to unify them. The potential unification offers a new understanding of the underlying mechanisms. Notably, the posterior default mode/salience spFNC exhibits sex-specific schizophrenia alteration during the state with the highest global network integration and correlates with genetic risk for schizophrenia. This dysfunction is also reflected in high-dimensional (voxel-level) space in regions with weak functional connectivity to corresponding networks. Conclusions Our method can effectively capture spatially dynamic networks, detect nuanced SZ effects, and reveal the intricate relationship of dynamic information to genomic data. The results also underscore the potential of dynamic spatial dependence and weak connectivity in the clinical landscape.
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Affiliation(s)
- A. Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - J. Chen
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA
| | - N. Lewis
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA
- Department of CSE, Georgia Institute of Technology, Atlanta, Georgia
| | - A. Faghiri
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA
| | - Z. Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA
| | - O. Agcaoglu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA
| | - P. Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, USA
| | - B. M. Adhikari
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, USA
| | - D.H. Mathalon
- Department of Psychiatry, University of California San Francisco, San Francisco, CA, USA
- San Francisco VA Medical Center, San Francisco, CA, USA
| | - G.D. Pearlson
- Departments of Psychiatry and Neuroscience, Yale University School of Medicine, New Haven, CT, USA
| | - F. Macciardi
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - A. Preda
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - T.G.M. van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - J. R. Bustillo
- Department of Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque, NM, USA
| | - C. M. Díaz-Caneja
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, Madrid, Spain
| | - P. Andrés-Camazón
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, Madrid, Spain
| | - M. Dhamala
- Department of Physics and Astronomy, Georgia State University, Atlanta, GA, USA
| | - T. Adali
- Department of CSEE, University of Maryland, Baltimore County, Baltimore, Maryland
| | - V.D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA
- Department of CSE, Georgia Institute of Technology, Atlanta, Georgia
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11
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Sun M, Gabrielson B, Akhonda MABS, Yang H, Laport F, Calhoun V, Adali T. A Scalable Approach to Independent Vector Analysis by Shared Subspace Separation for Multi-Subject fMRI Analysis. SENSORS (BASEL, SWITZERLAND) 2023; 23:5333. [PMID: 37300060 PMCID: PMC10256022 DOI: 10.3390/s23115333] [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: 04/10/2023] [Revised: 05/27/2023] [Accepted: 05/29/2023] [Indexed: 06/12/2023]
Abstract
Joint blind source separation (JBSS) has wide applications in modeling latent structures across multiple related datasets. However, JBSS is computationally prohibitive with high-dimensional data, limiting the number of datasets that can be included in a tractable analysis. Furthermore, JBSS may not be effective if the data's true latent dimensionality is not adequately modeled, where severe overparameterization may lead to poor separation and time performance. In this paper, we propose a scalable JBSS method by modeling and separating the "shared" subspace from the data. The shared subspace is defined as the subset of latent sources that exists across all datasets, represented by groups of sources that collectively form a low-rank structure. Our method first provides the efficient initialization of the independent vector analysis (IVA) with a multivariate Gaussian source prior (IVA-G) specifically designed to estimate the shared sources. Estimated sources are then evaluated regarding whether they are shared, upon which further JBSS is applied separately to the shared and non-shared sources. This provides an effective means to reduce the dimensionality of the problem, improving analyses with larger numbers of datasets. We apply our method to resting-state fMRI datasets, demonstrating that our method can achieve an excellent estimation performance with significantly reduced computational costs.
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Affiliation(s)
- Mingyu Sun
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA; (B.G.); (M.A.B.S.A.); (H.Y.); (F.L.)
| | - Ben Gabrielson
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA; (B.G.); (M.A.B.S.A.); (H.Y.); (F.L.)
| | - Mohammad Abu Baker Siddique Akhonda
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA; (B.G.); (M.A.B.S.A.); (H.Y.); (F.L.)
| | - Hanlu Yang
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA; (B.G.); (M.A.B.S.A.); (H.Y.); (F.L.)
| | - Francisco Laport
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA; (B.G.); (M.A.B.S.A.); (H.Y.); (F.L.)
- CITIC Research Center, University of A Coruña, 15008 A Coruña, Spain
| | - Vince Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA;
| | - Tülay Adali
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA; (B.G.); (M.A.B.S.A.); (H.Y.); (F.L.)
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12
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Gattuso JJ, Perkins D, Ruffell S, Lawrence AJ, Hoyer D, Jacobson LH, Timmermann C, Castle D, Rossell SL, Downey LA, Pagni BA, Galvão-Coelho NL, Nutt D, Sarris J. Default Mode Network Modulation by Psychedelics: A Systematic Review. Int J Neuropsychopharmacol 2023; 26:155-188. [PMID: 36272145 PMCID: PMC10032309 DOI: 10.1093/ijnp/pyac074] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 10/21/2022] [Indexed: 11/06/2022] Open
Abstract
Psychedelics are a unique class of drug that commonly produce vivid hallucinations as well as profound psychological and mystical experiences. A grouping of interconnected brain regions characterized by increased temporal coherence at rest have been termed the Default Mode Network (DMN). The DMN has been the focus of numerous studies assessing its role in self-referencing, mind wandering, and autobiographical memories. Altered connectivity in the DMN has been associated with a range of neuropsychiatric conditions such as depression, anxiety, post-traumatic stress disorder, attention deficit hyperactive disorder, schizophrenia, and obsessive-compulsive disorder. To date, several studies have investigated how psychedelics modulate this network, but no comprehensive review, to our knowledge, has critically evaluated how major classical psychedelic agents-lysergic acid diethylamide, psilocybin, and ayahuasca-modulate the DMN. Here we present a systematic review of the knowledge base. Across psychedelics there is consistent acute disruption in resting state connectivity within the DMN and increased functional connectivity between canonical resting-state networks. Various models have been proposed to explain the cognitive mechanisms of psychedelics, and in one model DMN modulation is a central axiom. Although the DMN is consistently implicated in psychedelic studies, it is unclear how central the DMN is to the therapeutic potential of classical psychedelic agents. This article aims to provide the field with a comprehensive overview that can propel future research in such a way as to elucidate the neurocognitive mechanisms of psychedelics.
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Affiliation(s)
- James J Gattuso
- MDHS, University of Melbourne, Parkville, Victoria, Australia
- Florey Department of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
| | - Daniel Perkins
- Psychae Institute, Melbourne, Victoria, Australia
- MDHS, University of Melbourne, Parkville, Victoria, Australia
- School of Social and Political Science, University of Melbourne, Australia
- Centre for Mental Health, Swinburne University, Hawthorn, Victoria, Australia
| | - Simon Ruffell
- The Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - Andrew J Lawrence
- Florey Department of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
| | - Daniel Hoyer
- MDHS, University of Melbourne, Parkville, Victoria, Australia
- Florey Department of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
- The Scripps Research Institute, Department of Molecular Medicine, La Jolla, California, USA
| | - Laura H Jacobson
- MDHS, University of Melbourne, Parkville, Victoria, Australia
- Florey Department of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
| | | | - David Castle
- Department of Psychiatry, University of Toronto, Canada
| | - Susan L Rossell
- Centre for Mental Health, Swinburne University, Hawthorn, Victoria, Australia
| | - Luke A Downey
- Centre for Human Psychopharmacology, Swinburne University, Hawthorn, Victoria, Australia
| | - Broc A Pagni
- College of Health Solutions, Arizona State University, Tempe, Arizona, USA
| | - Nicole L Galvão-Coelho
- Department of Physiology and Behavior, Universidade Federal do Rio Grande do Norte, Brazil
- NICM Health Research Institute, Western Sydney University, Westmead, New South Wales, Australia
| | - David Nutt
- Centre for Psychedelic Research, Division of Psychiatry, Imperial College London, UK
| | - Jerome Sarris
- Psychae Institute, Melbourne, Victoria, Australia
- Florey Department of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
- NICM Health Research Institute, Western Sydney University, Westmead, New South Wales, Australia
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de Sousa TR, Dt C, Novais F. Exploring the Hypothesis of a Schizophrenia and Bipolar Disorder Continuum: Biological, Genetic and Pharmacologic Data. CNS & NEUROLOGICAL DISORDERS DRUG TARGETS 2023; 22:161-171. [PMID: 34477537 DOI: 10.2174/1871527320666210902164235] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 07/19/2021] [Accepted: 08/08/2021] [Indexed: 12/16/2022]
Abstract
Present time nosology has its roots in Kraepelin's demarcation of schizophrenia and bipolar disorder. However, accumulating evidence has shed light on several commonalities between the two disorders, and some authors have advocated for the consideration of a disease continuum. Here, we review previous genetic, biological and pharmacological findings that provide the basis for this conceptualization. There is a cross-disease heritability, and they share single-nucleotide polymorphisms in some common genes. EEG and imaging patterns have a number of similarities, namely reduced white matter integrity and abnormal connectivity. Dopamine, serotonin, GABA and glutamate systems have dysfunctional features, some of which are identical among the disorders. Finally, cellular calcium regulation and mitochondrial function are, also, impaired in the two.
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Affiliation(s)
- Teresa Reynolds de Sousa
- Department of Neurosciences and Mental Health, Centro Hospitalar Universitário Lisboa Norte (CHULN), Hospital de Santa Maria, Lisbon, Portugal
| | - Correia Dt
- Department of Neurosciences and Mental Health, Centro Hospitalar Universitário Lisboa Norte (CHULN), Hospital de Santa Maria, Lisbon, Portugal
- Faculdade de Medicina da Universidade de Lisboa, Lisboa, Portugal
- ISAMB - Instituto de Saúde Ambiental, Lisboa, Portugal
| | - Filipa Novais
- Department of Neurosciences and Mental Health, Centro Hospitalar Universitário Lisboa Norte (CHULN), Hospital de Santa Maria, Lisbon, Portugal
- Faculdade de Medicina da Universidade de Lisboa, Lisboa, Portugal
- ISAMB - Instituto de Saúde Ambiental, Lisboa, Portugal
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14
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Ardesch DJ, Libedinsky I, Scholtens LH, Wei Y, van den Heuvel MP. Convergence of brain transcriptomic and neuroimaging patterns in schizophrenia, bipolar disorder, autism spectrum disorder and major depression disorder. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023. [DOI: 10.1016/j.bpsc.2022.12.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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15
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Hessenberger M, Haddad S, Obermair GJ. Pathophysiological Roles of Auxiliary Calcium Channel α 2δ Subunits. Handb Exp Pharmacol 2023; 279:289-316. [PMID: 36598609 DOI: 10.1007/164_2022_630] [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] [Indexed: 01/05/2023]
Abstract
α2δ proteins serve as auxiliary subunits of voltage-gated calcium channels, which are essential components of excitable cells such as skeletal and heart muscles, nerve cells of the brain and the peripheral nervous system, as well as endocrine cells. Over the recent years, α2δ proteins have been identified as critical regulators of synaptic functions, including the formation and differentiation of synapses. These functions require signalling mechanisms which are partly independent of calcium channels. Hence, in light of these features it is not surprising that the genes encoding for the four α2δ isoforms have recently been linked to neurological and neurodevelopmental disorders including epilepsy, autism spectrum disorders, schizophrenia, and depressive and bipolar disorders. Despite the increasing number of identified disease-associated mutations, the underlying pathophysiological mechanisms are only beginning to emerge. However, a thorough understanding of the pathophysiological role of α2δ proteins ideally serves two purposes: first, it will contribute to our understanding of general pathological mechanisms in synaptic disorders. Second, it may support the future development of novel and specific treatments for brain disorders. In this context, it is noteworthy that the antiepileptic and anti-allodynic drugs gabapentin and pregabalin both act via binding to α2δ proteins and are among the top sold drugs for treating neuropathic pain. In this book chapter, we will discuss recent developments in our understanding of the functions of α2δ proteins, both as calcium channel subunits and as independent regulatory entities. Furthermore, we present and summarize recently identified and likely pathogenic mutations in the genes encoding α2δ proteins and discuss potential underlying pathophysiological consequences at the molecular and structural level.
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Affiliation(s)
- Manuel Hessenberger
- Division Physiology, Department Pharmacology, Physiology and Microbiology, Karl Landsteiner University of Health Sciences, Krems, Austria
| | - Sabrin Haddad
- Division Physiology, Department Pharmacology, Physiology and Microbiology, Karl Landsteiner University of Health Sciences, Krems, Austria
- Institute of Physiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Gerald J Obermair
- Division Physiology, Department Pharmacology, Physiology and Microbiology, Karl Landsteiner University of Health Sciences, Krems, Austria.
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16
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Zou W, Song P, Lu W, Shao R, Zhang R, Yau SY, Yuan TF, Wang Y, Lin K. Global hippocampus functional connectivity as a predictive neural marker for conversion to future mood disorder in unaffected offspring of bipolar disorder parents. Asian J Psychiatr 2022; 78:103307. [PMID: 36332319 DOI: 10.1016/j.ajp.2022.103307] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Revised: 09/06/2022] [Accepted: 09/17/2022] [Indexed: 11/25/2022]
Abstract
OBJECTIVES Hippocampus-related functional alteration in genetically at-risk individuals may reflect an endophenotype of a mood disorder. Herein, we performed a prospective study to investigate whether baseline hippocampus functional connectivity (FC) in offspring of patients with bipolar disorder (BD) would predict subsequent conversion to mood disorder. METHODS Eighty bipolar offspring and 40 matched normal controls (NC) underwent resting state functional MRI (rsfMRI) scanning on a 3.0 Tesla MR scanner. The offspring were subdivided into asymptomatic offspring (AO) (n = 41) and symptomatic offspring (SO) (n = 39) according to whether they manifested subthreshold mood symptoms. After identifying the different hippocampus FCs between the AO and SO, a logistic regression analysis was conducted to investigate whether the baseline hippocampus FCs predicted a future mood disorder during a 6-year follow-up. RESULTS We identified seven baseline para/hippocampus FCs that showed differences between AO and SO, which were entered as predictive features in the logistic regressive model. Of the 80 bipolar offspring entering the analysis, the FCs between left hippocampus and left precuneus, and between right hippocampus and left posterior cingulate, showed a discriminative capacity for predicting future mood disorder (area-under-curve, or AUC=75.76 % and 75.00 % respectively), and for predicting BD onset (AUC=77.46 % and 81.63 %, respectively). CONCLUSIONS The present findings revealed high predictive utility of the hippocampus resting state FCs for future mood disorder and BD onset in individuals at familial risk. These neural markers can potentially improve early detection of individuals carrying particularly high risk for future mood disorder.
