CNeuroMod-THINGS：一个面向视觉神经科学的高密度采样fMRI数据集
CNeuroMod-THINGS, a densely-sampled fMRI dataset for visual neuroscience
AI总结：
本研究整合THINGS与CNeuroMod两大项目资源，构建了高密度采样的大规模fMRI数据集CNeuroMod-THINGS，覆盖720类共约4000张图像的神经表征，为神经AI建模提供高质量视觉神经数据支撑。
摘要：
数据需求旺盛的神经人工智能建模需要规模越来越大的神经成像数据集。CNeuroMod-THINGS通过构建一个全新的高密度采样大规模功能磁共振成像（fMRI）数据集，利用特征明确的图像捕获广泛语义概念对应的神经表征，满足了这一需求。\n值得注意的是，CNeuroMod-THINGS充分利用了两个现有项目之间的协同作用：THINGS倡议（THINGS）和Courtois神经建模项目（CNeuroMod）。THINGS开发了一套经过全面标注的通用图像集，广泛涵盖自然物体与人造物体，该图像集正被用于采集不断扩充的大规模多模态神经响应数据。与此同时，CNeuroMod正在从核心参与者组中采集数百小时的fMRI数据，采集场景包含受控任务与自然任务，其中包括看电影、玩电子游戏等视觉任务。\n在CNeuroMod-THINGS项目中，4名CNeuroMod参与者各完成了33至36个session的连续识别范式实验，实验使用了来自THINGS刺激集的约4000张图像，覆盖720个类别。我们报告了行为指标与神经成像指标，这些指标证明了数据集的质量。通过整合已有的大规模资源，CNeuroMod-THINGS提升了我们对人类视觉体验的广泛维度进行建模的能力。
英文摘要：
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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