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Ni H, Xue J, Qin J, Zhang Y. Accurate identification of individuals with subjective cognitive decline using 3D regional fractal dimensions on structural magnetic resonance imaging. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 254:108281. [PMID: 38924798 DOI: 10.1016/j.cmpb.2024.108281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 05/04/2024] [Accepted: 06/06/2024] [Indexed: 06/28/2024]
Abstract
BACKGROUND AND OBJECTIVE Accurate identification of individuals with subjective cognitive decline (SCD) is crucial for early intervention and prevention of neurodegenerative diseases. Fractal dimensionality (FD) has emerged as a robust and replicable measure, surpassing traditional geometric metrics, in characterizing the intricate fractal geometrical properties of brain structure. Nevertheless, the effectiveness of FD in identifying individuals with SCD remains largely unclear. A 3D regional FD method can be suggested to characterize and quantify the spatial complexity of the precise gray matter, providing insights into cognitive aging and aiding in the automated identification of individuals with SCD. METHODS This study introduces a novel integer ratio based 3D box-counting fractal analysis (IRBCFA) to quantify regional fractal dimensions (FDs) in structural magnetic resonance imaging (MRI) data. The innovative method overcomes limitations of conventional box-counting techniques by accommodating arbitrary box sizes, thereby enhancing the precision of FD estimation in small, yet neurologically significant, brain regions. RESULTS The application of IRBCFA to two publicly available datasets, OASIS-3 and ADNI, consisting of 520 and 180 subjects, respectively. The method identified discriminative regions of interest (ROIs) predominantly within the limbic system, fronto-parietal region, occipito-temporal region, and basal ganglia-thalamus region. These ROIs exhibited significant correlations with cognitive functions, including executive functioning, memory, social cognition, and sensory perception, suggesting their potential as neuroimaging markers for SCD. The identification model trained on these ROIs demonstrated exceptional performance achieving over 93 % accuracy on the discovery dataset and exceeding 87 % on the independent testing dataset. Furthermore, an exchange experiment between datasets revealed a substantial overlap in discriminative ROIs, highlighting the robustness of our method across diverse populations. CONCLUSION Our findings indicate that IRBCFA can serve as a valuable tool for quantifying the spatial complexity of gray matter, providing insights into cognitive aging and aiding in the automated identification of individuals with SCD. The demonstrated generalizability and robustness of this method position it as a promising tool for neurodegenerative disease research and offer potential for clinical applications.
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Affiliation(s)
- Huangjing Ni
- School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China
| | - Jing Xue
- School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China
| | - Jiaolong Qin
- Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.
| | - Yu Zhang
- Department of Clinical Psychology, Hangzhou First People's Hospital, Hangzhou, Zhejiang, 310006, China.
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2
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Sefik E, Guest RM, Aberizk K, Espana R, Goines K, Novacek DM, Murphy MM, Goldman-Yassen AE, Cubells JF, Ousley O, Li L, Shultz S, Walker EF, Mulle JG. Psychosis spectrum symptoms among individuals with schizophrenia-associated copy number variants and evidence of cerebellar correlates of symptom severity. Psychiatry Res 2024; 335:115867. [PMID: 38537595 DOI: 10.1016/j.psychres.2024.115867] [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: 09/21/2023] [Revised: 03/14/2024] [Accepted: 03/20/2024] [Indexed: 04/14/2024]
Abstract
The 3q29 deletion (3q29Del) is a copy number variant (CNV) with one of the highest effect sizes for psychosis-risk (>40-fold). Systematic research offers avenues for elucidating mechanism; however, compared to CNVs like 22q11.2Del, 3q29Del remains understudied. Emerging findings indicate that posterior fossa abnormalities are common among carriers, but their clinical relevance is unclear. We report the first in-depth evaluation of psychotic symptoms in participants with 3q29Del (N=23), using the Structured Interview for Psychosis-Risk Syndromes, and compare this profile to 22q11.2Del (N=31) and healthy controls (N=279). We also explore correlations between psychotic symptoms and posterior fossa abnormalities. Cumulatively, 48% of the 3q29Del sample exhibited a psychotic disorder or attenuated positive symptoms, with a subset meeting criteria for clinical high-risk. 3q29Del had more severe ratings than controls on all domains and only exhibited less severe ratings than 22q11.2Del in negative symptoms; ratings demonstrated select sex differences but no domain-wise correlations with IQ. An inverse relationship was identified between positive symptoms and cerebellar cortex volume in 3q29Del, documenting the first clinically-relevant neuroanatomical connection in this syndrome. Our findings characterize the profile of psychotic symptoms in the largest 3q29Del sample reported to date, contrast with another high-impact CNV, and highlight cerebellar involvement in psychosis-risk.
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Affiliation(s)
- Esra Sefik
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA; Department of Psychology, Emory University, Atlanta, GA, USA
| | - Ryan M Guest
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Katrina Aberizk
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Roberto Espana
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Katrina Goines
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Derek M Novacek
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA; Desert Pacific Mental Illness, Research, Education, and Clinical Center, VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA
| | - Melissa M Murphy
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA
| | - Adam E Goldman-Yassen
- Department of Radiology, Children's Healthcare of Atlanta, Atlanta, GA, USA; Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Joseph F Cubells
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA; Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Opal Ousley
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Longchuan Li
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA; Marcus Autism Center, Children's Healthcare of Atlanta and Emory University School of Medicine, Atlanta, GA, USA
| | - Sarah Shultz
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA; Marcus Autism Center, Children's Healthcare of Atlanta and Emory University School of Medicine, Atlanta, GA, USA
| | - Elaine F Walker
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Jennifer G Mulle
- Department of Psychiatry, Rutgers Robert Wood Johnson Medical School, Piscataway, NJ, USA; Center for Advanced Biotechnology and Medicine, Rutgers Robert Wood Johnson Medical School, Piscataway, NJ, USA.
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3
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Herkströter F, Zahedi A, Standke I, Dannlowski U, Lencer R, Schubotz RI, Trempler I. Gray matter matters: Cognitive stability and flexibility in schizophrenia spectrum disorder. Psychophysiology 2024:e14596. [PMID: 38691383 DOI: 10.1111/psyp.14596] [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: 12/20/2023] [Revised: 03/12/2024] [Accepted: 04/10/2024] [Indexed: 05/03/2024]
Abstract
Cognitive dysfunction constitutes a core characteristic of schizophrenia spectrum disorders (SZ). Specifically, deficits in updating generative models (i.e., cognitive flexibility) and shielding against distractions (i.e., cognitive stability) are considered critical contributors to cognitive impairment in these patients. Here, we examined the structural integrity of frontostriatal networks and their associations with reduced cognitive stability and flexibility in SZ patients. In a sample of 21 patients diagnosed with SZ and 22 healthy controls, we measured gray matter volume (GMV) using structural MRI. Further, cognitive stability and flexibility were assessed using a switch-drift paradigm, quantifying the successful ignoring of distracters and detection of rule switches. Compared to controls, patients showed significantly smaller GMV in the whole brain and three predefined regions of interest: the medial prefrontal cortex (mPFC), inferior frontal gyrus (IFG), and caudate nucleus (CN). Notably, GMV in these areas positively correlated with correct rule-switch detection but not with ignoring rule-compatible drifts. Further, the volumetric differences between SZ patients and controls were statistically explainable by considering the behavioral performance in the switch-drift task. Our results indicate that morphological abnormalities in frontostriatal networks are associated with deficient flexibility in SZ patients and highlight the necessity of minimizing neurodevelopmental and progressive brain atrophy in this population.
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Affiliation(s)
- Florentine Herkströter
- Department of Neurology, Niels-Stensen-Kliniken, Marienhospital Osnabrück-Standort Natruper Holz, Osnabrueck, Germany
| | - Anoushiravan Zahedi
- Institute of Psychology, University of Muenster, Muenster, Germany
- Otto Creutzfeldt-Center for Cognitive and Behavioral Neuroscience, University of Muenster, Muenster, Germany
| | - Isabel Standke
- Institute for Translational Psychiatry, University of Muenster, Muenster, Germany
| | - Udo Dannlowski
- Institute of Psychology, University of Muenster, Muenster, Germany
- Institute for Translational Psychiatry, University of Muenster, Muenster, Germany
| | - Rebekka Lencer
- Institute for Translational Psychiatry, University of Muenster, Muenster, Germany
- Department of Psychiatry and Psychotherapy, University of Luebeck, Luebeck, Germany
| | - Ricarda I Schubotz
- Institute of Psychology, University of Muenster, Muenster, Germany
- Otto Creutzfeldt-Center for Cognitive and Behavioral Neuroscience, University of Muenster, Muenster, Germany
| | - Ima Trempler
- Institute of Psychology, University of Muenster, Muenster, Germany
- Otto Creutzfeldt-Center for Cognitive and Behavioral Neuroscience, University of Muenster, Muenster, Germany
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4
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Wang Y, Yang Y, Xu W, Yao X, Xie X, Zhang L, Sun J, Wang L, Hua Q, He K, Tian Y, Wang K, Ji GJ. Heterogeneous Brain Abnormalities in Schizophrenia Converge on a Common Network Associated With Symptom Remission. Schizophr Bull 2024; 50:545-556. [PMID: 38253437 PMCID: PMC11059819 DOI: 10.1093/schbul/sbae003] [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: 01/24/2024]
Abstract
BACKGROUND AND HYPOTHESIS There is a huge heterogeneity of magnetic resonance imaging findings in schizophrenia studies. Here, we hypothesized that brain regions identified by structural and functional imaging studies of schizophrenia could be reconciled in a common network. STUDY DESIGN We systematically reviewed the case-control studies that estimated the brain morphology or resting-state local function for schizophrenia patients in the literature. Using the healthy human connectome (n = 652) and a validated technique "coordinate network mapping" to identify a common brain network affected in schizophrenia. Then, the specificity of this schizophrenia network was examined by independent data collected from 13 meta-analyses. The clinical relevance of this schizophrenia network was tested on independent data of medication, neuromodulation, and brain lesions. STUDY RESULTS We identified 83 morphological and 60 functional studies comprising 7389 patients with schizophrenia and 7408 control subjects. The "coordinate network mapping" showed that the atrophy and dysfunction coordinates were functionally connected to a common network although they were spatially distant from each other. Taking all 143 studies together, we identified the schizophrenia network with hub regions in the bilateral anterior cingulate cortex, insula, temporal lobe, and subcortical structures. Based on independent data from 13 meta-analyses, we showed that these hub regions were specifically connected with regions of cortical thickness changes in schizophrenia. More importantly, this schizophrenia network was remarkably aligned with regions involving psychotic symptom remission. CONCLUSIONS Neuroimaging abnormalities in cross-sectional schizophrenia studies converged into a common brain network that provided testable targets for developing precise therapies.
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Affiliation(s)
- Yingru Wang
- Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Yinian Yang
- Department of Clinical Psychiatry, School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China
| | - Wenqiang Xu
- Department of Clinical Psychiatry, School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China
| | - Xiaoqing Yao
- Department of Clinical Psychiatry, School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China
| | - Xiaohui Xie
- Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Long Zhang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Jinmei Sun
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Lu Wang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Qiang Hua
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Kongliang He
- Department of Psychiatry, Fourth People’s Hospital of Hefei, Anhui Mental Health Center, Hefei, China
| | - Yanghua Tian
- Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Kai Wang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders,Hefei, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China
- Anhui Institute of Translational Medicine, Hefei, China
| | - Gong-Jun Ji
- Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
- Department of Clinical Psychiatry, School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders,Hefei, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China
- Anhui Institute of Translational Medicine, Hefei, China
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5
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Fu Z, Batta I, Wu L, Abrol A, Agcaoglu O, Salman MS, Du Y, Iraji A, Shultz S, Sui J, Calhoun VD. Searching Reproducible Brain Features using NeuroMark: Templates for Different Age Populations and Imaging Modalities. Neuroimage 2024; 292:120617. [PMID: 38636639 DOI: 10.1016/j.neuroimage.2024.120617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 04/03/2024] [Accepted: 04/15/2024] [Indexed: 04/20/2024] Open
Abstract
A primary challenge to the data-driven analysis is the balance between poor generalizability of population-based research and characterizing more subject-, study- and population-specific variability. We previously introduced a fully automated spatially constrained independent component analysis (ICA) framework called NeuroMark and its functional MRI (fMRI) template. NeuroMark has been successfully applied in numerous studies, identifying brain markers reproducible across datasets and disorders. The first NeuroMark template was constructed based on young adult cohorts. We recently expanded on this initiative by creating a standardized normative multi-spatial-scale functional template using over 100,000 subjects, aiming to improve generalizability and comparability across studies involving diverse cohorts. While a unified template across the lifespan is desirable, a comprehensive investigation of the similarities and differences between components from different age populations might help systematically transform our understanding of the human brain by revealing the most well-replicated and variable network features throughout the lifespan. In this work, we introduced two significant expansions of NeuroMark templates first by generating replicable fMRI templates for infants, adolescents, and aging cohorts, and second by incorporating structural MRI (sMRI) and diffusion MRI (dMRI) modalities. Specifically, we built spatiotemporal fMRI templates based on 6,000 resting-state scans from four datasets. This is the first attempt to create robust ICA templates covering dynamic brain development across the lifespan. For the sMRI and dMRI data, we used two large publicly available datasets including more than 30,000 scans to build reliable templates. We employed a spatial similarity analysis to identify replicable templates and investigate the degree to which unique and similar patterns are reflective in different age populations. Our results suggest remarkably high similarity of the resulting adapted components, even across extreme age differences. With the new templates, the NeuroMark framework allows us to perform age-specific adaptations and to capture features adaptable to each modality, therefore facilitating biomarker identification across brain disorders. In sum, the present work demonstrates the generalizability of NeuroMark templates and suggests the potential of new templates to boost accuracy in mental health research and advance our understanding of lifespan and cross-modal alterations.
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Affiliation(s)
- Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States.
| | - Ishaan Batta
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Lei Wu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Anees Abrol
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Oktay Agcaoglu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Mustafa S Salman
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Yuhui Du
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Armin Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Sarah Shultz
- Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, United States
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
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6
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Van Assche L, Takamiya A, Van den Stock J, Van de Ven L, Luyten P, Emsell L, Vandenbulcke M. A voxel- and source-based morphometry analysis of grey matter volume differences in very-late-onset schizophrenia-like psychosis. Psychol Med 2024; 54:592-600. [PMID: 37577955 DOI: 10.1017/s0033291723002258] [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] [Indexed: 08/15/2023]
Abstract
BACKGROUND Very-late-onset schizophrenia-like psychosis (VLOSLP) is associated with significant burden. Its clinical importance is increasing as the global population of older adults rises, yet owing to limited research in this population, the neurobiological underpinnings of VLOSP remain insufficiently clarified. Here we address this knowledge gap using novel morphometry techniques to investigate grey matter volume (GMV) differences between VLOSLP and healthy older adults, and their correlations with neuropsychological scores. METHODS In this cross-sectional study, we investigated whole-brain GMV differences between 35 individuals with VLOSLP (mean age 76.7, 26 female) and 36 healthy controls (mean age 75.7, 27 female) using whole-brain voxel-based morphometry (VBM) and supplementary source-based morphometry (SBM) on high resolution 3D T1-weighted MRI images. Additionally, we investigated relationships between GMV differences and cognitive function assessed with an extensive neuropsychological battery. RESULTS VBM showed lower GMV in the thalamus, left inferior frontal gyrus and left insula in patients with VLOSLP compared to healthy controls. SBM revealed lower thalamo-temporal GMV in patients with VLOSLP. Processing speed, selective attention, mental flexibility, working memory, verbal memory, semantic fluency and confrontation naming were impaired in patients with VLOSLP. Correlations between thalamic volumes and memory function were significant within the group of individuals with VLOSLP, whereas no significant associations remained in the healthy controls. CONCLUSIONS Lower GMV in the thalamus and fronto-temporal regions may be part of the underlying neurobiology of VLOSLP, with lower thalamic GMV contributing to memory impairment in the disorder.
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Affiliation(s)
- Lies Van Assche
- Geriatric Psychiatry, University Psychiatric Center KU Leuven, Leuven, Belgium
- Neuropsychiatry, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Akihiro Takamiya
- Neuropsychiatry, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Jan Van den Stock
- Geriatric Psychiatry, University Psychiatric Center KU Leuven, Leuven, Belgium
- Neuropsychiatry, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Luc Van de Ven
- Geriatric Psychiatry, University Psychiatric Center KU Leuven, Leuven, Belgium
| | - Patrick Luyten
- Faculty of Psychology and Educational Sciences, University of Leuven, Leuven, Belgium
- Research Department of Clinical Educational and Health Psychology, University College London, London, UK
| | - Louise Emsell
- Geriatric Psychiatry, University Psychiatric Center KU Leuven, Leuven, Belgium
- Neuropsychiatry, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium
- Translational MRI, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Mathieu Vandenbulcke
- Geriatric Psychiatry, University Psychiatric Center KU Leuven, Leuven, Belgium
- Neuropsychiatry, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium
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Thakuri DS, Bhattarai P, Wong DF, Chand GB. Dysregulated Salience Network Control over Default-Mode and Central-Executive Networks in Schizophrenia Revealed Using Stochastic Dynamical Causal Modeling. Brain Connect 2024; 14:70-79. [PMID: 38164105 PMCID: PMC10890948 DOI: 10.1089/brain.2023.0054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2024] Open
Abstract
Introduction: Neuroimaging studies suggest that the human brain consists of intrinsically organized, large-scale neural networks. Among these networks, the interplay among the default-mode network (DMN), salience network (SN), and central-executive network (CEN) has been widely used to understand the functional interaction patterns in health and disease. This triple network model suggests that the SN causally controls over the DMN and CEN in healthy individuals. This interaction is often referred to as SN's dynamic regulating mechanism. However, such interactions are not well understood in individuals with schizophrenia. Methods: In this study, we leveraged resting-state functional magnetic resonance imaging data from schizophrenia (n = 67) and healthy controls (n = 81) and evaluated the directional functional interactions among DMN, SN, and CEN using stochastic dynamical causal modeling methodology. Results: In healthy controls, our analyses replicated previous findings that SN regulates DMN and CEN activities (Mann-Whitney U test; p < 10-8). In schizophrenia, however, our analyses revealed a disrupted SN-based controlling mechanism over the DMN and CEN (Mann-Whitney U test; p < 10-16). Conclusions: These results indicate that the disrupted controlling mechanism of SN over the other two neural networks may be a candidate neuroimaging phenotype in schizophrenia.
