A Multimodal Neuroimaging Dataset to Study Spatiotemporal Dynamics of Visual Processing in Humans

Abstract

We describe structural and multimodal functional neuroimaging data collected from 21 healthy volunteers. Functional magnetic resonance images (fMRI) and electroencephalography (EEG) signals were acquired in separate sessions from the same individuals while they were performing a visual one-back repetition task. During functional sessions, participants were presented with images from five categories, including animals, chairs, faces, fruits, and vehicles. The stimulus set and experimental parameters were chosen to be similar to that of an available electrocorticography (ECoG) dataset, therefore creating a unique opportunity to study vision in humans with multiple complementary neuroimaging modalities. Individual-specific head models can be constructed for each participant using T1-weighted MPRAGE images and the recorded positions of the EEG electrodes. By combining the three functional modalities and the structural data, this dataset provides a unique setting to explore spatiotemporal dynamics of invariant object recognition in humans. This multimodal data can also be used to develop new methods for combining fMRI and electrophysiological modalities to come up with more accurate spatiotemporally resolved maps of brain function, which is inaccessible by any of the modalities alone.