Predictive control of electrophysiological network architecture using direct, single-node neurostimulation in humans

Abstract

Chronically implantable neurostimulation devices are becoming a clinically viable option for treating patients with neurological disease and psychiatric disorders. Neurostimulation offers the ability to probe and manipulate distributed networks of interacting brain areas in dysfunctional circuits. Here, we use tools from network control theory to examine the dynamic reconfiguration of functionally interacting neuronal ensembles during targeted neurostimulation of cortical and subcortical brain structures. By integrating multi-modal intracranial recordings and diffusion tensor imaging from patients with drug-resistant epilepsy, we test hypothesized structural and functional rules that predict altered patterns of synchronized local field potentials. We demonstrate the ability to predictably reconfigure functional interactions depending on stimulation strength and location. Stimulation of areas with structurally weak connections largely modulates the functional hubness of downstream areas and concurrently propels the brain towards more difficult-to-reach dynamical states. By using focal perturbations to bridge large-scale structure, function, and markers of behavior, our findings suggest that stimulation may be tuned to influence different scales of network interactions driving cognition.

Navigation of brain networks

“…Exploring alternative spatial embeddings of brain networks (e.g., in hyperbolic space) may provide further insight into the navigability of nervous systems across species ( 40 ). In parallel, brain stimulation techniques could be used to evaluate evidence for competing communication strategies by means of electrophysiological tracking of local perturbations ( 41 , 42 ).…”

Transcranial direct current stimulation modulates brain functional connectivity in autism

“…We first calculated an optimal assignment between connectivity patterns of the two datasets. We then quantified the similarity of the connectivity patterns between assigned subnetwork pairs ( i, j ) by calculating the Pearson correlation coefficient ( Khambhati, 2018 ). This approach enables us to evaluate the reproducibility of each network component depending on the magnitude of the Pearson correlation similarity measure relative to accidental expectation ( Khambhati et al, 2018 ).…”

Network topology of neural systems supporting avalanche dynamics predicts stimulus propagation and recovery

“…Extensions of linear systems analysis, such as observability [12] and optimal control [10,21,68], follow immediately from this work and could provide added insights into other dynamical and computational properties of neural networks. Finally, it would be of interest to directly probe the effects of stimulation patterns defined by network controllability statistics on information transmission in vitro or behaviors in vivo, following work in a similar vein in large-scale human neuroimaging [34,43,45,65].…”