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Maximo JO, Armstrong WP, Kraguljac NV, Lahti AC. Higher-Order Intrinsic Brain Network Trajectories After Antipsychotic Treatment in Medication-Naïve Patients With First-Episode Psychosis. Biol Psychiatry 2024; 96:198-206. [PMID: 38272288 DOI: 10.1016/j.biopsych.2024.01.010] [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: 07/17/2023] [Revised: 12/19/2023] [Accepted: 01/17/2024] [Indexed: 01/27/2024]
Abstract
BACKGROUND Intrinsic brain network connectivity is already altered in first-episode psychosis (FEP), but the longitudinal trajectories of network connectivity, especially in response to antipsychotic treatment, remain poorly understood. The goal of this study was to investigate how antipsychotic medications affect higher-order intrinsic brain network connectivity in FEP. METHODS Data from 87 antipsychotic medication-naïve patients with FEP and 87 healthy control participants were used. Medication-naïve patients received antipsychotic treatment for 16 weeks. Resting-state functional connectivity (FC) of the default mode, salience, dorsal attention, and executive control networks were assessed prior to treatment and at 6 and 16 weeks after treatment. We evaluated baseline and FC changes using linear mixed models to test group × time interactions within each network. Associations between FC changes after 16 weeks and response to treatment were also evaluated. RESULTS Prior to treatment, significant group differences in all networks were found. However, significant trajectory changes in FC were found only in the default mode and executive control networks. Changes in FC in these networks were associated with treatment response. Several sensitivity analyses showed a consistent normalization of executive control network FC in response to antipsychotic treatment. CONCLUSIONS Here, we found that alterations in intrinsic brain network FC were not only alleviated with antipsychotic treatment, but the extent of this normalization was also associated with the degree of reduction in symptom severity. Taken together, our data suggest modulation of intrinsic brain network connectivity (mainly frontoparietal connectivity) as a mechanism underlying antipsychotic treatment response in FEP.
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Affiliation(s)
- Jose O Maximo
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, Alabama
| | - William P Armstrong
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Nina V Kraguljac
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Adrienne C Lahti
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, Alabama.
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2
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Park HG. Bayesian estimation of covariate assisted principal regression for brain functional connectivity. Biostatistics 2024:kxae023. [PMID: 38981041 DOI: 10.1093/biostatistics/kxae023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 03/25/2024] [Accepted: 06/02/2024] [Indexed: 07/11/2024] Open
Abstract
This paper presents a Bayesian reformulation of covariate-assisted principal regression for covariance matrix outcomes to identify low-dimensional components in the covariance associated with covariates. By introducing a geometric approach to the covariance matrices and leveraging Euclidean geometry, we estimate dimension reduction parameters and model covariance heterogeneity based on covariates. This method enables joint estimation and uncertainty quantification of relevant model parameters associated with heteroscedasticity. We demonstrate our approach through simulation studies and apply it to analyze associations between covariates and brain functional connectivity using data from the Human Connectome Project.
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Affiliation(s)
- Hyung G Park
- Division of Biostatistics, Department of Population Health, New York University Grossman School of Medicine, 180 Madison Ave., New York, NY 10016, USA
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3
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Secara MT, Oliver LD, Gallucci J, Dickie EW, Foussias G, Gold J, Malhotra AK, Buchanan RW, Voineskos AN, Hawco C. Heterogeneity in functional connectivity: Dimensional predictors of individual variability during rest and task fMRI in psychosis. Prog Neuropsychopharmacol Biol Psychiatry 2024; 132:110991. [PMID: 38484928 PMCID: PMC11034852 DOI: 10.1016/j.pnpbp.2024.110991] [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: 10/13/2023] [Revised: 02/27/2024] [Accepted: 03/07/2024] [Indexed: 03/21/2024]
Abstract
BACKGROUND Individuals with schizophrenia spectrum disorders (SSD) often demonstrate cognitive impairments, associated with poor functional outcomes. While neurobiological heterogeneity has posed challenges when examining social cognition in SSD, it provides a unique opportunity to explore brain-behavior relationships. The aim of this study was to investigate the relationship between individual variability in functional connectivity during resting state and the performance of a social task and social and non-social cognition in a large sample of controls and individuals diagnosed with SSD. METHODS Neuroimaging and behavioral data were analyzed for 193 individuals with SSD and 155 controls (total n = 348). Individual variability was quantified through mean correlational distance (MCD) of functional connectivity between participants; MCD was defined as a global 'variability score'. Pairwise correlational distance was calculated as 1 - the correlation coefficient between a given pair of participants, and averaging distance from one participant to all other participants provided the mean correlational distance metric. Hierarchical regressions were performed on variability scores derived from resting state and Empathic Accuracy (EA) task functional connectivity data to determine potential predictors (e.g., age, sex, neurocognitive and social cognitive scores) of individual variability. RESULTS Group comparison between SSD and controls showed greater SSD MCD during rest (p = 0.00038), while no diagnostic differences were observed during task (p = 0.063). Hierarchical regression analyses demonstrated the persistence of a significant diagnostic effect during rest (p = 0.008), contrasting with its non-significance during the task (p = 0.50), after social cognition was added to the model. Notably, social cognition exhibited significance in both resting state and task conditions (both p = 0.01). CONCLUSIONS Diagnostic differences were more prevalent during unconstrained resting scans, whereas the task pushed participants into a more common pattern which better emphasized transdiagnostic differences in cognitive abilities. Focusing on variability may provide new opportunities for interventions targeting specific cognitive impairments to improve functional outcomes.
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Affiliation(s)
- Maria T Secara
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Lindsay D Oliver
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Julia Gallucci
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Erin W Dickie
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - George Foussias
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - James Gold
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Anil K Malhotra
- Department of Psychiatry, Division of Research, The Zucker Hillside Hospital Division of Northwell Health, Glen Oaks, NY, USA
| | - Robert W Buchanan
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Aristotle N Voineskos
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Colin Hawco
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
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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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Becker M, Fischer DJ, Kühn S, Gallinat J. Videogame training increases clinical well-being, attention and hippocampal-prefrontal functional connectivity in patients with schizophrenia. Transl Psychiatry 2024; 14:218. [PMID: 38806461 PMCID: PMC11133354 DOI: 10.1038/s41398-024-02945-5] [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: 11/13/2023] [Revised: 05/14/2024] [Accepted: 05/17/2024] [Indexed: 05/30/2024] Open
Abstract
Recent research shows that videogame training enhances neuronal plasticity and cognitive improvements in healthy individuals. As patients with schizophrenia exhibit reduced neuronal plasticity linked to cognitive deficits and symptoms, we investigated whether videogame-related cognitive improvements and plasticity changes extend to this population. In a training study, patients with schizophrenia and healthy controls were randomly assigned to 3D or 2D platformer videogame training or E-book reading (active control) for 8 weeks, 30 min daily. After training, both videogame conditions showed significant increases in sustained attention compared to the control condition, correlated with increased functional connectivity in a hippocampal-prefrontal network. Notably, patients trained with videogames mostly improved in negative symptoms, general psychopathology, and perceived mental health recovery. Videogames, incorporating initiative, goal setting and gratification, offer a training approach closer to real life than current psychiatric treatments. Our results provide initial evidence that they may represent a possible adjunct therapeutic intervention for complex mental disorders.
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Affiliation(s)
- Maxi Becker
- University Medical Center Hamburg-Eppendorf, Clinic and Policlinic for Psychiatry and Psychotherapy, Martinistrasse 52, 20246, Hamburg, Germany.
- Humboldt-University Berlin, Department of Psychology, Berlin, Germany.
| | - Djo J Fischer
- University Medical Center Hamburg-Eppendorf, Clinic and Policlinic for Psychiatry and Psychotherapy, Martinistrasse 52, 20246, Hamburg, Germany
| | - Simone Kühn
- University Medical Center Hamburg-Eppendorf, Clinic and Policlinic for Psychiatry and Psychotherapy, Martinistrasse 52, 20246, Hamburg, Germany.
- Lise Meitner Group for Environmental Neuroscience, Max Planck Institute for Human Development, Berlin, Germany.
- Max Planck-UCL Center for Computational Psychiatry and Ageing Research, Berlin, Germany.
| | - Jürgen Gallinat
- University Medical Center Hamburg-Eppendorf, Clinic and Policlinic for Psychiatry and Psychotherapy, Martinistrasse 52, 20246, Hamburg, Germany
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Maximo JO, Briend F, Armstrong WP, Kraguljac NV, Lahti AC. Higher-order functional brain networks and anterior cingulate glutamate + glutamine (Glx) in antipsychotic-naïve first episode psychosis patients. Transl Psychiatry 2024; 14:183. [PMID: 38600117 PMCID: PMC11006887 DOI: 10.1038/s41398-024-02854-7] [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: 03/23/2022] [Revised: 02/07/2024] [Accepted: 02/26/2024] [Indexed: 04/12/2024] Open
Abstract
Human connectome studies have provided abundant data consistent with the hypothesis that functional dysconnectivity is predominant in psychosis spectrum disorders. Converging lines of evidence also suggest an interaction between dorsal anterior cingulate cortex (dACC) cortical glutamate with higher-order functional brain networks (FC) such as the default mode (DMN), dorsal attention (DAN), and executive control networks (ECN) in healthy controls (HC) and this mechanism may be impaired in psychosis. Data from 70 antipsychotic-medication naïve first-episode psychosis (FEP) and 52 HC were analyzed. 3T Proton magnetic resonance spectroscopy (1H-MRS) data were acquired from a voxel in the dACC and assessed correlations (positive FC) and anticorrelations (negative FC) of the DMN, DAN, and ECN. We then performed regressions to assess associations between glutamate + glutamine (Glx) with positive and negative FC of these same networks and compared them between groups. We found alterations in positive and negative FC in all networks (HC > FEP). A relationship between dACC Glx and positive and negative FC was found in both groups, but when comparing these relationships between groups, we found contrasting associations between these variables in FEP patients compared to HC. We demonstrated that both positive and negative FC in three higher-order resting state networks are already altered in antipsychotic-naïve FEP, underscoring the importance of also considering anticorrelations for optimal characterization of large-scale functional brain networks as these represent biological processes as well. Our data also adds to the growing body of evidence supporting the role of dACC cortical Glx as a mechanism underlying alterations in functional brain network connectivity. Overall, the implications for these findings are imperative as this particular mechanism may differ in untreated or chronic psychotic patients; therefore, understanding this mechanism prior to treatment could better inform clinicians.Clinical trial registration: Trajectories of Treatment Response as Window into the Heterogeneity of Psychosis: A Longitudinal Multimodal Imaging Study, NCT03442101 . Glutamate, Brain Connectivity and Duration of Untreated Psychosis (DUP), NCT02034253 .
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Affiliation(s)
- Jose O Maximo
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Frederic Briend
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA
- UMR1253, iBrain, Université de Tours, Inserm, Tours, France
| | - William P Armstrong
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Nina V Kraguljac
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Adrienne C Lahti
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA.
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7
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Yang Y, Jin X, Xue Y, Li X, Chen Y, Kang N, Yan W, Li P, Guo X, Luo B, Zhang Y, Liu Q, Shi H, Zhang L, Su X, Liu B, Lu L, Lv L, Li W. Right superior frontal gyrus: A potential neuroimaging biomarker for predicting short-term efficacy in schizophrenia. Neuroimage Clin 2024; 42:103603. [PMID: 38588618 PMCID: PMC11015154 DOI: 10.1016/j.nicl.2024.103603] [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: 01/11/2024] [Revised: 03/24/2024] [Accepted: 04/02/2024] [Indexed: 04/10/2024]
Abstract
Antipsychotic drug treatment for schizophrenia (SZ) can alter brain structure and function, but it is unclear if specific regional changes are associated with treatment outcome. Therefore, we examined the effects of antipsychotic drug treatment on regional grey matter (GM) density, white matter (WM) density, and functional connectivity (FC) as well as associations between regional changes and treatment efficacy. SZ patients (n = 163) and health controls (HCs) (n = 131) were examined by structural magnetic resonance imaging (sMRI) at baseline, and a subset of SZ patients (n = 77) were re-examined after 8 weeks of second-generation antipsychotic treatment to assess changes in regional GM and WM density. In addition, 88 SZ patients and 81 HCs were examined by resting-state functional MRI (rs-fMRI) at baseline and the patients were re-examined post-treatment to examine FC changes. The Positive and Negative Syndrome Scale (PANSS) and MATRICS Consensus Cognitive Battery (MCCB) were applied to measure psychiatric symptoms and cognitive impairments in SZ. SZ patients were then stratified into response and non-response groups according to PANSS score change (≥50 % decrease or <50 % decrease, respectively). The GM density of the right cingulate gyrus, WM density of the right superior frontal gyrus (SFG) plus 5 other WM tracts were reduced in the response group compared to the non-response group. The FC values between the right anterior cingulate and paracingulate gyrus and left thalamus were reduced in the entire SZ group (n = 88) after treatment, while FC between the right inferior temporal gyrus (ITG) and right medial superior frontal gyrus (SFGmed) was increased in the response group. There were no significant changes in regional FC among the non-response group after treatment and no correlations with symptom or cognition test scores. These findings suggest that the right SFG is a critical target of antipsychotic drugs and that WM density and FC alterations within this region could be used as potential indicators in predicting the treatment outcome of antipsychotics of SZ.
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Affiliation(s)
- Yongfeng Yang
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China; Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China; Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder, Xinxiang 453002, China
| | - Xueyan Jin
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China; Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder, Xinxiang 453002, China
| | - Yongjiang Xue
- The Second Clinical College of Xinxiang Medical University, Xinxiang 453002, China
| | - Xue Li
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China; Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder, Xinxiang 453002, China
| | - Yi Chen
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China; Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder, Xinxiang 453002, China
| | - Ning Kang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China; Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder, Xinxiang 453002, China
| | - Wei Yan
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China
| | - Peng Li
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China
| | - Xiaoge Guo
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China; Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder, Xinxiang 453002, China
| | - Binbin Luo
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China; Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder, Xinxiang 453002, China
| | - Yan Zhang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China; Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder, Xinxiang 453002, China
| | - Qing Liu
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China; Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder, Xinxiang 453002, China
| | - Han Shi
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China; Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder, Xinxiang 453002, China
| | - Luwen Zhang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China; Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder, Xinxiang 453002, China
| | - Xi Su
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China; Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder, Xinxiang 453002, China
| | - Bing Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Chinese Institute for Brain Research, Beijing 102206, China
| | - Lin Lu
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China; National Institute on Drug Dependence, Beijing Key Laboratory of Drug Dependence, Peking University, Beijing 100191, China; Peking-Tsinghua Centre for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China
| | - Luxian Lv
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China; Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder, Xinxiang 453002, China.
| | - Wenqiang Li
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China; Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder, Xinxiang 453002, China.
