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Fotiadis P, Parkes L, Davis KA, Satterthwaite TD, Shinohara RT, Bassett DS. Structure-function coupling in macroscale human brain networks. Nat Rev Neurosci 2024:10.1038/s41583-024-00846-6. [PMID: 39103609 DOI: 10.1038/s41583-024-00846-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/03/2024] [Indexed: 08/07/2024]
Abstract
Precisely how the anatomical structure of the brain gives rise to a repertoire of complex functions remains incompletely understood. A promising manifestation of this mapping from structure to function is the dependency of the functional activity of a brain region on the underlying white matter architecture. Here, we review the literature examining the macroscale coupling between structural and functional connectivity, and we establish how this structure-function coupling (SFC) can provide more information about the underlying workings of the brain than either feature alone. We begin by defining SFC and describing the computational methods used to quantify it. We then review empirical studies that examine the heterogeneous expression of SFC across different brain regions, among individuals, in the context of the cognitive task being performed, and over time, as well as its role in fostering flexible cognition. Last, we investigate how the coupling between structure and function is affected in neurological and psychiatric conditions, and we report how aberrant SFC is associated with disease duration and disease-specific cognitive impairment. By elucidating how the dynamic relationship between the structure and function of the brain is altered in the presence of neurological and psychiatric conditions, we aim to not only further our understanding of their aetiology but also establish SFC as a new and sensitive marker of disease symptomatology and cognitive performance. Overall, this Review collates the current knowledge regarding the regional interdependency between the macroscale structure and function of the human brain in both neurotypical and neuroatypical individuals.
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Affiliation(s)
- Panagiotis Fotiadis
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Anaesthesiology, University of Michigan, Ann Arbor, MI, USA.
| | - Linden Parkes
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Kathryn A Davis
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing & Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing & Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA, USA.
- Santa Fe Institute, Santa Fe, NM, USA.
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Pusuluri K, Fu Z, Miller R, Pearlson G, Kochunov P, Van Erp TGM, Iraji A, Calhoun VD. 4D dynamic spatial brain networks at rest linked to cognition show atypical variability and coupling in schizophrenia. Hum Brain Mapp 2024; 45:e26773. [PMID: 39045900 PMCID: PMC11267451 DOI: 10.1002/hbm.26773] [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: 08/24/2023] [Revised: 04/04/2024] [Accepted: 06/17/2024] [Indexed: 07/25/2024] Open
Abstract
Despite increasing interest in the dynamics of functional brain networks, most studies focus on the changing relationships over time between spatially static networks or regions. Here we propose an approach to study dynamic spatial brain networks in human resting state functional magnetic resonance imaging (rsfMRI) data and evaluate the temporal changes in the volumes of these 4D networks. Our results show significant volumetric coupling (i.e., synchronized shrinkage and growth) between networks during the scan, that we refer to as dynamic spatial network connectivity (dSNC). We find that several features of such dynamic spatial brain networks are associated with cognition, with higher dynamic variability in these networks and higher volumetric coupling between network pairs positively associated with cognitive performance. We show that these networks are modulated differently in individuals with schizophrenia versus typical controls, resulting in network growth or shrinkage, as well as altered focus of activity within a network. Schizophrenia also shows lower spatial dynamical variability in several networks, and lower volumetric coupling between pairs of networks, thus upholding the role of dynamic spatial brain networks in cognitive impairment seen in schizophrenia. Our data show evidence for the importance of studying the typically overlooked voxel-wise changes within and between brain networks.
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Affiliation(s)
- Krishna Pusuluri
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - Zening Fu
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - Robyn Miller
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - Godfrey Pearlson
- Department of PsychiatryYale School of MedicineNew HavenConnecticutUSA
| | - Peter Kochunov
- Department of PsychiatryUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Theo G. M. Van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
- Center for the Neurobiology of Learning and MemoryUniversity of California IrvineIrvineCaliforniaUSA
| | - Armin Iraji
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
- Department of Computer ScienceGeorgia State UniversityAtlantaGeorgiaUSA
| | - Vince D. Calhoun
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
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3
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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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Sawada T, Barbosa AR, Araujo B, McCord AE, D’Ignazio L, Benjamin KJM, Sheehan B, Zabolocki M, Feltrin A, Arora R, Brandtjen AC, Kleinman JE, Hyde TM, Bardy C, Weinberger DR, Paquola ACM, Erwin JA. Recapitulation of Perturbed Striatal Gene Expression Dynamics of Donors' Brains With Ventral Forebrain Organoids Derived From the Same Individuals With Schizophrenia. Am J Psychiatry 2024; 181:493-511. [PMID: 37915216 PMCID: PMC11209846 DOI: 10.1176/appi.ajp.20220723] [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] [Indexed: 11/03/2023]
Abstract
OBJECTIVE Schizophrenia is a brain disorder that originates during neurodevelopment and has complex genetic and environmental etiologies. Despite decades of clinical evidence of altered striatal function in affected patients, studies examining its cellular and molecular mechanisms in humans are limited. To explore neurodevelopmental alterations in the striatum associated with schizophrenia, the authors established a method for the differentiation of induced pluripotent stem cells (iPSCs) into ventral forebrain organoids (VFOs). METHODS VFOs were generated from postmortem dural fibroblast-derived iPSCs of four individuals with schizophrenia and four neurotypical control individuals for whom postmortem caudate genotypes and transcriptomic data were profiled in the BrainSeq neurogenomics consortium. Individuals were selected such that the two groups had nonoverlapping schizophrenia polygenic risk scores (PRSs). RESULTS Single-cell RNA sequencing analyses of VFOs revealed differences in developmental trajectory between schizophrenia and control individuals in which inhibitory neuronal cells from the patients exhibited accelerated maturation. Furthermore, upregulated genes in inhibitory neurons in schizophrenia VFOs showed a significant overlap with upregulated genes in postmortem caudate tissue of individuals with schizophrenia compared with control individuals, including the donors of the iPSC cohort. CONCLUSIONS The findings suggest that striatal neurons derived from high-PRS individuals with schizophrenia carry abnormalities that originated during early brain development and that the VFO model can recapitulate disease-relevant cell type-specific neurodevelopmental phenotypes in a dish.
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Affiliation(s)
- Tomoyo Sawada
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | | | - Bruno Araujo
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | | | - Laura D’Ignazio
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Kynon J. M. Benjamin
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Bonna Sheehan
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | - Michael Zabolocki
- South Australian Health and Medical Research Institute (SAHMRI), Laboratory for Human Neurophysiology and Genetics, Adelaide, SA, Australia
- Flinders University, Flinders Health and Medical Research Institute (FHMRI), College of Medicine and Public Health, Adelaide, SA, Australia
| | - Arthur Feltrin
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | - Ria Arora
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | | | - Joel E. Kleinman
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Thomas M. Hyde
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Cedric Bardy
- South Australian Health and Medical Research Institute (SAHMRI), Laboratory for Human Neurophysiology and Genetics, Adelaide, SA, Australia
- Flinders University, Flinders Health and Medical Research Institute (FHMRI), College of Medicine and Public Health, Adelaide, SA, Australia
| | - Daniel R. Weinberger
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Apuā C. M. Paquola
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Jennifer A. Erwin
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
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5
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Sapienza J, Pacchioni F, Spangaro M, Bosia M. Dysconnection in schizophrenia: Filling the dots from old to new evidence. Clin Neurophysiol 2024; 162:226-228. [PMID: 38555237 DOI: 10.1016/j.clinph.2024.03.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 03/14/2024] [Accepted: 03/14/2024] [Indexed: 04/02/2024]
Affiliation(s)
- Jacopo Sapienza
- Schizophrenia Research and Clinical Unit, IRCCS San Raffaele Hospital, Milan, Italy; Department of Humanities and Life Sciences, University School for Advanced Studies IUSS, Pavia, Italy.
| | - Federico Pacchioni
- Schizophrenia Research and Clinical Unit, IRCCS San Raffaele Hospital, Milan, Italy
| | - Marco Spangaro
- Schizophrenia Research and Clinical Unit, IRCCS San Raffaele Hospital, Milan, Italy
| | - Marta Bosia
- Schizophrenia Research and Clinical Unit, IRCCS San Raffaele Hospital, Milan, Italy; School of Medicine, Vita -Salute San Raffaele University, Milan, Italy
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Wang LN, Lin S, Tian L, Wu H, Jin WQ, Wang W, Pan WG, Yang CL, Ren YP, Ma X, Tang YL. Subregional thalamic functional connectivity abnormalities and cognitive impairments in first-episode schizophrenia. Asian J Psychiatr 2024; 96:104042. [PMID: 38615577 DOI: 10.1016/j.ajp.2024.104042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Revised: 03/15/2024] [Accepted: 03/31/2024] [Indexed: 04/16/2024]
Abstract
BACKGROUND Previous studies have documented thalamic functional connectivity (FC) abnormalities in schizophrenia, typically examining the thalamus as a whole. The specific link between subregional thalamic FC and cognitive deficits in first-episode schizophrenia (FES) remains unexplored. METHODS Using data from resting-state functional magnetic resonance imaging, we compared whole-brain FC with thalamic subregions between patients and HCs, and analyzed FC changes in drug-naïve patients separately. We then examined correlations between FC abnormalities with both cognitive impairment and clinical symptoms. RESULTS A total of 33 FES patients (20 drug-naïve) and 32 age- and sex-matched healthy controls (HCs) were included. Compared to HCs, FES patients exhibited increased FC between specific thalamic subregions and cortical regions, particularly bilateral middle temporal lobe and cuneus gyrus, left medial superior frontal gyrus, and right inferior/superior occipital gyrus. Decreased FC was observed between certain thalamic subregions and the left inferior frontal triangle. These findings were largely consistent in drug-naïve patients. Notably, deficits in social cognition and visual learning in FES patients correlated with increased FC between certain thalamic subregions and cortical regions involving the right superior occipital gyrus and cuneus gyrus. The severity of negative symptoms was associated with increased FC between a thalamic subregion and the left middle temporal gyrus. CONCLUSION Our findings suggest FC abnormalities between thalamic subregions and cortical areas in FES patients. Increased FC correlated with cognitive deficits and negative symptoms, highlighting the importance of thalamo-cortical connectivity in the pathophysiology of schizophrenia.
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Affiliation(s)
- Li-Na Wang
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Shuo Lin
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Lu Tian
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Han Wu
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Wen-Qing Jin
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Wen Wang
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Wei-Gang Pan
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Chun-Lin Yang
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
| | - Yan-Ping Ren
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
| | - Xin Ma
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
| | - Yi-Lang Tang
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA 30329, USA; Mental Health Service Line, Atlanta VA Medical Center, Decatur, GA 30033, USA
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Fouladivanda M, Iraji A, Wu L, van Erp TG, Belger A, Hawamdeh F, Pearlson GD, Calhoun VD. A spatially constrained independent component analysis jointly informed by structural and functional network connectivity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.08.13.553101. [PMID: 38853973 PMCID: PMC11160563 DOI: 10.1101/2023.08.13.553101] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
There are a growing number of neuroimaging studies motivating joint structural and functional brain connectivity. Brain connectivity of different modalities provides insight into brain functional organization by leveraging complementary information, especially for brain disorders such as schizophrenia. In this paper, we propose a multi-modal independent component analysis (ICA) model that utilizes information from both structural and functional brain connectivity guided by spatial maps to estimate intrinsic connectivity networks (ICNs). Structural connectivity is estimated through whole-brain tractography on diffusion-weighted MRI (dMRI), while functional connectivity is derived from resting-state functional MRI (rs-fMRI). The proposed structural-functional connectivity and spatially constrained ICA (sfCICA) model estimates ICNs at the subject level using a multi-objective optimization framework. We evaluated our model using synthetic and real datasets (including dMRI and rs-fMRI from 149 schizophrenia patients and 162 controls). Multi-modal ICNs revealed enhanced functional coupling between ICNs with higher structural connectivity, improved modularity, and network distinction, particularly in schizophrenia. Statistical analysis of group differences showed more significant differences in the proposed model compared to the unimodal model. In summary, the sfCICA model showed benefits from being jointly informed by structural and functional connectivity. These findings suggest advantages in simultaneously learning effectively and enhancing connectivity estimates using structural connectivity.
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Affiliation(s)
- Mahshid Fouladivanda
- Tri-institute Translational Research in Neuroimaging and Data Science (TReNDS Center), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- Georgia State University, Atlanta, GA, USA
| | - Armin Iraji
- Tri-institute Translational Research in Neuroimaging and Data Science (TReNDS Center), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- Georgia State University, Atlanta, GA, USA
| | - Lei Wu
- Tri-institute Translational Research in Neuroimaging and Data Science (TReNDS Center), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Theodorus G.M. van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior School of Medicine, University of California, Irvine, CA, USA
| | - Aysenil Belger
- Department of Psychiatry Director, Neuroimaging Research in Psychiatry Director, Clinical Translational Core, UNC Intellectual and Developmental Disabilities Research Center, University of North Carolina, Chapel Hill, NC, USA
| | - Faris Hawamdeh
- Center for Disaster Informatics and Computational Epidemiology (DICE), Georgia State University, Atlanta, GA, USA
| | - Godfrey D. Pearlson
- Olin Neuropsychiatry Research Center, Department of Psychiatry and Neuroscience, Yale University, School of Medicine, New Haven, CT, USA
| | - Vince D. Calhoun
- Tri-institute Translational Research in Neuroimaging and Data Science (TReNDS Center), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- Georgia State University, Atlanta, GA, USA
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8
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de Freitas MBL, Luna LP, Beatriz M, Pinto RK, Alves CHL, Bittencourt L, Nardi AE, Oertel V, Veras AB, de Lucena DF, Alves GS. Resting-state fMRI is associated with trauma experiences, mood and psychosis in Afro-descendants with bipolar disorder and schizophrenia. Psychiatry Res Neuroimaging 2024; 340:111766. [PMID: 38408419 DOI: 10.1016/j.pscychresns.2023.111766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 11/19/2023] [Accepted: 11/26/2023] [Indexed: 02/28/2024]
Abstract
BACKGROUND Bipolar disorder (BD) and schizophrenia (SCZ) may exhibit functional abnormalities in several brain areas, including the medial temporal and prefrontal cortex and hippocampus; however, a less explored topic is how brain connectivity is linked to premorbid trauma experiences and clinical features in non-Caucasian samples of SCZ and BD. METHODS Sixty-two individuals with SCZ (n = 20), BD (n = 21), and healthy controls (HC, n = 21) from indigenous and African ethnicity were submitted to clinical screening (Di-PAD), traumata experiences (ETISR-SF), cognitive and functional MRI assessment. The item psychosis/hallucinations in SCZ patients showed a negative correlation with the global efficiency (GE) in the right dorsal attention network. The items mania, irritable mood, and racing thoughts in the Di-PAD scale had a significant negative correlation with the GE in the parietal right default mode network. CONCLUSIONS Differences in the activation of specific networks were associated with earlier disease onset, history of physical abuse, and more severe psychotic and mood symptoms in SCZ and BD subjects of indigenous and black ethnicity. Findings provide further evidence on SZ and BD's brain connectivity disturbances, and their clinical significance, in non-Caucasian samples.