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Affiliation(s)
- Deepa S. Thakuri
- Department of Radiology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri, USA
- Departments of Medicine and Radiology, University of Missouri School of Medicine, Columbia, Missouri, USA
| | - Puskar Bhattarai
- Department of Radiology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Dean F. Wong
- Department of Radiology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri, USA
- Departments of Neuroscience, Psychiatry, and Neurology, Washington University School of Medicine, St. Louis, Missouri, USA
- Imaging Core, Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Ganesh B. Chand
- Department of Radiology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri, USA
- Imaging Core, Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, Missouri, USA
- Institute of Clinical and Translational Sciences, Washington University School of Medicine, St. Louis, Missouri, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, Missouri, USA
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8
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Sun H, Sun Q, Li Y, Zhang J, Xing H, Wang J. Mapping individual structural covariance network in development brain with dynamic time warping. Cereb Cortex 2024; 34:bhae039. [PMID: 38342688 DOI: 10.1093/cercor/bhae039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 01/04/2024] [Accepted: 01/21/2024] [Indexed: 02/13/2024] Open
Abstract
A conspicuous property of brain development or maturity is coupled with coordinated or synchronized brain structural co-variation. However, there is still a lack of effective approach to map individual structural covariance network. Here, we developed a novel individual structural covariance network method using dynamic time warping algorithm and applied it to delineate developmental trajectories of topological organizations of structural covariance network from childhood to early adulthood with a large sample of 655 individuals from Human Connectome Project-Development dataset. We found that the individual structural covariance network exhibited small-worldness property and the network global topological characteristics including small-worldness, global efficiency, local efficiency, and modularity linearly increase with age while the shortest path length linearly decreases with age. The nodal topological properties including betweenness and degree increased with age in language and emotion regulation related brain areas, while it decreased with age mainly in visual cortex, sensorimotor area, and hippocampus. Moreover, the topological attributes of structural covariance network as features could predict the age of each individual. Taken together, our results demonstrate that dynamic time warping can effectively map individual structural covariance network to uncover the developmental trajectories of network topology, which may facilitate future investigations to establish the links of structural co-variations with respect to cognition and disease vulnerability.
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Affiliation(s)
- Hui Sun
- College of Electrical Engineering, Sichuan University, Chengdu 610065, China
| | - Qinyao Sun
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 625014, China
| | - Yuanyuan Li
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, China
- Yunnan Key Laboratory of Primate Biomedical Research, Kunming 650500, China
| | - Jiang Zhang
- College of Electrical Engineering, Sichuan University, Chengdu 610065, China
| | - Haoyang Xing
- Magnetic Resonance Research Center, West China Hospital, Sichuan University, Chengdu 610065, China
- School of Physics, Sichuan University, Chengdu 610065, China
| | - Jiaojian Wang
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, China
- Yunnan Key Laboratory of Primate Biomedical Research, Kunming 650500, China
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9
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Samantaray T, Saini J, Pal PK, Gupta CN. Brain connectivity for subtypes of parkinson's disease using structural MRI. Biomed Phys Eng Express 2024; 10:025012. [PMID: 38224618 DOI: 10.1088/2057-1976/ad1e77] [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: 09/20/2023] [Accepted: 01/15/2024] [Indexed: 01/17/2024]
Abstract
Objective. Delineating Parkinson's disease (PD) into distinct subtypes is a major challenge. Most studies use clinical symptoms to label PD subtypes while our work uses an imaging-based data-mining approach to subtype PD. Our study comprises two major objectives - firstly, subtyping Parkinson's patients based on grey matter information from structural magnetic resonance imaging scans of human brains; secondly, comparative structural brain connectivity analysis of PD subtypes derived from the former step.Approach. Source-based-morphometry decomposition was performed on 131 Parkinson's patients and 78 healthy controls from PPMI dataset, to derive at components (regions) with significance in disease and high effect size. The loading coefficients of significant components were thresholded for arriving at subtypes. Further, regional grey matter maps of subtype-specific subjects were separately parcellated and employed for construction of subtype-specific association matrices using Pearson correlation. These association matrices were binarized using sparsity threshold and leveraged for structural brain connectivity analysis using network metrics.Main results. Two distinct Parkinson's subtypes (namely A and B) were detected employing loadings of two components satisfying the selection criteria, and a third subtype (AB) was detected, common to these two components. Subtype A subjects were highly weighted in inferior, middle and superior frontal gyri while subtype B subjects in inferior, middle and superior temporal gyri. Network metrics analyses through permutation test revealed significant inter-subtype differences (p < 0.05) in clustering coefficient, local efficiency, participation coefficient and betweenness centrality. Moreover, hubs were obtained using betweenness centrality and mean network degree.Significance. MRI-based data-driven subtypes show frontal and temporal lobes playing a key role in PD. Graph theory-driven brain network analyses could untangle subtype-specific differences in structural brain connections showing differential network architecture. Replication of these initial results in other Parkinson's datasets may be explored in future. Clinical Relevance- Investigating structural brain connections in Parkinson's disease may provide subtype-specific treatment.
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Affiliation(s)
- Tanmayee Samantaray
- Neural Engineering Lab, Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, 781039, India
| | - Jitender Saini
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bengaluru, 560029, India
| | - Pramod Kumar Pal
- Department of Neurology, National Institute of Mental Health & Neuro Sciences, Bengaluru, 560029, India
| | - Cota Navin Gupta
- Neural Engineering Lab, Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, 781039, India
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10
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Ge R, Ching CRK, Bassett AS, Kushan L, Antshel KM, van Amelsvoort T, Bakker G, Butcher NJ, Campbell LE, Chow EWC, Craig M, Crossley NA, Cunningham A, Daly E, Doherty JL, Durdle CA, Emanuel BS, Fiksinski A, Forsyth JK, Fremont W, Goodrich‐Hunsaker NJ, Gudbrandsen M, Gur RE, Jalbrzikowski M, Kates WR, Lin A, Linden DEJ, McCabe KL, McDonald‐McGinn D, Moss H, Murphy DG, Murphy KC, Owen MJ, Villalon‐Reina JE, Repetto GM, Roalf DR, Ruparel K, Schmitt JE, Schuite‐Koops S, Angkustsiri K, Sun D, Vajdi A, van den Bree M, Vorstman J, Thompson PM, Vila‐Rodriguez F, Bearden CE. Source-based morphometry reveals structural brain pattern abnormalities in 22q11.2 deletion syndrome. Hum Brain Mapp 2024; 45:e26553. [PMID: 38224541 PMCID: PMC10785196 DOI: 10.1002/hbm.26553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 11/12/2023] [Accepted: 11/19/2023] [Indexed: 01/17/2024] Open
Abstract
22q11.2 deletion syndrome (22q11DS) is the most frequently occurring microdeletion in humans. It is associated with a significant impact on brain structure, including prominent reductions in gray matter volume (GMV), and neuropsychiatric manifestations, including cognitive impairment and psychosis. It is unclear whether GMV alterations in 22q11DS occur according to distinct structural patterns. Then, 783 participants (470 with 22q11DS: 51% females, mean age [SD] 18.2 [9.2]; and 313 typically developing [TD] controls: 46% females, mean age 18.0 [8.6]) from 13 datasets were included in the present study. We segmented structural T1-weighted brain MRI scans and extracted GMV images, which were then utilized in a novel source-based morphometry (SBM) pipeline (SS-Detect) to generate structural brain patterns (SBPs) that capture co-varying GMV. We investigated the impact of the 22q11.2 deletion, deletion size, intelligence quotient, and psychosis on the SBPs. Seventeen GMV-SBPs were derived, which provided spatial patterns of GMV covariance associated with a quantitative metric (i.e., loading score) for analysis. Patterns of topographically widespread differences in GMV covariance, including the cerebellum, discriminated individuals with 22q11DS from healthy controls. The spatial extents of the SBPs that revealed disparities between individuals with 22q11DS and controls were consistent with the findings of the univariate voxel-based morphometry analysis. Larger deletion size was associated with significantly lower GMV in frontal and occipital SBPs; however, history of psychosis did not show a strong relationship with these covariance patterns. 22q11DS is associated with distinct structural abnormalities captured by topographical GMV covariance patterns that include the cerebellum. Findings indicate that structural anomalies in 22q11DS manifest in a nonrandom manner and in distinct covarying anatomical patterns, rather than a diffuse global process. These SBP abnormalities converge with previously reported cortical surface area abnormalities, suggesting disturbances of early neurodevelopment as the most likely underlying mechanism.
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Affiliation(s)
- Ruiyang Ge
- Department of PsychiatryUniversity of British ColumbiaVancouverBritish ColumbiaCanada
- Djavad Mowafaghian Centre for Brain HealthUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | | | - Anne S. Bassett
- Clinical Genetics Research ProgramCentre for Addiction and Mental HealthTorontoOntarioCanada
- The Dalglish Family 22q Clinic, Department of Psychiatry and Division of Cardiology, Department of Medicine, and Toronto General Hospital Research InstituteUniversity Health NetworkTorontoOntarioCanada
- Campbell Family Mental Health Research InstituteCentre for Addiction and Mental HealthTorontoOntarioCanada
- Department of PsychiatryUniversity of TorontoTorontoOntarioCanada
| | - Leila Kushan
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human BehaviorUniversity of California, Los AngelesLos AngelesCaliforniaUSA
| | | | | | - Geor Bakker
- Department of Psychiatry and NeuropsychologyMaastricht UniversityMaastrichtNetherlands
| | - Nancy J. Butcher
- Department of PsychiatryUniversity of TorontoTorontoOntarioCanada
- Child Health Evaluative SciencesThe Hospital for Sick ChildrenTorontoOntarioCanada
| | | | - Eva W. C. Chow
- Clinical Genetics Research ProgramCentre for Addiction and Mental HealthTorontoOntarioCanada
- Department of PsychiatryUniversity of TorontoTorontoOntarioCanada
| | - Michael Craig
- Sackler Institute for Translational Neurodevelopment and Department of Forensic and Neurodevelopmental Sciences, King's College LondonInstitute of Psychiatry, Psychology and NeuroscienceLondonUK
- National Autism UnitBethlem Royal HospitalBeckenhamUK
| | - Nicolas A. Crossley
- Department of PsychiatryPontificia Universidad Catolica de ChileSantiagoChile
| | - Adam Cunningham
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical NeurosciencesCardiff UniversityCardiffUK
| | - Eileen Daly
- Sackler Institute for Translational Neurodevelopment and Department of Forensic and Neurodevelopmental Sciences, King's College LondonInstitute of Psychiatry, Psychology and NeuroscienceLondonUK
| | - Joanne L. Doherty
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical NeurosciencesCardiff UniversityCardiffUK
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffUK
| | - Courtney A. Durdle
- Department of PediatricsUC Davis MIND InstituteDavisCaliforniaUSA
- Department of Psychological and Brain SciencesUC Santa BarbaraSanta BarbaraCaliforniaUSA
| | - Beverly S. Emanuel
- Division of Human GeneticsThe Children's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
- Department of Pediatrics, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Ania Fiksinski
- Department of Psychology and Department of Pediatrics, Wilhelmina Children's HospitalUniversity Medical Center UtrechtUtrechtNetherlands
- Department of Psychiatry and Neuropsychology, Division of Mental Health, MHeNSMaastricht UniversityMaastrichtNetherlands
| | - Jennifer K. Forsyth
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human BehaviorUniversity of California, Los AngelesLos AngelesCaliforniaUSA
- Department of PsychologyUniversity of WashingtonSeattleWashingtonUSA
| | - Wanda Fremont
- Department of Psychiatry and Behavioral Sciences State University of New YorkUpstate Medical University SyracuseNew YorkUSA
| | - Naomi J. Goodrich‐Hunsaker
- Department of PediatricsUC Davis MIND InstituteDavisCaliforniaUSA
- Department of NeurologyUniversity of UtahSalt Lake CityUtahUSA
| | - Maria Gudbrandsen
- Sackler Institute for Translational Neurodevelopment and Department of Forensic and Neurodevelopmental Sciences, King's College LondonInstitute of Psychiatry, Psychology and NeuroscienceLondonUK
- Centre for Research in Psychological Wellbeing (CREW), School of PsychologyUniversity of RoehamptonLondonUK
| | - Raquel E. Gur
- Department of Psychiatry, Perelman School of MedicineUniversity of Pennsylvania and Children's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
| | - Maria Jalbrzikowski
- Department of PsychiatryHarvard Medical SchoolBostonMassachusettsUSA
- Department of Psychiatry and Behavioral SciencesBoston Children's HospitalBostonMassachusettsUSA
| | - Wendy R. Kates
- Department of Psychiatry and Behavioral Sciences State University of New YorkUpstate Medical University SyracuseNew YorkUSA
| | - Amy Lin
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human BehaviorUniversity of California, Los AngelesLos AngelesCaliforniaUSA
- Graduate Interdepartmental Program in NeuroscienceUCLA School of MedicineLos AngelesCaliforniaUSA
| | - David E. J. Linden
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical NeurosciencesCardiff UniversityCardiffUK
| | - Kathryn L. McCabe
- School of PsychologyUniversity of NewcastleCallaghanAustralia
- Department of PediatricsUC Davis MIND InstituteDavisCaliforniaUSA
| | - Donna McDonald‐McGinn
- Department of Pediatrics, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- 22q and You Center, Clinical Genetics Center, and Division of Human GeneticsThe Children's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
- Department of Human Biology and Medical GeneticsSapienza UniversityRomeItaly
| | - Hayley Moss
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical NeurosciencesCardiff UniversityCardiffUK
| | - Declan G. Murphy
- Sackler Institute for Translational Neurodevelopment and Department of Forensic and Neurodevelopmental Sciences, King's College LondonInstitute of Psychiatry, Psychology and NeuroscienceLondonUK
- Behavioural Genetics Clinic, Adult Autism Service, Behavioural and Developmental Psychiatry Clinical Academic GroupSouth London and Maudsley Foundation NHS TrustLondonUK
| | - Kieran C. Murphy
- Department of PsychiatryRoyal College of Surgeons in IrelandDublinIreland
| | - Michael J. Owen
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical NeurosciencesCardiff UniversityCardiffUK
| | | | - Gabriela M. Repetto
- Centro de Genetica y Genomica, Facultad de MedicinaClinica Alemana Universidad del DesarrolloSantiagoChile
| | - David R. Roalf
- Department of PsychiatryUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Kosha Ruparel
- Department of PsychiatryUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - J. Eric Schmitt
- Department of Radiology and PsychiatryUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Sanne Schuite‐Koops
- Department of PsychiatryUniversity Medical Center Groningen, Rijksuniversiteit GroningenGroningenNetherlands
| | | | - Daqiang Sun
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human BehaviorUniversity of California, Los AngelesLos AngelesCaliforniaUSA
| | - Ariana Vajdi
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human BehaviorUniversity of California, Los AngelesLos AngelesCaliforniaUSA
- Kaiser Permanente Bernard J. Tyson School of Medicine PasadenaCaliforniaUSA
| | - Marianne van den Bree
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical NeurosciencesCardiff UniversityCardiffUK
| | - Jacob Vorstman
- Department of PsychiatryUniversity of TorontoTorontoOntarioCanada
- Program in Genetics and Genome Biology, Research Institute, and Department of PsychiatryThe Hospital for Sick ChildrenTorontoOntarioCanada
| | - Paul M. Thompson
- Departments of Neurology, Psychiatry, Radiology, Engineering, Pediatrics and OphthalmologyUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Fidel Vila‐Rodriguez
- Department of PsychiatryUniversity of British ColumbiaVancouverBritish ColumbiaCanada
- Djavad Mowafaghian Centre for Brain HealthUniversity of British ColumbiaVancouverBritish ColumbiaCanada
- School of Biomedical Engineering University of British Columbia VancouverBritish ColumbiaCanada
| | - Carrie E. Bearden
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human BehaviorUniversity of California, Los AngelesLos AngelesCaliforniaUSA
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11
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Moon SY, Park H, Lee W, Lee S, Lho SK, Kim M, Kim KW, Kwon JS. Magnetic resonance texture analysis reveals stagewise nonlinear alterations of the frontal gray matter in patients with early psychosis. Mol Psychiatry 2023; 28:5309-5318. [PMID: 37500824 DOI: 10.1038/s41380-023-02163-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 06/13/2023] [Accepted: 06/23/2023] [Indexed: 07/29/2023]
Abstract
Although gray matter (GM) abnormalities are present from the early stages of psychosis, subtle/miniscule changes may not be detected by conventional volumetry. Texture analysis (TA), which permits quantification of the complex interrelationship between contrasts at the individual voxel level, may capture subtle GM changes with more sensitivity than does volume or cortical thickness (CTh). We performed three-dimensional TA in nine GM regions of interest (ROIs) using T1 magnetic resonance images from 101 patients with first-episode psychosis (FEP), 85 patients at clinical high risk (CHR) for psychosis, and 147 controls. Via principal component analysis, three features of gray-level cooccurrence matrix - informational measure of correlation 1 (IMC1), autocorrelation (AC), and inverse difference (ID) - were selected to analyze cortical texture in the ROIs that showed a significant change in volume or CTh in the study groups. Significant reductions in GM volume and CTh of various frontotemporal regions were found in the FEP compared with the controls. Increased frontal AC was found in the FEP group compared to the controls after adjusting for volume and CTh changes. While volume and CTh were preserved in the CHR group, a stagewise nonlinear increase in frontal IMC1 was found, which exceeded both the controls and FEP group. Increased frontal IMC1 was also associated with a lesser severity of attenuated positive symptoms in the CHR group, while neither volume nor CTh was. The results of the current study suggest that frontal IMC1 may reflect subtle, dynamic GM changes and the symptomatology of the CHR stage with greater sensitivity, even in the absence of gross GM abnormalities. Some structural mechanisms that may contribute to texture changes (e.g., macrostructural cortical lamina, neuropil/myelination, cortical reorganization) and their possible implications are explored and discussed. Texture may be a useful tool to investigate subtle and dynamic GM abnormalities, especially during the CHR period.
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Affiliation(s)
- Sun Young Moon
- Department of Public Health Service, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
- Institute of Human Behavioral Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
| | - Hyungyou Park
- Department of Brain and Cognitive Science, Seoul National University College of Natural Science, Seoul, Republic of Korea
| | - Won Lee
- Department of Brain and Cognitive Science, Seoul National University College of Natural Science, Seoul, Republic of Korea
| | - Subin Lee
- Department of Brain and Cognitive Science, Seoul National University College of Natural Science, Seoul, Republic of Korea
| | | | - Minah Kim
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Ki Woong Kim
- Institute of Human Behavioral Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
- Department of Brain and Cognitive Science, Seoul National University College of Natural Science, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Jun Soo Kwon
- Institute of Human Behavioral Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea.
- Department of Brain and Cognitive Science, Seoul National University College of Natural Science, Seoul, Republic of Korea.
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea.
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea.
