This dataset accompanies the paper "Functional MRI connectivity accurately distinguishes cases with psychotic disorders from healthy controls, based on cortical features associated with brain network development", Morgan* and Young* et al, 2020.Pre-processed data:Regional CT, MD and FA values are provided here, as well as DTI networks. CT is given for the Maastricht, Dublin and Cobre datasets, whilst MD, FA and DTI are given for the Maastricht and Dublin datasets. Age, sex and group information is also provided (1=male, 2=female; 1=control, 2=case). Please note that fMRI data and the code to perform machine learning analyses is available on GitHub at: https://github.com/jmyoung36/fMRI_connectivity_accurately_distinguishes_cases.ML outputs:Predicted probabilities and regional ML feature weights are provided (see the paper for details).Notes:All data was parcellated according to an atlas with 308 regions, created by Dr Rafael Romero-Garcia- see Romero-Garcia et al, NeuroImage 2012 (https://doi.org/10.1016/j.neuroimage.2011.10.086). Please cite that paper, as well as Morgan* and Young* et al 2020, if you use this data for your own research.

The study was supported by grants from the European Commission (PSYSCAN - Translating neuroimaging findings from research into clinical practice; ID: 603196) and the NIHR Cambridge Biomedical Research Centre (Mental Health). The Cobre data was downloaded from the COllaborative Informatics and Neuroimaging Suite Data Exchange tool (COINS; http://coins.mrn.org/dx) and data collection was performed at the Mind Research Network, and funded by a Center of Biomedical Research Excellence (COBRE) grant 5P20RR021938/P20GM103472 from the NIH to Dr. Vince Calhoun. SEM was supported by a Henslow Fellowship at Lucy Cavendish College, University of Cambridge, funded by the Cambridge Philosophical Society. KJW was funded by an Alan Turing Institute Research Fellowship under EPSRC Research grant TU/A/000017. MPvdH was supported by a NWO VIDI and ALW open grant and a MQ fellowship. GD was supported by grants from the ERC (grant 677467) and SFI (12/IP/1359). ETB was supported by a NIHR Senior Investigator Award.

This dataset contains materials from the study "Brain correlates of speech perception in schizophrenia patients with and without auditory hallucinations" by Soler-Vidal et al. (2022), aimed at studying the activation patterns in response to speech perception in healthy participants and individuals with schizophrenia, with and without auditory hallucinations. The task has a block design with three experimental conditions: Word lists, Sentence lists and Reversed speech. A low-level control condition of white noise was also included.

Please note that the first 5 volumes (10 seconds) of the task were discarded before the analysis, although the whole sequence is included in this dataset. Events files start at the time of acquisition of the first volume after discarding the first five. Please also note that white noise periods are specified in the events file, but were not modeled in the original publication (and thus acted as an implicit baseline).

Detailed description of the tasks and analyses performed can be found in the published paper (https://doi.org/10.1371/journal.pone.0276975). Please cite:

Soler-Vidal, J., Fuentes-Claramonte, P., Salgado-Pineda, P., Ramiro, N., García-León, M. Á., Torres, M. L., Arévalo, A., Guerrero-Pedraza, A., Munuera, J., Sarró, S., Salvador, R., Hinzen, W., McKenna, P., & Pomarol-Clotet, E. (2022). Brain correlates of speech perception in schizophrenia patients with and without auditory hallucinations. PloS one, 17(12), e0276975. https://doi.org/10.1371/journal.pone.0276975

A collection of 1 brain maps. Each brain map is a 3D array of values representing properties of the brain at different locations.

Collection description

Pines AR, Frandsen SB, Drew W, Meyer GM, Howard C, Palm ST, Schaper FLWVJ, Lin C, Butenko K, Ferguson MA, Friedrich MU, Grafman JH, Kappel AD, Neudorfer C, Rost NS, Sanderson LL, Taylor JJ, Wu O, Kletenik I, Vogel JW, Cohen AL, Horn A, Fox MD, Silbersweig D, Siddiqi SH. Mapping Lesions That Cause Psychosis to a Human Brain Circuit and Proposed Stimulation Target. JAMA Psychiatry. 2025 Apr 1;82(4):368-378.