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8
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Feng S, Zheng S, Dong L, Li Z, Zhu H, Liu S, Li X, Ning Y, Jia H. Effects of aripiprazole on resting-state functional connectivity of large-scale brain networks in first-episode drug-naïve schizophrenia patients. J Psychiatr Res 2024; 171:215-221. [PMID: 38309211 DOI: 10.1016/j.jpsychires.2024.01.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 01/04/2024] [Accepted: 01/15/2024] [Indexed: 02/05/2024]
Abstract
Aripiprazole modulates functional connectivity (FC) between several brain regions in first-episode schizophrenia patients, contributing to improvement in clinical symptoms. However, the effects of aripiprazole on abnormal connections among extensive brain networks in schizophrenia patients remain unclear. We aimed to investigate the effects of 12 weeks of aripiprazole treatment on the FC of large-scale brain networks. Forty-five first-episode drug-naïve schizophrenia patients and 45 healthy controls were recruited for this longitudinal study. Resting-state functional magnetic resonance imaging (fMRI) data were collected at baseline and after 12 weeks of aripiprazole treatment. The patients were classified into those in response (SCHr group) and non-response (SCHnr group) according to the improvement of clinical symptoms after 12-weeks treatment. The FC were evaluated for seven large-scale brain networks. In addition, correlation analysis was performed to investigate associations between changes FC of large-scale brain networks and clinical symptoms. Before aripiprazole treatment, schizophrenia patients showed decreased FC of extensive brain networks compared to healthy controls. The 12-week aripiprazole treatment significantly prevented the constantly decreased FC of subcortical network, default mode network and other brain networks in patients with SCHr, in association with the improvement of clinical symptoms. Taken together, these findings have revealed the effects of aripiprazole on FC in large-scale networks in schizophrenia patients, which could provide new insight on interpreting symptom improvement in SCH.
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Affiliation(s)
- Sitong Feng
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Sisi Zheng
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Linrui Dong
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Ziyan Li
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Hong Zhu
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Shanshan Liu
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Xue Li
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Yanzhe Ning
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
| | - Hongxiao Jia
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
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9
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Jensen KM, Calhoun VD, Fu Z, Yang K, Faria AV, Ishizuka K, Sawa A, Andrés-Camazón P, Coffman BA, Seebold D, Turner JA, Salisbury DF, Iraji A. A whole-brain neuromark resting-state fMRI analysis of first-episode and early psychosis: Evidence of aberrant cortical-subcortical-cerebellar functional circuitry. Neuroimage Clin 2024; 41:103584. [PMID: 38422833 PMCID: PMC10944191 DOI: 10.1016/j.nicl.2024.103584] [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: 10/17/2023] [Revised: 01/31/2024] [Accepted: 02/25/2024] [Indexed: 03/02/2024]
Abstract
Psychosis (including symptoms of delusions, hallucinations, and disorganized conduct/speech) is a main feature of schizophrenia and is frequently present in other major psychiatric illnesses. Studies in individuals with first-episode (FEP) and early psychosis (EP) have the potential to interpret aberrant connectivity associated with psychosis during a period with minimal influence from medication and other confounds. The current study uses a data-driven whole-brain approach to examine patterns of aberrant functional network connectivity (FNC) in a multi-site dataset comprising resting-state functional magnetic resonance images (rs-fMRI) from 117 individuals with FEP or EP and 130 individuals without a psychiatric disorder, as controls. Accounting for age, sex, race, head motion, and multiple imaging sites, differences in FNC were identified between psychosis and control participants in cortical (namely the inferior frontal gyrus, superior medial frontal gyrus, postcentral gyrus, supplementary motor area, posterior cingulate cortex, and superior and middle temporal gyri), subcortical (the caudate, thalamus, subthalamus, and hippocampus), and cerebellar regions. The prominent pattern of reduced cerebellar connectivity in psychosis is especially noteworthy, as most studies focus on cortical and subcortical regions, neglecting the cerebellum. The dysconnectivity reported here may indicate disruptions in cortical-subcortical-cerebellar circuitry involved in rudimentary cognitive functions which may serve as reliable correlates of psychosis.
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Affiliation(s)
- Kyle M Jensen
- Georgia State University, Atlanta, GA, USA; Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA.
| | - Vince D Calhoun
- Georgia State University, Atlanta, GA, USA; Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA
| | - Zening Fu
- Georgia State University, Atlanta, GA, USA; Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA
| | - Kun Yang
- Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Andreia V Faria
- Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Koko Ishizuka
- Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Akira Sawa
- Johns Hopkins University School of Medicine, Baltimore, MD, USA; Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA
| | - Pablo Andrés-Camazón
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA; Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, Madrid, Spain
| | - Brian A Coffman
- University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Dylan Seebold
- University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Jessica A Turner
- Wexner Medical Center, The Ohio State University, Columbus, OH, USA
| | - Dean F Salisbury
- University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Armin Iraji
- Georgia State University, Atlanta, GA, USA; Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA
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10
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Kong L, Zhang Y, Wu XM, Wang XX, Wu HS, Li SB, Chu MY, Wang Y, Lui SSY, Lv QY, Yi ZH, Chan RCK. The network characteristics in schizophrenia with prominent negative symptoms: a multimodal fusion study. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2024; 10:10. [PMID: 38233433 PMCID: PMC10851703 DOI: 10.1038/s41537-023-00408-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 10/26/2023] [Indexed: 01/19/2024]
Abstract
Previous studies on putative neural mechanisms of negative symptoms in schizophrenia mainly used single modal imaging data, and seldom utilized schizophrenia patients with prominent negative symptoms (PNS).This study adopted the multimodal fusion method and recruited a homogeneous sample with PNS. We aimed to identify negative symptoms-related structural and functional neural correlates of schizophrenia. Structural magnetic resonance imaging (sMRI) and resting-state functional MRI (rs-fMRI) were performed in 31 schizophrenia patients with PNS and 33 demographically matched healthy controls.Compared to healthy controls, schizophrenia patients with PNS exhibited significantly altered functional activations in the default mode network (DMN) and had structural gray matter volume (GMV) alterations in the cerebello-thalamo-cortical network. Correlational analyses showed that negative symptoms severity was significantly correlated with the cerebello-thalamo-cortical structural network, but not with the DMN network in schizophrenia patients with PNS.Our findings highlight the important role of the cerebello-thalamo-cortical structural network underpinning the neuropathology of negative symptoms in schizophrenia. Future research should recruit a large sample and schizophrenia patients without PNS, and apply adjustments for multiple comparison, to verify our preliminary findings.
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Affiliation(s)
- Li Kong
- Department of Psychology, Shanghai Normal University, Shanghai, China
| | - Yao Zhang
- Shanghai Mental Health Centre, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Psychiatry, Huashan Hospital, Fudan University, Shanghai, China
| | - Xu-Ming Wu
- Nantong Fourth People's Hospital, Nantong, China
| | - Xiao-Xiao Wang
- Shanghai Mental Health Centre, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Psychiatry, Huashan Hospital, Fudan University, Shanghai, China
| | - Hai-Su Wu
- Shanghai Mental Health Centre, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shuai-Biao Li
- Shanghai Mental Health Centre, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Min-Yi Chu
- Shanghai Mental Health Centre, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yi Wang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Simon S Y Lui
- Department of Psychiatry, School of Clinical Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Qin-Yu Lv
- Shanghai Mental Health Centre, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Psychiatry, Huashan Hospital, Fudan University, Shanghai, China
| | - Zheng-Hui Yi
- Shanghai Mental Health Centre, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Department of Psychiatry, Huashan Hospital, Fudan University, Shanghai, China.
- Institute of Mental Health, Fudan University, Shanghai, China.
| | - Raymond C K Chan
- Shanghai Mental Health Centre, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
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11
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Levi PT, Chopra S, Pang JC, Holmes A, Gajwani M, Sassenberg TA, DeYoung CG, Fornito A. The effect of using group-averaged or individualized brain parcellations when investigating connectome dysfunction in psychosis. Netw Neurosci 2023; 7:1228-1247. [PMID: 38144692 PMCID: PMC10631788 DOI: 10.1162/netn_a_00329] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 06/27/2023] [Indexed: 12/26/2023] Open
Abstract
Functional magnetic resonance imaging (fMRI) is widely used to investigate functional coupling (FC) disturbances in a range of clinical disorders. Most analyses performed to date have used group-based parcellations for defining regions of interest (ROIs), in which a single parcellation is applied to each brain. This approach neglects individual differences in brain functional organization and may inaccurately delineate the true borders of functional regions. These inaccuracies could inflate or underestimate group differences in case-control analyses. We investigated how individual differences in brain organization influence group comparisons of FC using psychosis as a case study, drawing on fMRI data in 121 early psychosis patients and 57 controls. We defined FC networks using either a group-based parcellation or an individually tailored variant of the same parcellation. Individualized parcellations yielded more functionally homogeneous ROIs than did group-based parcellations. At the level of individual connections, case-control FC differences were widespread, but the group-based parcellation identified approximately 7.7% more connections as dysfunctional than the individualized parcellation. When considering differences at the level of functional networks, the results from both parcellations converged. Our results suggest that a substantial fraction of dysconnectivity previously observed in psychosis may be driven by the parcellation method, rather than by a pathophysiological process related to psychosis.
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Affiliation(s)
- Priscila T. Levi
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
| | - Sidhant Chopra
- Department of Psychology, Yale University, New Haven, CT, USA
| | - James C. Pang
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
| | - Alexander Holmes
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
| | - Mehul Gajwani
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
| | | | - Colin G. DeYoung
- Department of Psychology, University of Minnesota, Minnesota, MN, USA
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
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12
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Clementi L, Arnone E, Santambrogio MD, Franceschetti S, Panzica F, Sangalli LM. Anatomically compliant modes of variations: New tools for brain connectivity. PLoS One 2023; 18:e0292450. [PMID: 37934760 PMCID: PMC10629624 DOI: 10.1371/journal.pone.0292450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 09/20/2023] [Indexed: 11/09/2023] Open
Abstract
Anatomical complexity and data dimensionality present major issues when analysing brain connectivity data. The functional and anatomical aspects of the connections taking place in the brain are in fact equally relevant and strongly intertwined. However, due to theoretical challenges and computational issues, their relationship is often overlooked in neuroscience and clinical research. In this work, we propose to tackle this problem through Smooth Functional Principal Component Analysis, which enables to perform dimensional reduction and exploration of the variability in functional connectivity maps, complying with the formidably complicated anatomy of the grey matter volume. In particular, we analyse a population that includes controls and subjects affected by schizophrenia, starting from fMRI data acquired at rest and during a task-switching paradigm. For both sessions, we first identify the common modes of variation in the entire population. We hence explore whether the subjects' expressions along these common modes of variation differ between controls and pathological subjects. In each session, we find principal components that are significantly differently expressed in the healthy vs pathological subjects (with p-values < 0.001), highlighting clearly interpretable differences in the connectivity in the two subpopulations. For instance, the second and third principal components for the rest session capture the imbalance between the Default Mode and Executive Networks characterizing schizophrenia patients.
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Affiliation(s)
- Letizia Clementi
- MOX - Department of Mathematics, Politecnico di Milano, Milan, Italy
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
- CHDS, Center for Health Data Science, Human Technopole, Milan, Italy
| | | | - Marco D. Santambrogio
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | | | | | - Laura M. Sangalli
- MOX - Department of Mathematics, Politecnico di Milano, Milan, Italy
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13
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Lizano P, Kiely C, Mijalkov M, Meda SA, Keedy SK, Hoang D, Zeng V, Lutz O, Pereira JB, Ivleva EI, Volpe G, Xu Y, Lee AM, Rubin LH, Kristian Hill S, Clementz BA, Tamminga CA, Pearlson GD, Sweeney JA, Gershon ES, Keshavan MS, Bishop JR. Peripheral inflammatory subgroup differences in anterior Default Mode network and multiplex functional network topology are associated with cognition in psychosis. Brain Behav Immun 2023; 114:3-15. [PMID: 37506949 PMCID: PMC10592140 DOI: 10.1016/j.bbi.2023.07.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 07/17/2023] [Accepted: 07/22/2023] [Indexed: 07/30/2023] Open
Abstract
INTRODUCTION High-inflammation subgroups of patients with psychosis demonstrate cognitive deficits and neuroanatomical alterations. Systemic inflammation assessed using IL-6 and C-reactive protein may alter functional connectivity within and between resting-state networks, but the cognitive and clinical implications of these alterations remain unknown. We aim to determine the relationships of elevated peripheral inflammation subgroups with resting-state functional networks and cognition in psychosis spectrum disorders. METHODS Serum and resting-state fMRI were collected from psychosis probands (schizophrenia, schizoaffective, psychotic bipolar disorder) and healthy controls (HC) from the B-SNIP1 (Chicago site) study who were stratified into inflammatory subgroups based on factor and cluster analyses of 13 cytokines (HC Low n = 32, Proband Low n = 65, Proband High n = 29). Nine resting-state networks derived from independent component analysis were used to assess functional and multilayer connectivity. Inter-network connectivity was measured using Fisher z-transformation of correlation coefficients. Network organization was assessed by investigating networks of positive and negative connections separately, as well as investigating multilayer networks using both positive and negative connections. Cognition was assessed using the Brief Assessment of Cognition in Schizophrenia. Linear regressions, Spearman correlations, permutations tests and multiple comparison corrections were used for analyses in R. RESULTS Anterior default mode network (DMNa) connectivity was significantly reduced in the Proband High compared to Proband Low (Cohen's d = -0.74, p = 0.002) and HC Low (d = -0.85, p = 0.0008) groups. Inter-network connectivity between the DMNa and the right-frontoparietal networks was lower in Proband High compared to Proband Low (d = -0.66, p = 0.004) group. Compared to Proband Low, the Proband High group had lower negative (d = 0.54, p = 0.021) and positive network (d = 0.49, p = 0.042) clustering coefficient, and lower multiplex network participation coefficient (d = -0.57, p = 0.014). Network findings in high inflammation subgroups correlate with worse verbal fluency, verbal memory, symbol coding, and overall cognition. CONCLUSION These results expand on our understanding of the potential effects of peripheral inflammatory signatures and/or subgroups on network dysfunction in psychosis and how they relate to worse cognitive performance. Additionally, the novel multiplex approach taken in this study demonstrated how inflammation may disrupt the brain's ability to maintain healthy co-activation patterns between the resting-state networks while inhibiting certain connections between them.