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Affiliation(s)
| | - Licia P Luna
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Márcia Beatriz
- Neuroradiology Service, São Domingos Hospital, São Luís, Brazil; Translational Psychiatry Research Group, Federal University of Maranhão, São Luís, Brazil
| | | | - Candida H Lopes Alves
- Translational Psychiatry Research Group, Federal University of Maranhão, São Luís, Brazil
| | - Lays Bittencourt
- Neuropsychiatry Service, Nina Rodrigues Hospital, São Luís, Brazil
| | - Antônio E Nardi
- Post-Graduation in Psychiatry and Mental Health (PROPSAM), Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Viola Oertel
- Department of Psychiatry, Psychosomatics, and Psychotherapy, Frankfurt Goethe University, Germany
| | - André B Veras
- Post-Graduation in Psychiatry and Mental Health (PROPSAM), Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | | | - Gilberto Sousa Alves
- Translational Psychiatry Research Group, Federal University of Maranhão, São Luís, Brazil; Neuropsychiatry Service, Nina Rodrigues Hospital, São Luís, Brazil; Post-Graduation in Psychiatry and Mental Health (PROPSAM), Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.
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9
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Zhao C, Jiang R, Bustillo J, Kochunov P, Turner JA, Liang C, Fu Z, Zhang D, Qi S, Calhoun VD. Cross-cohort replicable resting-state functional connectivity in predicting symptoms and cognition of schizophrenia. Hum Brain Mapp 2024; 45:e26694. [PMID: 38727014 PMCID: PMC11083889 DOI: 10.1002/hbm.26694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 03/24/2024] [Accepted: 04/10/2024] [Indexed: 05/13/2024] Open
Abstract
Schizophrenia (SZ) is a debilitating mental illness characterized by adolescence or early adulthood onset of psychosis, positive and negative symptoms, as well as cognitive impairments. Despite a plethora of studies leveraging functional connectivity (FC) from functional magnetic resonance imaging (fMRI) to predict symptoms and cognitive impairments of SZ, the findings have exhibited great heterogeneity. We aimed to identify congruous and replicable connectivity patterns capable of predicting positive and negative symptoms as well as cognitive impairments in SZ. Predictable functional connections (FCs) were identified by employing an individualized prediction model, whose replicability was further evaluated across three independent cohorts (BSNIP, SZ = 174; COBRE, SZ = 100; FBIRN, SZ = 161). Across cohorts, we observed that altered FCs in frontal-temporal-cingulate-thalamic network were replicable in prediction of positive symptoms, while sensorimotor network was predictive of negative symptoms. Temporal-parahippocampal network was consistently identified to be associated with reduced cognitive function. These replicable 23 FCs effectively distinguished SZ from healthy controls (HC) across three cohorts (82.7%, 90.2%, and 86.1%). Furthermore, models built using these replicable FCs showed comparable accuracies to those built using the whole-brain features in predicting symptoms/cognition of SZ across the three cohorts (r = .17-.33, p < .05). Overall, our findings provide new insights into the neural underpinnings of SZ symptoms/cognition and offer potential targets for further research and possible clinical interventions.
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Affiliation(s)
- Chunzhi Zhao
- College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina
- Key Laboratory of Brain‐Machine Intelligence Technology, Ministry of EducationNanjing University of Aeronautics and AstronauticsNanjingChina
| | - Rongtao Jiang
- Department of Radiology and Biomedical ImagingYale School of MedicineNew HavenConnecticutUSA
| | - Juan Bustillo
- Department of Psychiatry and Behavioral SciencesUniversity of New MexicoAlbuquerqueNew MexicoUSA
| | - Peter Kochunov
- Department of Psychiatry and Behavioral SciencesUniversity of Texas Health Science Center HoustonHoustonTexasUSA
| | - Jessica A. Turner
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
| | - Chuang Liang
- College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina
- Key Laboratory of Brain‐Machine Intelligence Technology, Ministry of EducationNanjing University of Aeronautics and AstronauticsNanjingChina
| | - Zening Fu
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
| | - Daoqiang Zhang
- College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina
- Key Laboratory of Brain‐Machine Intelligence Technology, Ministry of EducationNanjing University of Aeronautics and AstronauticsNanjingChina
| | - Shile Qi
- College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina
- Key Laboratory of Brain‐Machine Intelligence Technology, Ministry of EducationNanjing University of Aeronautics and AstronauticsNanjingChina
| | - Vince D. Calhoun
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
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10
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Saha R, Saha DK, Fu Z, Duda M, Silva RF, Calhoun VD. Analysis of Longitudinal Change Patterns in Developing Brain Using Functional and Structural Magnetic Resonance Imaging via Multimodal Fusion. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.07.588473. [PMID: 38645216 PMCID: PMC11030394 DOI: 10.1101/2024.04.07.588473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Functional and structural magnetic resonance imaging (fMRI and sMRI) are complementary approaches that can be used to study longitudinal brain changes in adolescents. Each individual modality offers distinct insights into the brain. Each individual modality may overlook crucial aspects of brain analysis. By combining them, we can uncover hidden brain connections and gain a more comprehensive understanding. In previous work, we identified multivariate patterns of change in whole-brain function during adolescence. In this work, we focus on linking functional change patterns (FCPs) to brain structure. We introduce two approaches and applied them to data from the Adolescent Brain and Cognitive Development (ABCD) dataset. First, we evaluate voxelwise sMRI-FCP coupling to identify structural patterns linked to our previously identified FCPs. Our approach revealed multiple interesting patterns in functional network connectivity (FNC) and gray matter volume (GMV) data that were linked to subject level variation. FCP components 2 and 4 exhibit extensive associations between their loadings and voxel-wise GMV data. Secondly, we leveraged a symmetric multimodal fusion technique called multiset canonical correlation analysis (mCCA) + joint independent component analysis (jICA). Using this approach, we identify structured FCPs such as one showing increased connectivity between visual and sensorimotor domains and decreased connectivity between sensorimotor and cognitive control domains, linked to structural change patterns (SCPs) including alterations in the bilateral sensorimotor cortex. Interestingly, females exhibit stronger coupling between brain functional and structural changes than males, highlighting sex-related differences. The combined results from both asymmetric and symmetric multimodal fusion methods underscore the intricate sex-specific nuances in neural dynamics. By utilizing two complementary multimodal approaches, our study enhances our understanding of the dynamic nature of brain connectivity and structure during the adolescent period, shedding light on the nuanced processes underlying adolescent brain development.
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Affiliation(s)
- Rekha Saha
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University 55 Park Pl NE, Atlanta, GA 30303, USA
| | - Debbrata K. Saha
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University 55 Park Pl NE, Atlanta, GA 30303, 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 55 Park Pl NE, Atlanta, GA 30303, USA
| | - Marlena Duda
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University 55 Park Pl NE, Atlanta, GA 30303, USA
| | - Rogers F. Silva
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University 55 Park Pl NE, Atlanta, GA 30303, 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 55 Park Pl NE, Atlanta, GA 30303, USA
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11
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Xie Y, Li C, Guan M, Zhang T, Ma C, Wang Z, Ma Z, Wang H, Fang P. Low-frequency rTMS induces modifications in cortical structural connectivity - functional connectivity coupling in schizophrenia patients with auditory verbal hallucinations. Hum Brain Mapp 2024; 45:e26614. [PMID: 38375980 PMCID: PMC10878014 DOI: 10.1002/hbm.26614] [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: 08/17/2023] [Revised: 01/09/2024] [Accepted: 01/19/2024] [Indexed: 02/21/2024] Open
Abstract
Auditory verbal hallucinations (AVH) are distinctive clinical manifestations of schizophrenia. While low-frequency repetitive transcranial magnetic stimulation (rTMS) has demonstrated potential in mitigating AVH, the precise mechanisms by which it operates remain obscure. This study aimed to investigate alternations in structural connectivity and functional connectivity (SC-FC) coupling among schizophrenia patients with AVH prior to and following treatment with 1 Hz rTMS that specifically targets the left temporoparietal junction. Initially, patients exhibited significantly reduced macroscopic whole brain level SC-FC coupling compared to healthy controls. Notably, SC-FC coupling increased significantly across multiple networks, including the somatomotor, dorsal attention, ventral attention, frontoparietal control, and default mode networks, following rTMS treatment. Significant alternations in SC-FC coupling were noted in critical nodes comprising the somatomotor network and the default mode network, such as the precentral gyrus and the ventromedial prefrontal cortex, respectively. The alternations in SC-FC coupling exhibited a correlation with the amelioration of clinical symptom. The results of our study illuminate the intricate relationship between white matter structures and neuronal activity in patients who are receiving low-frequency rTMS. This advances our understanding of the foundational mechanisms underlying rTMS treatment for AVH.
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Affiliation(s)
- Yuanjun Xie
- Military Medical Psychology SchoolFourth Military Medical UniversityXi'anChina
- Department of Radiology, Xijing HospitalFourth Military Medical UniversityXi'anChina
| | - Chenxi Li
- Military Medical Psychology SchoolFourth Military Medical UniversityXi'anChina
| | - Muzhen Guan
- Department of Mental HealthXi'an Medical CollegeXi'anChina
| | - Tian Zhang
- Military Medical Psychology SchoolFourth Military Medical UniversityXi'anChina
| | - Chaozong Ma
- Military Medical Psychology SchoolFourth Military Medical UniversityXi'anChina
| | - Zhongheng Wang
- Department of Psychiatry, Xijing HospitalFourth Military Medical UniversityXi'anChina
| | - Zhujing Ma
- Military Medical Psychology SchoolFourth Military Medical UniversityXi'anChina
| | - Huaning Wang
- Department of Psychiatry, Xijing HospitalFourth Military Medical UniversityXi'anChina
| | - Peng Fang
- Military Medical Psychology SchoolFourth Military Medical UniversityXi'anChina
- Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent PerceptionXi'anChina
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Liang L, Li S, Huang Y, Zhou J, Xiong D, Li S, Li H, Zhu B, Li X, Ning Y, Hou X, Wu F, Wu K. Relationships among the gut microbiome, brain networks, and symptom severity in schizophrenia patients: A mediation analysis. Neuroimage Clin 2024; 41:103567. [PMID: 38271852 PMCID: PMC10835015 DOI: 10.1016/j.nicl.2024.103567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 01/12/2024] [Accepted: 01/12/2024] [Indexed: 01/27/2024]
Abstract
The microbiome-gut-brain axis (MGBA) plays a critical role in schizophrenia (SZ). However, the underlying mechanisms of the interactions among the gut microbiome, brain networks, and symptom severity in SZ patients remain largely unknown. Fecal samples, structural and functional magnetic resonance imaging (MRI) data, and Positive and Negative Syndrome Scale (PANSS) scores were collected from 38 SZ patients and 38 normal controls, respectively. The data of 16S rRNA gene sequencing were used to analyze the abundance of gut microbiome and the analysis of human brain networks was applied to compute the nodal properties of 90 brain regions. A total of 1,691,280 mediation models were constructed based on 261 gut bacterial, 810 nodal properties, and 4 PANSS scores in SZ patients. A strong correlation between the gut microbiome and brain networks (r = 0.89, false discovery rate (FDR) -corrected p < 0.05) was identified. Importantly, the PANSS scores were linearly correlated with both the gut microbiome (r = 0.5, FDR-corrected p < 0.05) and brain networks (r = 0.59, FDR-corrected p < 0.05). The abundance of genus Sellimonas significantly affected the PANSS negative scores of SZ patients via the betweenness centrality of white matter networks in the inferior frontal gyrus and amygdala. Moreover, 19 significant mediation models demonstrated that the nodal properties of 7 brain regions, predominately from the systems of visual, language, and control of action, showed significant mediating effects on the PANSS scores with the gut microbiome as mediators. Together, our findings indicated the tripartite relationships among the gut microbiome, brain networks, and PANSS scores and suggested their potential role in the neuropathology of SZ.
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Affiliation(s)
- Liqin Liang
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou 511442, China
| | - Shijia Li
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou 511442, China; Swammerdam Institute for Life Sciences (SILS), University of Amsterdam, Amsterdam, The Netherlands
| | - Yuanyuan Huang
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China
| | - Jing Zhou
- School of Material Science and Engineering, South China University of Technology, Guangzhou 510006, China; National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou 510006, China
| | - Dongsheng Xiong
- School of Material Science and Engineering, South China University of Technology, Guangzhou 510006, China; National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou 510006, China
| | - Shaochuan Li
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China; Realmeta Technology (Guangzhou) Co., Ltd, Guangzhou 510535, China
| | - Hehua Li
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China
| | - Baoyuan Zhu
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou 511442, China
| | - Xiaobo Li
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA
| | - Yuping Ning
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China
| | - Xiaohui Hou
- Guangdong Provincial Key Laboratory of Physical Activity and Health Promotion, Guangzhou Sport University, Guangzhou 510500, China.
| | - Fengchun Wu
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China.
| | - Kai Wu
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou 511442, China; National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou 510006, China; Department of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Tohoku University, Sendai 980-8575, Japan.
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13
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Van Dyken PC, MacKinley M, Khan AR, Palaniyappan L. Cortical Network Disruption Is Minimal in Early Stages of Psychosis. SCHIZOPHRENIA BULLETIN OPEN 2024; 5:sgae010. [PMID: 39144115 PMCID: PMC11207789 DOI: 10.1093/schizbullopen/sgae010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/16/2024]
Abstract
Background and Hypothesis Schizophrenia is associated with white matter disruption and topological reorganization of cortical connectivity but the trajectory of these changes, from the first psychotic episode to established illness, is poorly understood. Current studies in first-episode psychosis (FEP) patients using diffusion magnetic resonance imaging (dMRI) suggest such disruption may be detectable at the onset of psychosis, but specific results vary widely, and few reports have contextualized their findings with direct comparison to young adults with established illness. Study Design Diffusion and T1-weighted 7T MR scans were obtained from N = 112 individuals (58 with untreated FEP, 17 with established schizophrenia, 37 healthy controls) recruited from London, Ontario. Voxel- and network-based analyses were used to detect changes in diffusion microstructural parameters. Graph theory metrics were used to probe changes in the cortical network hierarchy and to assess the vulnerability of hub regions to disruption. The analysis was replicated with N = 111 (57 patients, 54 controls) from the Human Connectome Project-Early Psychosis (HCP-EP) dataset. Study Results Widespread microstructural changes were found in people with established illness, but changes in FEP patients were minimal. Unlike the established illness group, no appreciable topological changes in the cortical network were observed in FEP patients. These results were replicated in the early psychosis patients of the HCP-EP datasets, which were indistinguishable from controls in most metrics. Conclusions The white matter structural changes observed in established schizophrenia are not a prominent feature in the early stages of this illness.
