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Chang Z, Liu L, Lin L, Wang G, Zhang C, Tian H, Liu W, Wang L, Zhang B, Ren J, Zhang Y, Xie Y, Du X, Wei X, Wei L, Luo Y, Dong H, Li X, Zhao Z, Liang M, Zhang C, Wang X, Yu C, Qin W, Liu H. Selective disrupted gray matter volume covariance of amygdala subregions in schizophrenia. Front Psychiatry 2024; 15:1349989. [PMID: 38742128 PMCID: PMC11090100 DOI: 10.3389/fpsyt.2024.1349989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 04/11/2024] [Indexed: 05/16/2024] Open
Abstract
Objective Although extensive structural and functional abnormalities have been reported in schizophrenia, the gray matter volume (GMV) covariance of the amygdala remain unknown. The amygdala contains several subregions with different connection patterns and functions, but it is unclear whether the GMV covariance of these subregions are selectively affected in schizophrenia. Methods To address this issue, we compared the GMV covariance of each amygdala subregion between 807 schizophrenia patients and 845 healthy controls from 11 centers. The amygdala was segmented into nine subregions using FreeSurfer (v7.1.1), including the lateral (La), basal (Ba), accessory-basal (AB), anterior-amygdaloid-area (AAA), central (Ce), medial (Me), cortical (Co), corticoamygdaloid-transition (CAT), and paralaminar (PL) nucleus. We developed an operational combat harmonization model for 11 centers, subsequently employing a voxel-wise general linear model to investigate the differences in GMV covariance between schizophrenia patients and healthy controls across these subregions and the entire brain, while adjusting for age, sex and TIV. Results Our findings revealed that five amygdala subregions of schizophrenia patients, including bilateral AAA, CAT, and right Ba, demonstrated significantly increased GMV covariance with the hippocampus, striatum, orbitofrontal cortex, and so on (permutation test, P< 0.05, corrected). These findings could be replicated in most centers. Rigorous correlation analysis failed to identify relationships between the altered GMV covariance with positive and negative symptom scale, duration of illness, and antipsychotic medication measure. Conclusion Our research is the first to discover selectively impaired GMV covariance patterns of amygdala subregion in a large multicenter sample size of patients with schizophrenia.
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Affiliation(s)
- Zhongyu Chang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Liping Liu
- Department of Psychiatry, The First Psychiatric Hospital of Harbin, Harbin, Heilongjiang, China
| | - Liyuan Lin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Gang Wang
- Wuhan Mental Health Center, The Ninth Clinical School, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chen Zhang
- Department of Biochemistry and Psychopharmacology, Shanghai Mental Health Center, Shanghai, China
| | - Hongjun Tian
- Department of Psychiatry, Tianjin Fourth Center Hospital, The Fourth Central Clinical College, Tianjin Medical University, Tianjin, China
| | - Wei Liu
- Department of Psychiatry, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Lina Wang
- Department of Psychiatry, Tianjin Fourth Center Hospital, The Fourth Central Clinical College, Tianjin Medical University, Tianjin, China
| | - Bin Zhang
- Department of Psychiatry, Tianjin Fourth Center Hospital, The Fourth Central Clinical College, Tianjin Medical University, Tianjin, China
| | - Juanjuan Ren
- Department of Biochemistry and Psychopharmacology, Shanghai Mental Health Center, Shanghai, China
| | - Yu Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yingying Xie
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Xiaotong Du
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Xiaotong Wei
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Luli Wei
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yun Luo
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Haoyang Dong
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Xin Li
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Zhen Zhao
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Meng Liang
- School of Medical Imaging, Tianjin Medical University, Tianjin, China
| | - Congpei Zhang
- Department of Psychiatry, The First Psychiatric Hospital of Harbin, Harbin, Heilongjiang, China
| | - Xijin Wang
- Department of Psychiatry, The First Psychiatric Hospital of Harbin, Harbin, Heilongjiang, China
| | - Chunshui Yu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
- School of Medical Imaging, Tianjin Medical University, Tianjin, China
- State Key Laboratory of Experimental Hematology, Tianjin Medical University General Hospital, Tianjin, China
| | - Wen Qin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Huaigui Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
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Collantoni E, Alberti F, Dahmen B, von Polier G, Konrad K, Herpertz-Dahlmann B, Favaro A, Seitz J. Intra-individual cortical networks in Anorexia Nervosa: Evidence from a longitudinal dataset. EUROPEAN EATING DISORDERS REVIEW 2024; 32:298-309. [PMID: 37876109 DOI: 10.1002/erv.3043] [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: 03/20/2023] [Revised: 10/14/2023] [Accepted: 10/16/2023] [Indexed: 10/26/2023]
Abstract
OBJECTIVE This work investigates cortical thickness (CT) and gyrification patterns in Anorexia Nervosa (AN) before and after short-term weight restoration using graph theory tools. METHODS 38 female adolescents with AN underwent structural magnetic resonance imaging scans at baseline and after - on average - 3.5 months following short-term weight restoration while 53 age-matched healthy controls (HCs) were scanned once. Graph measures were compared between groups and longitudinally within the AN group. Associations with clinical measures such as age of onset, duration of illness, BMI standard deviation score (BMI-SDS), and longitudinal weight changes were tested via stepwise regression. RESULTS Cortical thickness graphs of patients with acute AN displayed lower modularity and small-world index (SWI) than HCs. Modularity recovered after weight gain. Reduced global efficiency and SWI were observed in patients at baseline compared to HCs based on gyrification networks. Significant associations between local clustering of CT at admission and BMI-SDS, and clustering/global efficiency of gyrification and duration of illness emerged. CONCLUSIONS Our results indicate a shift towards less organised CT networks in patients with acute AN. After weight recovery, the disarrangement seems to be partially reduced. However, longer-term follow-ups are needed to determine whether cortical organizational patterns fully return to normal.
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Affiliation(s)
- Enrico Collantoni
- Department of Neurosciences, University of Padua, Padova, Italy
- Padua Neuroscience Center, University of Padua, Padova, Italy
| | | | - Brigitte Dahmen
- Child and Adolescent Psychiatry, University Hospital, RWTH Aachen, Aachen, Germany
| | - Georg von Polier
- Child and Adolescent Psychiatry, University Hospital, RWTH Aachen, Aachen, Germany
- Child and Adolescent Psychiatry, University Hospital, Frankfurt, Germany
| | - Kerstin Konrad
- Child and Adolescent Psychiatry, University Hospital, RWTH Aachen, Aachen, Germany
- Section Neuropsychology, Child and Adolescent Psychiatry, University Hospital, RWTH Aachen, Aachen, Germany
| | | | - Angela Favaro
- Department of Neurosciences, University of Padua, Padova, Italy
- Padua Neuroscience Center, University of Padua, Padova, Italy
| | - Jochen Seitz
- Child and Adolescent Psychiatry, University Hospital, RWTH Aachen, Aachen, Germany
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Devignes Q, Ren B, Clancy KJ, Howell K, Pollmann Y, Martinez-Sanchez L, Beard C, Kumar P, Rosso IM. Trauma-related intrusive memories and anterior hippocampus structural covariance: an ecological momentary assessment study in posttraumatic stress disorder. Transl Psychiatry 2024; 14:74. [PMID: 38307849 PMCID: PMC10837434 DOI: 10.1038/s41398-024-02795-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 01/09/2024] [Accepted: 01/17/2024] [Indexed: 02/04/2024] Open
Abstract
Trauma-related intrusive memories (TR-IMs) are hallmark symptoms of posttraumatic stress disorder (PTSD), but their neural correlates remain partly unknown. Given its role in autobiographical memory, the hippocampus may play a critical role in TR-IM neurophysiology. The anterior and posterior hippocampi are known to have partially distinct functions, including during retrieval of autobiographical memories. This study aimed to investigate the relationship between TR-IM frequency and the anterior and posterior hippocampi morphology in PTSD. Ninety-three trauma-exposed adults completed daily ecological momentary assessments for fourteen days to capture their TR-IM frequency. Participants then underwent anatomical magnetic resonance imaging to obtain measures of anterior and posterior hippocampal volumes. Partial least squares analysis was applied to identify a structural covariance network that differentiated the anterior and posterior hippocampi. Poisson regression models examined the relationship of TR-IM frequency with anterior and posterior hippocampal volumes and the resulting structural covariance network. Results revealed no significant relationship of TR-IM frequency with hippocampal volumes. However, TR-IM frequency was significantly negatively correlated with the expression of a structural covariance pattern specifically associated with the anterior hippocampus volume. This association remained significant after accounting for the severity of PTSD symptoms other than intrusion symptoms. The network included the bilateral inferior temporal gyri, superior frontal gyri, precuneus, and fusiform gyri. These novel findings indicate that higher TR-IM frequency in individuals with PTSD is associated with lower structural covariance between the anterior hippocampus and other brain regions involved in autobiographical memory, shedding light on the neural correlates underlying this core symptom of PTSD.
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Affiliation(s)
- Quentin Devignes
- Center for Depression, Anxiety and Stress Disorders, McLean Hospital, Belmont, MA, USA.
