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Janssen J, Gallego AG, Díaz-Caneja CM, Lois NG, Janssen N, González-Peñas J, Gordaliza PM, Buimer EE, van Haren NE, Arango C, Kahn RS, Hulshoff Pol HE, Schnack HG. Heterogeneity of morphometric similarity networks in health and schizophrenia. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.26.586768. [PMID: 38948832 PMCID: PMC11212887 DOI: 10.1101/2024.03.26.586768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Introduction Morphometric similarity is a recently developed neuroimaging phenotype of inter-regional connectivity by quantifying the similarity of a region to other regions based on multiple MRI parameters. Altered average morphometric similarity has been reported in psychotic disorders at the group level, with considerable heterogeneity across individuals. We used normative modeling to address cross-sectional and longitudinal inter-individual heterogeneity of morphometric similarity in health and schizophrenia. Methods Morphometric similarity for 62 cortical regions was obtained from baseline and follow-up T1-weighted scans of healthy individuals and patients with chronic schizophrenia. Cortical regions were classified into seven predefined brain functional networks. Using Bayesian Linear Regression and taking into account age, sex, image quality and scanner, we trained and validated normative models in healthy controls from eleven datasets (n = 4310). Individual deviations from the norm (z-scores) in morphometric similarity were computed for each participant for each network and region at both timepoints. A z-score ≧ than 1.96 was considered supra-normal and a z-score ≦ -1.96 infra-normal. As a longitudinal metric, we calculated the change over time of the total number of infra- or supra-normal regions per participant. Results At baseline, patients with schizophrenia had decreased morphometric similarity of the default mode network and increased morphometric similarity of the somatomotor network when compared with healthy controls. The percentage of patients with infra- or supra-normal values for any region at baseline and follow-up was low (<6%) and did not differ from healthy controls. Mean intra-group changes over time in the total number of infra- or supra-normal regions were small in schizophrenia and healthy control groups (<1) and there were no significant between-group differences. Conclusions In a case-control setting, a decrease of morphometric similarity within the default mode network may be a robust finding implicated in schizophrenia. However, normative modeling suggests that significant reductions and changes over time of regional morphometric similarity are evident only in a minority of patients.
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Affiliation(s)
- Joost Janssen
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
- Ciber del Área de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
| | - Ana Guil Gallego
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
| | - Covadonga M. Díaz-Caneja
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
- Ciber del Área de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- School of Medicine, Universidad Complutense, Madrid, Spain
| | - Noemi González Lois
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
| | - Niels Janssen
- Department of Psychology, Universidad de la Laguna, Tenerife, Spain
- Institute of Biomedical Technologies, Universidad de La Laguna, Tenerife, Spain
- Institute of Neurosciences, Universidad de la Laguna, Santa Cruz de Tenerife, Spain
| | - Javier González-Peñas
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
- Ciber del Área de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
| | - Pedro M. Gordaliza
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Radiology Department, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Elizabeth E.L. Buimer
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Neeltje E.M. van Haren
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Centre, Sophia Children’s Hospital, Rotterdam, The Netherlands
| | - Celso Arango
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
- Ciber del Área de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- School of Medicine, Universidad Complutense, Madrid, Spain
| | - René S. Kahn
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, United States
| | - Hilleke E. Hulshoff Pol
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Hugo G. Schnack
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Centre, Sophia Children’s Hospital, Rotterdam, The Netherlands
- Department of Languages, Literature, and Communication, Faculty of Humanities, Utrecht University, Utrecht, The Netherlands
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Joo SW, Jo YT, Kim Y, Lee WH, Chung YC, Lee J. Structural variability of the cerebral cortex in schizophrenia and its association with clinical symptoms. Psychol Med 2024; 54:399-408. [PMID: 37485703 DOI: 10.1017/s0033291723001988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
BACKGROUND Substantial evidence indicates structural abnormalities in the cerebral cortex of patients with schizophrenia (SCZ), although their clinical implications remain unclear. Previous case-control studies have investigated group-level differences in structural abnormalities, although the study design cannot account for interindividual differences. Recent research has focused on the association between the heterogeneity of the cerebral cortex morphometric features and clinical heterogeneity. METHODS We used neuroimaging data from 420 healthy controls and 695 patients with SCZ from seven studies. Four cerebral cortex measures were obtained: surface area, gray matter volume, thickness, and local gyrification index. We calculated the coefficient of variation (CV) and person-based similarity index (PBSI) scores and performed group comparisons. Associations between the PBSI scores and cognitive functions were evaluated using Spearman's rho test and normative modeling. RESULTS Patients with SCZ had a greater CV of surface area and cortical thickness than those of healthy controls. All PBSI scores across cortical measures were lower in patients with SCZ than in HCs. In the patient group, the PBSI scores for gray matter volume and all cortical measures taken together positively correlated with the full-scale IQ scores. Patients with deviant PBSI scores for gray matter volume and all cortical measures taken together had lower full-scale IQ scores than those of other patients. CONCLUSIONS The cerebral cortex in patients with SCZ showed greater regional and global structural variability than that in healthy controls. Patients with deviant similarity of cortical structural profiles exhibited a lower general intelligence than those exhibited by the other patients.
