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Brain imaging (fMRI, EEG), neural recordings, connectome, cognitive experiments, psychology
1,730 datasets
11.6 MB of source data in XLSX format supports the research paper 'Disorder-mediated Non-equilibrium Photocurrent Redistribution Enables Homeostatic Synaptic Conditioning in AgBiS2 Heterostructure'. The dataset was authored by Hyun Woo Kim and last updated on April 20, 2026. It is licensed under CC-BY-4.0 and hosted on figshare.
1,300 distinct anatomical regions were mapped in a 21-hour real-time process to create this high-resolution human brain atlas. It serves as a technical bridge between the concepts of Geometric Stillness and Kinetic Flux Displacement, providing a standardized metric for AI risk management and volumetric neural parcellation. The dataset is technically verified under NIST Framework Inquiry #00476599 and targets the midbrain, pituitary, and localized coordinates.
A dataset of baseline participant characteristics from the Aging and Cognitive Health Evaluation in Elders (ACHIEVE) study conducted in 2018-19. The data shows distributions by randomized intervention assignment and recruitment source. Jennifer A. Deal authored this 9.5 KB Excel file, which is licensed for reuse under CC-BY-4.0.
A 412.0 KB PDF contains a pilot case series applying the SPECTRE method to analyze spatially resolved EEG data. The study examines theta-band brain electrical network changes following active versus sham intermittent theta-burst stimulation in cognitively normal older adults and individuals with mild cognitive impairment. Lawrence R. Frank authored this work, which was last updated in March 2026.
A PDF research article analyzes resting-state EEG data from 47 healthy adults (29 younger, 18 middle-aged) to identify age-group differences in brain dynamics. The study uses instantaneous frequency microstate analysis, a novel method distinct from amplitude-based approaches, to evaluate spatial distributions, mean dwell times, occupancy, and transition probabilities. Results show significant age-related changes in these dynamic properties within theta and alpha frequency bands.
Two previously collected datasets from the Dual Mechanisms of Cognitive Control (DMCC) task battery are used in a case study exploring Hierarchical Bayesian Regression. The 16.4 MB PDF, authored by Thomas A. Dudey and last updated on 2026-03-19, presents a methodological analysis of statistical inference practices in psychology. It illustrates how HBR models can provide insights into proactive and reactive control indicators across four DMCC tasks.
A database of 35 records containing figures and statistics on neurocognitive correlates of attention and working memory in digital environments across Latin America from 2021 to 2024. It includes country-level ADHD prevalence, screen time usage, scores from cognitive tests like WAIS-IV and Stroop, and EEG/fMRI findings on working memory under digital multitasking. The dataset also covers digital divide data for seven countries.
A 2.1 GB multimodal dataset demonstrating the application of graphene dry electrodes in a high-density 512-lead EEG cap and real-time monitoring system. The dataset, authored by jiawei liu and last updated on 2026-04-23, is available under a CC-BY-4.0 license on figshare. It includes OPJU, MP4, and PPTX files, suggesting a combination of experimental data, video recordings, and presentation materials.
This dataset integrates m6A-seq and CLIP-seq data with the SFARI gene database, finding that 41.59% (515 of 1,238 genes) of autism risk genes are m6A-enriched. It specifically identifies 28 syndromic genes overlapping with the Synaptic m6A Epitranscriptome (SME). The analysis was authored by Shreya Doijad and focuses on m6A readers like FMRP and YTHDF1.
This dataset integrates m6A-seq and CLIP-seq data with the SFARI gene database, finding 41.59% (515 of 1,238 genes) of Autism Spectrum Disorder risk genes are m6A-enriched. It specifically identifies 28 syndromic genes overlapping with the Synaptic m6A Epitranscriptome. The analysis was authored by Shreya Doijad and focuses on m6A readers like FMRP and YTHDF1.
Results from a study of 853 community-dwelling older adults aged 60+ from a northern Chinese urban area, investigating the association between Motoric Cognitive Risk syndrome and incident mild cognitive impairment over a one-year follow-up. The study used logistic regression to calculate adjusted odds ratios for MCI and cognitive decline risk.
Neal Bennett's dataset presents an interactive heatmap of high-confidence hits from parallel CRISPRi survival screens. The screens were performed in human iPSC-derived neurons across 12 distinct metabolic conditions, with data reflecting two replicates per condition. The dataset was last updated on 2026-05-15.
Over 650,000 cellular-resolution images from in situ hybridization experiments on the mouse brain are available for analysis. The Allen Institute produced this genome-scale collection of gene expression profiles, covering more than 20,000 genes with dense, uniform anatomical sampling. The dataset uses an inbred mouse strain to minimize variance, treating the brain as a reproducible three-dimensional tissue array.
142 individuals from a pre-birth cohort of 281 were tracked for 20 years to study links between early viral exposure and adult allergy. Parent-reported respiratory infections and seropositivity against 13 viruses at age two were analyzed alongside hereditary risk groups. Individuals with 11-20 early respiratory infections had a 58% allergy prevalence, compared to 34% for those with fewer than 10.
Experimental results comparing the performance of neural network models (EEGNet, AlexNet, Shallow ConvNet) combined with time-frequency transforms (CWT, STFT) for epileptic EEG signal detection. The data originates from a study by Canhui Wang, employing subject-independent validation and techniques like Focal Loss and dynamic data augmentation. The file is 5.5 KB in size.
Comprising experimental results comparing the performance of neural network models (EEGNet, AlexNet, Shallow ConvNet) combined with time-frequency transforms (CWT, STFT) for epileptic EEG signal detection. The study incorporates optimization techniques including Focal Loss, dynamic data augmentation, and an early stopping mechanism. The results show the CWT+Shallow ConvNet combination achieved optimal overall performance.
Comprising performance indicators from a study comparing continuous wavelet transform (CWT) and short-time Fourier transform (STFT) feature extraction methods combined with three neural network models for epileptic EEG signal detection. The study employed subject-independent validation and targeted optimizations including Focal Loss and dynamic data augmentation. The results indicate the CWT-based method with Shallow ConvNet achieved optimal overall performance.
A collection of performance metrics from a study comparing continuous wavelet transform (CWT) and short-time Fourier transform (STFT) feature extraction methods for epileptic EEG signal detection. The study evaluated three neural network models—EEGNet, AlexNet, and Shallow ConvNet—with targeted optimizations like Focal Loss and dynamic data augmentation. Results indicate the CWT-based method outperformed STFT, with the CWT+Shallow ConvNet combination showing optimal overall performance.
Encompassing performance indicators from a study comparing continuous wavelet transform (CWT) and short-time Fourier transform (STFT) feature extraction methods for epileptic EEG signal detection. The study evaluated three neural network models—EEGNet, AlexNet, and Shallow ConvNet—with targeted optimizations like Focal Loss and dynamic data augmentation. Results indicate the CWT-based method outperformed STFT, with the CWT+Shallow ConvNet combination showing optimal overall performance.
Lijun Zhou published a supplementary PDF on figshare on 2026-03-19. The file describes a multi-atlas graph neural network framework validated on the public ABIDE-I dataset for autism spectrum disorder and a private PTSD dataset with 138 subjects. The model achieved classification AUCs of 82.9% and 89.96% on the respective datasets.