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Affiliation(s)
- Paulo Lizano
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA; Division of Translational Neuroscience, Beth Israel Deaconess Medical Center, Boston, MA, USA.
| | - Chelsea Kiely
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Mite Mijalkov
- Neuro Division, Department of Clinical Neurosciences, Karolinska Institutet, Stockholm, Sweden
| | - Shashwath A Meda
- Department of Psychiatry, Yale University, New Haven, Connecticut, USA
| | - Sarah K Keedy
- Department of Psychiatry and Behavioral Neurosciences, University of Chicago, Chicago, IL, USA
| | - Dung Hoang
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Victor Zeng
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Olivia Lutz
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Joana B Pereira
- Neuro Division, Department of Clinical Neurosciences, Karolinska Institutet, Stockholm, Sweden; Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Sweden
| | - Elena I Ivleva
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX
| | - Giovanni Volpe
- Physics Department, University of Gothenburg, Gothenburg, Sweden
| | - Yanxun Xu
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA
| | - Adam M Lee
- Department of Experimental and Clinical Pharmacology and Psychiatry, University of Minnesota, Minneapolis, MN, USA
| | - Leah H Rubin
- Department of Neurology, Psychiatry and Behavioral Sciences, Molecular and Comparative Pathobiology, and Epidemiology, Johns Hopkins University, Baltimore, MD, USA
| | - S Kristian Hill
- Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago, IL, USA
| | - Brett A Clementz
- Department of Psychology, University of Georgia, Athens, Georgia
| | - Carol A Tamminga
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX
| | | | - John A Sweeney
- Department of Psychiatry, University of Cincinnati Medical Center, Cincinnati, OH, USA
| | - Elliot S Gershon
- Department of Psychiatry and Behavioral Neurosciences, University of Chicago, Chicago, IL, USA
| | - Matcheri S Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Jeffrey R Bishop
- Department of Experimental and Clinical Pharmacology and Psychiatry, University of Minnesota, Minneapolis, MN, USA
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14
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Ryan M, Glonek G, Tuke J, Humphries M. Capturing functional connectomics using Riemannian partial least squares. Sci Rep 2023; 13:17386. [PMID: 37833370 PMCID: PMC10576060 DOI: 10.1038/s41598-023-44687-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 10/10/2023] [Indexed: 10/15/2023] Open
Abstract
For neurological disorders and diseases, functional and anatomical connectomes of the human brain can be used to better inform targeted interventions and treatment strategies. Functional magnetic resonance imaging (fMRI) is a non-invasive neuroimaging technique that captures spatio-temporal brain function through change in blood-oxygen-level-dependent (BOLD) signals over time. FMRI can be used to study the functional connectome through the functional connectivity matrix; that is, Pearson's correlation matrix between time series from the regions of interest of an fMRI image. One approach to analysing functional connectivity is using partial least squares (PLS), a multivariate regression technique designed for high-dimensional predictor data. However, analysing functional connectivity with PLS ignores a key property of the functional connectivity matrix; namely, these matrices are positive definite. To account for this, we introduce a generalisation of PLS to Riemannian manifolds, called R-PLS, and apply it to symmetric positive definite matrices with the affine invariant geometry. We apply R-PLS to two functional imaging datasets: COBRE, which investigates functional differences between schizophrenic patients and healthy controls, and; ABIDE, which compares people with autism spectrum disorder and neurotypical controls. Using the variable importance in the projection statistic on the results of R-PLS, we identify key functional connections in each dataset that are well represented in the literature. Given the generality of R-PLS, this method has the potential to investigate new functional connectomes in the brain, and with future application to structural data can open up further avenues of research in multi-modal imaging analysis.
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Affiliation(s)
- Matthew Ryan
- School of Computer and Mathematical Sciences, The University of Adelaide, Adelaide, 5005, Australia.
| | - Gary Glonek
- School of Computer and Mathematical Sciences, The University of Adelaide, Adelaide, 5005, Australia
| | - Jono Tuke
- School of Computer and Mathematical Sciences, The University of Adelaide, Adelaide, 5005, Australia
| | - Melissa Humphries
- School of Computer and Mathematical Sciences, The University of Adelaide, Adelaide, 5005, Australia
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15
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Li F, Wang G, Jiang L, Yao D, Xu P, Ma X, Dong D, He B. Disease-specific resting-state EEG network variations in schizophrenia revealed by the contrastive machine learning. Brain Res Bull 2023; 202:110744. [PMID: 37591404 DOI: 10.1016/j.brainresbull.2023.110744] [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: 06/27/2023] [Revised: 08/03/2023] [Accepted: 08/14/2023] [Indexed: 08/19/2023]
Abstract
Given a multitude of genetic and environmental factors, when investigating the variability in schizophrenia (SCZ) and the first-degree relatives (R-SCZ), latent disease-specific variation is usually hidden. To reliably investigate the mechanism underlying the brain deficits from the aspect of functional networks, we newly iterated a framework of contrastive variational autoencoders (cVAEs) applied in the contrasts among three groups, to disentangle the latent resting-state network patterns specified for the SCZ and R-SCZ. We demonstrated that the comparison in reconstructed resting-state networks among SCZ, R-SCZ, and healthy controls (HC) revealed network distortions of the inner-frontal hypoconnectivity and frontal-occipital hyperconnectivity, while the original ones illustrated no differences. And only the classification by adopting the reconstructed network metrics achieved satisfying performances, as the highest accuracy of 96.80% ± 2.87%, along with the precision of 95.05% ± 4.28%, recall of 98.18% ± 3.83%, and F1-score of 96.51% ± 2.83%, was obtained. These findings consistently verified the validity of the newly proposed framework for the contrasts among the three groups and provided related resting-state network evidence for illustrating the pathological mechanism underlying the brain deficits in SCZ, as well as facilitating the diagnosis of SCZ.
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Affiliation(s)
- Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China; Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035 Chengdu, China
| | - Guangying Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Lin Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035 Chengdu, China; School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035 Chengdu, China; Radiation Oncology Key Laboratory of Sichuan Province, Chengdu 610041, China; Rehabilitation Center, Qilu Hospital of Shandong University, Jinan 250012, China.
| | - Xuntai Ma
- Clinical Medical College of Chengdu Medical College, Chengdu 610500, China; The First Affiliated Hospital of Chengdu Medical College, Chengdu 610599, China.
| | - Debo Dong
- Faculty of Psychology, Southwest University, Chongqing 400715, China; Institute of Neuroscience and Medicine, Brain and Behavior (INM-7), Research Center Jülich, Jülich, Germany.
| | - Baoming He
- Department of Neurology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 610072, China; Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu 610072, China.
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16
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Allebone J, Kanaan RA, Rayner G, Maller J, O'Brien TJ, Mullen SA, Cook M, Adams SJ, Vogrin S, Vaughan DN, Kwan P, Berkovic SF, D'Souza WJ, Jackson G, Velakoulis D, Wilson SJ. Neuropsychological function in psychosis of epilepsy. Epilepsy Res 2023; 196:107222. [PMID: 37717505 DOI: 10.1016/j.eplepsyres.2023.107222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 08/25/2023] [Accepted: 09/13/2023] [Indexed: 09/19/2023]
Abstract
OBJECTIVE The neuropsychological profile of patients with psychosis of epilepsy (POE) has received limited research attention. Recent neuroimaging work in POE has identified structural network pathology in the default mode network and the cognitive control network. This study examined the neuropsychological profile of POE focusing on cognitive domains subserved by these networks. METHODS Twelve consecutive patients with a diagnosis of POE were prospectively recruited from the Comprehensive Epilepsy Programmes at The Royal Melbourne, Austin and St Vincent's Hospitals, Melbourne, Australia between January 2015 and February 2017. They were compared to 12 matched patients with epilepsy but no psychosis and 42 healthy controls on standardised neuropsychological tests of memory and executive functioning in a case-control design. RESULTS Mean scores across all cognitive tasks showed a graded pattern of impairment, with the POE group showing the poorest performance, followed by the epilepsy without psychosis and the healthy control groups. This was associated with significant group-level differences on measures of working memory (p = < 0.01); immediate (p = < 0.01) and delayed verbal recall (p = < 0.01); visual memory (p < 0.001); and verbal fluency (p = 0.02). In particular, patients with POE performed significantly worse than the healthy control group on measures of both cognitive control (p = .005) and memory (p < .001), whereas the epilepsy without psychosis group showed only memory difficulties (delayed verbal recall) compared to healthy controls (p = .001). CONCLUSION People with POE show reduced performance in neuropsychological functions supported by the default mode and cognitive control networks, when compared to both healthy participants and people with epilepsy without psychosis.
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Affiliation(s)
- James Allebone
- Melbourne School of Psychological Sciences, University of Melbourne, Victoria, Australia; The Florey Institute of Neuroscience and Mental Health, Parkville, Victoria, Australia; Department of Clinical Neuropsychology, Austin Health, Heidelberg, Victoria, Australia
| | - Richard A Kanaan
- The Florey Institute of Neuroscience and Mental Health, Parkville, Victoria, Australia; Department of Psychiatry, University of Melbourne, Austin Health, Melbourne, Australia.
| | - Genevieve Rayner
- Melbourne School of Psychological Sciences, University of Melbourne, Victoria, Australia; The Florey Institute of Neuroscience and Mental Health, Parkville, Victoria, Australia; Comprehensive Epilepsy Program, Austin Health, University of Melbourne, Victoria, Australia; Department of Clinical Neuropsychology, Austin Health, Heidelberg, Victoria, Australia
| | - Jerome Maller
- ANU College of Health and Medicine, Australian National University, Canberra, Victoria, Australia; Monash Alfred Psychiatry Research Centre, The Alfred and Monash University, Melbourne, Australia; Department of Clinical Neuropsychology, Austin Health, Heidelberg, Victoria, Australia
| | - Terence J O'Brien
- Royal Melbourne Hospital, Melbourne, Victoria, Australia; Department of Neuroscience, Alfred Hospital, Monash University, Melbourne, Australia; Department of Clinical Neuropsychology, Austin Health, Heidelberg, Victoria, Australia
| | - Saul A Mullen
- The Florey Institute of Neuroscience and Mental Health, Parkville, Victoria, Australia; Department of Clinical Neuropsychology, Austin Health, Heidelberg, Victoria, Australia
| | - Mark Cook
- Graeme Clark Institute, University of Melbourne, Melbourne, Australia; Department of Clinical Neuropsychology, Austin Health, Heidelberg, Victoria, Australia
| | - Sophia J Adams
- Royal Melbourne Hospital, Melbourne, Victoria, Australia; Department of Clinical Neuropsychology, Austin Health, Heidelberg, Victoria, Australia
| | - Simon Vogrin
- St Vincent's Hospital, Melbourne, Victoria, Australia; Department of Clinical Neuropsychology, Austin Health, Heidelberg, Victoria, Australia
| | - David N Vaughan
- The Florey Institute of Neuroscience and Mental Health, Parkville, Victoria, Australia; Comprehensive Epilepsy Program, Austin Health, University of Melbourne, Victoria, Australia; Department of Clinical Neuropsychology, Austin Health, Heidelberg, Victoria, Australia
| | - Patrick Kwan
- Royal Melbourne Hospital, Melbourne, Victoria, Australia; Department of Neuroscience, Alfred Hospital, Monash University, Melbourne, Australia; Department of Clinical Neuropsychology, Austin Health, Heidelberg, Victoria, Australia
| | - Samuel F Berkovic
- Comprehensive Epilepsy Program, Austin Health, University of Melbourne, Victoria, Australia; Department of Clinical Neuropsychology, Austin Health, Heidelberg, Victoria, Australia
| | - Wendyl J D'Souza
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Australia; Department of Clinical Neuropsychology, Austin Health, Heidelberg, Victoria, Australia
| | - Graeme Jackson
- The Florey Institute of Neuroscience and Mental Health, Parkville, Victoria, Australia; Comprehensive Epilepsy Program, Austin Health, University of Melbourne, Victoria, Australia; Department of Clinical Neuropsychology, Austin Health, Heidelberg, Victoria, Australia
| | - Dennis Velakoulis
- Neuropsychiatry Unit, Royal Melbourne Hospital, Melbourne, Victoria, Australia; Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne, Health, Melbourne, Australia; Department of Clinical Neuropsychology, Austin Health, Heidelberg, Victoria, Australia
| | - Sarah J Wilson
- Melbourne School of Psychological Sciences, University of Melbourne, Victoria, Australia; The Florey Institute of Neuroscience and Mental Health, Parkville, Victoria, Australia; Comprehensive Epilepsy Program, Austin Health, University of Melbourne, Victoria, Australia; Department of Clinical Neuropsychology, Austin Health, Heidelberg, Victoria, Australia
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17
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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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18
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de Zoete RMJ, McMahon KL, Coombes JS, Sterling M. The effects of physical exercise on structural, functional, and biochemical brain characteristics in individuals with chronic whiplash-associated disorder: A pilot randomized clinical trial. Pain Pract 2023; 23:759-775. [PMID: 37157897 DOI: 10.1111/papr.13240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 02/01/2023] [Accepted: 04/24/2023] [Indexed: 05/10/2023]
Abstract
BACKGROUND Exercise for people with whiplash associated disorder (WAD) induces hypoalgesic effects in some, but hyperalgesic effects in others. We investigated the exercise-induced neurobiological effects of aerobic and strengthening exercise in individuals with chronic WAD. METHODS Sixteen participants (8 WAD, 8 pain-free [CON]) were randomised to either aerobic or strengthening exercise. MRI for brain morphometry, functional MRI for brain connectivity, and magnetic resonance spectroscopy for brain biochemistry, were used at baseline and after the 8-week intervention. RESULTS There were no differences in brain changes between exercise groups in either the WAD or CON group, therefore aerobic and strengthening data were combined to optimise sample size. After the exercise intervention, the CON group demonstrated increased cortical thickness (left parahippocampus: mean difference = 0.04, 95% CI = 0.07-0.00, p = 0.032; and left lateral orbital frontal cortex: mean difference = 0.03, 95% CI = 0.00-0.06, p = 0.048). The WAD group demonstrated an increase in prefrontal cortex (right medial orbital frontal) volume (mean difference = 95.57, 95% CI = 2.30-192.84, p = 0.046). Functional changes from baseline to follow-up between the default mode network and the insula, cingulate cortex, temporal lobe, and somatosensory and motor cortices, were found in the CON group, but not in the WAD group. There were no changes post-exercise in brain biochemistry. CONCLUSION Aerobic and strengthening exercises did not exert differential effects on brain characteristics, however differences in structural and functional changes were found between WAD and CON groups. This suggests that an altered central pain modulatory response may be responsible for differential effects of exercise in individuals with chronic WAD.
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Affiliation(s)
- Rutger M J de Zoete
- Recover Injury Research Centre, NHMRC Centre of Research Excellence in Recovery Following Road Traffic Injuries, The University of Queensland, Brisbane, Queensland, Australia
- School of Allied Health Science and Practice, The University of Adelaide, Adelaide, South Australia, Australia
| | - Katie L McMahon
- Herston Imaging Research Facility, Royal Brisbane & Women's Hospital, Brisbane, Queensland, Australia
- School of Clinical Sciences, Faculty of Health, Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Jeff S Coombes
- School of Human Movement and Nutrition Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, Brisbane, Queensland, Australia
| | - Michele Sterling
- Recover Injury Research Centre, NHMRC Centre of Research Excellence in Recovery Following Road Traffic Injuries, The University of Queensland, Brisbane, Queensland, Australia
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19
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Patel GH, Gruskin DC, Arkin SC, Jamerson EC, Ruiz-Betancourt DR, Klim CC, Sanchez-Peña JP, Bartel LP, Lee JK, Grinband J, Martinez A, Berman RA, Ochsner KN, Leopold DA, Javitt DC. The Road Not Taken: Disconnection of a Human-Unique Cortical Pathway Underlying Naturalistic Social Perception in Schizophrenia. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2023; 3:398-408. [PMID: 37519457 PMCID: PMC10382708 DOI: 10.1016/j.bpsgos.2022.03.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 03/02/2022] [Accepted: 03/06/2022] [Indexed: 11/18/2022] Open
Abstract
Background Efficient processing of complex and dynamic social scenes relies on intact connectivity of many underlying cortical areas and networks, but how connectivity anomalies affect the neural substrates of social perception remains unknown. Here we measured these relationships using functionally based localization of social perception areas, resting-state functional connectivity, and movie-watching data. Methods In 42 participants with schizophrenia (SzPs) and 41 healthy control subjects, we measured the functional connectivity of areas localized by face-emotion processing, theory-of-mind (ToM), and attention tasks. We quantified the weighted shortest path length between visual and medial prefrontal ToM areas in both populations to assess the impact of these changes in functional connectivity on network structure. We then correlated connectivity along the shortest path in each group with movie-evoked activity in a key node of the ToM network (posterior temporoparietal junction [TPJp]). Results SzPs had pronounced decreases in connectivity in TPJ/posterior superior temporal sulcus (TPJ-pSTS) areas involved in face-emotion processing (t81 = 4.4, p = .00002). In healthy control subjects, the shortest path connecting visual and medial prefrontal ToM areas passed through TPJ-pSTS, whereas in SzPs, the shortest path passed through the prefrontal cortex. While movie-evoked TPJp activity correlated with connectivity along the TPJ-pSTS pathway in both groups (r = 0.43, p = .002), it additionally correlated with connectivity along the prefrontal cortex pathway only in SzPs (rSzP = 0.56, p = .003). Conclusions These results suggest that connectivity along the human-unique TPJ-pSTS pathway affects both the network architecture and functioning of areas involved in processing complex dynamic social scenes. These results demonstrate how focal connectivity anomalies can have widespread impacts across the cortex.