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Affiliation(s)
- Peter C Van Dyken
- Neuroscience Graduate Program, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Michael MacKinley
- Lawson Health Research Institute, London Health Sciences Centre, London, ON, Canada
| | - Ali R Khan
- Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Lena Palaniyappan
- Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- Department of Psychiatry, Douglas Mental Health University Institute, McGill University, London, ON, Canada
- Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
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Ruiz-Torras S, Gudayol-Ferré E, Fernández-Vazquez O, Cañete-Massé C, Peró-Cebollero M, Guàrdia-Olmos J. Hypoconnectivity networks in schizophrenia patients: A voxel-wise meta-analysis of Rs-fMRI. Int J Clin Health Psychol 2023; 23:100395. [PMID: 37533450 PMCID: PMC10392089 DOI: 10.1016/j.ijchp.2023.100395] [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: 01/10/2023] [Accepted: 07/05/2023] [Indexed: 08/04/2023] Open
Abstract
In recent years several meta-analyses regarding resting-state functional connectivity in patients with schizophrenia have been published. The authors have used different data analysis techniques: regional homogeneity, seed-based data analysis, independent component analysis, and amplitude of low frequencies. Hence, we aim to perform a meta-analysis to identify connectivity networks with different activation patterns between people diagnosed with schizophrenia and healthy controls using voxel-wise analysis. METHOD We collected primary studies exploring whole brain connectivity by functional magnetic resonance imaging at rest in patients with schizophrenia compared with healthy controls. We identified 25 studies included high-quality studies that included 1285 patients with schizophrenia and 1279 healthy controls. RESULTS The results indicate hypoactivation in the right precentral gyrus and the left superior temporal gyrus of patients with schizophrenia compared with healthy controls. CONCLUSIONS These regions have been linked with some clinical symptoms usually present in Plea with schizophrenia, such as auditory verbal hallucinations, formal thought disorder, and the comprehension and production of gestures.
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Affiliation(s)
- Silvia Ruiz-Torras
- Clínica Psicològica de la Universitat de Barcelona, Fundació Josep Finestres, Universitat de Barcelona, Spain
| | | | | | - Cristina Cañete-Massé
- Facultat de Psicologia, Secció de Psicologia Quantitativa, Universitat de Barcelona, Spain
- UB Institute of Complex Systems, Universitat de Barcelona, Spain
| | - Maribel Peró-Cebollero
- Facultat de Psicologia, Secció de Psicologia Quantitativa, Universitat de Barcelona, Spain
- UB Institute of Complex Systems, Universitat de Barcelona, Spain
- Institute of Neuroscience, Universitat de Barcelona, Spain
| | - Joan Guàrdia-Olmos
- Facultat de Psicologia, Secció de Psicologia Quantitativa, Universitat de Barcelona, Spain
- UB Institute of Complex Systems, Universitat de Barcelona, Spain
- Institute of Neuroscience, Universitat de Barcelona, Spain
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15
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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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16
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Duda M, Faghiri A, Belger A, Bustillo JR, Ford JM, Mathalon DH, Mueller BA, Pearlson GD, Potkin SG, Preda A, Sui J, Van Erp TGM, Calhoun VD. Alterations in grey matter structure linked to frequency-specific cortico-subcortical connectivity in schizophrenia via multimodal data fusion. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.05.547840. [PMID: 37461731 PMCID: PMC10350020 DOI: 10.1101/2023.07.05.547840] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
Schizophrenia (SZ) is a complex psychiatric disorder that is currently defined by symptomatic and behavioral, rather than biological, criteria. Neuroimaging is an appealing avenue for SZ biomarker development, as several neuroimaging-based studies comparing individuals with SZ to healthy controls (HC) have shown measurable group differences in brain structure, as well as functional brain alterations in both static and dynamic functional network connectivity (sFNC and dFNC, respectively). The recently proposed filter-banked connectivity (FBC) method extends the standard dFNC sliding-window approach to estimate FNC within an arbitrary number of distinct frequency bands. The initial implementation used a set of filters spanning the full connectivity spectral range, providing a unified approach to examine both sFNC and dFNC in a single analysis. Initial FBC results found that individuals with SZ spend more time in a less structured, more disconnected low-frequency (i.e., static) FNC state than HC, as well as preferential SZ occupancy in high-frequency connectivity states, suggesting a frequency-specific component underpinning the functional dysconnectivity observed in SZ. Building on these findings, we sought to link such frequency-specific patterns of FNC to covarying data-driven structural brain networks in the context of SZ. Specifically, we employ a multi-set canonical correlation analysis + joint independent components analysis (mCCA + jICA) data fusion framework to study the connection between grey matter volume (GMV) maps and FBC states across the full connectivity frequency spectrum. Our multimodal analysis identified two joint sources that captured co-varying patterns of frequency-specific functional connectivity and alterations in GMV with significant group differences in loading parameters between the SZ group and HC. The first joint source linked frequency-modulated connections between the subcortical and sensorimotor networks and GMV alterations in the frontal and temporal lobes, while the second joint source identified a relationship between low-frequency cerebellar-sensorimotor connectivity and structural changes in both the cerebellum and motor cortex. Together, these results show a strong connection between cortico-subcortical functional connectivity at both high and low frequencies and alterations in cortical GMV that may be relevant to the pathogenesis and pathophysiology of SZ.
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Affiliation(s)
- Marlena Duda
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Ashkan Faghiri
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Aysenil Belger
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - Juan R Bustillo
- Department of Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque, NM, USA
| | - Judith M Ford
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, San Francisco, California, USA
- Department of Psychiatry and Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California, USA
| | - Daniel H Mathalon
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, San Francisco, California, USA
- Department of Psychiatry and Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California, USA
| | - Bryon A Mueller
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, Minnesota, 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, California, USA
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California, USA
| | - Jing Sui
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- IDG/McGovern Institute for Brain Research, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Theo G M Van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
- Center for the Neurobiology of Learning and Memory, 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, Emory University, Atlanta, GA, USA
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17
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Saha R, Saha DK, Fu Z, Silva RF, Calhoun VD. Functional and Structural Longitudinal Change Patterns in Adolescent Brain. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082649 DOI: 10.1109/embc40787.2023.10340079] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI) are two widely used techniques to analyze longitudinal brain functional and structural change in adolescents. Although longitudinal changes in intrinsic functional and structural changes have been studied separately, most studies focus on univariate change rather than estimating multivariate patterns of functional network connectivity (FNC) and gray matter (GM) changes with increased age. To analyze whole-brain structural and functional changes with increased age, we suggest two complementary techniques (1: linking of functional change pattern (FCP) to voxel-wise ∆GM and 2: the connection between FCP and structural change pattern (SCP)). In this study, we apply our approaches to the functional and GM data from the large-scale Adolescent Brain and Cognitive Development (ABCD) data. We find a significant correlation between FCP and voxel-wise ∆GM for two components. We also investigate the links between FCP and SCP and hypothesize that functional connectivity and GM continue to exhibit linked changes during adolescence.Clinical Relevance- This work captures the whole-brain functional and structural change patterns link by introducing two complementary techniques.
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18
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Falakshahi H, Rokham H, Miller R, Liu J, Calhoun VD. Network Differential in Gaussian Graphical Models from Multimodal Neuroimaging Data . ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-6. [PMID: 38083176 DOI: 10.1109/embc40787.2023.10340856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Multimodal brain network analysis has the potential to provide insights into the mechanisms of brain disorders. Most previous studies have analyzed either unimodal brain graphs or focused on local/global graphic metrics with little consideration of details of disrupted paths in the patient group. As we show, the combination of multimodal brain graphs and disrupted path-based analysis can be highly illuminating to recognize path-based disease biomarkers. In this study, we first propose a way to estimate multimodal brain graphs using static functional network connectivity (sFNC) and gray matter features using a Gaussian graphical model of schizophrenia versus controls. Next, applying the graph theory approach we identify disconnectors or connectors in the patient group graph that create additional paths or cause absent paths compared to the control graph. Results showed several edges in the schizophrenia group graph that trigger missing or additional paths. Identified edges associated with these disrupted paths were identified both within and between dFNC and gray matter which highlights the importance of considering multimodal studies and moving beyond pairwise edges to provide a more comprehensive understanding of brain disorders.Clinical Relevance- We identified a path-based biomarker in schizophrenia, by imitating the structure of paths in a multimodal (sMIR+fMRI) brain graph of the control group. Identified cross-modal edges associated with disrupted paths were related to the middle temporal gyrus and cerebellar regions.
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19
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Wang Q, Yao W, Bai D, Yi W, Yan W, Wang J. Schizophrenia MEG Network Analysis Based on Kernel Granger Causality. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1006. [PMID: 37509953 PMCID: PMC10378589 DOI: 10.3390/e25071006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Revised: 06/22/2023] [Accepted: 06/28/2023] [Indexed: 07/30/2023]
Abstract
Network analysis is an important approach to explore complex brain structures under different pathological and physiological conditions. In this paper, we employ the multivariate inhomogeneous polynomial kernel Granger causality (MKGC) to construct directed weighted networks to characterize schizophrenia magnetoencephalography (MEG). We first generate data based on coupled autoregressive processes to test the effectiveness of MKGC in comparison with the bivariate linear Granger causality and bivariate inhomogeneous polynomial kernel Granger causality. The test results suggest that MKGC outperforms the other two methods. Based on these results, we apply MKGC to construct effective connectivity networks of MEG for patients with schizophrenia (SCZs). We measure three network features, i.e., strength, nonequilibrium, and complexity, to characterize schizophrenia MEG. Our results suggest that MEG of the healthy controls (HCs) has a denser effective connectivity network than that of SCZs. The most significant difference in the in-connectivity strength is observed in the right frontal network (p=0.001). The strongest out-connectivity strength for all subjects occurs in the temporal area, with the most significant between-group difference in the left occipital area (p=0.0018). The total connectivity strength of the frontal, temporal, and occipital areas of HCs exhibits higher values compared with SCZs. The nonequilibrium feature over the whole brain of SCZs is significantly higher than that of the HCs (p=0.012); however, the results of Shannon entropy suggest that healthy MEG networks have higher complexity than schizophrenia networks. Overall, MKGC provides a reliable approach to construct MEG brain networks and characterize the network characteristics.
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Affiliation(s)
- Qiong Wang
- School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
- School of Physics and Information Engineering, Jiangsu Second Normal University, Nanjing 210013, China
| | - Wenpo Yao
- Smart Health Big Data Analysis and Location Services Engineering Research Center of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Dengxuan Bai
- School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
| | - Wanyi Yi
- School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
| | - Wei Yan
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing 210029, China
| | - Jun Wang
- Smart Health Big Data Analysis and Location Services Engineering Research Center of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
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20
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Zhao J, Huang CC, Zhang Y, Liu Y, Tsai SJ, Lin CP, Lo CYZ. Structure-function coupling in white matter uncovers the abnormal brain connectivity in Schizophrenia. Transl Psychiatry 2023; 13:214. [PMID: 37339983 DOI: 10.1038/s41398-023-02520-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 06/09/2023] [Accepted: 06/12/2023] [Indexed: 06/22/2023] Open
Abstract
Schizophrenia is characterized by dysconnectivity syndrome. Evidence of widespread impairment of structural and functional integration has been demonstrated in schizophrenia. Although white matter (WM) microstructural abnormalities have been commonly reported in schizophrenia, the dysfunction of WM as well as the relationship between structure and function in WM remains uncertain. In this study, we proposed a novel structure-function coupling measurement to reflect neuronal information transfer, which combined spatial-temporal correlations of functional signals with diffusion tensor orientations in the WM circuit from functional and diffusion magnetic resonance images (MRI). By analyzing MRI data from 75 individuals with schizophrenia (SZ) and 89 healthy volunteers (HV), the associations between structure and function in WM regions in schizophrenia were examined. Randomized validation of the measurement was performed in the HV group to confirm the capacity of the neural signal transferring along the WM tracts, referring to quantifying the association between structure and function. Compared to HV, SZ showed a widespread decrease in the structure-function coupling within WM regions, involving the corticospinal tract and the superior longitudinal fasciculus. Additionally, the structure-function coupling in the WM tracts was found to be significantly correlated with psychotic symptoms and illness duration in schizophrenia, suggesting that abnormal signal transfer of neuronal fiber pathways could be a potential mechanism of the neuropathology of schizophrenia. This work supports the dysconnectivity hypothesis of schizophrenia from the aspect of circuit function, and highlights the critical role of WM networks in the pathophysiology of schizophrenia.
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Affiliation(s)
- Jiajia Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Chu-Chung Huang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Health Center (ECNU), Institute of Cognitive Neuroscience, School of Psychology and Cognitive Science, East China Normal University, Shanghai, China.
- Shanghai Changning Mental Health Center, Shanghai, China.
| | - Yajuan Zhang
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Yuchen Liu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Shih-Jen Tsai
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
- Division of Psychiatry, Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Education and Research, Taipei City Hospital, Taipei, Taiwan
| | - Chun-Yi Zac Lo
- Department of Biomedical Engineering, Chung Yuan Christian University, Taoyuan, Taiwan.
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21
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Levitt JJ, Zhang F, Vangel M, Nestor PG, Rathi Y, Cetin-Karayumak S, Kubicki M, Coleman MJ, Lewandowski KE, Holt DJ, Keshavan M, Bouix S, Öngür D, Breier A, Shenton ME, O'Donnell LJ. The organization of frontostriatal brain wiring in non-affective early psychosis compared with healthy subjects using a novel diffusion imaging fiber cluster analysis. Mol Psychiatry 2023; 28:2301-2311. [PMID: 37173451 DOI: 10.1038/s41380-023-02031-0] [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: 09/13/2022] [Revised: 02/13/2023] [Accepted: 03/08/2023] [Indexed: 05/15/2023]
Abstract
BACKGROUND Alterations in brain connectivity may underlie neuropsychiatric conditions such as schizophrenia. We here assessed the degree of convergence of frontostriatal fiber projections in 56 young adult healthy controls (HCs) and 108 matched Early Psychosis-Non-Affective patients (EP-NAs) using our novel fiber cluster analysis of whole brain diffusion magnetic resonance imaging tractography. METHODS Using whole brain tractography and our fiber clustering methodology on harmonized diffusion magnetic resonance imaging data from the Human Connectome Project for Early Psychosis we identified 17 white matter fiber clusters that connect frontal cortex (FCtx) and caudate (Cd) per hemisphere in each group. To quantify the degree of convergence and, hence, topographical relationship of these fiber clusters, we measured the inter-cluster mean distances between the endpoints of the fiber clusters at the level of the FCtx and of the Cd, respectively. RESULTS We found (1) in both groups, bilaterally, a non-linear relationship, yielding convex curves, between FCtx and Cd distances for FCtx-Cd connecting fiber clusters, driven by a cluster projecting from inferior frontal gyrus; however, in the right hemisphere, the convex curve was more flattened in EP-NAs; (2) that cluster pairs in the right (p = 0.03), but not left (p = 0.13), hemisphere were significantly more convergent in HCs vs EP-NAs; (3) in both groups, bilaterally, similar clusters projected significantly convergently to the Cd; and, (4) a significant group by fiber cluster pair interaction for 2 right hemisphere fiber clusters (numbers 5, 11; p = .00023; p = .00023) originating in selective PFC subregions. CONCLUSIONS In both groups, we found the FCtx-Cd wiring pattern deviated from a strictly topographic relationship and that similar clusters projected significantly more convergently to the Cd. Interestingly, we also found a significantly more convergent pattern of connectivity in HCs in the right hemisphere and that 2 clusters from PFC subregions in the right hemisphere significantly differed in their pattern of connectivity between groups.