This dataset contains the data which accompanies the paper "Cortical patterning of abnormal morphometric similarity in psychosis is associated with brain expression of schizophrenia-related genes", Morgan et al, 2019.The data for the Maastricht GROUP, Dublin and Cobre datasets is in 3 separate folders. Each folder contains:1. 308 regional values of cortical thickness, grey matter volume, surface area, mean curvature, Gaussian curvature, mean diffusivity and fractional anisotropy, for each of the subjects (i.e. 7 tables with dimensions 308 cortical regions x N subjects)2. A vector of the subjects' ages3. A vector of the subjects' genders (1= male, 2=female)4. A vector of the subjects' group membership (1=control, 2=patient)A fourth folder contains miscellaneous required files, namely:1. Anatomical names and x, y, z co-ordinates in MNI space for each of the 308 cortical regions2. Yeo network and von Economo classes for the 308 cortical regionsPlease note that the 308 cortical region parcellation was created by Dr Rafael Romero-Garcia- see Romero-Garcia et al, NeuroImage 2012 (https://doi.org/10.1016/j.neuroimage.2011.10.086). The Yeo network mapping was performed by Jakob Seidlitz as part of the paper Váša et al, Cereb Cortex. 2018 (https://doi.org/10.1093/cercor/bhx249). The von Economo class mapping was performed by Dr Konrad Wagstyl and Dr Kirstie Whitaker as part of the paper Whitaker and Vértes, PNAS 2016 (https://doi.org/10.1073/pnas.1601745113). Please cite those papers if you use the mappings in your own work.Code to calculate morphometric similarity matrices and perform the analyses reported in the paper is available at: https://github.com/SarahMorgan/Morphometric_Similarity_SZ.

Numerous studies have examined gene expression profiles in post-mortem human brain samples from individuals with schizophrenia compared to healthy controls, to gain insight into the molecular mechanisms of the disease. While some findings have been replicated across studies,there is a general lack of consensus of which genes or pathways are affected. It has been unclear if these differences are due to the underlying cohorts, or methodological considerations. Here we present the most comprehensive analysis to date of expression patterns in the prefrontal cortex of schizophrenic compared to unaffected controls. Using data from seven independent studies, we assembled a data set of 153 affected and 153 control individuals. Remarkably, we identified expression differences in the brains of schizophrenics that are validated by up to seven laboratories using independent cohorts. Our combined analysis revealed a signature of 39 probes that are up-regulated in schizophrenia and 86 down-regulated. Some of these genes were previously identified in studies that were not included in our analysis, while others are novel to our analysis. In particular, we observe gene expression changes associated with various aspects of neuronal communication, and alterations of processes affected as a consequence of changes in synaptic functioning. A gene network analysis predicted previously unidentified functional relationships among the signature genes. Our results provide evidence for a common underlying expression signature in this heterogeneous disorder.

Background: Schizophrenia is a severe neuropsychiatric disorder that is hypothesized to result from disturbances in early brain development, and there is mounting evidence to support a role for developmentally-regulated epigenetic variation in the molecular etiology of the disorder. Here, we describe a systematic study of schizophrenia-associated methylomic variation in the adult brain and its relationship to changes in DNA methylation across human fetal brain development. Results: We profile methylomic variation in matched prefrontal cortex and cerebellum brain tissue from schizophrenia patients and controls, identifying disease-associated differential DNA methylation at multiple loci, particularly in the prefrontal cortex, and confirming these differences in an independent set of adult brain samples. Our data reveal discrete modules of co-methylated loci associated with schizophrenia that are enriched for genes involved in neurodevelopmental processes and include loci implicated by genetic studies of the disorder. Methylomic data from human fetal cortex samples, spanning 23 to 184 days post-conception, indicates that disease-associated differentially methylated positions are significantly enriched for loci at which DNA methylation is dynamically altered during human fetal brain development. Conclusions: Our data support the hypothesis that schizophrenia has an important early neurodevelopmental component, and suggest that epigenetic mechanisms may mediate these effects. 33 post-mortem brain (prefrontal cortex) samples (18 schizophrenia cases and 15 controls) were obtained from Douglas Bell-Canada Brain Bank (DBCBB), Montreal, Canada. Bisulfite converted DNA from these samples were hybridised to the Illumina Infinium 450k Human Methylation Beadchip v1.0.

