The energy landscape underpinning module dynamics in the human brain connectome

Abstract

Human brain dynamics can be viewed through the lens of statistical mechanics, where neurophysiological activity evolves around and between local attractors representing mental states. Many physically-inspired models of these dynamics define brain states based on instantaneous measurements of regional activity. Yet, recent work in network neuroscience has provided evidence that the brain might also be well-characterized by time-varying states composed of locally coherent activity or functional modules. We study this network-based notion of brain state to understand how functional modules dynamically interact with one another to perform cognitive functions. We estimate the functional relationships between regions of interest (ROIs) by fitting a pairwise maximum entropy model to each ROI’s pattern of allegiance to functional modules. This process uses an information theoretic notion of energy (as opposed to a metabolic one) to produce an energy landscape in which local minima represent attractor states characterized by specific patterns of modular structure. The clustering of local minima highlights three classes of ROIs with similar patterns of allegiance to community states. Visual, attention, sensorimotor, and subcortical ROIs are well-characterized by a single functional community. The remaining ROIs affiliate with a putative executive control community or a putative default mode and salience community. We simulate the brain’s dynamic transitions between these community states using a random walk process. We observe that simulated transition probabilities between basins are statistically consistent with empirically observed transitions in resting state fMRI data. These results offer a view of the brain as a dynamical system that transitions between basins of attraction characterized by coherent activity in groups of brain regions, and that the strength of these attractors depends on the ongoing cognitive computations.

Reconfigurations within resonating communities of brain regions following TMS reveal different scales of processing

“…Our analyses extend previous 133 research that has found network flexibility successfully characterizes large-scale functional differences 134 (e.g., Telesford et al, 2016) in executive function (Braun et al, 2015) and mood (Betzel et al, 2017). 135 Allegiance, on the other hand, has been used to describe observed network dynamics on a finer scale, 136 estimating alignment with a pre-defined functional architecture (Bassett et al, 2015) as well as 137 identifying transitions among certain network configurations (Ashourvan et al, 2017). Across the set 138 of network science metrics adapted for neuroscience application (for review, see Garcia et al, 2018), 139 allegiance and flexibility are the best suited to identify changes in scales of processing.…”

A dynamical systems framework to uncover the drivers of large-scale cortical activity

“…Therefore, we hypothesize that examination of the correlational structure of the estimated input should reveal that the ROIs within the intrinsically connected networks (or, resting state networks) in general also receive coherent (i.e., correlated) input. Moreover, it has been shown that at the large scale the resting state networks are roughly organized into three functionally coherent groups that form the basis of the main dynamic attractor states of the system (2). Therefore, we conjecture that the coherent output of these large-scale systems are in part the result of the coherent input to these systems.…”

The Active Inference Approach to Ecological Perception: General Information Dynamics for Natural and Artificial Embodied Cognition

“…At the ecological "behavior" scale (policies), these local minima provide a comprehensive re-description of affordances that unites the exteroceptive with the proprio-and interoceptive dimensions (Pezzulo and Cisek, 2016). They also generalize to the sub-ecological "action" scale, as reflex arcs, grounded in the physics of nerve electricity Sengupta et al, 2013), and the supra-ecological "activity" scale, as extended active and resting states, grounded in physiological homeostasis/allostasis dynamics (Ashourvan et al, 2017). 6. sKatinG UnCertainty: GeneraLiZed aFFordanCe tHeory, sKiLLed eXpertise, and tHe FraMe proBLeM This section considers how local minima of uncertainty in the projected temporal sequence of generative model states serve to unify developmental theory and the underspecified (by Gibson) notion of learned affordances. We then show concrete applications in skilled practical and cultural activities.…”