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12
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Chopra S, Segal A, Oldham S, Holmes A, Sabaroedin K, Orchard ER, Francey SM, O’Donoghue B, Cropley V, Nelson B, Graham J, Baldwin L, Tiego J, Yuen HP, Allott K, Alvarez-Jimenez M, Harrigan S, Fulcher BD, Aquino K, Pantelis C, Wood SJ, Bellgrove M, McGorry PD, Fornito A. Network-Based Spreading of Gray Matter Changes Across Different Stages of Psychosis. JAMA Psychiatry 2023; 80:1246-1257. [PMID: 37728918 PMCID: PMC10512169 DOI: 10.1001/jamapsychiatry.2023.3293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 06/21/2023] [Indexed: 09/22/2023]
Abstract
Importance Psychotic illness is associated with anatomically distributed gray matter reductions that can worsen with illness progression, but the mechanisms underlying the specific spatial patterning of these changes is unknown. Objective To test the hypothesis that brain network architecture constrains cross-sectional and longitudinal gray matter alterations across different stages of psychotic illness and to identify whether certain brain regions act as putative epicenters from which volume loss spreads. Design, Settings, and Participants This case-control study included 534 individuals from 4 cohorts, spanning early and late stages of psychotic illness. Early-stage cohorts included patients with antipsychotic-naive first-episode psychosis (n = 59) and a group of patients receiving medications within 3 years of psychosis onset (n = 121). Late-stage cohorts comprised 2 independent samples of people with established schizophrenia (n = 136). Each patient group had a corresponding matched control group (n = 218). A sample of healthy adults (n = 356) was used to derive representative structural and functional brain networks for modeling of network-based spreading processes. Longitudinal illness-related and antipsychotic-related gray matter changes over 3 and 12 months were examined using a triple-blind randomized placebo-control magnetic resonance imaging study of the antipsychotic-naive patients. All data were collected between April 29, 2008, and January 15, 2020, and analyses were performed between March 1, 2021, and January 14, 2023. Main Outcomes and Measures Coordinated deformation models were used to estimate the extent of gray matter volume (GMV) change in each of 332 parcellated areas by the volume changes observed in areas to which they were structurally or functionally coupled. To identify putative epicenters of volume loss, a network diffusion model was used to simulate the spread of pathology from different seed regions. Correlations between estimated and empirical spatial patterns of GMV alterations were used to quantify model performance. Results Of 534 included individuals, 354 (66.3%) were men, and the mean (SD) age was 28.4 (7.4) years. In both early and late stages of illness, spatial patterns of cross-sectional volume differences between patients and controls were more accurately estimated by coordinated deformation models constrained by structural, rather than functional, network architecture (r range, >0.46 to <0.57; P < .01). The same model also robustly estimated longitudinal volume changes related to illness (r ≥ 0.52; P < .001) and antipsychotic exposure (r ≥ 0.50; P < .004). Network diffusion modeling consistently identified, across all 4 data sets, the anterior hippocampus as a putative epicenter of pathological spread in psychosis. Epicenters of longitudinal GMV loss were apparent in posterior cortex early in the illness and shifted to the prefrontal cortex with illness progression. Conclusion and Relevance These findings highlight a central role for white matter fibers as conduits for the spread of pathology across different stages of psychotic illness, mirroring findings reported in neurodegenerative conditions. The structural connectome thus represents a fundamental constraint on brain changes in psychosis, regardless of whether these changes are caused by illness or medication. Moreover, the anterior hippocampus represents a putative epicenter of early brain pathology from which dysfunction may spread to affect connected areas.
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Affiliation(s)
- Sidhant Chopra
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Victoria, Australia
- Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia
- Department of Psychology, Yale University, New Haven, Connecticut
| | - Ashlea Segal
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Victoria, Australia
- Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia
| | - Stuart Oldham
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Victoria, Australia
- Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia
| | - Alexander Holmes
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Victoria, Australia
- Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia
| | - Kristina Sabaroedin
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Victoria, Australia
- Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia
- Department of Radiology, Hotchkiss Brain Institute and Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada
- Department of Paediatrics, Hotchkiss Brain Institute and Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada
| | - Edwina R. Orchard
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Victoria, Australia
- Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia
- Child Study Centre, Yale University, New Haven, Connecticut
| | - Shona M. Francey
- Orygen, Parkville, Victoria, Australia
- Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Brian O’Donoghue
- Orygen, Parkville, Victoria, Australia
- Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Vanessa Cropley
- Department of Psychiatry, Melbourne Neuropsychiatry Centre, The University of Melbourne, Carlton, Victoria, Australia
| | - Barnaby Nelson
- Orygen, Parkville, Victoria, Australia
- Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Jessica Graham
- Orygen, Parkville, Victoria, Australia
- Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Lara Baldwin
- Orygen, Parkville, Victoria, Australia
- Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Jeggan Tiego
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Victoria, Australia
- Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia
| | - Hok Pan Yuen
- Orygen, Parkville, Victoria, Australia
- Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Kelly Allott
- Orygen, Parkville, Victoria, Australia
- Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Mario Alvarez-Jimenez
- Orygen, Parkville, Victoria, Australia
- Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Susy Harrigan
- Orygen, Parkville, Victoria, Australia
- Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
- Centre for Mental Health, Melbourne School of Global and Population Health, The University of Melbourne, Parkville, Victoria, Australian
| | - Ben D. Fulcher
- School of Physics, University of Sydney, Sydney, New South Wales, Australia
| | - Kevin Aquino
- School of Physics, University of Sydney, Sydney, New South Wales, Australia
- Centre for Complex Systems, University of Sydney, Sydney, New South Wales, Australia
| | - Christos Pantelis
- Department of Psychiatry, Melbourne Neuropsychiatry Centre, The University of Melbourne, Carlton, Victoria, Australia
- NorthWestern Mental Health, Royal Melbourne Hospital, Melbourne, Victoria, Australia
- Western Health Sunshine Hospital, St Albans, Victoria, Australia
| | - Stephen J. Wood
- Orygen, Parkville, Victoria, Australia
- Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
- School of Psychology, University of Birmingham, Edgbaston, United Kingdom
| | - Mark Bellgrove
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Victoria, Australia
| | - Patrick D. McGorry
- Orygen, Parkville, Victoria, Australia
- Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Victoria, Australia
- Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia
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13
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Saha DK, Silva RF, Baker BT, Saha R, Calhoun VD. dcSBM: A federated constrained source-based morphometry approach for multivariate brain structure mapping. Hum Brain Mapp 2023; 44:5892-5905. [PMID: 37837630 PMCID: PMC10619413 DOI: 10.1002/hbm.26483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 08/09/2023] [Accepted: 08/29/2023] [Indexed: 10/16/2023] Open
Abstract
The examination of multivariate brain morphometry patterns has gained attention in recent years, especially for their powerful exploratory capabilities in the study of differences between patients and controls. Among the many existing methods and tools for the analysis of brain anatomy based on structural magnetic resonance imaging data, data-driven source-based morphometry (SBM) focuses on the exploratory detection of such patterns. Here, we implement a semi-blind extension of SBM, called constrained source-based morphometry (constrained SBM), which enables the extraction of maximally independent reference-alike sources using the constrained independent component analysis (ICA) approach. To do this, we combine SBM with a set of reference components covering the full brain, derived from a large independent data set (UKBiobank), to provide a fully automated SBM framework. This also allows us to implement a federated version of constrained SBM (cSBM) to allow analysis of data that is not locally accessible. In our proposed decentralized constrained source-based morphometry (dcSBM), the original data never leaves the local site. Each site operates constrained ICA on its private local data using a common distributed computation platform. Next, an aggregator/master node aggregates the results estimated from each local site and applies statistical analysis to estimate the significance of the sources. Finally, we utilize two additional multisite patient data sets to validate our model by comparing the resulting group difference estimates from both cSBM and dcSBM.
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Affiliation(s)
- Debbrata K. Saha
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - Rogers F. Silva
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - Bradley T. Baker
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - Rekha Saha
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - Vince D. Calhoun
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
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14
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Duda M, Faghiri A, Belger A, Bustillo JR, Ford JM, Mathalon DH, Mueller BA, Pearlson GD, Potkin SG, Preda A, Sui J, Van Erp TGM, Calhoun VD. Alterations in grey matter structure linked to frequency-specific cortico-subcortical connectivity in schizophrenia via multimodal data fusion. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.05.547840. [PMID: 37461731 PMCID: PMC10350020 DOI: 10.1101/2023.07.05.547840] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
Schizophrenia (SZ) is a complex psychiatric disorder that is currently defined by symptomatic and behavioral, rather than biological, criteria. Neuroimaging is an appealing avenue for SZ biomarker development, as several neuroimaging-based studies comparing individuals with SZ to healthy controls (HC) have shown measurable group differences in brain structure, as well as functional brain alterations in both static and dynamic functional network connectivity (sFNC and dFNC, respectively). The recently proposed filter-banked connectivity (FBC) method extends the standard dFNC sliding-window approach to estimate FNC within an arbitrary number of distinct frequency bands. The initial implementation used a set of filters spanning the full connectivity spectral range, providing a unified approach to examine both sFNC and dFNC in a single analysis. Initial FBC results found that individuals with SZ spend more time in a less structured, more disconnected low-frequency (i.e., static) FNC state than HC, as well as preferential SZ occupancy in high-frequency connectivity states, suggesting a frequency-specific component underpinning the functional dysconnectivity observed in SZ. Building on these findings, we sought to link such frequency-specific patterns of FNC to covarying data-driven structural brain networks in the context of SZ. Specifically, we employ a multi-set canonical correlation analysis + joint independent components analysis (mCCA + jICA) data fusion framework to study the connection between grey matter volume (GMV) maps and FBC states across the full connectivity frequency spectrum. Our multimodal analysis identified two joint sources that captured co-varying patterns of frequency-specific functional connectivity and alterations in GMV with significant group differences in loading parameters between the SZ group and HC. The first joint source linked frequency-modulated connections between the subcortical and sensorimotor networks and GMV alterations in the frontal and temporal lobes, while the second joint source identified a relationship between low-frequency cerebellar-sensorimotor connectivity and structural changes in both the cerebellum and motor cortex. Together, these results show a strong connection between cortico-subcortical functional connectivity at both high and low frequencies and alterations in cortical GMV that may be relevant to the pathogenesis and pathophysiology of SZ.
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Affiliation(s)
- Marlena Duda
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Ashkan Faghiri
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Aysenil Belger
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - Juan R Bustillo
- Department of Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque, NM, USA
| | - Judith M Ford
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, San Francisco, California, USA
- Department of Psychiatry and Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California, USA
| | - Daniel H Mathalon
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, San Francisco, California, USA
- Department of Psychiatry and Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California, USA
| | - Bryon A Mueller
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, Minnesota, USA
| | - Godfrey D Pearlson
- Departments of Psychiatry and Neuroscience, Yale University School of Medicine, New Haven, CT, USA
| | - Steven G Potkin
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California, USA
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California, USA
| | - Jing Sui
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- IDG/McGovern Institute for Brain Research, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Theo G M Van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine, CA, USA
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
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15
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Martin D, Basodi S, Panta S, Rootes-Murdy K, Prae P, Sarwate AD, Kelly R, Romero J, Baker BT, Gazula H, Bockholt J, Turner JA, Esper NB, Franco AR, Plis S, Calhoun VD. Enhancing collaborative neuroimaging research: introducing COINSTAC Vaults for federated analysis and reproducibility. Front Neuroinform 2023; 17:1207721. [PMID: 37404336 PMCID: PMC10315678 DOI: 10.3389/fninf.2023.1207721] [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: 04/18/2023] [Accepted: 06/02/2023] [Indexed: 07/06/2023] Open
Abstract
Collaborative neuroimaging research is often hindered by technological, policy, administrative, and methodological barriers, despite the abundance of available data. COINSTAC (The Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation) is a platform that successfully tackles these challenges through federated analysis, allowing researchers to analyze datasets without publicly sharing their data. This paper presents a significant enhancement to the COINSTAC platform: COINSTAC Vaults (CVs). CVs are designed to further reduce barriers by hosting standardized, persistent, and highly-available datasets, while seamlessly integrating with COINSTAC's federated analysis capabilities. CVs offer a user-friendly interface for self-service analysis, streamlining collaboration, and eliminating the need for manual coordination with data owners. Importantly, CVs can also be used in conjunction with open data as well, by simply creating a CV hosting the open data one would like to include in the analysis, thus filling an important gap in the data sharing ecosystem. We demonstrate the impact of CVs through several functional and structural neuroimaging studies utilizing federated analysis showcasing their potential to improve the reproducibility of research and increase sample sizes in neuroimaging studies.
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Affiliation(s)
- Dylan Martin
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State, Georgia Tech, Emory, Atlanta, GA, United States
| | - Sunitha Basodi
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State, Georgia Tech, Emory, Atlanta, GA, United States
| | - Sandeep Panta
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State, Georgia Tech, Emory, Atlanta, GA, United States
| | - Kelly Rootes-Murdy
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State, Georgia Tech, Emory, Atlanta, GA, United States
| | - Paul Prae
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State, Georgia Tech, Emory, Atlanta, GA, United States
| | - Anand D. Sarwate
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State, Georgia Tech, Emory, Atlanta, GA, United States
- Department of Electrical and Computer Engineering, Rutgers University–New Brunswick, Piscataway, NJ, United States
| | - Ross Kelly
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State, Georgia Tech, Emory, Atlanta, GA, United States
| | - Javier Romero
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State, Georgia Tech, Emory, Atlanta, GA, United States
| | - Bradley T. Baker
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State, Georgia Tech, Emory, Atlanta, GA, United States
| | - Harshvardhan Gazula
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Jeremy Bockholt
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State, Georgia Tech, Emory, Atlanta, GA, United States
| | - Jessica A. Turner
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State, Georgia Tech, Emory, Atlanta, GA, United States
| | - Nathalia B. Esper
- Center for the Developing Brain, Child Mind Institute, New York, NY, United States
| | - Alexandre R. Franco
- Center for the Developing Brain, Child Mind Institute, New York, NY, United States
- Center for Brain Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, United States
- Department of Psychiatry, NYU Grossman School of Medicine, New York, NY, United States
| | - Sergey Plis
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State, Georgia Tech, Emory, Atlanta, GA, United States
| | - Vince D. Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State, Georgia Tech, Emory, Atlanta, GA, United States
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16
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Wang K, Hu Y, Yan C, Li M, Wu Y, Qiu J, Zhu X. Brain structural abnormalities in adult major depressive disorder revealed by voxel- and source-based morphometry: evidence from the REST-meta-MDD Consortium. Psychol Med 2023; 53:3672-3682. [PMID: 35166200 DOI: 10.1017/s0033291722000320] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
BACKGROUND Neuroimaging studies on major depressive disorder (MDD) have identified an extensive range of brain structural abnormalities, but the exact neural mechanisms associated with MDD remain elusive. Most previous studies were performed with voxel- or surface-based morphometry which were univariate methods without considering spatial information across voxels/vertices. METHODS Brain morphology was investigated using voxel-based morphometry (VBM) and source-based morphometry (SBM) in 1082 MDD patients and 990 healthy controls (HCs) from the REST-meta-MDD Consortium. We first examined group differences in regional grey matter (GM) volumes and structural covariance networks between patients and HCs. We then compared first-episode, drug-naïve (FEDN) patients, and recurrent patients. Additionally, we assessed the effects of symptom severity and illness duration on brain alterations. RESULTS VBM showed decreased GM volume in various regions in MDD patients including the superior temporal cortex, anterior and middle cingulate cortex, inferior frontal cortex, and precuneus. SBM returned differences only in the prefrontal network. Comparisons between FEDN and recurrent MDD patients showed no significant differences by VBM, but SBM showed greater decreases in prefrontal, basal ganglia, visual, and cerebellar networks in the recurrent group. Moreover, depression severity was associated with volumes in the inferior frontal gyrus and precuneus, as well as the prefrontal network. CONCLUSIONS Simultaneous application of VBM and SBM methods revealed brain alterations in MDD patients and specified differences between recurrent and FEDN patients, which tentatively provide an effective multivariate method to identify potential neurobiological markers for depression.
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Affiliation(s)
- KangCheng Wang
- School of Psychology, Shandong Normal University, Jinan, Shandong, China
| | - YuFei Hu
- School of Psychology, Shandong Normal University, Jinan, Shandong, China
| | - ChaoGan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
- Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- International Big-Data Center for Depression Research, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - MeiLing Li
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA
| | - YanJing Wu
- Faculty of Foreign Languages, Ningbo University, Ningbo, Zhejiang, China
| | - Jiang Qiu
- Faculty of Psychology, Southwest University, Chongqing 400716, China
| | - XingXing Zhu
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
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17
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Li M, Deng W, Li Y, Zhao L, Ma X, Yu H, Li X, Meng Y, Wang Q, Du X, Sham PC, Palaniyappan L, Li T. Ameliorative patterns of grey matter in patients with first-episode and treatment-naïve schizophrenia. Psychol Med 2023; 53:3500-3510. [PMID: 35164887 PMCID: PMC10277763 DOI: 10.1017/s0033291722000058] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 12/30/2021] [Accepted: 01/05/2022] [Indexed: 02/05/2023]
Abstract
BACKGROUND Grey matter (GM) reduction is a consistent observation in established late stages of schizophrenia, but patients in the untreated early stages of illness display an increase as well as a decrease in GM distribution relative to healthy controls (HC). The relative excess of GM may indicate putative compensatory responses, though to date its relevance is unclear. METHODS 343 first-episode treatment-naïve patients with schizophrenia (FES) and 342 HC were recruited. Multivariate source-based morphometry was performed to identify covarying 'networks' of grey matter concentration (GMC). Neurocognitive scores using the Cambridge Neuropsychological Test Automated Battery (CANTAB) and symptom burden using the Positive and Negative Symptoms Scale (PANSS) were obtained. Bivariate linear relationships between GMC and cognition/symptoms were studied. RESULTS Compared to healthy subjects, FES had prominently lower GMC in two components; the first consists of the anterior insula, inferior frontal gyrus, anterior cingulate and the second component with the superior temporal gyrus, precuneus, inferior/superior parietal lobule, cuneus, and lingual gyrus. Higher GMC was seen in adjacent areas of the middle and superior temporal gyrus, middle frontal gyrus, inferior parietal cortex and putamen. Greater GMC of this component was associated with lower duration of untreated psychosis, less severe positive symptoms and better performance on cognitive tests. CONCLUSIONS In untreated stages of schizophrenia, both a distributed lower and higher GMC is observable. While the higher GMC is relatively modest, it occurs across frontoparietal, temporal and subcortical regions in association with reduced illness burden suggesting a compensatory role for higher GMC in the early stages of schizophrenia.