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Affiliation(s)
- Wenjin Zou
- Department of Radiology, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Peilun Song
- School of Information Engineering, Zhengzhou University, Zhengzhou, China
| | - Weicong Lu
- Department of Affective Disorders, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Robin Shao
- Laboratory of Neuropsychology and Laboratory of Social Cognitive Affective, Neuroscience, Department of Psychology, University of Hong Kong, Hong Kong
| | - Ruoxi Zhang
- Department of Affective Disorders, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Suk-Yu Yau
- Department of Rehabilitation Sciences, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong
| | - Ti-Fei Yuan
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Co-innovation Center of Neuroregeneration, Nantong University, Nantong, China.
| | - Yaping Wang
- School of Information Engineering, Zhengzhou University, Zhengzhou, China.
| | - Kangguang Lin
- Department of Affective Disorders, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; School of Health and Life Sciences, University of Health and Rehabilitation Sciences, No. 17, Shandong Road, Shinan district, Qingdao City, Shandong Province, China.
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17
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Chen WX, Liu B, Zhou L, Xiong X, Fu J, Huang ZF, Tan T, Tang M, Wang J, Tang YP. De novo mutations within metabolism networks of amino acid/protein/energy in Chinese autistic children with intellectual disability. Hum Genomics 2022; 16:52. [PMID: 36320054 PMCID: PMC9623983 DOI: 10.1186/s40246-022-00427-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 10/19/2022] [Indexed: 11/05/2022] Open
Abstract
BACKGROUND Autism spectrum disorder (ASD) is often accompanied by intellectual disability (ID). Despite extensive studies, however, the genetic basis for this comorbidity is still not clear. In this study, we tried to develop an analyzing pipeline for de novo mutations and possible pathways related to ID phenotype in ASD. Whole-exome sequencing (WES) was performed to screen de novo mutations and candidate genes in 79 ASD children together with their parents (trios). The de novo altering genes and relative pathways which were associated with ID phenotype were analyzed. The connection nodes (genes) of above pathways were selected, and the diagnostic value of these selected genes for ID phenotype in the study population was also evaluated. RESULTS We identified 89 de novo mutant genes, of which 34 genes were previously reported to be associated with ASD, including double hits in the EGF repeats of NOTCH1 gene (p.V999M and p.S1027L). Interestingly, of these 34 genes, 22 may directly affect intelligence quotient (IQ). Further analyses revealed that these IQ-related genes were enriched in protein synthesis, energy metabolism, and amino acid metabolism, and at least 9 genes (CACNA1A, ALG9, PALM2, MGAT4A, PCK2, PLEKHA1, PSME3, ADI1, and TLE3) were involved in all these three pathways. Seven patients who harbored these gene mutations showed a high prevalence of a low IQ score (< 70), a non-verbal language, and an early diagnostic age (< 4 years). Furthermore, our panel of these 9 genes reached a 10.2% diagnostic rate (5/49) in early diagnostic patients with a low IQ score and also reached a 10% diagnostic yield in those with both a low IQ score and non-verbal language (4/40). CONCLUSION We found some new genetic disposition for ASD accompanied with intellectual disability in this study. Our results may be helpful for etiologic research and early diagnoses of intellectual disability in ASD. Larger population studies and further mechanism studies are warranted.
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Affiliation(s)
- Wen-Xiong Chen
- grid.410737.60000 0000 8653 1072The Assessment and Intervention Center for Autistic Children, Department of Neurology, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, 510623 Guangdong China
| | - Bin Liu
- grid.410737.60000 0000 8653 1072Guangzhou Institute of Pediatrics, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, 510623 China ,grid.258164.c0000 0004 1790 3548Department of Biobank, Shenzhen Baoan Women’s and Children’s Hospital, Jinan University, Shenzhen, 518102 Guangdong China
| | - Lijie Zhou
- grid.412719.8Department of Pediatric Rehabilitation, Third Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052 China
| | - Xiaoli Xiong
- grid.410737.60000 0000 8653 1072Guangzhou Institute of Pediatrics, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, 510623 China
| | - Jie Fu
- grid.410737.60000 0000 8653 1072Guangzhou Institute of Pediatrics, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, 510623 China
| | - Zhi-Fang Huang
- grid.410737.60000 0000 8653 1072The Assessment and Intervention Center for Autistic Children, Department of Neurology, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, 510623 Guangdong China
| | - Ting Tan
- grid.410737.60000 0000 8653 1072Guangzhou Institute of Pediatrics, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, 510623 China
| | - Mingxi Tang
- grid.488387.8Department of Pathology, Affiliated Hospital of Southwest Medical University, Luzhou, 646000 Sichuan China
| | - Jun Wang
- grid.412719.8Department of Pediatric Rehabilitation, Third Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052 China
| | - Ya-Ping Tang
- grid.410737.60000 0000 8653 1072Guangzhou Institute of Pediatrics, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, 510623 China ,grid.412719.8Department of Pediatric Rehabilitation, Third Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052 China ,grid.12981.330000 0001 2360 039XGuangdong Provincial Key Laboratory of Brain Function and Disease, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080 Guangdong China
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18
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Tao S, Zhang Y, Wang Q, Qiao C, Deng W, Liang S, Wei J, Wei W, Yu H, Li X, Li M, Guo W, Ma X, Zhao L, Li T. Identifying transdiagnostic biological subtypes across schizophrenia, bipolar disorder, and major depressive disorder based on lipidomics profiles. Front Cell Dev Biol 2022; 10:969575. [PMID: 36133917 PMCID: PMC9483200 DOI: 10.3389/fcell.2022.969575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 08/01/2022] [Indexed: 11/23/2022] Open
Abstract
Emerging evidence has demonstrated overlapping biological abnormalities underlying schizophrenia (SCZ), bipolar disorder (BP), and major depressive disorder (MDD); these overlapping abnormalities help explain the high heterogeneity and the similarity of patients within and among diagnostic categories. This study aimed to identify transdiagnostic subtypes of these psychiatric disorders based on lipidomics abnormalities. We performed discriminant analysis to identify lipids that classified patients (N = 349, 112 with SCZ, 132 with BP, and 105 with MDD) and healthy controls (N = 198). Ten lipids that mainly regulate energy metabolism, inflammation, oxidative stress, and fatty acylation of proteins were identified. We found two subtypes (named Cluster 1 and Cluster 2 subtypes) across patients with SCZ, BP, and MDD by consensus clustering analysis based on the above 10 lipids. The distribution of clinical diagnosis, functional impairment measured by Global Assessment of Functioning (GAF) scales, and brain white matter abnormalities measured by fractional anisotropy (FA) and radial diffusivity (RD) differed in the two subtypes. Patients within the Cluster 2 subtype were mainly SCZ and BP patients and featured significantly elevated RD along the genu of corpus callosum (GCC) region and lower GAF scores than patients within the Cluster 1 subtype. The SCZ and BP patients within the Cluster 2 subtype shared similar biological patterns; that is, these patients had comparable brain white matter abnormalities and functional impairment, which is consistent with previous studies. Our findings indicate that peripheral lipid abnormalities might help identify homogeneous transdiagnostic subtypes across psychiatric disorders.
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Affiliation(s)
- Shiwan Tao
- Mental Health Center and Psychiatric Laboratory, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Yamin Zhang
- Department of Neurobiology, Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, MOE Frontier Science Center for Brain Science and Brain-Machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Qiang Wang
- Mental Health Center and Psychiatric Laboratory, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Chunxia Qiao
- Mental Health Center and Psychiatric Laboratory, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Wei Deng
- Department of Neurobiology, Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, MOE Frontier Science Center for Brain Science and Brain-Machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Sugai Liang
- Department of Neurobiology, Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, MOE Frontier Science Center for Brain Science and Brain-Machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Jinxue Wei
- Mental Health Center and Psychiatric Laboratory, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Wei Wei
- Department of Neurobiology, Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, MOE Frontier Science Center for Brain Science and Brain-Machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Hua Yu
- Department of Neurobiology, Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, MOE Frontier Science Center for Brain Science and Brain-Machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xiaojing Li
- Department of Neurobiology, Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, MOE Frontier Science Center for Brain Science and Brain-Machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Mingli Li
- Mental Health Center and Psychiatric Laboratory, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Wanjun Guo
- Department of Neurobiology, Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, MOE Frontier Science Center for Brain Science and Brain-Machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xiaohong Ma
- Mental Health Center and Psychiatric Laboratory, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Liansheng Zhao
- Mental Health Center and Psychiatric Laboratory, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Tao Li
- Department of Neurobiology, Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, MOE Frontier Science Center for Brain Science and Brain-Machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, Zhejiang, China
- Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Guangzhou, China
- *Correspondence: Tao Li,
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Cattarinussi G, Bellani M, Maggioni E, Sambataro F, Brambilla P, Delvecchio G. Resting-state functional connectivity and spontaneous brain activity in early-onset bipolar disorder: A review of functional Magnetic Resonance Imaging studies. J Affect Disord 2022; 311:463-471. [PMID: 35580695 DOI: 10.1016/j.jad.2022.05.055] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 05/04/2022] [Accepted: 05/10/2022] [Indexed: 10/18/2022]
Abstract
BACKGROUND Early-onset bipolar disorder (BD) is a complex psychiatric illness characterized by mood swings, irritability and functional impairments. To improve our understanding of the pathophysiology of the disorder, we collected the existing resting-state functional Magnetic Resonance Imaging (rs-fMRI) studies exploring resting-state functional connectivity (rs-FC) and spontaneous activity alterations in children and adolescents with BD. METHODS A search on PubMed, Web of Science and Scopus was conducted to identify all the relevant rs-fMRI investigations conducted in early-onset BD. A total of 14 studies employing different methodological approaches to explore rs-FC and spontaneous activity in early-onset BD were included (independent component analysis, n = 1; seed-based analysis, n = 7; amplitude of low frequency fluctuations analysis, n = 2; regional homogeneity analysis, n = 4). RESULTS Overall, the studies showed abnormalities within the Default Mode Network (DMN) and between the DMN and the Salience Network (SN). Moreover, widespread alterations in rs-FC and spontaneous brain activity within and between cortico-limbic structures, involving primarily the occipital and frontal lobes, amygdala, hippocampus, insula, thalamus and striatum were also reported. LIMITATIONS The small sample sizes, the use of medications, the presence of comorbidities and the heterogeneity in methods hamper the integration of the study findings. CONCLUSIONS Early-onset BD seems to be characterized by selective rs-FC and spontaneous activity dysfunctions in DMN and SN as well as in the cortico-limbic and cortico-striatal circuits, which could explain the emotive and cognitive deficits observed in this disabling psychiatric illness.
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Affiliation(s)
- Giulia Cattarinussi
- Department of Neuroscience (DNS), University of Padova, Padua, Italy; Padua Neuroscience Center, University of Padova, Padua, Italy
| | - Marcella Bellani
- Section of Psychiatry, Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - Eleonora Maggioni
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy; Department of Electronics Information and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Fabio Sambataro
- Department of Neuroscience (DNS), University of Padova, Padua, Italy; Padua Neuroscience Center, University of Padova, Padua, Italy
| | - Paolo Brambilla
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy; Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Giuseppe Delvecchio
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
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20
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Li J, Li J, Huang P, Huang LN, Ding QG, Zhan L, Li M, Zhang J, Zhang H, Cheng L, Li H, Liu DQ, Zhou HY, Jia XZ. Increased functional connectivity of white-matter in myotonic dystrophy type 1. Front Neurosci 2022; 16:953742. [PMID: 35979335 PMCID: PMC9377538 DOI: 10.3389/fnins.2022.953742] [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: 05/28/2022] [Accepted: 07/08/2022] [Indexed: 11/25/2022] Open
Abstract
Background Myotonic dystrophy type 1 (DM1) is the most common and dominant inherited neuromuscular dystrophy disease in adults, involving multiple organs, including the brain. Although structural measurements showed that DM1 is predominantly associated with white-matter damage, they failed to reveal the dysfunction of the white-matter. Recent studies have demonstrated that the functional activity of white-matter is of great significance and has given us insights into revealing the mechanisms of brain disorders. Materials and methods Using resting-state fMRI data, we adopted a clustering analysis to identify the white-matter functional networks and calculated functional connectivity between these networks in 16 DM1 patients and 18 healthy controls (HCs). A two-sample t-test was conducted between the two groups. Partial correlation analyzes were performed between the altered white-matter FC and clinical MMSE or HAMD scores. Results We identified 13 white-matter functional networks by clustering analysis. These white-matter functional networks can be divided into a three-layer network (superficial, middle, and deep) according to their spatial distribution. Compared to HCs, DM1 patients showed increased FC within intra-layer white-matter and inter-layer white-matter networks. For intra-layer networks, the increased FC was mainly located in the inferior longitudinal fasciculus, prefrontal cortex, and corpus callosum networks. For inter-layer networks, the increased FC of DM1 patients is mainly located in the superior corona radiata and deep networks. Conclusion Results demonstrated the abnormalities of white-matter functional connectivity in DM1 located in both intra-layer and inter-layer white-matter networks and suggested that the pathophysiology mechanism of DM1 may be related to the white-matter functional dysconnectivity. Furthermore, it may facilitate the treatment development of DM1.