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Affiliation(s)
- J J Levitt
- Department of Psychiatry, VA Boston Healthcare System, Brockton Division, Brockton, MA, 02301, USA.
- Harvard Medical School, Boston, MA, 02115, USA.
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
| | - F Zhang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - M Vangel
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - P G Nestor
- Department of Psychiatry, VA Boston Healthcare System, Brockton Division, Brockton, MA, 02301, USA
- Harvard Medical School, Boston, MA, 02115, USA
- Department of Psychology, University of Massachusetts, Boston, MA, 02125, USA
| | - Y Rathi
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - S Cetin-Karayumak
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - M Kubicki
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - M J Coleman
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - K E Lewandowski
- McLean Hospital, Harvard Medical School, Belmont, MA, 02478, USA
| | - D J Holt
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - M Keshavan
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA
| | - S Bouix
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Software Engineering and Information Technology, École de technologie supérieure, Université du Québec, Montréal, QC, H3C 1K3, Canada
| | - D Öngür
- McLean Hospital, Harvard Medical School, Belmont, MA, 02478, USA
| | - A Breier
- Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - M E Shenton
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - L J O'Donnell
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
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22
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Luna LP, Sousa MB, Passinho JS, Nardi AE, Oertel V, Veras AB, Alves GS. Resting-state fMRI functional connectivity and clinical correlates in Afro-descendants with schizophrenia and bipolar disorder. Psychiatry Res Neuroimaging 2023; 331:111628. [PMID: 36924740 DOI: 10.1016/j.pscychresns.2023.111628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 02/12/2023] [Accepted: 03/07/2023] [Indexed: 03/18/2023]
Abstract
Schizophrenia (SCZ) and bipolar disorder (BD) exhibited altered activation in several brain areas, including the prefrontal and temporal cortex; however, a less explored topic is how brain connectivity and functional disturbances occur in non-Caucasian samples of SCZ and BD. Individuals with SCZ (n=20), BD (n=21), and healthy controls (HC, n=21) from indigenous and African ethnicity were submitted to clinical screening and functional assessments. Mood, compulsive and psychotic symptoms were also correlated to network dysfunction in each group. Two distinct networks' subcomponents demonstrated significant lower global efficiency (GE) in SCZ versus HC, corresponding to left posterior dorsal attention and medial left ventral attention (VA) networks. Lower GE was found in BD versus controls in four subcomponents, including the left medial and right VA. Higher compulsion scores correlated in BD with lower GE in the left VA, whereas increased report of alcohol abuse was associated with higher GE in left default mode network. Although preliminary, differences in the activation of specific networks, notably the left hemisphere, in SCZ versus controls, and lower activation in VA areas, in BD versus controls. Results highlight default mode and salient network as relevant for the emotional processing of SCZ and BD of indigenous and black ethnicity. Abstract: schizophrenia, bipolar disorder, functional neuroimaging, ethnicity, default network.
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Affiliation(s)
- Licia P Luna
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Baltimore, MD, USA
| | | | - Jhule S Passinho
- Neuropsychology Laboratory, CEUMA University, São Luís, Maranhão, Brazil
| | - Antônio E Nardi
- Post-Graduation in Psychiatry and Mental Health (PROPSAM), Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Viola Oertel
- Department of Psychiatry, Psychosomatics, and Psychotherapy, Frankfurt Goethe University, Germany
| | - André Barciela Veras
- Post-Graduation in Psychiatry and Mental Health (PROPSAM), Federal University of Rio de Janeiro, Rio de Janeiro, Brazil; Translational Research Group on Mental Health (GPTranSMe), Dom Bosco Catholic University, Campo Grande, Mato Grosso do Sul, Brazil
| | - Gilberto Sousa Alves
- Post-Graduation in Psychiatry and Mental Health (PROPSAM), Federal University of Rio de Janeiro, Rio de Janeiro, Brazil; Translational Psychiatry Research Group, Federal University of Maranhão, São Luís, Maranhão, Brazil.
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23
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Teng X, Guo C, Lei X, Yang F, Wu Z, Yu L, Ren J, Zhang C. Comparison of brain network between schizophrenia and bipolar disorder: A multimodal MRI analysis of comparative studies. J Affect Disord 2023; 327:197-206. [PMID: 36736789 DOI: 10.1016/j.jad.2023.01.116] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 01/20/2023] [Accepted: 01/30/2023] [Indexed: 02/04/2023]
Abstract
OBJECTIVE Cognitive impairment is a shared symptom of Schizophrenia (SCZ) and bipolar disorder (BP), but the underlying neural mechanisms for both remain unclear. We aimed to identify abnormalities in the structural and functional brain network of patients with SCZ and BP. METHODS The study included 69 patients with SCZ, 40 with BP, and 63 healthy controls (HC). After neurocognitive function assessment, resting-state functional magnetic resonance imaging and diffusion tensor imaging were acquired respectively. We compared the network of structural connectivity (SC) and functional connectivity (FC) among the three groups and performed graph theoretical analyses. The SC-FC coupling was calculated, and the correlations between the cognitive function scores and network properties were ascertained. RESULTS The BP group showed significantly higher indicators in subnetworks and graph theory analysis than SCZ and HC. Several brain regions, such as the inferior parietal lobe, exhibited differences among all pairwise comparisons and showed significant correlations with cognitive scores in both SCZ and BP. SC-FC coupling did not significantly differ between the three groups but showed close associations with clinical performance. Interestingly, the direction of correlations between the network properties and cognition tends to present the opposite between SCZ and BP, especially regarding the working memory, attention, and language sections. CONCLUSIONS The FC and SC network of the SCZ group appeared more inefficient and disconnected than BP. The network demonstrated to be closely but differently associated with cognitive function at both local and global levels, indicating the potentially separated pathologies of cognition deficits in SCZ and BP.
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Affiliation(s)
- Xinyue Teng
- Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chaoyue Guo
- Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaoxia Lei
- Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fuyin Yang
- School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Zenan Wu
- Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lingfang Yu
- Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Juanjuan Ren
- Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chen Zhang
- Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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24
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Sheng D, Pu W, Linli Z, Tian GL, Guo S, Fei Y. Aberrant global and local dynamic properties in schizophrenia with instantaneous phase method based on Hilbert transform. Psychol Med 2023; 53:2125-2135. [PMID: 34588010 DOI: 10.1017/s0033291721003895] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
BACKGROUND Emerging functional imaging studies suggest that schizophrenia is associated with aberrant spatiotemporal interaction which may result in aberrant global and local dynamic properties. METHODS We investigated the dynamic functional connectivity (FC) by using instantaneous phase method based on Hilbert transform to detect abnormal spatiotemporal interaction in schizophrenia. Based on resting-state functional magnetic resonance imaging, two independent datasets were included, with 114 subjects from COBRE [51 schizophrenia patients (SZ) and 63 healthy controls (HCs)] and 96 from OpenfMRI (36 SZ and 60 HCs). Phase differences and instantaneous coupling matrices were firstly calculated at all time points by extracting instantaneous parameters. Global [global synchrony and intertemporal closeness (ITC)] and local dynamic features [strength of FC (sFC) and variability of FC (vFC)] were compared between two groups. Support vector machine (SVM) was used to estimate the ability to discriminate two groups by using all aberrant features. RESULTS We found SZ had lower global synchrony and ITC than HCs on both datasets. Furthermore, SZ had a significant decrease in sFC but an increase in vFC, which were mainly located at prefrontal cortex, anterior cingulate cortex, temporal cortex and visual cortex or temporal cortex and hippocampus, forming significant dynamic subnetworks. SVM analysis revealed a high degree of balanced accuracy (85.75%) on the basis of all aberrant dynamic features. CONCLUSIONS SZ has worse overall spatiotemporal stability and extensive FC subnetwork lesions compared to HCs, which to some extent elucidates the pathophysiological mechanism of schizophrenia, providing insight into time-variation properties of patients with other mental illnesses.
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Affiliation(s)
- Dan Sheng
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, PR China
- Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha, PR China
| | - Weidan Pu
- Medical Psychological Center, the Second Xiangya Hospital, Central South University, Changsha, Hunan, PR China
- China National Clinical Research Center for Mental Health Disorders, Changsha, PR China
- College of Mechatronics and Automation, National University of Defense Technology, Changsha, PR China
| | - Zeqiang Linli
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, PR China
- Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha, PR China
| | - Guo-Liang Tian
- Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, PR China
| | - Shuixia Guo
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, PR China
- Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha, PR China
| | - Yu Fei
- School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming, PR China
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25
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Bosma MJ, Cox SR, Ziermans T, Buchanan CR, Shen X, Tucker-Drob EM, Adams MJ, Whalley HC, Lawrie SM. White matter, cognition and psychotic-like experiences in UK Biobank. Psychol Med 2023; 53:2370-2379. [PMID: 37310314 PMCID: PMC10123836 DOI: 10.1017/s0033291721004244] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 09/09/2021] [Accepted: 09/29/2021] [Indexed: 11/05/2022]
Abstract
BACKGROUND Psychotic-like experiences (PLEs) are risk factors for the development of psychiatric conditions like schizophrenia, particularly if associated with distress. As PLEs have been related to alterations in both white matter and cognition, we investigated whether cognition (g-factor and processing speed) mediates the relationship between white matter and PLEs. METHODS We investigated two independent samples (6170 and 19 891) from the UK Biobank, through path analysis. For both samples, measures of whole-brain fractional anisotropy (gFA) and mean diffusivity (gMD), as indications of white matter microstructure, were derived from probabilistic tractography. For the smaller sample, variables whole-brain white matter network efficiency and microstructure were also derived from structural connectome data. RESULTS The mediation of cognition on the relationships between white matter properties and PLEs was non-significant. However, lower gFA was associated with having PLEs in combination with distress in the full available sample (standardized β = -0.053, p = 0.011). Additionally, lower gFA/higher gMD was associated with lower g-factor (standardized β = 0.049, p < 0.001; standardized β = -0.027, p = 0.003), and partially mediated by processing speed with a proportion mediated of 7% (p = < 0.001) for gFA and 11% (p < 0.001) for gMD. CONCLUSIONS We show that lower global white matter microstructure is associated with having PLEs in combination with distress, which suggests a direction of future research that could help clarify how and why individuals progress from subclinical to clinical psychotic symptoms. Furthermore, we replicated that processing speed mediates the relationship between white matter microstructure and g-factor.
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Affiliation(s)
- M. J. Bosma
- Department of Psychology, University of Amsterdam, Amsterdam, Netherlands
| | - S. R. Cox
- School of Philosophy, Psychology and Language Sciences, University of Edinburgh, Edinburgh, UK
| | - T. Ziermans
- Department of Psychology, University of Amsterdam, Amsterdam, Netherlands
| | - C. R. Buchanan
- School of Philosophy, Psychology and Language Sciences, University of Edinburgh, Edinburgh, UK
| | - X. Shen
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, Scotland, UK
| | - E. M. Tucker-Drob
- Department of Psychology, University of Texas at Austin, Austin, USA
| | - M. J. Adams
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, Scotland, UK
| | - H. C. Whalley
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, Scotland, UK
| | - S. M. Lawrie
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, Scotland, UK
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26
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Zhu W, Wang Z, Yu M, Zhang X, Zhang Z. Using support vector machine to explore the difference of function connection between deficit and non-deficit schizophrenia based on gray matter volume. Front Neurosci 2023; 17:1132607. [PMID: 37051145 PMCID: PMC10083255 DOI: 10.3389/fnins.2023.1132607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 03/06/2023] [Indexed: 03/28/2023] Open
Abstract
ObjectiveSchizophrenia can be divided into deficient schizophrenia (DS) and non-deficient schizophrenia (NDS) according to the presence of primary and persistent negative symptoms. So far, there are few studies that have explored the differences in functional connectivity (FC) between the different subtypes based on the region of interest (ROI) from GMV (Gray matter volume), especially since the characteristics of brain networks are still unknown. This study aimed to investigate the alterations of functional connectivity between DS and NDS based on the ROI obtained by machine learning algorithms and differential GMV. Then, the relationships between the alterations and the clinical symptoms were analyzed. In addition, the thalamic functional connection imbalance in the two groups was further explored.MethodsA total of 16 DS, 31 NDS, and 38 health controls (HC) underwent resting-state fMRI scans, patient group will further be evaluated by clinical scales including the Brief Psychiatric Rating Scale (BPRS), the Scale for the Assessment of Negative Symptoms (SANS), and the Scale for the Assessment of Positive Symptoms (SAPS). Based on GMV image data, a support vector machine (SVM) is used to classify DS and NDS. Brain regions with high weight in the classification were used as seed points in whole-brain FC analysis and thalamic FC imbalance analysis. Finally, partial correlation analysis explored the relationships between altered FC and clinical scale in the two subtypes.ResultsThe relatively high classification accuracy is obtained based on the SVM. Compared to HC, the FC increased between the right inferior parietal lobule (IPL.R) bilateral thalamus, and lingual gyrus, and between the right inferior temporal gyrus (ITG.R) and the Salience Network (SN) in NDS. The FC between the right thalamus (THA.R) and Visual network (VN), between ITG.R and right superior occipital gyrus in the DS group was higher than that in HC. Furthermore, compared with NDS, the FC between the ITG.R and the left superior and middle frontal gyrus decreased in the DS group. The thalamic FC imbalance, which is characterized by frontotemporal-THA.R hypoconnectivity and sensory motor network (SMN)-THA.R hyperconnectivity was found in both subtypes. The FC value of THA.R and SMN was negatively correlated with the SANS score in the DS group but positively correlated with the SAPS score in the NDS group.ConclusionUsing an SVM classification method and based on an ROI from GMV, we highlighted the difference in functional connectivity between DS and NDS from the local to the brain network, which provides new information for exploring the neural physiopathology of the two subtypes of schizophrenic.