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Affiliation(s)
- Mingli Li
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Wei Deng
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yinfei Li
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Liansheng Zhao
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Xiaohong Ma
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Hua Yu
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Xiaojing Li
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Yajing Meng
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Qiang Wang
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Xiangdong Du
- Suzhou Psychiatry Hospital, Affiliated Guangji Hospital of Soochow University, Suzhou, 215137, Jiangsu, China
| | - Pak Chung Sham
- Centre for Genomic Sciences and State Key Laboratory in Cognitive and Brain Sciences, LKS Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong, China
| | - Lena Palaniyappan
- Robarts Research Institute & The Brain and Mind Institute, University of Western Ontario, London, Ontario, Canada
- Department of Psychiatry, University of Western Ontario, London, Ontario, Canada
- Lawson Health Research Institute, London, Ontario, Canada
| | - Tao Li
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
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18
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Martin D, Basodi S, Panta S, Rootes-Murdy K, Prae P, Sarwate AD, Kelly R, Romero J, Baker BT, Gazula H, Bockholt J, Turner J, Esper NB, Franco AR, Plis S, Calhoun VD. Enhancing Collaborative Neuroimaging Research: Introducing COINSTAC Vaults for Federated Analysis and Reproducibility. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.08.539852. [PMID: 37214791 PMCID: PMC10197552 DOI: 10.1101/2023.05.08.539852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Collaborative neuroimaging research is often hindered by technological, policy, administrative, and methodological barriers, despite the abundance of available data. COINSTAC is a platform that successfully tackles these challenges through federated analysis, allowing researchers to analyze datasets without publicly sharing their data. This paper presents a significant enhancement to the COINSTAC platform: COINSTAC Vaults (CVs). CVs are designed to further reduce barriers by hosting standardized, persistent, and highly-available datasets, while seamlessly integrating with COINSTAC's federated analysis capabilities. CVs offer a user-friendly interface for self-service analysis, streamlining collaboration and eliminating the need for manual coordination with data owners. Importantly, CVs can also be used in conjunction with open data as well, by simply creating a CV hosting the open data one would like to include in the analysis, thus filling an important gap in the data sharing ecosystem. We demonstrate the impact of CVs through several functional and structural neuroimaging studies utilizing federated analysis showcasing their potential to improve the reproducibility of research and increase sample sizes in neuroimaging studies.
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19
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Le H, Dimitrakopoulou K, Patel H, Curtis C, Cordero-Grande L, Edwards AD, Hajnal J, Tournier JD, Deprez M, Cullen H. Effect of schizophrenia common variants on infant brain volumes: cross-sectional study in 207 term neonates in developing Human Connectome Project. Transl Psychiatry 2023; 13:121. [PMID: 37037832 PMCID: PMC10085987 DOI: 10.1038/s41398-023-02413-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 03/16/2023] [Accepted: 03/24/2023] [Indexed: 04/12/2023] Open
Abstract
Increasing lines of evidence suggest deviations from the normal early developmental trajectory could give rise to the onset of schizophrenia during adolescence and young adulthood, but few studies have investigated brain imaging changes associated with schizophrenia common variants in neonates. This study compared the brain volumes of both grey and white matter regions with schizophrenia polygenic risk scores (PRS) for 207 healthy term-born infants of European ancestry. Linear regression was used to estimate the relationship between PRS and brain volumes, with gestational age at birth, postmenstrual age at scan, ancestral principal components, sex and intracranial volumes as covariates. The schizophrenia PRS were negatively associated with the grey (β = -0.08, p = 4.2 × 10-3) and white (β = -0.13, p = 9.4 × 10-3) matter superior temporal gyrus volumes, white frontal lobe volume (β = -0.09, p = 1.5 × 10-3) and the total white matter volume (β = -0.062, p = 1.66 × 10-2). This result also remained robust when incorporating individuals of Asian ancestry. Explorative functional analysis of the schizophrenia risk variants associated with the right frontal lobe white matter volume found enrichment in neurodevelopmental pathways. This preliminary result suggests possible involvement of schizophrenia risk genes in early brain growth, and potential early life structural alterations long before the average age of onset of the disease.
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Affiliation(s)
- Hai Le
- Centre for the Developing Brain, Perinatal Imaging and Health Department, King's College London, London, UK.
| | - Konstantina Dimitrakopoulou
- Translational Bioinformatics Platform, NIHR Biomedical Research Centre, Guy's and St. Thomas' NHS Foundation Trust and King's College London, London, UK
| | - Hamel Patel
- NIHR Maudsley Biomedical Research Centre, King's College London, London, UK
| | - Charles Curtis
- NIHR Maudsley Biomedical Research Centre, King's College London, London, UK
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, Perinatal Imaging and Health Department, King's College London, London, UK
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid & CIBER-BBN, ISCIII, Madrid, Spain
| | - A David Edwards
- Centre for the Developing Brain, Perinatal Imaging and Health Department, King's College London, London, UK
| | - Joseph Hajnal
- Centre for the Developing Brain, Perinatal Imaging and Health Department, King's College London, London, UK
| | - Jacques-Donald Tournier
- Centre for the Developing Brain, Perinatal Imaging and Health Department, King's College London, London, UK
| | - Maria Deprez
- Centre for the Developing Brain, Perinatal Imaging and Health Department, King's College London, London, UK
| | - Harriet Cullen
- Centre for the Developing Brain, Perinatal Imaging and Health Department, King's College London, London, UK
- Department of Medical and Molecular Genetics, King's College London, London, UK
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20
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Baroreflex sensitivity is associated with markers of hippocampal gliosis and dysmyelination in patients with psychosis. Clin Auton Res 2023; 33:101-110. [PMID: 36877302 DOI: 10.1007/s10286-023-00929-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Accepted: 01/20/2023] [Indexed: 03/07/2023]
Abstract
PURPOSE Hippocampal dysfunction plays a key role in the pathology of psychosis. Given hippocampal sensitivity to changes in cerebral perfusion, decreased baroreflex function could contribute to psychosis pathogenesis. This study had two aims: (1) To compare baroreflex sensitivity in participants with psychosis to two control groups: participants with a nonpsychotic affective disorder and participants with no history of psychiatric disease; (2) to examine the relationship between hippocampal neurometabolites and baroreflex sensitivities in these three groups. We hypothesized that baroreflex sensitivity would be reduced and correlated with hippocampal neurometabolite levels in participants with psychosis, but not in the control groups. METHODS We assessed baroreflex sensitivity during the Valsalva maneuver separated into vagal and adrenergic components. Metabolite concentrations for cellular processes were quantitated in the entire multivoxel hippocampus using H1-MR spectroscopic (MRS) imaging and were compared with baroreflex sensitivities in the three groups. RESULTS Vagal baroreflex sensitivity (BRS-V) was reduced in a significantly larger proportion of participants with psychosis compared with patients with nonpsychotic affective disorders, whereas participants with psychosis had increased adrenergic baroreflex sensitivity (BRS-A) compared with participants with no history of psychiatric disease. Only in psychotic cases were baroreflex sensitivities associated with hippocampal metabolite concentrations. Specifically, BRS-V was inversely correlated with myo-inositol, a marker of gliosis, and BRS-A was positively correlated with energy dependent dysmyelination (choline, creatine) and excitatory activity (GLX). CONCLUSIONS Abnormal baroreflex sensitivity is common in participants with psychosis and is associated with MRS markers of hippocampal pathology. Future longitudinal studies are needed to examine causality.
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21
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Wang Y, Wang J, Su W, Hu H, Xia M, Zhang T, Xu L, Zhang X, Taylor H, Osipowicz K, Young IM, Lin YH, Nicholas P, Tanglay O, Sughrue ME, Tang Y, Doyen S. Symptom-circuit mappings of the schizophrenia connectome. Psychiatry Res 2023; 323:115122. [PMID: 36889161 DOI: 10.1016/j.psychres.2023.115122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 02/15/2023] [Accepted: 02/18/2023] [Indexed: 02/27/2023]
Abstract
OBJECTIVE This paper aims to model the anatomical circuits underlying schizophrenia symptoms, and to explore patterns of abnormal connectivity among brain networks affected by psychopathology. METHODS T1 magnetic resonance imaging (MRI), diffusion weighted imaging (DWI), and resting-state functional MRI (rsfMRI) were obtained from a total of 126 patients with schizophrenia who were recruited for the study. The images were processed using the Omniscient software (https://www.o8t. com). We further apply the use of the Hollow-tree Super (HoTS) method to gain insights into what brain regions had abnormal connectivity that might be linked to the symptoms of schizophrenia. RESULTS The Positive and Negative Symptom Scale is characterised into 6 factors. Each symptom is mapped with specific anatomical abnormalities and circuits. Comparison between factors reveals co-occurrence in parcels in Factor 1 and Factor 2. Multiple large-scale networks are involved in SCZ symptomatology, with functional connectivity within Default Mode Network (DMN) and Central Executive Network (CEN) regions most frequently associated with measures of psychopathology. CONCLUSION We present a summary of the relevant anatomy for regions of the cortical areas as part of a larger effort to understand its contribution in schizophrenia. This unique machine learning-type approach maps symptoms to specific brain regions and circuits by bridging the diagnostic subtypes and analysing the features of the connectome.
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Affiliation(s)
- Yingchan Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Jijun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China.
| | - Wenjun Su
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Hao Hu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Mengqing Xia
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Tianhong Zhang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Lihua Xu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Xia Zhang
- Xijia Medical Technology Company Limited, Shenzhen 518000, China; International Joint Research Center on Precision Brain Medicine, XD Group Hospital, Xi'an 710082, China
| | - Hugh Taylor
- Omniscient Neurotechnology, Sydney, Australia
| | | | | | - Yueh-Hsin Lin
- Department of Neurosurgery, Prince of Wales Private Hospital, Sydney, Australia
| | | | | | - Michael E Sughrue
- International Joint Research Center on Precision Brain Medicine, XD Group Hospital, Xi'an 710082, China; Omniscient Neurotechnology, Sydney, Australia
| | - Yingying Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China.
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22
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Lang X, Wang D, Zhou H, Wang L, Kosten TR, Zhang XY. P50 inhibition defects, psychopathology and gray matter volume in patients with first-episode drug-naive schizophrenia. Asian J Psychiatr 2023; 80:103421. [PMID: 36563611 DOI: 10.1016/j.ajp.2022.103421] [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: 07/12/2022] [Revised: 12/08/2022] [Accepted: 12/16/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND Sensory gating deficits and gray matter volume (GMV) abnormalities have been found to be associated with the pathogenesis and psychopathology of patients with schizophrenia (SCZ). However, no studies have investigated their interrelationship in first-episode treatment-naive (FETN) SCZ patients. METHODS We recruited 52 FETN SCZ patients and 57 healthy controls. The Positive and Negative Syndrome Scale (PANSS) was used to measure the psychopathology of the patients. We collected magnetic resonance imaging and P50 inhibition data from all participants. RESULTS Compared to healthy controls, patients had shorter S1 and S2 latencies but larger S2 amplitudes and P50 ratio (Bonferroni adjusted all p < 0.01). In patients, S2 latency was independently associated with PANSS total score, negative symptoms and general psychopathology (t = 2.26-2.58, both P < 0.05), whereas S1 (t = 2.44, P < 0.05) and S2 latencies (t = 2.13, P < 0.05) were associated with PANSS cognitive factor. Moreover, GMV in the left inferior temporal gyrus, left lingual gyrus and right superior occipital gyrus, and bilateral dorsolateral superior frontal gyrus were each associated with the P50 components (all p < 0.05). In addition, GMV associated with S2 latency was negatively correlated with PANSS general psychopathology (t = -2.46, p < 0.05) and total score (t = -2.34, p < 0.05). CONCLUSIONS Our findings indicate that FETN SCZ patients exhibit deficits in P50 inhibition and GMV of brain regions associated with these deficits may be associated with their psychopathological symptoms, suggesting that brain structures associated with P50 components may be important biomarkers of SCZ psychopathology. Future studies could use a prospective longitudinal design to investigate the potential causal relationship of brain structures associated with P50 components in the psychopathological symptoms of SCZ patients.
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Affiliation(s)
- XiaoE Lang
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, China.
| | - Dongmei Wang
- 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
| | - Huixia Zhou
- 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
| | - Li Wang
- 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
| | - Thomas R Kosten
- Department of Psychiatry, Baylor College of Medicine, Houston, TX, USA
| | - Xiang-Yang Zhang
- 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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Watanabe K, Okamoto N, Ueda I, Tesen H, Fujii R, Ikenouchi A, Yoshimura R, Kakeda S. Disturbed hippocampal intra-network in first-episode of drug-naïve major depressive disorder. Brain Commun 2023; 5:fcac323. [PMID: 36601619 PMCID: PMC9798279 DOI: 10.1093/braincomms/fcac323] [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: 03/14/2022] [Revised: 09/27/2022] [Accepted: 12/06/2022] [Indexed: 12/13/2022] Open
Abstract
Complex networks inside the hippocampus could provide new insights into hippocampal abnormalities in various psychiatric disorders and dementia. However, evaluating intra-networks in the hippocampus using MRI is challenging. Here, we employed a high spatial resolution of conventional structural imaging and independent component analysis to investigate intra-networks structural covariance in the hippocampus. We extracted the intra-networks based on the intrinsic connectivity of each 0.9 mm isotropic voxel to every other voxel using a data-driven approach. With a total volume of 3 cc, the hippocampus contains 4115 voxels for a 0.9 mm isotropic voxel size or 375 voxels for a 2 mm isotropic voxel of high-resolution functional or diffusion tensor imaging. Therefore, the novel method presented in the current study could evaluate the hippocampal intra-networks in detail. Furthermore, we investigated the abnormality of the intra-networks in major depressive disorders. A total of 77 patients with first-episode drug-naïve major depressive disorder and 79 healthy subjects were recruited. The independent component analysis extracted seven intra-networks from hippocampal structural images, which were divided into four bilateral networks and three networks along the longitudinal axis. A significant difference was observed in the bilateral hippocampal tail network between patients with major depressive disorder and healthy subjects. In the logistic regression analysis, two bilateral networks were significant predictors of major depressive disorder, with an accuracy of 78.1%. In conclusion, we present a novel method for evaluating intra-networks in the hippocampus. One advantage of this method is that a detailed network can be estimated using conventional structural imaging. In addition, we found novel bilateral networks in the hippocampus that were disturbed in patients with major depressive disorders, and these bilateral networks could predict major depressive disorders.
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Affiliation(s)
- Keita Watanabe
- Open Innovation Institute, Kyoto University, Kyoto 6068501, Japan
| | - Naomichi Okamoto
- Department of Psychiatry, University of Occupational and Environmental Health, Kitakyushu 8078555, Japan
| | - Issei Ueda
- Department of Radiology, Graduate School of Medicine, Hirosaki University, Hirosaki 0368502, Japan
| | - Hirofumi Tesen
- Department of Psychiatry, University of Occupational and Environmental Health, Kitakyushu 8078555, Japan
| | - Rintaro Fujii
- Department of Psychiatry, University of Occupational and Environmental Health, Kitakyushu 8078555, Japan
| | - Atsuko Ikenouchi
- Department of Psychiatry, University of Occupational and Environmental Health, Kitakyushu 8078555, Japan
| | - Reiji Yoshimura
- Department of Psychiatry, University of Occupational and Environmental Health, Kitakyushu 8078555, Japan
| | - Shingo Kakeda
- Department of Radiology, Graduate School of Medicine, Hirosaki University, Hirosaki 0368502, Japan
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Progressive brain abnormalities in schizophrenia across different illness periods: a structural and functional MRI study. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2023; 9:2. [PMID: 36604437 PMCID: PMC9816110 DOI: 10.1038/s41537-022-00328-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 11/16/2022] [Indexed: 01/07/2023]
Abstract
Schizophrenia is a chronic brain disorder, and neuroimaging abnormalities have been reported in different stages of the illness for decades. However, when and how these brain abnormalities occur and evolve remains undetermined. We hypothesized structural and functional brain abnormalities progress throughout the illness course at different rates in schizophrenia. A total of 115 patients with schizophrenia were recruited and stratified into three groups of different illness periods: 5-year group (illness duration: ≤5 years), 15-year group (illness duration: 12-18 years), and 25-year group (illness duration: ≥25 years); 230 healthy controls were matched by age and sex to the three groups, respectively. All participants underwent resting-state MRI scanning. Each group of patients with schizophrenia was compared with the corresponding controls in terms of voxel-based morphometry (VBM), fractional anisotropy (FA), global functional connectivity density (gFCD), and sample entropy (SampEn) abnormalities. In the 5-year group we observed only SampEn abnormalities in the putamen. In the 15-year group, we observed VBM abnormalities in the insula and cingulate gyrus and gFCD abnormalities in the temporal cortex. In the 25-year group, we observed FA abnormalities in nearly all white matter tracts, and additional VBM and gFCD abnormalities in the frontal cortex and cerebellum. By using two structural and two functional MRI analysis methods, we demonstrated that individual functional abnormalities occur in limited brain areas initially, functional connectivity and gray matter density abnormalities ensue later in wider brain areas, and structural connectivity abnormalities involving almost all white matter tracts emerge in the third decade of the course in schizophrenia.
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Ji GJ, Zalesky A, Wang Y, He K, Wang L, Du R, Sun J, Bai T, Chen X, Tian Y, Zhu C, Wang K. Linking Personalized Brain Atrophy to Schizophrenia Network and Treatment Response. Schizophr Bull 2023; 49:43-52. [PMID: 36318234 PMCID: PMC9810021 DOI: 10.1093/schbul/sbac162] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND HYPOTHESIS Schizophrenia manifests with marked heterogeneity in both clinical presentation and underlying biology. Modeling individual differences within clinical cohorts is critical to translate knowledge reliably into clinical practice. We hypothesized that individualized brain atrophy in patients with schizophrenia may explain the heterogeneous outcomes of repetitive transcranial magnetic stimulation (rTMS). STUDY DESIGN The magnetic resonance imaging (MRI) data of 797 healthy subjects and 91 schizophrenia patients (between January 1, 2015, and December 31, 2020) were retrospectively selected from our hospital database. The healthy subjects were used to establish normative reference ranges for cortical thickness as a function of age and sex. Then, a schizophrenia patient's personalized atrophy map was computed as vertex-wise deviations from the normative model. Each patient's atrophy network was mapped using resting-state functional connectivity MRI from a subgroup of healthy subjects (n = 652). In total 52 of the 91 schizophrenia patients received rTMS in a randomized clinical trial (RCT). Their longitudinal symptom changes were adopted to test the clinical utility of the personalized atrophy map. RESULTS The personalized atrophy maps were highly heterogeneous across patients, but functionally converged to a putative schizophrenia network that comprised regions implicated by previous group-level findings. More importantly, retrospective analysis of rTMS-RCT data indicated that functional connectivity of the personalized atrophy maps with rTMS targets was significantly associated with the symptom outcomes of schizophrenia patients. CONCLUSIONS Normative modeling can aid in mapping the personalized atrophy network associated with treatment outcomes of patients with schizophrenia.
