Showing 10 of 57 datasets · page 4 of 6
MEGMEM is a magnetoencephalography (MEG) dataset acquired during a memory task designed to investigate neural mechanisms of episodic memory encoding and retrieval. The dataset contains raw MEG recordings from multiple subjects performing structured memory experiments, providing a resource for studying oscillatory dynamics and neural correlates of memory processes.
This derivative dataset contains processed MEG data from a study investigating the neural mechanisms of model-based aversive learning in humans. The research examines how task state representations are preferentially reactivated during aversive learning, providing insights into the computational and neural basis of threat-related decision-making and learning processes.
ForrestGump-MEG is a multimodal neuroimaging dataset comprising magnetoencephalography (MEG) recordings from 11 subjects during passive viewing of a 2-hour audio-visual movie stimulus (Chinese-dubbed Forrest Gump), along with structural MRI data. The dataset includes raw 275-channel CTF MEG data, preprocessed MEG recordings with independent component analysis decomposition, anatomically preprocessed T1-weighted images, and MEG-MRI co-registration information. This resource enables investigation of neural dynamics during naturalistic audiovisual perception and brain-behavior relationships in ecologically valid contexts.
This dataset contains magnetoencephalography (MEG) and structural magnetic resonance imaging (MRI) data from adolescent participants with major depressive disorder and healthy controls during a monetary gambling mood induction task and resting state. The study investigates electrophysiological correlates of mood and reward dynamics in human adolescents through pre-registered analyses of task-based and resting-state neuroimaging data.
This dataset comprises neuroimaging data from a logical reasoning study conducted by the Cognitive and Computational Neuroscience Laboratory. The study investigates neural correlates of logical reasoning processes through multimodal brain imaging. Data were collected and organized according to BIDS standards for reproducibility and accessibility. This dataset is a NEMAR mirror of the OpenNeuro dataset ds003483.
This magnetoencephalography (MEG) dataset comprises recordings from 10 participants performing a human visual motion area (hMT+) localizer task involving passive viewing of coherent and incoherent moving dot stimuli. The dataset includes concurrent anatomical MRI scans and auxiliary physiological recordings (EOG, ECG), formatted according to MEG-BIDS standards. The data were acquired to investigate visual perceptual learning and motion perception mechanisms.
This dataset contains intracranial electrophysiological recordings acquired during visual stimulus presentation. Participants were exposed to eight spiral grating stimuli of varying colors and luminance levels, as well as color word stimuli, with each stimulus presented for 116 ms. The dataset includes event markers corresponding to each stimulus type, enabling investigation of neural responses to visual and linguistic stimuli.
This dataset contains somatosensory MEG recordings converted to BIDS format, including raw MEG data, anatomical MRI scans, and FreeSurfer-derived structural derivatives. The dataset includes forward models and boundary element models computed using MNE-BIDS, providing a complete neuroimaging resource for studying somatosensory processing and demonstrating MEG-MRI integration workflows.
This MEG dataset comprises auditory cortex mapping data from a single subject acquired using a CTF 275 system at 120 kHz sampling rate. The experiment involved passive listening to 1,795 iso-intensity pure tones ranging from 100 Hz to 21.5 kHz presented binaurally over a 12-minute acquisition run. The dataset includes anatomical MRI data processed with FreeSurfer and serves as a reference example for MEG-based auditory cortex mapping techniques.
This magnetoencephalography (MEG) dataset comprises human recordings during a sequential conflict task, designed to investigate neural mechanisms of conflict detection and decision-making. The dataset includes recordings from both healthy controls and patients with movement disorders, enabling investigation of cortico-subthalamic interactions and oscillatory dynamics in the beta frequency band during cognitive conflict processing.