A collection of 3 brain maps. Each brain map is a 3D array of values representing properties of the brain at different locations.

Collection description

Schizophrenia is thought to result from abnormal neurodevelopmental processes including alterations during the perinatal period that may have lifelong impacts on brain surface morphology and cortical folding. Many researches have focused on finding the stigmata of these early neurodevelopmental brain alterations and thus have examined gyrification in patients with schizophrenia and across the continuum of psychosis with mixed results. Here, we aimed to determine whether gyrification abnormalities were present along the psychosis continuum by conducting a systematic review and meta-analysis of studies investigating Local Gyrification Index (LGI) in patients with schizophrenia, patients with a first-episode of psychosis (FEP) and individuals with a high clinical and/or genetic risk for psychosis.
Twenty-one studies were included in the meta-analysis and were pooled with two large-scale public databases for a total of 1004 patients with schizophrenia, 317 FEP, 401 at-risk individuals, and 1299 healthy controls. Whole-brain meta-analyses of LGI findings were conducted with the Seed-based d Mapping (SDM) software on T-maps and coordinates from processed and raw data. Then, frequentist and Bayesian meta-analyses were carried out on 5 bilateral regions-of-interest (ROI). Finally, meta-regressions and linear regressions from raw data processing were used to examine the effects of clinical and demographic variables on LGI differences.
We found no difference in LGI between groups in either our whole-brain or ROI meta-analyses. In addition, the Bayesian ROI meta-analysis showed strong evidence for no difference. At the study level and at the individual level, our analyses revealed no significant association between LGI and clinical and demographic variables. Further studies are needed to determine the most appropriate method for measuring cortical folding in the schizophrenia spectrum and to clarify the effect of potential confounding variables.

This repository includes the unthresholded statistical maps from 3 comparisons: patients with schizophrenia vs. healthy controls, individuals with first-episode psychosis vs. healthy controls, individuals with an at-risk state for schizophrenia vs. healthy controls.

Collection description

Schizophrenia is thought to result from abnormal neurodevelopmental processes including alterations during the perinatal period that may have lifelong impacts on brain surface morphology and cortical folding. Many researches have focused on finding the stigmata of these early neurodevelopmental brain alterations and thus have examined gyrification in patients with schizophrenia and across the continuum of psychosis with mixed results. Here, we aimed to determine whether gyrification abnormalities were present along the psychosis continuum by conducting a systematic review and meta-analysis of studies investigating Local Gyrification Index (LGI) in patients with schizophrenia, patients with a first-episode of psychosis (FEP) and individuals with a high clinical and/or genetic risk for psychosis.
Twenty-one studies were included in the meta-analysis and were pooled with two large-scale public databases for a total of 1004 patients with schizophrenia, 317 FEP, 401 at-risk individuals, and 1299 healthy controls. Whole-brain meta-analyses of LGI findings were conducted with the Seed-based d Mapping (SDM) software on T-maps and coordinates from processed and raw data. Then, frequentist and Bayesian meta-analyses were carried out on 5 bilateral regions-of-interest (ROI). Finally, meta-regressions and linear regressions from raw data processing were used to examine the effects of clinical and demographic variables on LGI differences.
We found no difference in LGI between groups in either our whole-brain or ROI meta-analyses. In addition, the Bayesian ROI meta-analysis showed strong evidence for no difference. At the study level and at the individual level, our analyses revealed no significant association between LGI and clinical and demographic variables. Further studies are needed to determine the most appropriate method for measuring cortical folding in the schizophrenia spectrum and to clarify the effect of potential confounding variables.