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Affiliation(s)
- Gaurav H. Patel
- Department of Psychiatry, Columbia University, New York, New York
- Experimental Therapeutics, New York State Psychiatric Institute, New York, New York
| | - David C. Gruskin
- Department of Psychiatry, Columbia University, New York, New York
| | - Sophie C. Arkin
- University of California, Los Angeles, Los Angeles, California
| | | | | | | | - Juan P. Sanchez-Peña
- Department of Psychiatry, Columbia University, New York, New York
- Experimental Therapeutics, New York State Psychiatric Institute, New York, New York
| | - Laura P. Bartel
- Department of Psychiatry, Columbia University, New York, New York
- Experimental Therapeutics, New York State Psychiatric Institute, New York, New York
| | - Jessica K. Lee
- Department of Psychiatry, Columbia University, New York, New York
- Experimental Therapeutics, New York State Psychiatric Institute, New York, New York
| | - Jack Grinband
- Department of Psychiatry, Columbia University, New York, New York
- Experimental Therapeutics, New York State Psychiatric Institute, New York, New York
| | - Antígona Martinez
- Department of Psychiatry, Columbia University, New York, New York
- Schizophrenia Research Division, Nathan Kline Institute for Psychiatric Research, Orangeburg, New York
| | - Rebecca A. Berman
- Section on Cognitive Neurophysiology and Imaging, National Institute of Mental Health, Bethesda, Maryland
| | - Kevin N. Ochsner
- Department of Psychiatry, Columbia University, New York, New York
| | - David A. Leopold
- Section on Cognitive Neurophysiology and Imaging, National Institute of Mental Health, Bethesda, Maryland
| | - Daniel C. Javitt
- Department of Psychiatry, Columbia University, New York, New York
- Experimental Therapeutics, New York State Psychiatric Institute, New York, New York
- Schizophrenia Research Division, Nathan Kline Institute for Psychiatric Research, Orangeburg, New York
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20
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Keyvanfard F, Nasab AR, Nasiraei-Moghaddam A. Brain subnetworks most sensitive to alterations of functional connectivity in Schizophrenia: a data-driven approach. Front Neuroinform 2023; 17:1175886. [PMID: 37274751 PMCID: PMC10232974 DOI: 10.3389/fninf.2023.1175886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 04/24/2023] [Indexed: 06/06/2023] Open
Abstract
Functional connectivity (FC) of the brain changes in various brain disorders. Its complexity, however, makes it difficult to obtain a systematic understanding of these alterations, especially when they are found individually and through hypothesis-based methods. It would be easier if the variety of brain connectivity alterations is extracted through data-driven approaches and expressed as variation modules (subnetworks). In the present study, we modified a blind approach to determine inter-group brain variations at the network level and applied it specifically to schizophrenia (SZ) disorder. The analysis is based on the application of independent component analysis (ICA) over the subject's dimension of the FC matrices, obtained from resting-state functional magnetic resonance imaging (rs-fMRI). The dataset included 27 SZ people and 27 completely matched healthy controls (HC). This hypothesis-free approach led to the finding of three brain subnetworks significantly discriminating SZ from HC. The area associated with these subnetworks mostly covers regions in visual, ventral attention, and somatomotor areas, which are in line with previous studies. Moreover, from the graph perspective, significant differences were observed between SZ and HC for these subnetworks, while there was no significant difference when the same parameters (path length, network strength, global/local efficiency, and clustering coefficient) across the same limited data were calculated for the whole brain network. The increased sensitivity of those subnetworks to SZ-induced alterations of connectivity suggested whether an individual scoring method based on their connectivity values can be applied to classify subjects. A simple scoring classifier was then suggested based on two of these subnetworks and resulted in acceptable sensitivity and specificity with an area under the ROC curve of 77.5%. The third subnetwork was found to be a less specific building block (module) for describing SZ alterations. It projected a wider range of inter-individual variations and, therefore, had a lower chance to be considered as a SZ biomarker. These findings confirmed that investigating brain variations from a modular viewpoint can help to find subnetworks that are more sensitive to SZ-induced alterations. Altogether, our study results illustrated the developed method's ability to systematically find brain alterations caused by SZ disorder from a network perspective.
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Affiliation(s)
- Farzaneh Keyvanfard
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | - Alireza Rahimi Nasab
- Biomedical Engineering Department, Amirkabir University of Technology, Tehran, Iran
| | - Abbas Nasiraei-Moghaddam
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
- Biomedical Engineering Department, Amirkabir University of Technology, Tehran, Iran
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21
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Xu X, He B, Zeng J, Yin J, Wang X, Luo X, Liang C, Luo S, Yan H, Xiong S, Tan Z, Lv D, Dai Z, Lin Z, Lin J, Ye X, Chen R, Li Y, Wang Y, Chen W, Luo Z, Li K, Ma G. Genetic variations in DOCK4 contribute to schizophrenia susceptibility in a Chinese cohort: A genetic neuroimaging study. Behav Brain Res 2023; 443:114353. [PMID: 36822513 DOI: 10.1016/j.bbr.2023.114353] [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: 12/18/2022] [Revised: 02/13/2023] [Accepted: 02/15/2023] [Indexed: 02/23/2023]
Abstract
BACKGROUND Emerging evidence suggests that the DOCK4 gene increases susceptibility to schizophrenia. However, no study has hitherto repeated this association in Chinese, and further investigated the relationship between DOCK4 and clinical symptoms in schizophrenic patients using clinical scales and functional magnetic resonance imaging (fMRI). METHODS In this study, we genotyped three single nucleotide polymorphisms (SNPs) (rs2074127, rs2217262, and rs2074130) within the DOCK4 gene using a case-control design (including 1289 healthy controls and 1351 patients with schizophrenia). 55 first-episode schizophrenia (FES) patients and 59 healthy participants were divided by the genotypes of rs2074130 into CC and CT+TT groups. We further investigated the association with clinical symptoms and neural characteristics (brain activation/connectivity and nodal network metrics). RESULTS Our results showed significant associations between all selected SNPs and schizophrenia (all P < 0.05). In patients, letter fluency and motor speed scores of T allele carriers were significantly higher than the CC group (all P < 0.05). Interestingly, greater brain activity, functional connectivity, and betweenness centrality (BC) in language processing and motor coordination were also observed in the corresponding brain zones in patients with the T allele based on a two-way ANCOVA model. Moreover, a potential positive correlation was found between brain activity/connectivity of these brain regions and verbal fluency and motor speed. CONCLUSION Our findings suggest that the DOCK4 gene may contribute to the onset of schizophrenia and lead to language processing and motor coordination dysfunction in this patient population from China.
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Affiliation(s)
- Xusan Xu
- Institute of Neurology, Guangdong Medical University, Zhanjiang 524001, China; Maternal and Children's Health Research Institute, Shunde Maternal and Children's Hospital, Guangdong Medical University, Shunde 528300, China
| | - Bin He
- Department of Radiology, Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, China
| | - Jieqing Zeng
- Institute of Neurology, Guangdong Medical University, Zhanjiang 524001, China; Maternal and Children's Health Research Institute, Shunde Maternal and Children's Hospital, Guangdong Medical University, Shunde 528300, China
| | - Jingwen Yin
- Department of Psychiatry, Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, China
| | - Xiaoxia Wang
- Institute of Neurology, Guangdong Medical University, Zhanjiang 524001, China; Institute of Neurology, Longjiang Hospital, the Third Affiliated Hospital of Guangdong Medical University, Shunde 528300, China
| | - Xudong Luo
- Department of Psychiatry, Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, China
| | - Chunmei Liang
- Institute of Neurology, Guangdong Medical University, Zhanjiang 524001, China
| | - Shucun Luo
- Department of Radiology, Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, China
| | - Haifeng Yan
- Department of Psychiatry, Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, China
| | - Susu Xiong
- Department of Psychiatry, Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, China
| | - Zhi Tan
- Department of Radiology, Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, China
| | - Dong Lv
- Department of Psychiatry, Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, China
| | - Zhun Dai
- Department of Psychiatry, Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, China
| | - Zhixiong Lin
- Department of Psychiatry, Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, China
| | - Juda Lin
- Department of Psychiatry, Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, China
| | - Xiaoqing Ye
- Department of Psychiatry, Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, China
| | - Riling Chen
- Maternal and Children's Health Research Institute, Shunde Maternal and Children's Hospital, Guangdong Medical University, Shunde 528300, China
| | - You Li
- Institute of Neurology, Guangdong Medical University, Zhanjiang 524001, China
| | - Yajun Wang
- Maternal and Children's Health Research Institute, Shunde Maternal and Children's Hospital, Guangdong Medical University, Shunde 528300, China
| | - Wubiao Chen
- Department of Radiology, Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, China
| | - Zebin Luo
- Department of Radiology, Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, China.
| | - Keshen Li
- Clinical Neuroscience Institute, The First Affiliated Hospital, Jinan University, Guangzhou 510623, China.
| | - Guoda Ma
- Institute of Neurology, Guangdong Medical University, Zhanjiang 524001, China; Maternal and Children's Health Research Institute, Shunde Maternal and Children's Hospital, Guangdong Medical University, Shunde 528300, China.
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22
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Sampedro A, Ibarretxe-Bilbao N, Peña J, Cabrera-Zubizarreta A, Sánchez P, Gómez-Gastiasoro A, Iriarte-Yoller N, Pavón C, Tous-Espelosin M, Ojeda N. Analyzing structural and functional brain changes related to an integrative cognitive remediation program for schizophrenia: A randomized controlled trial. Schizophr Res 2023; 255:82-92. [PMID: 36965364 DOI: 10.1016/j.schres.2023.03.021] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 02/07/2023] [Accepted: 03/11/2023] [Indexed: 03/27/2023]
Abstract
Cognitive remediation has been shown to improve cognition in schizophrenia, but little is known about the specific functional and structural brain changes related to the implementation of an integrative cognitive remediation program. This study analyzed the functional and structural brain changes identified after implementing an integrative cognitive remediation program, REHACOP, in schizophrenia. The program combined cognitive remediation, social cognitive training, and functional and social skills training. The sample included 59 patients that were assigned to either the REHACOP group or an active control group for 20 weeks. In addition to a clinical and neuropsychological assessment, T1-weighted, diffusion-weighted and functional magnetic resonance images were acquired during a resting-state and during a memory paradigm, both at baseline and follow-up. Voxel-based morphometry, tract-based spatial statistics, resting-state functional connectivity, and brain activation analyses during the memory paradigm were performed. Brain changes were assessed with a 2 × 2 repeated-measure analysis of covariance for group x time interaction. Intragroup paired t-tests were also carried out. Repeated-measure analyses revealed improvements in cognition and functional outcome, but no significant brain changes associated with the integrative cognitive remediation program. Intragroup analyses showed greater gray matter volume and cortical thickness in right temporal regions at post-treatment in the REHACOP group. The absence of significant brain-level results associated with cognitive remediation may be partly due to the small sample size, which limited the statistical power of the study. Therefore, further research is needed to clarify whether the temporal lobe may be a key area involved in cognitive improvements following cognitive remediation.
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Affiliation(s)
- Agurne Sampedro
- University of Deusto, Faculty of Health Sciences, Department of Psychology, Bilbao, Spain
| | - Naroa Ibarretxe-Bilbao
- University of Deusto, Faculty of Health Sciences, Department of Psychology, Bilbao, Spain
| | - Javier Peña
- University of Deusto, Faculty of Health Sciences, Department of Psychology, Bilbao, Spain.
| | | | - Pedro Sánchez
- Bioaraba, New Therapies in Mental Health, Osakidetza Basque Health Service, Araba Mental Health Service, Alava Psychiatric Hospital, Vitoria-Gasteiz, Spain; University of Deusto, Faculty of Health Sciences, Department of Medicine, Bilbao, Spain
| | - Ainara Gómez-Gastiasoro
- University of the Basque Country (UPV/EHU), Faculty of Psychology, Department of Basic Psychological Processes and Development, Donostia, Spain
| | - Nagore Iriarte-Yoller
- Bioaraba, New Therapies in Mental Health, Osakidetza Basque Health Service, Araba Mental Health Service, Alava Psychiatric Hospital, Vitoria-Gasteiz, Spain
| | - Cristóbal Pavón
- Bioaraba, New Therapies in Mental Health, Osakidetza Basque Health Service, Araba Mental Health Service, Alava Psychiatric Hospital, Vitoria-Gasteiz, Spain
| | - Mikel Tous-Espelosin
- University of the Basque Country (UPV/EHU), Faculty of Education and Sport, Department of Physical Education and Sport, Vitoria-Gasteiz, Spain
| | - Natalia Ojeda
- University of Deusto, Faculty of Health Sciences, Department of Psychology, Bilbao, Spain
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23
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Impact of low-frequency repetitive transcranial magnetic stimulation on functional network connectivity in schizophrenia patients with auditory verbal hallucinations. Psychiatry Res 2023; 320:114974. [PMID: 36587467 DOI: 10.1016/j.psychres.2022.114974] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 11/10/2022] [Accepted: 11/19/2022] [Indexed: 11/22/2022]
Abstract
Auditory verbal hallucinations (AVH) are a key symptom of schizophrenia. Low-frequency repetitive transcranial magnetic stimulation (rTMS) has shown potential in the treatment of AVH. However, the underlying neural mechanismof rTMS in the treatment of AVH remains largely unknown. In this study, we used a static and dynamic functional network connectivity approach to investigate the connectivity changes among the brain functional networks in schizophrenia patients with AVH receiving 1 Hz rTMS treatment. The static functional network connectivity (sFNC) analysis revealed that patients at baseline had significantly decreased connectivity between the default mode network (DMN) and language network (LAN), and within the executive control network (ECN) as well as within the auditory network (AUD) compared to controls. However, the abnormal network connectivity patterns were normalized or restored after rTMS treatment in patients, instead of increased connectivity between the ECN and LAN, as well as within the AUD. Moreover, the dynamic functional network connectivity (dFNC) analysis showed that the patients at baseline spent more time in this state that was characterized by strongly negative connectivity between the ENC and AUD, as well as within the AUD relative to controls. While after rTMS treatment, the patients showed a higher occurrence rate in this state that was characterized by strongly positive connectivity among the LAN, DMN, and ENC, as well as within the ECN. In addition, the altered static and dynamic connectivity properties were associated with reduced severity of clinical symptoms. Both sFNC and dFNC analyses provided complementary information and suggested that low-frequency rTMS treatment could induce intrinsic functional network alternations and contribute to improvements in clinical symptoms in patients with AVH.
