Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Melbourne, Victoria, Australia.
Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia.
Hum Brain Mapp. 2022 Mar;43(4):1403-1418. doi: 10.1002/hbm.25732. Epub 2021 Dec 3.
Behavioral traits are rarely considered in task-evoked functional magnetic resonance imaging (MRI) studies, yet these traits can affect how an individual engages with the task, and thus lead to heterogeneity in task-evoked brain responses. We aimed to investigate whether interindividual variation in behavior associates with the accuracy of predicting task-evoked changes in the dynamics of functional brain connectivity measured with functional MRI. We developed a novel method called multi-timepoint pattern analysis (MTPA), in which binary logistic regression classifiers were trained to distinguish rest from each of 7 tasks (i.e., social cognition, working memory, language, relational, motor, gambling, emotion) based on functional connectivity dynamics measured in 1,000 healthy adults. We found that connectivity dynamics for multiple pairs of large-scale networks enabled individual classification between task and rest with accuracies exceeding 70%, with the most discriminatory connections relatively unique to each task. Crucially, interindividual variation in classification accuracy significantly associated with several behavioral, cognition and task performance measures. Classification between task and rest was generally more accurate for individuals with higher intelligence and task performance. Additionally, for some of the tasks, classification accuracy improved with lower perceived stress, lower aggression, higher alertness, and greater endurance. We conclude that heterogeneous dynamic adaptations of functional brain networks to changing cognitive demands can be reliably captured as linearly separable patterns by MTPA. Future studies should account for interindividual variation in behavior when investigating context-dependent dynamic functional connectivity.
行为特征在任务诱发功能磁共振成像（fMRI）研究中很少被考虑，但这些特征会影响个体对任务的参与程度，从而导致任务诱发脑反应的异质性。我们旨在研究个体间行为差异是否与预测功能磁共振测量的功能脑连接动态的任务诱发变化的准确性相关。我们开发了一种称为多时间点模式分析（MTPA）的新方法，该方法使用二进制逻辑回归分类器根据 1000 名健康成年人测量的功能连接动力学，从休息和 7 种任务（社交认知、工作记忆、语言、关系、运动、赌博、情绪）中的每一种中进行区分。我们发现，多个大规模网络之间的连接动力学可以以超过 70%的准确率对任务和休息进行个体分类，最具区分性的连接相对每个任务都是独特的。至关重要的是，分类准确性的个体间差异与多种行为、认知和任务表现测量显著相关。对于那些具有较高智力和任务表现的个体，任务和休息之间的分类通常更准确。此外，对于某些任务，分类准确性随着感知压力的降低、攻击性的降低、警觉性的提高和耐力的提高而提高。我们的结论是，功能大脑网络对不断变化的认知需求的异质动态适应可以通过 MTPA 可靠地捕捉为线性可分离模式。未来的研究在研究上下文相关的动态功能连接时，应考虑个体间行为差异。
