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2,640 results for “Schizophrenia”
Dataset: Partial-volume modeling reveals reduced gray matter in specific thalamic nuclei early in the time course of psychosis and chronic schizophrenia
<p>This repository contains the files needed to reproduce the results presented in the paper "Partial-volume modeling reveals reduced gray matter in specific thalamic nuclei early in the time course of psychosis and chronic schizophrenia" (DOI: 10.1002/hbm.25108) </p> <p>In order to ensure the reproducibility of our study, the individual gray matter concentration/probability images as well as the thalamic nuclei parcellations, in native and MNI spaces, are freely available in this repository. The demographic information table including age, gender and intracranial volume for each subject is also available.</p>
Transcranial Direct Current Stimulation Improves Action-Outcome Monitoring in Schizophrenia Spectrum Disorder
<p>Data set of the publication: Straube, B., van Kemenade, B.M., Kircher, T. & Schülke, R. (2020). Transcranial Direct Current Stimulation Improves Action-Outcome Monitoring in Schizophrenia Spectrum Disorder. <em>Brain Communications.</em> doi: 10.1093/braincomms/fcaa151</p> <p><strong>Abstract</strong></p> <p><strong>Background:</strong> Patients with schizophrenia spectrum disorder (SSD) often demonstrate impairments in action-outcome monitoring. Passivity phenomena and hallucinations, in particular, have been related to impairments of efference copy-based predictions which are relevant for the monitoring of outcomes produced by voluntary action. Frontal transcranial direct current stimulation (tDCS) has been shown to improve action-outcome monitoring in healthy subjects. However, whether tDCS can improve action monitoring in patients with SSD remains unknown.</p> <p><strong>Objective:</strong> We investigated whether tDCS can improve the detection of temporal action-outcome discrepancies in patients with SSD.</p> <p><strong>Methods:</strong> On 4 separate days, we applied sham or left cathodal/right anodal tDCS in a randomised order to frontal (F3/F4), parietal (CP3/CP4) and frontoparietal (F3/CP4) areas of 19 patients with SSD and 26 healthy control (HC) subjects. Action-outcome monitoring was assessed subsequent to 10 min of sham/tDCS (1.5 mA). After a self-generated (active) or externally generated (passive) key press, subjects were presented with a visual outcome (a dot on the screen), which was presented after various delays (0–417 ms). Participants had to detect delays between the key press and the visual consequence. Symptom subgroups were explored based on the presence or absence of symptoms related to a paranoid-hallucinatory syndrome (SSD phs+/phs-).</p> <p><strong>Results:</strong> In general, delay-detection performance was impaired in the SSD compared to the HC group. Interaction analyses showed group-specific (SSD vs HC) and symptom-specific (SSD phs+ vs SSD phs-) tDCS effects. Post-hoc tests revealed that frontal tDCS improved the detection of long delays in active conditions and reduced the proportion of false alarms in undelayed trials of the passive condition in patients. The SSD phs- group benefited especially from frontal tDCS in active conditions, while improvement in the SSD phs+ group was predominantly reflected in reduced false alarm rates in passive conditions.</p> <p><strong>Conclusion:</strong> These data provide some first evidence for the potential utility of tDCS in improving efference copy mechanisms and action-outcome monitoring in SSD. Current data indicate that improving efference copy-related processes can be especially effective in patients with no or few positive symptoms, while intersensory matching (i.e. task-relevant in passive conditions) could be more susceptible to improvement in patients with paranoid-hallucinatory symptoms.</p> <p> </p> <p><strong>Keywords:</strong> transcranial direct current stimulation; action-perception; action feedback; delay detection; schizophrenia.</p> <p> </p>
Superior Temporal Sulcus Disconnectivity During Processing of Metaphoric Gestures in Schizophrenia
<p><strong><em>Data related to the following publication:</em></strong></p> <p>Straube, B., Green, A., Sass, K., & Kircher, T. (2014). Superior Temporal Sulcus Disconnectivity During Processing of Metaphoric Gestures in Schizophrenia. <em>Schizophrenia Bulletin</em>, <em>40</em>(4), 936–944. http://doi.org/10.1093/schbul/sbt110</p> <p> </p>
NREM sleep EEG and wake ERP summary: The first wave of the Global Research Initiative on the neurophysiology of schizophrenia (GRINS)
<p>Motivated by the potential of objective neurophysiological markers to index thalamocortical function in patients with severe psychiatric illnesses, we comprehensively characterized key NREM sleep parameters across multiple domains, their interdependencies, and their relationship to waking event-related potentials and symptom severity. In 72 schizophrenia (SCZ) patients and 58 controls, we confirmed a marked reduction in sleep spindle density in SCZ and extended these findings to show that fast and slow spindle properties were largely uncorrelated. We also describe a novel measure of slow oscillation and spindle interaction that was attenuated in SCZ. The main sleep findings were replicated in a demographically distinct sample, and a joint model, based on multiple NREM components, statistically predicted disease status in the replication cohort. Although also altered in patients, auditory event-related potentials elicited during wake were unrelated to NREM metrics. Consistent with a growing literature implicating thalamocortical dysfunction in SCZ, our characterization identifies independent NREM and wake EEG biomarkers that may index distinct aspects of SCZ pathophysiology and point to multiple neural mechanisms underlying disease heterogeneity. This study lays the groundwork for evaluating these neurophysiological markers, individually or in combination, to guide efforts at treatment and prevention as well as identifying individuals most likely to benefit from specific interventions.</p>
Data related to "Upper cortical layer-driven network impairment in schizophrenia" paper, by Batiuk, Tyler et al.