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Affiliation(s)
- Wenjing Zhu
- Department of Neurology, School of Medicine, Affiliated Zhongda Hospital, Research Institution of Neuropsychiatry, Southeast University, Nanjing, China
- Affiliated Mental Health Center, Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Zan Wang
- Department of Neurology, School of Medicine, Affiliated Zhongda Hospital, Research Institution of Neuropsychiatry, Southeast University, Nanjing, China
| | - Miao Yu
- Department of Geriatric Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Xiangrong Zhang
- Department of Geriatric Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
- *Correspondence: Xiangrong Zhang,
| | - Zhijun Zhang
- Department of Neurology, School of Medicine, Affiliated Zhongda Hospital, Research Institution of Neuropsychiatry, Southeast University, Nanjing, China
- Affiliated Mental Health Center, Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Zhijun Zhang,
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Dey A, Luk CC, Ishaque A, Ta D, Srivastava O, Krebs D, Seres P, Hanstock C, Beaulieu C, Korngut L, Frayne R, Zinman L, Graham S, Genge A, Briemberg H, Kalra S. Motor cortex functional connectivity is associated with underlying neurochemistry in ALS. J Neurol Neurosurg Psychiatry 2023; 94:193-200. [PMID: 36379713 PMCID: PMC9985743 DOI: 10.1136/jnnp-2022-329993] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 10/18/2022] [Indexed: 11/16/2022]
Abstract
OBJECTIVE To identify structural and neurochemical properties that underlie functional connectivity impairments of the primary motor cortex (PMC) and how these relate to clinical findings in amyotrophic lateral sclerosis (ALS). METHODS 52 patients with ALS and 52 healthy controls, matched for age and sex, were enrolled from 5 centres across Canada for the Canadian ALS Neuroimaging Consortium study. Resting-state functional MRI, diffusion tensor imaging and magnetic resonance spectroscopy data were acquired. Functional connectivity maps, diffusion metrics and neurometabolite ratios were obtained from the analyses of the acquired multimodal data. A clinical assessment of foot tapping (frequency) was performed to examine upper motor neuron function in all participants. RESULTS Compared with healthy controls, the primary motor cortex in ALS showed reduced functional connectivity with sensory (T=5.21), frontal (T=3.70), temporal (T=3.80), putaminal (T=4.03) and adjacent motor (T=4.60) regions. In the primary motor cortex, N-acetyl aspartate (NAA, a neuronal marker) ratios and diffusion metrics (mean, axial and radial diffusivity, fractional anisotropy (FA)) were altered. Within the ALS cohort, foot tapping frequency correlated with NAA (r=0.347) and white matter FA (r=0.537). NAA levels showed associations with disturbed functional connectivity of the motor cortex. CONCLUSION In vivo neurochemistry may represent an effective imaging marker of impaired motor cortex functional connectivity in ALS.
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Affiliation(s)
- Avyarthana Dey
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta, Canada.,Division of Neurology, Department of Medicine, University of Alberta, Edmonton, Alberta, Canada
| | - Collin C Luk
- Division of Neurology, Department of Medicine, University of Alberta, Edmonton, Alberta, Canada
| | - Abdullah Ishaque
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta, Canada.,Faculty of Medicine & Dentistry, University of Alberta, Edmonton, Alberta, Canada
| | - Daniel Ta
- Faculty of Medicine & Dentistry, University of Alberta, Edmonton, Alberta, Canada
| | - Ojas Srivastava
- Faculty of Medicine & Dentistry, University of Alberta, Edmonton, Alberta, Canada
| | - Dennell Krebs
- Faculty of Medicine & Dentistry, University of Alberta, Edmonton, Alberta, Canada
| | - Peter Seres
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
| | - Chris Hanstock
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
| | - Christian Beaulieu
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
| | - Lawrence Korngut
- Department of Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Richard Frayne
- Seaman Family Magnetic Resonance Research Centre, Foothills Medical Centre, Alberta Health Services, Calgary, Alberta, Canada.,Department of Radiology, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Lorne Zinman
- Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada
| | - Simon Graham
- Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada
| | - Angela Genge
- The Montreal Neurological Institute and Hospital, McGill University, Montreal, Québec, Canada
| | - Hannah Briemberg
- Division of Neurology, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Sanjay Kalra
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta, Canada .,Division of Neurology, Department of Medicine, University of Alberta, Edmonton, Alberta, Canada
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Gao Z, Xiao Y, Zhu F, Tao B, Yu W, Lui S. The whole-brain connectome landscape in patients with schizophrenia: a systematic review and meta-analysis of graph theoretical characteristics. Neurosci Biobehav Rev 2023; 148:105144. [PMID: 36990373 DOI: 10.1016/j.neubiorev.2023.105144] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 03/14/2023] [Accepted: 03/24/2023] [Indexed: 03/29/2023]
Abstract
The alterations of connectome in schizophrenia have been reported, but the results remain inconsistent. We conducted a systematic review and random-effects meta-analysis on structural or functional connectome MRI studies comparing global graph theoretical characteristics between schizophrenia and healthy controls. Meta-regression and subgroup analyses were performed to examine confounding effects. Based on the included 48 studies, Structural connectome in schizophrenia showed a significant decrease in segregation (lower clustering coefficient and local efficiency, Hedge's g= -0.352 and -0.864, respectively) and integration (higher characteristic path length and lower global efficiency, Hedge's g= 0.532 and -0.577 respectively). The functional connectome showed no difference between groups except γ. Moderator analysis indicated that clinical and methodological factors exerted a potential effect on the graph theoretical characteristics. Our analysis revealed a weaker small-worldization trend in structural connectome of schizophrenia. For the relatively unchanged functional connectome, more homogenous and high-quality studies are warranted to elucidate whether the change was blurred by heterogeneity or the presentation of pathophysiological reconfiguration.
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29
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Liu Y, Li F, Shang S, Wang P, Yin X, Krishnan Muthaiah VP, Lu L, Chen YC. Functional-structural large-scale brain networks are correlated with neurocognitive impairment in acute mild traumatic brain injury. Quant Imaging Med Surg 2023; 13:631-644. [PMID: 36819289 PMCID: PMC9929413 DOI: 10.21037/qims-22-450] [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: 05/04/2022] [Accepted: 11/07/2022] [Indexed: 12/02/2022]
Abstract
Background This study was conducted to investigate topological changes in large-scale functional connectivity (FC) and structural connectivity (SC) networks in acute mild traumatic brain injury (mTBI) and determine their potential relevance to cognitive impairment. Methods Seventy-one patients with acute mTBI (29 males, 42 females, mean age 43.54 years) from Nanjing First Hospital and 57 matched healthy controls (HC) (33 males, 24 females, mean age 46.16 years) from the local community were recruited in this prospective study. Resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) were acquired within 14 days (mean 3.29 days) after the onset of mTBI. Then, large-scale FC and SC networks with 116 regions from the automated anatomical labeling (AAL) brain atlas were constructed. Graph theory analysis was used to analyze global and nodal metrics. Finally, correlations were assessed between topological properties and neurocognitive performances evaluated by the Montreal Cognitive Assessment (MoCA). Bonferroni correction was performed out for multiple comparisons in all involved analyses. Results Compared with HC, acute mTBI patients had a higher normalized clustering coefficient (γ) for FC (Cohen's d=4.076), and higher γ and small worldness (σ) for SC (Cohen's d=0.390 and Cohen's d=0.395). The mTBI group showed aberrant nodal degree (Dc), nodal efficiency (Ne), and nodal local efficiency (Nloc) for FC and aberrant Dc, nodal betweenness (Bc), nodal clustering coefficient (NCp) and Ne for SC mainly in the frontal and temporal, cerebellum, and subcortical areas. Acute mTBI patients also had higher functional-structural coupling strength at both the group and individual levels (Cohen's d=0.415). These aberrant global and nodal topological properties at functional and structural levels were associated with attention, orientation, memory, and naming performances (all P<0.05). Conclusions Our findings suggested that large-scale FC and SC network changes, higher correlation between FC and SC and cognitive impairment can be detected in the acute stage of mTBI. These network aberrances may be a compensatory mechanism for cognitive impairment in acute mTBI patients.
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Affiliation(s)
- Yin Liu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Fengfang Li
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Song’an Shang
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Peng Wang
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Xindao Yin
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Vijaya Prakash Krishnan Muthaiah
- Department of Rehabilitation Sciences, School of Public Health and Health Professions, State University of New York at Buffalo, Buffalo, NY, USA
| | - Liyan Lu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Yu-Chen Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
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Schizophrenia and psychedelic state: Dysconnection versus hyper-connection. A perspective on two different models of psychosis stemming from dysfunctional integration processes. Mol Psychiatry 2023; 28:59-67. [PMID: 35931756 DOI: 10.1038/s41380-022-01721-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 07/15/2022] [Accepted: 07/22/2022] [Indexed: 01/07/2023]
Abstract
Psychotic symptoms are a cross-sectional dimension affecting multiple diagnostic categories, despite schizophrenia represents the prototype of psychoses. Initially, dopamine was considered the most involved molecule in the neurobiology of schizophrenia. Over the next years, several biological factors were added to the discussion helping to constitute the concept of schizophrenia as a disease marked by a deficit of functional integration, contributing to the formulation of the Dysconnection Hypothesis in 1995. Nowadays the notion of dysconnection persists in the conceptualization of schizophrenia enriched by neuroimaging findings which corroborate the hypothesis. At the same time, in recent years, psychedelics received a lot of attention by the scientific community and astonishing findings emerged about the rearrangement of brain networks under the effect of these compounds. Specifically, a global decrease in functional connectivity was found, highlighting the disintegration of preserved and functional circuits and an increase of overall connectivity in the brain. The aim of this paper is to compare the biological bases of dysconnection in schizophrenia with the alterations of neuronal cyto-architecture induced by psychedelics and the consequent state of cerebral hyper-connection. These two models of psychosis, despite diametrically opposed, imply a substantial deficit of integration of neural signaling reached through two opposite paths.
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31
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Tran The J, Ansermet JP, Magistretti PJ, Ansermet F. Hyperactivity of the default mode network in schizophrenia and free energy: A dialogue between Freudian theory of psychosis and neuroscience. Front Hum Neurosci 2022; 16:956831. [PMID: 36590059 PMCID: PMC9795812 DOI: 10.3389/fnhum.2022.956831] [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: 05/30/2022] [Accepted: 11/21/2022] [Indexed: 12/15/2022] Open
Abstract
The economic conceptualization of Freudian metapsychology, based on an energetics model of the psyche's workings, offers remarkable commonalities with some recent discoveries in neuroscience, notably in the field of neuroenergetics. The pattern of cerebral activity at resting state and the identification of a default mode network (DMN), a network of areas whose activity is detectable at baseline conditions by neuroimaging techniques, offers a promising field of research in the dialogue between psychoanalysis and neuroscience. In this article we study one significant clinical application of this interdisciplinary dialogue by looking at the role of the DMN in the psychopathology of schizophrenia. Anomalies in the functioning of the DMN have been observed in schizophrenia. Studies have evidenced the existence of hyperactivity in this network in schizophrenia patients, particularly among those for whom a positive symptomatology is dominant. These data are particularly interesting when considered from the perspective of the psychoanalytic understanding of the positive symptoms of psychosis, most notably the Freudian hypothesis of delusions as an "attempt at recovery." Combining the data from research in neuroimaging of schizophrenia patients with the Freudian hypothesis, we propose considering the hyperactivity of the DMN as a consequence of a process of massive reassociation of traces occurring in schizophrenia. This is a process that may constitute an attempt at minimizing the excess of free energy present in psychosis. Modern models of active inference and the free energy principle (FEP) may shed some light on these processes.
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Affiliation(s)
- Jessica Tran The
- INSERM U1077 Neuropsychologie et Imagerie de la Mémoire Humaine, Caen, France,Centre Hospitalier Universitaire de Caen, Caen, France,Université de Caen Normandie, Caen, France,Ecole Pratique des Hautes Etudes, Université Paris Sciences et Lettres, Paris, France,Agalma Foundation Geneva, Geneva, Switzerland,Cyceron, Caen, France,*Correspondence: Jessica Tran The
| | | | - Pierre J. Magistretti
- Agalma Foundation Geneva, Geneva, Switzerland,Division of Biological and Environmental Sciences and Engineering (BESE), King Abdullah University of Science and Technology, Thuwal, Saudi Arabia,Brain Mind Institute, Swiss Federal Institute of Technology Lausanne, Lausanne, Switzerland
| | - Francois Ansermet
- Agalma Foundation Geneva, Geneva, Switzerland,Département de Psychiatrie, Faculté de Médecine, Université de Genève, Geneva, Switzerland
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32
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de Bartolomeis A, De Simone G, Ciccarelli M, Castiello A, Mazza B, Vellucci L, Barone A. Antipsychotics-Induced Changes in Synaptic Architecture and Functional Connectivity: Translational Implications for Treatment Response and Resistance. Biomedicines 2022; 10:3183. [PMID: 36551939 PMCID: PMC9776416 DOI: 10.3390/biomedicines10123183] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 12/02/2022] [Accepted: 12/04/2022] [Indexed: 12/14/2022] Open
Abstract
Schizophrenia is a severe mental illness characterized by alterations in processes that regulate both synaptic plasticity and functional connectivity between brain regions. Antipsychotics are the cornerstone of schizophrenia pharmacological treatment and, beyond occupying dopamine D2 receptors, can affect multiple molecular targets, pre- and postsynaptic sites, as well as intracellular effectors. Multiple lines of evidence point to the involvement of antipsychotics in sculpting synaptic architecture and remodeling the neuronal functional unit. Furthermore, there is an increasing awareness that antipsychotics with different receptor profiles could yield different interregional patterns of co-activation. In the present systematic review, we explored the fundamental changes that occur under antipsychotics' administration, the molecular underpinning, and the consequences in both acute and chronic paradigms. In addition, we investigated the relationship between synaptic plasticity and functional connectivity and systematized evidence on different topographical patterns of activation induced by typical and atypical antipsychotics.
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Affiliation(s)
- Andrea de Bartolomeis
- Section of Psychiatry, Laboratory of Translational and Molecular Psychiatry and Unit of Treatment-Resistant Psychosis, Department of Neuroscience, Reproductive Sciences and Odontostomatology, University Medical School of Naples “Federico II”, Via Pansini 5, 80131 Naples, Italy
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33
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Wang Y, Hu X, Li Y. Investigating cognitive flexibility deficit in schizophrenia using task-based whole-brain functional connectivity. Front Psychiatry 2022; 13:1069036. [PMID: 36479558 PMCID: PMC9719952 DOI: 10.3389/fpsyt.2022.1069036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 11/07/2022] [Indexed: 11/22/2022] Open
Abstract
Background Cognitive flexibility is a core cognitive control function supported by the brain networks of the whole-brain. Schizophrenic patients show deficits in cognitive flexibility in conditions such as task-switching. A large number of neuroimaging studies have revealed abnormalities in local brain activations associated with deficits in cognitive flexibility in schizophrenia, but the relationship between impaired cognitive flexibility and the whole-brain functional connectivity (FC) pattern is unclear. Method We investigated the task-based functional connectivity of the whole-brain in patients with schizophrenia and healthy controls during task-switching. Multivariate pattern analysis (MVPA) was utilized to investigate whether the FC pattern can be used as a feature to discriminate schizophrenia patients from healthy controls. Graph theory analysis was further used to quantify the degrees of integration and segregation in the whole-brain networks to interpret the different reconfiguration patterns of brain networks in schizophrenia patients and healthy controls. Results The results showed that the FC pattern classified schizophrenia patients and healthy controls with significant accuracy. Moreover, the altered whole-brain functional connectivity pattern was driven by a lower degree of network integration and segregation in schizophrenia, indicating that both global and local information transfers at the entire-network level were less efficient in schizophrenia patients than in healthy controls during task-switching processing. Conclusion These results investigated the group differences in FC profiles during task-switching and not only elucidated that FC patterns are changed in schizophrenic patients, suggesting that task-based FC could be used as a potential neuromarker to discriminate schizophrenia patients from healthy controls in cognitive flexibility but also provide increased insight into the brain network organization that may contribute to impaired cognitive flexibility.
