Rozovsky Renata, Bertocci Michele, Diwadkar Vaibhav, Stiffler Richelle S, Bebko Genna, Skeba Alexander S, Aslam Haris, Phillips Mary L
Department of Psychiatry, University of Pittsburgh School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania.
Department of Psychiatry, University of Pittsburgh School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania.
Biol Psychiatry Cogn Neurosci Neuroimaging. 2025 Jan 11. doi: 10.1016/j.bpsc.2025.01.006.
Effective connectivity (EC) analysis provides valuable insights into the directionality of neural interactions, which are crucial for understanding the mechanisms underlying cognitive and emotional regulation in depressive and anxiety disorders. In this study, we examined EC within key neural networks during working memory (WM) and emotional regulation (ER) tasks in young adults, both healthy individuals and those seeking help from mental health professionals for emotional distress.
Dynamic causal modeling was used to analyze EC in 2 independent samples (n = 97 and n = 94). Participants performed an emotional n-back task to assess EC across the central executive network (CEN), default mode network (DMN), salience network (SN), and face processing network. Group-level parametric empirical Bayes analyses were conducted to examine EC patterns, with subanalyses comparing individuals with and without depression and anxiety.
Consistent patterns of positive (posterior probability > .95) DMN→CEN and DMN→SN EC were observed in both samples, predominantly in low and high WM conditions without ER. However, individuals without depressive or anxiety disorders exhibited a significantly greater number of preserved connections that were replicated across both samples.
This study highlights the different patterns of DMN→CEN EC in conditions with high and low WM loads with and without ER, suggesting that in higher WM loads with ER, the integration of the DMN with the CEN is reduced to facilitate successful cognitive task performance. The findings also suggest that DMN→CEN and DMN→SN EC are significantly reduced in depressive and anxiety disorders, highlighting this pattern of reduced EC as a potential neural marker of these disorders.
有效连接性（EC）分析为神经交互的方向性提供了有价值的见解，这对于理解抑郁和焦虑障碍中认知和情绪调节的潜在机制至关重要。在本研究中，我们在工作记忆（WM）和情绪调节（ER）任务期间，对年轻成年人（包括健康个体和因情绪困扰寻求心理健康专业帮助的人）的关键神经网络内的EC进行了研究。
使用动态因果模型分析2个独立样本（n = 97和n = 94）中的EC。参与者执行情绪n-back任务，以评估中央执行网络（CEN）、默认模式网络（DMN）、突显网络（SN）和面部处理网络之间的EC。进行组水平的参数经验贝叶斯分析以检查EC模式，并进行亚分析比较有无抑郁和焦虑的个体。
在两个样本中均观察到一致的正向（后验概率> 0.95）DMN→CEN和DMN→SN EC模式，主要出现在无ER的低和高WM条件下。然而，没有抑郁或焦虑障碍的个体表现出在两个样本中重复出现的显著更多的保留连接。
本研究强调了在有和没有ER的高和低WM负荷条件下DMN→CEN EC的不同模式，表明在有ER的较高WM负荷下，DMN与CEN的整合减少以促进成功的认知任务表现。研究结果还表明，抑郁和焦虑障碍中DMN→CEN和DMN→SN EC显著降低，突出了这种EC降低模式作为这些障碍的潜在神经标志物。