This repository includes the unthresholded statistical maps from 3 comparisons: patients with schizophrenia vs. healthy controls, individuals with first-episode psychosis vs. healthy controls, individuals with an at-risk state for schizophrenia vs. healthy controls.

Subject species

homo sapiens

Modality

Structural MRI

Analysis level

meta-analysis

Cognitive paradigm (task)

None / Other

Map type

A

Tardive Dyskinesia (TD) is an iatrogenic movement disorder that most frequently impacts patients with schizophrenia (SCZ) who use antipsychotics (APs) for a month or longer. Although brain imaging studies focusing on TD have increased in recent decades, this research area is still underdeveloped. Prior research has shown numerous deficits associated with TD, suggesting that TD should not only be conceptualized as an abnormal movement disorder. Given the evidence of cognitive deterioration in patients with TD compared to the patients with SCZ without TD, linking cognitive deterioration with structural and physiological changes in the brain help improve assessment and prediction of cognitive changes in this population. This review examines the present literature on neuroimaging and TD and discusses key themes. TD is an iatrogenic movement disorder primarily affecting patients with schizophrenia who use antipsychotics for an extended period. Despite an increase in brain imaging studies, research on TD remains underdeveloped. TD should not be solely perceived as an abnormal movement disorder; prior research has identified various deficits associated with TD. Linking cognitive deterioration with structural and physiological brain changes can enhance the assessment and prediction of cognitive changes in individuals with TD.

This is the online data repository accompanying the following manuscript:
Consensus molecular environment of schizophrenia risk genes in coexpression networks shifting across age and brain regions

Giulio Pergola^1,2,3,*, Madhur Parihar¹, Leonardo Sportelli^1,2, Rahul Bharadwaj¹, Christopher Borcuk², Eugenia Radulescu¹, Loredana Bellantuono^2,5, Giuseppe Blasi^2,4, Qiang Chen¹, Joel E. Kleinman^1,3, Yanhong Wang¹, Srinidhi Rao Sripathy¹, Brady J. Maher^1,3,7, Alfonso Monaco^5,9, Fabiana Rossi^1,2, Joo Heon Shin¹, Thomas M. Hyde^1,3,6, Alessandro Bertolino^2,4,*, Daniel R. Weinberger^1,7,8,*

Affiliations:

1)Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD (USA)
2)Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
3)Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
4)Azienda Ospedaliero-Universitaria Consorziale Policlinico, Bari, Italy
5)Istituto Nazionale di Fisica Nucleare (INFN), Bari, Italy
6)Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland
7)Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland
8)Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
9)Dipartimento Interateneo di fisica, Università degli Studi di Bari Aldo Moro, Bari, Italy

Abstract:

Schizophrenia is a neurodevelopmental brain disorder whose genetic risk is associated with shifting clinical phenomena across the life span. We investigated the convergence of putative schizophrenia risk genes in brain coexpression networks in postmortem human prefrontal cortex (DLPFC), hippocampus, caudate nucleus, and dentate gyrus granule cells, parsed by specific age periods (total N = 833). The results support an early prefrontal involvement in the biology underlying schizophrenia and reveal a dynamic interplay of regions in which age parsing explains more variance in schizophrenia risk compared to lumping all age periods together. Across multiple data sources and publications, we identify 28 genes that are the most consistently found partners in modules enriched for schizophrenia risk genes in DLPFC; twenty-three are previously unidentified associations with schizophrenia. In iPSC-derived neurons, the relationship of these genes with schizophrenia risk genes is maintained. The genetic architecture of schizophrenia is embedded in shifting coexpression patterns across brain regions and time, potentially underwriting its shifting clinical presentation.

