Zhu Hao, Tong Xiaoyu, Carlisle Nancy B, Xie Hua, Keller Corey J, Oathes Desmond J, Liu Feng, Nemeroff Charles B, Fonzo Gregory A, Zhang Yu
Department of Bioengineering, Lehigh University, Bethlehem, PA, USA.
Department of Psychology, Lehigh University, Bethlehem, PA, USA.
Cell Rep Med. 2025 Jun 17;6(6):102151. doi: 10.1016/j.xcrm.2025.102151. Epub 2025 May 28.
Major depressive disorder (MDD) is highly heterogeneous, posing challenges for effective treatment due to complex interactions between clinical symptoms and neurobiological features. To address this, we apply contrastive principal-component analysis to fMRI-based resting-state functional connectivity, isolating disorder-specific variations by contrasting data from 233 MDD patients and 285 healthy controls. Subsequently, we use sparse canonical correlation analysis to identify two significant dimensions linking distinct brain circuits with clinical profiles. One dimension relates to an internalizing-externalizing symptom spectrum involving visual and limbic networks and is associated with cognitive task reaction times. The other dimension, linked to personality traits protective against depression (e.g., extraversion), is driven by dorsal attention network connections and correlates with cognitive control and psychomotor performance. This approach illuminates stable symptom dimensions and their neurophysiological underpinnings, aiding in precision phenotyping for MDD and supporting the development of targeted, individualized therapeutic strategies for mental health care.
重度抑郁症（MDD）具有高度异质性，由于临床症状与神经生物学特征之间存在复杂的相互作用，给有效治疗带来了挑战。为了解决这一问题，我们将对比主成分分析应用于基于功能磁共振成像（fMRI）的静息态功能连接，通过对比233例MDD患者和285例健康对照的数据，分离出特定疾病的变异。随后，我们使用稀疏典型相关分析来识别将不同脑回路与临床特征联系起来的两个重要维度。一个维度与涉及视觉和边缘网络的内化-外化症状谱相关，并且与认知任务反应时间有关。另一个维度与对抑郁症有保护作用的人格特质（如外向性）相关，由背侧注意网络连接驱动，与认知控制和精神运动表现相关。这种方法阐明了稳定的症状维度及其神经生理学基础，有助于对MDD进行精准表型分析，并支持为精神卫生保健制定有针对性的个体化治疗策略。