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Affiliation(s)
- Yanqing Wang
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Xueping Hu
- School of Linguistic Science and Art, Jiangsu Normal University, Xuzhou, China
- Key Laboratory of Language and Cognitive Neuroscience of Jiangsu Province, Collaborative Innovation Center for Language Ability, Xuzhou, China
| | - Yilu Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
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León-Ortiz P, Reyes-Madrigal F, Kochunov P, Gómez-Cruz G, Moncada-Habib T, Malacara M, Mora-Durán R, Rowland LM, de la Fuente-Sandoval C. White matter alterations and the conversion to psychosis: A combined diffusion tensor imaging and glutamate 1H MRS study. Schizophr Res 2022; 249:85-92. [PMID: 32595100 PMCID: PMC10025976 DOI: 10.1016/j.schres.2020.06.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 06/08/2020] [Accepted: 06/10/2020] [Indexed: 12/14/2022]
Abstract
INTRODUCTION Widespread white matter abnormalities and alterations in glutamate levels have been reported in patients with schizophrenia. We hypothesized that alterations in white matter integrity and glutamate levels in individuals at clinical high risk (CHR) for psychosis are associated with the subsequent development of psychosis. METHODS Participants included 33 antipsychotic naïve CHR (Female 7/Male 26, Age 19.55 (4.14) years) and 38 healthy controls (Female 10/Male 28, Age 20.92 (3.37) years). Whole brain diffusion tensor imaging for fractional anisotropy (FA) and right frontal white matter proton magnetic resonance spectroscopy for glutamate levels were acquired. CHR participants were clinically followed for 2 years to determine conversion to psychosis. RESULTS CHR participants that transitioned to psychosis (N = 7, 21%) were characterized by significantly lower FA values in the posterior thalamic radiation compared to those who did not transition and healthy controls. In the CHR group that transitioned to psychosis only, positive exploratory correlations between glutamate levels and FA values of the posterior thalamic radiation and the retrolenticular part of the internal capsule and a negative correlation between glutamate levels and the cingulum FA values were found. CONCLUSION The results of the present study highlight that alterations in white matter structure and glutamate are related with the conversion to psychosis.
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Affiliation(s)
- Pablo León-Ortiz
- Laboratory of Experimental Psychiatry, Instituto Nacional de Neurología y Neurocirugía, Mexico City, Mexico; Department of Education, Instituto Nacional de Neurología y Neurocirugía, Mexico City, Mexico
| | - Francisco Reyes-Madrigal
- Laboratory of Experimental Psychiatry, Instituto Nacional de Neurología y Neurocirugía, Mexico City, Mexico
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, United States of America
| | - Gladys Gómez-Cruz
- Laboratory of Experimental Psychiatry, Instituto Nacional de Neurología y Neurocirugía, Mexico City, Mexico
| | - Tomás Moncada-Habib
- Laboratory of Experimental Psychiatry, Instituto Nacional de Neurología y Neurocirugía, Mexico City, Mexico
| | - Melanie Malacara
- Laboratory of Experimental Psychiatry, Instituto Nacional de Neurología y Neurocirugía, Mexico City, Mexico
| | - Ricardo Mora-Durán
- Emergency Department, Hospital Fray Bernardino Álvarez, Mexico City, Mexico
| | - Laura M Rowland
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, United States of America
| | - Camilo de la Fuente-Sandoval
- Laboratory of Experimental Psychiatry, Instituto Nacional de Neurología y Neurocirugía, Mexico City, Mexico; Neuropsychiatry Department, Instituto Nacional de Neurología y Neurocirugía, Mexico City, Mexico.
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Menon V, Palaniyappan L, Supekar K. Integrative Brain Network and Salience Models of Psychopathology and Cognitive Dysfunction in Schizophrenia. Biol Psychiatry 2022:S0006-3223(22)01637-7. [PMID: 36702660 DOI: 10.1016/j.biopsych.2022.09.029] [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: 03/25/2022] [Revised: 08/09/2022] [Accepted: 09/06/2022] [Indexed: 01/28/2023]
Abstract
Brain network models of cognitive control are central to advancing our understanding of psychopathology and cognitive dysfunction in schizophrenia. This review examines the role of large-scale brain organization in schizophrenia, with a particular focus on a triple-network model of cognitive control and its role in aberrant salience processing. First, we provide an overview of the triple network involving the salience, frontoparietal, and default mode networks and highlight the central role of the insula-anchored salience network in the aberrant mapping of salient external and internal events in schizophrenia. We summarize the extensive evidence that has emerged from structural, neurochemical, and functional brain imaging studies for aberrancies in these networks and their dynamic temporal interactions in schizophrenia. Next, we consider the hypothesis that atypical striatal dopamine release results in misattribution of salience to irrelevant external stimuli and self-referential mental events. We propose an integrated triple-network salience-based model incorporating striatal dysfunction and sensitivity to perceptual and cognitive prediction errors in the insula node of the salience network and postulate that dysregulated dopamine modulation of salience network-centered processes contributes to the core clinical phenotype of schizophrenia. Thus, a powerful paradigm to characterize the neurobiology of schizophrenia emerges when we combine conceptual models of salience with large-scale cognitive control networks in a unified manner. We conclude by discussing potential therapeutic leads on restoring brain network dysfunction in schizophrenia.
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Affiliation(s)
- Vinod Menon
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California; Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California; Wu Tsai Neurosciences Institute, Stanford University School of Medicine, Stanford, California.
| | - Lena Palaniyappan
- Department of Psychiatry and Robarts Research Institute, University of Western Ontario, London, Ontario, Canada; Lawson Health Research Institute, London, Ontario, Canada; Douglas Mental Health University Institute, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Kaustubh Supekar
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California; Wu Tsai Neurosciences Institute, Stanford University School of Medicine, Stanford, California
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36
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Liu N, Lencer R, Yang Z, Zhang W, Yang C, Zeng J, Sweeney JA, Gong Q, Lui S. Altered functional synchrony between gray and white matter as a novel indicator of brain system dysconnectivity in schizophrenia. Psychol Med 2022; 52:2540-2548. [PMID: 33436114 DOI: 10.1017/s0033291720004420] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
BACKGROUND There is increasing evidence that blood oxygenation level-dependent signaling in white matter (WM) reflects WM functional activity. Whether this activity is altered in schizophrenia remains uncertain, as does whether it is related to established alterations of gray matter (GM) or the microstructure of WM tracts. METHODS A total of 153 antipsychotic-naïve schizophrenia patients and 153 healthy comparison subjects were assessed by resting-state functional magnetic resonance imaging, diffusion tensor imaging, and high-resolution T1-weighted imaging. We tested for case-control differences in the functional activity of WM, and examined their relation to the functional activity of GM and WM microstructure. The relations between fractional anisotropy (FA) in WM and GM-WM functional synchrony were investigated as well. Then, we examined the associations of identified abnormalities to age, duration of untreated psychosis (DUP), and symptom severity. RESULTS Schizophrenia patients displayed reductions of the amplitude of low-frequency fluctuations (ALFF), GM-WM functional synchrony, and FA in widespread regions. Specifically, the genu of corpus callosum not only had weakening in the synchrony of functional activity but also had reduced ALFF and FA. Positive associations were found between FA and functional synchrony in the genu of corpus callosum as well. No significant association was found between identified abnormalities and DUP, and symptom severity. CONCLUSIONS The widespread weakening in the synchrony of functional activity of GM and WM provided novel evidence for functional alterations in schizophrenia. Regarding the WM function as a component of brain systems and investigating its alternation represent a promising direction for future research.
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Affiliation(s)
- Naici Liu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Rebekka Lencer
- Department of Psychiatry and Psychotherapy, Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Muenster, Muenster, Germany
| | - Zhipeng Yang
- College of Electronic Engineering, Chengdu University of Information Technology, Chengdu, PR, China
| | - Wenjing Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Chengmin Yang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Jiaxin Zeng
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - John A Sweeney
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, USA
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Su Lui
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
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Network hub centrality and working memory performance in schizophrenia. SCHIZOPHRENIA 2022; 8:76. [PMID: 36151201 PMCID: PMC9508261 DOI: 10.1038/s41537-022-00288-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 09/12/2022] [Indexed: 11/08/2022]
Abstract
Cognitive impairment, and working memory deficits in particular, are debilitating, treatment-resistant aspects of schizophrenia. Dysfunction of brain network hubs, putatively related to altered neurodevelopment, is thought to underlie the cognitive symptoms associated with this illness. Here, we used weighted degree, a robust graph theory metric representing the number of weighted connections to a node, to quantify centrality in cortical hubs in 29 patients with schizophrenia and 29 age- and gender-matched healthy controls and identify the critical nodes that underlie working memory performance. In both patients and controls, elevated weighted degree in the default mode network (DMN) was generally associated with poorer performance (accuracy and reaction time). Higher degree in the ventral attention network (VAN) nodes in the right superior temporal cortex was associated with better performance (accuracy) in patients. Degree in several prefrontal and parietal areas was associated with cognitive performance only in patients. In regions that are critical for sustained attention, these correlations were primarily driven by between-network connectivity in patients. Moreover, a cross-validated prediction analysis showed that a linear model using a summary degree score can be used to predict an individual’s working memory accuracy (r = 0.35). Our results suggest that schizophrenia is associated with dysfunctional hubs in the cortical systems supporting internal and external cognition and highlight the importance of topological network analysis in the search of biomarkers for cognitive deficits in schizophrenia.
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Kristanto D, Liu X, Sommer W, Hildebrandt A, Zhou C. What do neuroanatomical networks reveal about the ontology of human cognitive abilities? iScience 2022; 25:104706. [PMID: 35865139 PMCID: PMC9293763 DOI: 10.1016/j.isci.2022.104706] [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: 12/20/2021] [Revised: 05/15/2022] [Accepted: 06/28/2022] [Indexed: 11/18/2022] Open
Abstract
Over the last decades, cognitive psychology has come to a fair consensus about the human intelligence ontological structure. However, it remains an open question whether anatomical properties of the brain support the same ontology. The present study explored the ontological structure derived from neuroanatomical networks associated with performance on 15 cognitive tasks indicating various abilities. Results suggest that the brain-derived (neurometric) ontology partly agrees with the cognitive performance-derived (psychometric) ontology complemented with interpretable differences. Moreover, the cortical areas associated with different inferred abilities are segregated, with little or no overlap. Nevertheless, these spatially segregated cortical areas are integrated via denser white matter structural connections as compared with the general brain connectome. The integration of ability-related cortical networks constitutes a neural counterpart to the psychometric construct of general intelligence, while the consistency and difference between psychometric and neurometric ontologies represent crucial pieces of knowledge for theory building, clinical diagnostics, and treatment. Psychometric and neurometric cognitive ontologies are partly equivalent Ability-related brain areas are ontologically segregated with little to no overlap However, ability-related brain areas are densely interconnected by fiber tracts
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Han S, Xu Y, Guo HR, Fang K, Wei Y, Liu L, Cheng J, Zhang Y, Cheng J. Two distinct subtypes of obsessive compulsive disorder revealed by a framework integrating multimodal neuroimaging information. Hum Brain Mapp 2022; 43:4254-4265. [PMID: 35726798 PMCID: PMC9435007 DOI: 10.1002/hbm.25951] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 03/14/2022] [Accepted: 05/06/2022] [Indexed: 12/03/2022] Open
Abstract
Patients with obsessive compulsive disorder (OCD) exhibit tremendous heterogeneity in structural and functional neuroimaging aberrance. However, most previous studies just focus on group‐level aberrance of a single modality ignoring heterogeneity and multimodal features. On that account, we aimed to uncover OCD subtypes integrating structural and functional neuroimaging features with the help of a multiview learning method and examined multimodal aberrance for each subtype. Ninety‐nine first‐episode untreated patients with OCD and 104 matched healthy controls (HCs) undergoing structural and functional MRI were included in this study. Voxel‐based morphometric and amplitude of low‐frequency fluctuation (ALFF) were adopted to assess gray matter volumes (GMVs) and the spontaneous neuronal fluctuations respectively. Structural/functional distance network was obtained by calculating Euclidean distance between pairs of regional GMVs/ALFF values across patients. Similarity network fusion, one of multiview learning methods capturing shared and complementary information from multimodal data sources, was used to fuse multimodal distance networks into one fused network. Then spectral clustering was adopted to categorize patients into subtypes. As a result, two robust subtypes were identified. These two subtypes presented opposite GMV aberrance and distinct ALFF aberrance compared with HCs while shared indistinguishable clinical and demographic features. In addition, these two subtypes exhibited opposite structure–function difference correlation reflecting distinct adaptive modifications between multimodal aberrance. Altogether, these results uncover two objective subtypes with distinct multimodal aberrance and provide a new insight into taxonomy of OCD.
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Affiliation(s)
- Shaoqiang Han
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, China.,Henan Engineering Research Center of Brain Function Development and Application, China
| | - Yinhuan Xu
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, China.,Henan Engineering Research Center of Brain Function Development and Application, China
| | - Hui-Rong Guo
- Department of Psychiatry, the First Affiliated Hospital of Zhengzhou University, China
| | - Keke Fang
- Department of Pharmacy, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, China
| | - Yarui Wei
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, China.,Henan Engineering Research Center of Brain Function Development and Application, China
| | - Liang Liu
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, China.,Henan Engineering Research Center of Brain Function Development and Application, China
| | - Junying Cheng
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, China.,Henan Engineering Research Center of Brain Function Development and Application, China
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, China.,Henan Engineering Research Center of Brain Function Development and Application, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, China.,Henan Engineering Research Center of Brain Function Development and Application, China
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Bortolin K, Delavari F, Preti MG, Sandini C, Mancini V, Mullier E, Van De Ville D, Eliez S. Neural substrates of psychosis revealed by altered dependencies between brain activity and white-matter architecture in individuals with 22q11 deletion syndrome. NEUROIMAGE: CLINICAL 2022; 35:103075. [PMID: 35717884 PMCID: PMC9218553 DOI: 10.1016/j.nicl.2022.103075] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 05/10/2022] [Accepted: 06/01/2022] [Indexed: 11/29/2022] Open
Abstract
Function-structural dependency is altered in patients with 22q11 deletion syndrome. Stronger dependency in subcortical regions correlates with psychotic symptoms. Weaker dependency in cingulate cortex correlates with psychotic symptoms. Multimodal and not unimodal indexes were correlated with psychosis emergence.
Background Dysconnectivity has been consistently proposed as a major key mechanism in psychosis. Indeed, disruptions in large-scale structural and functional brain networks have been associated with psychotic symptoms. However, brain activity is largely constrained by underlying white matter pathways and the study of function-structure dependency, compared to conventional unimodal analysis, allows a biologically relevant assessment of neural mechanisms. The 22q11.2 deletion syndrome (22q11DS) constitutes a remarkable opportunity to study the pathophysiological processes of psychosis. Methods 58 healthy controls and 57 deletion carriers, aged from 16 to 32 years old, underwent resting-state functional and diffusion-weighted magnetic resonance imaging. Deletion carriers were additionally fully assessed for psychotic symptoms. Firstly, we used a graph signal processing method to combine brain activity and structural connectivity measures to obtain regional structural decoupling indexes (SDIs). We use SDI to assess the differences of functional structural dependency (FSD) across the groups. Subsequently we investigated how alterations in FSDs are associated with the severity of positive psychotic symptoms in participants with 22q11DS. Results In line with previous findings, participants in both groups showed a spatial gradient of FSD ranging from sensory-motor regions (stronger FSD) to regions involved in higher-order function (weaker FSD). Compared to controls, in participants with 22q11DS, and further in deletion carriers with more severe positive psychotic symptoms, the functional activity was more strongly dependent on the structure in parahippocampal gyrus and subcortical dopaminergic regions, while it was less dependent within the cingulate cortex. This analysis revealed group differences not otherwise detected when assessing the structural and functional nodal measures separately. Conclusions Our findings point toward a disrupted modulation of functional activity on the underlying structure, which was further associated to psychopathology for candidate critical regions in 22q11DS. This study provides the first evidence for the clinical relevance of function-structure dependency and its contribution to the emergence of psychosis.
