Activity flow underlying abnormalities in brain activations and cognition in schizophrenia.

Activity flow underlying abnormalities in brain activations and cognition in schizophrenia.
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DOI:
10.1126/sciadv.abf2513
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发表时间:
2021-07
期刊:
影响因子:
13.6
通讯作者:
Cole MW
Cole MW
中科院分区:
综合性期刊1区
文献类型:
--
作者:
Hearne LJ;Mill RD;Keane BP;Repovš G;Anticevic A;Cole MW

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大脑活动流经大脑线路的方式有助于解释精神分裂症患者的异常大脑反应和行为。认知功能障碍是包括精神分裂症(SZ)在内的许多脑部疾病的核心特征,并且与异常的大脑激活有关。然而,目前尚不清楚这些激活异常是如何出现的。我们认为,通过功能连接(FC)通路的异常脑活动流导致SZ中产生认知功能障碍的激活改变。我们使用活动流映射来验证这一假设,这是一种模拟大脑区域之间与任务相关的活动作为FC功能的运动的方法。利用SZ个体和健康对照者在工作记忆任务中的功能磁共振成像数据,我们发现活动流模型准确地预测了多个脑网络的异常认知激活。在相同的框架内,我们模拟了基于连接的临床干预,预测了使患者大脑激活和行为正常化的具体治疗方法。我们的研究结果表明,功能失调的任务诱发活动流是导致SZ认知功能障碍的大规模网络机制。
The way that brain activity flows across brain wiring helps explain abnormal brain responses and behaviors in schizophrenia. 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.
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