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Affiliation(s)
- Gong-Jun Ji
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 230032, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province, 230032, China
- Anhui Institute of Translational Medicine, Hefei, 230032, China
| | - Andrew Zalesky
- Departments of Psychiatry and Biomedical Engineering, Melbourne Neuropsychiatry Centre, The University of Melbourne, Victoria, 3010, Australia
| | - Yingru Wang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 230032, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province, 230032, China
| | - Kongliang He
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province, 230032, China
- Anhui Institute of Translational Medicine, Hefei, 230032, China
- Department of Psychiatry, Anhui Mental Health Center, Hefei, 230022, China
| | - Lu Wang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 230032, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province, 230032, China
| | - Rongrong Du
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 230032, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province, 230032, China
| | - Jinmei Sun
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 230032, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province, 230032, China
| | - Tongjian Bai
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 230032, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province, 230032, China
| | - Xingui Chen
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 230032, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province, 230032, China
| | - Yanghua Tian
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 230032, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province, 230032, China
- Anhui Institute of Translational Medicine, Hefei, 230032, China
| | - Chunyan Zhu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 230032, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province, 230032, China
- Anhui Institute of Translational Medicine, Hefei, 230032, China
| | - Kai Wang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 230032, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province, 230032, China
- Anhui Institute of Translational Medicine, Hefei, 230032, China
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26
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Schmitt JE, DeBevits JJ, Roalf DR, Ruparel K, Gallagher RS, Gur RC, Alexander-Bloch A, Eom TY, Alam S, Steinberg J, Akers W, Khairy K, Crowley TB, Emanuel B, Zakharenko SS, McDonald-McGinn DM, Gur RE. A Comprehensive Analysis of Cerebellar Volumes in the 22q11.2 Deletion Syndrome. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:79-90. [PMID: 34848384 PMCID: PMC9162086 DOI: 10.1016/j.bpsc.2021.11.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 10/12/2021] [Accepted: 11/08/2021] [Indexed: 01/17/2023]
Abstract
BACKGROUND The presence of a 22q11.2 microdeletion (22q11.2 deletion syndrome [22q11DS]) ranks among the greatest known genetic risk factors for the development of psychotic disorders. There is emerging evidence that the cerebellum is important in the pathophysiology of psychosis. However, there is currently limited information on cerebellar neuroanatomy in 22q11DS specifically. METHODS High-resolution 3T magnetic resonance imaging was acquired in 79 individuals with 22q11DS and 70 typically developing control subjects (N = 149). Lobar and lobule-level cerebellar volumes were estimated using validated automated segmentation algorithms, and subsequently group differences were compared. Hierarchical clustering, principal component analysis, and graph theoretical models were used to explore intercerebellar relationships. Cerebrocerebellar structural connectivity with cortical thickness was examined via linear regression models. RESULTS Individuals with 22q11DS had, on average, 17.3% smaller total cerebellar volumes relative to typically developing subjects (p < .0001). The lobules of the superior posterior cerebellum (e.g., VII and VIII) were particularly affected in 22q11DS. However, all cerebellar lobules were significantly smaller, even after adjusting for total brain volumes (all cerebellar lobules p < .0002). The superior posterior lobule was disproportionately associated with cortical thickness in the frontal lobes and cingulate cortex, brain regions known be affected in 22q11DS. Exploratory analyses suggested that the superior posterior lobule, particularly Crus I, may be associated with psychotic symptoms in 22q11DS. CONCLUSIONS The cerebellum is a critical but understudied component of the 22q11DS neuroendophenotype.
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Affiliation(s)
- J Eric Schmitt
- Brain Behavior Laboratory, Neurodevelopment and Psychosis Section, Department of Psychiatry, Philadelphia, Pennsylvania; Division of Neuroradiology, Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania.
| | - John J DeBevits
- Division of Neuroradiology, Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania
| | - David R Roalf
- Brain Behavior Laboratory, Neurodevelopment and Psychosis Section, Department of Psychiatry, Philadelphia, Pennsylvania
| | - Kosha Ruparel
- Brain Behavior Laboratory, Neurodevelopment and Psychosis Section, Department of Psychiatry, Philadelphia, Pennsylvania
| | - R Sean Gallagher
- Brain Behavior Laboratory, Neurodevelopment and Psychosis Section, Department of Psychiatry, Philadelphia, Pennsylvania
| | - Ruben C Gur
- Brain Behavior Laboratory, Neurodevelopment and Psychosis Section, Department of Psychiatry, Philadelphia, Pennsylvania
| | - Aaron Alexander-Bloch
- Brain Behavior Laboratory, Neurodevelopment and Psychosis Section, Department of Psychiatry, Philadelphia, Pennsylvania
| | - Tae-Yeon Eom
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Shahinur Alam
- Center for Bioimage Informatics, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Jeffrey Steinberg
- Center for Bioimage Informatics, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Walter Akers
- Center for Bioimage Informatics, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Khaled Khairy
- Center for In Vivo Imaging and Therapeutics, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - T Blaine Crowley
- Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Beverly Emanuel
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Stanislav S Zakharenko
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Donna M McDonald-McGinn
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Raquel E Gur
- Brain Behavior Laboratory, Neurodevelopment and Psychosis Section, Department of Psychiatry, Philadelphia, Pennsylvania; Division of Neuroradiology, Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania
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27
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Rootes-Murdy K, Edmond JT, Jiang W, Rahaman MA, Chen J, Perrone-Bizzozero NI, Calhoun VD, van Erp TGM, Ehrlich S, Agartz I, Jönsson EG, Andreassen OA, Westlye LT, Wang L, Pearlson GD, Glahn DC, Hong E, Buchanan RW, Kochunov P, Voineskos A, Malhotra A, Tamminga CA, Liu J, Turner JA. Clinical and cortical similarities identified between bipolar disorder I and schizophrenia: A multivariate approach. Front Hum Neurosci 2022; 16:1001692. [PMID: 36438633 PMCID: PMC9684186 DOI: 10.3389/fnhum.2022.1001692] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Accepted: 10/17/2022] [Indexed: 01/06/2024] Open
Abstract
BACKGROUND Structural neuroimaging studies have identified similarities in the brains of individuals diagnosed with schizophrenia (SZ) and bipolar I disorder (BP), with overlap in regions of gray matter (GM) deficits between the two disorders. Recent studies have also shown that the symptom phenotypes associated with SZ and BP may allow for a more precise categorization than the current diagnostic criteria. In this study, we sought to identify GM alterations that were unique to each disorder and whether those alterations were also related to unique symptom profiles. MATERIALS AND METHODS We analyzed the GM patterns and clinical symptom presentations using independent component analysis (ICA), hierarchical clustering, and n-way biclustering in a large (N ∼ 3,000), merged dataset of neuroimaging data from healthy volunteers (HV), and individuals with either SZ or BP. RESULTS Component A showed a SZ and BP < HV GM pattern in the bilateral insula and cingulate gyrus. Component B showed a SZ and BP < HV GM pattern in the cerebellum and vermis. There were no significant differences between diagnostic groups in these components. Component C showed a SZ < HV and BP GM pattern bilaterally in the temporal poles. Hierarchical clustering of the PANSS scores and the ICA components did not yield new subgroups. N-way biclustering identified three unique subgroups of individuals within the sample that mapped onto different combinations of ICA components and symptom profiles categorized by the PANSS but no distinct diagnostic group differences. CONCLUSION These multivariate results show that diagnostic boundaries are not clearly related to structural differences or distinct symptom profiles. Our findings add support that (1) BP tend to have less severe symptom profiles when compared to SZ on the PANSS without a clear distinction, and (2) all the gray matter alterations follow the pattern of SZ < BP < HV without a clear distinction between SZ and BP.
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Affiliation(s)
- Kelly Rootes-Murdy
- Department of Psychology, Georgia State University, Atlanta, GA, United States
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Jesse T. Edmond
- Department of Psychology, Georgia State University, Atlanta, GA, United States
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Wenhao Jiang
- Department of Psychosomatics and Psychiatry, Medical School, Zhongda Hospital, Institute of Psychosomatics, Southeast University, Nanjing, China
| | - Md A. Rahaman
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Jiayu Chen
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | | | - Vince D. Calhoun
- Department of Psychology, Georgia State University, Atlanta, GA, United States
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Theo G. M. van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, United States
- Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, CA, United States
| | - Stefan Ehrlich
- Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, TU Dresden, Dresden, Germany
| | - Ingrid Agartz
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo, Norway
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institute and Stockholm Health Care Services, Stockholm, Sweden
- K. G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Erik G. Jönsson
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo, Norway
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institute and Stockholm Health Care Services, Stockholm, Sweden
| | - Ole A. Andreassen
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo, Norway
- K. G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Lars T. Westlye
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo, Norway
- K. G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Lei Wang
- Psychiatry and Behavioral Health, Ohio State Wexner Medical Center, Columbus, OH, United States
| | - Godfrey D. Pearlson
- Department of Psychiatry, Yale University, New Haven, CT, United States
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford Hospital, Hartford, CT, United States
| | - David C. Glahn
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford Hospital, Hartford, CT, United States
- Boston Children’s Hospital and Harvard Medical School, Boston, MA, United States
| | - Elliot Hong
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Robert W. Buchanan
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Peter Kochunov
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Aristotle Voineskos
- Department of Psychiatry, Centre for Addiction and Mental Health, University of Toronto, Toronto, ON, Canada
| | - Anil Malhotra
- Division of Psychiatry Research, Zucker Hillside Hospital, Queens, NY, United States
| | - Carol A. Tamminga
- Department of Psychiatry, University of Texas Southwestern Medical School, Dallas, TX, United States
| | - Jingyu Liu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Jessica A. Turner
- Psychiatry and Behavioral Health, Ohio State Wexner Medical Center, Columbus, OH, United States
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28
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Abram SV, Hua JPY, Ford JM. Consider the pons: bridging the gap on sensory prediction abnormalities in schizophrenia. Trends Neurosci 2022; 45:798-808. [PMID: 36123224 PMCID: PMC9588719 DOI: 10.1016/j.tins.2022.08.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 08/04/2022] [Accepted: 08/23/2022] [Indexed: 01/18/2023]
Abstract
A shared mechanism across species heralds the arrival of self-generated sensations, helping the brain to anticipate, and therefore distinguish, self-generated from externally generated sensations. In mammals, this sensory prediction mechanism is supported by communication within a cortico-ponto-cerebellar-thalamo-cortical loop. Schizophrenia is associated with impaired sensory prediction as well as abnormal structural and functional connections between nodes in this circuit. Despite the pons' principal role in relaying and processing sensory information passed from the cortex to cerebellum, few studies have examined pons connectivity in schizophrenia. Here, we first briefly describe how the pons contributes to sensory prediction. We then summarize schizophrenia-related abnormalities in the cortico-ponto-cerebellar-thalamo-cortical loop, emphasizing the dearth of research on the pons relative to thalamic and cerebellar connections. We conclude with recommendations for advancing our understanding of how the pons relates to sensory prediction failures in schizophrenia.
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Affiliation(s)
- Samantha V Abram
- San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA; University of California, San Francisco, CA, USA
| | - Jessica P Y Hua
- San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA; University of California, San Francisco, CA, USA; Sierra Pacific Mental Illness Research Education and Clinical Centers, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA; Department of Psychiatry and Behavioral Sciences, The University of California, San Francisco, CA, USA
| | - Judith M Ford
- San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA; University of California, San Francisco, CA, USA.
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29
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Premi E, Cotelli M, Gobbi E, Pagnoni I, Binetti G, Gadola Y, Libri I, Mattioli I, Pengo M, Iraji A, Calhoun VD, Alberici A, Borroni B, Manenti R. Neuroanatomical correlates of screening for aphasia in NeuroDegeneration (SAND) battery in non-fluent/agrammatic variant of primary progressive aphasia. Front Aging Neurosci 2022; 14:942095. [PMID: 36389058 PMCID: PMC9660243 DOI: 10.3389/fnagi.2022.942095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 10/11/2022] [Indexed: 06/04/2024] Open
Abstract
Background Non-fluent/agrammatic variant of Primary Progressive Aphasia (avPPA) is primarily characterized by language impairment due to atrophy of the inferior frontal gyrus and the insula cortex in the dominant hemisphere. The Screening for Aphasia in NeuroDegeneration (SAND) battery has been recently proposed as a screening tool for PPA, with several tasks designed to be specific for different language features. Applying multivariate approaches to neuroimaging data and verbal fluency tasks, Aachener Aphasie Test (AAT) naming subtest and SAND data may help in elucidating the neuroanatomical correlates of language deficits in avPPA. Objective To investigate the neuroanatomical correlates of language deficits in avPPA using verbal fluency tasks, AAT naming subtest and SAND scores as proxies of brain structural imaging abnormalities. Methods Thirty-one avPPA patients were consecutively enrolled and underwent extensive neuropsychological assessment and MRI scan. Raw scores of verbal fluency tasks, AAT naming subtest, and SAND subtests, namely living and non-living picture naming, auditory sentence comprehension, single-word comprehension, words and non-words repetition and sentence repetition, were used as proxies to explore structural (gray matter volume) neuroanatomical correlates. We assessed univariate (voxel-based morphometry, VBM) as well as multivariate (source-based morphometry, SBM) approaches. Age, gender, educational level, and disease severity were considered nuisance variables. Results SAND picture naming (total, living and non-living scores) and AAT naming scores showed a direct correlation with the left temporal network derived from SBM. At univariate analysis, the left middle temporal gyrus was directly correlated with SAND picture naming (total and non-living scores) and AAT naming score. When words and non-words repetition (total score) was considered, a direct correlation with the left temporal network (SBM) and with the left fusiform gyrus (VBM) was also evident. Conclusion Naming impairments that characterize avPPA are related to specific network-based involvement of the left temporal network, potentially expanding our knowledge on the neuroanatomical basis of this neurodegenerative condition.
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Affiliation(s)
- Enrico Premi
- Stroke Unit, Azienda Socio Sanitaria Territoriale Spedali Civili Brescia, Brescia, Italy
| | - Maria Cotelli
- Neuropsychology Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Elena Gobbi
- Neuropsychology Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Ilaria Pagnoni
- Neuropsychology Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Giuliano Binetti
- MAC Memory Clinic and Molecular Markers Laboratory, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Yasmine Gadola
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Owensboro, Italy
| | - Ilenia Libri
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Owensboro, Italy
| | - Irene Mattioli
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Owensboro, Italy
| | - Marta Pengo
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Owensboro, Italy
| | - Armin Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
- Departments of Psychology and Computer Science, Georgia State University, Atlanta, GA, United States
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
- Departments of Psychology and Computer Science, Georgia State University, Atlanta, GA, United States
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Antonella Alberici
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Owensboro, Italy
| | - Barbara Borroni
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Owensboro, Italy
| | - Rosa Manenti
- Neuropsychology Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
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Prognostic associations of cortical gyrification in minimally medicated schizophrenia in an early intervention setting. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2022; 8:88. [PMID: 36309534 PMCID: PMC9617870 DOI: 10.1038/s41537-022-00296-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 10/03/2022] [Indexed: 12/03/2022]
Abstract
The aberration in cortical gyrification seen in schizophrenia likely originates in the earliest phase of life, as gyrification begins in utero and reaches its peak in infancy. However, emerging observations have indicated a later reduction in gyrification, especially in early adulthood, may also occur in schizophrenia. At present, it is unclear whether the baseline and later gyrification reduction has any prognostic importance in schizophrenia. We address this question in a longitudinal design in patients minimally medicated at inception. About 108 minimally medicated (duration of medication = <14 days of antipsychotics) patients and 106 healthy controls underwent structural magnetic resonance imaging, with 34 patients being selectively re-scanned when clinically stable following antipsychotic treatment. The cortical surface from each structural image was reconstructed, and the local gyrification index and cortical thickness were computed for each vertex on the surface. We found minimally medicated schizophrenia patients during the first episode had a relatively higher gyrification in bilateral supramarginal, left superior temporal, and right posterior cingulate and paracentral regions. However, poor prognostic features were more likely in patients with lower baseline gyrification. Longitudinal reductions in left superior parietal and right precentral gyrification were associated with lower improvements in both positive and negative symptoms over time. The spatial pattern of longitudinal changes in gyrification was distinct from the changes in cortical thickness. These results indicated that schizophrenia is characterized by a relative hypergyrification in parieto-temporal and medial cortical areas at a group level at first presentation, but poor outcomes relate to lower-gyrification elsewhere both at the onset and during the early course. The early post-onset reduction of gyrification is rather limited in space and magnitude, but occurs unrelated to the progressive thinning, representing a distinct, prognostically important structural trajectory.
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31
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Kong L, Lui SSY, Wang Y, Hung KSY, Ho KKH, Wang Y, Huang J, Mak HKF, Sham PC, Cheung EFC, Chan RCK. Structural network alterations and their association with neurological soft signs in schizophrenia: Evidence from clinical patients and unaffected siblings. Schizophr Res 2022; 248:345-352. [PMID: 34872833 DOI: 10.1016/j.schres.2021.11.042] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 09/24/2021] [Accepted: 11/26/2021] [Indexed: 11/16/2022]
Abstract
BACKGROUND Grey matter abnormalities and neurological soft signs (NSS) have been found in schizophrenia patients and their unaffected relatives. Evidence suggested that NSS are associated with grey matter morphometrical alterations in multiple regions in schizophrenia. However, the association between NSS and structural abnormalities at network level remains largely unexplored, especially in the schizophrenia and unaffected siblings. METHOD We used source-based morphometry (SBM) to examine the association of structural brain network characteristics with NSS in 62 schizophrenia patients, 25 unaffected siblings, and 60 healthy controls. RESULTS Two components, namely the IC-5 (superior temporal gyrus, inferior frontal gyrus and insula network) and the IC-10 (parahippocampal gyrus, fusiform, thalamus and insula network) showed significant grey matter reductions in schizophrenia patients compared to healthy controls and unaffected siblings. Further association analysis demonstrated separate NSS-related grey matter covarying patterns in schizophrenia, unaffected siblings and healthy controls. Specifically, NSS were negatively associated with IC-1 (hippocampus, caudate and thalamus network) and IC-5 in schizophrenia, but with IC-3 (caudate, superior and middle frontal cortices network) in unaffected siblings and with IC-5 in healthy controls. CONCLUSION Our results confirmed the key cortical and subcortical network abnormalities and NSS-related grey matter covarying patterns in the schizophrenia and unaffected siblings. Our findings suggest that brain regions implicating genetic liability to schizophrenia are partly separated from brain regions implicating neural abnormalities.
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Affiliation(s)
- Li Kong
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; Department of Psychology, Shanghai Normal University, Shanghai, China
| | - Simon S Y Lui
- Department of Psychiatry, The University of Hong Kong, Hong Kong, China; Castle Peak Hospital, Hong Kong, China
| | - Ya 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, the University of Chinese Academy of Sciences, Beijing, 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, the University of Chinese Academy of Sciences, Beijing, China
| | - Jia Huang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; Department of Psychology, the University of Chinese Academy of Sciences, Beijing, China
| | - Henry K F Mak
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China; State Key Laboratory of Brain and Cognitive Sciences, the University of Hong Kong, Hong Kong, China
| | - Pak C Sham
- Department of Psychiatry, The University of Hong Kong, Hong Kong, China; State Key Laboratory of Brain and Cognitive Sciences, the University of Hong Kong, Hong Kong, China; Centre for PanorOmic Sciences, the University of Hong Kong, Hong Kong, China
| | | | - Raymond C K Chan
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; Department of Psychology, the University of Chinese Academy of Sciences, Beijing, China.