Citation: Giulio Pergola et al. ,Consensus molecular environment of schizophrenia risk genes in coexpression networks shifting across age and brain regions.Sci. Adv.9, eade2812(2023).DOI:10.1126/sciadv.ade2812

Data Files:
DLPFC hit.genes_kb_200_online.version.zip:
Interactive Sankey plot for age-parsed DLPFC networks with SCZ genes (200 kbp list) only. For Sankey plots, hover mouse over the links to see the list of genes. Also supports zoom, drag and selection.
DLPFC hit.genes_kb_200_paper.version.zip:
Interactive Sankey plot for age-parsed DLPFC networks with SCZ genes (200 kbp list) only. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)
DLPFC all.genes_kb_200_online.version.zip:
Interactive Sankey plot for age-parsed DLPFC networks with all genes
DLPFC all.genes_kb_200_paper.version.zip:
Interactive Sankey plot for age-parsed DLPFC networks with all genes. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)
HP hit.genes_kb_200_online.version.zip:
Interactive Sankey plot for age-parsed Hippocampus networks with SCZ genes (200 kbp list) only
HP hit.genes_kb_200_paper.version.zip:
Interactive Sankey plot for age-parsed Hippocampus networks with SCZ genes (200 kbp list) only. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)
HP all.genes_kb_200_online.version.zip:
Interactive Sankey plot for age-parsed Hippocampus networks with all genes
HP all.genes_kb_200_paper.version.zip:
Interactive Sankey plot for age-parsed Hippocampus networks with all genes. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)
Modulewise SCZ enrichment(1.0).xlsx:
Excel file contains module level SCZ enrichment results for all networks
wide_form_test_slidingwindow_NC_SchizoNew(v1.4)_final.xlsx:
Excel file contains WGCNA output for sliding window networks
wide_form_WGCNA(v3.7.1)_final.xlsx:
Excel file contains WGCNA output for our generated networks and from previously published networks
libdnetworks(NC).preprocessed.exp.RData:
Preprocessed ranknormalised expression assay for age-parsed/nonparsed NC networks (DLPFC, HP, CAUDATE, DENTATE). For fixed window and sliding window study.
libdnetworks(SCZ).preprocessed.exp.RData:
Preprocessed ranknormalised expression assay for nonparsed SCZ networks (DLPFC, HP, CAUDATE, DENTATE). For the sliding window study.
sample_matched_HP_DG_qsva(NC).preprocessed.exp.RData:
Preprocessed ranknormalised expression assay for the sample-matched HP-DG. QSVA removed pipeline. For Cell population enrichment study.
sample_matched_HP_DG_noqsva(NC).preprocessed.exp.RData:
Preprocessed ranknormalised expression assay for the sample-matched HP-DG. No QSVA removed pipeline. For Cell population enrichment study.
stemcell.preprocessed.exp.RData:
Preprocessed ranknormalised expression assay for the iPSC network. For replication in human iPSC data study. Neuronal samples averaged for each “RealGenome”.
SCZ.ref.list.sciadv.ade2812.rds: List of All Biotypes/ Protein Coding Schizophrenia reference genelist for following bins: PGC3, 0 kbp, 20 kbp, 50 kbp, 100 kbp, 150 kbp, 200 kbp, 250 kbp, 500 kbp.

Accompanying code can be found at: https://github.com/LieberInstitute/Brain_WGCNA
Data from this repository is also available at: https://nets.libd.org/age_wgcna/

For any data inquiries please contact:
Giulio Pergola: Giulio.Pergola@libd.org

The Brain Dynamics Centre (BDC) is a network of centers and units. It achieves a unique exploration of the healthy brain and disorders of brain function. It translates these insights into new ways to tailor treatments to the individual. There approach is: "integrative neuroscience" - bringing together clinical observations, theory, and modern imaging technologies. And it's theoretical framework derives from linking physiology, psychology and evolution. Additionally, BDC also actively researches ADHD and conduct disorder, stress and trauma-related problems, depression and anxiety, anorexia nervosa, psychosis (including early onset) and conversion disorders. The research facilities DBC include assessment, rooms, two cognition-brain function laboratories, genotyping and an MRI Suite with 1.5 and 3T GE systems. BDC is the coordinating site for an international network - BRAINnet. It has over 180 members, and coordinates access to the first standardized database on the human brain for scientific purposes: Brain Resource International Database.

