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Brain imaging (fMRI, EEG), neural recordings, connectome, cognitive experiments, psychology
1,723 datasets
A 9-month experimental protocol investigates the effects of velocity-based resistance training on institutionalized older adults. The study, registered as NCT07027397, compares two velocity loss thresholds and includes a 6-month follow-up period after exercise cessation. Outcomes likely include muscle size, physical performance, disability, cognitive function, and frailty measures.
Between 30 July and 30 September 2010, the R.V. Solander conducted the SOL5117 survey in the Joseph Bonaparte Gulf, collecting underwater video and 12-megapixel still images from 79 stations across two legs. The survey aimed to map geohazards and sensitive seabed environments, gathering multibeam acoustic, sub-bottom profile, geological, and biological data across four offshore grids. This dataset represents a follow-up to a 2009 survey and was a collaboration between Geoscience Australia, the Australian Institute of Marine Science, and the Museum and Art Gallery of the Northern Territory.
This systematic review quantitatively evaluates how video game practice affects visual attention, oculomotor control, and perceptual processing. It compares video-game players versus non-players and analyzes pre-post intervention designs, exploring moderators like age, game type. The findings support experience-dependent visual plasticity and highlight gaming's translational potential for visual rehabilitation tools.
Data Sheet 1 by Meng Chen, uploaded to figshare in May 2026, contains results from a study integrating neuroimaging and transcriptomic data. The 113.2 KB Excel file presents findings from 27 adolescents with ADHD and 34 typically developing controls. It includes results from voxel-based morphometry, gene expression correlations, and machine learning model performance for diagnostic classification.
27 adolescents with ADHD and 34 typically developing controls were analyzed using structural MRI and voxel-based morphometry. The spatial pattern of gray matter volume changes was correlated with whole-brain gene expression profiles from the Allen Human Brain Atlas. The dataset includes results from three machine learning models, with Random Forest achieving an AUC of 0.871 for diagnostic classification.
30 trigeminal neuralgia patients and 20 matched controls had their whole-brain structural networks reconstructed from diffusion tensor imaging data. The dataset, authored by Xiao-Yi Guo and last updated in 2026, contains 90x90 connectivity matrices analyzed using Network-Based Statistics and graph theory. Results show side-specific alterations in subnetworks involving regions like the anterior cingulate gyrus and postcentral gyrus.
A graph dataset of whole-brain structural networks reconstructed from diffusion tensor imaging (DTI) tractography data for 30 trigeminal neuralgia patients and 20 matched controls. The data includes 90x90 connectivity matrices analyzed with Network-Based Statistics and graph theoretical methods to profile global and nodal properties. The dataset was authored by Xiao-Yi Guo and last updated on 2026-05-29.
A 24.3 MB dataset of intracranial electroencephalography (iEEG) recordings from patients undergoing functional brain mapping prior to epilepsy surgery. The data, published by Sai Pavan Beeram under CC-BY-4.0, captures brain activity during brief pulse electrical stimulation and subsequent cognitive tasks designed to suppress epilepsy-associated after discharges. Analysis using detrended fluctuation analysis and logistic regression models achieved an average accuracy of 77% in classifying effective suppression trials.
A multi-stage validation study of the C-reactive protein–triglyceride–glucose index (CTI) for predicting mild cognitive impairment. The dataset includes results from a primary cohort of 1,580 patients with type 2 diabetes and an external validation cohort of 4,575 individuals from the China Health and Retirement Longitudinal Study (CHARLS). The research was authored by Yiwen Chen and last updated on 2026-05-19.
46 participants' resting-state fMRI data analyzed for a potential objective biomarker for insomnia disorder. The dataset includes results from a discovery and validation set split, showing reduced spontaneous activity in the right Heschl's gyrus. Authored by Miao Li and last updated in 2026.
1,581 articles from Web of Science and Scopus databases were analyzed to map the evolution of neuroimaging research on cognitive control in Autism Spectrum Disorder. The analysis, conducted by Jing Hu, reveals an annual publication growth rate of 21.61% and a paradigm shift from functional localization to brain network dysregulation. The dataset, last updated in May 2026, includes visualizations of collaboration networks, keyword co-occurrence, and thematic evolution.
Fig 6 string MCL cluster descriptions dataset contains bulk RNA sequencing data from the hippocampus of high-excitability (LT) and low-excitability (HT) rat strains. The data was generated by Marina Pavlova and is available on figshare under a CC-BY-4.0 license. It provides a transcriptomic reference for studying polygenic excitability-related traits.
Marina Pavlova published bulk RNA sequencing data from the hippocampus of selectively bred rat strains on figshare in June 2026. The dataset compares transcriptomic profiles of high-excitability (LT) and low-excitability (HT) rats, highlighting differences in synaptic, glial, and cellular maintenance pathways. The data is stored in an XLS file sized 42.5 KB.
7.3 KB of bulk RNA sequencing data from the hippocampus of two selectively bred rat strains, high-excitability (LT) and low-excitability (HT), generated by Marina Pavlova. The dataset provides a transcriptomic reference for polygenic excitability traits, highlighting differences in synaptic, glial, and cellular maintenance pathways.
RNA sequencing data from the hippocampus of selectively bred high-excitability (LT) and low-excitability (HT) rats provides a baseline transcriptomic profile. The dataset likely contains differential expression results for genes related to synaptic function, glial activity, and cellular maintenance pathways. Marina Pavlova authored this 8.7 KB Excel file, last updated on June 3, 2026.
18.5 KB of bulk RNA sequencing data from the hippocampus of two rat strains bred for contrasting neural excitability thresholds. Marina Pavlova published this transcriptomic reference profile on figshare in June 2026 under a CC-BY-4.0 license. The analysis reveals strain-specific transcriptional profiles involving synapse-related genes, glial/immune functions, and cellular maintenance pathways.
RNA sequencing data from the hippocampus of selectively bred rat strains contrasts high-excitability (LT) and low-excitability (HT) thresholds. Marina Pavlova authored this transcriptomic reference profile for studying polygenic excitability traits. The dataset was last updated on June 3, 2026.
Marina Pavlosa's dataset contains bulk RNA sequencing results from the hippocampus of rat strains selectively bred for contrasting neural excitability thresholds. The data, last updated on 2026-06-03, includes transcriptomic profiles for high-excitability (LT) and low-excitability (HT) rats, highlighting differences in synaptic, glial, and cellular maintenance pathways. It is stored in an XLS file of 39.0 KB.
22.1 MB DOC file by Guangjun Hu, last updated June 3, 2026. The file contains supplementary data for a study investigating the role of neuron-derived exosomes in regulating microglial polarization and neuroinflammation. The research utilized a neuron–microglia transwell co-culture system and molecular assays.
A 2026 systematic review by Valentina Napoli synthesizes data from 72 studies on children aged 0 to 18 years with Developmental Coordination Disorder (DCD). The review, published on figshare, analyzes neurophysiological and neurobiological principles using EEG, fMRI, fNIRS, eye-tracking, and gait analysis. It reports on structural, functional, and connectivity abnormalities in brain areas like the DLPFC, cerebellum, and basal ganglia, including cases with ADHD and ASD comorbidities.