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Cattarinussi G, Kubera KM, Hirjak D, Wolf RC, Sambataro F. Neural Correlates of the Risk for Schizophrenia and Bipolar Disorder: A Meta-analysis of Structural and Functional Neuroimaging Studies. Biol Psychiatry 2022; 92:375-384. [PMID: 35523593 DOI: 10.1016/j.biopsych.2022.02.960] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 01/28/2022] [Accepted: 02/23/2022] [Indexed: 11/19/2022]
Abstract
BACKGROUND Clinical features and genetics overlap in schizophrenia (SCZ) and bipolar disorder (BD). Identifying brain alterations associated with genetic vulnerability for SCZ and BD could help to discover intermediate phenotypes, quantifiable biological traits with greater prevalence in unaffected relatives (RELs), and early recognition biomarkers in ultrahigh risk populations. However, a comprehensive meta-analysis of structural and functional magnetic resonance imaging (MRI) studies examining relatives of patients with SCZ and BD has not been performed yet. METHODS We systematically searched PubMed, Scopus, and Web of Science for structural and functional MRI studies investigating relatives and healthy control subjects. A total of 230 eligible neuroimaging studies (6274 SCZ-RELs, 1900 BD-RELs, 10,789 healthy control subjects) were identified. We conducted coordinate-based activation likelihood estimation meta-analyses on 26 structural MRI and 81 functional MRI investigations, including stratification by task type. We also meta-analyzed regional and global volumetric changes. Finally, we performed a meta-analysis of all MRI studies combined. RESULTS Reduced thalamic volume was present in both SCZ and BD RELs. Moreover, SCZ-RELs showed alterations in corticostriatal-thalamic networks, spanning the dorsolateral prefrontal cortex and temporal regions, while BD-RELs showed altered thalamocortical and limbic regions, including the ventrolateral prefrontal, superior parietal, and medial temporal cortices, with frontoparietal alterations in RELs of BD type I. CONCLUSIONS Familiarity for SCZ and BD is associated with alterations in the thalamocortical circuits, which may be the expression of the shared genetic mechanism underlying both disorders. Furthermore, the involvement of different prefrontocortical and temporal nodes may be associated with a differential symptom expression in the two disorders.
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Affiliation(s)
- Giulia Cattarinussi
- Department of Neuroscience, Università degli studi di Padova, Padova, Italy; Padova Neuroscience Center, Università degli studi di Padova, Padova, Italy
| | - Katharina M Kubera
- Department of General Psychiatry, Center for Psychosocial Medicine, Heidelberg University, Heidelberg, Germany
| | - Dusan Hirjak
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Robert C Wolf
- Department of General Psychiatry, Center for Psychosocial Medicine, Heidelberg University, Heidelberg, Germany
| | - Fabio Sambataro
- Department of Neuroscience, Università degli studi di Padova, Padova, Italy; Padova Neuroscience Center, Università degli studi di Padova, Padova, Italy.
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Shi W, Fan L, Wang H, Liu B, Li W, Li J, Cheng L, Chu C, Song M, Sui J, Luo N, Cui Y, Dong Z, Lu Y, Ma Y, Ma L, Li K, Chen J, Chen Y, Guo H, Li P, Lu L, Lv L, Wan P, Wang H, Wang H, Yan H, Yan J, Yang Y, Zhang H, Zhang D, Jiang T. Two subtypes of schizophrenia identified by an individual-level atypical pattern of tensor-based morphometric measurement. Cereb Cortex 2022; 33:3683-3700. [PMID: 36005854 DOI: 10.1093/cercor/bhac301] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 07/11/2022] [Accepted: 07/12/2022] [Indexed: 11/12/2022] Open
Abstract
Difficulties in parsing the multiaspect heterogeneity of schizophrenia (SCZ) based on current nosology highlight the need to subtype SCZ using objective biomarkers. Here, utilizing a large-scale multisite SCZ dataset, we identified and validated 2 neuroanatomical subtypes with individual-level abnormal patterns of the tensor-based morphometric measurement. Remarkably, compared with subtype 1, which showed moderate deficits of some subcortical nuclei and an enlarged striatum and cerebellum, subtype 2, which showed cerebellar atrophy and more severe subcortical nuclei atrophy, had a higher subscale score of negative symptoms, which is considered to be a core aspect of SCZ and is associated with functional outcome. Moreover, with the neuroimaging-clinic association analysis, we explored the detailed relationship between the heterogeneity of clinical symptoms and the heterogeneous abnormal neuroanatomical patterns with respect to the 2 subtypes. And the neuroimaging-transcription association analysis highlighted several potential heterogeneous biological factors that may underlie the subtypes. Our work provided an effective framework for investigating the heterogeneity of SCZ from multilevel aspects and may provide new insights for precision psychiatry.
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Affiliation(s)
- Weiyang Shi
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lingzhong Fan
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China.,Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Haiyan Wang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, 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
| | - Wen Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jin Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Luqi Cheng
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China
| | - Congying Chu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Ming Song
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Na Luo
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Yue Cui
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhenwei Dong
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yuheng Lu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yawei Ma
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,Sino-Danish College, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Liang Ma
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Kaixin Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Jun Chen
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Yunchun Chen
- Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University, Xi'an 710032, China
| | - Hua Guo
- Zhumadian Psychiatric Hospital, Zhumadian 463000, China
| | - Peng Li
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China.,Key Laboratory of Mental Health, Ministry of Health, National Clinical Research Center for Mental Disorders, Peking University, Beijing 100191, China
| | - Lin Lu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China.,Key Laboratory of Mental Health, Ministry of Health, National Clinical Research Center for Mental Disorders, Peking University, Beijing 100191, 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 of Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China
| | - Ping Wan
- Zhumadian Psychiatric Hospital, Zhumadian 463000, China
| | - Huaning Wang
- Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University, Xi'an 710032, China
| | - Huiling Wang
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan 430060, China.,Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Hao Yan
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China.,Key Laboratory of Mental Health, Ministry of Health, National Clinical Research Center for Mental Disorders, Peking University, Beijing 100191, China
| | - Jun Yan
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China.,Key Laboratory of Mental Health, Ministry of Health, National Clinical Research Center for Mental Disorders, Peking University, Beijing 100191, China
| | - Yongfeng Yang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China.,Henan Key Lab of Biological Psychiatry of Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China
| | - Hongxing Zhang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China.,Henan Key Lab of Biological Psychiatry of Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China.,Department of Psychology, Xinxiang Medical University, Xinxiang 453002, China
| | - Dai Zhang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China.,Key Laboratory of Mental Health, Ministry of Health, National Clinical Research Center for Mental Disorders, Peking University, Beijing 100191, China.,Center for Life Sciences/PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100191, China
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China.,Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China.,Innovation Academy for Artificial Intelligence, Chinese Academy of Sciences, Beijing 100190, China
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Benussi A, Libri I, Premi E, Alberici A, Cantoni V, Gadola Y, Rivolta J, Pengo M, Gazzina S, Calhoun VD, Gasparotti R, Zetterberg H, Ashton NJ, Blennow K, Padovani A, Borroni B. Differences and similarities between familial and sporadic frontotemporal dementia: An Italian single-center cohort study. ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2022; 8:e12326. [PMID: 35898667 PMCID: PMC9310192 DOI: 10.1002/trc2.12326] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 05/11/2022] [Accepted: 06/17/2022] [Indexed: 06/15/2023]
Abstract
Introduction The possibility to generalize our understandings on treatments and assessments to both familial frontotemporal dementia (f-FTD) and sporadic FTD (s-FTD) is a fundamental perspective for the near future, considering the constant advancement in potential disease-modifying therapies that target particular genetic forms of FTD. We aimed to investigate differences in clinical features, cerebrospinal fluid (CSF), and blood-based biomarkers between f-FTD and s-FTD. Methods In this longitudinal cohort study, we evaluated a consecutive sample of symptomatic FTD patients, classified as f-FTD and s-FTD according to Goldman scores (GS). All patients underwent clinical, behavioral, and neuropsychiatric symptom assessment, CSF biomarkers and serum neurofilament light (NfL) analysis, and brain atrophy evaluation with magnetic resonance imaging. Results Of 570 patients with FTD, 123 were classified as f-FTD, and 447 as s-FTD. In the f-FTD group, 95 had a pathogenic FTD mutation while 28 were classified as GS = 1 or 2; of the s-FTD group, 133 were classified as GS = 3 and 314 with GS = 4. f-FTD and s-FTD cases showed comparable demographic features, except for younger age at disease onset, age at diagnosis, and higher years of education in the f-FTD group (all P < .05). f-FTD showed worse behavioral disturbances as measured with Frontal Behavioral Inventory (FBI) negative behaviors (14.0 ± 7.6 vs. 11.6 ± 7.4, P = .002), and positive behaviors (20.0 ± 11.0 vs. 17.4 ± 11.8, P = .031). Serum NfL concentrations were higher in patients with f-FTD (70.9 ± 37.9 pg/mL) compared to s-FTD patients (37.3 ± 24.2 pg/mL, P < .001), and f-FTD showed greater brain atrophy in the frontal and temporal regions and basal ganglia. Patients with f-FTD had significantly shorter survival than those with s-FTD (P = .004). Discussion f-FTD and s-FTD are very similar clinical entities, but with different biological mechanisms, and different rates of progression. The parallel characterization of both f-FTD and s-FTD will improve our understanding of the disease, and aid in designing future clinical trials for both genetic and sporadic forms of FTD. Highlights Do clinical features and biomarkers differ between patients with familial frontotemporal dementia (f-FTD) and sporadic FTD (s-FTD)?In this cohort study of 570 patients with FTD, f-FTD and s-FTD share similar demographic features, but with younger age at disease onset and diagnosis in the f-FTD group.f-FTD showed higher serum neurofilament light concentrations, greater brain damage, and shorter survival, compared to s-FTD.f-FTD and s-FTD are very similar clinical entities, but with different cognitive reserve mechanisms and different rates of progression.
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Affiliation(s)
- Alberto Benussi
- Neurology UnitDepartment of Clinical and Experimental SciencesUniversity of BresciaBresciaItaly
- Neurology UnitDepartment of Neurological and Vision SciencesASST Spedali CiviliBresciaItaly
| | - Ilenia Libri
- Neurology UnitDepartment of Clinical and Experimental SciencesUniversity of BresciaBresciaItaly
| | - Enrico Premi
- Stroke UnitDepartment of Neurological and Vision SciencesASST Spedali CiviliBresciaItaly
| | - Antonella Alberici
- Neurology UnitDepartment of Neurological and Vision SciencesASST Spedali CiviliBresciaItaly
| | - Valentina Cantoni
- Neurology UnitDepartment of Clinical and Experimental SciencesUniversity of BresciaBresciaItaly
| | - Yasmine Gadola
- Neurology UnitDepartment of Clinical and Experimental SciencesUniversity of BresciaBresciaItaly
| | - Jasmine Rivolta
- Neurology UnitDepartment of Clinical and Experimental SciencesUniversity of BresciaBresciaItaly
| | - Marta Pengo
- Department of Molecular and Translational MedicineUniversity of BresciaBresciaItaly
| | - Stefano Gazzina
- Neurophysiology UnitDepartment of Neurological and Vision SciencesASST Spedali CiviliBresciaItaly
| | - Vince D. Calhoun
- The Mind Research NetworkDepartment of Electrical and Computer EngineeringUniversity of New MexicoAlbuquerqueNew MexicoUSA
| | | | - Henrik Zetterberg
- Institute of Neuroscience and PhysiologyDepartment of Psychiatry and NeurochemistryThe Sahlgrenska Academy at the University of GothenburgMölndalSweden
- Clinical Neurochemistry LaboratorySahlgrenska University HospitalMölndalSweden
- UK Dementia Research Institute at UCLLondonUK
- Department of Neurodegenerative DiseaseUCL Institute of NeurologyLondonUK
- Hong Kong Center for Neurodegenerative DiseasesHong KongChina
| | - Nicholas J. Ashton
- Institute of Neuroscience and PhysiologyDepartment of Psychiatry and NeurochemistryThe Sahlgrenska Academy at the University of GothenburgMölndalSweden
- Wallenberg Centre for Molecular and Translational MedicineUniversity of GothenburgMölndalSweden
- King's College LondonInstitute of PsychiatryPsychology & NeuroscienceMaurice Wohl Clinical Neuroscience InstituteLondonUK
- NIHR Biomedical Research Centre for Mental Health & Biomedical Research Unit for Dementia at South London & Maudsley NHS FoundationLondonUK
| | - Kaj Blennow
- Institute of Neuroscience and PhysiologyDepartment of Psychiatry and NeurochemistryThe Sahlgrenska Academy at the University of GothenburgMölndalSweden
- Clinical Neurochemistry LaboratorySahlgrenska University HospitalMölndalSweden
| | - Alessandro Padovani
- Neurology UnitDepartment of Clinical and Experimental SciencesUniversity of BresciaBresciaItaly
- Neurology UnitDepartment of Neurological and Vision SciencesASST Spedali CiviliBresciaItaly
| | - Barbara Borroni
- Neurology UnitDepartment of Clinical and Experimental SciencesUniversity of BresciaBresciaItaly
- Neurology UnitDepartment of Neurological and Vision SciencesASST Spedali CiviliBresciaItaly
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Correlación entre el metabolismo de la glucosa cerebral (18F-FDG) y el flujo sanguíneo cerebral con marcadores de amiloide (18F-florbetapir) en práctica clínica: evidencias preliminares. Rev Esp Med Nucl Imagen Mol 2022. [DOI: 10.1016/j.remn.2021.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Demro C, Shen C, Hendrickson TJ, Arend JL, Disner SG, Sponheim SR. Advanced Brain-Age in Psychotic Psychopathology: Evidence for Transdiagnostic Neurodevelopmental Origins. Front Aging Neurosci 2022; 14:872867. [PMID: 35527740 PMCID: PMC9074783 DOI: 10.3389/fnagi.2022.872867] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 03/11/2022] [Indexed: 11/13/2022] Open
Abstract
Schizophrenia is characterized by abnormal brain structure such as global reductions in gray matter volume. Machine learning models trained to estimate the age of brains from structural neuroimaging data consistently show advanced brain-age to be associated with schizophrenia. Yet, it is unclear whether advanced brain-age is specific to schizophrenia compared to other psychotic disorders, and whether evidence that brain structure is "older" than chronological age actually reflects neurodevelopmental rather than atrophic processes. It is also unknown whether advanced brain-age is associated with genetic liability for psychosis carried by biological relatives of people with schizophrenia. We used the Brain-Age Regression Analysis and Computation Utility Software (BARACUS) prediction model and calculated the residualized brain-age gap of 332 adults (163 individuals with psychotic disorders: 105 schizophrenia, 17 schizoaffective disorder, 41 bipolar I disorder with psychotic features; 103 first-degree biological relatives; 66 controls). The model estimated advanced brain-ages for people with psychosis in comparison to controls and relatives, with no differences among psychotic disorders or between relatives and controls. Specifically, the model revealed an enlarged brain-age gap for schizophrenia and bipolar disorder with psychotic features. Advanced brain-age was associated with lower cognitive and general functioning in the full sample. Among relatives, cognitive performance and schizotypal symptoms were related to brain-age gap, suggesting that advanced brain-age is associated with the subtle expressions associated with psychosis. Exploratory longitudinal analyses suggested that brain aging was not accelerated in individuals with a psychotic disorder. In sum, we found that people with psychotic disorders, irrespective of specific diagnosis or illness severity, show indications of non-progressive, advanced brain-age. These findings support a transdiagnostic, neurodevelopmental formulation of structural brain abnormalities in psychotic psychopathology.
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Affiliation(s)
- Caroline Demro
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, United States
- Department of Psychology, University of Minnesota, Minneapolis, MN, United States
| | - Chen Shen
- Department of Psychology, University of Minnesota, Minneapolis, MN, United States
| | | | - Jessica L. Arend
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, United States
- Department of Psychology, University of Minnesota, Minneapolis, MN, United States
| | - Seth G. Disner
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, United States
- Minneapolis Veterans Affairs Health Care System, Minneapolis, MN, United States
| | - Scott R. Sponheim
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, United States
- Department of Psychology, University of Minnesota, Minneapolis, MN, United States
- Minneapolis Veterans Affairs Health Care System, Minneapolis, MN, United States
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Penzel N, Sanfelici R, Antonucci LA, Betz LT, Dwyer D, Ruef A, Cho KIK, Cumming P, Pogarell O, Howes O, Falkai P, Upthegrove R, Borgwardt S, Brambilla P, Lencer R, Meisenzahl E, Schultze-Lutter F, Rosen M, Lichtenstein T, Kambeitz-Ilankovic L, Ruhrmann S, Salokangas RKR, Pantelis C, Wood SJ, Quednow BB, Pergola G, Bertolino A, Koutsouleris N, Kambeitz J, Dwyer D, Ruef A, Kambeitz-Ilankovic L, Sen Dong M, Erkens A, Gussmann E, Haas S, Hasan A, Hoff C, Khanyaree I, Melo A, Muckenhuber-Sternbauer S, Kohler J, Ozturk OF, Popovic D, Rangnick A, von Saldern S, Sanfelici R, Spangemacher M, Tupac A, Urquijo MF, Weiske J, Wosgien A, Kambeitz J, Ruhrmann S, Rosen M, Betz L, Lichtenstein T, Blume K, Seves M, Kaiser N, Penzel N, Pilgram T, Lichtenstein T, Wenzel J, Woopen C, Borgwardt S, Andreou C, Egloff L, Harrisberger F, Lenz C, Leanza L, Mackintosh A, Smieskova R, Studerus E, Walter A, Widmayer S, Upthegrove R, Wood SJ, Chisholm K, Day C, Griffiths SL, Lalousis PA, Iqbal M, Pelton M, Mallikarjun P, Stainton A, Lin A, Salokangas RKR, Denissoff A, Ellila A, From T, Heinimaa M, Ilonen T, Jalo P, Laurikainen H, Lehtinen M, Luutonen A, Makela A, Paju J, Pesonen H, Armio Säilä RL, Sormunen E, Toivonen A, Turtonen O, Solana AB, Abraham M, Hehn N, Schirmer T, Brambilla P, Altamura C, Belleri M, Bottinelli F, Ferro A, Re M, Monzani E, Percudani M, Sberna M, D’Agostino A, Del Fabro L, Perna G, Nobile M, Alciati A, Balestrieri M, Bonivento C, Cabras G, Fabbro F, Garzitto M, PiCCuin S, Bertolino A, Blasi G, Antonucci LA, Pergola G, Caforio G, Faio L, Quarto T, Gelao B, Romano R, Andriola I, Falsetti A, Barone M, Passatiore R, Sangiuliano M, Lencer R, Surman M, Bienek O, Romer G, Dannlowski U, Meisenzahl E, Schultze-Lutter F, Schmidt-Kraepelin C, Neufang S, Korda A, Rohner H. Pattern of predictive features of continued cannabis use in patients with recent-onset psychosis and clinical high-risk for psychosis. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2022; 8:19. [PMID: 35264631 PMCID: PMC8907166 DOI: 10.1038/s41537-022-00218-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 01/31/2022] [Indexed: 11/09/2022]
Abstract
Continued cannabis use (CCu) is an important predictor for poor long-term outcomes in psychosis and clinically high-risk patients, but no generalizable model has hitherto been tested for its ability to predict CCu in these vulnerable patient groups. In the current study, we investigated how structured clinical and cognitive assessments and structural magnetic resonance imaging (sMRI) contributed to the prediction of CCu in a group of 109 patients with recent-onset psychosis (ROP). We tested the generalizability of our predictors in 73 patients at clinical high-risk for psychosis (CHR). Here, CCu was defined as any cannabis consumption between baseline and 9-month follow-up, as assessed in structured interviews. All patients reported lifetime cannabis use at baseline. Data from clinical assessment alone correctly classified 73% (p < 0.001) of ROP and 59 % of CHR patients. The classifications of CCu based on sMRI and cognition were non-significant (ps > 0.093), and their addition to the interview-based predictor via stacking did not improve prediction significantly, either in the ROP or CHR groups (ps > 0.065). Lower functioning, specific substance use patterns, urbanicity and a lack of other coping strategies contributed reliably to the prediction of CCu and might thus represent important factors for guiding preventative efforts. Our results suggest that it may be possible to identify by clinical measures those psychosis-spectrum patients at high risk for CCu, potentially allowing to improve clinical care through targeted interventions. However, our model needs further testing in larger samples including more diverse clinical populations before being transferred into clinical practice.