<p>This is Data for "Upper cortical layer-driven network impairment in schizophrenia" paper, by Batiuk, Tyler et al., 2022</p> <p>This repository contains:</p> <p>Supplementary Dataset Tables 1-4 (Supplementary_Dataset_Tables_1-4.xlsx). They contain DE genes and GO terms from snRNA-seq and visium analysis.</p> <p>Single nuclei and Visium spatial transcriptomics sequencing data (snRNA-seq_and_spatial_transcriptomics.zip) containing raw count matrices of snRNA-seq samples; Conos object with aligned snRNA-seq samples; snRNA-seq single nuclei cell subtype annotations; raw count matrices of Visium spatial transcriptomics samples; Visium spatial transcriptomics manual histological cortical layer annotations; and 10x Genomics spaceranger count pipeline output for Visium spatial transcriptomics data.</p> <p>Histological images of H&E stained Visium spatial transcriptomics samples mounted on visium slide capture area (visium_sample_images.zip)</p>
Data from: Signature of altered retinal microstructures and electrophysiology in schizophrenia spectrum disorders is associated with disease severity and polygenic risk
<p>This dataset contains supporting data for the publication: </p> <p>Boudriot, E.<em> et al.</em> Signature of altered retinal microstructures and electrophysiology in schizophrenia spectrum disorders is associated with disease severity and polygenic risk. <em>Biological Psychiatry</em><span> </span><a href="https://doi.org/10.1016/j.biopsych.2024.04.014">https://doi.org/10.1016/j.biopsych.2024.04.014</a></p> <p> </p> <p>Files:</p> <ul> <li><em>clinical.csv </em>contains data from clinical assessment and polygenic risk scores for schizophrenia</li> <li><em>ophthalmic_examination.csv </em>contains data on spherical equivalent, intraocular pressure and visual acuity</li> <li><em>oct.csv </em>contains segmentation output from Iowa Reference Algorithms</li> <li><em>erg.csv </em>contains pre-processed ERG data for the four ERG conditions</li> <li><em>mri.csv </em>contains ICV-corrected MRI volumes</li> </ul>
Fig. 1 in Fig. 1 in Positive association between PTN polymorphisms and schizophrenia in Northeast Chinese Han population.
Fig. 1. Vertigo lilljeborgi distribution map.
Fig. 2 in Fig. 1 in Positive association between PTN polymorphisms and schizophrenia in Northeast Chinese Han population.
Fig. 2. Vertigo genesii distribution map.
Fig. 4 in Fig. 1 in Positive association between PTN polymorphisms and schizophrenia in Northeast Chinese Han population.
Fig. 4. Histograms showing frequency of all assembled records over time.
Fig. 3 in Fig. 1 in Positive association between PTN polymorphisms and schizophrenia in Northeast Chinese Han population.
Fig. 3. Vertigo geyeri distribution map.
Fig. 2 in Fig. 3 in Positive association between PTN polymorphisms and schizophrenia in Northeast Chinese Han population.
Fig. 2. Distribution of suitable habitat reconstructed with MaxEnt, a: LIG, and b: LGM.
Fig. 4 in Fig. 3 in Positive association between PTN polymorphisms and schizophrenia in Northeast Chinese Han population.
Fig. 4. Periodical suitable habitat changes of the macaques from LIG to the 2050s.
