Davison Elizabeth N, Schlesinger Kimberly J, Bassett Danielle S, Lynall Mary-Ellen, Miller Michael B, Grafton Scott T, Carlson Jean M
Department of Mechanical & Aerospace Engineering, Princeton University, Princeton, New Jersey, United States of America; Department of Physics, University of California, Santa Barbara, Santa Barbara, California, United States of America.
Department of Physics, University of California, Santa Barbara, Santa Barbara, California, United States of America.
PLoS Comput Biol. 2015 Jan 8;11(1):e1004029. doi: 10.1371/journal.pcbi.1004029. eCollection 2015 Jan.
Activity in the human brain moves between diverse functional states to meet the demands of our dynamic environment, but fundamental principles guiding these transitions remain poorly understood. Here, we capitalize on recent advances in network science to analyze patterns of functional interactions between brain regions. We use dynamic network representations to probe the landscape of brain reconfigurations that accompany task performance both within and between four cognitive states: a task-free resting state, an attention-demanding state, and two memory-demanding states. Using the formalism of hypergraphs, we identify the presence of groups of functional interactions that fluctuate coherently in strength over time both within (task-specific) and across (task-general) brain states. In contrast to prior emphases on the complexity of many dyadic (region-to-region) relationships, these results demonstrate that brain adaptability can be described by common processes that drive the dynamic integration of cognitive systems. Moreover, our results establish the hypergraph as an effective measure for understanding functional brain dynamics, which may also prove useful in examining cross-task, cross-age, and cross-cohort functional change.
人类大脑的活动在不同功能状态之间转换，以满足我们动态环境的需求，但指导这些转换的基本原则仍知之甚少。在这里，我们利用网络科学的最新进展来分析脑区之间的功能相互作用模式。我们使用动态网络表示法来探究在四种认知状态（无任务静息状态、注意力需求状态和两种记忆需求状态）内和之间伴随任务执行的大脑重构情况。使用超图形式，我们识别出在大脑状态内（特定任务）和跨大脑状态（一般任务）强度随时间连贯波动的功能相互作用组的存在。与之前对许多二元（区域到区域）关系复杂性的强调不同，这些结果表明大脑适应性可以通过驱动认知系统动态整合的共同过程来描述。此外，我们的结果确立了超图作为理解功能性脑动力学的有效度量，这在检查跨任务、跨年龄和跨队列的功能变化方面可能也很有用。