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Affiliation(s)
- Jing Li
- School of Teacher Education, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China
| | - Jie Li
- Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian, China
- Key Laboratory of Brain and Cognitive Neuroscience, Dalian, China
| | - Pei Huang
- Department of Neurology & Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Li-Na Huang
- Department of Radiology, Changshu No. 2 People’s Hospital, The Affiliated Changshu Hospital of Xuzhou Medical University, Changshu, China
| | - Qing-Guo Ding
- Department of Radiology, Changshu No. 2 People’s Hospital, The Affiliated Changshu Hospital of Xuzhou Medical University, Changshu, China
| | - Linlin Zhan
- Faculty of Western Languages, Heilongjiang University, Harbin, China
| | - Mengting Li
- School of Teacher Education, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China
| | - Jiaxi Zhang
- School of Teacher Education, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China
| | - Hongqiang Zhang
- Department of Radiology, Changshu No. 2 People’s Hospital, The Affiliated Changshu Hospital of Xuzhou Medical University, Changshu, China
| | - Lulu Cheng
- School of Foreign Studies, China University of Petroleum, Qingdao, China
- Shanghai Center for Research in English Language Education, Shanghai International Studies University, Shanghai, China
| | - Huayun Li
- School of Teacher Education, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China
| | - Dong-Qiang Liu
- Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian, China
- Key Laboratory of Brain and Cognitive Neuroscience, Dalian, China
| | - Hai-Yan Zhou
- Department of Neurology & Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xi-Ze Jia
- School of Teacher Education, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China
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21
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Guardiola-Ripoll M, Almodóvar-Payá C, Lubeiro A, Salvador R, Salgado-Pineda P, Gomar JJ, Guerrero-Pedraza A, Sarró S, Maristany T, Fernández-Linsenbarth I, Hernández-García M, Papiol S, Molina V, Pomarol-Clotet E, Fatjó-Vilas M. New insights of the role of the KCNH2 gene in schizophrenia: An fMRI case-control study. Eur Neuropsychopharmacol 2022; 60:38-47. [PMID: 35635995 DOI: 10.1016/j.euroneuro.2022.04.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 04/14/2022] [Accepted: 04/19/2022] [Indexed: 11/04/2022]
Abstract
The KCNH2 gene, encoding for a subunit of a voltage-gated potassium channel, has been identified as a key element of neuronal excitability and a promising novel therapeutic target for schizophrenia (SZ). Nonetheless, evidence highlighting the role of KCNH2 on cognitive and brain activity phenotypes comes mainly from studies based on healthy controls (HC). Therefore, we aimed to study the role of KCNH2 on the brain functional differences between patients with SZ and HC. The fMRI sample comprised 78 HC and 79 patients with SZ (matched for age, sex and premorbid IQ). We studied the effect of the polymorphism KCNH2-rs3800779 on attention and working memory-related brain activity, evaluated through the N-back task, in regions with detected diagnostic differences (regression model, controlled for age, sex and premorbid IQ, FEAT-FSL). We report a significant diagnosis x KCNH2 interaction on brain activity (1-back vs baseline contrast) at the medial superior prefrontal cortex (Zmax=3.55, p = 0.00861). In this region, patients with SZ carrying the risk genotype (AA) show a deactivation failure, while HC depict the opposite pattern towards deactivation. The brain region with significant diagnosis x KCNH2 interaction has been previously associated with SZ. The results of this study, in which the role of KCNH2 on fMRI response is analysed for the first time in patients, suggest that KCNH2 variability contributes to inefficient brain activity modulation during the N-back task in affected subjects. These data may pave the way to further understand how KCNH2 genetic variability is related to the pathophysiological mechanisms underlying schizophrenia.
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Affiliation(s)
- Maria Guardiola-Ripoll
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain; CIBERSAM (Biomedical Research Network in Mental Health, Instituto de Salud Carlos III), Madrid, Spain
| | - Carmen Almodóvar-Payá
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain; CIBERSAM (Biomedical Research Network in Mental Health, Instituto de Salud Carlos III), Madrid, Spain
| | - Alba Lubeiro
- Psychiatry Department, School of Medicine, University of Valladolid, Valladolid, Spain
| | - Raymond Salvador
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain; CIBERSAM (Biomedical Research Network in Mental Health, Instituto de Salud Carlos III), Madrid, Spain
| | - Pilar Salgado-Pineda
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain; CIBERSAM (Biomedical Research Network in Mental Health, Instituto de Salud Carlos III), Madrid, Spain
| | - Jesús J Gomar
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain; The Litwin-Zucker Alzheimer's Research Center, NY, United States
| | - Amalia Guerrero-Pedraza
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain; Hospital Benito Menni-CASM, Sant Boi de Llobregat, Barcelona, Spain
| | - Salvador Sarró
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain; CIBERSAM (Biomedical Research Network in Mental Health, Instituto de Salud Carlos III), Madrid, Spain
| | - Teresa Maristany
- Diagnostic Imaging Department, Hospital Sant Joan de Déu Research Foundation, Barcelona, Spain
| | | | - Marta Hernández-García
- Neurosciences Institute of Castilla y León (INCYL), University of Salamanca, Salamanca, Spain
| | - Sergi Papiol
- CIBERSAM (Biomedical Research Network in Mental Health, Instituto de Salud Carlos III), Madrid, Spain; Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany; Department of Psychiatry, University Hospital, Ludwig Maximilian University, Munich Germany
| | - Vicente Molina
- Psychiatry Department, School of Medicine, University of Valladolid, Valladolid, Spain; Neurosciences Institute of Castilla y León (INCYL), University of Salamanca, Salamanca, Spain; Psychiatry Service, University Hospital of Valladolid, Valladolid, Spain
| | - Edith Pomarol-Clotet
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain; CIBERSAM (Biomedical Research Network in Mental Health, Instituto de Salud Carlos III), Madrid, Spain.
| | - Mar Fatjó-Vilas
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain; CIBERSAM (Biomedical Research Network in Mental Health, Instituto de Salud Carlos III), Madrid, Spain; Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals, Facultat de Biologia, Universitat de Barcelona; Barcelona, Spain.
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22
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Yang Y, Li X, Cui Y, Liu K, Qu H, Lu Y, Li W, Zhang L, Zhang Y, Song J, Lv L. Reduced Gray Matter Volume in Orbitofrontal Cortex Across Schizophrenia, Major Depressive Disorder, and Bipolar Disorder: A Comparative Imaging Study. Front Neurosci 2022; 16:919272. [PMID: 35757556 PMCID: PMC9226907 DOI: 10.3389/fnins.2022.919272] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 05/02/2022] [Indexed: 11/29/2022] Open
Abstract
Schizophrenia (SZ), major depressive disorder (MDD), and bipolar disorder (BD) are severe psychiatric disorders and share common characteristics not only in clinical symptoms but also in neuroimaging. The purpose of this study was to examine common and specific neuroanatomical features in individuals with these three psychiatric conditions. In this study, 70 patients with SZ, 85 patients with MDD, 42 patients with BD, and 95 healthy controls (HCs) were recruited. Voxel-based morphometry (VBM) analysis was used to explore brain imaging characteristics. Psychopathology was assessed using the Beck Depression Inventory (BDI), the Beck Anxiety Inventory (BAI), the Young Mania Rating Scale (YMRS), and the Positive and Negative Syndrome Scale (PANSS). Cognition was assessed using the digit symbol substitution test (DSST), forward-digital span (DS), backward-DS, and semantic fluency. Common reduced gray matter volume (GMV) in the orbitofrontal cortex (OFC) region was found across the SZ, MDD, and BD. Specific reduced GMV of brain regions was also found. For patients with SZ, we found reduced GMV in the frontal lobe, temporal pole, occipital lobe, thalamus, hippocampus, and cerebellum. For patients with MDD, we found reduced GMV in the frontal and temporal lobes, insular cortex, and occipital regions. Patients with BD had reduced GMV in the medial OFC, inferior temporal and fusiform regions, insular cortex, hippocampus, and cerebellum. Furthermore, the OFC GMV was correlated with processing speed as assessed with the DSST across four groups (r = 0.17, p = 0.004) and correlated with the PANSS positive symptoms sub-score in patients with SZ (r = − 0.27, p = 0.026). In conclusion, common OFC alterations in SZ, MDD, and BD provided evidence that this region dysregulation may play a critical role in the pathophysiology of these three psychiatric disorders.
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Affiliation(s)
- Yongfeng Yang
- Department of Psychiatry, Henan Mental Hospital, Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China.,Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China.,International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Xue Li
- Department of Psychiatry, Henan Mental Hospital, Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China.,Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China.,International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Yue Cui
- Brainnetome Center and Institute of Automation, Chinese Academy of Sciences, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Kang Liu
- Department of Psychiatry, Henan Mental Hospital, Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China.,Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China.,International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Haoyang Qu
- Department of Psychiatry, The Second Clinic College of Xinxiang Medical University, Xinxiang, China
| | - Yanli Lu
- Department of Psychiatry, Henan Mental Hospital, Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
| | - Wenqiang Li
- Department of Psychiatry, Henan Mental Hospital, Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China.,Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China.,International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Luwen Zhang
- Department of Psychiatry, Henan Mental Hospital, Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China.,Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China.,International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Yan Zhang
- Department of Psychiatry, Henan Mental Hospital, Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China.,Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China.,International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Jinggui Song
- Department of Psychiatry, Henan Mental Hospital, Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China.,Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China.,International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Luxian Lv
- Department of Psychiatry, Henan Mental Hospital, Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China.,Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China.,International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
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23
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Iraji A, Faghiri A, Fu Z, Rachakonda S, Kochunov P, Belger A, Ford JM, McEwen S, Mathalon DH, Mueller BA, Pearlson GD, Potkin SG, Preda A, Turner JA, van Erp TGM, Calhoun VD. Multi-spatial-scale dynamic interactions between functional sources reveal sex-specific changes in schizophrenia. Netw Neurosci 2022; 6:357-381. [PMID: 35733435 PMCID: PMC9208002 DOI: 10.1162/netn_a_00196] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 04/12/2021] [Indexed: 11/04/2022] Open
Abstract
We introduce an extension of independent component analysis (ICA), called multiscale ICA, and design an approach to capture dynamic functional source interactions within and between multiple spatial scales. Multiscale ICA estimates functional sources at multiple spatial scales without imposing direct constraints on the size of functional sources, overcomes the limitation of using fixed anatomical locations, and eliminates the need for model-order selection in ICA analysis. We leveraged this approach to study sex-specific and sex-common connectivity patterns in schizophrenia. Results show dynamic reconfiguration and interaction within and between multi-spatial scales. Sex-specific differences occur (a) within the subcortical domain, (b) between the somatomotor and cerebellum domains, and (c) between the temporal domain and several others, including the subcortical, visual, and default mode domains. Most of the sex-specific differences belong to between-spatial-scale functional interactions and are associated with a dynamic state with strong functional interactions between the visual, somatomotor, and temporal domains and their anticorrelation patterns with the rest of the brain. We observed significant correlations between multi-spatial-scale functional interactions and symptom scores, highlighting the importance of multiscale analyses to identify potential biomarkers for schizophrenia. As such, we recommend such analyses as an important option for future functional connectivity studies.
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Affiliation(s)
- Armin Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
- * Corresponding Authors: ;
| | - Ashkan Faghiri
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - Srinivas Rachakonda
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, USA
| | - Aysenil Belger
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - Judy M. Ford
- Department of Psychiatry, University of California San Francisco, San Francisco, CA, USA
- San Francisco VA Medical Center, San Francisco, CA, USA
| | - Sarah McEwen
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA
| | - Daniel H. Mathalon
- Department of Psychiatry, University of California San Francisco, San Francisco, CA, USA
- San Francisco VA Medical Center, San Francisco, CA, USA
| | - Bryon A. Mueller
- Department of Psychiatry, University of Minnesota, Minneapolis, MN, USA
| | - Godfrey D. Pearlson
- Departments of Psychiatry and Neuroscience, Yale University, School of Medicine, New Haven, CT, USA
| | - Steven G. Potkin
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - Jessica A. Turner
- Department of Psychology, Georgia State University, Atlanta, GA, USA
| | - Theodorus G. M. van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
- * Corresponding Authors: ;
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Bi B, Che D, Bai Y. Neural network of bipolar disorder: Toward integration of neuroimaging and neurocircuit-based treatment strategies. Transl Psychiatry 2022; 12:143. [PMID: 35383150 PMCID: PMC8983759 DOI: 10.1038/s41398-022-01917-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 03/22/2022] [Accepted: 03/23/2022] [Indexed: 01/23/2023] Open
Abstract
Bipolar disorder (BD) is a complex psychiatric disorder characterized by dysfunctions in three domains including emotional processing, cognitive processing, and psychomotor dimensions. However, the neural underpinnings underlying these clinical profiles are not well understood. Based on the reported data, we hypothesized that (i) the core neuropathology in BD is damage in fronto-limbic network, which is associated with emotional dysfunction; (ii) changes in intrinsic brain network, such as sensorimotor network, salience network, default-mode network, central executive network are associated with impaired cognition function; and (iii) beyond the dopaminergic-driven basal ganglia-thalamo-cortical motor circuit modulated by other neurotransmitter systems, such as serotonin (subcortical-cortical modulation), the sensorimotor network and related motor function modulated by other non-motor networks such as the default-mode network are involved in psychomotor function. In this review, we propose a neurocircuit-based clinical characteristics and taxonomy to guide the treatment of BD. We draw on findings from neuropsychological and neuroimaging studies in BD and link variations in these clinical profiles to underlying neurocircuit dysfunctions. We consider pharmacological, psychotherapy, and neuromodulatory treatments that could target those specific neurocircuit dysfunctions in BD. Finally, it is suggested that the methods of testing the neurocircuit-based taxonomy and important limitations to this approach should be considered in future.
