Chopra Sidhant, Cocuzza Carrisa V, Lawhead Connor, Ricard Jocelyn A, Labache Loïc, Patrick Lauren M, Kumar Poornima, Rubenstein Arielle, Moses Julia, Chen Lia, Blankenbaker Crystal, Gillis Bryce, Germine Laura T, Harpaz-Rote Ilan, Yeo Bt Thomas, Baker Justin T, Holmes Avram J
1. Department of Psychology, Yale University, New Haven, CT, USA.
2. Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, NJ, USA.
medRxiv. 2024 Jun 21:2024.06.18.24309054. doi: 10.1101/2024.06.18.24309054.
An important aim in psychiatry is the establishment of valid and reliable associations linking profiles of brain functioning to clinically relevant symptoms and behaviors across patient populations. To advance progress in this area, we introduce an open dataset containing behavioral and neuroimaging data from 241 individuals aged 18 to 70, comprising 148 individuals meeting diagnostic criteria for a broad range of psychiatric illnesses and a healthy comparison group of 93 individuals. These data include high-resolution anatomical scans, multiple resting-state, and task-based functional MRI runs. Additionally, participants completed over 50 psychological and cognitive assessments. Here, we detail available behavioral data as well as raw and processed MRI derivatives. Associations between data processing and quality metrics, such as head motion, are reported. Processed data exhibit classic task activation effects and canonical functional network organization. Overall, we provide a comprehensive and analysis-ready transdiagnostic dataset, which we hope will accelerate the identification of illness-relevant features of brain functioning, enabling future discoveries in basic and clinical neuroscience.
精神病学的一个重要目标是建立有效的、可靠的关联,将大脑功能特征与不同患者群体的临床相关症状和行为联系起来。为了推动该领域的进展,我们引入了一个开放数据集,其中包含241名年龄在18至70岁之间个体的行为和神经影像数据,包括148名符合广泛精神疾病诊断标准的个体以及93名健康对照个体组成的对照组。这些数据包括高分辨率解剖扫描、多个静息态和基于任务的功能磁共振成像扫描。此外,参与者完成了50多项心理和认知评估。在此,我们详细介绍了可用的行为数据以及原始和处理后的磁共振成像衍生数据。报告了数据处理与质量指标(如头部运动)之间的关联。处理后的数据呈现出典型的任务激活效应和标准的功能网络组织。总体而言,我们提供了一个全面且可供分析的跨诊断数据集,希望它能加速对大脑功能中与疾病相关特征的识别,推动基础和临床神经科学未来的发现。