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Affiliation(s)
- Sung Woo Joo
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Young Tak Jo
- Department of Psychiatry, Kangdong Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Republic of Korea
| | - Yangsik Kim
- Department of Psychiatry, Inha University Hospital, Incheon, Republic of Korea
| | - Won Hee Lee
- Department of Software Convergence, Kyung Hee University, Yongin, Republic of Korea
| | - Young-Chul Chung
- Department of Psychiatry, Chonbuk National University Medical School, Jeonju, Republic of Korea
| | - Jungsun Lee
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
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Yehuda B, Rabinowich A, Link-Sourani D, Avisdris N, Ben-Zvi O, Specktor-Fadida B, Joskowicz L, Ben-Sira L, Miller E, Ben Bashat D. Automatic Quantification of Normal Brain Gyrification Patterns and Changes in Fetuses with Polymicrogyria and Lissencephaly Based on MRI. AJNR Am J Neuroradiol 2023; 44:1432-1439. [PMID: 38050002 PMCID: PMC10714858 DOI: 10.3174/ajnr.a8046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 09/23/2023] [Indexed: 12/06/2023]
Abstract
BACKGROUND AND PURPOSE The current imaging assessment of fetal brain gyrification is performed qualitatively and subjectively using sonography and MR imaging. A few previous studies have suggested methods for quantification of fetal gyrification based on 3D reconstructed MR imaging, which requires unique data and is time-consuming. In this study, we aimed to develop an automatic pipeline for gyrification assessment based on routinely acquired fetal 2D MR imaging data, to quantify normal changes with gestation, and to measure differences in fetuses with lissencephaly and polymicrogyria compared with controls. MATERIALS AND METHODS We included coronal T2-weighted MR imaging data of 162 fetuses retrospectively collected from 2 clinical sites: 134 controls, 12 with lissencephaly, 13 with polymicrogyria, and 3 with suspected lissencephaly based on sonography, yet with normal MR imaging diagnoses. Following brain segmentation, 5 gyrification parameters were calculated separately for each hemisphere on the basis of the area and ratio between the contours of the cerebrum and its convex hull. Seven machine learning classifiers were evaluated to differentiate control fetuses and fetuses with lissencephaly or polymicrogyria. RESULTS In control fetuses, all parameters changed significantly with gestational age (P < .05). Compared with controls, fetuses with lissencephaly showed significant reductions in all gyrification parameters (P ≤ .02). Similarly, significant reductions were detected for fetuses with polymicrogyria in several parameters (P ≤ .001). The 3 suspected fetuses showed normal gyrification values, supporting the MR imaging diagnosis. An XGBoost-linear algorithm achieved the best results for classification between fetuses with lissencephaly and control fetuses (n = 32), with an area under the curve of 0.90 and a recall of 0.83. Similarly, a random forest classifier showed the best performance for classification of fetuses with polymicrogyria and control fetuses (n = 33), with an area under the curve of 0.84 and a recall of 0.62. CONCLUSIONS This study presents a pipeline for automatic quantification of fetal brain gyrification and provides normal developmental curves from a large cohort. Our method significantly differentiated fetuses with lissencephaly and polymicrogyria, demonstrating lower gyrification values. The method can aid radiologic assessment, highlight fetuses at risk, and may improve early identification of fetuses with cortical malformations.