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Affiliation(s)
- Bo Bi
- Department of Clinical Psychology, The Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China.
| | - Dongfang Che
- grid.452787.b0000 0004 1806 5224Neurosurgery department, Shenzhen Children’s Hospital, Shenzhen, China
| | - Yuyin Bai
- grid.12981.330000 0001 2360 039XDepartment of Clinical Psychology, The Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China
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Yan K, Shi WQ, Su T, Liao XL, Wu SN, Li QY, Yu J, Shu HY, Zhang LJ, Pan YC, Shao Y. Brain Activity Changes in Slow 5 and Slow 4 Frequencies in Patients With Optic Neuritis: A Resting State Functional MRI Study. Front Neurol 2022; 13:823919. [PMID: 35265028 PMCID: PMC8900534 DOI: 10.3389/fneur.2022.823919] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Accepted: 01/13/2022] [Indexed: 11/13/2022] Open
Abstract
Objective We used the amplitude of low-frequency fluctuation (ALFF) method to investigate spontaneous brain activity in patients with optic neuritis (ON) in specific frequency bands. Data and Methods A sample of 21 patients with ON (13 female and eight male) and 21 healthy controls (HCs) underwent functional magnetic resonance imaging (fMRI) scans in the resting state. We analyzed the ALFF values at different frequencies (slow-4 band: 0.027–0.073 Hz; slow-5 band: 0.01–0.027 Hz) in ON patients and HCs. Results In the slow-4 frequency range, compared with HCs, ON patients had apparently lower ALFF in the insula and the whack precuneus. In the slow-5 frequency range, ON patients showed significantly increased ALFF in the left parietal inferior and the left postcentral. Conclusion Our results suggest that ON may be involved in abnormal brain function and can provide a basis for clinical research.
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Affiliation(s)
- Kai Yan
- Department of Radiology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China
| | - Wen-Qing Shi
- Department of Ophthalmology, Jiangxi Centre of National Clinical Ophthalmology Institute, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Ting Su
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, United States
| | - Xu-Lin Liao
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Shi-Nan Wu
- Department of Ophthalmology, Jiangxi Centre of National Clinical Ophthalmology Institute, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Qiu-Yu Li
- Department of Ophthalmology, Jiangxi Centre of National Clinical Ophthalmology Institute, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Jing Yu
- Department of Acupuncture and Moxibustion, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China
| | - Hui-Ye Shu
- Department of Ophthalmology, Jiangxi Centre of National Clinical Ophthalmology Institute, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Li-Juan Zhang
- Department of Ophthalmology, Jiangxi Centre of National Clinical Ophthalmology Institute, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yi-Cong Pan
- Department of Ophthalmology, Jiangxi Centre of National Clinical Ophthalmology Institute, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yi Shao
- Department of Ophthalmology, Jiangxi Centre of National Clinical Ophthalmology Institute, The First Affiliated Hospital of Nanchang University, Nanchang, China
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26
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Li W, Xu J, Xiang Q, Zhuo K, Zhang Y, Liu D, Li Y. Neurometabolic and functional changes of default-mode network relate to clinical recovery in first-episode psychosis patients: A longitudinal 1H-MRS and fMRI study. Neuroimage Clin 2022; 34:102970. [PMID: 35240468 PMCID: PMC8889416 DOI: 10.1016/j.nicl.2022.102970] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 02/12/2022] [Accepted: 02/21/2022] [Indexed: 02/06/2023]
Abstract
BACKGROUND Antipsychotic treatment has improved the disrupted functional connectivity (FC) and neurometabolites levels of the default mode network (DMN) in schizophrenia patients, but a direct relationship between FC change, neurometabolic level alteration, and symptom improvement has not been built. This study examined the association between the alterations in DMN FC, the changes of neurometabolites levels in the medial prefrontal cortex (MPFC), and the improvementsinpsychopathology in a longitudinal study of drug-naïve first-episode psychosis (FEP) patients. METHODS Thirty-two drug-naïve FEP patients and 30 matched healthy controls underwent repeated assessments with the Positive and Negative Syndrome Scale (PANSS) and 3T proton magnetic resonance spectroscopy as well as resting-state functional magnetic resonance imaging. The levels of γ-aminobutyric acid, glutamate, N-acetyl-aspartate in MPFC, and the FC of DMN were measured. After 8-week antipsychotic treatment, 24 patients were re-examined. RESULTS After treatment, the changes in γ-aminobutyric acid were correlated with the alterations of FC between the MPFC and DMN, while the changes in N-acetyl-aspartate were associated with the alterations of FC between the posterior cingulate cortex/precuneus and DMN. The FC changes of both regions were correlated with patients PANSS positive score reductions. The structural equation modeling analyses revealed that the changes of DMN FC mediated the relationship between the changes of neurometabolites and the symptom improvements of the patients. CONCLUSIONS The derived neurometabolic-functional changes underlying the clinical recovery provide insights into the prognosis of FEP patients. It is noteworthy that this is an exploratory study, and future work with larger sample size is needed to validate our findings.
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Affiliation(s)
- Wenli Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, PR China
| | - Jiale Xu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, PR China
| | - Qiong Xiang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China
| | - Kaiming Zhuo
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China
| | - Yaoyu Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, PR China
| | - Dengtang Liu
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China; Huashan Hospital, Fudan University, Shanghai 200040, PR China; Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China; Institute of Mental Health, Fudan University, Shanghai 200030, PR China.
| | - Yao Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, PR China.
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Costanzo F, Zanni G, Fucà E, Di Paola M, Barresi S, Travaglini L, Colafati GS, Gambardella A, Bellacchio E, Bertini E, Menghini D, Vicari S. Cerebellar Agenesis and Bilateral Polimicrogyria Associated with Rare Variants of CUB and Sushi Multiple Domains 1 Gene (CSMD1): A Longitudinal Neuropsychological and Neuroradiological Case Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19031224. [PMID: 35162247 PMCID: PMC8835405 DOI: 10.3390/ijerph19031224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 01/12/2022] [Accepted: 01/17/2022] [Indexed: 12/04/2022]
Abstract
Cerebellar agenesis is an extremely rare condition characterized by a near complete absence of the cerebellum. The pathogenesis and molecular basis remain mostly unknown. We report the neuroradiological, molecular, neuropsychological and behavioral characterization of a 5-year-old girl, with cerebellar agenesis associated with parietal and peri-Sylvian polymicrogyria, followed-up for 10 years at four time points. Whole exome sequencing identified two rare variants in CSMD1, a gene associated with neurocognitive and psychiatric alterations. Mild intellectual impairment, cerebellar ataxia and deficits in language, memory and executive functions, with relatively preserved adaptive and psychopathological domains, were initially showed. Phonological awareness and verbal memory declined at 11 years of age, and social and anxiety problems emerged. Adaptive and psychopathological characteristics dramatically worsened at 15 years. In summary, the developmental clinical outcome showed impairment in multiple cognitive functions in childhood, with a progressive decline in cognitive and adaptive abilities and the emergence of psychopathological symptoms in adolescence. The observed phenotype could be the result of a complex interplay between cerebellar abnormality, brain malformation and the relations with CSMD1 variants. These findings may provide insights into the developmental clinical outcomes of a co-occurrence between rare brain malformation and rare genetic variants associated to neurodevelopmental disorders.
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Affiliation(s)
- Floriana Costanzo
- Child and Adolescent Neuropsychiatry Unit, Department of Neurosciences, Bambino Gesù Children’s Hospital IRCCS, Via Ferdinando Baldelli 41, I-00146 Rome, Italy; (F.C.); (E.F.); (S.V.)
| | - Ginevra Zanni
- Unit of Neuromuscular and Neurodegenerative Disorders, Department of Neurosciences, Bambino Gesù Children’s Hospital, IRCCS, Viale di San Paolo 15, I-00146 Rome, Italy; (G.Z.); (L.T.); (E.B.)
| | - Elisa Fucà
- Child and Adolescent Neuropsychiatry Unit, Department of Neurosciences, Bambino Gesù Children’s Hospital IRCCS, Via Ferdinando Baldelli 41, I-00146 Rome, Italy; (F.C.); (E.F.); (S.V.)
| | - Margherita Di Paola
- Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Via Ardeatina 306, I-00179 Rome, Italy;
- Department of Mental Health, King Faisal Specialist Hospital & Research Center, Riyadh 12713, Saudi Arabia
| | - Sabina Barresi
- Pathology Unit, Department of Laboratories, Bambino Gesù Children’s Hospital, IRCCS, Viale di San Paolo 15, I-00146 Rome, Italy;
| | - Lorena Travaglini
- Unit of Neuromuscular and Neurodegenerative Disorders, Department of Neurosciences, Bambino Gesù Children’s Hospital, IRCCS, Viale di San Paolo 15, I-00146 Rome, Italy; (G.Z.); (L.T.); (E.B.)
| | - Giovanna Stefania Colafati
- Oncological Neuroradiology Unit, Department of Imaging, Bambino Gesù Children’s Hospital, IRCCS, Piazza Sant’Onofrio 4, I-00100 Rome, Italy;
| | - Antonio Gambardella
- Institute of Neurology, University Magna Græcia, I-88100 Catanzaro, Italy;
- Institute of Molecular Bioimaging and Physiology, National Research Council, I-88100 Catanzaro, Italy
| | - Emanuele Bellacchio
- Genetics and Rare Diseases Research Division, Bambino Gesù Children’s Hospital, Viale di San Paolo 15, I-00146 Rome, Italy;
| | - Enrico Bertini
- Unit of Neuromuscular and Neurodegenerative Disorders, Department of Neurosciences, Bambino Gesù Children’s Hospital, IRCCS, Viale di San Paolo 15, I-00146 Rome, Italy; (G.Z.); (L.T.); (E.B.)
| | - Deny Menghini
- Child and Adolescent Neuropsychiatry Unit, Department of Neurosciences, Bambino Gesù Children’s Hospital IRCCS, Via Ferdinando Baldelli 41, I-00146 Rome, Italy; (F.C.); (E.F.); (S.V.)
- Correspondence: ; Tel.: +39-0668597091
| | - Stefano Vicari
- Child and Adolescent Neuropsychiatry Unit, Department of Neurosciences, Bambino Gesù Children’s Hospital IRCCS, Via Ferdinando Baldelli 41, I-00146 Rome, Italy; (F.C.); (E.F.); (S.V.)
- Department of Life Science and Public Health, Catholic University of the Sacred Heart, Largo Agostino Gemelli 1, I-00168 Rome, Italy
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Abstract
The cognitive dysfunction experienced by patients with schizophrenia represents a major unmet clinical need. We believe that enhancing synaptic function and plasticity by targeting kalirin may provide a novel means to remediate these symptoms. Karilin (a protein encoded by the KALRN gene) has multiple functional domains, including two Dbl homology (DH) guanine exchange factor (GEF) domains, which act to enhance the activity of the Rho family guanosine triphosphate (GTP)-ases. Here, we provide an overview of kalirin's roles in brain function and its therapeutic potential in schizophrenia. We outline how it mediates diverse effects via a suite of distinct isoforms that couple to members of the Rho GTPase family to regulate synapse formation and stabilisation, and how genomic and post-mortem data implicate it in schizophrenia. We then review the current state of knowledge about the influence of kalirin on brain function at a systems level, based largely on evidence from transgenic mouse models, which support its proposed role in regulating dendritic spine function and plasticity. We demonstrate that, whilst the GTPases are classically considered to be 'undruggable', targeting kalirin and other Rho GEFs provides a means to indirectly modulate their activity. Finally, we integrate across the information presented to assess the therapeutic potential of kalirin for schizophrenia and highlight the key outstanding questions required to advance it in this capacity; namely, the need for more information about the diversity and function of its isoforms, how these change across neurodevelopment, and how they affect brain function in vivo.
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Reardon AM, Li K, Hu XP. Improving Between-Group Effect Size for Multi-Site Functional Connectivity Data via Site-Wise De-Meaning. Front Comput Neurosci 2021; 15:762781. [PMID: 34924984 PMCID: PMC8674307 DOI: 10.3389/fncom.2021.762781] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Accepted: 11/04/2021] [Indexed: 11/18/2022] Open
Abstract
Background: Multi-site functional MRI (fMRI) databases are becoming increasingly prevalent in the study of neurodevelopmental and psychiatric disorders. However, multi-site databases are known to introduce site effects that may confound neurobiological and measures such as functional connectivity (FC). Although studies have been conducted to mitigate site effects, these methods often result in reduced effect size in FC comparisons between controls and patients. Methods: We present a site-wise de-meaning (SWD) strategy in multi-site FC analysis and compare its performance with two common site-effect mitigation methods, i.e., generalized linear model (GLM) and Combining Batches (ComBat) Harmonization. For SWD, after FC was calculated and Fisher z-transformed, the site-wise FC mean was removed from each subject before group-level statistical analysis. The above methods were tested on two multi-site psychiatric consortiums [Autism Brain Imaging Data Exchange (ABIDE) and Bipolar and Schizophrenia Network on Intermediate Phenotypes (B-SNIP)]. Preservation of consistent FC alterations in patients were evaluated for each method through the effect sizes (Hedge’s g) of patients vs. controls. Results: For the B-SNIP dataset, SWD improved the effect size between schizophrenic and control subjects by 4.5–7.9%, while GLM and ComBat decreased the effect size by 22.5–42.6%. For the ABIDE dataset, SWD improved the effect size between autistic and control subjects by 2.9–5.3%, while GLM and ComBat decreased the effect size by up to 11.4%. Conclusion: Compared to the original data and commonly used methods, the SWD method demonstrated superior performance in preserving the effect size in FC features associated with disorders.
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Affiliation(s)
- Alexandra M Reardon
- Department of Bioengineering, University of California, Riverside, Riverside, CA, United States
| | - Kaiming Li
- Department of Bioengineering, University of California, Riverside, Riverside, CA, United States
| | - Xiaoping P Hu
- Department of Bioengineering, University of California, Riverside, Riverside, CA, United States.,Center for Advanced Neuroimaging, University of California, Riverside, Riverside, CA, United States
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30
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Jiang W, Rootes-Murdy K, Chen J, Bizzozero NIP, Calhoun VD, van Erp TGM, Ehrlich S, Agartz I, Jönsson EG, Andreassen OA, Wang L, Pearlson GD, Glahn DC, Hong E, Liu J, Turner JA. Multivariate alterations in insula - Medial prefrontal cortex linked to genetics in 12q24 in schizophrenia. Psychiatry Res 2021; 306:114237. [PMID: 34655926 PMCID: PMC8643340 DOI: 10.1016/j.psychres.2021.114237] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 10/06/2021] [Accepted: 10/08/2021] [Indexed: 11/29/2022]
Abstract
The direct effect of genetic variations on clinical phenotypes within schizophrenia (SZ) remains elusive. We examined the previously identified association of reduced gray matter concentration in the insula - medial prefrontal cortex and a quantitative trait locus located in 12q24 in a SZ dataset. The main analysis was performed on 1461 SNPs and 830 participants. The highest contributing SNPs were localized in five genes including TMEM119, which encodes a microglial marker, that is associated with neuroinflammation and Alzheimer's disease. The gene set in 12q4 may partially explain brain alterations in SZ, but they may also relate to other psychiatric and developmental disorders.
