Sustained Attention Task (gradCPT) Dataset using simultaneous EEG-fMRI and DTI
DOI: 10.1038/s41597-026-06616-6
archive: archived pipeline: cataloged verified
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Summary
This paper introduces a multimodal neuroimaging dataset designed to investigate human attentional fluctuations by combining simultaneous electroencephalography (EEG), functional magnetic resonance imaging (fMRI), and diffusion-weighted imaging (DWI). The study addresses the complementary limitations of these modalities: fMRI offers high spatial resolution but slow temporal dynamics, while EEG provides millisecond-level temporal resolution but limited spatial precision. By integrating these techniques, the dataset enables precise tracking of brain states during sustained attention tasks, specifically the gradual onset continuous performance task (gradCPT). The research aims to explore the interaction between external attention and internal cognitive states, such as mind-wandering, by combining gradCPT with imagery tasks. The dataset comprises data from 28 healthy adult participants who underwent a single-session experiment at Sungkyunkwan University. The experimental protocol included two conditions: "Scan OFF" (EEG-only, scanner silent) and "Scan ON" (simultaneous EEG-fMRI). Tasks performed included resting states (eyes-open and eyes-closed), a visual flickering checkerboard task, gradCPT, an imagery task, and a dual-task condition combining gradCPT with imagery. EEG was recorded using a 64-channel MR-compatible system at 5000 Hz, while fMRI and DWI were acquired using a 3 T Siemens Magnetom Prisma scanner. The gradCPT paradigm required participants to respond to city scenes and withhold responses for mountain scenes, allowing for the measurement of reaction time changes and attentional lapses. Data preprocessing involved rigorous artifact removal to ensure signal quality. EEG data were processed using EEGLAB and the FMRIB plugin to remove gradient artifacts, ballistocardiogram (BCG) noise, and power line interference. fMRI data were preprocessed using the fMRIPrep pipeline, including motion correction, normalization to MNI space, and nuisance regression. DWI data were processed with MRtrix3 for denoising, distortion correction, and tractography to map structural white-matter connectivity. Quality control metrics, such as temporal signal-to-noise ratio (tSNR) and framewise displacement, were used to assess data integrity, with specific thresholds applied to exclude poor-quality runs or participants. The significance of this work lies in the public release of raw and preprocessed data via OpenNeuro and GitHub, providing a valuable resource for the neuroscience community. This dataset facilitates the investigation of spatiotemporal brain dynamics during attentional fluctuations, multitasking, and internal cognitive states. By offering high-resolution multimodal data, it supports future research into the neural mechanisms underlying sustained attention and the interplay between external task engagement and internal mind-wandering. The inclusion of both Scan OFF and Scan ON conditions also allows for the evaluation of artifact correction methods in simultaneous EEG-fMRI recordings.
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-08-09 |
| archive | success | canonical_url | — | — | 1 | 2026-08-09 |
| extract | success | cached | — | — | 3 | 2026-08-10 |
| clean | success | clean | — | — | 1 | 2026-08-09 |
| chunk | success | chunk | — | — | 1 | 2026-08-09 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-08-09 |
| enrich | success | semantic_scholar | — | — | 1 | 2026-08-09 |
| promote | success | — | — | — | 1 | 2026-08-09 |
| summarize | success | llm | qwen3.6-27b-nvidia | summ-v5 | 2 | 2026-08-10 |
| tag | success | vector_similarity | — | — | 10 | 2026-08-11 |
| verify | success | — | — | — | 2 | 2026-08-10 |
Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified.
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