Brain activity during rest is organized into spatio-temporal coactivation patterns [1]. This emergent order can be seen as the result of self-organized activity, as the brain transiently shifts from a state of incoherent dynamics to coherent and oscillatory [2,3,4]. Although such activity is expected to be governed by meaningful low-dimensional manifolds, that description is still missing [5]. In this study we show that the resting state manifold follows the deformation of the underlying energy landscapes as the dynamics alternate between low coherence state (LCS) and high coherence state (HCS). Methods Blood-oxygen-level-dependent (BOLD) signal from 200 healthy subjects was analyzed [6]. Instantaneous phase coherence identified the LCS and HCS [7]. Temporal organization was quantified using mean dwell times, fractional occupancy, and transition probability matrices. After removing spatiotemporal outliers, stationary density functions were extracted via the first principal component (PC) of whole-brain activity. Bayesian hierarchical modeling fitted reduced quadratic potential functions [8] to infer resting-state networks (RSNs) stationary dynamics. Model comparison, using the Bayesian information criterion, quantified candidate model fit. State-space modeling eventually characterized the geometry and flow of two-dimensional manifolds [9]. Results We showed that although the HCS is of transient nature, it generates a richer variety of coactivation patterns. Spatially, across the first PC, globally and within the RSNs, the HCS stationary dynamics were bistable, contrasting monostability for LCS. Moreover, HCS and LCS were driven by the sensory-motor/dorsal attention and association networks activity, respectively (Figure). These two findings qualified the idea that active inference takes place during HCS [10], which now explains bistability as the best model for interacting with the environment. Incorporating the second PC we constructed the RSNs’ manifolds, which transformed bistability into degenerate solutions that formed approximate continuous attractors. Discussion Resting-state activity is the most widely used paradigm in functional neuroimaging research. In addition to enhancing our understanding of its underlying dynamics and geometry, our work introduces novel metrics that can serve as comparable features, providing a comprehensive basis for distinguishing healthy controls from clinical populations.
Figure 1. The fitted Fokker-Planck probability density functions PDFs inherit the form of the quadratic potential functions that correspond to the dynamics of the RSNs. A) The variance of the PDFs quantified how dynamics and activated the different RSNs are, showing a clear alignment with the cortical hierarchy, which reverses from HCS to LCS. B) The stability was quantified with the criticality parameter. Acknowledgements Funded by the European Union (Grant agreement No 101057429).
