标题： CNeuroMod-THINGS，用于视觉神经科学的高采样fMRI数据集 显示英文标题

标题： CNeuroMod-THINGS, a densely-sampled fMRI dataset for visual neuroscience

摘要： 数据饥渴的神经人工智能建模需要越来越大的神经影像数据集。CNeuroMod-THINGS通过使用一个新的密集采样、大规模的fMRI数据集中的经过良好表征的图像，捕捉一系列语义概念的神经表征来满足这一需求。重要的是，CNeuroMod-THINGS利用了两个现有项目之间的协同效应：THINGS倡议（THINGS）和Courtois神经建模项目（CNeuroMod）。THINGS开发了一组共同的彻底标注的图像，广泛采样自然和人造物体，用于获取不断增长的大规模多模态神经反应集合。同时，CNeuroMod正在核心参与者群体中收集数百小时的fMRI数据，在受控和自然任务中进行，包括如电影观看和视频游戏播放等视觉任务。对于CNeuroMod-THINGS，四个CNeuroMod参与者各自完成了33至36次连续识别范式的会话，使用大约4000张来自THINGS刺激集的图像，涵盖720个类别。我们报告了展示数据质量的行为和神经影像指标。通过结合现有的大量资源，CNeuroMod-THINGS扩展了我们对建模人类视觉体验广泛部分的能力。 显示英文摘要

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.