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24
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Ho NF, Lin AY, Tng JXJ, Chew QH, Cheung MWL, Javitt DC, Sim K. Abnormalities in visual cognition and associated impaired interactions between visual and attentional networks in schizophrenia and brief psychotic disorder. Psychiatry Res Neuroimaging 2022; 327:111545. [PMID: 36272310 DOI: 10.1016/j.pscychresns.2022.111545] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 09/08/2022] [Accepted: 09/23/2022] [Indexed: 12/04/2022]
Abstract
The extent and nature of cognitive impairment in brief psychotic disorder remains unclear, being rarely studied unlike schizophrenia. The present study hence sought to directly compare the visual cognitive dysfunction and its associated brain networks in brief psychotic disorder and schizophrenia. Data from picture completion (a complex visual task) and whole-brain functional connectome from resting-state fMRI were acquired from a sample of clinically stable patients with an established psychotic disorder (twenty with brief psychotic disorder, twenty with schizophrenia) and twenty-nine healthy controls. Group differences and the inter-relationships in task performances and brain networks were tested. Picture completion task deficits were found in brief psychotic disorder compared with healthy controls, though the deficits were less than schizophrenia. Task performance also correlated with severity of psychotic symptoms in patients. The task performance was inversely correlated with the functional connectivity between peripheral visual and attentional networks (dorsal attention and salience ventral attention), with increased functional connectivity in brief psychotic disorder compared with healthy controls and in schizophrenia compared with brief psychotic disorder. Present findings showed pronounced visual cognitive impairments in brief psychotic disorder that were worse in schizophrenia, underpinned by abnormal interactions between higher-order attentional and lower-order visual processing networks.
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Affiliation(s)
- New Fei Ho
- Institute of Mental Health, Singapore; Duke-National University of Singapore Medical School, Singapore.
| | | | | | | | | | | | - Kang Sim
- Institute of Mental Health, Singapore
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25
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Dabiri M, Dehghani Firouzabadi F, Yang K, Barker PB, Lee RR, Yousem DM. Neuroimaging in schizophrenia: A review article. Front Neurosci 2022; 16:1042814. [PMID: 36458043 PMCID: PMC9706110 DOI: 10.3389/fnins.2022.1042814] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 10/28/2022] [Indexed: 11/16/2022] Open
Abstract
In this review article we have consolidated the imaging literature of patients with schizophrenia across the full spectrum of modalities in radiology including computed tomography (CT), morphologic magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), magnetic resonance spectroscopy (MRS), positron emission tomography (PET), and magnetoencephalography (MEG). We look at the impact of various subtypes of schizophrenia on imaging findings and the changes that occur with medical and transcranial magnetic stimulation (TMS) therapy. Our goal was a comprehensive multimodality summary of the findings of state-of-the-art imaging in untreated and treated patients with schizophrenia. Clinical imaging in schizophrenia is used to exclude structural lesions which may produce symptoms that may mimic those of patients with schizophrenia. Nonetheless one finds global volume loss in the brains of patients with schizophrenia with associated increased cerebrospinal fluid (CSF) volume and decreased gray matter volume. These features may be influenced by the duration of disease and or medication use. For functional studies, be they fluorodeoxyglucose positron emission tomography (FDG PET), rs-fMRI, task-based fMRI, diffusion tensor imaging (DTI) or MEG there generally is hypoactivation and disconnection between brain regions. However, these findings may vary depending upon the negative or positive symptomatology manifested in the patients. MR spectroscopy generally shows low N-acetylaspartate from neuronal loss and low glutamine (a neuroexcitatory marker) but glutathione may be elevated, particularly in non-treatment responders. The literature in schizophrenia is difficult to evaluate because age, gender, symptomatology, comorbidities, therapy use, disease duration, substance abuse, and coexisting other psychiatric disorders have not been adequately controlled for, even in large studies and meta-analyses.
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Affiliation(s)
- Mona Dabiri
- Department of Radiology, Children’s Medical Center, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Kun Yang
- Department of Psychiatry, Molecular Psychiatry Program, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Peter B. Barker
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Medical Institution, Baltimore, MD, United States
| | - Roland R. Lee
- Department of Radiology, UCSD/VA Medical Center, San Diego, CA, United States
| | - David M. Yousem
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Medical Institution, Baltimore, MD, United States
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26
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Cheng J, Ren Y, Gu Q, He Y, Wang Z. QEEG Biomarkers for ECT Treatment Response in Schizophrenia. Clin EEG Neurosci 2022; 53:499-505. [PMID: 34792399 DOI: 10.1177/15500594211058260] [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] [Indexed: 11/17/2022]
Abstract
Background: Electroconvulsive therapy (ECT) is a clinically effective treatment for schizophrenia (SZD). However, studies have shown that only about 50 to 80% of patients show response to ECT. To identify the most suitable patients for ECT, developing biomarkers predicting ECT response remains an important goal. This study aimed to explore the quantitative electroencephalography (QEEG) biomarkers to predict ECT efficacy. Methods: Thirty patients who met DSM-5 criteria for SZD and had been assigned to ECT were recruited. 32-lead Resting-EEG recordings were collected one hour before the initial ECT treatment. Positive and negative symptoms scale (PANSS) was assessed at baseline and after the eighth ECT session. EEG data were analyzed using mutual information. Results: In the brain network density threshold range of 0.05 to 0.2, the assortativity of the right temporal, right parietal, and right occipital cortex in the response group was significantly higher than that in the non-response group (p < .05) in the beta band. In the theta band, the left frontal, parietal, right occipital cortex, and central area assortativity were higher in the response group than in the non-response group (p < .05). Conclusions: QEEG might be a useful approach to identify the candidate biomarker for ECT in clinical practice.
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Affiliation(s)
- Jiayue Cheng
- 364236Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Yanyan Ren
- 364236Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Qiumeng Gu
- 364236Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Yongguang He
- Institute of Psychological and Behavioral Science, Shanghai Jiao Tong University, Shanghai, PR China
| | - Zhen Wang
- 364236Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China.,Institute of Psychological and Behavioral Science, Shanghai Jiao Tong University, Shanghai, PR China
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27
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Haller S, Montandon ML, Rodriguez C, Giannakopoulos P. Wearing a KN95/FFP2 facemask induces subtle yet significant brain functional connectivity modifications restricted to the salience network. Eur Radiol Exp 2022; 6:50. [PMID: 36210391 PMCID: PMC9548384 DOI: 10.1186/s41747-022-00301-0] [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: 03/30/2022] [Accepted: 08/03/2022] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
The use of facemasks is one of the consequences of the coronavirus disease 2019 (COVID-19) pandemic. We used resting-state functional magnetic resonance imaging (fMRI) to search for subtle changes in brain functional connectivity, expected notably related to the high-level salience network (SN) and default mode network (DMN).
Methods
Prospective crossover design resting 3-T fMRI study with/without wearing a tight FFP2/KN95 facemask, including 23 community-dwelling male healthy controls aged 29.9 ± 6.9 years (mean ± standard deviation). Physiological parameters, respiration frequency, and heart rate were monitored. The data analysis was performed using the CONN toolbox.
Results
Wearing an FFP2/KN95 facemask did not impact respiration or heart rate but resulted in a significant reduction in functional connectivity between the SN as the seed region and the left middle frontal and precentral gyrus. No difference was found when the DMN, sensorimotor, visual, dorsal attention, or language networks were used as seed regions. In the absence of significant changes of physiological parameter respiration and heart rate, and in the absence of changes in lower-level functional networks, we assume that those subtle modifications are cognitive consequence of wearing facemasks.
Conclusions
The effect of wearing a tight FFP2/KN95 facemask in men is limited to high-level functional networks. Using the SN as seed network, we observed subtle yet significant decreases between the SN and the left middle frontal and precentral gyrus. Our observations suggest that wearing a facemask may change the patterns of functional connectivity with the SN known to be involved in communication, social behavior, and self-awareness.
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28
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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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29
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Ji R, Zhou M, Ou N, Chen H, Li Y, Zhuo L, Huang X, Huang G. Large-scale networks underlie cognitive insight differs between untreated adolescents ongoing their first schizophrenic episode and their reference non-schizophrenic mates. Heliyon 2022; 8:e10818. [PMID: 36217472 PMCID: PMC9547213 DOI: 10.1016/j.heliyon.2022.e10818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 06/03/2022] [Accepted: 09/22/2022] [Indexed: 10/25/2022] Open
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30
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Corr R, Glier S, Bizzell J, Pelletier-Baldelli A, Campbell A, Killian-Farrell C, Belger A. Triple Network Functional Connectivity During Acute Stress in Adolescents and the Influence of Polyvictimization. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2022; 7:867-875. [PMID: 35292406 PMCID: PMC9464656 DOI: 10.1016/j.bpsc.2022.03.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 02/28/2022] [Accepted: 03/02/2022] [Indexed: 11/25/2022]
Abstract
BACKGROUND Exposure to both chronic and acute stressors can disrupt functional connectivity (FC) of the default mode network (DMN), salience network (SN), and central executive network (CEN), increasing risk for negative health outcomes. During adolescence, these stress-sensitive triple networks undergo critical neuromaturation that is altered by chronic exposure to general forms of trauma or victimization. However, no work has directly examined how acute stress affects triple network FC in adolescents or whether polyvictimization-exposure to multiple categories/subtypes of victimization-influences adolescent triple network neural acute stress response. METHODS This functional magnetic resonance imaging study examined seed-to-voxel FC of the DMN, SN, and CEN during the Montreal Imaging Stress Task. Complete data from 73 participants aged 9 to 16 years (31 female) are reported. RESULTS During acute stress, FC was increased between DMN and CEN regions and decreased between the SN and the DMN and CEN. Greater polyvictimization was associated with reduced FC during acute stress exposure between the DMN seed and a cluster containing the left insula of the SN. CONCLUSIONS These results indicate that acute stress exposure alters FC between the DMN, SN, and CEN in adolescents. In addition, FC changes during stress between the DMN and SN are further moderated by polyvictimization exposure.
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Affiliation(s)
- Rachel Corr
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Duke-UNC Brain Imaging and Analysis Center, Duke University Medical Center, Durham, North Carolina.
| | - Sarah Glier
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Duke-UNC Brain Imaging and Analysis Center, Duke University Medical Center, Durham, North Carolina
| | - Joshua Bizzell
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Frank Porter Graham Child Development Institute, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Duke-UNC Brain Imaging and Analysis Center, Duke University Medical Center, Durham, North Carolina
| | - Andrea Pelletier-Baldelli
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Duke-UNC Brain Imaging and Analysis Center, Duke University Medical Center, Durham, North Carolina
| | - Alana Campbell
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Candace Killian-Farrell
- Department of Child and Adolescent Psychiatry & Behavioral Health Sciences, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Aysenil Belger
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Frank Porter Graham Child Development Institute, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Duke-UNC Brain Imaging and Analysis Center, Duke University Medical Center, Durham, North Carolina
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31
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Sarpal DK, Tarcijonas G, Calabro FJ, Foran W, Haas GL, Luna B, Murty VP. Context-specific abnormalities of the central executive network in first-episode psychosis: relationship with cognition. Psychol Med 2022; 52:2299-2308. [PMID: 33222723 PMCID: PMC9805803 DOI: 10.1017/s0033291720004201] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
BACKGROUND Cognitive impairments, which contribute to the profound functional deficits observed in psychotic disorders, have found to be associated with abnormalities in trial-level cognitive control. However, neural tasks operate within the context of sustained cognitive states, which can be assessed with 'background connectivity' following the removal of task effects. To date, little is known about the integrity of brain processes supporting the maintenance of a cognitive state in individuals with psychotic disorders. Thus, here we examine background connectivity during executive processing in a cohort of participants with first-episode psychosis (FEP). METHODS The following fMRI study examined background connectivity of the dorsolateral prefrontal cortex (DLPFC), during working memory engagement in a group of 43 patients with FEP, relative to 35 healthy controls (HC). Findings were also examined in relation to measures of executive function. RESULTS The FEP group relative to HC showed significantly lower background DLPFC connectivity with bilateral superior parietal lobule (SPL) and left inferior parietal lobule. Background connectivity between DLPFC and SPL was also positively associated with overall cognition across all subjects and in our FEP group. In comparison, resting-state frontoparietal connectivity did not differ between groups and was not significantly associated with overall cognition, suggesting that psychosis-related alterations in executive networks only emerged during states of goal-oriented behavior. CONCLUSIONS These results provide novel evidence indicating while frontoparietal connectivity at rest appears intact in psychosis, when engaged during a cognitive state, it is impaired possibly undermining cognitive control capacities in FEP.
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Affiliation(s)
- Deepak K. Sarpal
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Goda Tarcijonas
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Finnegan J. Calabro
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - William Foran
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Gretchen L. Haas
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Beatriz Luna
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Vishnu P. Murty
- Department of Psychology, Temple University, Philadelphia, PA, USA
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Schiwy LC, Forlim CG, Fischer DJ, Kühn S, Becker M, Gallinat J. Aberrant functional connectivity within the salience network is related to cognitive deficits and disorganization in psychosis. Schizophr Res 2022; 246:103-111. [PMID: 35753120 DOI: 10.1016/j.schres.2022.06.008] [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] [Received: 11/22/2021] [Revised: 05/10/2022] [Accepted: 06/11/2022] [Indexed: 01/09/2023]
Abstract
In schizophrenia and schizoaffective disorder cognitive deficits are a reliable characteristic predicting a poor functional outcome. It has been theorized that both the default mode network (DMN) and the salience network (SN) play a crucial role in cognitive processes and aberrant functional connectivity within these networks in psychotic patients has been reported. The goal of this study was to reveal potential links between aberrant functional connectivity within these networks and impaired cognitive performance in psychosis. We chose two approaches for cognitive assessment, first the MATRICS Consensus Cognitive Battery (MCCB) combined into a global score and second the disorganization factor derived from a five-factor model of the Positive and Negative Syndrome Scale (PANSS) known to be relevant for cognitive performance. DMN and SN were identified using independent component analysis on resting-state functional magnetic resonance imaging data. We found significantly decreased connectivity within the right supplementary motor area (SMA) and bilateral putamen in patients with psychosis (n = 70; 27F/43M) compared to healthy controls (n = 72; 28F/44M). Within patients, linear regression analysis revealed that aberrant SMA connectivity was associated with impaired global cognition, while dysfunctional bilateral putamen connectivity predicted disorganization. There were no significant changes in connectivity within the DMN. Results support the hypothesis that SN dysfunctional connectivity is important in the pathobiology of cognitive deficits in psychosis. For the first time we were able to show the involvement of dysfunctional SMA connectivity in this context. We interpret the decreased SN connectivity as evidence of reduced functionality in recruiting brain areas necessary for cognitive processing.