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Affiliation(s)
- Bossmat Yehuda
- From the Sagol Brain Institute (B.Y., A.R., D.L.-S., N.A., O.B.-Z., D.B.B.), Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Sagol School of Neuroscience (B.Y., L.B.-S., D.B.B.), Tel Aviv University, Tel Aviv, Israel
| | - Aviad Rabinowich
- From the Sagol Brain Institute (B.Y., A.R., D.L.-S., N.A., O.B.-Z., D.B.B.), Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Sackler Faculty of Medicine (A.R., L.B.-S., D.B.B.), Tel Aviv University, Tel Aviv, Israel
- Division of Radiology (A.R., L.B.-S.), Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Daphna Link-Sourani
- From the Sagol Brain Institute (B.Y., A.R., D.L.-S., N.A., O.B.-Z., D.B.B.), Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Netanell Avisdris
- From the Sagol Brain Institute (B.Y., A.R., D.L.-S., N.A., O.B.-Z., D.B.B.), Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- School of Computer Science and Engineering (N.A., L.J.), The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Ori Ben-Zvi
- From the Sagol Brain Institute (B.Y., A.R., D.L.-S., N.A., O.B.-Z., D.B.B.), Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Bella Specktor-Fadida
- School of Computer Science and Engineering (B.S.-F.), The Hebrew University of Jerusalem, Israel
| | - Leo Joskowicz
- School of Computer Science and Engineering (N.A., L.J.), The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Liat Ben-Sira
- Sagol School of Neuroscience (B.Y., L.B.-S., D.B.B.), Tel Aviv University, Tel Aviv, Israel
- Sackler Faculty of Medicine (A.R., L.B.-S., D.B.B.), Tel Aviv University, Tel Aviv, Israel
- Division of Radiology (A.R., L.B.-S.), Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Elka Miller
- Department of Medical Imaging (E.M.), Children's Hospital of Eastern Ontario, University of Ottawa, Ottawa, Ontario, Canada
| | - Dafna Ben Bashat
- From the Sagol Brain Institute (B.Y., A.R., D.L.-S., N.A., O.B.-Z., D.B.B.), Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Sagol School of Neuroscience (B.Y., L.B.-S., D.B.B.), Tel Aviv University, Tel Aviv, Israel
- Sackler Faculty of Medicine (A.R., L.B.-S., D.B.B.), Tel Aviv University, Tel Aviv, Israel
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Xie Y, Sun J, Man W, Zhang Z, Zhang N. Personalized estimates of brain cortical structural variability in individuals with Autism spectrum disorder: the predictor of brain age and neurobiology relevance. Mol Autism 2023; 14:27. [PMID: 37507798 PMCID: PMC10375633 DOI: 10.1186/s13229-023-00558-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023] Open
Abstract
BACKGROUND Autism spectrum disorder (ASD) is a heritable condition related to brain development that affects a person's perception and socialization with others. Here, we examined variability in the brain morphology in ASD children and adolescent individuals at the level of brain cortical structural profiles and the level of each brain regional measure. METHODS We selected brain structural MRI data in 600 ASDs and 729 normal controls (NCs) from Autism Brain Imaging Data Exchange (ABIDE). The personalized estimate of similarity between gray matter volume (GMV) profiles of an individual to that of others in the same group was assessed by using the person-based similarity index (PBSI). Regional contributions to PBSI score were utilized for brain age gap estimation (BrainAGE) prediction model establishment, including support vector regression (SVR), relevance vector regression (RVR), and Gaussian process regression (GPR). The association between BrainAGE prediction in ASD and clinical performance was investigated. We further explored the related inter-regional profiles of gene expression from the Allen Human Brain Atlas with variability differences in the brain morphology between groups. RESULTS The PBSI score of GMV was negatively related to age regardless of the sample group, and the PBSI score was significantly lower in ASDs than in NCs. The regional contributions to the PBSI score of 126 brain regions in ASDs showed significant differences compared to NCs. RVR model achieved the best performance for predicting brain age. Higher inter-individual brain morphology variability was related to increased brain age, specific to communication symptoms. A total of 430 genes belonging to various pathways were identified as associated with brain cortical morphometric variation. The pathways, including short-term memory, regulation of system process, and regulation of nervous system process, were dominated mainly by gene sets for manno midbrain neurotypes. LIMITATIONS There is a sample mismatch between the gene expression data and brain imaging data from ABIDE. A larger sample size can contribute to the model training of BrainAGE and the validation of the results. CONCLUSIONS ASD has personalized heterogeneity brain morphology. The brain age gap estimation and transcription-neuroimaging associations derived from this trait are replenished in an additional direction to boost the understanding of the ASD brain.