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Affiliation(s)
- Wenhao Jiang
- Department of Psychology, Georgia State University, United States of America; Department of Psychosomatics and Psychiatry, Zhongda Hospital, Institute of Psychosomatics, Medical School, Southeast University, Nanjing, China
| | - Kelly Rootes-Murdy
- Department of Psychology, Georgia State University, United States of America
| | - Jiayu Chen
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology and Emory University, United States of America
| | | | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology and Emory University, United States of America
| | - Theo G M van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, United States of America; Qureshey Research Laboratory, Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine, CA,United States of America
| | - Stefan Ehrlich
- Department of Psychiatry, Massachusetts General Hospital, United States of America; Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, TU Dresden, Germany
| | - Ingrid Agartz
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway; Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Stockholm Region, Stockholm, Sweden; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Erik G Jönsson
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway; Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Stockholm Region, Stockholm, Sweden
| | - Ole A Andreassen
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway
| | - Lei Wang
- Psychiatry and Behavioral Health, Ohio State Wexner Medical Center, United States of America
| | | | - David C Glahn
- Boston Children's Hospital and Harvard Medical School, United States of America
| | - Elliot Hong
- Maryland Psychiatric Research Center, University of Maryland School of Medicine, United States of America
| | - Jingyu Liu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology and Emory University, United States of America
| | - Jessica A Turner
- Department of Psychology, Georgia State University, United States of America; Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology and Emory University, United States of America
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Pan Y, Liu Z, Xue Z, Sheng Y, Cai Y, Cheng Y, Chen X. Abnormal Network Properties and Fiber Connections of DMN across Major Mental Disorders: A Probability Tracing and Graph Theory Study. Cereb Cortex 2021; 32:3127-3136. [PMID: 34849632 DOI: 10.1093/cercor/bhab405] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 10/09/2021] [Accepted: 10/10/2021] [Indexed: 12/25/2022] Open
Abstract
The default mode network (DMN) is related to brain functions and its abnormalities were associated with mental disorders' pathophysiology. To further understand the common and distinct DMN alterations across disorders, we capitalized on the probability tracing method and graph theory to analyze the role of DMN across three major mental disorders. A total of 399 participants (156 schizophrenia [SCZ], 90 bipolar disorder [BP], 58 major depression disorder [MDD], and 95 healthy controls [HC]) completed magnetic resonance imaging (MRI)-scanning, clinical, and cognitive assessment. The MRI preprocessing of diffusion-tensor-imaging was conducted in FMRIB Software Library and probabilistic fiber tracking was applied by PANDA. This study had three main findings. First, patient groups showed significantly lower cluster coefficient in whole-brain compared with HC. SCZ showed significantly longer characteristic path compared with HC. Second, patient groups showed inter-group specificity in abnormalities of DMN connections. Third, SCZ was sensitive to left_medial_superior_frontal_gyrus (L_SFGmed)-right_anterior_cingulate_gyrus (R_ACG) connection relating to positive symptoms; left_ACG-right_ACG connection was the mania's antagonistic factor in BP. This trans-diagnostic study found disorder-specific structural abnormalities in the fiber connection of R_SFGmed-L_SFGmed-R_ACG_L_ACG within DMN, where SCZ showed more disconnections compared with other disorders. And these connections are diagnosis-specifically correlated to phenotypes. The current study may provide further evidence of shared and distinct endo-phenotypes across psychopathology.
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Affiliation(s)
- Yunzhi Pan
- Second Xiangya Hospital, National Clinical Research Center for Mental Disorders, and Department of Psychiatry, Changsha, Hunan, China.,Robarts Research Institution, University of Western Ontario, London, Ontario, Canada
| | - Zhening Liu
- Second Xiangya Hospital, National Clinical Research Center for Mental Disorders, and Department of Psychiatry, Changsha, Hunan, China
| | - Zhimin Xue
- Second Xiangya Hospital, National Clinical Research Center for Mental Disorders, and Department of Psychiatry, Changsha, Hunan, China
| | - Yaoyao Sheng
- Second Xiangya Hospital, National Clinical Research Center for Mental Disorders, and Department of Psychiatry, Changsha, Hunan, China
| | - Yan Cai
- Second Xiangya Hospital, National Clinical Research Center for Mental Disorders, and Department of Psychiatry, Changsha, Hunan, China
| | - Yixin Cheng
- Second Xiangya Hospital, National Clinical Research Center for Mental Disorders, and Department of Psychiatry, Changsha, Hunan, China
| | - Xudong Chen
- Second Xiangya Hospital, National Clinical Research Center for Mental Disorders, and Department of Psychiatry, Changsha, Hunan, China
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Miranda L, Paul R, Pütz B, Koutsouleris N, Müller-Myhsok B. Systematic Review of Functional MRI Applications for Psychiatric Disease Subtyping. Front Psychiatry 2021; 12:665536. [PMID: 34744805 PMCID: PMC8569315 DOI: 10.3389/fpsyt.2021.665536] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 09/07/2021] [Indexed: 12/12/2022] Open
Abstract
Background: Psychiatric disorders have been historically classified using symptom information alone. Recently, there has been a dramatic increase in research interest not only in identifying the mechanisms underlying defined pathologies but also in redefining their etiology. This is particularly relevant for the field of personalized medicine, which searches for data-driven approaches to improve diagnosis, prognosis, and treatment selection for individual patients. Methods: This review aims to provide a high-level overview of the rapidly growing field of functional magnetic resonance imaging (fMRI) from the perspective of unsupervised machine learning applications for disease subtyping. Following the PRISMA guidelines for protocol reproducibility, we searched the PubMed database for articles describing functional MRI applications used to obtain, interpret, or validate psychiatric disease subtypes. We also employed the active learning framework ASReview to prioritize publications in a machine learning-guided way. Results: From the 20 studies that met the inclusion criteria, five used functional MRI data to interpret symptom-derived disease clusters, four used it to interpret clusters derived from biomarker data other than fMRI itself, and 11 applied clustering techniques involving fMRI directly. Major depression disorder and schizophrenia were the two most frequently studied pathologies (35% and 30% of the retrieved studies, respectively), followed by ADHD (15%), psychosis as a whole (10%), autism disorder (5%), and the consequences of early exposure to violence (5%). Conclusions: The increased interest in personalized medicine and data-driven disease subtyping also extends to psychiatric disorders. However, to date, this subfield is at an incipient exploratory stage, and all retrieved studies were mostly proofs of principle where further validation and increased sample sizes are craved for. Whereas results for all explored diseases are inconsistent, we believe this reflects the need for concerted, multisite data collection efforts with a strong focus on measuring the generalizability of results. Finally, whereas functional MRI is the best way of measuring brain function available to date, its low signal-to-noise ratio and elevated monetary cost make it a poor clinical alternative. Even with technology progressing and costs decreasing, this might incentivize the search for more accessible, clinically ready functional proxies in the future.
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Affiliation(s)
- Lucas Miranda
- Department of Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany
| | - Riya Paul
- Department of Precision Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
- Department of Psychiatry and Psychotherapy, Section for Neurodiagnostic Applications, Ludwig-Maximilian University, Munich, Germany
| | - Benno Pütz
- Department of Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany
| | - Nikolaos Koutsouleris
- Department of Precision Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
- Department of Psychiatry and Psychotherapy, Section for Neurodiagnostic Applications, Ludwig-Maximilian University, Munich, Germany
| | - Bertram Müller-Myhsok
- Department of Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany
- Department of Health Data Science, Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom
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33
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Piguet C, Karahanoğlu FI, Saccaro LF, Van De Ville D, Vuilleumier P. Mood disorders disrupt the functional dynamics, not spatial organization of brain resting state networks. Neuroimage Clin 2021; 32:102833. [PMID: 34619652 PMCID: PMC8498469 DOI: 10.1016/j.nicl.2021.102833] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 09/10/2021] [Accepted: 09/19/2021] [Indexed: 12/24/2022]
Abstract
Spontaneous fluctuations in the blood oxygenation level dependent signal measured through resting-state functional magnetic resonance imaging have been corroborated to aggregate into multiple functional networks. Abnormal resting brain activity is observed in mood disorder patients, however with inconsistent results. How do such alterations relate to clinical symptoms; e.g., level of depression and rumination tendencies? Here we recovered spatially and temporally overlapping functional networks from 31 mood disorder patients and healthy controls during rest, by applying novel methods that identify transient changes in spontaneous brain activity. Our unique approach disentangles the dynamic engagement of resting-state networks unconstrained by the slow hemodynamic response. This time-varying characterization provides moment-to-moment information about functional networks in terms of their durations and dynamic coupling, and offers novel evidence for selective contributionsto particular clinical symptoms. Patients showed increased duration of default-mode network (DMN), increased duration and occurrence of posterior DMN as well as insula- and amygdala-centered networks, but decreased occurrence of visual and anterior salience networks. Coupling between limbic (insula and amygdala) networks was also reduced. Depression level modulated DMN duration, whereas intrusive thoughts correlated with occurrence of insula and posterior DMN. Anatomical network organization was similar to controls. In sum, altered brain dynamics in mood disorder patients appear to mediate distinct clinical dimensions including increased self-processing, and decreased attention to external world.
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Affiliation(s)
- Camille Piguet
- Department of Psychiatry, Faculty of Medicine, University of Geneva, Switzerland
| | - Fikret Işık Karahanoğlu
- MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, USA
- Department of Radiology, Harvard Medical School, MA, USA
| | | | - Dimitri Van De Ville
- Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Switzerland
- Institute of Bioengineering, School of Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Patrik Vuilleumier
- Swiss Center for Affective Sciences, Campus Biotech, Geneva, Switzerland
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Sheng J, Wang L, Cheng H, Zhang Q, Zhou R, Shi Y. Strategies for multivariate analyses of imaging genetics study in Alzheimer's disease. Neurosci Lett 2021; 762:136147. [PMID: 34332030 DOI: 10.1016/j.neulet.2021.136147] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 03/27/2021] [Accepted: 07/26/2021] [Indexed: 11/16/2022]
Abstract
Alzheimer's disease (AD) is an incurable neurodegenerative disease primarily affecting the elderly population. Early diagnosis of AD is critical for the management of this disease. Imaging genetics examines the influence of genetic variants (i.e., single nucleotide polymorphisms (SNPs)) on brain structure and function and many novel approaches of imaging genetics are proposed for studying AD. We review and synthesize the Alzheimer's Disease Neuroimaging Initiative (ADNI) genetic associations with quantitative disease endophenotypes including structural and functional neuroimaging, diffusion tensor imaging (DTI), positron emission tomography (PET), and fluid biomarker assays. In this review, we survey recent publications using neuroimaging and genetic data of AD, with a focus on methods capturing multivariate effects accommodating the large number variables from both imaging data and genetic data. We review methods focused on bridging the imaging and genetic data by establishing genotype-phenotype association, including sparse canonical correlation analysis, parallel independent component analysis, sparse reduced rank regression, sparse partial least squares, genome-wide association study, and so on. The broad availability and wide scope of ADNI genetic and phenotypic data has advanced our understanding of the genetic basis of AD and has nominated novel targets for future pharmaceutical therapy and biomarker development.
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Affiliation(s)
- Jinhua Sheng
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang 310018, China.
