Abstract
The function of many biological systems is made possible by a network along which items of interest whether nutrients, goods, or information–can be routed. The human brain is a notable example. It is comprised of regions that perform specific functions and engage in particular computations. Those regions are interconnected by large white matter tracts. Each tract is a bundle of neuronal axons along which information-bearing electrical signals can propagate. Collectively, the tracts evince a pattern of connectivity–or network–that constrains the passage of information. In turn, that pattern of information flow determines the sorts of functions that the brain can support. Understanding structural network constraints is hence key to understanding healthy human brain function and its alteration in disease. Recent efforts have expanded the investigation of structural constraints in several ways. First, non-invasive measurements of white matter tracts using diffusion-weighted magnetic resonance imaging techniques have become increasingly sensitive to microstructural integrity and provided estimates of tract locations at finer spatial resolutions. These gains are made possible by an increase in the scan time (from 10 minutes to 1 hour), and in the number of diffusion directions acquired (from 30 to 720). Second, data-informed computational models have been developed to quantitatively assess how the particular network architecture these tracts comprise affects the brain’s dynamical repertoire. One such model that has proven particularly promising is the network control model, which draws upon and extends theoretical work in systems engineering. Third, a conceptual shift has expanded the types of explanations we use for cognitive processes from activity-based to structurally-based. For example, the hallmark of adult mental function–cognitive control–is now being studied not only as a regional activation state or computation but also as a dynamical process constrained by the structural network connecting the regions involved. Collectively, these measurement, modeling, and conceptual expansions are providing a richer understanding of structural constraints on human brain function. To better highlight the importance of structural network constraints upon neural dynamics, I will focus on a simple example. Cognitive effort has long been an important explanatory factor in the study of human behavior in health and disease. Yet, the biophysical nature of cognitive effort remains far from understood. Here, I will cast cognitive effort in the framework of network control theory, which describes how much energy is required to move the brain from one activity state to another when that activity is constrained to pass along physical pathways in a network. I will then turn to empirical studies that link this theoretical notion of energy with cognitive effort in a behaviorally demanding task. Finally, I will ask how this structurally-constrained activity flow can provide us with insights about the brain’s non-equilibrium nature. Using a general tool for quantifying entropy production in macroscopic systems, I will provide evidence to suggest that states of marked cognitive effort are also states of greater entropy production. Collectively, the work I discuss offers a complementary view of cognitive effort as a dynamical process structurally constrained by an underlying network.