Fig. 1 in Positive association between PTN polymorphisms and schizophrenia in Northeast Chinese Han population.
Fig. 1. Map showing the current known global distribution of Scyllaea fulva and Scyllaea pelagica.
Fig. 1 in Positive association between PTN polymorphisms and schizophrenia in Northeast Chinese Han population.
Fig. 1. The study area with the point-count locations (black dots) on Tenerife Island.
Enhancing Collaborative Care in Schizophrenia: A Comparative Analysis of ICF Core Sets Assessments by Occupational Therapy and Mental Health Social Work Students
<p>This repository contains the R code, dataset, and README file for the network analysis of ICF (International Classification of Functioning, Disability, and Health) assessments. The study compares the assessments conducted by occupational therapy students and mental health social work students, analyzing centrality measures and Bridge Expected Influence within their respective networks.</p>
Data supporting the article: "Comparative analysis of fecal microbiota between adolescents with early-onset psychosis and adults with schizophrenia"
<p>This dataset supports the article titled <em>"Comparative analysis of fecal microbiota between adolescents with early-onset psychosis and adults with schizophrenia.", </em>available at<em> <a href="https://doi.org/10.3390/microorganisms12102071">https://doi.org/10.3390/microorganisms12102071</a></em><em>.</em></p> <p>The dataset includes fecal microbiota sequencing data from adolescent patients with early-onset psychosis, adult patients with schizophrenia, and non-psychotic controls. The data were generated using 16S rRNA gene sequencing and analyzed with QIIME2 and PICRUSt2 to assess microbial diversity and functional pathways. Variables such as age, diagnosis, and medication use are included.</p> <p>The dataset contains:</p> <ul> <li><strong>Processed results</strong> from fecal microbiota analysis (OTUs and taxonomic classifications)</li> <li><strong>Metadata</strong> associated with each sample (age, diagnosis, medication)</li> <li><strong>Results from diversity analysis</strong> (alpha and beta diversity metrics)</li> <li><strong>Functional analysis</strong> of microbial pathways (PICRUSt2)</li> </ul> <p>These data are essential for reproducing the findings discussed in the article. Note that the raw sequencing data are available upon request.</p>
Modality-specific dysfunctional neural processing of social-abstract and non-social-concrete information in schizophrenia
<p>Schizophrenia is characterized by marked communication dysfunctions encompassing potential impairments in the processing of social-abstract and non-social-concrete information, especially in everyday situations where multiple modalities are present in the form of speech and gesture. To date, the neurobiological basis of these deficits remains elusive. In a functional magnetic resonance imaging (fMRI) study, 17 patients with schizophrenia or schizoaffective disorder, and 18 matched controls watched videos of an actor speaking, gesturing (unimodal), and both speaking and gesturing (bimodal) about social or non-social events in a naturalistic way. Participants were asked to judge whether each video contains person-related (social) or object-related (non-social) information. When processing social-abstract content, patients showed reduced activation in the medial prefrontal cortex (mPFC) only in the gesture but not in the speech condition. For non-social-concrete content, remarkably, reduced neural activation for patients in the left postcentral gyrus and the right insula was observed only in the speech condition. Moreover, in the bimodal conditions, patients displayed improved task performance and comparable activation to controls in both social and non-social content. To conclude, patients with schizophrenia displayed modality-specific aberrant neural processing of social and non-social information, which is not present for the bimodal conditions. This finding provides novel insights into dysfunctional multimodal communication in schizophrenia, and may have potential therapeutic implications.</p>
Evidence for the association between the intronic haplotypes of ionotropic glutamate receptors and schizophrenia
<p>VCF and BED files for the publication "Evidence for the association between the intronic haplotypes of ionotropic glutamate receptors and schizophrenia".</p>
Data for "Brain cell-type shifts in Alzheimer's disease, autism and schizophrenia interrogated using methylomics and genetics"
