Source imaging of high-density visual evoked potentials with multi-scale brain parcellations and connectomes

Area of Science:

• Neuroscience
• Cognitive Science
• Medical Imaging

Background:

• Multimodal neuroimaging datasets are crucial for understanding complex brain functions.
• Existing datasets may lack comprehensive integration of electroencephalography (EEG) and magnetic resonance imaging (MRI) data with detailed behavioral metrics.
• The VEPCON dataset addresses this gap by providing synchronized EEG, MRI, and behavioral data.

Purpose of the Study:

• To introduce and describe the VEPCON multimodal neuroimaging dataset.
• To provide a rich resource for studying structure-function relationships in the brain.
• To facilitate the development and optimization of neuroimaging analysis techniques, including source imaging and graph analysis.

Main Methods:

• Acquisition of high-density EEG, structural MRI, and diffusion-weighted images (DWI) from 20 participants.
• Recording of single-trial behavioral data (accuracy, reaction time) during visual evoked potential (VEP) tasks.
• Generation of individualized brain parcellations and structural connectomes, along with EEG source imaging solutions.

Main Results:

• The VEPCON dataset includes raw and pre-processed EEG, structural MRI, DWI, and behavioral data.
• It provides individualized brain parcellations across 5 resolutions and corresponding structural connectomes.
• Includes Python and Matlab scripts for deriving regional activity time-series from EEG source imaging.

Conclusions:

• The VEPCON dataset is a valuable, BIDS-compatible resource for multimodal neuroimaging research.
• It enables advanced investigations into brain structure-function relationships.
• Supports unimodal and multimodal analysis method development for EEG and MRI data.