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Affiliation(s)
- Yingying Xie
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin, 300052, China
| | - Jie Sun
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin, 300052, China
| | - Weiqi Man
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin, 300052, China
- Department of Radiology, Tianjin First Central Hospital, Tianjin, 300192, China
| | - Zhang Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin, 300052, China.
| | - Ningnannan Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin, 300052, China.
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Sun J, Zhao W, Xie Y, Zhou F, Wu L, Li Y, Li H, Li Y, Zeng C, Han X, Liu Y, Zhang N. Personalized estimates of morphometric similarity in multiple sclerosis and neuromyelitis optica spectrum disorders. Neuroimage Clin 2023; 39:103454. [PMID: 37343344 PMCID: PMC10509529 DOI: 10.1016/j.nicl.2023.103454] [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: 03/10/2023] [Revised: 05/21/2023] [Accepted: 06/16/2023] [Indexed: 06/23/2023]
Abstract
Brain morphometric alterations involve multiple brain regions on progression of the disease in multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) and exhibit age-related degenerative changes during the pathological aging. Recent advance in brain morphometry as measured using MRI have leveraged Person-Based Similarity Index (PBSI) approach to assess the extent of within-diagnosis similarity or heterogeneity of brain neuroanatomical profiles between individuals of healthy populations and validate in neuropsychiatric disorders. Brain morphometric changes throughout the lifespan would be invaluable for understanding regional variability of age-related structural degeneration and the substrate of inflammatory demyelinating disease. Here, we aimed to quantify the neuroanatomical profiles with PBSI measures of cortical thickness (CT) and subcortical volumes (SV) in 263 MS, 207 NMOSD, and 338 healthy controls (HC) from six separate central datasets (aged 11-80). We explored the between-group comparisons of PBSI measures, as well as the advancing age and sex effects on PBSI measures. Compared to NMOSD, MS showed a lower extent of within-diagnosis similarity. Significant differences in regional contributions to PBSI score were observed in 29 brain regions between MS and NMOSD (P < 0.05/164, Bonferroni corrected), of which bilateral cerebellum in MS and bilateral parahippocampal gyrus in NMOSD represented the highest divergence between the two patient groups, with a high similarity effect within each group. The PBSI scores were generally lower with advancing age, but their associations showed different patterns depending on the age range. For MS, CT profiles were significantly negatively correlated with age until the early 30 s (ρ = -0.265, P = 0.030), while for NMOSD, SV profiles were significantly negatively correlated with age with 51 year-old and older (ρ = -0.365, P = 0.008). The current study suggests that PBSI approach could be used to quantify the variation in brain morphometric changes in CNS inflammatory demyelinating disease, and exhibited a greater neuroanatomical heterogeneity pattern in MS compared with NMOSD. Our results reveal that, as an MR marker, PBSI may be sensitive to distribute the disease-associated grey matter diversity and complexity. Disease-driven production of regionally selective and age stage-dependency changes in the neuroanatomical profile of MS and NMOSD should be considered to facilitate the prediction of clinical outcomes and assessment of treatment responses.
