Loading...
Loading...
Brain imaging (fMRI, EEG), neural recordings, connectome, cognitive experiments, psychology
2,276 datasets
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.
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.
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 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.
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.
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.
Task-aware EEG-to-text data, likely containing segmented recordings related to a 'smoke' task. The dataset is hosted on Kaggle, but detailed metadata such as author, collection date, and sample size are not provided. Columns and data structure are unknown, requiring verification after download.
Saudi Arabia's Al Hayit region is the focus of this dataset, which appears to catalog megalithic structures. The dataset is hosted on Kaggle, but its specific contents, creation date, and authorship are not detailed in the provided metadata. The actual data volume, collection methods, and specific attributes require verification after download.
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.
Algonauts 2023 NSD Subject 01 With Test fMRI is a dataset from the Algonauts Project 2023 challenge. It likely contains functional Magnetic Resonance Imaging (fMRI) data from a single subject (Subject 01) of the Natural Scenes Dataset (NSD), including test data. The dataset is hosted on Kaggle.
Task-aware-eeg2text-task-segmented-schedule is a dataset from Kaggle. It likely contains electroencephalogram (EEG) recordings paired with text, segmented according to specific tasks or schedules. The dataset's purpose appears to be for exploring the relationship between brain activity and language generation within defined experimental contexts.
An EEG dataset published on huggingface by ChenglinLiuChris. The dataset was last updated on 2026-06-13. Its specific content, scale, and collection methodology are not detailed in the provided metadata.
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.
Kaggle hosts a dataset titled 'task-aware-eeg2text-task-segmented-protocol'. The data likely contains electroencephalogram (EEG) recordings paired with corresponding text descriptions, segmented by specific cognitive or motor tasks. The dataset's author, organization, and specific collection details are not provided in the available metadata.
A dataset titled 'task-aware-eeg2text-task-treatment-pilots' published on Kaggle. The dataset likely contains electroencephalogram (EEG) recordings paired with textual descriptions, inferred from the title. Its specific scale, authorship, and temporal details are not provided in the available metadata.
Seventy healthy young adults (age 28.8 ± 9.1 years, 27 females) underwent multimodal MRI scanning to map brain connectivity. The dataset includes structural connectivity from diffusion spectrum imaging (DSI) and functional connectivity from eyes-open resting-state fMRI, processed into brain region parcels at five spatial scales (83, 129, 234, 463, and 1015 parcels). This provides a core resource for studying the human connectome's structural and functional organization in a healthy population.
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.