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Affiliation(s)
- Lennart Christopher Schiwy
- University Medical Centre Hamburg-Eppendorf, Clinic and Policlinic for Psychiatry and Psychotherapy, Martinistraße 52, 20246 Hamburg, Germany.
| | - Caroline Garcia Forlim
- University Medical Centre Hamburg-Eppendorf, Clinic and Policlinic for Psychiatry and Psychotherapy, Martinistraße 52, 20246 Hamburg, Germany
| | - Djo Juliette Fischer
- University Medical Centre Hamburg-Eppendorf, Clinic and Policlinic for Psychiatry and Psychotherapy, Martinistraße 52, 20246 Hamburg, Germany
| | - Simone Kühn
- University Medical Centre Hamburg-Eppendorf, Clinic and Policlinic for Psychiatry and Psychotherapy, Martinistraße 52, 20246 Hamburg, Germany; Max Planck Institute for Human Development, Center for Lifespan Psychology, Lentzeallee 94, 14195 Berlin, Germany
| | - Maxi Becker
- University Medical Centre Hamburg-Eppendorf, Clinic and Policlinic for Psychiatry and Psychotherapy, Martinistraße 52, 20246 Hamburg, Germany
| | - Jürgen Gallinat
- University Medical Centre Hamburg-Eppendorf, Clinic and Policlinic for Psychiatry and Psychotherapy, Martinistraße 52, 20246 Hamburg, Germany
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33
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King VL, Lahti AC, Maximo JO, ver Hoef LW, John S, Kraguljac NV. Contrasting Frontoparietal Network Connectivity in Antipsychotic Medication-Naive First-Episode Psychosis Patients Who Do and Do Not Display Features of the Deficit Syndrome. Schizophr Bull 2022; 48:1344-1353. [PMID: 35869578 PMCID: PMC9673254 DOI: 10.1093/schbul/sbac081] [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] [Indexed: 12/14/2022]
Abstract
BACKGROUND The deficit syndrome is a clinical subtype of schizophrenia that is characterized by enduring negative symptoms. Several lines of evidence point to frontoparietal involvement, but the frontoparietal control network (FPCN) and its subsystems (FPCNA and FPCNB) proposed by Yeo et al. have not been systematically characterized at rest in patients with the deficit syndrome. METHODS We used resting-state fMRI to investigate the FPCN and its subnetworks in 72 healthy controls and 65 antipsychotic medication-naive, first-episode psychosis patients (22 displayed deficit syndrome features, 43 did not). To assess whole-brain FPCN connectivity, we used the right posterior parietal cortex as the seed region. We then performed region of interest analyses in FPCN subsystems. RESULTS We found that patterns of FPCN dysconnectivity to the whole brain differed in patients who displayed deficit syndrome features compared with those who did not. Examining the FPCN on a more granular level revealed reduced within-FPCN(A) connectivity only in patients displaying deficit features. FPCNB connectivity did not differ between patient groups. DISCUSSION Here, we describe a neurobiological signature of aberrant FPCN connectivity in antipsychotic-naive, first-episode patients who display clinical features of the deficit syndrome. Importantly, frontoparietal subnetwork connectivity differentiated subgroups, where the FPCNA is selectively involved in patients with deficit features. Our findings add to the growing body of literature supporting a neurobiological distinction between two clinical subtypes of schizophrenia, which has the potential to be leveraged for patient stratification in clinical trials and the development of novel treatments.
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Affiliation(s)
- Victoria L King
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Adrienne C Lahti
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Jose O Maximo
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Lawrence W ver Hoef
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Sooraj John
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Nina V Kraguljac
- To whom correspondence should be addressed; Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, SC 501, 1720 7th Ave S, Birmingham, AL 35294-0017, USA; tel: 205-996-7171, e-mail:
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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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Iraji A, Faghiri A, Fu Z, Kochunov P, Adhikari BM, Belger A, Ford JM, McEwen S, Mathalon DH, Pearlson GD, Potkin SG, Preda A, Turner JA, Van Erp TGM, Chang C, Calhoun VD. Moving beyond the 'CAP' of the Iceberg: Intrinsic connectivity networks in fMRI are continuously engaging and overlapping. Neuroimage 2022; 251:119013. [PMID: 35189361 PMCID: PMC9107614 DOI: 10.1016/j.neuroimage.2022.119013] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 02/11/2022] [Accepted: 02/17/2022] [Indexed: 11/05/2022] Open
Abstract
Resting-state functional magnetic resonance imaging is currently the mainstay of functional neuroimaging and has allowed researchers to identify intrinsic connectivity networks (aka functional networks) at different spatial scales. However, little is known about the temporal profiles of these networks and whether it is best to model them as continuous phenomena in both space and time or, rather, as a set of temporally discrete events. Both categories have been supported by series of studies with promising findings. However, a critical question is whether focusing only on time points presumed to contain isolated neural events and disregarding the rest of the data is missing important information, potentially leading to misleading conclusions. In this work, we argue that brain networks identified within the spontaneous blood oxygenation level-dependent (BOLD) signal are not limited to temporally sparse burst moments and that these event present time points (EPTs) contain valuable but incomplete information about the underlying functional patterns. We focus on the default mode and show evidence that is consistent with its continuous presence in the BOLD signal, including during the event absent time points (EATs), i.e., time points that exhibit minimum activity and are the least likely to contain an event. Moreover, our findings suggest that EPTs may not contain all the available information about their corresponding networks. We observe distinct default mode connectivity patterns obtained from all time points (AllTPs), EPTs, and EATs. We show evidence of robust relationships with schizophrenia symptoms that are both common and unique to each of the sets of time points (AllTPs, EPTs, EATs), likely related to transient patterns of connectivity. Together, these findings indicate the importance of leveraging the full temporal data in functional studies, including those using event-detection approaches.
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Affiliation(s)
- A 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, United States of America.
| | - A Faghiri
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, United States of America
| | - Z 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, United States of America
| | - P Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, United States of America
| | - B M Adhikari
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, United States of America
| | - A Belger
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, United States of America
| | - J M Ford
- Department of Psychiatry, University of California San Francisco, San Francisco, CA, United States of America; San Francisco VA Medical Center, San Francisco, CA, United States of America
| | - S McEwen
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, United States of America
| | - D H Mathalon
- Department of Psychiatry, University of California San Francisco, San Francisco, CA, United States of America; San Francisco VA Medical Center, San Francisco, CA, United States of America
| | - G D Pearlson
- Departments of Psychiatry and Neuroscience, Yale University, School of Medicine, New Haven, CT, United States of America
| | - S G Potkin
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, United States of America
| | - A Preda
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, United States of America
| | - J A Turner
- Department of Psychology, Georgia State University, Atlanta, GA, United States of America
| | - T G M Van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, United States of America
| | - C Chang
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States of America
| | - V 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, United States of America.
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36
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Jiang L, Wang J, Dai J, Li F, Chen B, He R, Liao Y, Yao D, Dong W, Xu P. Altered temporal variability in brain functional connectivity identified by fuzzy entropy underlines schizophrenia deficits. J Psychiatr Res 2022; 148:315-324. [PMID: 35193035 DOI: 10.1016/j.jpsychires.2022.02.011] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 01/13/2022] [Accepted: 02/14/2022] [Indexed: 11/18/2022]
Abstract
Investigation of the temporal variability of resting-state brain networks informs our understanding of how neural connectivity aggregates and disassociates over time, further shedding light on the aberrant neural interactions that underlie symptomatology and psychosis development. In the current work, an electroencephalogram-based sliding window analysis was utilized for the first time to measure the nonlinear complexity of dynamic resting-state brain networks of schizophrenia (SZ) patients by applying fuzzy entropy. The results of this study demonstrated the attenuated temporal variability among multiple electrodes that were distributed in the frontal and right parietal lobes for SZ patients when compared with healthy controls (HCs). Meanwhile, a concomitant strengthening of the posterior and peripheral flexible connections that may be attributed to the excessive alertness or sensitivity of SZ patients to the external environment was also revealed. These temporal fluctuation distortions combined reflect an abnormality in the coordination of functional network switching in SZ, which is further the source of worse task performance (i.e., P300 amplitude) and the negative relationship between individual complexity metrics and P300 amplitude. Notably, when using the network metrics as features, multiple linear regressions of P300 amplitudes were also exactly achieved for both the SZ and HC groups. These findings shed light on the pathophysiological mechanisms of SZ from a temporal variability perspective and provide potential biomarkers for quantifying SZ's progressive neurophysiological deterioration.
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Affiliation(s)
- Lin Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 610054, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Jiuju Wang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
| | - Jing Dai
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 610054, China; Chengdu Mental Health Center, Chengdu, 610036, China
| | - Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 610054, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, China.
| | - Baodan Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 610054, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Runyang He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 610054, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Yuanyuan Liao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 610054, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 610054, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, China; School of Electrical Engineering, Zhengzhou University, Zhengzhou, 450001, China
| | - Wentian Dong
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China.
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 610054, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, China.
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37
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Wang YM, Cai XL, Zhang RT, Zhang YJ, Zhou HY, Wang Y, Wang Y, Huang J, Wang YY, Cheung EFC, Chan RCK. Altered brain structural and functional connectivity in schizotypy. Psychol Med 2022; 52:834-843. [PMID: 32677599 DOI: 10.1017/s0033291720002445] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
BACKGROUND Schizotypy refers to schizophrenia-like traits below the clinical threshold in the general population. The pathological development of schizophrenia has been postulated to evolve from the initial coexistence of 'brain disconnection' and 'brain connectivity compensation' to 'brain connectivity decompensation'. METHODS In this study, we examined the brain connectivity changes associated with schizotypy by combining brain white matter structural connectivity, static and dynamic functional connectivity analysis of diffusion tensor imaging data and resting-state functional magnetic resonance imaging data. A total of 87 participants with a high level of schizotypal traits and 122 control participants completed the experiment. Group differences in whole-brain white matter structural connectivity probability, static mean functional connectivity strength, dynamic functional connectivity variability and stability among 264 brain sub-regions of interests were investigated. RESULTS We found that individuals with high schizotypy exhibited increased structural connectivity probability within the task control network and within the default mode network; increased variability and decreased stability of functional connectivity within the default mode network and between the auditory network and the subcortical network; and decreased static mean functional connectivity strength mainly associated with the sensorimotor network, the default mode network and the task control network. CONCLUSIONS These findings highlight the specific changes in brain connectivity associated with schizotypy and indicate that both decompensatory and compensatory changes in structural connectivity within the default mode network and the task control network in the context of whole-brain functional disconnection may be an important neurobiological correlate in individuals with high schizotypy.
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Affiliation(s)
- Yong-Ming Wang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing100101, PR China
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing100190, PR China
- Sino-Danish Center for Education and Research, Beijing100190, PR China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, PR China
| | - Xin-Lu Cai
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing100101, PR China
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing100190, PR China
- Sino-Danish Center for Education and Research, Beijing100190, PR China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, PR China
| | - Rui-Ting Zhang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing100101, PR China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, PR China
| | - Yi-Jing Zhang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing100101, PR China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, PR China
| | - Han-Yu Zhou
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing100101, PR China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, PR China
| | - Yi Wang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing100101, PR China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, PR China
| | - Ya Wang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing100101, PR China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, PR China
| | - Jia Huang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing100101, PR China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, PR China
| | - Yan-Yu Wang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing100101, PR China
- Department of Psychology, Weifang Medical University, Shandong Province, PR China
| | - Eric F C Cheung
- Castle Peak Hospital, Hong Kong Special Administrative Region, PR China
| | - Raymond C K Chan
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing100101, PR China
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing100190, PR China
- Sino-Danish Center for Education and Research, Beijing100190, PR China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, PR China
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Wei J, Wang X, Cui X, Wang B, Xue J, Niu Y, Wang Q, Osmani A, Xiang J. Functional Integration and Segregation in a Multilayer Network Model of Patients with Schizophrenia. Brain Sci 2022; 12:brainsci12030368. [PMID: 35326324 PMCID: PMC8946586 DOI: 10.3390/brainsci12030368] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 03/08/2022] [Accepted: 03/08/2022] [Indexed: 12/24/2022] Open
Abstract
Research has shown that abnormal brain networks in patients with schizophrenia appear at different frequencies, but the relationship between these different frequencies is unclear. Therefore, it is necessary to use a multilayer network model to evaluate the integration of information from different frequency bands. To explore the mechanism of integration and separation in the multilayer network of schizophrenia, we constructed multilayer frequency brain network models in 50 patients with schizophrenia and 69 healthy subjects, and the entropy of the multiplex degree (EMD) and multilayer clustering coefficient (MCC) were calculated. The results showed that the ability to integrate and separate information in the multilayer network of patients was significantly higher than that of normal people. This difference was mainly reflected in the default mode network, sensorimotor network, subcortical network, and visual network. Among them, the subcortical network was different in both MCC and EMD outcomes. Furthermore, differences were found in the posterior cingulate gyrus, hippocampus, amygdala, putamen, pallidum, and thalamus. The thalamus and posterior cingulate gyrus were associated with the patient’s symptom scores. Our results showed that the cross-frequency interaction ability of patients with schizophrenia was significantly enhanced, among which the subcortical network was the most active. This interaction may serve as a compensation mechanism for intralayer dysfunction.
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Affiliation(s)
- Jing Wei
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China; (J.W.); (X.W.); (X.C.); (B.W.); (J.X.); (Y.N.); (Q.W.); (A.O.)
- School of Information, Shanxi University of Finance and Economics, Taiyuan 030024, China
| | - Xiaoyue Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China; (J.W.); (X.W.); (X.C.); (B.W.); (J.X.); (Y.N.); (Q.W.); (A.O.)
| | - Xiaohong Cui
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China; (J.W.); (X.W.); (X.C.); (B.W.); (J.X.); (Y.N.); (Q.W.); (A.O.)
| | - Bin Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China; (J.W.); (X.W.); (X.C.); (B.W.); (J.X.); (Y.N.); (Q.W.); (A.O.)
| | - Jiayue Xue
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China; (J.W.); (X.W.); (X.C.); (B.W.); (J.X.); (Y.N.); (Q.W.); (A.O.)
| | - Yan Niu
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China; (J.W.); (X.W.); (X.C.); (B.W.); (J.X.); (Y.N.); (Q.W.); (A.O.)
| | - Qianshan Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China; (J.W.); (X.W.); (X.C.); (B.W.); (J.X.); (Y.N.); (Q.W.); (A.O.)
| | - Arezo Osmani
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China; (J.W.); (X.W.); (X.C.); (B.W.); (J.X.); (Y.N.); (Q.W.); (A.O.)
| | - Jie Xiang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China; (J.W.); (X.W.); (X.C.); (B.W.); (J.X.); (Y.N.); (Q.W.); (A.O.)
- Correspondence:
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Fan L, Yu M, Pinkham A, Zhu Y, Tang X, Wang X, Zhang X, Ma J, Zhang J, Zhang X, Dai Z. Aberrant large-scale brain modules in deficit and non-deficit schizophrenia. Prog Neuropsychopharmacol Biol Psychiatry 2022; 113:110461. [PMID: 34688810 DOI: 10.1016/j.pnpbp.2021.110461] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 10/16/2021] [Accepted: 10/18/2021] [Indexed: 01/12/2023]
Abstract
OBJECTIVE Schizophrenia is a heterogenous psychiatric disease, and deficit schizophrenia (DS) is a clinical subgroup with primary and enduring negative symptoms. Although previous neuroimaging studies have identified functional connectome alterations in schizophrenia, the modular organizations in DS and nondeficit schizophrenia (NDS) remain poorly understood. Therefore, this study aimed to investigate the modular-level alterations in DS patients compared with the NDS and healthy control (HC) groups. METHODS A previously collected dataset was re-analyzed, in which 74 chronic male schizophrenia patients (33 DS and 41 NDS) and 40 HC underwent resting-state functional magnetic resonance imaging with eyes closed in a Siemens 3 T scanner (scanning duration = 8 min). Modular- (intramodule and intermodule connectivity) and nodal- [normalized within-module degree (Zi) and participation coefficient (PCi)] level graph theory properties were computed and compared among the three groups. Receiver operating characteristic curve (ROC) analyses were performed to examine the classification ability of these measures, and partial correlations were conducted between network measures and symptom severity. Validation analyses on head motion, network sparsity, and parcellation scheme were also performed. RESULTS Both schizophrenia subgroups showed decreased intramodule connectivity in salience network (SN), somatosensory-motor network (SMN), and visual network (VN), and increased intermodule connectivity in SMN-default mode network (DMN) and SMN-frontoparietal network (FPN). Compared with NDS patients, DS patients showed weaker intramodule connectivity in SN and stronger intermodule connectivity in SMN-FPN and SMN-VN. At the nodal level, the schizophrenia-related alterations were distributed in SN, SMN, VN, and DMN, and 7 DS-specific nodal alterations were identified. Intramodule connectivity of SN, intermodule connectivity of SMN-VN, and Zi of left precuneus successfully distinguished the three groups. Partial correlational analyses revealed that these measures were related to negative symptoms, general psychiatric symptoms, and neurocognitive function. CONCLUSION Our findings suggest that functional connectomes, especially SN, SMN, and VN, may capture the distinct and common disruptions of DS and NDS. These findings may help to understand the neuropathology of negative symptoms of schizophrenia and inform targets for treating different schizophrenia subtypes.
