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Brain imaging (fMRI, EEG), neural recordings, connectome, cognitive experiments, psychology
2,271 datasets
A finite element brain model incorporating axonal fiber tracts derived from a group-averaged tractography atlas. The model was validated against experimental data from postmortem human subject tests and used in reconstruction simulations of eight real-world mTBI cases. The dataset, authored by Noritoshi Atsumi and last updated in March 2026, is a 3.4 MB PDF file shared under a CC-BY-4.0 license.
A research paper analyzing neuroimaging and behavioral data from 80 matched youth pairs from the Adolescent Brain Cognitive Development (ABCD) Study. The study investigates neural differences in inhibitory control between youth who initiated low-level alcohol use and those who remained alcohol-naïve, using the Stop Signal Task. The document was authored by Faith Adams and last updated on March 18, 2026.
A single clinical case report documents a 61-year-old female patient with a 22-year history of drug-refractory nausea and vomiting later diagnosed as Neuronal Intranuclear Inclusion Disease (NIID). The report details diagnostic findings from diffusion-weighted imaging, genetic analysis of the NOTCH2NLC gene, and skin biopsy, along with the patient's response to corticosteroid therapy over a six-month follow-up. The dataset, a 19.1 KB DOCX file, was authored by Long Luo and published on figshare under a CC-BY-4.0 license.
Elena Carbone's study dataset from 2026 includes survey responses from 552 participants aged 50–84 years. It examines relationships between personal views of aging, quality of life, and cognitive reserve proxies. The data was collected using standardized questionnaires including the Attitudes Toward Own Aging scale and the Awareness of Age-Related Change questionnaire.
Avinash Veerappa's study profiles transcriptomes from four brain regions—midbrain, dorsolateral prefrontal cortex (DLPFC), nucleus accumbens (NAc), and amygdala—to investigate substance use disorders. The dataset, last updated in March 2026, contains results from clustering, biclustering, WGCNA, and pathway enrichment analyses, identifying unique and shared gene signatures across regions. It includes findings on 186 genes exclusive to midbrain, 29 to DLPFC, 160 to NAc, and 442 in amygdala, with specific genes like CSF3, GADD45B, SOCS3, and NPAS4 highlighted.
186 to 442 unique differentially expressed genes were identified across four brain regions (midbrain, DLPFC, NAc, amygdala) in a study of chronic substance use. The dataset contains results from transcriptome profiling, clustering, and network analysis, authored by Avinash Veerappa and last updated in March 2026. It is shared under a CC-BY-4.0 license on figshare.
Transcriptomic data from four brain regions—midbrain, dorsolateral prefrontal cortex (DLPFC), nucleus accumbens (NAc), and amygdala—profiled to study substance use disorders. The dataset identifies 186, 29, 160, and 442 unique differentially expressed genes for each region respectively, along with shared signatures. It was published by Avinash Veerappa on figshare under a CC-BY-4.0 license and last updated in March 2026.
186 to 442 unique genes were identified in each of four brain regions from a transcriptomic analysis of substance use disorders. The dataset contains results from clustering, biclustering, WGCNA, and pathway enrichment analyses of midbrain, DLPFC, NAc, and amygdala samples. Authored by Avinash Veerappa and shared under CC-BY-4.0 in March 2026.
186 to 442 unique differentially expressed genes were identified in each of four brain regions (midbrain, DLPFC, NAc, amygdala) from cases versus controls. The dataset contains results from transcriptome profiling and network analysis, authored by Avinash Veerappa and last updated in March 2026. It is a 27.5 KB Excel file shared under a CC-BY-4.0 license on figshare.
Avinash Veerappa's dataset, published on figshare in March 2026, contains transcriptomic analysis results from four brain regions related to substance use disorders. The data, stored in a 104.1 KB XLSX file, identifies unique and shared gene expression signatures, including 186 genes exclusive to the midbrain and 442 in the amygdala. It results from clustering, biclustering, WGCNA, and pathway enrichment analyses on case versus control samples.
Avinash Veerappa published transcriptomic data from four brain regions in 2026. The dataset contains gene expression signatures from the midbrain, dorsolateral prefrontal cortex, nucleus accumbens, and amygdala, comparing cases with chronic substance use to controls. Analysis identified 186, 29, 160, and 442 unique differentially expressed genes per region, respectively, and shared pathways like CREB Signaling in Neurons.
186 to 442 unique differentially expressed genes were identified in each of four brain regions (midbrain, DLPFC, NAc, amygdala) from cases versus controls. This dataset, created by Avinash Veerappa and last updated in March 2026, contains results from transcriptome profiling, clustering, and network analyses to study addiction. It highlights shared and unique gene signatures, including upregulation of CSF3, GADD45B, SOCS3, and NPAS4, linked to CREB signaling pathways.
186 unique genes were identified in the midbrain, 29 in the DLPFC, 160 in the NAc, and 442 in the amygdala in this transcriptomic study of substance use disorders. The dataset, created by Avinash Veerappa and last updated in March 2026, profiles gene expression across four brain regions to identify shared and unique molecular signatures associated with addiction. It results from clustering, biclustering, WGCNA, and pathway enrichment analyses of case versus control samples.
Avinash Veerappa's dataset, last updated March 2026, profiles transcriptomes from four brain regions to study substance use disorders. The analysis identifies 186, 29, 160, and 442 uniquely dysregulated genes in the midbrain, DLPFC, NAc, and amygdala, respectively. It also reveals shared gene upregulation and network interactions via a neuropeptide-neurotransmitter axis.
Avinash Veerappa published this dataset on figshare in March 2026. It contains transcriptomic data from four brain regions—midbrain, DLPFC, NAc, and amygdala—profiled to study substance use disorders. The analysis identified unique and shared gene signatures, including 186 genes exclusive to the midbrain and 442 in the amygdala.
A transcriptomic analysis of four brain regions—midbrain, dorsolateral prefrontal cortex (DLPFC), nucleus accumbens (NAc), and amygdala—from cases versus controls. The dataset, created by Avinash Veerappa and last updated in March 2026, contains results identifying 186, 29, 160, and 442 uniquely dysregulated genes per region, respectively, and shared pathway enrichments. It is a 921.7 KB Excel file licensed under CC-BY-4.0.
186 to 442 unique genes were identified in four key brain regions of substance-use cases versus controls. This dataset contains transcriptomic profiles from the midbrain, dorsolateral prefrontal cortex, nucleus accumbens, and amygdala, analyzed with clustering and network methods. The data was uploaded by Avinash Veerappa in March 2026 under a CC-BY-4.0 license.
A 2026 study by Avinash Veerappa profiles gene expression in four brain regions to investigate substance use disorders. The dataset contains transcriptomic signatures from the midbrain, dorsolateral prefrontal cortex, nucleus accumbens, and amygdala, identifying unique and shared genes. Analysis includes clustering, biclustering, WGCNA, and pathway enrichment results.
Avinash Veerappa published this transcriptomic dataset on figshare in March 2026. It contains gene expression profiles from four brain regions—midbrain, dorsolateral prefrontal cortex, nucleus accumbens, and amygdala—comparing cases with chronic substance use to controls. The analysis identified unique and shared differentially expressed genes and enriched pathways related to addiction neurocircuitry.
A curated dataset of mega-hit and mainstream-popular web series across 20+ countries. The dataset is hosted on Kaggle and covers a 20-year period from 2006 to 2026. The author, organization, and specific data collection method are unknown.