Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, USA.
University Behavioral Health Care, Department of Psychiatry, and Center for Cognitive Science, Rutgers University, Piscataway, NJ, USA.
Sci Adv. 2021 Jul 14;7(29). doi: 10.1126/sciadv.abf2513. Print 2021 Jul.
Cognitive dysfunction is a core feature of many brain disorders, including schizophrenia (SZ), and has been linked to aberrant brain activations. However, it is unclear how these activation abnormalities emerge. We propose that aberrant flow of brain activity across functional connectivity (FC) pathways leads to altered activations that produce cognitive dysfunction in SZ. We tested this hypothesis using activity flow mapping, an approach that models the movement of task-related activity between brain regions as a function of FC. Using functional magnetic resonance imaging data from SZ individuals and healthy controls during a working memory task, we found that activity flow models accurately predict aberrant cognitive activations across multiple brain networks. Within the same framework, we simulated a connectivity-based clinical intervention, predicting specific treatments that normalized brain activations and behavior in patients. Our results suggest that dysfunctional task-evoked activity flow is a large-scale network mechanism contributing to cognitive dysfunction in SZ.
认知功能障碍是许多脑部疾病的核心特征,包括精神分裂症(SZ),并与异常的大脑活动有关。然而,这些激活异常是如何出现的还不清楚。我们提出,异常的大脑活动在功能连接(FC)通路上的流动导致了改变的激活,从而导致 SZ 中的认知功能障碍。我们使用活动流映射来测试这个假设,这是一种将任务相关活动在大脑区域之间的运动作为 FC 的函数来建模的方法。使用工作记忆任务期间来自 SZ 个体和健康对照的功能磁共振成像数据,我们发现活动流模型可以准确地预测多个大脑网络中异常的认知激活。在同一个框架内,我们模拟了基于连接的临床干预,预测了特定的治疗方法,使患者的大脑活动和行为正常化。我们的结果表明,功能失调的任务诱发的活动流是导致 SZ 认知功能障碍的一个大规模网络机制。