Characterizing large-scale dynamic organization of the brain relies on both data-driven and mechanistic modeling, which demands a low versus high level of prior knowledge and assumptions about how constituents of the brain interact. However, the conceptual translation between the two is not straightforward. The present work aims to provide a bridge between data-driven and mechanistic modeling. We conceptualize brain dynamics as a complex landscape that is continuously modulated by internal and external changes. The modulation can induce transitions between one stable brain state (attractor) to another. Here, we provide a novel method—Temporal Mapper—built upon established tools from the field of topological data analysis to retrieve the network of attractor transitions from time series data alone. For theoretical validation, we use a biophysical network model to induce transitions in a controlled manner, which provides simulated time series equipped with a ground-truth attractor transition network. Our approach reconstructs the ground-truth transition network from simulated time series data better than existing time-varying approaches. For empirical relevance, we apply our approach to fMRI data gathered during a continuous multitask experiment. We found that occupancy of the high-degree nodes and cycles of the transition network was significantly associated with subjects’ behavioral performance. Taken together, we provide an important first step toward integrating data-driven and mechanistic modeling of brain dynamics.

Brain dynamics are often described by data-driven models or mechanistic dynamical systems models to understand how specific brain states persist or change (transition). However, there lacks a computational framework that explicitly connects states/transitions discovered by data-driven methods to those of mechanistic models, leading to a disconnection between data analysis and theoretical modeling. To begin bridging this gap, we develop a data-driven method, the Temporal Mapper, to extract dynamical systems features from time series and represent them as attractor transition networks. The Temporal Mapper can reconstruct ground-truth transition networks of mechanistic models. When applied to human fMRI data, the method helps predict behavioral performance from the topology of transition networks. Potential applications include characterizing brain dynamic organization in health and diseases and designing brain stimulation protocols.

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Author notes

Competing Interests: The authors have declared that no competing interests exist.

These authors contributed equally to this work.

Handling Editor: Christopher Honey

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