Ghost Attractors in Spontaneous Brain Activity: Recurrent Excursions Into Functionally-Relevant BOLD Phase-Locking States.

Jakub Vohryzek,Morten L Kringelbach,Bruno Cessac,Joana Cabral,Gustavo Deco

doi:10.3389/fnsys.2020.00020

Abstract

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.

Highlights

For healthy human cognition, the brain needs to engage in functionally meaningful activity through an integration of information incoming from various segregated brain areas (Tononi and Edelman, 1998; Sporns et al, 2000)
We obtain a set of BOLD phase-locking patterns from the first session of resting-state fMRI of 99 unrelated subjects using the Leading Eigenvector Dynamics Analysis (LEiDA) approach
Each BOLD phase-locking pattern is represented as a vector with N elements, each element representing the projection of the BOLD phase of a brain area into the leading eigenvector of all BOLD phases

Summary

Introduction

The brain needs to engage in functionally meaningful activity through an integration of information incoming from various segregated brain areas (Tononi and Edelman, 1998; Sporns et al, 2000). The choice of the “window” size introduces limitations which hinders the temporal resolution as well as statistical validation (Hindriks et al, 2016; Preti et al, 2016) To overcome these caveats, recent development has focused on describing single frame functional connectivity [FC(t)] either by considering BOLD co-activations (Karahanoglu and Van De Ville, 2015; Tagliazucchi et al, 2016) or BOLD phase coherence (Glerean et al, 2012; Cabral et al, 2017b). Phase coherence techniques represent the time instances as relative phase relationships between brain regions and do not require thresholding and are sensitive to phase-shifted patterns (Glerean et al, 2012; Cabral et al, 2017b)

Results

Discussion

Conclusion