The energy landscape underpinning module dynamics in the human brain connectome.
The energy landscape underpinning module dynamics in the human brain connectome.
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DOI：
10.1016/j.neuroimage.2017.05.067
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发表时间：
2017-08-15
期刊：
影响因子：
5.7
通讯作者：
Bassett DS
中科院分区：
文献类型：
--
作者：
Ashourvan A;Gu S;Mattar MG;Vettel JM;Bassett DS
人脑动力学可以通过统计力学的透镜来观察，在统计力学中，神经生理活动围绕代表精神状态的局部吸引子以及在局部吸引子之间演变。许多受物理启发的这些动力学模型基于对区域活动的瞬时测量来定义大脑状态。然而，网络神经科学的最新研究提供了证据，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。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。该过程使用能量的信息论概念（与代谢概念相反）来产生能量景观，其中局部最小值表示以模块化结构的特定模式为特征的吸引子状态。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的特征在于单一的功能社区。其余的投资回报率隶属于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。我们模拟大脑的动态转换之间的这些社区状态使用随机游走过程。我们观察到，盆地之间的模拟过渡概率统计上与经验观察到的静息状态fMRI数据的过渡一致。这些结果提供了一个视图的大脑作为一个动态系统，其特征在于连贯的活动在大脑区域的群体之间的吸引力盆地之间的过渡，这些吸引力的强度取决于正在进行的认知计算。
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 pairwise 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.
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影响因子：
5.7
作者：
Britz, Juliane;Van De Ville, Dimitri;Michel, Christoph M.
通讯作者：
Michel, Christoph M.
影响因子：
4.7
作者：
Chai, Lucy R.;Khambhati, Ankit N.;Bassett, Danielle S.
通讯作者：
Bassett, Danielle S.
影响因子：
3.7
作者：
Allen, Elena A.;Damaraju, Eswar;Calhoun, Vince D.
通讯作者：
Calhoun, Vince D.
影响因子：
5.2
作者：
Andrews-Hanna JR;Smallwood J;Spreng RN
通讯作者：
Spreng RN
DOI：
10.1073/pnas.1018985108
发表时间：
2011-05-03
影响因子：
11.1
作者：
Bassett, Danielle S.;Wymbs, Nicholas F.;Grafton, Scott T.
通讯作者：
Grafton, Scott T.