Severe Traumatic Brain Injury (sTBI)
The overarching goal of our laboratory is to combine electrical engineering and computational neuroscience to develop an intelligent neurostimulation paradigm to improve the pathological state of the brain in disorders of consciousness. This approach can be extended to other pathological neurological conditions. We have identified the Thalamo-cortical loop in the brain to be responsible for consciousness and recovery in coma patients. We are exploring the safety and effectiveness of deep brain stimulation to restore this connectivity. In parallel, we are working towards identifying objective clinical metrics that can be used in prognosis as well as to assess the improvement or deterioration of the patient condition. Not only does this guide physician decisions, but this would improve our ability to measure whether a prospective treatment is taking effect.
Electrocorticography reveals thalamic control of cortical dynamics following traumatic brain injury
Here, we use direct recordings of local field potentials (LFP) from frontal areas implicated in consciousness to understand how neural signals associated with consciousness arise. We had a rare and unique clinical opportunity: we recorded and stimulated depth electrodes implanted in the PFC and anterior cingulate cortex (ACC) for seizure monitoring after sTBI. Due to the current limitations for direct recording from the thalamus in sTBI patients, we used in silico modeling to gain insight into the role of the thalamus in shaping the functional state of the cortex in the context of recovery of consciousness. Our model suggests that thalamocortical projections to the frontoparietal network (FPN) facilitate the complex dynamics needed for consciousness. By contrast, injury to these connections results in a dysfunctional state of cortical networks, incapable of maintaining neuronal ensembles required for consciousness.
Novel Facial Recognition Software to Identify Mood
In the current study, we use a novel, real-time mood assessment tool that tracks subtle facial movements in order to reliably differentiate happy and sad mood states in our healthy subjects. These minute changes in facial expression with mood state go unnoticed to the naked eye yet are readily discernible after our analysis. Our results highlight the intriguing utility of this technique in mood detection as well as in more broad clinical monitoring settings.