构建大规模视觉神经数据集，支持脑-人工智能建模研究。
CNeuroMod-THINGS, a densely-sampled fMRI dataset for visual neuroscience
- 整合THINGS图像与CNeuroMod fMRI数据，覆盖720类概念
- 4名参与者完成33-36次扫描，使用约4000张图像进行连续识别任务
- 适合视觉神经科学与脑启发人工智能研究者使用
神经人工智能建模依赖大规模神经影像数据。CNeuroMod-THINGS通过结合已有两大项目——THINGS（图像标注丰富，涵盖自然与人工物体）与CNeuroMod（长期收集受试者在控制与自然任务下的fMRI数据），构建了一个高密度采样的大型功能性磁共振成像数据集。本研究中，4名核心受试者在33至36次会话中，使用来自THINGS刺激集的约4000张图像（覆盖720个类别）完成了连续识别任务。研究报告了行为与神经影像指标，验证了数据质量。该数据集融合现有资源，显著拓展了对人类视觉体验大范围建模的能力。
原文摘要 · Abstract (English)
Data-hungry neuro-AI modelling requires ever larger neuroimaging datasets. CNeuroMod-THINGS meets this need by capturing neural representations for a wide set of semantic concepts using well-characterized images in a new densely-sampled, large-scale fMRI dataset. Importantly, CNeuroMod-THINGS exploits synergies between two existing projects: the THINGS initiative (THINGS) and the Courtois Project on Neural Modelling (CNeuroMod). THINGS has developed a common set of thoroughly annotated images broadly sampling natural and man-made objects which is used to acquire a growing collection of large-scale multimodal neural responses. Meanwhile, CNeuroMod is acquiring hundreds of hours of fMRI data from a core set of participants during controlled and naturalistic tasks, including visual tasks like movie watching and videogame playing. For CNeuroMod-THINGS, four CNeuroMod participants each completed 33-36 sessions of a continuous recognition paradigm using approximately 4000 images from the THINGS stimulus set spanning 720 categories. We report behavioural and neuroimaging metrics that showcase the quality of the data. By bridging together large existing resources, CNeuroMod-THINGS expands our capacity to model broad slices of the human visual experience.
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