Zhao Bingxin, Li Tengfei, Li Yujue, Fan Zirui, Xiong Di, Wang Xifeng, Gao Mufeng, Smith Stephen M, Zhu Hongtu
Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA.
Department of Statistics, Purdue University, West Lafayette, IN 47907, USA.
Imaging Neurosci (Camb). 2023;1:1-23. doi: 10.1162/imag_a_00015. Epub 2023 Sep 7.
Functional magnetic resonance imaging (fMRI) has been widely used to identify brain regions linked to critical functions, such as language and vision, and to detect tumors, strokes, brain injuries, and diseases. It is now known that large sample sizes are necessary for fMRI studies to detect small effect sizes and produce reproducible results. Here we report a systematic association analysis of 647 traits with imaging features extracted from resting-state and task-evoked fMRI data of more than 40,000 UK Biobank participants. We used a parcellation-based approach to generate 64,620 functional connectivity measures to reveal fine-grained details about cerebral cortex functional organizations. The difference between functional organizations at rest and during task was examined, and we have prioritized important brain regions and networks associated with a variety of human traits and clinical outcomes. For example, depression was most strongly associated with decreased connectivity in the somatomotor network. We have made our results publicly available and developed a browser framework to facilitate the exploration of brain function-trait association results (http://fmriatlas.org/).
功能磁共振成像（fMRI）已被广泛用于识别与语言和视觉等关键功能相关的脑区，以及检测肿瘤、中风、脑损伤和疾病。现在已知，功能磁共振成像研究需要大样本量才能检测到小效应量并产生可重复的结果。在此，我们报告了一项对647种性状与从40000多名英国生物银行参与者的静息态和任务诱发功能磁共振成像数据中提取的成像特征进行的系统关联分析。我们采用基于脑区划分的方法生成了64620个功能连接测量值，以揭示大脑皮质功能组织的细粒度细节。研究了静息态和任务期间功能组织的差异，并确定了与多种人类性状和临床结果相关的重要脑区和神经网络。例如，抑郁症与躯体运动网络中连接性降低的关联最为强烈。我们已将研究结果公开，并开发了一个浏览器框架，以方便探索脑功能-性状关联结果（http://fmriatlas.org/）。