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
| | - Boyu Ren
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Psychiatric Biostatistics Laboratory, McLean Hospital, Belmont, MA, USA
| | - Kevin J Clancy
- Center for Depression, Anxiety and Stress Disorders, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Kristin Howell
- Center for Depression, Anxiety and Stress Disorders, McLean Hospital, Belmont, MA, USA
| | - Yara Pollmann
- Center for Depression, Anxiety and Stress Disorders, McLean Hospital, Belmont, MA, USA
| | | | - Courtney Beard
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Division of Depression and Anxiety Disorders, McLean Hospital, Belmont, MA, USA
| | - Poornima Kumar
- Center for Depression, Anxiety and Stress Disorders, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Isabelle M Rosso
- Center for Depression, Anxiety and Stress Disorders, McLean Hospital, Belmont, MA, USA.
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
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Paunova R, Ramponi C, Kandilarova S, Todeva-Radneva A, Latypova A, Stoyanov D, Kherif F. Degeneracy and disordered brain networks in psychiatric patients using multivariate structural covariance analyzes. Front Psychiatry 2023; 14:1272933. [PMID: 37908595 PMCID: PMC10614636 DOI: 10.3389/fpsyt.2023.1272933] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 10/02/2023] [Indexed: 11/02/2023] Open
Abstract
Introduction In this study, we applied multivariate methods to identify brain regions that have a critical role in shaping the connectivity patterns of networks associated with major psychiatric diagnoses, including schizophrenia (SCH), major depressive disorder (MDD) and bipolar disorder (BD) and healthy controls (HC). We used T1w images from 164 subjects: Schizophrenia (n = 17), bipolar disorder (n = 25), major depressive disorder (n = 68) and a healthy control group (n = 54). Methods We extracted regions of interest (ROIs) using a method based on the SHOOT algorithm of the SPM12 toolbox. We then performed multivariate structural covariance between the groups. For the regions identified as significant in t term of their covariance value, we calculated their eigencentrality as a measure of the influence of brain regions within the network. We applied a significance threshold of p = 0.001. Finally, we performed a cluster analysis to determine groups of regions that had similar eigencentrality profiles in different pairwise comparison networks in the observed groups. Results As a result, we obtained 4 clusters with different brain regions that were diagnosis-specific. Cluster 1 showed the strongest discriminative values between SCH and HC and SCH and BD. Cluster 2 had the strongest discriminative value for the MDD patients, cluster 3 - for the BD patients. Cluster 4 seemed to contribute almost equally to the discrimination between the four groups. Discussion Our results suggest that we can use the multivariate structural covariance method to identify specific regions that have higher predictive value for specific psychiatric diagnoses. In our research, we have identified brain signatures that suggest that degeneracy shapes brain networks in different ways both within and across major psychiatric disorders.
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Affiliation(s)
- Rositsa Paunova
- Department of Psychiatry and Medical Psychology, Medical University Plovdiv, Plovdiv, Bulgaria
- Research Institute, Medical University Plovdiv, Plovdiv, Bulgaria
| | - Cristina Ramponi
- Laboratory for Research in Neuroimaging, Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Sevdalina Kandilarova
- Department of Psychiatry and Medical Psychology, Medical University Plovdiv, Plovdiv, Bulgaria
- Research Institute, Medical University Plovdiv, Plovdiv, Bulgaria
| | - Anna Todeva-Radneva
- Department of Psychiatry and Medical Psychology, Medical University Plovdiv, Plovdiv, Bulgaria
- Research Institute, Medical University Plovdiv, Plovdiv, Bulgaria
| | - Adeliya Latypova
- Laboratory for Research in Neuroimaging, Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Drozdstoy Stoyanov
- Department of Psychiatry and Medical Psychology, Medical University Plovdiv, Plovdiv, Bulgaria
- Research Institute, Medical University Plovdiv, Plovdiv, Bulgaria
| | - Ferath Kherif
- Laboratory for Research in Neuroimaging, Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
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Tsai CL, Chou KH, Lee PL, Liang CS, Kuo CY, Lin GY, Lin YK, Hsu YC, Ko CA, Yang FC, Lin CP. Shared alterations in hippocampal structural covariance in subjective cognitive decline and migraine. Front Aging Neurosci 2023; 15:1191991. [PMID: 37409010 PMCID: PMC10318340 DOI: 10.3389/fnagi.2023.1191991] [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: 03/22/2023] [Accepted: 06/05/2023] [Indexed: 07/07/2023] Open
Abstract
Introduction Subjective cognitive decline (SCD) and migraine are often comorbid. Hippocampal structural abnormalities have been observed in individuals with both SCD and migraine. Given the known structural and functional heterogeneity along the long axis (anterior to posterior) of the hippocampus, we aimed to identify altered patterns of structural covariance within hippocampal subdivisions associated with SCD and migraine comorbidities. Methods A seed-based structural covariance network analysis was applied to examine large-scale anatomical network changes of the anterior and posterior hippocampus in individuals with SCD, migraine and healthy controls. Conjunction analyses were used to identify shared network-level alterations in the hippocampal subdivisions in individuals with both SCD and migraine. Results Altered structural covariance integrity of the anterior and posterior hippocampus was observed in the temporal, frontal, occipital, cingulate, precentral, and postcentral areas in individuals with SCD and migraine compared with healthy controls. Conjunction analysis revealed that, in both SCD and migraine, altered structural covariance integrity was shared between the anterior hippocampus and inferior temporal gyri and between the posterior hippocampus and precentral gyrus. Additionally, the structural covariance integrity of the posterior hippocampus-cerebellum axis was associated with the duration of SCD. Conclusion This study highlighted the specific role of hippocampal subdivisions and specific structural covariance alterations within these subdivisions in the pathophysiology of SCD and migraine. These network-level changes in structural covariance may serve as potential imaging signatures for individuals who have both SCD and migraine.
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Affiliation(s)
- Chia-Lin Tsai
- Department of Neurology, Tri-Service General Hospital, National Defense Medical Center, Taipei City, Taiwan
| | - Kun-Hsien Chou
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei City, Taiwan
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei City, Taiwan
| | - Pei-Lin Lee
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei City, Taiwan
| | - Chih-Sung Liang
- Department of Psychiatry, Beitou Branch, Tri-Service General Hospital, National Defense Medical Center, Taipei City, Taiwan
| | - Chen-Yuan Kuo
- Aging and Health Research Center, National Yang Ming Chiao Tung University, Taipei City, Taiwan
| | - Guan-Yu Lin
- Department of Neurology, Tri-Service General Hospital, National Defense Medical Center, Taipei City, Taiwan
| | - Yu-Kai Lin
- Department of Neurology, Tri-Service General Hospital, National Defense Medical Center, Taipei City, Taiwan
- Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei City, Taiwan
| | - Yi-Chih Hsu
- Department of Radiology, Tri-Service General Hospital, National Defense Medical Center, Taipei City, Taiwan
| | - Chien-An Ko
- Department of Neurology, Tri-Service General Hospital, National Defense Medical Center, Taipei City, Taiwan
| | - Fu-Chi Yang
- Department of Neurology, Tri-Service General Hospital, National Defense Medical Center, Taipei City, Taiwan
- Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei City, Taiwan
| | - Ching-Po Lin
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei City, Taiwan
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei City, Taiwan
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei City, Taiwan
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Liu H, Li W, Liu N, Tang J, Sun L, Xu J, Ji Y, Xie Y, Ding H, Ye Z, Yu C, Qin W. Structural covariances of prefrontal subregions selectively associate with dopamine-related gene coexpression and schizophrenia. Cereb Cortex 2023; 33:8035-8045. [PMID: 36935097 DOI: 10.1093/cercor/bhad096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 02/24/2023] [Accepted: 02/25/2023] [Indexed: 03/20/2023] Open
Abstract
Evidence highlights that dopamine (DA) system dysregulation and prefrontal cortex (PFC) dysfunction may underlie the pathophysiology of schizophrenia. However, the associations among DA genes, PFC morphometry, and schizophrenia have not yet been fully clarified. Based on the brain gene expression dataset from Allen Human Brain Atlas and structural magnetic resonance imaging data (NDIS = 1727, NREP = 408), we first identified 10 out of 22 PFC subregions whose gray matter volume (GMV) covariance profiles were reliably associated with their DA genes coexpression profiles, then four out of the identified 10 PFC subregions demonstrated abnormally increased GMV covariance with the hippocampus, insula, and medial frontal areas in schizophrenia patients (NCASE = 100; NCONTROL = 102). Moreover, based on a schizophrenia postmortem expression dataset, we found that the DA genes coexpression of schizophrenia was significantly reduced between the middle frontal gyrus and hippocampus, in which 21 DA genes showed significantly unsynchronized expression changes, and the 21 genes' brain expression were enriched in brain activity invoked by working memory, reward, speech production, and episodic memory. Our findings indicate the DA genes selectively regulate the structural covariance of PFC subregions by their coexpression profiles, which may underlie the disrupted GMV covariance and impaired cognitive functions in schizophrenia.
