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Establishing Network Neuroscience Mechanisms of Efficiency of Evidence Accumulation in a Well-Characterized Sample with Bipolar Disorder: A Multi-Modal Clinical Imaging Study

在充分表征的双相情感障碍样本中建立证据积累效率的网络神经科学机制：多模式临床影像研究

基本信息

批准号：
10628028
负责人：
Chandra Sekhar Sripada
金额：
$ 75.06万
依托单位：
UNIVERSITY OF MICHIGAN AT ANN ARBOR
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-06-01 至 2027-03-31
项目状态：
未结题

项目摘要

Abstract Bipolar disorder is a serious chronic condition, and there is great interest in understanding brain-based mechanisms that contribute to disorder symptoms. In this proposal, we focus on one promising candidate: efficiency of evidence accumulation (EEA). EEA is measured in specialized models from computational psychiatry, and it quantifies a basic neurocognitive ability to accumulate information from a stimulus in noisy conditions in order to select appropriate responses. Substantial reductions in EEA are found in bipolar disorder, as well as other major psychiatric disorders, and they contribute to impulsivity and disease severity. There is a critical gap in knowledge, however: At the current time, we know little about the brain mechanism that produce reduced EEA in bipolar disorder, or in any other psychiatric disorder. In this project, we address this gap using the methods of network neuroscience. Substantial evidence from large datasets strongly supports a flexible network reconfiguration model of EEA. This model says EEA depends on the brain’s ability to adaptively reconfigure connectivity patterns of brain networks across cognitive demands and task contexts. The model suggests the novel hypothesis that reduced EEA in bipolar disorder arises from deficits in flexible network reconfiguration. We test this hypothesis with U. of Michigan’s unique Prechter Longitudinal Study of Bipolar Disorder (headed by Co-I McInnis). We study 130 healthy adults and 130 adults with bipolar disorder, who complete a battery of behavioral tasks to measure EEA and a battery of neuroimaging tasks optimized to measure flexibility of brain network reconfiguration. A centerpiece of our approach is the use of brain basis set (BBS), a multivariate predictive modeling framework. This method lets us “summarize” tens of thousands of changes in connectivity patterns across the brain in terms of a modest number of basic reconfiguration components. BBS lets us identify what networks reconfigure as well as how much they reconfigure. Using BBS, we will quantify brain network reconfiguration deficits in bipolar disorder. We in addition link deficits in EEA and reduced brain network reconfiguration specifically to an impulsive/affectively-unstable subtype of bipolar disorder and to impulsivity factor scores. Finally, we elucidate the etiology of deficits in task-evoked brain network reconfiguration. We use multivariate methods to delineate how reduced task-evoked network flexibility arises from alterations in the brain’s task-free functional and structural architecture. EEA is a computational metric that rigorously quantifies core neurocognitive deficits in bipolar disorder. This project leverages computational psychiatry, network neuroscience, and multi-modal imaging to delineate brain network mechanisms that underpin EEA. Success here lays the foundation for a broader network neuroscience research program examining impairments in reconfiguration/flexibility of brain networks across multiple disorders, with the aim of pinpointing etiology and identifying potential interventions.

摘要