| | - Luyun Wang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang 310018, China; College of Information Engineering, Hangzhou Vocational & Technical College, Hangzhou, Zhejiang 310018, China
| | - Hu Cheng
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | | | - Rougang Zhou
- Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang 310018, China; School of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China; Mstar Technologies Inc., Hangzhou, Zhejiang 310018, China
| | - Yuchen Shi
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang 310018, China
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35
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Onofrj M, Di Iorio A, Carrarini C, Russo M, Franciotti R, Espay AJ, Boylan LS, Taylor JP, Di Giannantonio M, Martinotti G, Valente EM, Thomas A, Bonanni L, Delli Pizzi S, Dono F, Sensi S. Preexisting Bipolar Disorder Influences the Subsequent Phenotype of Parkinson's Disease. Mov Disord 2021; 36:2840-2852. [PMID: 34427338 PMCID: PMC9292484 DOI: 10.1002/mds.28745] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 07/15/2021] [Accepted: 07/19/2021] [Indexed: 12/13/2022] Open
Abstract
Background Patients with bipolar spectrum disorders (BSDs) exhibit an increased risk of Parkinson's disease (PD). Objective The aim is to investigate whether a previous diagnosis of BSDs influences the phenotype of PD. Methods Of 2660 PD patients followed for at least 6 years (6–27), 250 (BSD‐PD) had BSDs, 6–20 years before PD diagnosis; 48%–43% had a PD or BSD family history, and 34 carried glucocerebrosidase (GBA) and Parkin (PRKN) mutations. The cohort was split into a subset of 213 BSD‐PD patients, compared with 426 matched PD patients without BSDs, and a subset of 34 BSD‐PD and 79 PD patients carrying GBA or PRKN mutations. Carriers of mutations absent in BSD‐PD patients and of synuclein triplication were excluded. Structured clinical interviews and mood disorder questionnaires assessed BSDs. Linear mixed models evaluated the assessment scales over time. Thirteen BSD‐PD patients underwent subthalamic nucleus deep brain stimulation (STN‐DBS) and were compared with 27 matched STN‐DBS‐treated PD patients. Results Compared to PD patients, BSD‐PD showed (1) higher frequency of family history of PD (odds ratio [OR] 3.31; 2.32–4.71) and BSDs (OR 6.20; 4.11–9.35) 5); (2) higher incidence of impulse control disorders (hazard ratio [HR] 5.95, 3.89–9.09); (3) higher frequency of functional disorders occurring before PD therapy (HR, 5.67, 3.95–8.15); (4) earlier occurrence of delusions or mild dementia (HR, 7.70, 5.55–10.69; HR, 1.43, 1.16–1.75); and (5) earlier mortality (1.48; 1.11–1.97). Genetic BSD‐PD subjects exhibited clinical features indistinguishable from nongenetic BSD‐PD subjects. STN‐DBS‐treated BSD‐PD patients showed no improvements in quality of life compared to the control group. Conclusions BSDs as a prodrome to PD unfavorably shape their course and are associated with detrimental neuropsychiatric features and treatment outcomes. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society
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Affiliation(s)
- Marco Onofrj
- Department of Neuroscience, Imaging, and Clinical Sciences, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy.,Center of Advanced Studies and Technology, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy.,YDA Foundation, Institute of Immune Therapy and Advanced Biological Treatments, Pescara, Italy
| | - Angelo Di Iorio
- Department of Aging Sciences, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy
| | - Claudia Carrarini
- Department of Neuroscience, Imaging, and Clinical Sciences, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy
| | - Mirella Russo
- Department of Neuroscience, Imaging, and Clinical Sciences, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy
| | - Raffaella Franciotti
- Department of Neuroscience, Imaging, and Clinical Sciences, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy
| | - Alberto J Espay
- Department of Neurology, James J. and Joan A. Gardner Family Center for Parkinson's Disease and Movement Disorders, University of Cincinnati, Cincinnati, Ohio, USA
| | - Laura S Boylan
- Department of Neurology, Bellevue Hospital, New York University School of Medicine, New York, New York, USA
| | - John-Paul Taylor
- Translational and Clinical Research Institute, Biomedical Research Building, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Massimo Di Giannantonio
- Department of Neuroscience, Imaging, and Clinical Sciences, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy
| | - Giovanni Martinotti
- Department of Neuroscience, Imaging, and Clinical Sciences, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy
| | - Enza M Valente
- IRCCS Mondino Foundation and Department of Molecular Medicine, University of Pavia, Pavia, Italy
| | - Astrid Thomas
- Department of Neuroscience, Imaging, and Clinical Sciences, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy
| | - Laura Bonanni
- Department of Neuroscience, Imaging, and Clinical Sciences, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy
| | - Stefano Delli Pizzi
- Department of Neuroscience, Imaging, and Clinical Sciences, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy
| | - Fedele Dono
- Department of Neuroscience, Imaging, and Clinical Sciences, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy
| | - StefanoL Sensi
- Department of Neuroscience, Imaging, and Clinical Sciences, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy.,Center of Advanced Studies and Technology, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy.,Institute for Mind Impairments and Neurological Disorders (IMIND), University of California-Irvine, Irvine, California, USA
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36
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Chen H, Lu F, Guo X, Pang Y, He C, Han S, Duan X, Chen H. Dimensional Analysis of Atypical Functional Connectivity of Major Depression Disorder and Bipolar Disorder. Cereb Cortex 2021; 32:1307-1317. [PMID: 34416760 DOI: 10.1093/cercor/bhab296] [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: 11/14/2022] Open
Abstract
Literatures have reported considerable heterogeneity with atypical functional connectivity (FC) pattern of psychiatric disorders. However, traditional statistical methods are hard to explore this heterogeneity pattern. We proposed a "brain dimension" method to describe the atypical FC patterns of major depressive disorder and bipolar disorder (BD). The approach was firstly applied to a simulation dataset. It was then utilized to a real resting-state functional magnetic resonance imaging dataset of 47 individuals with major depressive disorder, 32 individuals with BD, and 52 well matched health controls. Our method showed a better ability to extract the FC dimensions than traditional methods. The results of the real dataset revealed atypical FC dimensions for major depressive disorder and BD. Especially, an atypical FC dimension which exhibited decreased FC strength of thalamus and basal ganglia was found with higher severity level of individuals with BD than the ones with major depressive disorder. This study provided a novel "brain dimension" method to view the atypical FC patterns of major depressive disorder and BD and revealed shared and specific atypical FC patterns between major depressive disorder and BD.
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Affiliation(s)
- Heng Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, PR China.,School of Life Science and Technology, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, PR China.,School of Medicine, Guizhou University, Guizhou 550025, China
| | - Fengmei Lu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, PR China
| | - Xiaonan Guo
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066000, China.,Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao 066000, China
| | - Yajing Pang
- Research Center for Intelligent Science and Engineering Technology of Traditional Chinese Medicine, School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, PR China.,Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, PR China
| | - Changchun He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, PR China.,School of Life Science and Technology, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, PR China
| | - Shaoqiang Han
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450001, PR China
| | - Xujun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, PR China.,School of Life Science and Technology, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, PR China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, PR China.,School of Life Science and Technology, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, PR China
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37
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Moreau CA, Raznahan A, Bellec P, Chakravarty M, Thompson PM, Jacquemont S. Dissecting autism and schizophrenia through neuroimaging genomics. Brain 2021; 144:1943-1957. [PMID: 33704401 PMCID: PMC8370419 DOI: 10.1093/brain/awab096] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 12/24/2020] [Accepted: 01/08/2021] [Indexed: 12/23/2022] Open
Abstract
Neuroimaging genomic studies of autism spectrum disorder and schizophrenia have mainly adopted a 'top-down' approach, beginning with the behavioural diagnosis, and moving down to intermediate brain phenotypes and underlying genetic factors. Advances in imaging and genomics have been successfully applied to increasingly large case-control studies. As opposed to diagnostic-first approaches, the bottom-up strategy begins at the level of molecular factors enabling the study of mechanisms related to biological risk, irrespective of diagnoses or clinical manifestations. The latter strategy has emerged from questions raised by top-down studies: why are mutations and brain phenotypes over-represented in individuals with a psychiatric diagnosis? Are they related to core symptoms of the disease or to comorbidities? Why are mutations and brain phenotypes associated with several psychiatric diagnoses? Do they impact a single dimension contributing to all diagnoses? In this review, we aimed at summarizing imaging genomic findings in autism and schizophrenia as well as neuropsychiatric variants associated with these conditions. Top-down studies of autism and schizophrenia identified patterns of neuroimaging alterations with small effect-sizes and an extreme polygenic architecture. Genomic variants and neuroimaging patterns are shared across diagnostic categories suggesting pleiotropic mechanisms at the molecular and brain network levels. Although the field is gaining traction; characterizing increasingly reproducible results, it is unlikely that top-down approaches alone will be able to disentangle mechanisms involved in autism or schizophrenia. In stark contrast with top-down approaches, bottom-up studies showed that the effect-sizes of high-risk neuropsychiatric mutations are equally large for neuroimaging and behavioural traits. Low specificity has been perplexing with studies showing that broad classes of genomic variants affect a similar range of behavioural and cognitive dimensions, which may be consistent with the highly polygenic architecture of psychiatric conditions. The surprisingly discordant effect sizes observed between genetic and diagnostic first approaches underscore the necessity to decompose the heterogeneity hindering case-control studies in idiopathic conditions. We propose a systematic investigation across a broad spectrum of neuropsychiatric variants to identify putative latent dimensions underlying idiopathic conditions. Gene expression data on temporal, spatial and cell type organization in the brain have also considerable potential for parsing the mechanisms contributing to these dimensions' phenotypes. While large neuroimaging genomic datasets are now available in unselected populations, there is an urgent need for data on individuals with a range of psychiatric symptoms and high-risk genomic variants. Such efforts together with more standardized methods will improve mechanistically informed predictive modelling for diagnosis and clinical outcomes.
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Affiliation(s)
- Clara A Moreau
- Sainte Justine Research Center, University of Montréal, Montréal, Québec H3T 1C5, Canada
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, Montreal, Québec H3W 1W5, Canada
- Human Genetics and Cognitive Functions, CNRS UMR 3571, Université de Paris, Institut Pasteur, Paris, France
| | - Armin Raznahan
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, Bethesda, MD 20892, USA
| | - Pierre Bellec
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, Montreal, Québec H3W 1W5, Canada
| | - Mallar Chakravarty
- Cerebral Imaging Centre, Douglas Hospital Mental Health University Institute, Verdun, Québec H4H 1R3, Canada
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Institute for Neuroimaging and Informatics, USC Keck School of Medicine, Marina del Rey, CA 90033, USA
| | - Sebastien Jacquemont
- Sainte Justine Research Center, University of Montréal, Montréal, Québec H3T 1C5, Canada
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Dynamics of amygdala connectivity in bipolar disorders: a longitudinal study across mood states. Neuropsychopharmacology 2021; 46:1693-1701. [PMID: 34099869 PMCID: PMC8280117 DOI: 10.1038/s41386-021-01038-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 04/30/2021] [Accepted: 05/03/2021] [Indexed: 11/25/2022]
Abstract
Alterations in activity and connectivity of brain circuits implicated in emotion processing and emotion regulation have been observed during resting-state for different clinical phases of bipolar disorders (BD), but longitudinal investigations across different mood states in the same patients are still rare. Furthermore, measuring dynamics of functional connectivity patterns offers a powerful method to explore changes in the brain's intrinsic functional organization across mood states. We used a novel co-activation pattern (CAP) analysis to explore the dynamics of amygdala connectivity at rest in a cohort of 20 BD patients prospectively followed-up and scanned across distinct mood states: euthymia (20 patients; 39 sessions), depression (12 patients; 18 sessions), or mania/hypomania (14 patients; 18 sessions). We compared them to 41 healthy controls scanned once or twice (55 sessions). We characterized temporal aspects of dynamic fluctuations in amygdala connectivity over the whole brain as a function of current mood. We identified six distinct networks describing amygdala connectivity, among which an interoceptive-sensorimotor CAP exhibited more frequent occurrences during hypomania compared to other mood states, and predicted more severe symptoms of irritability and motor agitation. In contrast, a default-mode CAP exhibited more frequent occurrences during depression compared to other mood states and compared to controls, with a positive association with depression severity. Our results reveal distinctive interactions between amygdala and distributed brain networks in different mood states, and foster research on interoception and default-mode systems especially during the manic and depressive phase, respectively. Our study also demonstrates the benefits of assessing brain dynamics in BD.
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Takagi Y, Okada N, Ando S, Yahata N, Morita K, Koshiyama D, Kawakami S, Sawada K, Koike S, Endo K, Yamasaki S, Nishida A, Kasai K, Tanaka SC. Intergenerational transmission of the patterns of functional and structural brain networks. iScience 2021; 24:102708. [PMID: 34258550 PMCID: PMC8253972 DOI: 10.1016/j.isci.2021.102708] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 05/04/2021] [Accepted: 06/08/2021] [Indexed: 01/22/2023] Open
Abstract
There is clear evidence of intergenerational transmission of life values, cognitive traits, psychiatric disorders, and even aspects of daily decision making. To investigate biological substrates of this phenomenon, the brain has received increasing attention as a measurable biomarker and potential target for intervention. However, no previous study has quantitatively and comprehensively investigated the effects of intergenerational transmission on functional and structural brain networks. Here, by employing an unusually large cohort dataset (N = 84 parent-child dyads; 45 sons, 39 daughters, 81 mothers, and 3 fathers), we show that patterns of functional and structural brain networks are preserved over a generation. We also demonstrate that several demographic factors and behavioral/physiological phenotypes have a relationship with brain similarity. Collectively, our results provide a comprehensive picture of neurobiological substrates of intergenerational transmission and demonstrate the usability of our dataset for investigating the neurobiological substrates of intergenerational transmission.
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Affiliation(s)
- Yu Takagi
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- ATR Brain Information Communication Research Laboratory Group, Kyoto, Japan
| | - Naohiro Okada
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- International Research Center for Neurointelligence (WPI-IRCN), University of Tokyo Institutes for Advanced Study (UTIAS), The University of Tokyo, Tokyo, Japan
| | - Shuntaro Ando
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Research Center for Social Science & Medicine, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
| | - Noriaki Yahata
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Institute for Quantum Life Science, National Institutes for Quantum and Radiological Science and Technology, Chiba, Japan
- Department of Molecular Imaging and Theranostics, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Chiba, Japan
| | - Kentaro Morita
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Department of Rehabilitation, The University of Tokyo Hospital, Tokyo, Japan
| | - Daisuke Koshiyama
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Shintaro Kawakami
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Kingo Sawada
- Office for Mental Health Support, Mental Health Unit, Division for Practice Research, Center for Research on Counseling and Support Services, The University of Tokyo, Tokyo, Japan
| | - Shinsuke Koike
- International Research Center for Neurointelligence (WPI-IRCN), University of Tokyo Institutes for Advanced Study (UTIAS), The University of Tokyo, Tokyo, Japan
- University of Tokyo Institute for Diversity and Adaptation of Human Mind (UTIDAHM), The University of Tokyo, Tokyo, Japan
- Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, The University of Tokyo, Tokyo, Japan
- University of Tokyo Center for Integrative Science of Human Behavior (CiSHuB), Tokyo, Japan
| | - Kaori Endo
- Research Center for Social Science & Medicine, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
| | - Syudo Yamasaki
- Research Center for Social Science & Medicine, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
| | - Atsushi Nishida
- Research Center for Social Science & Medicine, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
| | - Kiyoto Kasai
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- International Research Center for Neurointelligence (WPI-IRCN), University of Tokyo Institutes for Advanced Study (UTIAS), The University of Tokyo, Tokyo, Japan
- University of Tokyo Institute for Diversity and Adaptation of Human Mind (UTIDAHM), The University of Tokyo, Tokyo, Japan
- University of Tokyo Center for Integrative Science of Human Behavior (CiSHuB), Tokyo, Japan
| | - Saori C Tanaka
- ATR Brain Information Communication Research Laboratory Group, Kyoto, Japan
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40
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Ji JL, Helmer M, Fonteneau C, Burt JB, Tamayo Z, Demšar J, Adkinson BD, Savić A, Preller KH, Moujaes F, Vollenweider FX, Martin WJ, Repovš G, Cho YT, Pittenger C, Murray JD, Anticevic A. Mapping brain-behavior space relationships along the psychosis spectrum. eLife 2021; 10:e66968. [PMID: 34313219 PMCID: PMC8315806 DOI: 10.7554/elife.66968] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 06/14/2021] [Indexed: 12/29/2022] Open
Abstract
Difficulties in advancing effective patient-specific therapies for psychiatric disorders highlight a need to develop a stable neurobiologically grounded mapping between neural and symptom variation. This gap is particularly acute for psychosis-spectrum disorders (PSD). Here, in a sample of 436 PSD patients spanning several diagnoses, we derived and replicated a dimensionality-reduced symptom space across hallmark psychopathology symptoms and cognitive deficits. In turn, these symptom axes mapped onto distinct, reproducible brain maps. Critically, we found that multivariate brain-behavior mapping techniques (e.g. canonical correlation analysis) do not produce stable results with current sample sizes. However, we show that a univariate brain-behavioral space (BBS) can resolve stable individualized prediction. Finally, we show a proof-of-principle framework for relating personalized BBS metrics with molecular targets via serotonin and glutamate receptor manipulations and neural gene expression maps derived from the Allen Human Brain Atlas. Collectively, these results highlight a stable and data-driven BBS mapping across PSD, which offers an actionable path that can be iteratively optimized for personalized clinical biomarker endpoints.