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Affiliation(s)
- Huaigui Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Wei Li
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Nana Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Jie Tang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Lixin Sun
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Jiayuan Xu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Yuan Ji
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Yingying Xie
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Hao Ding
- School of Medical Imaging, Tianjin Medical University, Tianjin 300070, China
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Chunshui Yu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Wen Qin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
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Wu X, Palaniyappan L, Yu G, Zhang K, Seidlitz J, Liu Z, Kong X, Schumann G, Feng J, Sahakian BJ, Robbins TW, Bullmore E, Zhang J. Morphometric dis-similarity between cortical and subcortical areas underlies cognitive function and psychiatric symptomatology: a preadolescence study from ABCD. Mol Psychiatry 2023; 28:1146-1158. [PMID: 36473996 DOI: 10.1038/s41380-022-01896-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Revised: 11/16/2022] [Accepted: 11/17/2022] [Indexed: 12/12/2022]
Abstract
Preadolescence is a critical period characterized by dramatic morphological changes and accelerated cortico-subcortical development. Moreover, the coordinated development of cortical and subcortical regions underlies the emerging cognitive functions during this period. Deviations in this maturational coordination may underlie various psychiatric disorders that begin during preadolescence, but to date these deviations remain largely uncharted. We constructed a comprehensive whole-brain morphometric similarity network (MSN) from 17 neuroimaging modalities in a large preadolescence sample (N = 8908) from Adolescent Brain Cognitive Development (ABCD) study and investigated its association with 10 cognitive subscales and 27 psychiatric subscales or diagnoses. Based on the MSNs, each brain was clustered into five modules with distinct cytoarchitecture and evolutionary relevance. While morphometric correlation was positive within modules, it was negative between modules, especially between isocortical and paralimbic/subcortical modules; this developmental dissimilarity was genetically linked to synapse and neurogenesis. The cortico-subcortical dissimilarity becomes more pronounced longitudinally in healthy children, reflecting developmental differentiation of segregated cytoarchitectonic areas. Higher cortico-subcortical dissimilarity (between the isocortical and paralimbic/subcortical modules) were related to better cognitive performance. In comparison, children with poor modular differentiation between cortex and subcortex displayed higher burden of externalizing and internalizing symptoms. These results highlighted cortical-subcortical morphometric dissimilarity as a dynamic maturational marker of cognitive and psychiatric status during the preadolescent stage and provided insights into brain development.
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Affiliation(s)
- Xinran Wu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Lena Palaniyappan
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Quebec, QC, Canada
- Robarts Research Institute, University of Western Ontario, London, ON, Canada
| | - Gechang Yu
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, 999077, Hong Kong SAR, China
| | - Kai Zhang
- School of Computer Science and Technology, East China Normal University, 200062, Shanghai, China
| | - Jakob Seidlitz
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Zhaowen Liu
- Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Xiangzhen Kong
- Department of Psychology and Behavioral Sciences, Zhejiang University, Zhejiang, China
| | - Gunter Schumann
- The Centre for Population Neuroscience and Stratified Medicine (PONS), ISTBI, Fudan University, Shanghai, China
- PONS Centre and SGDP Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- PONS Centre, Charite Mental Health, Dept. of Psychiatry and Psychotherapie, CCM, Charite Universitaetsmedizin Berlin, Berlin, Germany
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
- Shanghai Center for Mathematical Sciences, Shanghai, 200433, China
- Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, 200433, China
| | - Barbara J Sahakian
- Department of Psychiatry, Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK
| | - Trevor W Robbins
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China
- Cambridge shire and Peterborough NHS Trust, Elizabeth House, Fulbourn Hospital, Cambridge, UK
| | - Edward Bullmore
- Department of Psychiatry and Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK
- Department of Psychology and Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK
| | - Jie Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China.
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China.
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8
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Fan YS, Xu Y, Li Q, Chen Y, Guo X, Yang S, Guo J, Sheng W, Wang C, Gao Q, Chen H. Systematically mapping gray matter abnormal patterns in drug-naïve first-episode schizophrenia from childhood to adolescence. Cereb Cortex 2023; 33:1452-1461. [PMID: 35396845 DOI: 10.1093/cercor/bhac148] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Revised: 03/23/2022] [Accepted: 03/24/2022] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Schizophrenia originates early in neurodevelopment, underscoring the need to elaborate on anomalies in the still maturing brain of early-onset schizophrenia (EOS). METHODS Gray matter (GM) volumes were evaluated in 94 antipsychotic-naïve first-episode EOS patients and 100 typically developing (TD) controls. The anatomical profiles of changing GM deficits in EOS were detected using 2-way analyses of variance with diagnosis and age as factors, and its timing was further charted using stage-specific group comparisons. Interregional relationships of GM alterations were established using structural covariance network analyses. RESULTS Antagonistic interaction results suggested dynamic GM abnormalities of the left fusiform gyrus, inferior occipital gyrus, and lingual gyrus in EOS. These regions comprise a dominating part of the ventral stream, a ventral occipitotemporal (vOT) network engaged in early social information processing. GM abnormalities were mainly located in the vOT regions in childhood-onset patients, whereas in the rostral prefrontal cortex (rPFC) in adolescent-onset patients. Moreover, compared with TD controls, patients' GM synchronization with the ventral stream was disrupted in widespread high-order social perception regions including the rPFC and salience network. CONCLUSIONS The current findings reveal age-related anatomical abnormalities of the social perception system in pediatric patients with schizophrenia.
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Affiliation(s)
- Yun-Shuang Fan
- Sichuan Provincial Center for Mental Health, The Center of Psychosomatic Medicine of Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 611731, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yong Xu
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan 030000, China
| | - Qiang Li
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan 030000, China
| | - Yuyan Chen
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.,MOE Key Lab for Neuroinformation; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Xiaonan Guo
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
| | - Siqi Yang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.,MOE Key Lab for Neuroinformation; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Jing Guo
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.,MOE Key Lab for Neuroinformation; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Wei Sheng
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.,MOE Key Lab for Neuroinformation; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Chong Wang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.,MOE Key Lab for Neuroinformation; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Qing Gao
- MOE Key Lab for Neuroinformation; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Huafu Chen
- Sichuan Provincial Center for Mental Health, The Center of Psychosomatic Medicine of Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 611731, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.,MOE Key Lab for Neuroinformation; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, China
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9
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Dang M, Yang C, Chen K, Lu P, Li H, Zhang Z. Hippocampus-centred grey matter covariance networks predict the development and reversion of mild cognitive impairment. Alzheimers Res Ther 2023; 15:27. [PMID: 36732782 PMCID: PMC9893696 DOI: 10.1186/s13195-023-01167-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 01/09/2023] [Indexed: 02/04/2023]
Abstract
BACKGROUND Mild cognitive impairment (MCI) has been thought of as the transitional stage between normal ageing and Alzheimer's disease, involving substantial changes in brain grey matter structures. As most previous studies have focused on single regions (e.g. the hippocampus) and their changes during MCI development and reversion, the relationship between grey matter covariance among distributed brain regions and clinical development and reversion of MCI remains unclear. METHODS With samples from two independent studies (155 from the Beijing Aging Brain Rejuvenation Initiative and 286 from the Alzheimer's Disease Neuroimaging Initiative), grey matter covariance of default, frontoparietal, and hippocampal networks were identified by seed-based partial least square analyses, and random forest models were applied to predict the progression from normal cognition to MCI (N-t-M) and the reversion from MCI to normal cognition (M-t-N). RESULTS With varying degrees, the grey matter covariance in the three networks could predict N-t-M progression (AUC = 0.692-0.792) and M-t-N reversion (AUC = 0.701-0.809). Further analyses indicated that the hippocampus has emerged as an important region in reversion prediction within all three brain networks, and even though the hippocampus itself could predict the clinical reversion of M-t-N, the grey matter covariance showed higher prediction accuracy for early progression of N-t-M. CONCLUSIONS Our findings are the first to report grey matter covariance changes in MCI development and reversion and highlight the necessity of including grey matter covariance changes along with hippocampal degeneration in the early detection of MCI and Alzheimer's disease.
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Affiliation(s)
- Mingxi Dang
- grid.20513.350000 0004 1789 9964State Key Laboratory of Cognitive Neuroscience and Learning, Faculty of Psychology, Beijing Normal University, Beijing, 100875 China
| | - Caishui Yang
- grid.20513.350000 0004 1789 9964State Key Laboratory of Cognitive Neuroscience and Learning, Faculty of Psychology, Beijing Normal University, Beijing, 100875 China ,grid.20513.350000 0004 1789 9964School of Systems Science, Beijing Normal University, Beijing, 100875 China
| | - Kewei Chen
- grid.418204.b0000 0004 0406 4925Banner Alzheimer’s Institute, Phoenix, AZ 85006 USA
| | - Peng Lu
- grid.20513.350000 0004 1789 9964State Key Laboratory of Cognitive Neuroscience and Learning, Faculty of Psychology, Beijing Normal University, Beijing, 100875 China
| | - He Li
- grid.410318.f0000 0004 0632 3409Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, 100700 China
| | - Zhanjun Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Faculty of Psychology, Beijing Normal University, Beijing, 100875, China.
