THINGS-MEG

# THINGS-MEG Understanding object representations visual and semantic processing of objects requires a broad, comprehensive sampling of the objects in our visual world with dense measurements of brain activity and behavior. This densely sampled fMRI dataset is part of THINGS-data, a multimodal collection of large-scale datasets comprising functional MRI, magnetoencephalographic recordings, and 4.70 million behavioral judgments in response to thousands of photographic images for up to 1,854 object concepts. THINGS-data is unique in its breadth of richly-annotated objects, allowing for testing countless novel hypotheses at scale while assessing the reproducibility of previous findings. The multimodal data allows for studying both the temporal and spatial dynamics of object representations and their relationship to behavior and additionally provides the means for combining these datasets for novel insights into object processing. THINGS-data constitutes the core release of the [THINGS initiative](https://things-initiative.org) for bridging the gap between disciplines and the advancement of cognitive neuroscience. # Dataset overview We collected extensively sampled object representations using magnetoencephalography (MEG). To this end, we drew on the THINGS database [(Hebart et al., 2019)](https://doi.org/10.1371/journal.pone.0223792), a richly-annotated database of 1,854 object concepts representative of the American English language which contains 26,107 manually-curated naturalistic object images. During the fMRI experiment, participants were shown a representative subset of THINGS images, spread across 12 separate sessions (N=4, 22,448 unique images of 1,854 objects). Images were shown in fast succession (1.5±0.2s), and participants were instructed to maintain central fixation. To ensure engagement, participants performed an oddball detection task responding to occasional artificially-generated images. A subset of images (n=200) were shown repeatedly in each session. Beyond the core functional imaging data in response to THINGS images, we acquired T1-weighted MRI scans to allow for cortical source localization. Eye movements were monitored in the MEG to ensure participants maintained central fixation.# THINGS-MEG 要理解物体的视觉表征与语义加工，需对视觉世界中的物体开展广泛且全面的采样，并结合高密度的脑活动与行为测量数据。本高密度采样的功能磁共振成像（fMRI）数据集隶属于THINGS-data，这是一个多模态大规模数据集集合，涵盖功能磁共振成像、脑磁图（MEG）记录，以及针对数千张摄影图像的470万份行为判断数据，共涉及1854个物体概念。THINGS-data的独特之处在于其对物体的丰富注释广度，可支持在大规模尺度下验证无数全新假设，同时用于评估既往研究发现的可重复性。该多模态数据既可用于研究物体表征的时空动态特征及其与行为的关联，还为整合这些数据集以获取物体加工的全新见解提供了可能。THINGS-data是[THINGS研究计划（THINGS initiative）](https://things-initiative.org)的核心发布成果，旨在打通不同学科间的壁垒，推动认知神经科学的发展。 # 数据集概览 我们通过脑磁图（MEG）采集了大量经过充分采样的物体表征数据。为此，我们采用了THINGS数据库[(Hebart等人，2019)](https://doi.org/10.1371/journal.pone.0223792)，该数据库包含1854个具有美国英语代表性的物体概念，内含26107张经人工精心筛选的自然物体图像，注释信息丰富。 在功能磁共振成像实验中，我们向被试展示了THINGS图像的代表性子集，实验分为12个独立扫描会话（共4名被试，涵盖1854个物体的22448张独特图像）。图像快速连续呈现（间隔1.5±0.2秒），并要求被试始终保持中央注视。为确保被试投入任务，我们设置了偏差刺激检测任务，要求被试对偶尔出现的人工生成图像做出反应。每个会话中均会重复呈现200张特定图像作为子集。 除针对THINGS图像的核心功能成像数据外，我们还采集了T1加权磁共振成像（T1-weighted MRI）扫描结果，用于皮层源定位分析。在脑磁图记录期间，我们同步监测了被试的眼动情况，以确认其始终保持中央注视。

搜集汇总

背景与挑战

背景概述

THINGS-MEG是一个多模态脑磁图（MEG）数据集，属于THINGS-data项目的一部分，旨在研究物体在视觉和语义处理中的神经表征。该数据集包含来自4名参与者的MEG记录，涉及1,854个物体概念的图像刺激，并辅以T1加权MRI扫描以支持皮质源定位，数据规模为237.69GB，采用BIDS格式和CC0许可。