Embodied theories of cognition emphasize the central role of sensorimotor transformations in the representation of others' actions. Support for these theories is derived from the discovery of the mirror neuron system (MNS) in primates, from noninvasive techniques in humans, and from a limited number of intracranial studies. To understand the neural dynamics of the human MNS, more studies with precise spatial and temporal resolutions are essential. We used electrocorticography to define activation patterns in sensorimotor, parietal and/or frontal neuronal populations, during a viewing and grasping task. Our results show robust high gamma activation for both conditions in classic MNS sites. Furthermore, we provide novel evidence for 2 different populations of neurons: sites that were only active for viewing and grasping ("pure mirroring") and sites that were also active between viewing and grasping, and perhaps serve a more general attentional role. Lastly, a subgroup of parietal electrodes showed earlier peaks than all other regions. These results highlight the complexity of spatial-temporal patterns within the MNS and provide a critical link between single-unit research in monkeys and noninvasive techniques in human.

In recent years there has been increasing interest in applying network science tools to EEG data. At the 2018 American Epilepsy Society conference in New Orleans, LA, the yearly session of the Engineering and Neurostimulation Special Interest Group focused on emerging, translational technologies to analyze seizure networks. Each speaker demonstrated practical examples of how network tools can be utilized in clinical care and provide additional data to help care for patients with intractable epilepsy. The groups presented advances using tools from functional connectivity, control theory, and graph theory to analyze human EEG data. These tools have great potential to augment clinical interpretation of EEG signals.

Optimizing direct electrical stimulation for the treatment of neurological disease remains difficult due to an incomplete understanding of its physical propagation through brain tissue. Here, we use network control theory to predict how stimulation spreads through white matter to influence spatially distributed dynamics. We test the theory's predictions using a unique dataset comprising diffusion weighted imaging and electrocorticography in epilepsy patients undergoing grid stimulation. We find statistically significant shared variance between the predicted activity state transitions and the observed activity state transitions. We then use an optimal control framework to posit testable hypotheses regarding which brain states and structural properties will efficiently improve memory encoding when stimulated. Our work quantifies the role that white matter architecture plays in guiding the dynamics of direct electrical stimulation and offers empirical support for the utility of network control theory in explaining the brain's response to stimulation.