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Affiliation(s)
- Jie Lisa Ji
- Department of Psychiatry, Yale University School of MedicineNew HavenUnited States
- Interdepartmental Neuroscience Program, Yale University School of MedicineNew HavenUnited States
| | - Markus Helmer
- Department of Psychiatry, Yale University School of MedicineNew HavenUnited States
| | - Clara Fonteneau
- Department of Psychiatry, Yale University School of MedicineNew HavenUnited States
| | | | - Zailyn Tamayo
- Department of Psychiatry, Yale University School of MedicineNew HavenUnited States
| | - Jure Demšar
- Department of Psychology, University of LjubljanaLjubljanaSlovenia
- Faculty of Computer and Information Science, University of LjubljanaLjubljanaSlovenia
| | - Brendan D Adkinson
- Department of Psychiatry, Yale University School of MedicineNew HavenUnited States
- Interdepartmental Neuroscience Program, Yale University School of MedicineNew HavenUnited States
| | | | - Katrin H Preller
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital for Psychiatry ZurichZurichSwitzerland
| | - Flora Moujaes
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital for Psychiatry ZurichZurichSwitzerland
| | - Franz X Vollenweider
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital for Psychiatry ZurichZurichSwitzerland
| | - William J Martin
- The Janssen Pharmaceutical Companies of Johnson and JohnsonSan FranciscoUnited States
| | - Grega Repovš
- Department of Psychiatry, University of ZagrebZagrebCroatia
| | - Youngsun T Cho
- Department of Psychiatry, Yale University School of MedicineNew HavenUnited States
- Child Study Center, Yale University School of MedicineNew HavenUnited States
| | - Christopher Pittenger
- Department of Psychiatry, Yale University School of MedicineNew HavenUnited States
- Child Study Center, Yale University School of MedicineNew HavenUnited States
| | - John D Murray
- Department of Psychiatry, Yale University School of MedicineNew HavenUnited States
- Interdepartmental Neuroscience Program, Yale University School of MedicineNew HavenUnited States
- Department of Physics, Yale UniversityNew HavenUnited States
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of MedicineNew HavenUnited States
- Interdepartmental Neuroscience Program, Yale University School of MedicineNew HavenUnited States
- Department of Psychology, Yale University School of MedicineNew HavenUnited States
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41
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Zbtb16 regulates social cognitive behaviors and neocortical development. Transl Psychiatry 2021; 11:242. [PMID: 33895774 PMCID: PMC8068730 DOI: 10.1038/s41398-021-01358-y] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 03/28/2021] [Accepted: 04/09/2021] [Indexed: 02/02/2023] Open
Abstract
Zinc finger and BTB domain containing 16 (ZBTB16) play the roles in the neural progenitor cell proliferation and neuronal differentiation during development, however, how the function of ZBTB16 is involved in brain function and behaviors unknown. Here we show the deletion of Zbtb16 in mice leads to social impairment, repetitive behaviors, risk-taking behaviors, and cognitive impairment. To elucidate the mechanism underlying the behavioral phenotypes, we conducted histological analyses and observed impairments in thinning of neocortical layer 6 (L6) and a reduction of TBR1+ neurons in Zbtb16 KO mice. Furthermore, we found increased dendritic spines and microglia, as well as developmental defects in oligodendrocytes and neocortical myelination in the prefrontal cortex (PFC) of Zbtb16 KO mice. Using genomics approaches, we identified the Zbtb16 transcriptome that includes genes involved in neocortical maturation such as neurogenesis and myelination, and both autism spectrum disorder (ASD) and schizophrenia (SCZ) pathobiology. Co-expression networks further identified Zbtb16-correlated modules that are unique to ASD or SCZ, respectively. Our study provides insight into the novel roles of ZBTB16 in behaviors and neocortical development related to the disorders.
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42
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Daikoku T, Wiggins GA, Nagai Y. Statistical Properties of Musical Creativity: Roles of Hierarchy and Uncertainty in Statistical Learning. Front Neurosci 2021; 15:640412. [PMID: 33958983 PMCID: PMC8093513 DOI: 10.3389/fnins.2021.640412] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Accepted: 03/10/2021] [Indexed: 12/18/2022] Open
Abstract
Creativity is part of human nature and is commonly understood as a phenomenon whereby something original and worthwhile is formed. Owing to this ability, humans can produce innovative information that often facilitates growth in our society. Creativity also contributes to esthetic and artistic productions, such as music and art. However, the mechanism by which creativity emerges in the brain remains debatable. Recently, a growing body of evidence has suggested that statistical learning contributes to creativity. Statistical learning is an innate and implicit function of the human brain and is considered essential for brain development. Through statistical learning, humans can produce and comprehend structured information, such as music. It is thought that creativity is linked to acquired knowledge, but so-called "eureka" moments often occur unexpectedly under subconscious conditions, without the intention to use the acquired knowledge. Given that a creative moment is intrinsically implicit, we postulate that some types of creativity can be linked to implicit statistical knowledge in the brain. This article reviews neural and computational studies on how creativity emerges within the framework of statistical learning in the brain (i.e., statistical creativity). Here, we propose a hierarchical model of statistical learning: statistically chunking into a unit (hereafter and shallow statistical learning) and combining several units (hereafter and deep statistical learning). We suggest that deep statistical learning contributes dominantly to statistical creativity in music. Furthermore, the temporal dynamics of perceptual uncertainty can be another potential causal factor in statistical creativity. Considering that statistical learning is fundamental to brain development, we also discuss how typical versus atypical brain development modulates hierarchical statistical learning and statistical creativity. We believe that this review will shed light on the key roles of statistical learning in musical creativity and facilitate further investigation of how creativity emerges in the brain.
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Affiliation(s)
- Tatsuya Daikoku
- International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo, Tokyo, Japan
| | - Geraint A. Wiggins
- AI Lab, Vrije Universiteit Brussel, Brussels, Belgium
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom
| | - Yukie Nagai
- International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo, Tokyo, Japan
- Institute for AI and Beyond, The University of Tokyo, Tokyo, Japan
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43
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Wang P, Yu Y, Liu J, Li B, Zhang Y, Li D, Xu W, Liu Q, Wang Z. IMCC: A Novel Quantitative Approach Revealing Variation of Global Modular Map and Local Inter-Module Coordination Among Differential Drug's Targeted Cerebral Ischemic Networks. Front Pharmacol 2021; 12:637253. [PMID: 33935725 PMCID: PMC8087074 DOI: 10.3389/fphar.2021.637253] [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: 12/03/2020] [Accepted: 02/23/2021] [Indexed: 02/01/2023] Open
Abstract
Stroke is a common disease characterized by multiple genetic dysfunctions. In this complex disease, detecting the strength of inter-module coordination (genetic community interaction) and subsequent modular rewiring is essential to characterize the reactive biosystematic variation (biosystematic perturbation) brought by multiple-target drugs, whose effects are achieved by hitting on a series of targets (target profile) jointly. Here, a quantitative approach for inter-module coordination and its transition, named as IMCC, was developed. Applying IMCC to mouse cerebral ischemia–related gene microarray, we investigated a holistic view of modular map and its rewiring from ischemic stroke to drugs (baicalin, BA; ursodeoxycholic acid, UA; and jasminoidin, JA) perturbation states and locally identified the cooperative pathological module pair and its dissection. Our result suggested the global modular map in cerebral ischemia exhibited a characteristic “core–periphery” architecture, and this architecture was rewired by the effective drugs heterogeneously: BA and UA converged modules into an intensively connected integrity, whereas JA diverged partial modules and widened the remaining inter-module paths. Locally, the PMP dissociation brought by drugs contributed to the reversion of the pathological condition: the focus of the cellular function shift from survival after nervous system injury into development and repair, including neurotrophin regulation, hormone releasing, and chemokine signaling activation. The core targets and mechanisms were validated by in vivo experiments. Overall, our result highlights the holistic inter-module coordination rearrangement rather than a target or a single module that brings phenotype alteration. This strategy may lead to systematically explore detailed variation of inter-module pharmacological action mode of multiple-target drugs, which is the principal problem of module pharmacology for network-based drug discovery.
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Affiliation(s)
- Pengqian Wang
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yanan Yu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Jun Liu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Bing Li
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China.,Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yingying Zhang
- Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Dongfeng Li
- Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Wenjuan Xu
- School of Mathematical Sciences, Peking University, Beijing, China
| | - Qiong Liu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Zhong Wang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
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44
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Dauvermann MR, Mothersill D, Rokita KI, King S, Holleran L, Kane R, McKernan DP, Kelly JP, Morris DW, Corvin A, Hallahan B, McDonald C, Donohoe G. Changes in Default-Mode Network Associated With Childhood Trauma in Schizophrenia. Schizophr Bull 2021; 47:1482-1494. [PMID: 33823040 PMCID: PMC8379545 DOI: 10.1093/schbul/sbab025] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
BACKGROUND There is considerable evidence of dysconnectivity within the default-mode network (DMN) in schizophrenia, as measured during resting-state functional MRI (rs-fMRI). History of childhood trauma (CT) is observed at a higher frequency in schizophrenia than in the general population, but its relationship to DMN functional connectivity has yet to be investigated. METHODS CT history and rs-fMRI data were collected in 65 individuals with schizophrenia and 132 healthy controls. Seed-based functional connectivity between each of 4 a priori defined seeds of the DMN (medial prefrontal cortex, right and left lateral parietal lobes, and the posterior cingulate cortex) and all other voxels of the brain were compared across groups. Effects of CT on functional connectivity were examined using multiple regression analyses. Where significant associations were observed, regression analyses were further used to determine whether variance in behavioral measures of Theory of Mind (ToM), previously associated with DMN recruitment, were explained by these associations. RESULTS Seed-based analyses revealed evidence of widespread reductions in functional connectivity in patients vs controls, including between the left/right parietal lobe (LP) and multiple other regions, including the parietal operculum bilaterally. Across all subjects, increased CT scores were associated with reduced prefrontal-parietal connectivity and, in patients, with increased prefrontal-cerebellar connectivity also. These CT-associated differences in DMN connectivity also predicted variation in behavioral measures of ToM. CONCLUSIONS These findings suggest that CT history is associated with variation in DMN connectivity during rs-fMRI in patients with schizophrenia and healthy participants, which may partly mediate associations observed between early life adversity and cognitive performance.
