Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; U.S. Army Research Laboratory, Aberdeen Proving Ground, MD 21005, USA.
Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Applied Mathematics and Computational Science Graduate Program, University of Pennsylvania, Philadelphia, PA 19104, USA.
Neuroimage. 2017 Aug 15;157:364-380. doi: 10.1016/j.neuroimage.2017.05.067. Epub 2017 Jun 7.
Human brain dynamics can be viewed through the lens of statistical mechanics, where neurophysiological activity evolves around and between local attractors representing mental states. Many physically-inspired models of these dynamics define brain states based on instantaneous measurements of regional activity. Yet, recent work in network neuroscience has provided evidence that the brain might also be well-characterized by time-varying states composed of locally coherent activity or functional modules. We study this network-based notion of brain state to understand how functional modules dynamically interact with one another to perform cognitive functions. We estimate the functional relationships between regions of interest (ROIs) by fitting a pair-wise maximum entropy model to each ROI's pattern of allegiance to functional modules. This process uses an information theoretic notion of energy (as opposed to a metabolic one) to produce an energy landscape in which local minima represent attractor states characterized by specific patterns of modular structure. The clustering of local minima highlights three classes of ROIs with similar patterns of allegiance to community states. Visual, attention, sensorimotor, and subcortical ROIs are well-characterized by a single functional community. The remaining ROIs affiliate with a putative executive control community or a putative default mode and salience community. We simulate the brain's dynamic transitions between these community states using a random walk process. We observe that simulated transition probabilities between basins are statistically consistent with empirically observed transitions in resting state fMRI data. These results offer a view of the brain as a dynamical system that transitions between basins of attraction characterized by coherent activity in groups of brain regions, and that the strength of these attractors depends on the ongoing cognitive computations.
人类大脑的动力学可以通过统计力学的视角来观察，其中神经生理活动围绕着代表心理状态的局部吸引子而演变。许多基于物理的这些动力学模型基于区域活动的瞬时测量来定义大脑状态。然而，最近的网络神经科学研究提供了证据，表明大脑也可以通过由局部相干活动或功能模块组成的时变状态来很好地描述。我们研究这种基于网络的大脑状态概念，以了解功能模块如何动态地相互作用以执行认知功能。我们通过对每个 ROI 的功能模块归属模式拟合成对最大熵模型来估计感兴趣区域（ROI）之间的功能关系。这个过程使用了一种信息论意义上的能量（而不是代谢意义上的能量）来产生一个能量景观，其中局部最小值代表了具有特定模块结构模式的吸引子状态。局部最小值的聚类突出了具有类似功能模块归属模式的三类 ROI。视觉、注意力、感觉运动和皮质下 ROI 由单个功能社区很好地描述。其余的 ROI 与假设的执行控制社区或假设的默认模式和显着性社区相关联。我们使用随机游走过程模拟大脑在这些社区状态之间的动态转变。我们观察到，模拟的盆地之间的跃迁概率与静息状态 fMRI 数据中观察到的跃迁在统计上是一致的。这些结果提供了一种观点，即大脑是一个动态系统，它在由大脑区域群的相干活动表征的吸引子盆地之间转换，并且这些吸引子的强度取决于正在进行的认知计算。
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