Deep brain stimulation for data-driven circuit-specific neuromodulation

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My research program is focused on the application of data science and engineering methods to drive innovation and advancement of deep brain stimulation (DBS) technologies. DBS is a neurosurgical therapy that has applications in Parkinson's disease and many other brain disorders. It involves the surgical implantation of permanent electrodes deep within the brain that are connected to a neurostimulator (much like a cardiac pacemaker) in the chest. Currently, DBS is "open-loop," meaning that it delivers electrical stimulation 24hours/day, even though (i) clinical features can fluctuate on a moment-to-moment basis and (ii) DBS may elicit unwanted side-effects. A "closed-loop" system would instead monitor brain activity and deliver stimulation only when necessary, thereby reducing side-effects and power consumption. A successful closed-loop system requires the identification of suitable neurofeedback signals (i.e. brain "biomarkers" that tell the DBS device when to stimulate) and a comprehensive understanding of how DBS changes brain activity. My lab has access to a vast database of intracranial brain recordings from neurosurgical patients of the Toronto Western Hospital. This includes 10s of thousands of recordings of individual brain cells (neurons) from hundreds of patients. My first objective is to use this data to develop software which uses artificial intelligence to automatically disaggregate distinct neuronal populations to aid in real-time surgical identification of the ideal implantation location for DBS interventions. Moreover, we will apply statistical methods to seek out patterns of brain activity (defined by the behaviour of individual brain cells) that are indicative of symptom presence. This will lead to the discovery of biomarkers that can be used for closed-loop DBS. My second objective is to enhance our knowledge of how DBS works by applying advanced brain signal analysis techniques to prospective intracranial data from the cortex (outermost layer of the brain) and other deep brain regions during DBS. This data collection and analysis is a specialty of my lab and will enable us to gain insights into how we can apply DBS to restore healthy brain function in the context of brain circuits that regulate movements and memory function. Understanding how DBS can influence these circuits will promote the development of new DBS methods in disorders like Parkinson's and Alzheimer's. Combining insights from the first two objectives, my third objective is to develop and test novel neurofeedback-driven closed-loop DBS strategies. This will involve the development of a system which continuously monitors the activity of individual brain cells, automatically detects abnormalities, then triggers DBS to abolish this unhealthy brain activity. This method of closed-loop DBS is unique in that it uses the activity of individual neurons to control stimulation delivery and may incite a fundamental change in the operation of future generation DBS devices.