Stimulation-Based Control of Dynamic Brain Networks.

The ability to modulate brain states using targeted stimulation is increasingly being employed to treat neurological disorders and to enhance human performance. Despite the growing interest in brain stimulation as a form of neuromodulation, much remains unknown about the network-level impact of these focal perturbations. To study the system wide impact of regional stimulation, we employ a data-driven computational model of nonlinear brain dynamics to systematically explore the effects of targeted stimulation. Validating predictions from network control theory, we uncover the relationship between regional controllability and the focal versus global impact of stimulation, and we relate these findings to differences in the underlying network architecture. Finally, by mapping brain regions to cognitive systems, we observe that the default mode system imparts large global change despite being highly constrained by structural connectivity. This work forms an important step towards the development of personalized stimulation protocols for medical treatment or performance enhancement.

GABAergic inhibition shapes interictal dynamics in awake epileptic mice.

Epilepsy is characterized by recurrent seizures and brief, synchronous bursts called interictal spikes that are present in-between seizures and observed as transient events in EEG signals. While GABAergic transmission is known to play an important role in shaping healthy brain activity, the role of inhibition in these pathological epileptic dynamics remains unclear. Examining the microcircuits that participate in interictal spikes is thus an important first step towards addressing this issue, as the function of these transient synchronizations in either promoting or prohibiting seizures is currently under debate. To identify the microcircuits recruited in spontaneous interictal spikes in the absence of any proconvulsive drug or anaesthetic agent, we combine a chronic model of epilepsy with in vivo two-photon calcium imaging and multiunit extracellular recordings to map cellular recruitment within large populations of CA1 neurons in mice free to run on a self-paced treadmill. We show that GABAergic neurons, as opposed to their glutamatergic counterparts, are preferentially recruited during spontaneous interictal activity in the CA1 region of the epileptic mouse hippocampus. Although the specific cellular dynamics of interictal spikes are found to be highly variable, they are consistently associated with the activation of GABAergic neurons, resulting in a perisomatic inhibitory restraint that reduces neuronal spiking in the principal cell layer. Given the role of GABAergic neurons in shaping brain activity during normal cognitive function, their aberrant unbalanced recruitment during these transient events could have important downstream effects with clinical implications.

Personalized brain network models for assessing structure-function relationships.

Many recent efforts in computational modeling of macro-scale brain dynamics have begun to take a data-driven approach by incorporating structural and/or functional information derived from subject data. Here, we discuss recent work using personalized brain network models to study structure-function relationships in human brains. We describe the steps necessary to build such models and show how this computational approach can provide previously unobtainable information through the ability to perform virtual experiments. Finally, we present examples of how personalized brain network models can be used to gain insight into the effects of local stimulation and improve surgical outcomes in epilepsy.

Functional Brain States Measure Mentor-Trainee Trust during Robot-Assisted Surgery.

Mutual trust is important in surgical teams, especially in robot-assisted surgery (RAS) where interaction with robot-assisted interface increases the complexity of relationships within the surgical team. However, evaluation of trust between surgeons is challenging and generally based on subjective measures. Mentor-Trainee trust was defined as assessment of mentor on trainee's performance quality and approving trainee's ability to continue performing the surgery. Here, we proposed a novel method of objectively assessing mentor-trainee trust during RAS based on patterns of brain activity of surgical mentor observing trainees. We monitored the EEG activity of a mentor surgeon while he observed procedures performed by surgical trainees and quantified the mentor's brain activity using functional and cognitive brain state features. We used methods from machine learning classification to identity key features that distinguish trustworthiness from concerning performances. Results showed that during simple surgical task, functional brain features are sufficient to classify trust. While, during more complex tasks, the addition of cognitive features could provide additional accuracy, but functional brain state features drive classification performance. These results indicate that functional brain network interactions hold information that may help objective trainee specific mentorship and aid in laying the foundation of automation in the human-robot shared control environment during RAS.

Small-World Propensity and Weighted Brain Networks.

Quantitative descriptions of network structure can provide fundamental insights into the function of interconnected complex systems. Small-world structure, diagnosed by high local clustering yet short average path length between any two nodes, promotes information flow in coupled systems, a key function that can differ across conditions or between groups. However, current techniques to quantify small-worldness are density dependent and neglect important features such as the strength of network connections, limiting their application in real-world systems. Here, we address both limitations with a novel metric called the Small-World Propensity (SWP). In its binary instantiation, the SWP provides an unbiased assessment of small-world structure in networks of varying densities. We extend this concept to the case of weighted brain networks by developing (i) a standardized procedure for generating weighted small-world networks, (ii) a weighted extension of the SWP, and (iii) a method for mapping observed brain network data onto the theoretical model. In applying these techniques to compare real-world brain networks, we uncover the surprising fact that the canonical biological small-world network, the C. elegans neuronal network, has strikingly low SWP. These metrics, models, and maps form a coherent toolbox for the assessment and comparison of architectural properties in brain networks.