<p>Data for "Genetic and methylomic interrogation of brain cell-type shifts in autism, schizophrenia, and Alzheimer’s disease" (Yap et al. 2023).</p> <p>Source data from ROSMAP, LIBD and UCLA_ASD post-mortem brain datasets.</p> <p>This data repository contains 3 files:</p> <p><strong>220819_Supplementary_Tables.xlsx</strong></p> <p>Supplementary Tables for the manuscript:</p> <ol> <li>Supplementary Table 1: Comparison of CTP deconvolution methods in the ROSMAP dataset. mcc* denotes methylCC deconvolution; sSV* denotes smartSVA; h* indicates Houseman using array reference data; hseq* indicates Houseman with sequencing reference data; celfie* indicates CelFIE (and includes an output titled "unknown1"); VAEe* indicates variational autoencoder embeddings.</li> <li>Supplementary Table 2: Comparison of CTP deconvolution methods in the LIBD dataset. mcc* denotes methylCC deconvolution; sSV* denotes smartSVA; h* indicates Houseman using array reference data; hseq* indicates Houseman with sequencing reference data; celfie* indicates CelFIE (and includes an output titled "unknown1"); VAEe* indicates variational autoencoder embeddings.</li> <li>Supplementary Table 3: Comparison of CTP deconvolution methods in the UCLA_ASD dataset. mcc* denotes methylCC deconvolution; sSV* denotes smartSVA; h* indicates Houseman using array reference data; hseq* indicates Houseman with sequencing reference data; celfie* indicates CelFIE (and includes an output titled "unknown1"); VAEe* indicates variational autoencoder embeddings.</li> <li>Supplementary Table 4: Deconvolved brain CTPs (raw), with covariates.</li> <li>Supplementary Table 5: Deconvolved brain CTPs (clr-transform, offset 1e-3), with covariates and raw PGS for all ancestries. Comp* indicates compositionally-aware principal components of the CTP data; *pgs_raw indicates PGS calculated for using genotyping across all ancestries. This table has a total of n=1,098 across all ancestries, including n=885 EUR. After applying a rel<0.05 threshold on the n=885 EUR, there were n=878 EUR which were used in the PGS analysis so that population stratification PCs did not simply capture family structure.</li> <li>Supplementary Table 6: Deconvolved brain CTPs (clr-transform, offset 1e-3), with covariates and standardised PGS for n=878 Europeans. *pgs_raw indicates unstandardised PGS, PC* indicates genotyping PCs within the European dataset, *_PGS indicates standardised PGS within the European subset.</li> <li>Supplementary Table 7: Deconvolved brain CTPs (clr-transform, offset 1e-3), adjusted for oligodendrocyte proportions, with covariates.</li> <li>Supplementary Table 8: Deconvolved brain CTPs (raw), adjusted for oligodendrocyte proportions, with covariates.</li> </ol> <p><strong>20220108_maf05_gwas_ctp.tar.gz</strong></p> <p>GWAS summary statistics for the 7 brainCTPs (clr-transformed): Exc, Inh, Astro, Endo, Micro, Oligo, OPC</p> <p>METAL output format:<br> MarkerName: SNP<br> Allele1<br> Allele2<br> Freq1: Allele1 frequency<br> FreqSE: frequency standard error<br> MinFreq: minimum Allele1 frequency in meta-analysis<br> MaxFreq: maximum Allele1 frequency in meta-analysis<br> Effect: effect size<br> StdErr: standard error of effect size<br> P-value: calculated in inverse variance weighted meta-analysis<br> Direction: directions of effects across the 3 datasets<br> HetISq: heterozygosity I-squared<br> HetChiSq: heterozygosity chi-squared<br> HetDf: heterozygosity degress of freedom<br> HetPVal: heterozygosity test p-value</p> <p><strong>20220108_maf05_gwas_ctp_pc.tar.gz</strong></p> <p>GWAS summary statistics for the 5 CTP_PCs</p> <p>METAL output format (see above)</p> <p> </p> <p><strong>Source data</strong>:</p> <p>ROSMAP: Raw methylation .idat files were obtained from Synapse accession syn7357283. Whole genome sequencing .vcf files (variants jointly called with MSBB and Mayo studies) were obtained from Synapse accession syn11707420.</p> <p>LIBD: Raw methylation .idat files were obtained from GEO accession GSE74193. SNP genotypes were downloaded from dbGaP accession phs000417.v2.p1.</p> <p>UCLA-ASD: The processed methylation beta matrix was downloaded from Synapse accession syn8263588. SNP genotypes were downloaded from Synapse accession syn10537134.</p> <p>GWAS summary statistics are available at: 10.5281/zenodo.7604231</p> <p>Code is available on GitHub: gandallab/brain_CTP_deconv</p>
Study to Evaluate the Efficacy of ALKS 3831 on Body Weight in Young Adults Who Have Been Recently Diagnosed With Schizophrenia, Schizophreniform, or Bipolar I Disorder
ClinicalTrials.gov study NCT03187769. IPD Sharing: NO. Countries: 12. Publications: 1.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.