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Affiliation(s)
- Jie Sun
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Wenjin Zhao
- 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
| | - Fuqing Zhou
- Department of Radiology, The First Afliated Hospital, Nanchang University, Nanchang 330006, Jiangxi Province, China
- Neuroimaging Lab, Jiangxi Province Medical Imaging Research Institute, Nanchang 330006, Jiangxi Province, China
| | - Lin Wu
- Department of Radiology, The First Afliated Hospital, Nanchang University, Nanchang 330006, Jiangxi Province, China
- Neuroimaging Lab, Jiangxi Province Medical Imaging Research Institute, Nanchang 330006, Jiangxi Province, China
| | - Yuxin Li
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Haiqing Li
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Yongmei Li
- Department of Radiology, The First Afliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Chun Zeng
- Department of Radiology, The First Afliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Xuemei Han
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun 130031, Jilin Province, China
| | - Yaou Liu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, No.119, The West Southern 4th Ring Road, Fengtai District, Beijing 100070, China
| | - Ningnannan Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
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Lee-Hughes R, Lancaster TM. Cumulative Impact of Morphometric Features in Schizophrenia in Two Independent Samples. SCHIZOPHRENIA BULLETIN OPEN 2023; 4:sgad031. [PMID: 39145335 PMCID: PMC11207677 DOI: 10.1093/schizbullopen/sgad031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/16/2024]
Abstract
Schizophrenia and bipolar disorder share a common structural brain alteration profile. However, there is considerable between- and within-diagnosis variability in these features, which may underestimate informative individual differences. Using a recently established morphometric risk score (MRS) approach, we aim to provide confirmation that individual MRS scores are higher in individuals with a psychosis diagnosis, helping to parse individual heterogeneity. Using the Human Connectome Project Early Psychosis (N = 124), we estimate MRS for psychosis and specifically for bipolar/schizophrenia using T1-weighted MRI data and prior meta-analysis effect sizes. We confirm associations in an independent replication sample (N = 69). We assess (1) the impact of diagnosis on these MRS, (2) compare effect sizes of MRS to all individual, cytoarchitecturally defined brain regions, and (3) perform negative control analyses to assess MRS specificity. The MRS specifically for SCZ was higher in the whole psychosis group (Cohen's d = 0.56; P = 0.003) and outperformed any single region of interest in standardized mean difference (ZMRS>75 ROIS = 2.597; P = 0.009) and correlated with previously reported effect sizes (PSPIN/SHUFFLE < 0.005). MRS without Enhancing Neuroimaging Genomics through Meta-Analysis weights did not delineate groups with empirically null associations (t = 2.29; P = 0.02). We replicate MRS specifically for SCZ associations in the independent sample. Akin to polygenic risk scoring and individual allele effect sizes, these observations suggest that assessing the combined impact of regional structural alterations may be more informative than any single cytoarchitecturally constrained cortical region, where well-powered, meta-analytical samples are informative in the delineation of diagnosis and within psychosis case differences, in smaller independent samples.
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Janssen J, Alloza C, Díaz-Caneja CM, Santonja J, Pina-Camacho L, Gordaliza PM, Fernández-Pena A, Lois NG, Buimer EEL, van Haren NEM, Cahn W, Vieta E, Castro-Fornieles J, Bernardo M, Arango C, Kahn RS, Hulshoff Pol HE, Schnack HG. Longitudinal Allometry of Sulcal Morphology in Health and Schizophrenia. J Neurosci 2022; 42:3704-3715. [PMID: 35318286 PMCID: PMC9087719 DOI: 10.1523/jneurosci.0606-21.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 02/24/2022] [Accepted: 03/01/2022] [Indexed: 11/21/2022] Open
Abstract
Scaling between subcomponents of folding and total brain volume (TBV) in healthy individuals (HIs) is allometric. It is unclear whether this is true in schizophrenia (SZ) or first-episode psychosis (FEP). This study confirmed normative allometric scaling norms in HIs using discovery and replication samples. Cross-sectional and longitudinal diagnostic differences in folding subcomponents were then assessed using an allometric framework. Structural imaging from a longitudinal (Sample 1: HI and SZ, nHI Baseline = 298, nSZ Baseline = 169, nHI Follow-up = 293, nSZ Follow-up = 168, totaling 1087 images, all individuals ≥ 2 images, age 16-69 years) and a cross-sectional sample (Sample 2: nHI = 61 and nFEP = 89, age 10-30 years), all human males and females, is leveraged to calculate global folding and its nested subcomponents: sulcation index (SI, total sulcal/cortical hull area) and determinants of sulcal area: sulcal length and sulcal depth. Scaling of SI, sulcal area, and sulcal length with TBV in SZ and FEP was allometric and did not differ from HIs. Longitudinal age trajectories demonstrated steeper loss of SI and sulcal area through adulthood in SZ. Longitudinal allometric analysis revealed that both annual change in SI and sulcal area was significantly stronger related to change in TBV in SZ compared with HIs. Our results detail the first evidence of the disproportionate contribution of changes in SI and sulcal area to TBV changes in SZ. Longitudinal allometric analysis of sulcal morphology provides deeper insight into lifespan trajectories of cortical folding in SZ.SIGNIFICANCE STATEMENT Psychotic disorders are associated with deficits in cortical folding and brain size, but we lack knowledge of how these two morphometric features are related. We leverage cross-sectional and longitudinal samples in which we decompose folding into a set of nested subcomponents: sulcal and hull area, and sulcal depth and length. We reveal that, in both schizophrenia and first-episode psychosis, (1) scaling of subcomponents with brain size is different from expected scaling laws and (2) caution is warranted when interpreting results from traditional methods for brain size correction. Longitudinal allometric scaling points to loss of sulcal area as a principal contributor to loss of brain size in schizophrenia. These findings advance the understanding of cortical folding atypicalities in psychotic disorders.