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Affiliation(s)
- Karin Bortolin
- Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland; Medical Image Processing Laboratory, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Farnaz Delavari
- Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland; Medical Image Processing Laboratory, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
| | - Maria Giulia Preti
- Medical Image Processing Laboratory, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland; CIBM Center for Biomedical Imaging, Lausanne, Switzerland.
| | - Corrado Sandini
- Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland.
| | - Valentina Mancini
- Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland.
| | - Emeline Mullier
- Autism Brain and Behavior Laboratory, Department of Psychiatry, University of Geneva, Geneva, Switzerland
| | - Dimitri Van De Ville
- Medical Image Processing Laboratory, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland; CIBM Center for Biomedical Imaging, Lausanne, Switzerland.
| | - Stephan Eliez
- Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland; Department of Genetic Medicine and Development, University of Geneva School of Medicine, Geneva, Switzerland.
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41
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Biagianti B, Bigoni D, Maggioni E, Brambilla P. Can neuroimaging-based biomarkers predict response to cognitive remediation in patients with psychosis? A state-of-the-art review. J Affect Disord 2022; 305:196-205. [PMID: 35283181 DOI: 10.1016/j.jad.2022.03.006] [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: 05/11/2021] [Revised: 03/04/2022] [Accepted: 03/06/2022] [Indexed: 11/30/2022]
Abstract
BACKGROUND Cognitive Remediation (CR) is designed to halt the pathological neural systems that characterize major psychotic disorders (MPD), and its main objective is to improve cognitive functioning. The magnitude of CR-induced cognitive gains greatly varies across patients with MPD, with up to 40% of patients not showing gains in global cognitive performance. This is likely due to the high degree of heterogeneity in neural activation patterns underlying cognitive endophenotypes, and to inter-individual differences in neuroplastic potential, cortical organization and interaction between brain systems in response to learning. Here, we review studies that used neuroimaging to investigate which biomarkers could potentially serve as predictors of treatment response to CR in MPD. METHODS This systematic review followed the PRISMA guidelines. An electronic database search (Embase, Elsevier; Scopus, PsycINFO, APA; PubMed, APA) was conducted in March 2021. peer-reviewed, English-language studies were included if they reported data for adults aged 18+ with MPD, reported findings from randomized controlled trials or single-arm trials of CR; and presented neuroimaging data. RESULTS Sixteen studies were included and eight neuroimaging-based biomarkers were identified. Auditory mismatch negativity (3 studies), auditory steady-state response (1), gray matter morphology (3), white matter microstructure (1), and task-based fMRI (7) can predict response to CR. Efference copy corollary/discharge, resting state, and thalamo-cortical connectivity (1) require further research prior to being implemented. CONCLUSIONS Translational research on neuroimaging-based biomarkers can help elucidate the mechanisms by which CR influences the brain's functional architecture, better characterize psychotic subpopulations, and ultimately deliver CR that is optimized and personalized.
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Affiliation(s)
- Bruno Biagianti
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy.
| | - Davide Bigoni
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Eleonora Maggioni
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Paolo Brambilla
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy; Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
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42
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Zhang Y, Peng Y, Song Y, Zhou Y, Zhang S, Yang G, Yang Y, Li W, Yue W, Lv L, Zhang D. Abnormal functional connectivity of the striatum in first-episode drug-naive early-onset Schizophrenia. Brain Behav 2022; 12:e2535. [PMID: 35384392 PMCID: PMC9120884 DOI: 10.1002/brb3.2535] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 11/03/2021] [Accepted: 01/27/2022] [Indexed: 11/17/2022] Open
Abstract
Abnormal brain network connectivity is strongly implicated in the pathogenesis of schizophrenia. The striatum, consisting of the caudate and putamen, is the major treatment target for antipsychotics, the primary treatments for schizophrenia; however, there are few studies on the functional connectivity (FC) of striatum in drug-naive early-onset schizophrenia (EOS) patients. We examined the FC values of the caudate nucleus and putamen with whole brain by resting-state functional magnetic resonance imaging (RS-fMRI) and the associations with indices of clinical severity. Patients demonstrated abnormal FC between subregions of the putamen and both the visual network (left middle occipital gyrus) and default mode network (bilateral anterior cingulate, left superior frontal, and right middle frontal gyri). Furthermore, FC between dorsorostral putamen and left superior frontal gyrus correlated with both positive symptom subscore and total score on the Positive and Negative Syndrome Scale (PANSS). These findings demonstrate abnormal FC between the striatum and other brain areas even in the early stages of schizophrenia, supporting neurodevelopmental disruption in disease etiology and expression.
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Affiliation(s)
- Yan Zhang
- Psychiatry Institute of Mental Health/Peking University Sixth Hospital, Peking University, Beijing, China.,Henan Mental Hospital, The 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 Peng
- Department of Pediatric Rehabilitation Medicine, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Yichen Song
- Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China.,International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Youqi Zhou
- Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China.,Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
| | - Sen Zhang
- Child and Adolescent Psychiatry Department, Mental Health Center of Shantou University, Shantou, Guangdong, China
| | - Ge Yang
- Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
| | - Yongfeng Yang
- Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China.,International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Wenqiang Li
- Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China.,Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
| | - Weihua Yue
- Psychiatry Institute of Mental Health/Peking University Sixth Hospital, Peking University, Beijing, China
| | - Luxian Lv
- Henan Mental Hospital, The 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
| | - Dai Zhang
- Psychiatry Institute of Mental Health/Peking University Sixth Hospital, Peking University, Beijing, China
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Falakshahi H, Rokham H, Fu Z, Iraji A, Mathalon DH, Ford JM, Mueller BA, Preda A, van Erp TGM, Turner JA, Plis S, Calhoun VD. Path Analysis: A Method to Estimate Altered Pathways in Time-varying Graphs of Neuroimaging Data. Netw Neurosci 2022; 6:634-664. [PMID: 36204419 PMCID: PMC9531579 DOI: 10.1162/netn_a_00247] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 03/23/2022] [Indexed: 11/16/2022] Open
Abstract
Graph-theoretical methods have been widely used to study human brain networks in psychiatric disorders. However, the focus has primarily been on global graphic metrics with little attention to the information contained in paths connecting brain regions. Details of disruption of these paths may be highly informative for understanding disease mechanisms. To detect the absence or addition of multistep paths in the patient group, we provide an algorithm estimating edges that contribute to these paths with reference to the control group. We next examine where pairs of nodes were connected through paths in both groups by using a covariance decomposition method. We apply our method to study resting-state fMRI data in schizophrenia versus controls. Results show several disconnectors in schizophrenia within and between functional domains, particularly within the default mode and cognitive control networks. Additionally, we identify new edges generating additional paths. Moreover, although paths exist in both groups, these paths take unique trajectories and have a significant contribution to the decomposition. The proposed path analysis provides a way to characterize individuals by evaluating changes in paths, rather than just focusing on the pairwise relationships. Our results show promise for identifying path-based metrics in neuroimaging data.
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Affiliation(s)
- Haleh Falakshahi
- 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
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Hooman Rokham
- 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
- School of Electrical and Computer Engineering, Georgia Institute of Technology, 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
| | - 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
| | - Daniel H. Mathalon
- Department of Psychiatry, University of California, San Francisco, CA, USA
- San Francisco VA Medical Center, San Francisco, CA, USA
| | - Judith M. Ford
- Department of Psychiatry, University of California, 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
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - Theo G. M. van Erp
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine, CA, USA
| | - Jessica A. Turner
- 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
- Department of Psychology, Georgia State University, Atlanta, GA, USA
| | - Sergey Plis
- 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
- Department of Computer Science, Georgia State University, Atlanta, GA, 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
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
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Ye C, Huang J, Liang L, Yan Z, Qi Z, Kang X, Liu Z, Dong H, Lv H, Ma T, Lu J. Coupling of brain activity and structural network in multiple sclerosis: A graph frequency analysis study. J Neurosci Res 2022; 100:1226-1238. [PMID: 35184336 DOI: 10.1002/jnr.25028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 12/06/2021] [Accepted: 01/27/2022] [Indexed: 11/10/2022]
Affiliation(s)
| | - Jing Huang
- Department of Radiology and Nuclear Medicine Xuanwu Hospital, Capital Medical University Beijing China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics Capital Medical University Beijing China
| | - Li Liang
- Department of Electronic and Information Engineering Harbin Institute of Technology at Shenzhen Shenzhen China
| | - Zehong Yan
- Department of Electronic and Information Engineering Harbin Institute of Technology at Shenzhen Shenzhen China
| | - Zhigang Qi
- Department of Radiology and Nuclear Medicine Xuanwu Hospital, Capital Medical University Beijing China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics Capital Medical University Beijing China
| | - Xiong Kang
- Department of Radiology and Nuclear Medicine Xuanwu Hospital, Capital Medical University Beijing China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics Capital Medical University Beijing China
| | - Zheng Liu
- Department of Neurology Xuanwu Hospital, Capital Medical University Beijing China
| | - Huiqing Dong
- Department of Neurology Xuanwu Hospital, Capital Medical University Beijing China
| | - Haiyan Lv
- Mindsgo Life Science Shenzhen Co. Ltd Shenzhen China
| | - Ting Ma
- Peng Cheng Laboratory Shenzhen China
- Department of Electronic and Information Engineering Harbin Institute of Technology at Shenzhen Shenzhen China
- Advanced Innovation Center for Human Brain Protection Capital Medical University Beijing China
- National Clinical Research Center for Geriatric Disorders Xuanwu Hospital Capital Medical University Beijing China
| | - Jie Lu
- Department of Radiology and Nuclear Medicine Xuanwu Hospital, Capital Medical University Beijing China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics Capital Medical University Beijing China
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Antonucci LA, Fazio L, Pergola G, Blasi G, Stolfa G, Di Palo P, Mucci A, Rocca P, Brasso C, di Giannantonio M, Maria Giordano G, Monteleone P, Pompili M, Siracusano A, Bertolino A, Galderisi S, Maj M. Joint structural-functional magnetic resonance imaging features are associated with diagnosis and real-world functioning in patients with schizophrenia. Schizophr Res 2022; 240:193-203. [PMID: 35032904 DOI: 10.1016/j.schres.2021.12.029] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 10/20/2021] [Accepted: 12/22/2021] [Indexed: 12/28/2022]
Abstract
OBJECTIVE Earlier evidence suggested that structural-functional covariation in schizophrenia patients (SCZ) is associated with cognition, a predictor of functioning. Moreover, studies suggested that functional brain abnormalities of schizophrenia may be related with structural network features. However, only few studies have investigated the relationship between structural-functional covariation and both diagnosis and functioning in SCZ. We hypothesized that structural-functional covariation networks associated with diagnosis are related to real-world functioning in SCZ. METHODS We performed joint Independent Component Analysis on T1 images and resting-state fMRI-based Degree Centrality (DC) maps from 89 SCZ and 285 controls. Structural-functional covariation networks in which we found a main effect of diagnosis underwent correlation analysis to investigate their relationship with functioning. Covariation networks showing a significant association with both diagnosis and functioning underwent univariate analysis to better characterize group-level differences at the spatial level. RESULTS A structural-functional covariation network characterized by frontal, temporal, parietal and thalamic structural estimates significantly covaried with temporo-parietal resting-state DC. Compared with controls, SCZ had reduced structural-functional covariation within this network (pFDR = 0.005). The same measure correlated positively with both social and occupational functioning (both pFDR = 0.042). Univariate analyses revealed grey matter deviations in SCZ compared with controls within this structural-functional network in hippocampus, cerebellum, thalamus, orbito-frontal cortex, and insula. No group differences were found in DC. CONCLUSIONS Findings support the existence of a phenotypical association between group-level differences and inter-individual heterogeneity of functional deficits in SCZ. Given that only the joint structural/functional analysis revealed this association, structural-functional covariation may be a potentially relevant schizophrenia phenotype.
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Affiliation(s)
- Linda A Antonucci
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Leonardo Fazio
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Giulio Pergola
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Giuseppe Blasi
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Giuseppe Stolfa
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Piergiuseppe Di Palo
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Armida Mucci
- Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Paola Rocca
- Department of Neuroscience, Section of Psychiatry, University of Turin, Turin, Italy
| | - Claudio Brasso
- Department of Neuroscience, Section of Psychiatry, University of Turin, Turin, Italy
| | | | | | - Palmiero Monteleone
- Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", Section of Neuroscience, University of Salerno, Salerno, Italy
| | - Maurizio Pompili
- Department of Neurosciences, Mental Health, and Sensory Organs, S. Andrea Hospital, Sapienza University of Rome, Rome, Italy
| | - Alberto Siracusano
- Department of Systems Medicine, Psychiatry and Clinical Psychology Unit, Tor Vergata University of Rome, Rome, Italy
| | - Alessandro Bertolino
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy.
| | - Silvana Galderisi
- Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Mario Maj
- Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
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46
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Stein F, Buckenmayer E, Brosch K, Meller T, Schmitt S, Ringwald KG, Pfarr JK, Steinsträter O, Enneking V, Grotegerd D, Heindel W, Meinert S, Leehr EJ, Lemke H, Thiel K, Waltemate L, Winter A, Hahn T, Dannlowski U, Jansen A, Nenadić I, Krug A, Kircher T. Dimensions of Formal Thought Disorder and Their Relation to Gray- and White Matter Brain Structure in Affective and Psychotic Disorders. Schizophr Bull 2022; 48:902-911. [PMID: 35064667 PMCID: PMC9212109 DOI: 10.1093/schbul/sbac002] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Factorial dimensions and neurobiological underpinnings of formal thought disorders (FTD) have been extensively investigated in schizophrenia spectrum disorders (SSD). However, FTD are also highly prevalent in other disorders. Still, there is a lack of knowledge about transdiagnostic, structural brain correlates of FTD. In N = 1071 patients suffering from DSM-IV major depressive disorder, bipolar disorder, or SSD, we calculated a psychopathological factor model of FTD based on the SAPS and SANS scales. We tested the association of FTD dimensions with 3 T MRI measured gray matter volume (GMV) and white matter fractional anisotropy (FA) using regression and interaction models in SPM12. We performed post hoc confirmatory analyses in diagnostically equally distributed, age- and sex-matched sub-samples to test whether results were driven by diagnostic categories. Cross-validation (explorative and confirmatory) factor analyses revealed three psychopathological FTD factors: disorganization, emptiness, and incoherence. Disorganization was negatively correlated with a GMV cluster comprising parts of the middle occipital and angular gyri and positively with FA in the right posterior cingulum bundle and inferior longitudinal fascicle. Emptiness was negatively associated with left hippocampus and thalamus GMV. Incoherence was negatively associated with FA in bilateral anterior thalamic radiation, and positively with the hippocampal part of the right cingulum bundle. None of the gray or white matter associations interacted with diagnosis. Our results provide a refined mapping of cross-disorder FTD phenotype dimensions. For the first time, we demonstrated that their neuroanatomical signatures are associated with language-related gray and white matter structures independent of diagnosis.
