A Data-Driven Latent Variable Approach to Validating the Research Domain Criteria Framework.
A Data-Driven Latent Variable Approach to Validating the Research Domain Criteria Framework.
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验证研究领域标准框架的数据驱动潜变量方法。
DOI：
10.1101/2024.01.31.577486
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发表时间：
2024
期刊：
影响因子：
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通讯作者：
Saggar,M
中科院分区：
文献类型：
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作者：
Quah,SKL;Jo,B;Geniesse,C;Uddin,LQ;Mumford,JA;Barch,DM;Fair,DA;Gotlib,IH;Poldrack,RA;Saggar,M
尽管在精神病学和神经科学中广泛使用研究领域标准（RDoC）框架，但最近的研究表明，RDoC相对于其试图阐明的潜在脑回路而言不够具体或过于宽泛。为了解决这些问题，我们采用了潜变量的方法，使用双因素分析。我们检查了84个全脑任务为基础的功能磁共振成像（tfMRI）激活地图，从19个研究6192名参与者。具有RDoC域的平衡表示的37个映射的策展子集构成训练集，其余保留的映射形成内部验证集。使用Neurosynth的36个峰值坐标激活图进行外部验证，使用RDoC结构的术语作为主题荟萃分析的种子。在这里，我们表明，一个双因素模型，将任务一般域和分裂的认知系统域更好地适应检查语料库的tfMRI数据比目前的RDoC框架。我们还确定唤醒和调节系统的领域代表性不足。我们的数据驱动验证支持修改RDoC框架，以更准确地反映潜在的大脑回路。
Despite the widespread use of the Research Domain Criteria (RDoC) framework in psychiatry and neuroscience, recent studies suggest that the RDoC is insufficiently specific or excessively broad relative to the underlying brain circuitry it seeks to elucidate. To address these concerns, we employ a latent variable approach using bifactor analysis. We examine 84 whole-brain task-based fMRI (tfMRI) activation maps from 19 studies with 6192 participants. A curated subset of 37 maps with a balanced representation of RDoC domains constitute the training set, and the remaining held-out maps form the internal validation set. External validation is conducted using 36 peak coordinate activation maps from Neurosynth, using terms of RDoC constructs as seeds for topic meta-analysis. Here, we show that a bifactor model incorporating a task-general domain and splitting the cognitive systems domain better fits the examined corpus of tfMRI data than the current RDoC framework. We also identify the domain of arousal and regulatory systems as underrepresented. Our data-driven validation supports revising the RDoC framework to reflect underlying brain circuitry more accurately.