Shappell Heather M, Liu Zhiyuan, Khodaei Mohammadreza, He George, Gee Dylan G, Lindquist Martin A, Sukhodolsky Denis G, McCarthy Gregory, Ibrahim Karim
Wake Forest University School of Medicine, Department of Biostatistics and Data Science.
Yale University School of Medicine, Child Study Center.
bioRxiv. 2025 May 20:2025.05.15.654366. doi: 10.1101/2025.05.15.654366.
Childhood disruptive behavior problems are linked to aberrant integrity within large-scale cognitive control networks. However, it is unclear if transitory or dynamic variation in the functional brain architecture is a marker of disruptive behavior problems. The current study tested whether functional connectivity across dynamic networks is distinctly associated with the transdiagnostic symptom domain of disruptive behavior problems in children.
Participants were aged 9-10 years from the Adolescent Brain Cognitive Development (ABCD) Study, who completed resting-state fMRI (N=877). We employed a dynamic connectivity approach leveraging a hidden semi-Markov model (HSMM) to identify transient properties of brain networks and states. Models estimated the time spent in each state (occupancy time) and the number of consecutive timepoints in a state (dwell time) for each participant. Linear regression models were utilized to identify distinct associations between dynamic properties (occupancy and sojourn times) and severity of disruptive behavior problems, accounting for other commonly co-occurring symptoms.
Dynamic network markers of disruptive behavior problems included increased time in network states characterized by globally aberrant connectivity patterns in circuitry involved in cognitive control including frontoparietal and dorsal attention networks. Replication of findings was found in a held-out sample of resting-state fMRI runs in which greater severity of disruptive behavior problems was uniquely linked to greater occupancy time in similarly characterized brain states.
Transdiagnostic, dynamic resting-state markers of disruptive behavior problems in youth may assist in the development of brain-based biomarkers for monitoring treatment outcomes, assessing circuit target engagement and informing clinical decisions.
儿童期破坏性行为问题与大规模认知控制网络内的异常完整性有关。然而，尚不清楚大脑功能结构的短暂或动态变化是否是破坏性行为问题的一个标志。本研究测试了动态网络间的功能连接是否与儿童破坏性行为问题的跨诊断症状领域有显著关联。
参与者为来自青少年大脑认知发展（ABCD）研究的9至10岁儿童，他们完成了静息态功能磁共振成像（N = 877）。我们采用了一种动态连接方法，利用隐藏半马尔可夫模型（HSMM）来识别脑网络和状态的瞬态特性。模型估计了每个参与者在每种状态下花费的时间（占用时间）以及在一种状态下连续时间点的数量（驻留时间）。利用线性回归模型来识别动态特性（占用时间和驻留时间）与破坏性行为问题严重程度之间的显著关联，并考虑了其他常见的共发症状。
破坏性行为问题的动态网络标志物包括在以认知控制相关回路（包括额顶叶和背侧注意网络）中全局异常连接模式为特征的网络状态下停留时间增加。在一个独立的静息态功能磁共振成像样本中发现了研究结果的重复，其中破坏性行为问题的严重程度越高，与在类似特征脑状态下的占用时间越长唯一相关。
青少年破坏性行为问题的跨诊断、动态静息态标志物可能有助于开发基于大脑的生物标志物，以监测治疗结果、评估回路靶点参与情况并为临床决策提供信息。