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Affiliation(s)
- Linlin Fan
- Department of Psychology, Sun Yat-sen University, Guangzhou, Guangdong, China; School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, TX, United States
| | - Miao Yu
- Department of Neurology, Affiliated Nanjing Brain Hospital, Nanjing Medical University, No. 264 Guangzhou Road, Nanjing, Jiangsu, China
| | - Amy Pinkham
- School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, TX, United States
| | - Yiyi Zhu
- School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, TX, United States
| | - Xiaowei Tang
- Department of Geriatric Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, Jiangsu, China; Department of Psychiatry, Affiliated WuTaiShan Hospital of Medical College of Yangzhou University, Yangzhou, Jiangsu, China
| | - Xiang Wang
- Medical Psychological Institute of the Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Xiaobin Zhang
- Department of Psychiatry, Affiliated WuTaiShan Hospital of Medical College of Yangzhou University, Yangzhou, Jiangsu, China
| | - Junji Ma
- Department of Psychology, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Jinbo Zhang
- Department of Psychology, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Xiangrong Zhang
- Department of Geriatric Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, Jiangsu, China.
| | - Zhengjia Dai
- Department of Psychology, Sun Yat-sen University, Guangzhou, Guangdong, China.
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40
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Functional network connectivity and topology during naturalistic stimulus is altered in first-episode psychosis. Schizophr Res 2022; 241:83-91. [PMID: 35092893 DOI: 10.1016/j.schres.2022.01.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Revised: 01/01/2022] [Accepted: 01/02/2022] [Indexed: 11/23/2022]
Abstract
BACKGROUND Psychotic disorders have been suggested to derive from dysfunctional integration of signaling between brain regions. Earlier studies have found several changes in functional network synchronization as well as altered network topology in patients with psychotic disorders. However, studies have used mainly resting-state that makes it more difficult to link functional alterations to any specific stimulus or experience. We set out to examine functional connectivity as well as graph (topological) measures and their association to symptoms in first-episode psychosis patients during movie viewing. Our goal was to understand whole-brain functional dynamics of complex naturalistic information processing in psychosis and changes in brain functional organization related to symptoms. METHODS 71 first-episode psychosis patients and 57 control subjects watched scenes from the movie Alice in Wonderland during 3 T fMRI. We compared functional connectivity and graph measures indicating integration, segregation and centrality between groups, and examined the association between topology and symptom scores in the patient group. RESULTS We identified a subnetwork with predominantly decreased links of functional connectivity in first-episode psychosis patients. The subnetwork was mainly comprised of nodes of and links between the cingulo-opercular, sensorimotor and default-mode networks. In topological measures, we observed between-group differences in properties of centrality. CONCLUSIONS Functional brain networks are affected during naturalistic information processing already in the early stages of psychosis, concentrated in salience- and cognitive control-related hubs and subnetworks. Understanding these aberrant dynamics could add to better targeted cognitive and behavioral interventions in the early stages of psychotic disorders.
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41
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Schutte MJL, Voppel A, Collin G, Abramovic L, Boks MPM, Cahn W, van Haren NEM, Hugdahl K, Koops S, Mandl RCW, Sommer IEC. Modular-Level Functional Connectome Alterations in Individuals With Hallucinations Across the Psychosis Continuum. Schizophr Bull 2022; 48:684-694. [PMID: 35179210 PMCID: PMC9077417 DOI: 10.1093/schbul/sbac007] [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] [Indexed: 11/14/2022]
Abstract
Functional connectome alterations, including modular network organization, have been related to the experience of hallucinations. It remains to be determined whether individuals with hallucinations across the psychosis continuum exhibit similar alterations in modular brain network organization. This study assessed functional connectivity matrices of 465 individuals with and without hallucinations, including patients with schizophrenia and bipolar disorder, nonclinical individuals with hallucinations, and healthy controls. Modular brain network organization was examined at different scales of network resolution, including (1) global modularity measured as Qmax and Normalised Mutual Information (NMI) scores, and (2) within- and between-module connectivity. Global modular organization was not significantly altered across groups. However, alterations in within- and between-module connectivity were observed for higher-order cognitive (e.g., central-executive salience, memory, default mode), and sensory modules in patients with schizophrenia and nonclinical individuals with hallucinations relative to controls. Dissimilar patterns of altered within- and between-module connectivity were found bipolar disorder patients with hallucinations relative to controls, including the visual, default mode, and memory network, while connectivity patterns between visual, salience, and cognitive control modules were unaltered. Bipolar disorder patients without hallucinations did not show significant alterations relative to controls. This study provides evidence for alterations in the modular organization of the functional connectome in individuals prone to hallucinations, with schizophrenia patients and nonclinical individuals showing similar alterations in sensory and higher-order cognitive modules. Other higher-order cognitive modules were found to relate to hallucinations in bipolar disorder patients, suggesting differential neural mechanisms may underlie hallucinations across the psychosis continuum.
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Affiliation(s)
- Maya J L Schutte
- Department of Biomedical Sciences of Cells and Systems, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands,Department of Psychiatry, UMC Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Alban Voppel
- To whom correspondence should be addressed; Neuroimaging Center, PO Box 196, 9700 AD, Groningen, The Netherlands; tel: +31 88 75 58672, fax: +31887555487, e-mail:
| | - Guusje Collin
- Department of Psychiatry, UMC Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands,Department of Psychiatry, Beth Israel Deaconess Medical Center and Massachusetts Mental Health Center, Harvard Medical School, Boston, MA, USA,McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA,Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Lucija Abramovic
- Department of Psychiatry, UMC Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Marco P M Boks
- Department of Psychiatry, UMC Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Wiepke Cahn
- Department of Psychiatry, UMC Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Neeltje E M van Haren
- Department of Psychiatry, UMC Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands,Department of Child and adolescent psychiatry/psychology, Erasmus University Medical Center, Sophia’s Children’s Hospital, Rotterdam, Netherlands
| | - Kenneth Hugdahl
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway,Department of Psychiatry, Haukeland University Hospital, Bergen, Norway,Department of Radiology, Haukeland University Hospital, Bergen, Norway,NORMENT Norwegian Center for the Study of Mental Disorders, Haukeland University hospital, Bergen, Norway
| | - Sanne Koops
- Department of Psychiatry, UMC Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - René C W Mandl
- Department of Psychiatry, UMC Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Iris E C Sommer
- Department of Biomedical Sciences of Cells and Systems, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands,Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
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Huang LJ, Hu FC, Wu C, Yang YH, Lee SC, Huang HC, Yu CY, Lai KY. Are the measurement structures of the Traditional Chinese Dispositional Flow Scale-2 equivalent between schizophrenic patients and healthy subjects? J Formos Med Assoc 2022; 121:1981-1992. [DOI: 10.1016/j.jfma.2022.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 11/29/2021] [Accepted: 02/07/2022] [Indexed: 10/19/2022] Open
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Wu Y, Ren P, Chen R, Xu H, Xu J, Zeng L, Wu D, Jiang W, Tang N, Liu X. Detection of functional and structural brain alterations in female schizophrenia using elastic net logistic regression. Brain Imaging Behav 2022; 16:281-290. [PMID: 34313906 PMCID: PMC8825615 DOI: 10.1007/s11682-021-00501-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/05/2021] [Indexed: 12/19/2022]
Abstract
Neuroimaging technique is a powerful tool to characterize the abnormality of brain networks in schizophrenia. However, the neurophysiological substrate of schizophrenia is still unclear. Here we investigated the patterns of brain functional and structural changes in female patients with schizophrenia using elastic net logistic regression analysis of resting-state functional magnetic resonance imaging data. Data from 52 participants (25 female schizophrenia patients and 27 healthy controls) were obtained. Using an elastic net penalty, the brain regions most relevant to schizophrenia pathology were defined in the models using the amplitude of low-frequency fluctuations (ALFF) and gray matter, respectively. The receiver operating characteristic analysis showed reliable classification accuracy with 85.7% in ALFF analysis, and 77.1% in gray matter analysis. Notably, our results showed eight common regions between the ALFF and gray matter analyses, including the Frontal-Inf-Orb-R, Rolandic-Oper-R, Olfactory-R, Angular-L, Precuneus-L, Precuenus-R, Heschl-L, and Temporal-Pole-Mid-R. In addition, the severity of symptoms was found positively associated with the ALFF within the Rolandic-Oper-R and Frontal-Inf-Orb-R. Our findings indicated that elastic net logistic regression could be a useful tool to identify the characteristics of schizophrenia -related brain deterioration, which provides novel insights into schizophrenia diagnosis and prediction.
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Affiliation(s)
- Ying Wu
- Key Lab of Statistical Modeling and Data Analysis of Yunnan, Yunnan University, Kunming, China
| | - Ping Ren
- Lab of Brain Health Assessment and Research, Shenzhen Mental Health Center, Shenzhen, China
- Department of Geriatric Psychiatry, Shenzhen Kangning Hospital, Shenzhen, China
| | - Rong Chen
- Female Ward of Acute Psychiatric Department, Shenzhen Kangning Hospital, Shenzhen, China
| | - Hong Xu
- Female Ward of Acute Psychiatric Department, Shenzhen Kangning Hospital, Shenzhen, China
| | - Jianxing Xu
- Neuropsychiatry Imaging Center, Shenzhen Mental Health Center, Shenzhen, China
- Department of Radiology, Shenzhen Kangning Hospital, Shenzhen, China
| | - Lin Zeng
- Neuropsychiatry Imaging Center, Shenzhen Mental Health Center, Shenzhen, China
- Department of Radiology, Shenzhen Kangning Hospital, Shenzhen, China
| | - Donghui Wu
- Department of Geriatric Psychiatry, Shenzhen Kangning Hospital, Shenzhen, China
- School of Mental Health, Jining Medical University, Jining, China
| | - Wentao Jiang
- Neuropsychiatry Imaging Center, Shenzhen Mental Health Center, Shenzhen, China
- Department of Radiology, Shenzhen Kangning Hospital, Shenzhen, China
| | - NianSheng Tang
- Key Lab of Statistical Modeling and Data Analysis of Yunnan, Yunnan University, Kunming, China.
| | - Xia Liu
- Neuropsychiatry Imaging Center, Shenzhen Mental Health Center, Shenzhen, China.
- Department of Radiology, Shenzhen Kangning Hospital, Shenzhen, China.
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Hoptman MJ, Tural U, Lim KO, Javitt DC, Oberlin LE. Relationships between Diffusion Tensor Imaging and Resting State Functional Connectivity in Patients with Schizophrenia and Healthy Controls: A Preliminary Study. Brain Sci 2022; 12:brainsci12020156. [PMID: 35203920 PMCID: PMC8870342 DOI: 10.3390/brainsci12020156] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 01/02/2022] [Accepted: 01/19/2022] [Indexed: 11/16/2022] Open
Abstract
Schizophrenia is widely seen as a disorder of dysconnectivity. Neuroimaging studies have examined both structural and functional connectivity in the disorder, but these modalities have rarely been integrated directly. We scanned 29 patients with schizophrenia and 25 healthy control subjects, and we acquired resting state fMRI and diffusion tensor imaging. We used the Functional and Tractographic Connectivity Analysis Toolbox (FATCAT) to estimate functional and structural connectivity of the default mode network. Correlations between modalities were investigated, and multimodal connectivity scores (MCS) were created using principal component analysis. Of the 28 possible region pairs, 9 showed consistent (>80%) tracts across participants. Correlations between modalities were found among those with schizophrenia for the prefrontal cortex, posterior cingulate, and lateral temporal lobes, with frontal and parietal regions, consistent with frontotemporoparietal network involvement in the disorder. In patients, MCS correlated with several aspects of the Positive and Negative Syndrome Scale, with higher multimodal connectivity associated with outward-directed (externalizing) behavior and lower multimodal connectivity related to psychosis per se. In this preliminary sample, we found FATCAT to be a useful toolbox to directly integrate and examine connectivity between imaging modalities. A consideration of conjoint structural and functional connectivity can provide important information about the network mechanisms of schizophrenia.
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Affiliation(s)
- Matthew J. Hoptman
- Clinical Research Division, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA;
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
- Correspondence: or ; Tel.: +1-845-398-6569
| | - Umit Tural
- Clinical Research Division, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA;
| | - Kelvin O. Lim
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55454, USA;
| | - Daniel C. Javitt
- Schizophrenia Research Division, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA; or
- Division of Experimental Therapeutics, College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA
| | - Lauren E. Oberlin
- Department of Psychiatry, Weill Cornell Medicine, New York, NY 10065, USA;
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Brakowski J, Manoliu A, Homan P, Bosch OG, Herdener M, Seifritz E, Kaiser S, Kirschner M. Aberrant striatal coupling with default mode and central executive network relates to self-reported avolition and anhedonia in schizophrenia. J Psychiatr Res 2022; 145:263-275. [PMID: 33187692 DOI: 10.1016/j.jpsychires.2020.10.047] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Revised: 10/13/2020] [Accepted: 10/30/2020] [Indexed: 11/17/2022]
Abstract
BACKGROUND Avolition and anhedonia are common symptoms in schizophrenia and are related to poor long-term prognosis. There is evidence for aberrant cortico-striatal function and connectivity as neural substrate of avolition and anhedonia. However, it remains unclear how both relate to shared or distinct striatal coupling with large-scale intrinsic networks. Using resting state functional magnetic resonance imaging (rs-fMRI) this study investigated the association of large-scale cortico-striatal functional connectivity with self-reported and clinician-rated avolition and anhedonia in subjects with schizophrenia. METHODS Seventeen subjects with schizophrenia (SZ) and 28 healthy controls (HC) underwent rs-fMRI. Using Independent Component Analysis (ICA), we assessed Independent Components (ICs) reflecting intrinsic connectivity networks (ICNs), intra intrinsic functional connectivity within the ICs (intra-iFC), and intrinsic functional connectivity between different ICs (inter-iFC). Avolition and anhedonia were assessed using the Self Evaluation Scale for Negative Symptoms and the Brief Negative Symptom Scale. RESULTS ICA revealed three striatal components and six cortical ICNs. Both self-rated avolition and anhedonia correlated with increased inter-iFC between the caudate and posterior Default Mode Network (pDMN) and between the caudate and Central Executive Network (CEN). In contrast, clinician-rated avolition and anhedonia were not correlated with cortico-striatal connectivity. Group comparison revealed trend-wise decreased inter-iFC between the caudate and Salience Network (SN) in schizophrenia patients compared to HC. DISCUSSION Self-rated, but not clinician-rated, avolition and anhedonia was associated with aberrant striatal coupling with the default mode and the central executive network. These findings suggest that self-reported and clinician-rated scores might capture different aspects of motivational and hedonic deficits in schizophrenia and therefore relate to different cortico-striatal functional abnormalities.
