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Sustained Attention Task (gradCPT) Dataset using simultaneous EEG-fMRI and DTI.

Younghwa Cha1,2,3, Yeji Lee1,2, Eunhee Ji1,2

  • 1Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, 16419, Republic of Korea.

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Summary
This summary is machine-generated.

This study introduces a multimodal neuroimaging dataset for studying human attention fluctuations. It combines EEG, fMRI, and DWI data to explore brain activity during various attentional states.

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Area of Science:

  • Cognitive Neuroscience
  • Neuroimaging
  • Human Brain Research

Background:

  • Understanding human attentional fluctuations is crucial for cognitive neuroscience.
  • Existing datasets may lack multimodal integration for comprehensive analysis.

Purpose of the Study:

  • To introduce a novel multimodal dataset for studying sustained attention.
  • To facilitate research on brain states during attentional fluctuations using combined neuroimaging techniques.

Main Methods:

  • Development of the Sustained Attention Task (gradual onset continuous performance: gradCPT).
  • Collection of multimodal neuroimaging data: electroencephalography (EEG), functional magnetic resonance imaging (fMRI), and diffusion-weighted imaging (DWI).
  • Inclusion of data from 28 participants across various tasks (gradCPT, imagery, visual, resting states).

Main Results:

  • A comprehensive multimodal dataset is now publicly available.
  • The dataset includes raw and preprocessed data for each imaging modality.
  • Data covers diverse cognitive states, including sustained attention and resting states.

Conclusions:

  • The gradCPT multimodal dataset offers a valuable resource for cognitive neuroscience research.
  • This dataset will enable deeper investigations into the neural correlates of attention dynamics.
  • Public accessibility promotes collaborative research on brain activity across multiple imaging modalities.