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Affiliation(s)
- Joost Janssen
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, 28007 Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón, 28007 Madrid, Spain
- Ciber del Área de Salud Mental, 28007 Madrid, Spain
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands
| | - Clara Alloza
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, 28007 Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón, 28007 Madrid, Spain
- Ciber del Área de Salud Mental, 28007 Madrid, Spain
| | - Covadonga M Díaz-Caneja
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, 28007 Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón, 28007 Madrid, Spain
- Ciber del Área de Salud Mental, 28007 Madrid, Spain
- School of Medicine, Universidad Complutense, 28040 Madrid, Spain
| | - Javier Santonja
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, 28007 Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón, 28007 Madrid, Spain
| | - Laura Pina-Camacho
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, 28007 Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón, 28007 Madrid, Spain
- Ciber del Área de Salud Mental, 28007 Madrid, Spain
- School of Medicine, Universidad Complutense, 28040 Madrid, Spain
| | - Pedro M Gordaliza
- Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, 28911 Madrid, Spain
| | - Alberto Fernández-Pena
- Instituto de Investigación Sanitaria Gregorio Marañón, 28007 Madrid, Spain
- Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, 28911 Madrid, Spain
| | - Noemi González Lois
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, 28007 Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón, 28007 Madrid, Spain
| | - Elizabeth E L Buimer
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands
| | - Neeltje E M van Haren
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Centre, Sophia Children's Hospital, 3015 GD Rotterdam, The Netherlands
| | - Wiepke Cahn
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands
| | - Eduard Vieta
- Ciber del Área de Salud Mental, 28007 Madrid, Spain
- Bipolar Disorders Unit, Clinical Institute of Neurosciences, Hospital Clínic, University of Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer, 08036 Barcelona, Spain
| | - Josefina Castro-Fornieles
- Ciber del Área de Salud Mental, 28007 Madrid, Spain
- Department of Child and Adolescent Psychiatry and Psychology, Clinical Institute of Neurosciences, Hospital Clínic, University of Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer, 08036 Barcelona, Spain
| | - Miquel Bernardo
- Ciber del Área de Salud Mental, 28007 Madrid, Spain
- Barcelona Clinic Schizophrenia Unit, Hospital Clinic of Barcelona, Neuroscience Institute, Institut d'Investigacions Biomèdiques August Pi i Sunyer, University of Barcelona, 08036 Barcelona, Spain
| | - Celso Arango
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, 28007 Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón, 28007 Madrid, Spain
- Ciber del Área de Salud Mental, 28007 Madrid, Spain
- School of Medicine, Universidad Complutense, 28040 Madrid, Spain
| | - René S Kahn
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, 10029 New York
| | - Hilleke E Hulshoff Pol
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands
| | - Hugo G Schnack
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands
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8
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Cheon EJ, Bearden CE, Sun D, Ching CRK, Andreassen OA, Schmaal L, Veltman DJ, Thomopoulos SI, Kochunov P, Jahanshad N, Thompson PM, Turner JA, van Erp TG. Cross disorder comparisons of brain structure in schizophrenia, bipolar disorder, major depressive disorder, and 22q11.2 deletion syndrome: A review of ENIGMA findings. Psychiatry Clin Neurosci 2022; 76:140-161. [PMID: 35119167 PMCID: PMC9098675 DOI: 10.1111/pcn.13337] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Revised: 11/29/2021] [Accepted: 01/21/2022] [Indexed: 12/25/2022]
Abstract
This review compares the main brain abnormalities in schizophrenia (SZ), bipolar disorder (BD), major depressive disorder (MDD), and 22q11.2 Deletion Syndrome (22q11DS) determined by ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) consortium investigations. We obtained ranked effect sizes for subcortical volumes, regional cortical thickness, cortical surface area, and diffusion tensor imaging abnormalities, comparing each of these disorders relative to healthy controls. In addition, the studies report on significant associations between brain imaging metrics and disorder-related factors such as symptom severity and treatments. Visual comparison of effect size profiles shows that effect sizes are generally in the same direction and scale in severity with the disorders (in the order SZ > BD > MDD). The effect sizes for 22q11DS, a rare genetic syndrome that increases the risk for psychiatric disorders, appear to be much larger than for either of the complex psychiatric disorders. This is consistent with the idea of generally larger effects on the brain of rare compared to common genetic variants. Cortical thickness and surface area effect sizes for 22q11DS with psychosis compared to 22q11DS without psychosis are more similar to those of SZ and BD than those of MDD; a pattern not observed for subcortical brain structures and fractional anisotropy effect sizes. The observed similarities in effect size profiles for cortical measures across the psychiatric disorders mimic those observed for shared genetic variance between these disorders reported based on family and genetic studies and are consistent with shared genetic risk for SZ and BD and structural brain phenotypes.