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Affiliation(s)
- Maria R Dauvermann
- School of Psychology, National University of Ireland Galway, Galway, Ireland,Center for Neuroimaging, Genetics and Cognition (NICOG), National University of Ireland Galway, Galway, Ireland,McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA
| | - David Mothersill
- School of Psychology, National University of Ireland Galway, Galway, Ireland,Center for Neuroimaging, Genetics and Cognition (NICOG), National University of Ireland Galway, Galway, Ireland,Department of Psychology, National College of Ireland, Dublin, Ireland
| | - Karolina I Rokita
- School of Psychology, National University of Ireland Galway, Galway, Ireland,Center for Neuroimaging, Genetics and Cognition (NICOG), National University of Ireland Galway, Galway, Ireland
| | - Sinead King
- School of Psychology, National University of Ireland Galway, Galway, Ireland,Center for Neuroimaging, Genetics and Cognition (NICOG), National University of Ireland Galway, Galway, Ireland
| | - Laurena Holleran
- School of Psychology, National University of Ireland Galway, Galway, Ireland,Center for Neuroimaging, Genetics and Cognition (NICOG), National University of Ireland Galway, Galway, Ireland
| | - Ruan Kane
- School of Psychology, National University of Ireland Galway, Galway, Ireland,Center for Neuroimaging, Genetics and Cognition (NICOG), National University of Ireland Galway, Galway, Ireland
| | - Declan P McKernan
- Center for Neuroimaging, Genetics and Cognition (NICOG), National University of Ireland Galway, Galway, Ireland,Pharmacology and Therapeutics, National University of Ireland Galway, Galway, Ireland
| | - John P Kelly
- Center for Neuroimaging, Genetics and Cognition (NICOG), National University of Ireland Galway, Galway, Ireland,Pharmacology and Therapeutics, National University of Ireland Galway, Galway, Ireland
| | - Derek W Morris
- School of Psychology, National University of Ireland Galway, Galway, Ireland,Center for Neuroimaging, Genetics and Cognition (NICOG), National University of Ireland Galway, Galway, Ireland,School of Natural Sciences, National University of Ireland Galway, Galway, Ireland
| | - Aiden Corvin
- Department of Psychiatry, Trinity Centre for Health Sciences, St. James’s Hospital, Dublin, Ireland
| | - Brian Hallahan
- School of Psychology, National University of Ireland Galway, Galway, Ireland,Center for Neuroimaging, Genetics and Cognition (NICOG), National University of Ireland Galway, Galway, Ireland,Department of Psychiatry, Clinical Science Institute, National University of Ireland Galway, Galway, Ireland
| | - Colm McDonald
- School of Psychology, National University of Ireland Galway, Galway, Ireland,Center for Neuroimaging, Genetics and Cognition (NICOG), National University of Ireland Galway, Galway, Ireland,Department of Psychiatry, Clinical Science Institute, National University of Ireland Galway, Galway, Ireland
| | - Gary Donohoe
- School of Psychology, National University of Ireland Galway, Galway, Ireland,Center for Neuroimaging, Genetics and Cognition (NICOG), National University of Ireland Galway, Galway, Ireland,To whom correspondence should be addressed; Centre for Neuroimaging and Cognitive Genomics (NICOG), School of Psychology, National University of Ireland Galway, University Road, Galway, Ireland; tel: +353-(0)91-495-122, e-mail:
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45
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Zink N, Lenartowicz A, Markett S. A new era for executive function research: On the transition from centralized to distributed executive functioning. Neurosci Biobehav Rev 2021; 124:235-244. [PMID: 33582233 DOI: 10.1016/j.neubiorev.2021.02.011] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 02/04/2021] [Indexed: 02/07/2023]
Abstract
"Executive functions" (EFs) is an umbrella term for higher cognitive control functions such as working memory, inhibition, and cognitive flexibility. One of the most challenging problems in this field of research has been to explain how the wide range of cognitive processes subsumed as EFs are controlled without an all-powerful but ill-defined central executive in the brain. Efforts to localize control mechanisms in circumscribed brain regions have not led to a breakthrough in understanding how the brain controls and regulates itself. We propose to re-conceptualize EFs as emergent consequences of highly distributed brain processes that communicate with a pool of highly connected hub regions, thus precluding the need for a central executive. We further discuss how graph-theory driven analysis of brain networks offers a unique lens on this problem by providing a reference frame to study brain connectivity in EFs in a holistic way and helps to refine our understanding of the mechanisms underlying EFs by providing new, testable hypotheses and resolves empirical and theoretical inconsistencies in the EF literature.
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Affiliation(s)
- Nicolas Zink
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, United States.
| | - Agatha Lenartowicz
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, United States
| | - Sebastian Markett
- Department of Psychology, Humboldt University Berlin, Berlin, Germany
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46
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Yoon S, Kim TD, Kim J, Lyoo IK. Altered functional activity in bipolar disorder: A comprehensive review from a large-scale network perspective. Brain Behav 2021; 11:e01953. [PMID: 33210461 PMCID: PMC7821558 DOI: 10.1002/brb3.1953] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 09/08/2020] [Accepted: 10/25/2020] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Growing literature continues to identify brain regions that are functionally altered in bipolar disorder. However, precise functional network correlates of bipolar disorder have yet to be determined due to inconsistent results. The overview of neurological alterations from a large-scale network perspective may provide more comprehensive results and elucidate the neuropathology of bipolar disorder. Here, we critically review recent neuroimaging research on bipolar disorder using a network-based approach. METHODS A systematic search was conducted on studies published from 2009 through 2019 in PubMed and Google Scholar. Articles that utilized functional magnetic resonance imaging technique to examine altered functional activity of major regions belonging to a large-scale brain network in bipolar disorder were selected. RESULTS A total of 49 studies were reviewed. Within-network hypoconnectivity was reported in bipolar disorder at rest among the default mode, salience, and central executive networks. In contrast, when performing a cognitive task, hyperconnectivity among the central executive network was found. Internetwork functional connectivity in the brain of bipolar disorder was greater between the salience and default mode networks, while reduced between the salience and central executive networks at rest, compared to control. CONCLUSION This systematic review suggests disruption in the functional activity of large-scale brain networks at rest as well as during a task stimuli in bipolar disorder. Disrupted intra- and internetwork functional connectivity that are also associated with clinical symptoms suggest altered functional connectivity of and between large-scale networks plays an important role in the pathophysiology of bipolar disorder.
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Affiliation(s)
- Sujung Yoon
- Ewha Brain Institute, Ewha W. University, Seoul, South Korea.,Department of Brain and Cognitive Sciences, Ewha W. University, Seoul, South Korea
| | - Tammy D Kim
- Ewha Brain Institute, Ewha W. University, Seoul, South Korea
| | - Jungyoon Kim
- Ewha Brain Institute, Ewha W. University, Seoul, South Korea.,Department of Brain and Cognitive Sciences, Ewha W. University, Seoul, South Korea
| | - In Kyoon Lyoo
- Ewha Brain Institute, Ewha W. University, Seoul, South Korea.,Department of Brain and Cognitive Sciences, Ewha W. University, Seoul, South Korea.,Graduate School of Pharmaceutical Sciences, Ewha W. University, Seoul, South Korea.,The Brain Institute and Department of Psychiatry, University of Utah, Salt Lake City, UT, USA
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47
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Tamminga CA, Clementz BA, Pearlson G, Keshavan M, Gershon ES, Ivleva EI, McDowell J, Meda SA, Keedy S, Calhoun VD, Lizano P, Bishop JR, Hudgens-Haney M, Alliey-Rodriguez N, Asif H, Gibbons R. Biotyping in psychosis: using multiple computational approaches with one data set. Neuropsychopharmacology 2021; 46:143-155. [PMID: 32979849 PMCID: PMC7689458 DOI: 10.1038/s41386-020-00849-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 08/24/2020] [Accepted: 08/26/2020] [Indexed: 12/15/2022]
Abstract
Focusing on biomarker identification and using biomarkers individually or in clusters to define biological subgroups in psychiatry requires a re-orientation from behavioral phenomenology to quantifying brain features, requiring big data approaches for data integration. Much still needs to be accomplished, not only to refine but also to build support for the application and customization of such an analytical phenotypic approach. In this review, we present some of what Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) has learned so far to guide future applications of multivariate phenotyping and their analyses to understanding psychosis. This paper describes several B-SNIP projects that use phenotype data and big data computations to generate novel outcomes and glimpse what phenotypes contribute to disease understanding and, with aspiration, to treatment. The source of the phenotypes varies from genetic data, structural neuroanatomic localization, immune markers, brain physiology, and cognition. We aim to see guiding principles emerge and areas of commonality revealed. And, we will need to demonstrate not only data stability but also the usefulness of biomarker information for subgroup identification enhancing target identification and treatment development.
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Affiliation(s)
- Carol A Tamminga
- Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX, 75390, USA.
| | - Brett A Clementz
- Departments of Psychology, Neuroscience, and BioImaging Research Center, University of Georgia, Athens, GA, 30602, USA
| | - Godfrey Pearlson
- Olin Neuropsychiatry Research Center, Institute of Living at Hartford Hospital, Hartford, CT, USA
- Departments of Psychiatry & Neuroscience, Yale University, New Haven, CT, USA
| | - Macheri Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, United States
| | - Elliot S Gershon
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, 60637, USA
| | - Elena I Ivleva
- Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Jennifer McDowell
- Departments of Psychology, Neuroscience, and BioImaging Research Center, University of Georgia, Athens, GA, 30602, USA
| | - Shashwath A Meda
- Olin Neuropsychiatry Research Center, Institute of Living at Hartford Hospital, Hartford, CT, USA
- Departments of Psychiatry & Neuroscience, Yale University, New Haven, CT, USA
| | - Sarah Keedy
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, 60637, USA
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - Paulo Lizano
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, United States
| | - Jeffrey R Bishop
- Department of Experimental and Clinical Pharmacology, University of Minnesota College of Pharmacy, Minneapolis, United States
- Department of Psychiatry, University of Minnesota Medical School, Minneapolis, MN, 55455, USA
| | | | - Ney Alliey-Rodriguez
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, 60637, USA
| | - Huma Asif
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, 60637, USA
| | - Robert Gibbons
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, 60637, USA
- Departments of Medicine and Public Health Sciences, University of Chicago, Chicago, Ill, USA
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48
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Begum Ali J, Charman T, Johnson MH, Jones EJH. Early Motor Differences in Infants at Elevated Likelihood of Autism Spectrum Disorder and/or Attention Deficit Hyperactivity Disorder. J Autism Dev Disord 2020; 50:4367-4384. [PMID: 32328858 PMCID: PMC7677154 DOI: 10.1007/s10803-020-04489-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
We investigated infant's manual motor behaviour; specifically behaviours crossing the body midline. Infants at elevated likelihood of Autism Spectrum Disorder (ASD) and/or Attention Deficit Hyperactivity Disorder (ADHD) produced fewer manual behaviours that cross the midline compared to infants with a typical likelihood of developing these disorders; however this effect was limited to 10-month-olds and not apparent at age 5 and 14 months. Although, midline crossing did not predict ASD traits, it was related to ADHD traits at 2 years of age. We rule out motor ability and hand dominance as possible explanations for this pattern of behaviour, positing that these results may be a consequence of multisensory integration abilities, and the neurobehavioural shift period, in the first year of life.
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Affiliation(s)
- Jannath Begum Ali
- Centre for Brain and Cognitive Development, Department of Psychological Sciences, Birkbeck, University of London, Henry Wellcome Building, Malet Street, London, WC1E 7HX, UK.
| | - Tony Charman
- Psychology Department, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Mark H Johnson
- Centre for Brain and Cognitive Development, Department of Psychological Sciences, Birkbeck, University of London, Henry Wellcome Building, Malet Street, London, WC1E 7HX, UK
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Emily J H Jones
- Centre for Brain and Cognitive Development, Department of Psychological Sciences, Birkbeck, University of London, Henry Wellcome Building, Malet Street, London, WC1E 7HX, UK
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49
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Sobczak AM, Bohaterewicz B, Marek T, Fafrowicz M, Dudek D, Siwek M, Tereszko A, Krupa A, Bryll A, Chrobak AA. Altered Functional Connectivity Differences in Salience Network as a Neuromarker of Suicide Risk in Euthymic Bipolar Disorder Patients. Front Hum Neurosci 2020; 14:585766. [PMID: 33281585 PMCID: PMC7705642 DOI: 10.3389/fnhum.2020.585766] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 10/16/2020] [Indexed: 12/13/2022] Open
Abstract
Objective The occurrence of death by suicide in patients diagnosed with bipolar disorder is as much as 60 times greater than in the general population. Even during the state of euthymia patients are characterized by suicide risk. The aim of the study is to investigate the baseline brain activity in euthymic bipolar disorder patients in regard to suicide risk. We hypothesized that patients compared to healthy control group will demonstrate altered functional connectivity among resting state networks which will be directly related to current suicide risk. Method 41 subjects were enrolled in the study consisting control group (n = 21) and euthymic bipolar disorder patients group (n = 20). Functional magnetic resonance imaging was used to evaluate resting state brain activity and ROI-ROI functional connectivity analysis was performed. Suicidal risk was estimated using The Suicide Behaviors Questionnaire-Revised. Results A two sample t-test revealed decreased functional connectivity between regions involved in the salience network in patients compared to the control group. This decrease was negatively correlated with current suicide risk. Conclusion Obtained results suggest the association between risk of suicide and activity of regions responsible for functions such as learning from mistakes, prospective thinking, and sensory integration.
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Affiliation(s)
- Anna Maria Sobczak
- Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, Kraków, Poland
| | - Bartosz Bohaterewicz
- Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, Kraków, Poland.,Department of Psychology of Individual Differences, Psychological Diagnosis, and Psychometrics, Institute of Psychology, University of Social Sciences and Humanities, Warsaw, Poland
| | - Tadeusz Marek
- Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, Kraków, Poland
| | - Magdalena Fafrowicz
- Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, Kraków, Poland
| | - Dominika Dudek
- Department of Adult Psychiatry, Jagiellonian University Medical College, Kraków, Poland
| | - Marcin Siwek
- Department of Affective Disorders, Jagiellonian University Medical College, Kraków, Poland
| | - Anna Tereszko
- Chair of Psychiatry, Jagiellonian University Medical College, Kraków, Poland
| | - Anna Krupa
- Chair of Psychiatry, Jagiellonian University Medical College, Kraków, Poland
| | - Amira Bryll
- Chair of Radiology, Jagiellonian University Medical College, Kraków, Poland
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Person-based similarity in brain structure and functional connectivity in bipolar disorder. J Affect Disord 2020; 276:38-44. [PMID: 32697714 PMCID: PMC7568424 DOI: 10.1016/j.jad.2020.06.041] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 05/26/2020] [Accepted: 06/16/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Bipolar disorder shows significant variability in clinical presentation. Here we adopt a personalized approach to quantify the brain structural and functional similarity of each individual patient to other patients and to healthy individuals. METHODS Brain morphometric and resting-state functional connectivity measures from two independent samples of patients with bipolar disorder and healthy individuals (total number of participants=215) were modeled as single vectors to generated individualized morphometric and connectivity profiles. These profiles were then used to compute a person-based similarity indices which quantified the similarity in neuroimaging profiles amongst patients and between patients and health individuals. RESULTS The morphometric and connectivity profiles of patients showed within-diagnosis similarity which was comparable to that observed in healthy individuals. They also showed minimal deviance from those of healthy individuals; the correlation between the profiles of patients and healthy individuals was high (range: 0.71-0.94, p<10-5). The degree of similarity between imaging profiles was associated with IQ (for cortical thickness) and age (functional integration) rather than clinical variables. Patients who were prescribed lithium, compared to those who were not, showed greater similarity to healthy individuals in terms of network integration (t = 2.2, p = 0.03). LIMITATIONS We focused on patients with Bipolar disorder, type I only. CONCLUSIONS High inter-individual similarity in neuroimaging profiles was observed amongst patients with bipolar disorder and between patients and healthy individuals. We infer that brain alterations associated with bipolar disorder may be nested within the normal biological diversity consistent with the high prevalence of mood symptoms in the general population.
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