Functionally relevant network patterns form transiently in brain activity during rest, where a given subset of brain areas exhibits temporally synchronized BOLD signals. To adequately assess the biophysical mechanisms governing intrinsic brain activity, a detailed characterization of the dynamical features of functional networks is needed from the experimental side to constrain theoretical models. In this work, we use an open-source fMRI dataset from 100 healthy participants from the Human Connectome Project and analyze whole-brain activity using Leading Eigenvector Dynamics Analysis (LEiDA), which serves to characterize brain activity at each time point by its whole-brain BOLD phase-locking pattern. Clustering these BOLD phase-locking patterns into a set of k states, we demonstrate that the cluster centroids closely overlap with reference functional subsystems. Borrowing tools from dynamical systems theory, we characterize spontaneous brain activity in the form of trajectories within the state space, calculating the Fractional Occupancy and the Dwell Times of each state, as well as the Transition Probabilities between states. Finally, we demonstrate that within-subject reliability is maximized when including the high frequency components of the BOLD signal (>0.1 Hz), indicating the existence of individual fingerprints in dynamical patterns evolving at least as fast as the temporal resolution of acquisition (here TR = 0.72 s). Our results reinforce the mechanistic scenario that resting-state networks are the expression of erratic excursions from a baseline synchronous steady state into weakly-stable partially-synchronized states – which we term ghost attractors. To better understand the rules governing the transitions between ghost attractors, we use methods from dynamical systems theory, giving insights into high-order mechanisms underlying brain function.

中文翻译：

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自发大脑活动中的幽灵吸引子：反复进入功能相关的 BOLD 锁相状态


功能相关的网络模式在休息期间的大脑活动中短暂形成，其中给定的大脑区域子集表现出时间同步的 BOLD 信号。为了充分评估控制内在大脑活动的生物物理机制，需要从实验方面详细描述功能网络的动态特征，以约束理论模型。在这项工作中，我们使用来自人类连接组项目的 100 名健康参与者的开源 fMRI 数据集，并使用领先特征向量动力学分析 (LEiDA) 来分析全脑活动，该分析用于通过整个大脑活动来表征每个时间点的大脑活动。大脑大胆的锁相模式。将这些 BOLD 锁相模式聚类成一组 k 个状态，我们证明簇质心与参考功能子系统紧密重叠。借用动力系统理论的工具，我们以状态空间内的轨迹形式描述自发的大脑活动，计算每个状态的分数占用和停留时间，以及状态之间的转换概率。最后，我们证明，当包含 BOLD 信号的高频分量（>0.1 Hz）时，受试者内的可靠性最大化，表明动态模式中个体指纹的存在至少与采集的时间分辨率一样快（此处为 TR = 0.72 秒）。我们的结果强化了这样一种机制：静止状态网络是从基线同步稳态到弱稳定部分同步状态的不稳定偏移的表现——我们称之为幽灵吸引子。 为了更好地理解幽灵吸引子之间转换的规则，我们使用动力系统理论的方法，深入了解大脑功能背后的高阶机制。