School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada.
Mind, Brain Imaging and Neuroethics Research Unit, Institute of Mental Health Research, Royal Ottawa Mental Health Centre and University of Ottawa, Ottawa, Canada.
Commun Biol. 2021 Mar 4;4(1):277. doi: 10.1038/s42003-021-01785-z.
The human cortex exhibits intrinsic neural timescales that shape a temporal hierarchy. Whether this temporal hierarchy follows the spatial hierarchy of its topography, namely the core-periphery organization, remains an open issue. Using magnetoencephalography data, we investigate intrinsic neural timescales during rest and task states; we measure the autocorrelation window in short (ACW-50) and, introducing a novel variant, long (ACW-0) windows. We demonstrate longer ACW-50 and ACW-0 in networks located at the core compared to those at the periphery with rest and task states showing a high ACW correlation. Calculating rest-task differences, i.e., subtracting the shared core-periphery organization, reveals task-specific ACW changes in distinct networks. Finally, employing kernel density estimation, machine learning, and simulation, we demonstrate that ACW-0 exhibits better prediction in classifying a region's time window as core or periphery. Overall, our findings provide fundamental insight into how the human cortex's temporal hierarchy converges with its spatial core-periphery hierarchy.
人类大脑皮层表现出内在的神经时间尺度，这些时间尺度构成了一个时间层次结构。这种时间层次结构是否遵循其地形的空间层次结构，即核心-边缘组织，仍然是一个悬而未决的问题。使用脑磁图数据，我们在休息和任务状态下研究内在神经时间尺度；我们测量短自相关窗口（ACW-50），并引入新的变体，长自相关窗口（ACW-0）。我们证明，与位于边缘的网络相比，位于核心的网络在休息和任务状态下具有更长的 ACW-50 和 ACW-0，并且具有较高的 ACW 相关性。计算休息-任务差异，即减去共享的核心-边缘组织，揭示了不同网络中特定于任务的 ACW 变化。最后，我们通过核密度估计、机器学习和模拟证明，ACW-0 在分类区域的时间窗口作为核心或边缘时具有更好的预测能力。总的来说，我们的研究结果为理解人类大脑皮层的时间层次结构如何与其空间核心-边缘层次结构相融合提供了重要的见解。