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10
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Lin S, Guo Z, Chen S, Lin X, Ye M, Qiu Y. Progressive Brain Structural Impairment Assessed via Network and Causal Analysis in Patients With Hepatitis B Virus-Related Cirrhosis. Front Neurol 2022; 13:849571. [PMID: 35599731 PMCID: PMC9120530 DOI: 10.3389/fneur.2022.849571] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 04/12/2022] [Indexed: 12/03/2022] Open
Abstract
Objectives This research amid to elucidate the disease stage-specific spatial patterns and the probable sequences of gray matter (GM) deterioration as well as the causal relationship among structural network components in hepatitis B virus-related cirrhosis (HBV-RC) patients. Methods Totally 30 HBV-RC patients and 38 healthy controls (HC) were recruited for this study. High-resolution T1-weighted magnetic resonance imaging and psychometric hepatic encephalopathy score (PHES) were evaluated in all participants. Voxel-based morphometry (VBM), structural covariance network (SCN), and causal SCN (CaSCN) were applied to identify the disease stage-specific GM abnormalities in morphology and network, as well as their causal relationship. Results Compared to HC (0.443 ± 0.073 cm3), the thalamus swelled significantly in the no minimal hepatic encephalopathy (NMHE) stage (0.607 ± 0.154 cm3, p <0.05, corrected) and further progressed and expanded to the bilateral basal ganglia, the cortices, and the cerebellum in the MHE stage (p < 0.05, corrected). Furthermore, the thalamus swelling had a causal effect on other parts of cortex-basal ganglia-thalamus circuits (p < 0.05, corrected), which was negatively correlated with cognitive performance (r = −0.422, p < 0.05). Moreover, the thalamus-related SCN also displayed progressive deterioration as the disease advanced in HBV-RC patients (p < 0.05, corrected). Conclusion Progressive deterioration of GM morphology and SCN exists in HBV-RC patients during advanced disease, displaying thalamus-related causal effects. These findings indicate that bilateral thalamus morphology as well as the thalamus-related network may serve as an in vivo biomarker for monitoring the progression of the disease in HBV-RC patients.
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Affiliation(s)
- Shiwei Lin
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
- Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, China
| | - Zheng Guo
- Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, China
| | - Shengli Chen
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
- Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, China
| | - Xiaoshan Lin
- Department of Hematology and Oncology, International Cancer Center, Shenzhen Key Laboratory of Precision Medicine for Hematological Malignancies, Shenzhen University General Hospital, Shenzhen University Clinical Medical Academy, Shenzhen University Health Science Center, Shenzhen, China
| | - Min Ye
- Department of Geriatrics, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
- Department of Geriatrics, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, China
- *Correspondence: Min Ye
| | - Yingwei Qiu
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
- Yingwei Qiu
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11
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Structural covariance changes in major cortico-basal ganglia and thalamic networks in amyloid-positive patients with white matter hyperintensities. Neurobiol Aging 2022; 117:117-127. [DOI: 10.1016/j.neurobiolaging.2022.05.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 05/19/2022] [Accepted: 05/23/2022] [Indexed: 11/23/2022]
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12
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Lai H, Kong X, Zhao Y, Pan N, Zhang X, He M, Wang S, Gong Q. Patterns of a structural covariance network associated with dispositional optimism during late adolescence. Neuroimage 2022; 251:119009. [PMID: 35182752 DOI: 10.1016/j.neuroimage.2022.119009] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 01/28/2022] [Accepted: 02/15/2022] [Indexed: 02/08/2023] Open
Abstract
Dispositional optimism (hereinafter, optimism), as a vital character strength, reflects the tendency to hold generalized positive expectancies for future outcomes. A great number of studies have consistently shown the importance of optimism to a spectrum of physical and mental health outcomes. However, less attention has been given to the intrinsic neurodevelopmental patterns associated with interindividual differences in optimism. Here, we investigated this important question in a large sample comprising 231 healthy adolescents (16-20 years old) via structural magnetic resonance imaging and behavioral tests. We constructed individual structural covariance networks based on cortical gyrification using a recent novel approach combining probability density estimation and Kullback-Leibler divergence and estimated global (global efficiency, local efficiency and small-worldness) and regional (betweenness centrality) properties of these constructed networks using graph theoretical analysis. Partial correlations adjusted for age, sex and estimated total intracranial volume showed that optimism was positively related to global and local efficiency but not small-worldness. Partial least squares correlations indicated that optimism was positively linked to a pronounced betweenness centrality pattern, in which twelve cognition-, emotion-, and motivation-related regions made robust and reliable contributions. These findings remained basically consistent after additionally controlling for family socioeconomic status and showed significant correlations with optimism scores from 2.5 years before, which replicated the main findings. The current work, for the first time, delineated characteristics of the cortical gyrification covariance network associated with optimism, extending previous neurobiological understandings of optimism, which may navigate the development of interventions on a neural network level aimed at raising optimism.
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Affiliation(s)
- Han Lai
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China; Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Department of Psychology, Army Medical University, Chongqing, China
| | - Xiangzhen Kong
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China
| | - Yajun Zhao
- School of Education and Psychology, Southwest Minzu University, Chengdu, China
| | - Nanfang Pan
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China; Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Xun Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China; Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Min He
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China; Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Song Wang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China; Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China.
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China; Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China.
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13
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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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14
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Resolving heterogeneity in schizophrenia through a novel systems approach to brain structure: individualized structural covariance network analysis. Mol Psychiatry 2021; 26:7719-7731. [PMID: 34316005 DOI: 10.1038/s41380-021-01229-4] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 06/15/2021] [Accepted: 07/05/2021] [Indexed: 12/12/2022]
Abstract
Reliable mapping of system-level individual differences is a critical first step toward precision medicine for complex disorders such as schizophrenia. Disrupted structural covariance indicates a system-level brain maturational disruption in schizophrenia. However, most studies examine structural covariance at the group level. This prevents subject-level inferences. Here, we introduce a Network Template Perturbation approach to construct individual differential structural covariance network (IDSCN) using regional gray-matter volume. IDSCN quantifies how structural covariance between two nodes in a patient deviates from the normative covariance in healthy subjects. We analyzed T1 images from 1287 subjects, including 107 first-episode (drug-naive) patients and 71 controls in the discovery datasets and established robustness in 213 first-episode (drug-naive), 294 chronic, 99 clinical high-risk patients, and 494 controls from the replication datasets. Patients with schizophrenia were highly variable in their altered structural covariance edges; the number of altered edges was related to severity of hallucinations. Despite this variability, a subset of covariance edges, including the left hippocampus-bilateral putamen/globus pallidus edges, clustered patients into two distinct subgroups with opposing changes in covariance compared to controls, and significant differences in their anxiety and depression scores. These subgroup differences were stable across all seven datasets with meaningful genetic associations and functional annotation for the affected edges. We conclude that the underlying physiology of affective symptoms in schizophrenia involves the hippocampus and putamen/pallidum, predates disease onset, and is sufficiently consistent to resolve morphological heterogeneity throughout the illness course. The two schizophrenia subgroups identified thus have implications for the nosology and clinical treatment.
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15
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Shu J, Qiang Q, Yan Y, Ren Y, Wei W, Zhang L. Aberrant Topological Patterns of Structural Covariance Networks in Cognitively Normal Elderly Adults With Mild Behavioral Impairment. Front Neuroanat 2021; 15:738100. [PMID: 34658800 PMCID: PMC8511486 DOI: 10.3389/fnana.2021.738100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 09/09/2021] [Indexed: 11/13/2022] Open
Abstract
Mild behavioral impairment (MBI), characterized by the late-life onset of sustained and meaningful neuropsychiatric symptoms, is increasingly recognized as a prodromal stage of dementia. However, the underlying neural mechanisms of MBI remain unclear. Here, we examined alterations in the topological organization of the structural covariance networks of patients with MBI (N = 32) compared with normal controls (N = 38). We found that the gray matter structural covariance networks of both the patients with MBI and controls exhibited a small-world topology evidenced by sigma value larger than one. The patients with MBI had significantly decreased clustering coefficients at several network densities and local efficiency at densities ranging from 0.05 to 0.26, indicating decreased local segregation. No significant differences in the characteristic path length, gamma value, sigma value, or global efficiency were detected. Locally, the patients with MBI showed significantly decreased nodal betweenness centrality in the left middle frontal gyrus, right inferior frontal gyrus (opercular part), and left Heschl gyrus and increased betweenness centrality in the left gyrus rectus, right insula, bilateral precuneus, and left thalamus. Moreover, the difference in the bilateral precuneus survived after correcting for multiple comparisons. In addition, a different number and distribution of hubs was identified in patients with MBI, showing more paralimbic hubs than observed in the normal controls. In conclusion, we revealed abnormal topological patterns of the structural covariance networks in patients with MBI and offer new insights into the network dysfunctional mechanisms of MBI.