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Affiliation(s)
- Eun-Jin Cheon
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, 5251 California Ave, Irvine, CA, 92617, USA
- Department of Psychiatry, Yeungnam University College of Medicine, Yeungnam University Medical Center, Daegu, Republic of Korea
| | - Carrie E. Bearden
- Departments of Psychiatry and Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles
| | - Daqiang Sun
- Departments of Psychiatry and Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles
- Department of Mental Health, Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA, USA
| | - Christopher R. K. Ching
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Ole A. Andreassen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Lianne Schmaal
- Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia
- Orygen, Parkville, Australia
| | - Dick J. Veltman
- Department of Psychiatry, Amsterdam UMC, location VUMC, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Sophia I. Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Jessica A. Turner
- Psychology Department and Neuroscience Institute, Georgia State University, Atlant, GA, USA
| | - Theo G.M. van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, 5251 California Ave, Irvine, CA, 92617, USA
- Center for the Neurobiology of Learning and Memory, University of California Irvine, 309 Qureshey Research Lab, Irvine, CA, 92697, USA
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9
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West A, Hamlin N, Frangou S, Wilson TW, Doucet GE. Person-Based Similarity Index for Cognition and Its Neural Correlates in Late Adulthood: Implications for Cognitive Reserve. Cereb Cortex 2021; 32:397-407. [PMID: 34255824 DOI: 10.1093/cercor/bhab215] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 06/10/2021] [Accepted: 06/10/2021] [Indexed: 11/13/2022] Open
Abstract
Healthy aging is typically associated with some level of cognitive decline, but there is substantial variation in such decline among older adults. The mechanisms behind such heterogeneity remain unclear but some have suggested a role for cognitive reserve. In this work, we propose the "person-based similarity index" for cognition (PBSI-Cog) as a proxy for cognitive reserve in older adults, and use the metric to quantify similarity between the cognitive profiles of healthy older and younger participants. In the current study, we computed this metric in 237 healthy older adults (55-88 years) using a reference group of 156 younger adults (18-39 years) taken from the Cambridge Center for Ageing and Neuroscience dataset. Our key findings revealed that PBSI-Cog scores in older adults were: 1) negatively associated with age (rho = -0.25, P = 10-4) and positively associated with higher education (t = 2.4, P = 0.02), 2) largely explained by fluid intelligence and executive function, and 3) predicted more by functional connectivity between lower- and higher-order resting-state networks than brain structural morphometry or education. Particularly, we found that higher segregation between the sensorimotor and executive networks predicted higher PBSI-Cog scores. Our results support the notion that brain network functional organization may underly variability in cognitive reserve in late adulthood.
