Network Spreading Dynamics In Cognition And Neurodegeneration

The macroscale network structure of the brain is fundamental to the pathophysiology and treatment of several neuropsychiatric diseases, including epilepsy, neurodegenerative disease, depression, and psychosis. Functional interactions at this scale index disease symptoms and guide exogenous interventions, such as brain stimulation or pharmacology. However, a lack of tools for measuring the underlying neurobiological drivers of these functional interactions, as well as phenotypic heterogeneity within disorders, hinders the ability to expand upon existing treatments and target them to the appropriate populations. Dynamical systems models have the potential to move beyond a statistical description of neural systems by positing mechanisms that link the physical form of a system with its emergent function. A subset of dynamical systems models, linear network spreading models, have proved especially useful for capturing activity fluctuations in neural systems. Existing tools allow these models to be studied through the lens of network control theory, which captures the system’s response to external inputs. Here, we use network spreading models and other computational tools to study structure-function relationships in the human brain and the mechanisms of Parkinson’s disease pathophysiology. First, we employed a network spreading model to characterize the neural substrates of individual differences in impulse control throughout development. Second, we incorporated external inputs into those models in order to explain the temporal progression of large- scale cortical activity patterns. Third, we used a network spreading model to confirm that endogenous levels of α-synuclein, along with both anterograde and retrograde transsynaptic diffusion drive Parkinson’s disease progression. Finally, we identify latent patterns of co- occurring pathologies in neuropathological autopsy data that can be predicted from in vivo biomarkers using statistical models. This collection of studies expands our understanding of how brain activity and misfolded proteins spread throughout the brain’s white matter connections and provides a computational framework for addressing heterogeneity in neurologic diseases. These findings are complementary to the aim of developing network-oriented therapies and lay a general framework for parsing disease heterogeneity across multiple fields of medicine

Non-equilibrium dynamics and entropy production in the human brain

Living systems operate out of thermodynamic equilibrium at small scales, consuming energy and producing entropy in the environment in order to perform molecular and cellular functions. However, it remains unclear whether non-equilibrium dynamics manifest at macroscopic scales, and if so, how such dynamics support higher-order biological functions. Here we present a framework to probe for non-equilibrium dynamics by quantifying entropy production in macroscopic systems. We apply our method to the human brain, an organ whose immense metabolic consumption drives a diverse range of cognitive functions. Using whole-brain imaging data, we demonstrate that the brain fundamentally operates out of equilibrium at large scales. Moreover, we find that the brain produces more entropy -- operating further from equilibrium -- when performing physically and cognitively demanding tasks. By simulating an Ising model, we show that macroscopic non-equilibrium dynamics can arise from asymmetries in the interactions at the microscale. Together, these results suggest that non-equilibrium dynamics are vital for cognition, and provide a general tool for quantifying the non-equilibrium nature of macroscopic systems.Comment: 18 pages, 14 figure

Information content of brain states is explained by structural constraints on state energetics

Signal propagation along the structural connectome of the brain induces changes in the patterns of activity. These activity patterns define global brain states and contain information in accordance with their expected probability of occurrence. The structural connectome, in conjunction with the dynamics, determines the set of possible brain states and constrains the transition between accessible states. Yet, precisely how these structural constraints on state-transitions relate to their information content remains unexplored. To address this gap in knowledge, we defined the information content as a function of the activation distribution, where statistically rare values of activation correspond to high information content. With this numerical definition in hand, we studied the spatiotemporal distribution of information content in fMRI data from the Human Connectome Project during different tasks, and report four key findings. First, information content strongly depends on the cognitive task. Second, while information content shows similarities to other measures of brain activity, it is distinct from both Neurosynth maps and task contrast maps generated by a general linear model applied to the fMRI data. Third, the brain's structural wiring constrains the cost to control its state, where the cost to transition into high information content states is larger than that to transition into low information content states. Finally, all state transitions - especially those to high information content states - are less costly than expected from random network null models, thereby indicating the brain's marked efficiency. Taken together, our findings establish an explanatory link between the information contained in a brain state and the energetic cost of attaining that state, thereby laying important groundwork for our understanding of large-scale cognitive computations.Comment: 16 pages, 4 figures + supplement (5 pages, 5 figures

Context-dependent architecture of brain state dynamics is explained by white matter connectivity and theories of network control

A diverse white matter network and finely tuned neuronal membrane properties allow the brain to transition seamlessly between cognitive states. However, it remains unclear how static structural connections guide the temporal progression of large-scale brain activity patterns in different cogni- tive states. Here, we deploy an unsupervised machine learning algorithm to define brain states as time point level activity patterns from functional magnetic resonance imaging data acquired dur- ing passive visual fixation (rest) and an n-back working memory task. We find that brain states are composed of interdigitated functional networks and exhibit context-dependent dynamics. Using diffusion-weighted imaging acquired from the same subjects, we show that structural connectivity constrains the temporal progression of brain states. We also combine tools from network control theory with geometrically conservative null models to demonstrate that brains are wired to sup- port states of high activity in default mode areas, while requiring relatively low energy. Finally, we show that brain state dynamics change throughout development and explain working mem- ory performance. Overall, these results elucidate the structural underpinnings of cognitively and developmentally relevant spatiotemporal brain dynamics