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Affiliation(s)
- Janis Brakowski
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital, University of Zurich, Lenggstrasse 31, 8032, Zurich, Switzerland.
| | - Andrei Manoliu
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital, University of Zurich, Lenggstrasse 31, 8032, Zurich, Switzerland; Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Russell Square House, 10-12, Russell Square London, WC1B 5EH, United Kingdom
| | - Philipp Homan
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital, University of Zurich, Lenggstrasse 31, 8032, Zurich, Switzerland
| | - Oliver G Bosch
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital, University of Zurich, Lenggstrasse 31, 8032, Zurich, Switzerland
| | - Marcus Herdener
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital, University of Zurich, Lenggstrasse 31, 8032, Zurich, Switzerland
| | - Erich Seifritz
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital, University of Zurich, Lenggstrasse 31, 8032, Zurich, Switzerland
| | - Stefan Kaiser
- Division of Adult Psychiatry, Department of Psychiatry, Geneva University Hospitals, Chemin Du Petit-Bel-Air, 1226, Thônex, Switzerland
| | - Matthias Kirschner
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital, University of Zurich, Lenggstrasse 31, 8032, Zurich, Switzerland; Montreal Neurological Institute, McGill University, 3801 University St, Montréal, QC, H3A 2B4, Canada.
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Culbreth AJ, Kasanova Z, Ross TJ, Salmeron BJ, Gold JM, Stein EA, Waltz JA. Schizophrenia Patients Show Largely Similar Salience Signaling Compared to Healthy Controls in an Observational Task Environment. Brain Sci 2021; 11:brainsci11121610. [PMID: 34942913 PMCID: PMC8699423 DOI: 10.3390/brainsci11121610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 11/30/2021] [Indexed: 11/30/2022] Open
Abstract
Recent evidence suggests that the aberrant signaling of salience is associated with psychotic illness. Salience, however, can take many forms in task environments. For example, salience may refer to any of the following: (1) the valence of an outcome, (2) outcomes that are unexpected, called reward prediction errors (PEs), or (3) cues associated with uncertain outcomes. Here, we measure brain responses to different forms of salience in the context of a passive PE-signaling task, testing whether patients with schizophrenia (SZ) showed aberrant signaling of particular types of salience. We acquired event-related MRI data from 29 SZ patients and 23 controls during the performance of a passive outcome prediction task. Across groups, we found that the anterior insula and posterior parietal cortices were activated to multiple different types of salience, including PE magnitude and heightened levels of uncertainty. However, BOLD activation to salient events was not significantly different between patients and controls in many regions, including the insula, posterior parietal cortices, and default mode network nodes. Such results suggest that deficiencies in salience processing in SZ may not result from an impaired ability to signal salience per se, but instead the ability to use such signals to guide future actions. Notably, no between-group differences were observed in BOLD signal changes associated with PE-signaling in the striatum. However, positive symptom severity was found to significantly correlate with the magnitudes of salience contrasts in default mode network nodes. Our results suggest that, in an observational environment, SZ patients may show an intact ability to activate striatal and cortical regions to rewarding and non-rewarding salient events. Furthermore, reduced deactivation of a hypothesized default mode network node for SZ participants with high levels of positive symptoms, following salient events, point to abnormalities in interactions of the salience network with other brain networks, and their potential importance to positive symptoms.
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Affiliation(s)
- Adam J. Culbreth
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD 21228, USA; (J.M.G.); (J.A.W.)
- Correspondence:
| | - Zuzana Kasanova
- Leuven Research & Development Spin-off & Innovation Unit, KU Leuven, Waaistraat 6-Box 5105, 3000 Leuven, Belgium;
| | - Thomas J. Ross
- Neuroimaging Research Branch, National Institute on Drug Abuse-Intramural Research Program, Baltimore, MD 21224, USA; (T.J.R.); (B.J.S.); (E.A.S.)
| | - Betty J. Salmeron
- Neuroimaging Research Branch, National Institute on Drug Abuse-Intramural Research Program, Baltimore, MD 21224, USA; (T.J.R.); (B.J.S.); (E.A.S.)
| | - James M. Gold
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD 21228, USA; (J.M.G.); (J.A.W.)
| | - Elliot A. Stein
- Neuroimaging Research Branch, National Institute on Drug Abuse-Intramural Research Program, Baltimore, MD 21224, USA; (T.J.R.); (B.J.S.); (E.A.S.)
| | - James A. Waltz
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD 21228, USA; (J.M.G.); (J.A.W.)
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Gallos IK, Mantonakis L, Spilioti E, Kattoulas E, Savvidou E, Anyfandi E, Karavasilis E, Kelekis N, Smyrnis N, Siettos CI. The relation of integrated psychological therapy to resting state functional brain connectivity networks in patients with schizophrenia. Psychiatry Res 2021; 306:114270. [PMID: 34775295 DOI: 10.1016/j.psychres.2021.114270] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 10/22/2021] [Accepted: 10/31/2021] [Indexed: 01/05/2023]
Abstract
Functional brain dysconnectivity measured with resting state functional magnetic resonance imaging (rsfMRI) has been linked to cognitive impairment in schizophrenia. This study investigated the effects on functional brain connectivity of Integrated Psychological Therapy (IPT), a cognitive behavioral oriented group intervention program, in 31 patients with schizophrenia. Patients received IPT or an equal intensity non-specific psychological treatment in a non-randomized design. Evidence of improvement in executive and social functions, psychopathology and overall level of functioning was observed after treatment completion at six months only in the IPT treatment group and was partially sustained at one-year follow up. Independent Component Analysis and Isometric Mapping (ISOMAP), a non-linear manifold learning algorithm, were used to construct functional connectivity networks from the rsfMRI data. Functional brain dysconnectivity was observed in patients compared to a group of 17 healthy controls, both globally and specifically including the default mode (DMN) and frontoparietal network (FPN). DMN and FPN connectivity were reversed towards healthy control patterns only in the IPT treatment group and these effects were sustained at follow up for DMN but not FPN. These data suggest the use of rsfMRI as a biomarker for accessing and monitoring the therapeutic effects of cognitive remediation therapy in schizophrenia.
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Affiliation(s)
- I K Gallos
- School of Applied Mathematics and Physical Sciences, National Technical University of Athens, Athens, Greece
| | - L Mantonakis
- Laboratory of Cognitive Neuroscience and Sensorimotor Control, University Mental Health, Neurosciences and Precision Medicine Research Institute "COSTAS STEFANIS", Athens, Greece; First Psychiatry Department, National and Kapodistrian University of Athens, School of Medicine, Eginition Hospital, Athens, Greece
| | - E Spilioti
- Laboratory of Cognitive Neuroscience and Sensorimotor Control, University Mental Health, Neurosciences and Precision Medicine Research Institute "COSTAS STEFANIS", Athens, Greece; First Psychiatry Department, National and Kapodistrian University of Athens, School of Medicine, Eginition Hospital, Athens, Greece
| | - E Kattoulas
- Laboratory of Cognitive Neuroscience and Sensorimotor Control, University Mental Health, Neurosciences and Precision Medicine Research Institute "COSTAS STEFANIS", Athens, Greece
| | - E Savvidou
- Laboratory of Cognitive Neuroscience and Sensorimotor Control, University Mental Health, Neurosciences and Precision Medicine Research Institute "COSTAS STEFANIS", Athens, Greece
| | - E Anyfandi
- First Psychiatry Department, National and Kapodistrian University of Athens, School of Medicine, Eginition Hospital, Athens, Greece
| | - E Karavasilis
- Second Department of Radiology, National and Kapodistrian University of Athens, School of Medicine, University General Hospital "ATTIKON", Athens, Greece
| | - N Kelekis
- Second Department of Radiology, National and Kapodistrian University of Athens, School of Medicine, University General Hospital "ATTIKON", Athens, Greece
| | - N Smyrnis
- Laboratory of Cognitive Neuroscience and Sensorimotor Control, University Mental Health, Neurosciences and Precision Medicine Research Institute "COSTAS STEFANIS", Athens, Greece; Second Psychiatry Department, National and Kapodistrian University of Athens, School of Medicine, University General Hospital "ATTIKON", Athens, Greece.
| | - C I Siettos
- Dipartimento di Matematica e Applicazioni "Renato Caccioppoli", Università degli Studi di Napoli Federico II, Naples, Italy
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Lyu H, Jiao J, Feng G, Wang X, Sun B, Zhao Z, Shang D, Pan F, Xu W, Duan J, Zhou Q, Hu S, Xu Y, Xu D, Huang M. Abnormal causal connectivity of left superior temporal gyrus in drug-naïve first- episode adolescent-onset schizophrenia: A resting-state fMRI study. Psychiatry Res Neuroimaging 2021; 315:111330. [PMID: 34280873 DOI: 10.1016/j.pscychresns.2021.111330] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 06/06/2021] [Accepted: 07/02/2021] [Indexed: 12/19/2022]
Abstract
This study aimed to investigate the alterations of causal connectivity between the brain regions in Adolescent-onset schizophrenia (AOS) patients. Thirty-two first-episode drug-naïve AOS patients and 27 healthy controls (HC) were recruited for resting-state functional MRI scanning. The brain region with the between-group difference in regional homogeneity (ReHo) values was chosen as a seed to perform the Granger causality analysis (GCA) and further detect the alterations of causal connectivity in AOS. AOS patients exhibited increased ReHo values in left superior temporal gyrus (STG) compared with HCs. Significantly decreased values of outgoing Granger causality from left STG to right superior frontal gyrus and right angular gyrus were observed in GC mapping for AOS. Significantly stronger causal outflow from left STG to right insula and stronger causal inflow from right middle occipital gyrus (MOG) to left STG were also observed in AOS patients. Based on assessments of the two strengthened causal connectivity of the left STG with insula and MOG, a discriminant model could identify all patients from controls with 94.9% accuracy. This study indicated that alterations of directional connections in left STG may play an important role in the pathogenesis of AOS and serve as potential biomarkers for the disease.
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Affiliation(s)
- Hailong Lyu
- The First Affiliated Hospital, Zhejiang University School of Medicine, The Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Jianping Jiao
- Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Guoxun Feng
- Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Ningbo Mental Hospital, Ningbo, Zhejiang, China
| | - Xinxin Wang
- Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Bin Sun
- Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Ningbo Mental Hospital, Ningbo, Zhejiang, China
| | - Zhiyong Zhao
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China; Columbia University & New York State Psychiatric Institute, New York, United States
| | - Desheng Shang
- The First Affiliated Hospital, Zhejiang University School of Medicine, The Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Fen Pan
- The First Affiliated Hospital, Zhejiang University School of Medicine, The Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Weijuan Xu
- The First Affiliated Hospital, Zhejiang University School of Medicine, The Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Jinfeng Duan
- The First Affiliated Hospital, Zhejiang University School of Medicine, The Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou, Zhejiang, China
| | | | - Shaohua Hu
- The First Affiliated Hospital, Zhejiang University School of Medicine, The Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Yi Xu
- The First Affiliated Hospital, Zhejiang University School of Medicine, The Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Dongrong Xu
- Columbia University & New York State Psychiatric Institute, New York, United States.
| | - Manli Huang
- The First Affiliated Hospital, Zhejiang University School of Medicine, The Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou, Zhejiang, China.
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Maximo JO, Kraguljac NV, Rountree BG, Lahti AC. Structural and Functional Default Mode Network Connectivity and Antipsychotic Treatment Response in Medication-Naïve First Episode Psychosis Patients. ACTA ACUST UNITED AC 2021; 2:sgab032. [PMID: 34414373 PMCID: PMC8364918 DOI: 10.1093/schizbullopen/sgab032] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Introduction Only a few studies have comprehensively characterized default mode network (DMN) pathology on a structural and functional level, and definite conclusions cannot be drawn due to antipsychotic medication exposure and illness chronicity. The objective of this study was to characterize DMN pathology in medication-naïve first episode psychosis (FEP) patients, and determine if DMN structural and functional connectivity (FC) have potential utility as a predictor for subsequent antipsychotic treatment response. Methods Diffusion imaging and resting state FC data from 42 controls and 52 FEP were analyzed. Patients then received 16 weeks of antipsychotic treatment. Using region of interest analyses, we quantified FC of the DMN and structural integrity of the white matter tracts supporting DMN function. We then did linear regressions between DMN structural and FC indices and antipsychotic treatment response. Results We detected reduced DMN fractional anisotropy and axial diffusivity in FEP compared to controls. No DMN FC abnormalities nor correlations between DMN structural and FC were found. Finally, DMN fractional anisotropy and radial diffusivity were associated with response to treatment. Conclusion Our study highlights the critical role of the DMN in the pathophysiology suggesting that axonal damage may already be present in FEP patients. We also demonstrated that DMN pathology is clinically relevant, as greater structural DMN alterations were associated with a less favorable clinical response to antipsychotic medications.
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Affiliation(s)
- Jose O Maximo
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, AL, USA
| | - Nina V Kraguljac
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, AL, USA
| | - Boone G Rountree
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, AL, USA
| | - Adrienne C Lahti
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, AL, USA
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Qiu X, Xu W, Zhang R, Yan W, Ma W, Xie S, Zhou M. Regional Homogeneity Brain Alterations in Schizophrenia: An Activation Likelihood Estimation Meta-Analysis. Psychiatry Investig 2021; 18:709-717. [PMID: 34333896 PMCID: PMC8390947 DOI: 10.30773/pi.2021.0062] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 05/24/2021] [Indexed: 01/08/2023] Open
Abstract
OBJECTIVE Resting state functional magnetic resonance imaging (rsfMRI) provides a lot of evidence for local abnormal brain activity in schizophrenia, but the results are not consistent. Our aim is to find out the consistent abnormal brain regions of the patients with schizophrenia by using regional homogeneity (ReHo), and indirectly understand the degree of brain damage of the patients with drug-naive first episode schizophrenia (Dn-FES) and chronic schizophrenia. METHODS We performed the experiment by activation likelihood estimation (ALE) software to analysis the differences between people with schizophrenia group (all schizophrenia group and chronic schizophrenia group) and healthy controls. RESULTS Thirteen functional imaging studies were included in quantitative meta-analysis. All schizophrenia group showed decreased ReHo in bilateral precentral gyrus (PreCG) and left middle occipital gyrus (MOG), and increased ReHo in bilateral superior frontal gyrus (SFG) and right insula. Chronic schizophrenia group showed decreased ReHo in bilateral MOG, right fusiform gyrus, left PreCG, left cerebellum, right precuneus, left medial frontal gyrus and left anterior cingulate cortex (ACC). No significant increased brain areas were found in patients with chronic schizophrenia. CONCLUSION Our findings suggest that patients with chronic schizophrenia have more extensive brain damage than FES, which may contribute to our understanding of the progressive pathophysiology of schizophrenia.
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Affiliation(s)
- Xiaolei Qiu
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Wenwen Xu
- Department of Neurology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Rongrong Zhang
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Wei Yan
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Wenying Ma
- Department of Neurology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Shiping Xie
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Min Zhou
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
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