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Affiliation(s)
- Nora Penzel
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Psychiatry and Psychotherapy, Cologne, Germany.,Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.,Group of Psychiatric Neuroscience, Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari 'Aldo Moro', Bari, Italy
| | - Rachele Sanfelici
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.,Max-Planck Institute of Psychiatry, Munich, Germany
| | - Linda A Antonucci
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.,Department of Education, Psychology, Communication, University of Bari, Bari, Italy
| | - Linda T Betz
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Psychiatry and Psychotherapy, Cologne, Germany
| | - Dominic Dwyer
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany
| | - Anne Ruef
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany
| | - Kang Ik K Cho
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Paul Cumming
- Department of Nuclear Medicine, Bern University Hospital, Bern, Switzerland.,School of Psychology and Counselling, Queensland University of Technology, Brisbane, QLD, Australia.,International Research Lab in Neuropsychiatry, Neuroscience Research Institute, Samara State Medical University, Samara, Russia
| | - Oliver Pogarell
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany
| | - Oliver Howes
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London, SE5 8AF, UK.,MRC London Institute of Medical Sciences, Hammersmith Hospital, London, W12 0NN, UK.,Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, W12 0NN, UK.,South London and Maudsley NHS Foundation Trust, London, SE5 8AF, UK
| | - Peter Falkai
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.,Max-Planck Institute of Psychiatry, Munich, Germany
| | - Rachel Upthegrove
- Institute for Mental Health, University of Birmingham, Birmingham, UK.,Early Intervention Service, Birmingham Womens and Childrens NHS Foundation Trust, Birmingham, UK
| | - Stefan Borgwardt
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland.,Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
| | - Paolo Brambilla
- Department of Neurosciences and Mental Health, Fondazione IRCCUS Ca' Granda Ospedale Maggiore Policlinico, University of Milan, Milan, Italy.,Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Rebekka Lencer
- Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany.,Department of Psychiatry and Psychotherapy, University of Münster, Münster, Germany.,Otto Creutzfeldt Center for Behavioral and Cognitive Neuroscience, University of Münster, Münster, Germany
| | - Eva Meisenzahl
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany
| | - Frauke Schultze-Lutter
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany.,Department of Psychology, Faculty of Psychology, Airlangga University, Surabaya, Indonesia.,University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Marlene Rosen
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Psychiatry and Psychotherapy, Cologne, Germany
| | - Theresa Lichtenstein
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Psychiatry and Psychotherapy, Cologne, Germany
| | - Lana Kambeitz-Ilankovic
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Psychiatry and Psychotherapy, Cologne, Germany.,Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany
| | - Stephan Ruhrmann
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Psychiatry and Psychotherapy, Cologne, Germany
| | | | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, University of Melbourne & Melbourne Health, Melbourne, VIC, Australia
| | - Stephen J Wood
- Institute for Mental Health, University of Birmingham, Birmingham, UK.,Orygen, Melbourne, VIC, Australia.,Centre for Youth Mental Health, University of Melbourne, Melbourne, VIC, Australia
| | - Boris B Quednow
- Experimental and Clinical Pharmacopsychology, Department of Psychiatry, Psychotherapy, and Psychosomatics, Psychiatric Hospital of the University of Zurich, Lenggstr. 31, 8032, Zurich, Switzerland
| | - Giulio Pergola
- Group of Psychiatric Neuroscience, Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari 'Aldo Moro', Bari, Italy
| | - Alessandro Bertolino
- Group of Psychiatric Neuroscience, Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari 'Aldo Moro', Bari, Italy
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.,Max-Planck Institute of Psychiatry, Munich, Germany.,Institute of Psychiatry, Psychology & Neuroscience, Department of Psychosis Studies, King's College London, London, UK
| | - Joseph Kambeitz
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Psychiatry and Psychotherapy, Cologne, Germany.
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Liddle PF, Liddle EB. Imprecise Predictive Coding Is at the Core of Classical Schizophrenia. Front Hum Neurosci 2022; 16:818711. [PMID: 35308615 PMCID: PMC8928728 DOI: 10.3389/fnhum.2022.818711] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 02/14/2022] [Indexed: 12/23/2022] Open
Abstract
Current diagnostic criteria for schizophrenia place emphasis on delusions and hallucinations, whereas the classical descriptions of schizophrenia by Kraepelin and Bleuler emphasized disorganization and impoverishment of mental activity. Despite the availability of antipsychotic medication for treating delusions and hallucinations, many patients continue to experience persisting disability. Improving treatment requires a better understanding of the processes leading to persisting disability. We recently introduced the term classical schizophrenia to describe cases with disorganized and impoverished mental activity, cognitive impairment and predisposition to persisting disability. Recent evidence reveals that a polygenic score indicating risk for schizophrenia predicts severity of the features of classical schizophrenia: disorganization, and to a lesser extent, impoverishment of mental activity and cognitive impairment. Current understanding of brain function attributes a cardinal role to predictive coding: the process of generating models of the world that are successively updated in light of confirmation or contradiction by subsequent sensory information. It has been proposed that abnormalities of these predictive processes account for delusions and hallucinations. Here we examine the evidence provided by electrophysiology and fMRI indicating that imprecise predictive coding is the core pathological process in classical schizophrenia, accounting for disorganization, psychomotor poverty and cognitive impairment. Functional imaging reveals aberrant brain activity at network hubs engaged during encoding of predictions. We discuss the possibility that frequent prediction errors might promote excess release of the neurotransmitter, dopamine, thereby accounting for the occurrence of episodes of florid psychotic symptoms including delusions and hallucinations in classical schizophrenia. While the predictive coding hypotheses partially accounts for the time-course of classical schizophrenia, the overall body of evidence indicates that environmental factors also contribute. We discuss the evidence that chronic inflammation is a mechanism that might link diverse genetic and environmental etiological factors, and contribute to the proposed imprecision of predictive coding.
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Affiliation(s)
- Peter F. Liddle
- Centre for Translational Neuroimaging for Mental Health, School of Medicine, Institute of Mental Health, University of Nottingham, Nottingham, United Kingdom
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Oomen PP, Gangadin SS, Begemann MJH, Visser E, Mandl RCW, Sommer IEC. The neurobiological characterization of distinct cognitive subtypes in early-phase schizophrenia-spectrum disorders. Schizophr Res 2022; 241:228-237. [PMID: 35176721 DOI: 10.1016/j.schres.2022.02.006] [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: 09/20/2021] [Revised: 01/28/2022] [Accepted: 02/04/2022] [Indexed: 10/19/2022]
Abstract
INTRODUCTION Cognitive deficits are present in some, but not all patients with schizophrenia-spectrum disorders (SSD). We and others have demonstrated three cognitive clusters: cognitively intact patients, patients with deficits in a few domains and those with global cognitive deficits. This study aimed to identify cognitive subtypes of early-phase SSD with matched controls as a reference group, and evaluated cognitive subgroups regarding clinical and brain volumetric measures. METHODS Eighty-six early-phase SSD patients were included. Hierarchical cluster analysis was conducted using global performance on the Brief Assessment of Cognition in Schizophrenia (BACS). Cognitive subgroups were subsequently related to clinical and brain volumetric measures (cortical, subcortical and cortical thickness) using ANCOVA. RESULTS Three distinct cognitive clusters emerged: relative to controls we found one cluster of patients with preserved cognition (n = 25), one moderately impaired cluster (n = 38) and one severely impaired cluster (n = 23). Cognitive subgroups were characterized by differences in volume of the left postcentral gyrus, left middle caudal frontal gyrus and left insula, while differences in cortical thickness were predominantly found in fronto-parietal regions. No differences were demonstrated in subcortical brain volume. DISCUSSION Current results replicate the existence of three distinct cognitive subgroups including one relatively large group with preserved cognitive function. Cognitive subgroups were characterized by differences in cortical regional brain volume and cortical thickness, suggesting associations with cortical, but not subcortical development and cognitive functioning such as attention, executive functions and speed of processing.
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Affiliation(s)
- P P Oomen
- Department of Biomedical Sciences of Cells & Systems, Section Cognitive Neurosciences, University Medical Centre Groningen, University of Groningen, Groningen, the Netherlands.
| | - S S Gangadin
- Department of Biomedical Sciences of Cells & Systems, Section Cognitive Neurosciences, University Medical Centre Groningen, University of Groningen, Groningen, the Netherlands
| | - M J H Begemann
- Department of Biomedical Sciences of Cells & Systems, Section Cognitive Neurosciences, University Medical Centre Groningen, University of Groningen, Groningen, the Netherlands
| | - E Visser
- Department of Psychiatry, University Medical Center, Utrecht Brain Center, Utrecht University, Utrecht, the Netherlands
| | - R C W Mandl
- Department of Psychiatry, University Medical Center, Utrecht Brain Center, Utrecht University, Utrecht, the Netherlands
| | - I E C Sommer
- Department of Biomedical Sciences of Cells & Systems, Section Cognitive Neurosciences, University Medical Centre Groningen, University of Groningen, Groningen, the Netherlands
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Insula volumes in first-episode and chronic psychosis: A longitudinal MRI study. Schizophr Res 2022; 241:14-23. [PMID: 35074528 DOI: 10.1016/j.schres.2021.12.048] [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: 04/01/2021] [Revised: 07/21/2021] [Accepted: 12/28/2021] [Indexed: 12/22/2022]
Abstract
BACKGROUND Alterations in insular grey matter (GM) volume has been consistently reported for affective and non-affective psychoses both in chronic and first-episode patients, ultimately suggesting that the insula might represent a good region to study in order to assess the longitudinal course of psychotic disorders. Therefore, in this longitudinal Magnetic Resonance Imaging (MRI) study, we aimed at further investigating the key role of insular volumes in psychosis. MATERIAL AND METHODS 68 First-Episode Psychosis (FEP) patients, 68 patients with Schizophrenia (SCZ), 47 Bipolar Disorder (BD) patients, and 94 Healthy Controls (HC) were enrolled and underwent a 1.5 T MRI evaluation. A subsample of 99 subjects (10 HC, 23 BD, 29 SCZ, 37 FEP) was rescanned after 2,53 ± 1,68 years. The insular cortex was manually traced and then divided into an anterior and posterior portion. Group and correlation analyses were then performed both at baseline and at follow-up. RESULTS At baseline, greater anterior and lower posterior insular GM volumes were observed in chronic patients. At follow-up, we found that FEP patients had a significant GM volume increase from baseline to follow-up, especially in the posterior insula whereas chronic patients showed a relative stability. Finally, significant negative correlations between illness severity and pharmacological treatment and insular GM volumes were observed in the whole group of psychotic patients. CONCLUSIONS The longitudinal assessment of both chronic and first-episode patients allowed us to detect a complex pattern of GM abnormalities in selective sub-portions of insular volumes, ultimately suggesting that this structure could represent a key biological marker of psychotic disorders.
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Prasad K, Rubin J, Mitra A, Lewis M, Theis N, Muldoon B, Iyengar S, Cape J. Structural covariance networks in schizophrenia: A systematic review Part I. Schizophr Res 2022; 240:1-21. [PMID: 34906884 PMCID: PMC8917984 DOI: 10.1016/j.schres.2021.11.035] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 08/02/2021] [Accepted: 11/23/2021] [Indexed: 02/03/2023]
Abstract
BACKGROUND Schizophrenia is proposed as a disorder of dysconnectivity. However, examination of complexities of dysconnectivity has been challenging. Structural covariance networks (SCN) provide important insights into the nature of dysconnectivity. This systematic review examines the SCN studies that employed statistical approaches to elucidate covariation of regional morphometric variations. METHODS A systematic search of literature was conducted for peer-reviewed publications using different keywords and keyword combinations for schizophrenia. Fifty-two studies met the criteria. RESULTS Early SCN studies began using correlational structure of selected regions. Over the last 3 decades, methodological approaches have grown increasingly sophisticated from examining selected brain regions using correlation tests on small sample sizes to recent approaches that use advanced statistical methods to examine covariance structure of whole-brain parcellations on larger samples. Although the results are not fully consistent across all studies, a pattern of fronto-temporal, fronto-parietal and fronto-thalamic covariation is reported. Attempts to associate SCN alterations with functional connectivity, to differentiate between disease-related and neurodevelopment-related morphometric changes, and to develop "causality-based" models are being reported. Clinical correlation with outcome, psychotic symptoms, neurocognitive and social cognitive performance are also reported. CONCLUSIONS Application of advanced statistical methods are beginning to provide insights into interesting patterns of regional covariance including correlations with clinical and cognitive data. Although these findings appear similar to morphometric studies, SCNs have the advantage of highlighting topology of these regions and their relationship to the disease and associated variables. Further studies are needed to investigate neurobiological underpinnings of shared covariance, and causal links to clinical domains.
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Affiliation(s)
- Konasale Prasad
- University of Pittsburgh School of Medicine, Western Psychiatric Institute and Clinic, 3811 O'Hara St, Pittsburgh, PA 15213, United States of America; University of Pittsburgh Swanson School of Engineering, 3700 O'Hara St, Pittsburgh, PA 15213, United States of America; VA Pittsburgh Healthcare System, University Dr C, Pittsburgh, PA 15240, United States of America.
| | - Jonathan Rubin
- Department of Mathematics, University of Pittsburgh, 301 Thackeray Hall, Pittsburgh PA 15260
| | - Anirban Mitra
- Department of Statistics, University of Pittsburgh, 1826 Wesley W. Posvar Hall, Pittsburgh PA 15260
| | - Madison Lewis
- University of Pittsburgh Swanson School of Engineering, 3700 O’Hara St, Pittsburgh PA 15213
| | - Nicholas Theis
- University of Pittsburgh School of Medicine, Western Psychiatric Institute and Clinic, 3811 O’Hara St, Pittsburgh PA 15213
| | - Brendan Muldoon
- University of Pittsburgh School of Medicine, Western Psychiatric Institute and Clinic, 3811 O’Hara St, Pittsburgh PA 15213
| | - Satish Iyengar
- Department of Statistics, University of Pittsburgh, 1826 Wesley W. Posvar Hall, Pittsburgh PA 15260
| | - Joshua Cape
- Department of Statistics, University of Pittsburgh, 1826 Wesley W. Posvar Hall, Pittsburgh PA 15260
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Antonucci LA, Fazio L, Pergola G, Blasi G, Stolfa G, Di Palo P, Mucci A, Rocca P, Brasso C, di Giannantonio M, Maria Giordano G, Monteleone P, Pompili M, Siracusano A, Bertolino A, Galderisi S, Maj M. Joint structural-functional magnetic resonance imaging features are associated with diagnosis and real-world functioning in patients with schizophrenia. Schizophr Res 2022; 240:193-203. [PMID: 35032904 DOI: 10.1016/j.schres.2021.12.029] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 10/20/2021] [Accepted: 12/22/2021] [Indexed: 12/28/2022]
Abstract
OBJECTIVE Earlier evidence suggested that structural-functional covariation in schizophrenia patients (SCZ) is associated with cognition, a predictor of functioning. Moreover, studies suggested that functional brain abnormalities of schizophrenia may be related with structural network features. However, only few studies have investigated the relationship between structural-functional covariation and both diagnosis and functioning in SCZ. We hypothesized that structural-functional covariation networks associated with diagnosis are related to real-world functioning in SCZ. METHODS We performed joint Independent Component Analysis on T1 images and resting-state fMRI-based Degree Centrality (DC) maps from 89 SCZ and 285 controls. Structural-functional covariation networks in which we found a main effect of diagnosis underwent correlation analysis to investigate their relationship with functioning. Covariation networks showing a significant association with both diagnosis and functioning underwent univariate analysis to better characterize group-level differences at the spatial level. RESULTS A structural-functional covariation network characterized by frontal, temporal, parietal and thalamic structural estimates significantly covaried with temporo-parietal resting-state DC. Compared with controls, SCZ had reduced structural-functional covariation within this network (pFDR = 0.005). The same measure correlated positively with both social and occupational functioning (both pFDR = 0.042). Univariate analyses revealed grey matter deviations in SCZ compared with controls within this structural-functional network in hippocampus, cerebellum, thalamus, orbito-frontal cortex, and insula. No group differences were found in DC. CONCLUSIONS Findings support the existence of a phenotypical association between group-level differences and inter-individual heterogeneity of functional deficits in SCZ. Given that only the joint structural/functional analysis revealed this association, structural-functional covariation may be a potentially relevant schizophrenia phenotype.