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Affiliation(s)
- Jun Shu
- Department of Neurology, Cognitive Disorders Center, Huadong Hospital, Fudan University, Shanghai, China
| | - Qiang Qiang
- Department of Neurology, Cognitive Disorders Center, Huadong Hospital, Fudan University, Shanghai, China
| | - Yuning Yan
- Department of Neurology, Cognitive Disorders Center, Huadong Hospital, Fudan University, Shanghai, China
| | - Yiqing Ren
- Department of Neurology, Cognitive Disorders Center, Huadong Hospital, Fudan University, Shanghai, China
| | - Wenshi Wei
- Department of Neurology, Cognitive Disorders Center, Huadong Hospital, Fudan University, Shanghai, China
| | - Li Zhang
- Department of Neurology, Cognitive Disorders Center, Huadong Hospital, Fudan University, Shanghai, China
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16
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Zhao K, Zheng Q, Che T, Dyrba M, Li Q, Ding Y, Zheng Y, Liu Y, Li S. Regional radiomics similarity networks (R2SNs) in the human brain: Reproducibility, small-world properties and a biological basis. Netw Neurosci 2021; 5:783-797. [PMID: 34746627 PMCID: PMC8567836 DOI: 10.1162/netn_a_00200] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Accepted: 05/08/2021] [Indexed: 12/13/2022] Open
Abstract
A structural covariance network (SCN) has been used successfully in structural magnetic resonance imaging (sMRI) studies. However, most SCNs have been constructed by a unitary marker that is insensitive for discriminating different disease phases. The aim of this study was to devise a novel regional radiomics similarity network (R2SN) that could provide more comprehensive information in morphological network analysis. R2SNs were constructed by computing the Pearson correlations between the radiomics features extracted from any pair of regions for each subject (AAL atlas). We further assessed the small-world property of R2SNs, and we evaluated the reproducibility in different datasets and through test-retest analysis. The relationships between the R2SNs and general intelligence/interregional coexpression of genes were also explored. R2SNs could be replicated in different datasets, regardless of the use of different feature subsets. R2SNs showed high reproducibility in the test-retest analysis (intraclass correlation coefficient > 0.7). In addition, the small-word property (σ > 2) and the high correlation between gene expression (R = 0.29, p < 0.001) and general intelligence were determined for R2SNs. Furthermore, the results have also been repeated in the Brainnetome atlas. R2SNs provide a novel, reliable, and biologically plausible method to understand human morphological covariance based on sMRI.
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Affiliation(s)
- Kun Zhao
- School of Biological Science & Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
| | - Qiang Zheng
- School of Computer and Control Engineering, Yantai University, Yantai, China
| | - Tongtong Che
- School of Biological Science & Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
| | - Martin Dyrba
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
| | - Qiongling Li
- School of Biological Science & Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
| | - Yanhui Ding
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Yuanjie Zheng
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Yong Liu
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, China
| | - Shuyu Li
- School of Biological Science & Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
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17
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Jiang Y, Wang Y, Huang H, He H, Tang Y, Su W, Xu L, Wei Y, Zhang T, Hu H, Wang J, Yao D, Wang J, Luo C. Antipsychotics Effects on Network-Level Reconfiguration of Cortical Morphometry in First-Episode Schizophrenia. Schizophr Bull 2021; 48:231-240. [PMID: 34313782 PMCID: PMC8781340 DOI: 10.1093/schbul/sbab082] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Cortical thickness reductions are evident in schizophrenia (SZ). Associations between antipsychotic medications (APMs) and cortical morphometry have been explored in SZ patients. This raises the question of whether the reconfiguration of morphological architecture by APM plays potential compensatory roles for abnormalities in the cerebral cortex. Structural magnetic resonance imaging was obtained from 127 medication-naive first-episode SZ patients and 133 matched healthy controls. Patients received 12 weeks of APM and were categorized as responders (n = 75) or nonresponders (NRs, n = 52) at follow-up. Using surface-based morphometry and structural covariance (SC) analysis, this study investigated the short-term effects of antipsychotics on cortical thickness and cortico-cortical covariance. Global efficiency was computed to characterize network integration of the large-scale structural connectome. The relationship between covariance and cortical thinning was examined by SC analysis among the top-n regions with thickness reduction. Widespread cortical thickness reductions were observed in pre-APM patients. Post-APM patients showed more reductions in cortical thickness, even in the frontotemporal regions without baseline reductions. Covariance analysis revealed strong cortico-cortical covariance and higher network integration in responders than in NRs. For the NRs, some of the prefrontal and temporal nodes were not covariant between the top-n regions with cortical thickness reduction. Antipsychotic effects are not restricted to a single brain region but rather exhibit a network-level covariance pattern. Neuroimaging connectomics highlights the positive effects of antipsychotics on the reconfiguration of brain architecture, suggesting that abnormalities in regional morphology may be compensated by increasing interregional covariance when symptoms are controlled by antipsychotics.
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Affiliation(s)
- Yuchao Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, PR China,High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, PR China
| | - Yingchan Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Huan Huang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, PR China,High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, PR China
| | - Hui He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, PR China
| | - Yingying Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Wenjun Su
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Lihua Xu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Yanyan Wei
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Tianhong Zhang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Hao Hu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Jinhong Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, PR China,Research Unit of NeuroInformation (2019RU035), Chinese Academy of Medical Sciences, Chengdu, PR China
| | - Jijun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China,CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Science, Shanghai, PR China,Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai, PR China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, PR China,High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, PR China,Research Unit of NeuroInformation (2019RU035), Chinese Academy of Medical Sciences, Chengdu, PR China,To whom correspondence should be addressed; University of Electronic Science and Technology of China, Second North Jianshe Road, Chengdu 610054, PR China; tel: 86-28-83201018, fax: 86-28-83208238, e-mail:
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18
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Chou KH, Lee PL, Liang CS, Lee JT, Kao HW, Tsai CL, Lin GY, Lin YK, Lin CP, Yang FC. Identifying neuroanatomical signatures in insomnia and migraine comorbidity. Sleep 2021; 44:5911972. [PMID: 32979047 DOI: 10.1093/sleep/zsaa202] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 08/26/2020] [Indexed: 01/03/2023] Open
Abstract
STUDY OBJECTIVES While insomnia and migraine are often comorbid, the shared and distinct neuroanatomical substrates underlying these disorders and the brain structures associated with the comorbidity are unknown. We aimed to identify patterns of neuroanatomical substrate alterations associated with migraine and insomnia comorbidity. METHODS High-resolution T1-weighted images were acquired from subjects with insomnia, migraine, and comorbid migraine and insomnia, respectively, and healthy controls (HC). Direct group comparisons with HC followed by conjunction analyses identified shared regional gray matter volume (GMV) alterations between the disorders. To further examine large-scale anatomical network changes, a seed-based structural covariance network (SCN) analysis was applied. Conjunction analyses also identified common SCN alterations in two disease groups, and we further evaluated these shared regional and global neuroanatomical signatures in the comorbid group. RESULTS Compared with controls, patients with migraine and insomnia showed GMV changes in the cerebellum and the lingual, precentral, and postcentral gyri (PCG). The bilateral PCG were common GMV alteration sites in both groups, with decreased structural covariance integrity observed in the cerebellum. In patients with comorbid migraine and insomnia, shared regional GMV and global SCN changes were consistently observed. The GMV of the right PCG also correlated with sleep quality in these patients. CONCLUSION These findings highlight the specific role of the PCG in the shared pathophysiology of insomnia and migraine from a regional and global brain network perspective. These multilevel neuroanatomical changes could be used as potential image markers to decipher the comorbidity of the two disorders.
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Affiliation(s)
- Kun-Hsien Chou
- Brain Research Center, National Yang-Ming University, Taipei, Taiwan.,Institute of Neuroscience, National Yang-Ming University, Taipei, Taiwan
| | - Pei-Lin Lee
- Institute of Neuroscience, National Yang-Ming University, Taipei, Taiwan
| | - Chih-Sung Liang
- Department of Psychiatry, Beitou Branch, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Jiunn-Tay Lee
- Department of Neurology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan.,Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei, Taiwan
| | - Hung-Wen Kao
- Department of Radiology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Chia-Lin Tsai
- Department of Neurology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Guan-Yu Lin
- Department of Neurology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Yu-Kai Lin
- Department of Neurology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan.,Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei, Taiwan
| | - Ching-Po Lin
- Brain Research Center, National Yang-Ming University, Taipei, Taiwan.,Institute of Neuroscience, National Yang-Ming University, Taipei, Taiwan.,Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan
| | - Fu-Chi Yang
- Department of Neurology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
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19
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Tuominen L, DeCross SN, Boeke E, Cassidy CM, Freudenreich O, Shinn AK, Tootell RBH, Holt DJ. Neural Abnormalities in Fear Generalization in Schizophrenia and Associations With Negative Symptoms. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2021; 6:1165-1175. [PMID: 33524600 DOI: 10.1016/j.bpsc.2021.01.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 01/04/2021] [Accepted: 01/12/2021] [Indexed: 10/22/2022]
Abstract
BACKGROUND Associative learning and memory processes, including the generalization of previously learned associations, may be altered in schizophrenia. Deficits in schizophrenia in stimulus generalization, one of the simplest forms of memory, could interfere with the ability to efficiently categorize related, similar information, potentially leading to impairments in daily functioning. METHODS To measure generalization in schizophrenia, 37 individuals with a nonaffective psychotic disorder and 32 demographically matched healthy control subjects underwent a Pavlovian fear conditioning and generalization procedure, which accounted for variation in perceptual ability across participants, while undergoing functional magnetic resonance imaging. Skin conductance and neural responses to conditioned (CS+), neutral (CS-), and generalization stimuli were measured. Explicit memory ratings reflecting successful generalization were also collected after the scanning, as well as measures of symptom severity. RESULTS Compared with healthy control subjects, individuals with nonaffective psychotic disorders showed significant deficits in fear generalization across multiple measurements, with impairments in memory ratings and reductions in activation and deactivation of the salience and default networks, respectively, during fear generalization. Moreover, in the psychotic disorder group, greater behavioral and neural abnormalities in generalization were associated with higher levels of negative symptoms. CONCLUSIONS Fear generalization is impaired in psychotic illness. Given that successful generalization relies on a dynamic balance between excitatory and inhibitory neurotransmission, these results reveal a potentially quantifiable mechanism linked to negative symptoms that can be investigated further in future human and experimental animal studies.