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Affiliation(s)
- Anna West
- Institute for Human Neuroscience, Boys Town National Research Hospital, Omaha, NE 68010, USA
| | - Noah Hamlin
- Institute for Human Neuroscience, Boys Town National Research Hospital, Omaha, NE 68010, USA
| | - Sophia Frangou
- Department of Psychiatry, Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC V6T 1Z3, Canada.,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Tony W Wilson
- Institute for Human Neuroscience, Boys Town National Research Hospital, Omaha, NE 68010, USA
| | - Gaelle E Doucet
- Institute for Human Neuroscience, Boys Town National Research Hospital, Omaha, NE 68010, USA
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10
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Antoniades M, Haas SS, Modabbernia A, Bykowsky O, Frangou S, Borgwardt S, Schmidt A. Personalized Estimates of Brain Structural Variability in Individuals With Early Psychosis. Schizophr Bull 2021; 47:1029-1038. [PMID: 33547470 PMCID: PMC8266574 DOI: 10.1093/schbul/sbab005] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
BACKGROUND Early psychosis in first-episode psychosis (FEP) and clinical high-risk (CHR) individuals has been associated with alterations in mean regional measures of brain morphology. Examination of variability in brain morphology could assist in quantifying the degree of brain structural heterogeneity in clinical relative to healthy control (HC) samples. METHODS Structural magnetic resonance imaging data were obtained from CHR (n = 71), FEP (n = 72), and HC individuals (n = 55). Regional brain variability in cortical thickness (CT), surface area (SA), and subcortical volume (SV) was assessed with the coefficient of variation (CV). Furthermore, the person-based similarity index (PBSI) was employed to quantify the similarity of CT, SA, and SV profile of each individual to others within the same diagnostic group. Normative modeling of the PBSI-CT, PBSI-SA, and PBSI-SV was used to identify CHR and FEP individuals whose scores deviated markedly from those of the healthy individuals. RESULTS There was no effect of diagnosis on the CV for any regional measure (P > .38). CHR and FEP individuals differed significantly from the HC group in terms of PBSI-CT (P < .0001), PBSI-SA (P < .0001), and PBSI-SV (P = .01). In the clinical groups, normative modeling identified 32 (22%) individuals with deviant PBSI-CT, 12 (8.4%) with deviant PBSI-SA, and 21 (15%) with deviant PBSI-SV; differences of small effect size indicated that individuals with deviant PBSI scores had lower IQ and higher psychopathology. CONCLUSIONS Examination of brain structural variability in early psychosis indicated heterogeneity at the level of individual profiles and encourages further large-scale examination to identify individuals that deviate markedly from normative reference data.
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Affiliation(s)
- Mathilde Antoniades
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Shalaila S Haas
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY
| | | | - Oleg Bykowsky
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
| | - Sophia Frangou
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY
- Centre for Brain Health, Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Stefan Borgwardt
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
- Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
| | - André Schmidt
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
- To whom correspondence should be addressed; Wilhelm Klein-Strasse 27, 4002 Basel, Switzerland; tel: +41 0(61) 325 59 29, fax: +41 (0)61 325 55 82, e-mail:
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11
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Doucet GE, Lin D, Du Y, Fu Z, Glahn DC, Calhoun VD, Turner J, Frangou S. Personalized estimates of morphometric similarity in bipolar disorder and schizophrenia. NPJ SCHIZOPHRENIA 2020; 6:39. [PMID: 33277498 PMCID: PMC7718905 DOI: 10.1038/s41537-020-00128-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Accepted: 10/06/2020] [Indexed: 12/29/2022]
Abstract
Bipolar disorder and schizophrenia are associated with brain morphometry alterations. This study investigates inter-individual variability in brain structural profiles, both within diagnostic groups and between patients and healthy individuals. Brain morphometric measures from three independent samples of patients with schizophrenia (n = 168), bipolar disorder (n = 122), and healthy individuals (n = 180) were modeled as single vectors to generated individualized profiles of subcortical volumes and regional cortical thickness. These profiles were then used to compute a person-based similarity index (PBSI) for subcortical volumes and for regional cortical thickness, to quantify the within-group similarity of the morphometric profile of each individual to that of the other participants in the same diagnostic group. There was no effect of diagnosis on the PBSI for subcortical volumes. In contrast, compared to healthy individuals, the PBSI for cortical thickness was lower in patients with schizophrenia (effect size = 0.4, p ≤ 0.0002), but not in patients with bipolar disorder. The results were robust and reproducible across samples. We conclude that disease mechanisms for these disorders produce modest inter-individual variations in brain morphometry that should be considered in future studies attempting to cluster patients in subgroups.
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Affiliation(s)
- Gaelle E Doucet
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Boys Town National Research Hospital, Omaha, NE, USA
| | - Dongdong Lin
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - Yuhui Du
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA.,School of Computer & Information Technology, Shanxi University, Taiyuan, China
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - David C Glahn
- Tommy Fuss Center for Neuropsychiatric Disease Research, Boston Children's Hospital, Harvard University, Boston, MA, USA
| | - Vincent D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - Jessica Turner
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA.,Department of Psychology, Neuroscience Institute, Georgia State University, Atlanta, GA, USA
| | - Sophia Frangou
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA. .,Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada.
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