Autism spectrum disorder and schizophrenia share a substantial number of etiologic and phenotypic characteristics. Still, no direct comparison of both disorders has been performed to identify differences and commonalities in brain structure. In this voxel based morphometry study, 34 patients with autism spectrum disorder, 21 patients with schizophrenia and 26 typically developed control subjects were included to identify global and regional brain volume alterations. No global gray matter or white matter differences were found between groups. In regional data, patients with autism spectrum disorder compared to typically developed control subjects showed smaller gray matter volume in the amygdala, insula, and anterior medial prefrontal cortex. Compared to patients with schizophrenia, patients with autism spectrum disorder displayed smaller gray matter volume in the left insula. Disorder specific positive correlations were found between mentalizing ability and left amygdala volume in autism spectrum disorder, and hallucinatory behavior and insula volume in schizophrenia. Results suggest the involvement of social brain areas in both disorders. Further studies are needed to replicate these findings and to quantify the amount of distinct and overlapping neural correlates in autism spectrum disorder and schizophrenia.

We used the cnv-enrichment-test to test whether any of four brain function gene sets were enriched in two CNV published datasets. In the first column we list the tested gene set. In the second two columns we list results for the ISC data set, and in the final two columns we list results for the Walsh et al data set. For each data set we present the odds ratio for schizophrenia for each gene set and the one-tailed empirical p-value.

A database of brain neuroanatomic volumetric observations spanning various species, diagnoses, and structures for both individual and group results. A major thrust effort is to enable electronic access to the results that exist in the published literature. Currently, there is quite limited electronic or searchable methods for the data observations that are contained in publications. This effort will facilitate the dissemination of volumetric observations by making a more complete corpus of volumetric observations findable to the neuroscience researcher. This also enhances the ability to perform comparative and integrative studies, as well as metaanalysis. Extensions that permit pre-published, non-published and other representation are planned, again to facilitate comparative analyses. Design strategy: The principle organizing data structure is the "publication". Publications report on "groups" of subjects. These groups have "demographic" information as well as "volume" information for the group as a whole. Groups are comprised of "individuals", which also have demographic and volume information for each of the individuals. The finest-grained data structure is the "individual volume record" which contains a volume observation, the units for the observation, and a pointer to the demographic record for individual upon which the observation is derived. A collection of individual volumes can be grouped into a "group volume" observation; the group can be demographically characterized by the distribution of individual demographic observations for the members of the group.

The Australian Schizophrenia Research Bank (ASRB) was established in 2006 as a collaborative research initiative between the Schizophrenia Research Institute; the Priority Research Centre for Brain and Mental Health Research, University of Newcastle; the Medical Genetics Laboratory, Hunter Area Pathology Service; the Centre for Rural and Remote Health; the Queensland Centre for Mental Health Research, University of Queensland: the Centre for Clinical Research in Neuropsychiatry, University of Western Australia and the Melbourne Neuropsychiatry Centre, University of Melbourne.

The ASRB is a medical research database and storage facility that links clinical and neuropsychological information, blood samples and structural MRI brain scans from people with schizophrenia and healthy non-psychiatric controls. A proportion of these volunteers have also indicated a willingness to be contacted about participation in future schizophrenia research projects. Information about the ASRB is available on line at http://www.schizophreniaresearch.org.au/bank/index.php

The primary aim of the ASRB is to facilitate scientific research into schizophrenia by: 1) Collecting, storing and providing comprehensive, cross-referenced clinical, neuropsychological, genetic and brain imaging data from people with schizophrenia and healthy controls, and 2) Providing researchers with access to a pool of volunteers as an ancillary recruitment resource for participation in schizophrenia research projects.