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Affiliation(s)
- Frederike Stein
- To whom correspondence should be addressed; Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany; tel: +49-6421-58 63831, fax: +49-6421-58 65197, e-mail:
| | - Elena Buckenmayer
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Katharina Brosch
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany,Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Tina Meller
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany,Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Simon Schmitt
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany,Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany,Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, Hannover, Germany
| | - Kai Gustav Ringwald
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany,Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Julia Katharina Pfarr
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany,Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Olaf Steinsträter
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany,Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany,Core-Facility Brainimaging, Faculty of Medicine, Philipps-University Marburg, Marburg, Germany
| | - Verena Enneking
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Walter Heindel
- Department of Radiology, University of Münster, Münster, Germany
| | - Susanne Meinert
- Institute for Translational Psychiatry, University of Münster, Münster, Germany,Institute for Translational Neuroscience, University of Münster, Münster, Germany
| | - Elisabeth J Leehr
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Hannah Lemke
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Katharina Thiel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Lena Waltemate
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Alexandra Winter
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Tim Hahn
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Andreas Jansen
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany,Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany,Core-Facility Brainimaging, Faculty of Medicine, Philipps-University Marburg, Marburg, Germany
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany,Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Axel Krug
- Department of Psychiatry und Psychotherapy, University of Bonn, Bonn, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany,Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
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47
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Yang W, Xu X, Wang C, Cheng Y, Li Y, Xu S, Li J. Alterations of dynamic functional connectivity between visual and executive-control networks in schizophrenia. Brain Imaging Behav 2022; 16:1294-1302. [PMID: 34997915 DOI: 10.1007/s11682-021-00592-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 10/20/2021] [Indexed: 01/28/2023]
Abstract
Schizophrenia is a chronic mental disorder characterized by continuous or relapsing episodes of psychosis. While previous studies have detected functional network connectivity alterations in patients with schizophrenia, and most have focused on static functional connectivity. However, brain activity is believed to change dynamically over time. Therefore, we computed dynamic functional network connectivity using the sliding window method in 38 patients with schizophrenia and 31 healthy controls. We found that patients with schizophrenia exhibited higher occurrences in the weakly and sparsely connected state (state 3) than healthy controls, positively correlated with negative symptoms. In addition, patients exhibited fewer occurrences in a strongly connected state (state 4) than healthy controls. Lastly, the dynamic functional network connectivity between the right executive-control network and the medial visual network was decreased in schizophrenia patients compared to healthy controls. Our results further prove that brain activity is dynamic, and that alterations of dynamic functional network connectivity features might be a fundamental neural mechanism in schizophrenia.
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Affiliation(s)
- Weiliang Yang
- Laboratory of Biological Psychiatry, Institute of Mental Health, Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, Tianjin, 300222, China
| | - Xuexin Xu
- Department of Radiology, MRI Center, Tianjin Children Hospital, Tianjin Medical University Affiliated Tianjin Children Hospital, Tianjin, China
| | - Chunxiang Wang
- Department of Radiology, MRI Center, Tianjin Children Hospital, Tianjin Medical University Affiliated Tianjin Children Hospital, Tianjin, China
| | - Yongying Cheng
- Laboratory of Biological Psychiatry, Institute of Mental Health, Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, Tianjin, 300222, China
| | - Yan Li
- Laboratory of Biological Psychiatry, Institute of Mental Health, Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, Tianjin, 300222, China
| | - Shuli Xu
- Laboratory of Biological Psychiatry, Institute of Mental Health, Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, Tianjin, 300222, China
| | - Jie Li
- Laboratory of Biological Psychiatry, Institute of Mental Health, Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, Tianjin, 300222, China.
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48
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Sharaev M, Malashenkova I, Maslennikova A, Zakharova N, Bernstein A, Burnaev E, Mamedova G, Krynskiy S, Ogurtsov D, Kondrateva E, Druzhinina P, Zubrikhina M, Arkhipov A, Strelets V, Ushakov V. Diagnosis of Schizophrenia Based on the Data of Various Modalities: Biomarkers and Machine Learning Techniques (Review). Sovrem Tekhnologii Med 2022; 14:53-75. [PMID: 37181835 PMCID: PMC10171060 DOI: 10.17691/stm2022.14.5.06] [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: 05/20/2022] [Indexed: 05/16/2023] Open
Abstract
Schizophrenia is a socially significant mental disorder resulting frequently in severe forms of disability. Diagnosis, choice of treatment tactics, and rehabilitation in clinical psychiatry are mainly based on the assessment of behavioral patterns, socio-demographic data, and other investigations such as clinical observations and neuropsychological testing including examination of patients by the psychiatrist, self-reports, and questionnaires. In many respects, these data are subjective and therefore a large number of works have appeared in recent years devoted to the search for objective characteristics (indices, biomarkers) of the processes going on in the human body and reflected in the behavioral and psychoneurological patterns of patients. Such biomarkers are based on the results of instrumental and laboratory studies (neuroimaging, electro-physiological, biochemical, immunological, genetic, and others) and are successfully being used in neurosciences for understanding the mechanisms of the emergence and development of nervous system pathologies. Presently, with the advent of new effective neuroimaging, laboratory, and other methods of investigation and also with the development of modern methods of data analysis, machine learning, and artificial intelligence, a great number of scientific and clinical studies is being conducted devoted to the search for the markers which have diagnostic and prognostic value and may be used in clinical practice to objectivize the processes of establishing and clarifying the diagnosis, choosing and optimizing treatment and rehabilitation tactics, predicting the course and outcome of the disease. This review presents the analysis of the works which describe the correlates between the diagnosis of schizophrenia, established by health professionals, various manifestations of the psychiatric disorder (its subtype, variant of the course, severity degree, observed symptoms, etc.), and objectively measured characteristics/quantitative indicators (anatomical, functional, immunological, genetic, and others) obtained during instrumental and laboratory examinations of patients. A considerable part of these works has been devoted to correlates/biomarkers of schizophrenia based on the data of structural and functional (at rest and under cognitive load) MRI, EEG, tractography, and immunological data. The found correlates/biomarkers reflect anatomic disorders in the specific brain regions, impairment of functional activity of brain regions and their interconnections, specific microstructure of the brain white matter and the levels of connectivity between the tracts of various structures, alterations of electrical activity in various parts of the brain in different EEG spectral ranges, as well as changes in the innate and adaptive links of immunity. Current methods of data analysis and machine learning to search for schizophrenia biomarkers using the data of diverse modalities and their application during building and interpretation of predictive diagnostic models of schizophrenia have been considered in the present review.
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Affiliation(s)
- M.G. Sharaev
- Senior Researcher; Skolkovo Institute of Science and Technology (Skoltech), Territory of Skolkovo Innovation Center, Bldg 1, 30 Bolshoy Boulevard, Moscow, 121205, Russia; Department Senior Researcher; N.A. Alekseyev Psychiatric Clinical Hospital No.1, 2 Zagorodnoye Shosse, Moscow, 117152, Russia
- Corresponding author: Maksim G. Sharaev, e-mail:
| | - I.K. Malashenkova
- Head of the Laboratory of Molecular Immunology and Virology; National Research Center “Kurchatov Institute”, 1 Akademika Kurchatova Square, Moscow, 123182, Russia; Senior Researcher, Laboratory of Clinical Immunology; Federal Research and Clinical Center of Physical-Chemical Medicine, Federal Medical Biological Agency of Russia, 1A Malaya Pirogovskaya St., Moscow, 119435, Russia
| | - A.V. Maslennikova
- Researcher, Laboratory of Human Higher Nervous Activity; Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Sciences, 5A Butlerova St., Moscow, 117485, Russia
| | - N.V. Zakharova
- Head of the Laboratory for Fundamental Research Methods, Research Clinical Center of Neuropsychiatry; N.A. Alekseyev Psychiatric Clinical Hospital No.1, 2 Zagorodnoye Shosse, Moscow, 117152, Russia
| | - A.V. Bernstein
- Professor, Professor of the Center of Applied Artificial Intelligence; Skolkovo Institute of Science and Technology (Skoltech), Territory of Skolkovo Innovation Center, Bldg 1, 30 Bolshoy Boulevard, Moscow, 121205, Russia
| | - E.V. Burnaev
- Associate Professor, Professor of the Center of Applied Artificial Intelligence; Skolkovo Institute of Science and Technology (Skoltech), Territory of Skolkovo Innovation Center, Bldg 1, 30 Bolshoy Boulevard, Moscow, 121205, Russia
| | - G.S. Mamedova
- Junior Researcher, Laboratory for Fundamental Research Methods, Research Clinical Center of Neuropsychiatry; N.A. Alekseyev Psychiatric Clinical Hospital No.1, 2 Zagorodnoye Shosse, Moscow, 117152, Russia
| | - S.A. Krynskiy
- Researcher, Laboratory of Molecular Immunology and Virology; National Research Center “Kurchatov Institute”, 1 Akademika Kurchatova Square, Moscow, 123182, Russia
| | - D.P. Ogurtsov
- Researcher, Laboratory of Molecular Immunology and Virology; National Research Center “Kurchatov Institute”, 1 Akademika Kurchatova Square, Moscow, 123182, Russia
| | - E.A. Kondrateva
- PhD Student; Skolkovo Institute of Science and Technology (Skoltech), Territory of Skolkovo Innovation Center, Bldg 1, 30 Bolshoy Boulevard, Moscow, 121205, Russia
| | - P.V. Druzhinina
- PhD Student; Skolkovo Institute of Science and Technology (Skoltech), Territory of Skolkovo Innovation Center, Bldg 1, 30 Bolshoy Boulevard, Moscow, 121205, Russia
| | - M.O. Zubrikhina
- PhD Student; Skolkovo Institute of Science and Technology (Skoltech), Territory of Skolkovo Innovation Center, Bldg 1, 30 Bolshoy Boulevard, Moscow, 121205, Russia
| | - A.Yu. Arkhipov
- Researcher, Laboratory of Human Higher Nervous Activity; Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Sciences, 5A Butlerova St., Moscow, 117485, Russia
| | - V.B. Strelets
- Chief Researcher, Laboratory of Human Higher Nervous Activity; Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Sciences, 5A Butlerova St., Moscow, 117485, Russia
| | - V.L. Ushakov
- Associate Professor, Chief Researcher, Institute for Advanced Brain Research; Lomonosov Moscow State University, 27/1 Lomonosov Avenue, Moscow, 119192, Russia; Head of the Department; N.A. Alekseyev Psychiatric Clinical Hospital No.1, 2 Zagorodnoye Shosse, Moscow, 117152, Russia; Senior Researcher; National Research Nuclear University MEPhI, 31 Kashirskoye Shosse, Moscow, 115409, Russia
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49
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Plechawska-Wójcik M, Karczmarek P, Krukow P, Kaczorowska M, Tokovarov M, Jonak K. Recognition of Electroencephalography-Related Features of Neuronal Network Organization in Patients With Schizophrenia Using the Generalized Choquet Integrals. Front Neuroinform 2022; 15:744355. [PMID: 34970131 PMCID: PMC8712566 DOI: 10.3389/fninf.2021.744355] [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: 07/20/2021] [Accepted: 11/09/2021] [Indexed: 11/13/2022] Open
Abstract
In this study, we focused on the verification of suitable aggregation operators enabling accurate differentiation of selected neurophysiological features extracted from resting-state electroencephalographic recordings of patients who were diagnosed with schizophrenia (SZ) or healthy controls (HC). We built the Choquet integral-based operators using traditional classification results as an input to the procedure of establishing the fuzzy measure densities. The dataset applied in the study was a collection of variables characterizing the organization of the neural networks computed using the minimum spanning tree (MST) algorithms obtained from signal-spaced functional connectivity indicators and calculated separately for predefined frequency bands using classical linear Granger causality (GC) measure. In the series of numerical experiments, we reported the results of classification obtained using numerous generalizations of the Choquet integral and other aggregation functions, which were tested to find the most appropriate ones. The obtained results demonstrate that the classification accuracy can be increased by 1.81% using the extended versions of the Choquet integral called in the literature, namely, generalized Choquet integral or pre-aggregation operators.
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Affiliation(s)
| | - Paweł Karczmarek
- Department of Computer Science, Lublin University of Technology, Lublin, Poland
| | - Paweł Krukow
- Department of Clinical Neuropsychiatry, Medical University of Lublin, Lublin, Poland
| | - Monika Kaczorowska
- Department of Computer Science, Lublin University of Technology, Lublin, Poland
| | - Mikhail Tokovarov
- Department of Computer Science, Lublin University of Technology, Lublin, Poland
| | - Kamil Jonak
- Department of Computer Science, Lublin University of Technology, Lublin, Poland.,Department of Clinical Neuropsychiatry, Medical University of Lublin, Lublin, Poland
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50
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Robotically-induced hallucination triggers subtle changes in brain network transitions. Neuroimage 2021; 248:118862. [PMID: 34971766 DOI: 10.1016/j.neuroimage.2021.118862] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 11/19/2021] [Accepted: 12/23/2021] [Indexed: 01/20/2023] Open
Abstract
The perception that someone is nearby, although nobody can be seen or heard, is called presence hallucination (PH). Being a frequent hallucination in patients with Parkinson's disease, it has been argued to be indicative of a more severe and rapidly advancing form of the disease, associated with psychosis and cognitive decline. PH may also occur in healthy individuals and has recently been experimentally induced, in a controlled manner during fMRI, using MR-compatible robotics and sensorimotor stimulation. Previous neuroimaging correlates of such robot-induced PH, based on conventional time-averaged fMRI analysis, identified altered activity in the posterior superior temporal sulcus and inferior frontal gyrus in healthy individuals. However, no link with the strength of the robot-induced PH was observed, and such activations were also associated with other sensations induced by robotic stimulation. Here we leverage recent advances in dynamic functional connectivity, which have been applied to different psychiatric conditions, to decompose fMRI data during PH-induction into a set of co-activation patterns that are tracked over time, as to characterize their occupancies, durations, and transitions. Our results reveal that, when PH is induced, the identified brain patterns significantly and selectively increase their transition probabilities towards a specific brain pattern, centred on the posterior superior temporal sulcus, angular gyrus, dorso-lateral prefrontal cortex, and middle prefrontal cortex. This change is not observed in any other control conditions, nor is it observed in association with other sensations induced by robotic stimulation. The present findings describe the neural mechanisms of PH in healthy individuals and identify a specific disruption of the dynamics of network interactions, extending previously reported network dysfunctions in psychotic patients with hallucinations to an induced robot-controlled specific hallucination in healthy individuals.
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