Avniel Singh Ghuman, PhD

Over the past year, Dr. Ghuman’s lab has made a number of new and ongoing discoveries. Using intracranial recordings in epilepsy patients and MEG in Parkinson’s patients, the lab has illuminated how brain networks behave during real world behavior, how deep brain stimulation modulates cortical brain networks, and described a novel model regarding how the brain processes written words.

The mechanism of action of deep brain stimulation (DBS) to the basal ganglia for Parkinson’s disease remains unclear. Studies have shown that DBS decreases pathological beta hypersynchrony between the basal ganglia and motor cortex. However, little is known about DBS’s effects on long range corticocortical synchronization. Here, Dr. Ghuman uses machine learning combined with graph theory to compare resting-state cortical connectivity between the off and on-stimulation states and to healthy controls. He found that turning DBS on increased high beta and gamma band synchrony (26 to 50 Hz) in a cortical circuit spanning the motor, occipitoparietal, middle temporal, and prefrontal cortices. The synchrony in this network was greater in DBS on relative to both DBS off and controls, with no significant difference between DBS off and controls. Turning DBS on also increased network efficiency and strength and subnetwork modularity relative to both DBS off and controls in the beta and gamma band. Thus, unlike DBS’s subcortical normalization of pathological basal ganglia activity, it introduces greater synchrony relative to healthy controls in cortical circuitry that includes both motor and non-motor systems. This increased high beta/gamma synchronization may reflect compensatory mechanisms related to DBS’s clinical benefits, as well as undesirable non-motor side effects.

During the course of a day, our brains must accomplish a wide range of tasks and demonstrate a remarkable amount of flexibility despite their anatomic stability. How do ecologically valid brain states balance the tension between these demands of flexibility and stability? To answer this question, Dr. Ghuman explored how the human functional connectome changes using continuous intracranial electroencephalography recordings in six epilepsy patients while they went about their day: eating, talking with visitors, reading, etc. over the course of a week. By tracking how the coherence between all pairs of the100-120 electrodes implanted in each patient changes over each five second time window over the course of the entire week, he was able to use unsupervised autoregressive methods to identify the prevalent dynamic patterns of connectivity. 

Two major patterns emerged. First, brain networks had a stable baseline state that the brain would consistently return to after individual subnetworks took excursions of various types throughout the day. This stable state was similar across all our subjects, consisting of elevated lower beta coherence and decreased theta and gamma coherence. His second finding was that there was a discrete set of probable ways to leave this baseline state. Different sub-networks of the brain were not activated or inactivated randomly to each other: they formed a specific set of patterns of which networks could be activated together over which frequencies. These patterns were well-preserved from day to day: if one network’s beta activation were linked to another network’s gamma inactivation in one day, the same would generally hold true in other days. Additionally, the length of the excursion (e.g. the autocorrelation of each dynamic pattern) was consistent from day-to-day. 

These patterns show that, after perturbations, the brain’s functional networks are pulled to return within a stable baseline dynamic range, which may represent an optimal homeostatic state for the functional connectome. Excursions from this state occur frequently, presumably to accomplish tasks such as sleep or heightened activity, but the excursions are always marked by a return back to homeostasis. The day to day consistency of the largest excursions from homeostasis may indicate some underlying anatomic or energy limitation that forces departures from homeostasis to follow characteristic trajectories. Taken together, these results suggest a homeostasis-like mechanism by which the functional connectome achieves stability, while allowing for neurocognitive flexibility, through characteristic perturbations and return to this homeostatic state.

Scientists have long debated the nature of the visual networks that support humans’ unique ability to read. Reading is built upon visuo-linguistic transformations that map written words to their sounds and meanings. Independent of reading, computationally parallel visuo-linguistic transformations well-suited to perform operations necessary for word recognition underpin the perception of social-communication and visual object and face naming. A key node of the reading brain, the visual word form area (VWFA), lies where circuits that underpin visuo-linguistic transformations diverge from earlier visual processing in ventral occipitotemporal cortex. Dr. Ghuman proposes a model in which literacy leverages preexisting circuits that perform visuo-linguistic transformations well-suited to those required for fluent reading.