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Affiliation(s)
- Lauri Tuominen
- University of Ottawa Institute of Mental Health Research, Ottawa, Ontario, Canada; Department of Psychiatry, Massachusetts General Hospital, Charlestown, Massachusetts; Harvard Medical School, Boston, Massachusetts
| | - Stephanie N DeCross
- Department of Psychiatry, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Emily Boeke
- Department of Psychiatry, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Clifford M Cassidy
- University of Ottawa Institute of Mental Health Research, Ottawa, Ontario, Canada
| | - Oliver Freudenreich
- Department of Psychiatry, Massachusetts General Hospital, Charlestown, Massachusetts; Harvard Medical School, Boston, Massachusetts
| | - Ann K Shinn
- Harvard Medical School, Boston, Massachusetts; Psychotic Disorders Division, McLean Hospital, Belmont, Massachusetts
| | - Roger B H Tootell
- Harvard Medical School, Boston, Massachusetts; Department of Radiology, Harvard Medical School, Boston, Massachusetts; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts
| | - Daphne J Holt
- Department of Psychiatry, Massachusetts General Hospital, Charlestown, Massachusetts; Harvard Medical School, Boston, Massachusetts; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts.
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20
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Muller AM, Pennington DL, Meyerhoff DJ. Substance-Specific and Shared Gray Matter Signatures in Alcohol, Opioid, and Polysubstance Use Disorder. Front Psychiatry 2021; 12:795299. [PMID: 35115969 PMCID: PMC8803650 DOI: 10.3389/fpsyt.2021.795299] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 12/27/2021] [Indexed: 11/13/2022] Open
Abstract
Substance use disorders (SUD) have been shown to be associated with gray matter (GM) loss, particularly in the frontal cortex. However, unclear is to what degree these regional GM alterations are substance-specific or shared across different substances, and if these regional GM alterations are independent of each other or the result of system-level processes at the intrinsic connectivity network level. The T1 weighted MRI data of 65 treated patients with alcohol use disorder (AUD), 27 patients with opioid use disorder (OUD) on maintenance therapy, 21 treated patients with stimulant use disorder comorbid with alcohol use disorder (polysubstance use disorder patients, PSU), and 21 healthy controls were examined via data-driven vertex-wise and voxel-wise GM analyses. Then, structural covariance analyses and open-access fMRI database analyses were used to map the cortical thinning patterns found in the three SUD groups onto intrinsic functional systems. Among AUD and OUD, we identified both common cortical thinning in right anterior brain regions as well as SUD-specific regional GM alterations that were not present in the PSU group. Furthermore, AUD patients had not only the most extended regional thinning but also significantly smaller subcortical structures and cerebellum relative to controls, OUD and PSU individuals. The system-level analyses revealed that AUD and OUD showed cortical thinning in several functional systems. In the AUD group the default mode network was clearly most affected, followed by the salience and executive control networks, whereas the salience and somatomotor network were highlighted as critical for understanding OUD. Structural brain alterations in groups with different SUDs are largely unique in their spatial extent and functional network correlates.
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Affiliation(s)
- Angela M Muller
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States.,VA Advanced Imaging Research Center (VAARC), San Francisco VA Medical Center, San Francisco, CA, United States
| | - David L Pennington
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, San Francisco, CA, United States.,San Francisco Veterans Affairs Health Care System (SFVAHCS), San Francisco, CA, United States
| | - Dieter J Meyerhoff
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States.,VA Advanced Imaging Research Center (VAARC), San Francisco VA Medical Center, San Francisco, CA, United States
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21
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Rollins CPE, Garrison JR, Arribas M, Seyedsalehi A, Li Z, Chan RCK, Yang J, Wang D, Liò P, Yan C, Yi ZH, Cachia A, Upthegrove R, Deakin B, Simons JS, Murray GK, Suckling J. Evidence in cortical folding patterns for prenatal predispositions to hallucinations in schizophrenia. Transl Psychiatry 2020; 10:387. [PMID: 33159044 PMCID: PMC7648757 DOI: 10.1038/s41398-020-01075-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 09/30/2020] [Accepted: 10/22/2020] [Indexed: 12/26/2022] Open
Abstract
All perception is a construction of the brain from sensory input. Our first perceptions begin during gestation, making fetal brain development fundamental to how we experience a diverse world. Hallucinations are percepts without origin in physical reality that occur in health and disease. Despite longstanding research on the brain structures supporting hallucinations and on perinatal contributions to the pathophysiology of schizophrenia, what links these two distinct lines of research remains unclear. Sulcal patterns derived from structural magnetic resonance (MR) images can provide a proxy in adulthood for early brain development. We studied two independent datasets of patients with schizophrenia who underwent clinical assessment and 3T MR imaging from the United Kingdom and Shanghai, China (n = 181 combined) and 63 healthy controls from Shanghai. Participants were stratified into those with (n = 79 UK; n = 22 Shanghai) and without (n = 43 UK; n = 37 Shanghai) hallucinations from the PANSS P3 scores for hallucinatory behaviour. We quantified the length, depth, and asymmetry indices of the paracingulate and superior temporal sulci (PCS, STS), which have previously been associated with hallucinations in schizophrenia, and constructed cortical folding covariance matrices organized by large-scale functional networks. In both ethnic groups, we demonstrated a significantly shorter left PCS in patients with hallucinations compared to those without, and to healthy controls. Reduced PCS length and STS depth corresponded to focal deviations in their geometry and to significantly increased covariance within and between areas of the salience and auditory networks. The discovery of neurodevelopmental alterations contributing to hallucinations establishes testable models for these enigmatic, sometimes highly distressing, perceptions and provides mechanistic insight into the pathological consequences of prenatal origins.
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Affiliation(s)
- Colleen P. E. Rollins
- grid.5335.00000000121885934Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Jane R. Garrison
- grid.5335.00000000121885934Department of Psychology, University of Cambridge, Cambridge, UK
| | - Maite Arribas
- grid.5335.00000000121885934Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK ,grid.13097.3c0000 0001 2322 6764Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, UK
| | - Aida Seyedsalehi
- grid.13097.3c0000 0001 2322 6764Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, UK ,grid.450563.10000 0004 0412 9303Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - Zhi Li
- grid.9227.e0000000119573309Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Raymond C. K. Chan
- grid.9227.e0000000119573309Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Junwei Yang
- grid.5335.00000000121885934Department of Computer Science and Technology, University of Cambridge, Cambridge, UK
| | - Duo Wang
- grid.5335.00000000121885934Department of Computer Science and Technology, University of Cambridge, Cambridge, UK
| | - Pietro Liò
- grid.5335.00000000121885934Department of Computer Science and Technology, University of Cambridge, Cambridge, UK
| | - Chao Yan
- grid.22069.3f0000 0004 0369 6365Key Laboratory of Brain Functional Genomics (MOE & STCSM), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Zheng-hui Yi
- grid.16821.3c0000 0004 0368 8293Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Arnaud Cachia
- Université de Paris, LaPsyDÉ, CNRS, F-75005 Paris, France ,Université de Paris, IPNP, INSERM, F-75005 Paris, France
| | - Rachel Upthegrove
- grid.6572.60000 0004 1936 7486Institute for Mental Health, University of Birmingham, Birmingham, UK
| | - Bill Deakin
- grid.5379.80000000121662407Neuroscience and Psychiatry Unit, The University of Manchester, Manchester, UK
| | - Jon S. Simons
- grid.5335.00000000121885934Department of Psychology, University of Cambridge, Cambridge, UK
| | - Graham K. Murray
- grid.5335.00000000121885934Department of Psychiatry, University of Cambridge, Cambridge, UK ,grid.450563.10000 0004 0412 9303Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - John Suckling
- grid.5335.00000000121885934Department of Psychiatry, University of Cambridge, Cambridge, UK
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22
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Carmon J, Heege J, Necus JH, Owen TW, Pipa G, Kaiser M, Taylor PN, Wang Y. Reliability and comparability of human brain structural covariance networks. Neuroimage 2020; 220:117104. [PMID: 32621973 DOI: 10.1016/j.neuroimage.2020.117104] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 05/01/2020] [Accepted: 06/25/2020] [Indexed: 12/11/2022] Open
Abstract
Structural covariance analysis is a widely used structural MRI analysis method which characterises the co-relations of morphology between brain regions over a group of subjects. To our knowledge, little has been investigated in terms of the comparability of results between different data sets of healthy human subjects, as well as the reliability of results over the same subjects in different rescan sessions, image resolutions, or FreeSurfer versions. In terms of comparability, our results show substantial differences in the structural covariance matrix between data sets of age- and sex-matched healthy human adults. These differences persist after univariate site correction, they are exacerbated by low sample sizes, and they are most pronounced when using average cortical thickness as a morphological measure. Down-stream graph theoretic analyses further show statistically significant differences. In terms of reliability, substantial differences were also found when comparing repeated scan sessions of the same subjects, image resolutions, and even FreeSurfer versions of the same image. We could further estimate the relative measurement error and showed that it is largest when using cortical thickness as a morphological measure. Using simulated data, we argue that cortical thickness is least reliable because of larger relative measurement errors. Practically, we make the following recommendations (1) combining subjects across sites into one group should be avoided, particularly if sites differ in image resolutions, subject demographics, or preprocessing steps; (2) surface area and volume should be preferred as morphological measures over cortical thickness; (3) a large number of subjects (n≫30 for the Desikan-Killiany parcellation) should be used to estimate structural covariance; (4) measurement error should be assessed where repeated measurements are available; (5) if combining sites is critical, univariate (per ROI) site-correction is insufficient, but error covariance (between ROIs) should be explicitly measured and modelled.
