人脑连接体中支撑模块动力学的能量景观
The Energy Landscape Underpinning Module Dynamics in the Human Brain Connectome
摘要：
人类的大脑动力学可以通过统计力学的镜头进行有益的观察，其中神经生理活动在代表首选精神状态的局部吸引子周围和之间演变。许多受物理启发的这些动态模型基于对区域活动的瞬时测量来定义大脑的状态。然而，最近在网络神经科学方面的研究提供了初步证据，表明大脑也可能具有由局部相干活动或功能模块组成的时变状态的特征。在这里，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。我们估计感兴趣的区域（ROI）之间的功能关系，通过拟合成对的最大熵模型，每个ROI的模式效忠功能模块。在这个模型中的局部极小值表示吸引子状态，其特征在于模块化结构的特定模式。局部最小值的聚类突出了三类具有类似模式的忠诚社区状态的ROI。视觉、注意力、感觉运动和皮质下ROI倾向于形成单一的功能社区。其余的ROI倾向于形成一个假定的执行控制社区或一个假定的默认模式和显着社区。我们使用马尔可夫链蒙特卡罗随机游走来模拟大脑在这些社区状态之间的动态转换。我们观察到，盆地之间的模拟过渡概率类似于经验观察到的社区效忠状态之间的过渡，在静息状态的功能磁共振成像数据。这些结果共同提供了一个视图的大脑作为一个动态系统之间的过渡盆地的吸引力的特点是连贯的活动在小群体的大脑区域，这些吸引力的强度取决于正在执行的认知计算。
英文摘要：
Human brain dynamics can be profitably viewed through the lens of statistical mechanics, where neurophysiological activity evolves around and between local attractors representing preferred mental states. Many physically-inspired models of these dynamics define the state of the brain based on instantaneous measurements of regional activity. Yet, recent work in network neuroscience has provided initial evidence that the brain might also be well-characterized by time-varying states composed of locally coherent activity or functional modules. Here 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. Local minima in this model 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 tend to form a single functional community. The remaining ROIs tend to form 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 Markov Chain Monte Carlo random walk. We observe that simulated transition probabilities between basins resemble empirically observed transitions between community allegiance states in resting state fMRI data. These results collectively offer a view of the brain as a dynamical system that transitions between basins of attraction characterized by coherent activity in small groups of brain regions, and that the strength of these attractors depends on the cognitive computations being performed.
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