Department of Psychiatry & Behavioral Sciences, Stanford University, 401 Quarry Rd, St 1356, Stanford, CA, 94305, USA.
Department of Psychological & Brain Sciences & Network Science Institute, Indiana University, 1101 East 10th Street, Bloomington, IN, 47405, USA.
Nat Commun. 2018 Apr 11;9(1):1399. doi: 10.1038/s41467-018-03664-4.
Little is known about how our brains dynamically adapt for efficient functioning. Most previous work has focused on analyzing changes in co-fluctuations between a set of brain regions over several temporal segments of the data. We argue that by collapsing data in space or time, we stand to lose useful information about the brain's dynamical organization. Here we use Topological Data Analysis to reveal the overall organization of whole-brain activity maps at a single-participant level-as an interactive representation-without arbitrarily collapsing data in space or time. Using existing multitask fMRI datasets, with the known ground truth about the timing of transitions from one task-block to next, our approach tracks both within- and between-task transitions at a much faster time scale (~4-9 s) than before. The individual differences in the revealed dynamical organization predict task performance. In summary, our approach distills complex brain dynamics into interactive and behaviorally relevant representations.
关于大脑如何动态适应以实现高效运作，我们知之甚少。大多数先前的工作都集中在分析数据的几个时间片段中一组大脑区域之间的共波动变化。我们认为，通过在空间或时间上进行数据折叠，我们可能会失去有关大脑动态组织的有用信息。在这里，我们使用拓扑数据分析来揭示单个参与者水平上的整个大脑活动图谱的整体组织 - 作为一种交互表示 - 而不会在空间或时间上任意折叠数据。使用现有的多任务 fMRI 数据集，以及关于从一个任务块到下一个任务块的时间的已知真实信息，我们的方法以比以前快得多的时间尺度（约 4-9 秒）跟踪了任务内和任务间的转换。所揭示的动态组织的个体差异可以预测任务表现。总之，我们的方法将复杂的大脑动力学简化为交互和与行为相关的表示。
