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<p>We provide a multimodal naturalistic neuroimaging dataset (NNDb-3T+), designed to support the study of brain function under both naturalistic and controlled experimental conditions. The dataset includes high-quality 3 T fMRI data from 40 participants acquired during full-length movie-watching and somatotopic, retinotopic, and tonotopic sensory mapping tasks. Each participant also completed synchronised eye-tracking during movie-watching and retinotopic mapping tasks, physiological recordings, and a battery of behavioural and cognitive assessments. Data were collected across two MRI sessions and a remote testing session, with all data organised in a BIDS-compliant format. Technical validation confirms high data quality, with minimal head motion, accurate eye-tracker calibration, and robust task-evoked activation patterns. The dataset provides a unique resource for investigating individual differences, functional topographies, multimodal integration, and naturalistic cognition. All raw and preprocessed data, quality metrics, and preprocessing scripts are publicly available to support reproducible research.</p>

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A neuroimaging dataset combining movie-watching, eye-tracking, sensorimotor mapping, and cognitive tasks

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月9日

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  • Egor Levchenko,
  • Hugo Chow-Wing-Bom,
  • Fred Dick,
  • Greg Cooper,
  • Adam Tierney,
  • Jeremy I. Skipper

摘要

We provide a multimodal naturalistic neuroimaging dataset (NNDb-3T+), designed to support the study of brain function under both naturalistic and controlled experimental conditions. The dataset includes high-quality 3 T fMRI data from 40 participants acquired during full-length movie-watching and somatotopic, retinotopic, and tonotopic sensory mapping tasks. Each participant also completed synchronised eye-tracking during movie-watching and retinotopic mapping tasks, physiological recordings, and a battery of behavioural and cognitive assessments. Data were collected across two MRI sessions and a remote testing session, with all data organised in a BIDS-compliant format. Technical validation confirms high data quality, with minimal head motion, accurate eye-tracker calibration, and robust task-evoked activation patterns. The dataset provides a unique resource for investigating individual differences, functional topographies, multimodal integration, and naturalistic cognition. All raw and preprocessed data, quality metrics, and preprocessing scripts are publicly available to support reproducible research.