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Affiliation(s)
- Linda A Antonucci
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Leonardo Fazio
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Giulio Pergola
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Giuseppe Blasi
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Giuseppe Stolfa
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Piergiuseppe Di Palo
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Armida Mucci
- Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Paola Rocca
- Department of Neuroscience, Section of Psychiatry, University of Turin, Turin, Italy
| | - Claudio Brasso
- Department of Neuroscience, Section of Psychiatry, University of Turin, Turin, Italy
| | | | | | - Palmiero Monteleone
- Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", Section of Neuroscience, University of Salerno, Salerno, Italy
| | - Maurizio Pompili
- Department of Neurosciences, Mental Health, and Sensory Organs, S. Andrea Hospital, Sapienza University of Rome, Rome, Italy
| | - Alberto Siracusano
- Department of Systems Medicine, Psychiatry and Clinical Psychology Unit, Tor Vergata University of Rome, Rome, Italy
| | - Alessandro Bertolino
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy.
| | - Silvana Galderisi
- Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Mario Maj
- Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
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Rootes-Murdy K, Goldsmith DR, Turner JA. Clinical and Structural Differences in Delusions Across Diagnoses: A Systematic Review. Front Integr Neurosci 2022; 15:726321. [PMID: 35140591 PMCID: PMC8818879 DOI: 10.3389/fnint.2021.726321] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 12/16/2021] [Indexed: 12/18/2022] Open
Abstract
Delusions are marked, fixed beliefs that are incongruent with reality. Delusions, with comorbid hallucinations, are a hallmark of certain psychotic disorders (e.g., schizophrenia). Delusions can present transdiagnostically, in neurodegenerative (e.g., Alzheimer's disease and fronto-temporal dementia), nervous system disorders (e.g., Parkinson's disease) and across other psychiatric disorders (e.g., bipolar disorder). The burden of delusions is severe and understanding the heterogeneity of delusions may delineate a more valid nosology of not only psychiatric disorders but also neurodegenerative and nervous system disorders. We systematically reviewed structural neuroimaging studies reporting on delusions in four disorder types [schizophrenia (SZ), bipolar disorder (BP), Alzheimer's disease (AD), and Parkinson's disease (PD)] to provide a comprehensive overview of neural changes and clinical presentations associated with delusions. Twenty-eight eligible studies were identified. This review found delusions were most associated with gray matter reductions in the dorsolateral prefrontal cortex (SZ, BP, and AD), left claustrum (SZ and AD), hippocampus (SZ and AD), insula (SZ, BP, and AD), amygdala (SZ and BP), thalamus (SZ and AD), superior temporal gyrus (SZ, BP, and AD), and middle frontal gyrus (SZ, BP, AD, and PD). However, there was a great deal of variability in the findings of each disorder. There is some support for the current dopaminergic hypothesis of psychosis, but we also propose new hypotheses related to the belief formation network and cognitive biases. We also propose a standardization of assessments to aid future transdiagnostic study approaches. Future studies should explore the neural and biological underpinnings of delusions to hopefully, inform future treatment.
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Affiliation(s)
- Kelly Rootes-Murdy
- Department of Psychology, Georgia State University, Atlanta, GA, United States
- 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
- *Correspondence: Kelly Rootes-Murdy
| | - David R. Goldsmith
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States
| | - Jessica A. Turner
- Department of Psychology, Georgia State University, Atlanta, GA, United States
- 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
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44
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Jensen DM, Zendrehrouh E, Calhoun V, Turner JA. Cognitive Implications of Correlated Structural Network Changes in Schizophrenia. Front Integr Neurosci 2022; 15:755069. [PMID: 35126065 PMCID: PMC8811375 DOI: 10.3389/fnint.2021.755069] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Accepted: 12/28/2021] [Indexed: 11/13/2022] Open
Abstract
Background Schizophrenia is a brain disorder characterized by diffuse, diverse, and wide-spread changes in gray matter volume (GM) and white matter structure (fractional anisotropy, FA), as well as cognitive impairments that greatly impact an individual’s quality of life. While the relationship of each of these image modalities and their links to schizophrenia status and cognitive impairment has been investigated separately, a multimodal fusion via parallel independent component analysis (pICA) affords the opportunity to explore the relationships between the changes in GM and FA, and the implications these network changes have on cognitive performance. Methods Images from 73 subjects with schizophrenia (SZ) and 82 healthy controls (HC) were drawn from an existing dataset. We investigated 12 components from each feature (FA and GM). Loading coefficients from the images were used to identify pairs of features that were significantly correlated and showed significant group differences between HC and SZ. MANCOVA analysis uncovered the relationships the identified spatial maps had with age, gender, and a global cognitive performance score. Results Three component pairs showed significant group differences (HC > SZ) in both gray and white matter measurements. Two of the component pairs identified networks of gray matter that drove significant relationships with cognition (HC > SZ) after accounting for age and gender. The gray and white matter structural networks identified in these three component pairs pull broadly from many regions, including the right and left thalamus, lateral occipital cortex, multiple regions of the middle temporal gyrus, precuneus cortex, postcentral gyrus, cingulate gyrus/cingulum, lingual gyrus, and brain stem. Conclusion The results of this multimodal analysis adds to our understanding of how the relationship between GM, FA, and cognition differs between HC and SZ by highlighting the correlated intermodal covariance of these structural networks and their differential relationships with cognitive performance. Previous unimodal research has found similar areas of GM and FA differences between these groups, and the cognitive deficits associated with SZ have been well documented. This study allowed us to evaluate the intercorrelated covariance of these structural networks and how these networks are involved the differences in cognitive performance between HC and SZ.
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Affiliation(s)
- Dawn M. Jensen
- Neuroscience Institute, Georgia State University, Atlanta, GA, United States
- *Correspondence: Dawn M. Jensen,
| | - Elaheh Zendrehrouh
- Department of Computer Science, Georgia State University, Atlanta, GA, United States
| | - Vince Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Jessica A. Turner
- Department of Psychology, Georgia State University, Atlanta, GA, United States
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Li X, Liu N, Yang C, Zhang W, Lui S. Cerebellar gray matter volume changes in patients with schizophrenia: A voxel-based meta-analysis. Front Psychiatry 2022; 13:1083480. [PMID: 36620665 PMCID: PMC9814486 DOI: 10.3389/fpsyt.2022.1083480] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Accepted: 11/28/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND In schizophrenia, the structural changes in the cerebellum are associated with patients' cognition and motor deficits. However, the findings are inconsistent owing to the heterogeneity in sample size, magnetic resonance imaging (MRI) scanners, and other factors among them. In this study, we conducted a meta-analysis to characterize the anatomical changes in cerebellar subfields in patients with schizophrenia. METHODS Systematic research was conducted to identify studies that compare the gray matter volume (GMV) differences in the cerebellum between patients with schizophrenia and healthy controls with a voxel-based morphometry (VBM) method. A coordinate-based meta-analysis was adopted based on seed-based d mapping (SDM) software. An exploratory meta-regression analysis was conducted to associate clinical and demographic features with cerebellar changes. RESULTS Of note, 25 studies comprising 996 patients with schizophrenia and 1,109 healthy controls were included in the present meta-analysis. In patients with schizophrenia, decreased GMVs were demonstrated in the left Crus II, right lobule VI, and right lobule VIII, while no increased GMV was identified. In the meta-regression analysis, the mean age and illness duration were negatively associated with the GMV in the left Crus II in patients with schizophrenia. CONCLUSION The most significant structural changes in the cerebellum are mainly located in the posterior cerebellar hemisphere in patients with schizophrenia. The decreased GMVs of these regions might partly explain the cognitive deficits and motor symptoms in patients with schizophrenia.
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Affiliation(s)
- Xing Li
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
| | - Naici Liu
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
| | - Chengmin Yang
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
| | - Wenjing Zhang
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
| | - Su Lui
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
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Schulz J, Zimmermann J, Sorg C, Menegaux A, Brandl F. Magnetic resonance imaging of the dopamine system in schizophrenia - A scoping review. Front Psychiatry 2022; 13:925476. [PMID: 36203848 PMCID: PMC9530597 DOI: 10.3389/fpsyt.2022.925476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 07/08/2022] [Indexed: 11/30/2022] Open
Abstract
For decades, aberrant dopamine transmission has been proposed to play a central role in schizophrenia pathophysiology. These theories are supported by human in vivo molecular imaging studies of dopamine transmission, particularly positron emission tomography. However, there are several downsides to such approaches, for example limited spatial resolution or restriction of the measurement to synaptic processes of dopaminergic neurons. To overcome these limitations and to measure complementary aspects of dopamine transmission, magnetic resonance imaging (MRI)-based approaches investigating the macrostructure, metabolism, and connectivity of dopaminergic nuclei, i.e., substantia nigra pars compacta and ventral tegmental area, can be employed. In this scoping review, we focus on four dopamine MRI methods that have been employed in patients with schizophrenia so far: neuromelanin MRI, which is thought to measure long-term dopamine function in dopaminergic nuclei; morphometric MRI, which is assumed to measure the volume of dopaminergic nuclei; diffusion MRI, which is assumed to measure fiber-based structural connectivity of dopaminergic nuclei; and resting-state blood-oxygenation-level-dependent functional MRI, which is thought to measure functional connectivity of dopaminergic nuclei based on correlated blood oxygenation fluctuations. For each method, we describe the underlying signal, outcome measures, and downsides. We present the current state of research in schizophrenia and compare it to other disorders with either similar (psychotic) symptoms, i.e., bipolar disorder and major depressive disorder, or dopaminergic abnormalities, i.e., substance use disorder and Parkinson's disease. Finally, we discuss overarching issues and outline future research questions.
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Affiliation(s)
- Julia Schulz
- Department of Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany.,TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Juliana Zimmermann
- Department of Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany.,TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Christian Sorg
- Department of Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany.,TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany.,Department of Psychiatry and Psychotherapy, School of Medicine, Technical University of Munich, Munich, Germany
| | - Aurore Menegaux
- Department of Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany.,TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Felix Brandl
- Department of Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany.,TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany.,Department of Psychiatry and Psychotherapy, School of Medicine, Technical University of Munich, Munich, Germany
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47
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Ge R, Hassel S, Arnott SR, Davis AD, Harris JK, Zamyadi M, Milev R, Frey BN, Strother SC, Müller DJ, Rotzinger S, MacQueen GM, Kennedy SH, Lam RW, Vila-Rodriguez F. Structural covariance pattern abnormalities of insula in major depressive disorder: A CAN-BIND study report. Prog Neuropsychopharmacol Biol Psychiatry 2021; 111:110194. [PMID: 33296696 DOI: 10.1016/j.pnpbp.2020.110194] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 09/25/2020] [Accepted: 11/30/2020] [Indexed: 11/26/2022]
Abstract
BACKGROUND AND METHODS Investigation of the insula may inform understanding of the etiopathogenesis of major depressive disorder (MDD). In the present study, we introduced a novel gray matter volume (GMV) based structural covariance technique, and applied it to a multi-centre study of insular subregions of 157 patients with MDD and 93 healthy controls from the Canadian Biomarker Integration Network in Depression (CAN-BIND, https://www.canbind.ca/). Specifically, we divided the unilateral insula into three subregions, and investigated their coupling with whole-brain GMV-based structural brain networks (SBNs). We compared between-group difference of the structural coupling patterns between the insular subregions and SBNs. RESULTS The insula was divided into three subregions, including an anterior one, a superior-posterior one and an inferior-posterior one. In the comparison between MDD patients and controls we found that patients' right anterior insula showed increased inter-network coupling with the default mode network, and it showed decreased inter-network coupling with the central executive network; whereas patients' right ventral-posterior insula showed decreased inter-network coupling with the default mode network, and it showed increased inter-network coupling with the central executive network. We also demonstrated that patients' loading parameters of the right ventral-posterior insular structural covariance negatively correlated with their suicidal ideation scores; and controls' loading parameters of the right ventral-posterior insular structural covariance positively correlated with their motor and psychomotor speed scores, whereas these phenomena were not found in patients. Additionally, we did not find significant inter-network coupling between the whole-brain SBNs, including salience network, default mode network, and central executive network. CONCLUSIONS Our work proposed a novel technique to investigate the structural covariance coupling between large-scale structural covariance networks, and provided further evidence that MDD is a system-level disorder that shows disrupted structural coupling between brain networks.
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Affiliation(s)
- Ruiyang Ge
- Non-Invasive Neurostimulation Therapies (NINET) Laboratory, Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Stefanie Hassel
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, AB, Canada
| | | | - Andrew D Davis
- Department of Psychology, Neuroscience & Behaviour, McMaster University, St. Joseph's Healthcare Hamilton, Hamilton, ON, Canada
| | | | - Mojdeh Zamyadi
- Rotman Research Institute, Baycrest, Toronto, ON, Canada
| | - Roumen Milev
- Department of Psychiatry, Queen's University and Providence Care Hospital, Kingston, ON, Canada; Department of Psychology, Queen's University, Kingston, ON, Canada
| | - Benicio N Frey
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada; Mood Disorders Program and Women's Health Concerns Clinic, St. Joseph's Healthcare, Hamilton, ON, Canada
| | | | - Daniel J Müller
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Susan Rotzinger
- Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, ON, Canada; Department of Psychiatry, Krembil Research Centre, University Health Network, University of Toronto, Toronto, ON, Canada; Department of Psychiatry, St. Michael's Hospital, University of Toronto, Toronto, ON, Canada
| | - Glenda M MacQueen
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Sidney H Kennedy
- Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, ON, Canada; Department of Psychiatry, Krembil Research Centre, University Health Network, University of Toronto, Toronto, ON, Canada; Department of Psychiatry, St. Michael's Hospital, University of Toronto, Toronto, ON, Canada; Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, ON, Canada
| | - Raymond W Lam
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Fidel Vila-Rodriguez
- Non-Invasive Neurostimulation Therapies (NINET) Laboratory, Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada.
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Jiang W, Rootes-Murdy K, Chen J, Bizzozero NIP, Calhoun VD, van Erp TGM, Ehrlich S, Agartz I, Jönsson EG, Andreassen OA, Wang L, Pearlson GD, Glahn DC, Hong E, Liu J, Turner JA. Multivariate alterations in insula - Medial prefrontal cortex linked to genetics in 12q24 in schizophrenia. Psychiatry Res 2021; 306:114237. [PMID: 34655926 PMCID: PMC8643340 DOI: 10.1016/j.psychres.2021.114237] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 10/06/2021] [Accepted: 10/08/2021] [Indexed: 11/29/2022]
Abstract
The direct effect of genetic variations on clinical phenotypes within schizophrenia (SZ) remains elusive. We examined the previously identified association of reduced gray matter concentration in the insula - medial prefrontal cortex and a quantitative trait locus located in 12q24 in a SZ dataset. The main analysis was performed on 1461 SNPs and 830 participants. The highest contributing SNPs were localized in five genes including TMEM119, which encodes a microglial marker, that is associated with neuroinflammation and Alzheimer's disease. The gene set in 12q4 may partially explain brain alterations in SZ, but they may also relate to other psychiatric and developmental disorders.
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Affiliation(s)
- Wenhao Jiang
- Department of Psychology, Georgia State University, United States of America; Department of Psychosomatics and Psychiatry, Zhongda Hospital, Institute of Psychosomatics, Medical School, Southeast University, Nanjing, China
| | - Kelly Rootes-Murdy
- Department of Psychology, Georgia State University, United States of America
| | - Jiayu Chen
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology and Emory University, United States of America
| | | | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology and Emory University, United States of America
| | - Theo G M van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, United States of America; Qureshey Research Laboratory, Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine, CA,United States of America
| | - Stefan Ehrlich
- Department of Psychiatry, Massachusetts General Hospital, United States of America; Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, TU Dresden, Germany
| | - Ingrid Agartz
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway; Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Stockholm Region, Stockholm, Sweden; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Erik G Jönsson
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway; Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Stockholm Region, Stockholm, Sweden
| | - Ole A Andreassen
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway
| | - Lei Wang
- Psychiatry and Behavioral Health, Ohio State Wexner Medical Center, United States of America
| | | | - David C Glahn
- Boston Children's Hospital and Harvard Medical School, United States of America
| | - Elliot Hong
- Maryland Psychiatric Research Center, University of Maryland School of Medicine, United States of America
| | - Jingyu Liu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology and Emory University, United States of America
| | - Jessica A Turner
- Department of Psychology, Georgia State University, United States of America; Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology and Emory University, United States of America
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Hirano Y, Uhlhaas PJ. Current findings and perspectives on aberrant neural oscillations in schizophrenia. Psychiatry Clin Neurosci 2021; 75:358-368. [PMID: 34558155 DOI: 10.1111/pcn.13300] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 08/20/2021] [Accepted: 09/09/2021] [Indexed: 12/11/2022]
Abstract
There is now consistent evidence that neural oscillation at low- and high-frequencies constitute an important aspect of the pathophysiology of schizophrenia. Specifically, impaired rhythmic activity may underlie the deficit to generate coherent cognition and behavior, leading to the characteristic symptoms of psychosis and cognitive deficits. Importantly, the generating mechanisms of neural oscillations are relatively well-understood and thus enable the targeted search for the underlying circuit impairments and novel treatment targets. In the following review, we will summarize and assess the evidence for aberrant rhythmic activity in schizophrenia through evaluating studies that have utilized Electro/Magnetoencephalography to examine neural oscillations during sensory and cognitive tasks as well as during resting-state measurements. These data will be linked to current evidence from post-mortem, neuroimaging, genetics, and animal models that have implicated deficits in GABAergic interneurons and glutamatergic neurotransmission in oscillatory deficits in schizophrenia. Finally, we will highlight methodological and analytical challenges as well as provide recommendations for future research.
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Affiliation(s)
- Yoji Hirano
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
- Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
| | - Peter J Uhlhaas
- Department of Child and Adolescent Psychiatry, Charité - Universitätsmedizin, Berlin, Germany
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, UK
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50
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Rootes-Murdy K, Zendehrouh E, Calhoun VD, Turner JA. Spatially Covarying Patterns of Gray Matter Volume and Concentration Highlight Distinct Regions in Schizophrenia. Front Neurosci 2021; 15:708387. [PMID: 34720851 PMCID: PMC8551386 DOI: 10.3389/fnins.2021.708387] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 09/20/2021] [Indexed: 11/29/2022] Open
Abstract
Introduction: Individuals with schizophrenia have consistent gray matter reduction throughout the cortex when compared to healthy individuals. However, the reduction patterns vary based on the quantity (concentration or volume) utilized by study. The objective of this study was to identify commonalities between gray matter concentration and gray matter volume effects in schizophrenia. Methods: We performed both univariate and multivariate analyses of case/control effects on 145 gray matter images from 66 participants with schizophrenia and 79 healthy controls, and processed to compare the concentration and volume estimates. Results: Diagnosis effects in the univariate analysis showed similar areas of volume and concentration reductions in the insula, occipitotemporal gyrus, temporopolar area, and fusiform gyrus. In the multivariate analysis, healthy controls had greater gray matter volume and concentration additionally in the superior temporal gyrus, prefrontal cortex, cerebellum, calcarine, and thalamus. In the univariate analyses there was moderate overlap between gray matter concentration and volume across the entire cortex (r = 0.56, p = 0.02). The multivariate analyses revealed only low overlap across most brain patterns, with the largest correlation (r = 0.37) found in the cerebellum and vermis. Conclusions: Individuals with schizophrenia showed reduced gray matter volume and concentration in previously identified areas of the prefrontal cortex, cerebellum, and thalamus. However, there were only moderate correlations across the cortex when examining the different gray matter quantities. Although these two quantities are related, concentration and volume do not show identical results, and therefore, should not be used interchangeably in the literature.
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Affiliation(s)
- Kelly Rootes-Murdy
- Department of Psychology, Georgia State University, Atlanta, GA, United States.,Neuroscience Institute, Georgia State University, Atlanta, GA, United States
| | - Elaheh Zendehrouh
- Department of Computer Science, Georgia State University, Atlanta, GA, United States.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Vince D Calhoun
- Department of Psychology, Georgia State University, Atlanta, GA, United States.,Neuroscience Institute, Georgia State University, Atlanta, GA, United States.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Jessica A Turner
- Department of Psychology, Georgia State University, Atlanta, GA, United States.,Neuroscience Institute, Georgia State University, Atlanta, GA, United States.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
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