Quah S K L, Jo B, Geniesse C, Uddin L Q, Mumford J A, Barch D M, Fair D A, Gotlib I H, Poldrack R A, Saggar M
Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, USA.
Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, CA USA.
bioRxiv. 2024 Nov 8:2024.01.31.577486. doi: 10.1101/2024.01.31.577486.
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 employed a latent variable approach using bifactor analysis. We examined 84 whole-brain task-based fMRI (tfMRI) activation maps from 19 studies with 6,192 participants. A curated subset of 37 maps with a balanced representation of RDoC domains constituted the training set, and the remaining held-out maps formed the internal validation set. External validation was 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.
尽管研究领域标准（RDoC）框架在精神病学和神经科学中得到了广泛应用，但最近的研究表明，相对于其试图阐明的潜在脑回路而言，RDoC不够具体或过于宽泛。为了解决这些问题，我们采用了一种使用双因素分析的潜在变量方法。我们检查了来自19项研究的84个基于全脑任务的功能磁共振成像（tfMRI）激活图，这些研究共有6192名参与者。一个精心挑选的包含37个图的子集，其中RDoC领域有平衡的代表性，构成了训练集，其余保留的图形成了内部验证集。外部验证使用来自Neurosynth的36个峰值坐标激活图进行，使用RDoC结构的术语作为主题元分析的种子。在此，我们表明，与当前的RDoC框架相比，一个包含任务通用领域并拆分认知系统领域的双因素模型更适合所检查的tfMRI数据集。我们还发现唤醒和调节系统领域的代表性不足。我们的数据驱动验证支持修订RDoC框架，以更准确地反映潜在的脑回路。
