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Brain imaging (fMRI, EEG), neural recordings, connectome, cognitive experiments, psychology
1,737 datasets
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.
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.
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.
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.
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.
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.
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.
Data and code from 'Repetition-related reductions in neural activity support improved behavior through increases in oscillatory power' by Gilmore, Adrian. This dataset includes behavioral and EEG power data for a within-participants analysis correlating behavioral priming with EEG induced power differences for novel and repeat items. The data was last updated on April 25, 2026.
Eight days of longitudinal resting-state fMRI data from awake mice during habituation, acquired at 15.2 T. The dataset includes measurements of plasma corticosterone levels, head motion, and functional connectivity, collected by Sang-Han Choi and last updated in March 2026. It compares three groups: controls, mice habituated outside the MRI magnet, and mice habituated within the fMRI environment.
A Data Management and Sharing Plan outlines the scientific data to be generated for research on translation regulation during human cytomegalovirus infection. Authored by Nathaniel Moorman, the plan describes the data types and a strategy for managing and sharing project data. The record was last updated on May 25, 2026.
Source estimated EEG data from 40 participants in an overt naming experiment. The data has been processed using a complete denoising procedure including gradient artifact and ECG removal, plus regression of signals near the eyes and upper jaw. The dataset was authored by Adrian Gilmore and last updated on April 25, 2026.
Gilmore, Adrian provides estimated EEG data from 25 participants in a covert naming experiment. The data has undergone a complete denoising procedure including gradient artifact and ECG removal, as well as regression of signals near the eyes and upper jaw. ICA components summing to 90% of the variance around response times have also been removed.
EEG spectral power density data was calculated using the PWelch function in Matlab from recordings in two animal cohorts (C and D). The dataset includes measurements from frontal (ch1) and occipital (ch2) derivations under baseline and sleep deprivation conditions. An accompanying information file provides details on the baseline and sleep deprivation experiments.
Supporting data for the manuscript 'Rapid, Growth Factor-Reduced Differentiation of Functional Neurons from hiPSCs.' The dataset contains raw and processed files from differentiation, characterization, and functional assessment experiments. It was contributed by Natalie Parker of the Iyer Lab and last updated on April 27, -2026.
ST-CORE-TOKENS is an ultra-refined, high-density tokenized dataset developed by SKT AI LABS. The dataset is intended for training Indian large language models and is described as containing distilled logic. It was last updated on the platform in April 2026.
Juan Chen's dataset, updated in April 2026, supports research on how individuals learn to use Generative AI. It examines dual pathways: cognitive-utility factors like task-technology fit and socio-affective factors like emotion and appraisal. The data likely contains survey measures related to behavioral intention and learning outcomes.
Juan Moises de la Serna compiled this integrated database in 2026, synthesizing molecular biomarkers, neuroimaging metrics, and clinical progression scales for Alzheimer's, Parkinson's, and Multiple Sclerosis. The data covers research and clinical reports published between 2013 and 2026, focusing specifically on neuroinflammatory mechanisms across these three conditions.
76,831 user queries annotated with complexity labels (easy, medium, or hard) to indicate the cognitive effort required for an answer. The dataset was created by regolo.ai to train a LoRA adapter for the Brick Semantic Router. It was last updated on Hugging Face in April 2026.
These data show responses to sensory stimulation in the nodulus/uvula region of the cerebellum. The dataset contains sensitivity to motion data for Purkinje cells in response to motion in anteroposterior and mediolateral directions. It also includes gain coefficients for velocity, acceleration, and jerk kinematic terms, which could be used for additional computations on neuron responses.