Nathan Baune

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Investigator Bio:

Dr. Baune is dedicated to understanding how the brain estimates and controls body state during goal-directed actions, with the ultimate goal of developing precision interventions for neurological conditions. His early research explored cortical reorganization following deafferentation and reafferentation, revealing critical insights into neural plasticity and sensorimotor learning. Building on this foundation, he has developed novel assessments of sensory integration using virtual reality and motion capture and led research in machine learning approaches to monitoring upper limb activity in stroke patients. Currently, Dr. Baune investigates cortical dynamics and sensory integration relevant to changes in sensation and posture using EEG and robotics to study motor control strategies. He is broadening his research into examining the neural mechanisms underlying balance impairments in Parkinson’s disease, aiming to understand variability in clinical manifestations and differences from healthy aging. As part of this research he aims to leverage his machine learning experience to develop brain state-based approaches to guide treatments such as TMS and DBS, working toward individualized therapies to enhance outcomes for individuals with neurological conditions.

Objective:

To identify electrophysiological biomarkers of FoG using mobile electroencephalography (EEG).  

Background:

FoG, occurring in 50% of individuals with PD, manifests as brief, episodic halts in forward progression despite the intention to walk, reducing mobility and increasing fall risk. A limited understanding of the neural mechanisms behind FoG, compounded by its sporadic nature and the temporal resolution limitations of functional imaging techniques, impedes treatment development. 

Methods/Design:

I will assess a person’s freeze risk without a freezing event using mobile electroencephalography (HD-EEG) during well-controlled perturbations to standing balance as a “behavioral probe,” which excites brain circuits implicated in FoG. I hypothesize that individuals that typically experience FoG will show increased brain activity in response to balance challenges than those who do not experience FoG. Characterizing consistent and reliable brain activity using a probe that does not require a freeze event addresses the inherent unpredictability and sporadic nature of FoG, providing deeper insights into the individual risk of developing FoG. Next, I will employ mobile EEG to characterize irregular brain activity patterns occurring before and during FoG during a clinical gait assessment. I hypothesize that FoG episodes are preceded by irregular brain activity stemming from an increased reliance on cortical resources.  

Relevance to Diagnosis/Treatment of Parkinson’s Disease:

This approach holds translational potential for real-time detection, monitoring, and prevention of FoG. I envision using electrophysiological biomarkers from the brain to provide neurophysiological input for adaptive interventions, such as deep brain stimulators or wearable cueing devices, to prevent or intervene during FoG.