The ASRB is based on a case (schizophrenia) and control (non-psychiatric) sampling design. The sample is drawn from five Australian States and Territories: NSW (Sydney, Hunter, Illawarra, Orange), Canberra, Queensland (Brisbane), Western Australia (Perth) and Victoria (Melbourne), collected through new and existing recruitment resources associated with the ASRB collaborators, including media advertisements, inpatient, outpatient and community mental health service providers, non-government organisations, rehabilitation services, and cold telephoning using the electoral rolls in each State (recruitment of healthy controls only).

MNC have been involved in the collection and analysis of 180 MRI scans from Victorian patients.

Time period: 2008-present

Schizophrenic patients present abnormalities in a variety of eye movement tasks. Exploratory eye movement (EEM) dysfunction appears to be particularly specific to schizophrenia. However, the underlying mechanisms of EEM dysfunction in schizophrenia are not clearly understood. To assess the potential neuroanatomical substrates of EEM, we recorded EEM performance and conducted a voxel-based morphometric analysis of gray matter in 33 schizophrenic patients and 29 well matched healthy controls. In schizophrenic patients, decreased responsive search score (RSS) and widespread gray matter density (GMD) reductions were observed. Moreover, the RSS was positively correlated with GMD in distributed brain regions in schizophrenic patients. Furthermore, in schizophrenic patients, some brain regions with neuroanatomical deficits overlapped with some ones associated with RSS. These brain regions constituted an occipito-tempro-frontal circuitry involved in visual information processing and eye movement control, including the left calcarine cortex [Brodmann area (BA) 17], the left cuneus (BA 18), the left superior occipital cortex (BA 18/19), the left superior frontal gyrus (BA 6), the left cerebellum, the right lingual cortex (BA 17/18), the right middle occipital cortex (BA19), the right inferior temporal cortex (BA 37), the right dorsolateral prefrontal cortex (BA 46) and bilateral precentral gyri (BA 6) extending to the frontal eye fields (FEF, BA 8). To our knowledge, we firstly reported empirical evidence that gray matter loss in the occipito-tempro-frontal neuroanatomical circuitry of visual processing system was associated with EEM performance in schizophrenia, which may be helpful for the future effort to reveal the underlying neural mechanisms for EEM disturbances in schizophrenia.

ObjectiveAlthough distinctive structural abnormalities occur in patients with schizophrenia, detecting schizophrenia with magnetic resonance imaging (MRI) remains challenging. This study aimed to detect schizophrenia in structural MRI data sets using a trained deep learning algorithm.MethodFive public MRI data sets (BrainGluSchi, COBRE, MCICShare, NMorphCH, and NUSDAST) from schizophrenia patients and normal subjects, for a total of 873 structural MRI data sets, were used to train a deep convolutional neural network.ResultsThe deep learning algorithm trained with structural MR images detected schizophrenia in randomly selected images with reliable performance (area under the receiver operating characteristic curve [AUC] of 0.96). The algorithm could also identify MR images from schizophrenia patients in a previously unencountered data set with an AUC of 0.71 to 0.90. The deep learning algorithm’s classification performance degraded to an AUC of 0.71 when a new data set with younger patients and a shorter duration of illness than the training data sets was presented. The brain region contributing the most to the performance of the algorithm was the right temporal area, followed by the right parietal area. Semitrained clinical specialists hardly discriminated schizophrenia patients from healthy controls (AUC: 0.61) in the set of 100 randomly selected brain images.ConclusionsThe deep learning algorithm showed good performance in detecting schizophrenia and identified relevant structural features from structural brain MRI data; it had an acceptable classification performance in a separate group of patients at an earlier stage of the disease. Deep learning can be used to delineate the structural characteristics of schizophrenia and to provide supplementary diagnostic information in clinical settings.

SZC bold time series

Platform for mediation and integration of schizophrenia neuroimaging-related databases. It provides access to federated databases, novel mediation software, and large-scale data-sharing features.

A collection of 5 brain maps. Each brain map is a 3D array of values representing properties of the brain at different locations.

Collection description

Randomized, double-blind, placebo-controlled, cross-over design to compare the impacts of a single intranasal oxytocin dose on amygdala connectivity among individuals with schizophrenia (n = 22) versus healthy controls (n = 24).