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Affiliation(s)
- Jona Carmon
- Institute of Cognitive Science, Osnabrueck University, Osnabrueck, Germany
| | - Jil Heege
- Humboldt University Berlin, Berlin, Germany
| | - Joe H Necus
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Complex Systems Group, School of Computing, Newcastle University, Newcastle upon Tyne, UK; Institute of Neuroscience, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Thomas W Owen
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Complex Systems Group, School of Computing, Newcastle University, Newcastle upon Tyne, UK
| | - Gordon Pipa
- Institute of Cognitive Science, Osnabrueck University, Osnabrueck, Germany
| | - Marcus Kaiser
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Complex Systems Group, School of Computing, Newcastle University, Newcastle upon Tyne, UK; Institute of Neuroscience, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK; School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Peter N Taylor
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Complex Systems Group, School of Computing, Newcastle University, Newcastle upon Tyne, UK; Institute of Neuroscience, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK; Institute of Neurology, University College London, UK
| | - Yujiang Wang
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Complex Systems Group, School of Computing, Newcastle University, Newcastle upon Tyne, UK; Institute of Neuroscience, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK; Institute of Neurology, University College London, UK.
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23
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Zhang X, Liu W, Guo F, Li C, Wang X, Wang H, Yin H, Zhu Y. Disrupted structural covariance network in first episode schizophrenia patients: Evidence from a large sample MRI-based morphometric study. Schizophr Res 2020; 224:24-32. [PMID: 33203611 DOI: 10.1016/j.schres.2020.11.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 09/30/2020] [Accepted: 11/02/2020] [Indexed: 10/23/2022]
Abstract
BACKGROUND Recent progress in neuroscience research has provided evidence that schizophrenia is a disease that involves dysconnectivity of brain networks. Widespread gray matter loss was commonly observed but how these gray matter abnormalities are characterized at the large-scale network-level in schizophrenia, especially patients with first-episode (FE-SCZ) remains unclear. METHODS In this study, gray matter structural network aberrations were investigated by applying structural covariance network analysis to 193 first episode schizophrenia patients and 178 age and gender-matched healthy controls (HCs). The mean gray matter volume in seed regions relating to eight specific networks (visual, auditory, sensorimotor, speech, semantic, default-mode, executive control, and salience) were extracted, and voxel-wise analyses of covariance were conducted to compare the association between whole-brain gray matter volume and each seed region for FE-SCZ and HCs. RESULTS The auditory network was less extended in FE-SCZ compared with HCs, with a significant decrease in the structural association between the Hesch's gyrus and the middle frontal gyrus and the superior frontal gyrus. Hyperconnectivity was observed in executive control network with a significant increase in the structural association between the dorsal lateral prefrontal cortex and the superior frontal gyrus and supplementary motor area. CONCLUSION Our research shows that seed based structural covariance analysis can well characterize multiple large-scale networks, the observed changes might underly the hallucinations and cognitive impairments observed in FE-SCZ. Given that these patients were experiencing their first episode of schizophrenia, our findings suggest that such structural network deficits are present at an early stage in this disorder.
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Affiliation(s)
- Xiao Zhang
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Wenming Liu
- Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Fan Guo
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Chen Li
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Xingrui Wang
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Huaning Wang
- Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Hong Yin
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China.
| | - Yuanqiang Zhu
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China.
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24
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Wang YM, Yang ZY, Wang Y, Wang YY, Cai XL, Zhang RT, Hu HX, Cheung EFC, Chan RCK. Grey matter volume and structural covariance associated with schizotypy. Schizophr Res 2020; 224:88-94. [PMID: 33046333 DOI: 10.1016/j.schres.2020.09.021] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 06/16/2020] [Accepted: 09/24/2020] [Indexed: 02/09/2023]
Abstract
In this study, we applied brain grey matter volume and structural covariance methods on T1 weighted images to delineate potential structural brain changes in individuals with high schizotypy, who were defined as healthy individuals scoring in the top tenth percentile of the Schizotypal Personality Questionnaire (SPQ). Eighty-seven college students with high schizotypy and 122 controls were recruited in China. Differences in grey matter volume and volume covariance between the two groups, and correlations of grey matter volume with SPQ scores in the high schizotypy group were examined. We found that individuals with high schizotypy had decreased grey matter volume at the left medial superior frontal gyrus (medsFG) extending towards the superior frontal gyrus, decreased structural covariance within the right medsFG, between the right superior frontal gyrus (sFG), the right superior temporal gyrus and the right anterior insula; and increased structural covariance between the caudate and the right inferior temporal gyrus. Correlation analysis revealed that grey matter volume of the left middle temporal pole and the right sFG correlated positively with the SPQ total scores, volume of the bilateral cerebellum 9 sub-region correlated negatively with the SPQ cognitive-perceptual sub-scale scores, volume of the bilateral striatum correlated positively with the SPQ interpersonal sub-scale scores, and volume of the bilateral superior temporal pole correlated positively with the SPQ disorganization sub-scale scores in the high schizotypy group. These results highlight important grey matter structural changes in the medsFG in individuals with high schizotypy.
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Affiliation(s)
- Yong-Ming Wang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing 100101, PR China; Sino-Danish College, University of Chinese Academy of Sciences, Beijing 100190, PR China; Sino-Danish Center for Education and Research, Beijing 100190, PR China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, PR China
| | - Zhou-Ya Yang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing 100101, PR China
| | - Yi Wang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing 100101, PR China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, PR China
| | - Yan-Yu Wang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing 100101, PR China; Department of Psychology, Weifang Medical University, Shandong Province, PR China
| | - Xin-Lu Cai
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing 100101, PR China; Sino-Danish College, University of Chinese Academy of Sciences, Beijing 100190, PR China; Sino-Danish Center for Education and Research, Beijing 100190, PR China
| | - Rui-Ting Zhang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing 100101, PR China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, PR China
| | - Hui-Xin Hu
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing 100101, PR China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, PR China
| | - Eric F C Cheung
- Castle Peak Hospital, Hong Kong Special Administrative Region
| | - Raymond C K Chan
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing 100101, PR China; Sino-Danish College, University of Chinese Academy of Sciences, Beijing 100190, PR China; Sino-Danish Center for Education and Research, Beijing 100190, PR China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, PR China.
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25
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Yun JY, Boedhoe PSW, Vriend C, Jahanshad N, Abe Y, Ameis SH, Anticevic A, Arnold PD, Batistuzzo MC, Benedetti F, Beucke JC, Bollettini I, Bose A, Brem S, Calvo A, Cheng Y, Cho KIK, Ciullo V, Dallaspezia S, Denys D, Feusner JD, Fouche JP, Giménez M, Gruner P, Hibar DP, Hoexter MQ, Hu H, Huyser C, Ikari K, Kathmann N, Kaufmann C, Koch K, Lazaro L, Lochner C, Marques P, Marsh R, Martínez-Zalacaín I, Mataix-Cols D, Menchón JM, Minuzzi L, Morgado P, Moreira P, Nakamae T, Nakao T, Narayanaswamy JC, Nurmi EL, O'Neill J, Piacentini J, Piras F, Piras F, Reddy YCJ, Sato JR, Simpson HB, Soreni N, Soriano-Mas C, Spalletta G, Stevens MC, Szeszko PR, Tolin DF, Venkatasubramanian G, Walitza S, Wang Z, van Wingen GA, Xu J, Xu X, Zhao Q, Thompson PM, Stein DJ, van den Heuvel OA, Kwon JS. Brain structural covariance networks in obsessive-compulsive disorder: a graph analysis from the ENIGMA Consortium. Brain 2020; 143:684-700. [PMID: 32040561 PMCID: PMC7009583 DOI: 10.1093/brain/awaa001] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Revised: 11/24/2019] [Accepted: 11/26/2019] [Indexed: 12/13/2022] Open
Abstract
Brain structural covariance networks reflect covariation in morphology of different brain areas and are thought to reflect common trajectories in brain development and maturation. Large-scale investigation of structural covariance networks in obsessive-compulsive disorder (OCD) may provide clues to the pathophysiology of this neurodevelopmental disorder. Using T1-weighted MRI scans acquired from 1616 individuals with OCD and 1463 healthy controls across 37 datasets participating in the ENIGMA-OCD Working Group, we calculated intra-individual brain structural covariance networks (using the bilaterally-averaged values of 33 cortical surface areas, 33 cortical thickness values, and six subcortical volumes), in which edge weights were proportional to the similarity between two brain morphological features in terms of deviation from healthy controls (i.e. z-score transformed). Global networks were characterized using measures of network segregation (clustering and modularity), network integration (global efficiency), and their balance (small-worldness), and their community membership was assessed. Hub profiling of regional networks was undertaken using measures of betweenness, closeness, and eigenvector centrality. Individually calculated network measures were integrated across the 37 datasets using a meta-analytical approach. These network measures were summated across the network density range of K = 0.10-0.25 per participant, and were integrated across the 37 datasets using a meta-analytical approach. Compared with healthy controls, at a global level, the structural covariance networks of OCD showed lower clustering (P < 0.0001), lower modularity (P < 0.0001), and lower small-worldness (P = 0.017). Detection of community membership emphasized lower network segregation in OCD compared to healthy controls. At the regional level, there were lower (rank-transformed) centrality values in OCD for volume of caudate nucleus and thalamus, and surface area of paracentral cortex, indicative of altered distribution of brain hubs. Centrality of cingulate and orbito-frontal as well as other brain areas was associated with OCD illness duration, suggesting greater involvement of these brain areas with illness chronicity. In summary, the findings of this study, the largest brain structural covariance study of OCD to date, point to a less segregated organization of structural covariance networks in OCD, and reorganization of brain hubs. The segregation findings suggest a possible signature of altered brain morphometry in OCD, while the hub findings point to OCD-related alterations in trajectories of brain development and maturation, particularly in cingulate and orbitofrontal regions.
