Hudetz Anthony G, Humphries Colin J, Binder Jeffrey R
Department of Anesthesiology, Medical College of Wisconsin Milwaukee, WI, USA.
Department of Neurology, Medical College of Wisconsin Milwaukee, WI, USA.
Front Syst Neurosci. 2014 Dec 11;8:234. doi: 10.3389/fnsys.2014.00234. eCollection 2014.
Patterns of resting state connectivity change dynamically and may represent modes of cognitive information processing. The diversity of connectivity patterns (global brain states) reflects the information capacity of the brain and determines the state of consciousness. In this work, computer simulation was used to explore the repertoire of global brain states as a function of cortical activation level. We implemented a modified spin glass model to describe UP/DOWN state transitions of neuronal populations at a mesoscopic scale based on resting state BOLD fMRI data. Resting state fMRI was recorded in 20 participants and mapped to 10,000 cortical regions (sites) defined on a group-aligned cortical surface map. Each site represented the population activity of a ~20 mm(2) area of the cortex. Cross-correlation matrices of the mapped BOLD time courses of the set of sites were calculated and averaged across subjects. In the model, each cortical site was allowed to interact with the 16 other sites that had the highest pair-wise correlation values. All sites stochastically transitioned between UP and DOWN states under the net influence of their 16 pairs. The probability of local state transitions was controlled by a single parameter T corresponding to the level of global cortical activation. To estimate the number of distinct global states, first we ran 10,000 simulations at T = 0. Simulations were started from random configurations that converged to one of several distinct patterns. Using hierarchical clustering, at 99% similarity, close to 300 distinct states were found. At intermediate T, metastable state configurations were formed suggesting critical behavior with a sharp increase in the number of metastable states at an optimal T. Both reduced activation (anesthesia, sleep) and increased activation (hyper-activation) moved the system away from equilibrium, presumably incompatible with conscious mentation. During equilibrium, the diversity of large-scale brain states was maximum, compatible with maximum information capacity-a presumed condition of consciousness.
静息态连接模式会动态变化，可能代表认知信息处理模式。连接模式（全脑状态）的多样性反映了大脑的信息容量，并决定意识状态。在这项研究中，我们使用计算机模拟来探索全脑状态的组成，将其作为皮层激活水平的函数。我们实施了一个改进的自旋玻璃模型，基于静息态BOLD功能磁共振成像数据，在介观尺度上描述神经元群体的兴奋/抑制状态转换。对20名参与者进行了静息态功能磁共振成像记录，并将其映射到基于群体对齐的皮层表面图定义的10000个皮层区域（位点）。每个位点代表约20平方毫米皮层区域的群体活动。计算了这些位点的映射BOLD时间序列的互相关矩阵，并在受试者之间进行平均。在模型中，每个皮层位点被允许与其他16个具有最高成对相关值的位点相互作用。在其16对位点的净影响下，所有位点在兴奋和抑制状态之间随机转换。局部状态转换的概率由一个对应于全皮层激活水平的单一参数T控制。为了估计不同全局状态的数量，首先我们在T = 0时运行了10000次模拟。模拟从随机配置开始，收敛到几种不同模式之一。使用层次聚类，在99%的相似度下，发现了近300种不同的状态。在中间T值时，形成了亚稳态配置，表明存在临界行为，在最佳T值时亚稳态的数量急剧增加。激活降低（麻醉、睡眠）和激活增加（过度激活）都会使系统远离平衡，这可能与有意识的思维不相容。在平衡期间，大规模脑状态的多样性最大，与最大信息容量兼容——这是意识的一个假定条件。
