Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China.
Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.
Hum Brain Mapp. 2020 Jun 1;41(8):2160-2172. doi: 10.1002/hbm.24937. Epub 2020 Jan 21.
The human brain has been demonstrated to rapidly and continuously form and dissolve networks on a subsecond timescale, offering effective and essential substrates for cognitive processes. Understanding how the dynamic organization of brain functional networks on a subsecond level varies across individuals is, therefore, of great interest for personalized neuroscience. However, it remains unclear whether features of such rapid network organization are reliably unique and stable in single subjects and, therefore, can be used in characterizing individual networks. Here, we used two sets of 5-min magnetoencephalography (MEG) resting data from 39 healthy subjects over two consecutive days and modeled the spontaneous brain activity as recurring networks fast shifting between each other in a coordinated manner. MEG cortical maps were obtained through source reconstruction using the beamformer method and subjects' temporal structure of recurring networks was obtained via the Hidden Markov Model. Individual organization of dynamic brain activity was quantified with the features of the network-switching pattern (i.e., transition probability and mean interval time) and the time-allocation mode (i.e., fractional occupancy and mean lifetime). Using these features, we were able to identify subjects from the group with significant accuracies (~40% on average in 0.5-48 Hz). Notably, the default mode network displayed a distinguishable pattern, being the least frequently visited network with the longest duration for each visit. Together, we provide initial evidence suggesting that the rapid dynamic temporal organization of brain networks achieved in electrophysiology is intrinsic and subject specific.
人类大脑被证明能够在亚秒时间尺度上快速而连续地形成和瓦解网络，为认知过程提供了有效且必要的基础。因此，了解亚秒级大脑功能网络的动态组织在个体之间如何变化，对于个性化神经科学非常重要。然而，目前尚不清楚这种快速网络组织的特征在单个主体中是否可靠且稳定，因此是否可以用于描述个体网络。在这里，我们使用了两组来自 39 名健康受试者的连续两天的 5 分钟脑磁图 (MEG) 静息数据，并通过模型将自发脑活动模拟为在协调方式下快速相互切换的周期性网络。使用波束形成器方法通过源重建获得 MEG 皮质图，通过隐马尔可夫模型获得受试者周期性网络的时间结构。通过网络切换模式（即转移概率和平均间隔时间）和时间分配模式（即分数占用和平均寿命）的特征来量化动态大脑活动的个体组织。使用这些特征，我们能够以较高的准确率（在 0.5-48Hz 频段内平均约为 40%）从组中识别出个体。值得注意的是，默认模式网络显示出可区分的模式，是访问频率最低但每次访问持续时间最长的网络。总之，我们提供了初步证据表明，在电生理学中实现的大脑网络的快速动态时间组织是内在的且具有个体特异性。