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Affiliation(s)
- Je-Yeon Yun
- Seoul National University Hospital, Seoul, Republic of Korea
- Yeongeon Student Support Center, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Premika S W Boedhoe
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Psychiatry, Amsterdam Neuroscience, Amsterdam, The Netherlands
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy & Neurosciences, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Chris Vriend
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Psychiatry, Amsterdam Neuroscience, Amsterdam, The Netherlands
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy & Neurosciences, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, CA, USA
| | - Yoshinari Abe
- Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Stephanie H Ameis
- The Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health, Campbell Family Mental Health Research Institute, The Centre for Addiction and Mental Health, Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, Canada
- Centre for Brain and Mental Health, The Hospital for Sick Children, Toronto, Canada
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Paul D Arnold
- Mathison Centre for Mental Health Research and Education, Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Marcelo C Batistuzzo
- Departamento e Instituto de Psiquiatria do Hospital das Clinicas, IPQ HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, SP, Brazil
| | - Francesco Benedetti
- Psychiatry and Clinical Psychobiology, Division of Neuroscience, Scientific Institute Ospedale San Raffaele, Milano, Italy
| | - Jan C Beucke
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Irene Bollettini
- Psychiatry and Clinical Psychobiology, Division of Neuroscience, Scientific Institute Ospedale San Raffaele, Milano, Italy
| | - Anushree Bose
- Obsessive-Compulsive Disorder (OCD) Clinic Department of Psychiatry National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Silvia Brem
- Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric Hospital, University of Zurich, Zurich, Switzerland
| | - Anna Calvo
- Magnetic Resonance Image Core Facility, IDIBAPS (Institut d'Investigacions Biomèdiques August Pi i Sunyer), Barcelona, Spain
| | - Yuqi Cheng
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Kang Ik K Cho
- Institute of Human Behavioral Medicine, SNU-MRC, Seoul, Republic of Korea
| | - Valentina Ciullo
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Sara Dallaspezia
- Psychiatry and Clinical Psychobiology, Division of Neuroscience, Scientific Institute Ospedale San Raffaele, Milano, Italy
| | - Damiaan Denys
- Amsterdam UMC, University of Amsterdam, Department of Psychiatry, Amsterdam Neuroscience, Amsterdam, The Netherlands
- Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands
| | - Jamie D Feusner
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Jean-Paul Fouche
- SAMRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry, University of Cape Town, Cape Town, South Africa
| | - Mònica Giménez
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Carlos III Health Institute, Barcelona, Spain
- Department of Psychiatry, Bellvitge University Hospital, Bellvitge Biomedical Research Institute-IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain
| | - Patricia Gruner
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Derrek P Hibar
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, CA, USA
| | - Marcelo Q Hoexter
- Departamento e Instituto de Psiquiatria do Hospital das Clinicas, IPQ HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, SP, Brazil
| | - Hao Hu
- Shanghai Mental Health Center Shanghai Jiao Tong University School of Medicine, PR China
| | - Chaim Huyser
- De Bascule, Academic Center for Child and Adolescent Psychiatry, Amsterdam, The Netherlands
- Amsterdam UMC, Department of Child and Adolescent Psychiatry, Amsterdam, The Netherlands
| | - Keisuke Ikari
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi Higashi-ku, Fukuoka, Japan
| | - Norbert Kathmann
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Christian Kaufmann
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Kathrin Koch
- Department of Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Germany
- TUM-Neuroimaging Center (TUM-NIC) of Klinikum rechts der Isar, Technische Universität München, Germany
| | - Luisa Lazaro
- Department of Child and Adolescent Psychiatry and Psychology, Institute of Neurosciences, Hospital Clínic Universitari, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Department of Medicine, University of Barcelona, Barcelona, Spain
- Centro de Investigación Biomèdica en Red de Salud Mental (CIBERSAM), Barcelona, Spain
| | - Christine Lochner
- SAMRC Unit on Anxiety and Stress Disorders, Department of Psychiatry, University of Stellenbosch, South Africa
| | - Paulo Marques
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
| | - Rachel Marsh
- Columbia University Medical College, Columbia University, New York, NY, USA
- The New York State Psychiatric Institute, New York, NY, USA
| | - Ignacio Martínez-Zalacaín
- Department of Psychiatry, Bellvitge University Hospital, Bellvitge Biomedical Research Institute-IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain
- Department of Clinical Sciences, University of Barcelona, Spain
| | - David Mataix-Cols
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet, Stockholm, Sweden
| | - José M Menchón
- Department of Psychiatry, Bellvitge University Hospital, Bellvitge Biomedical Research Institute-IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain
- Centro de Investigación Biomèdica en Red de Salud Mental (CIBERSAM), Barcelona, Spain
- Department of Clinical Sciences, University of Barcelona, Spain
| | - Luciano Minuzzi
- McMaster University, Department of Psychiatry and Behavioural Neurosciences, Hamilton, Ontario, Canada
| | - Pedro Morgado
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- Clinical Academic Center-Braga, Braga, Portugal
- ICVS-3Bs PT Government Associate Laboratory, Braga, Portugal
| | - Pedro Moreira
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- Clinical Academic Center-Braga, Braga, Portugal
- ICVS-3Bs PT Government Associate Laboratory, Braga, Portugal
| | - Takashi Nakamae
- Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Tomohiro Nakao
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Janardhanan C Narayanaswamy
- Obsessive-Compulsive Disorder (OCD) Clinic Department of Psychiatry National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Erika L Nurmi
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Joseph O'Neill
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
- Division of Child and Adolescent Psychiatry, University of California, Los Angeles, CA, USA
| | - John Piacentini
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
- Division of Child and Adolescent Psychiatry, University of California, Los Angeles, CA, USA
| | - Fabrizio Piras
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Federica Piras
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Y C Janardhan Reddy
- Obsessive-Compulsive Disorder (OCD) Clinic Department of Psychiatry National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Joao R Sato
- Center for Mathematics, Computing and Cognition, Universidade Federal do ABC, Santo Andre, Brazil
| | - H Blair Simpson
- Columbia University Medical College, Columbia University, New York, NY, USA
- Center for OCD and Related Disorders, New York State Psychiatric Institute, New York, NY, USA
| | - Noam Soreni
- Pediatric OCD Consultation Service, Anxiety Treatment and Research Center, St. Joseph's HealthCare, Hamilton, Ontario, Canada
| | - Carles Soriano-Mas
- Department of Psychiatry, Bellvitge University Hospital, Bellvitge Biomedical Research Institute-IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain
- Centro de Investigación Biomèdica en Red de Salud Mental (CIBERSAM), Barcelona, Spain
- Department of Psychobiology and Methodology of Health Sciences, Universitat Autònoma de Barcelona, Spain
| | - Gianfranco Spalletta
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy
- Beth K. and Stuart C. Yudofsky Division of Neuropsychiatry, Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, Texas, USA
| | - Michael C Stevens
- Yale University School of Medicine, New Haven, Connecticut, USA
- Clinical Neuroscience and Development Laboratory, Olin Neuropsychiatry Research Center, Hartford, Connecticut, USA
| | - Philip R Szeszko
- Icahn School of Medicine at Mount Sinai, New York, USA
- James J. Peters VA Medical Center, Bronx, New York, USA
| | - David F Tolin
- Yale University School of Medicine, New Haven, Connecticut, USA
- Institute of Living/Hartford Hospital, Hartford, Connecticut, USA
| | - Ganesan Venkatasubramanian
- Obsessive-Compulsive Disorder (OCD) Clinic Department of Psychiatry National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Susanne Walitza
- Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric Hospital, University of Zurich, Zurich, Switzerland
| | - Zhen Wang
- Shanghai Mental Health Center Shanghai Jiao Tong University School of Medicine, PR China
- Shanghai Key Laboratory of Psychotic Disorders, PR China
| | - Guido A van Wingen
- Amsterdam UMC, University of Amsterdam, Department of Psychiatry, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Jian Xu
- Department of Psychiatry, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, PR China
| | - Xiufeng Xu
- Department of Psychiatry, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, PR China
| | - Qing Zhao
- Shanghai Mental Health Center Shanghai Jiao Tong University School of Medicine, PR China
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, CA, USA
| | - Dan J Stein
- SAMRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry, University of Cape Town, Cape Town, South Africa
| | - Odile A van den Heuvel
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Psychiatry, Amsterdam Neuroscience, Amsterdam, The Netherlands
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy & Neurosciences, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Jun Soo Kwon
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
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