Defining Physiological Subtypes in Essential Tremor - Project Abstract Essential tremor (ET) is a common, devastating neurological disorder affecting approximately 7 million people in the U.S., causing involuntary rhythmic movement (action tremor due to abnormal physiology) that disrupts daily life. Current treatments are often ineffective, as patients have heterogeneous responses, likely due to different underlying cerebellar physiological differences. To address this challenge, therefore we need to probe these physiological differences in ET. As the cerebellum is the key brain region implicated in ET pathophysiology, preliminary data from 26 ET patients using a novel cerebellar electroencephalography (EEG) technique revealed four distinct physiological subtypes. Types 1 and 2 resemble two well-established mouse models of tremor, while Types 3 and 4 suggest less cerebellar involvement, demonstrating that cerebellar EEG can capture the underlying physiological heterogeneity in ET. However, critical knowledge gaps remain: the population prevalence and clinical characteristics of these subtypes are unknown, their cerebello-cortical circuit mechanisms have yet to be defined, and further refinement of subtype classification using advanced computational approaches, such as machine learning, remains unexplored. Through this K99/R00 proposal, I aim to address these gaps by recruiting a large cohort of ET patients (n = 150 total; K99 Years 1-2: 20 patients/year; R00 Years 3-4: 40 patients/year; Year 5: 30 patients) and determining the prevalence and tremor-related as well as clinical features of cerebellar physiological ET subtypes, using cerebellar EEG and wearable senor data (Aim 1). I will characterize the network dynamics of these subtypes using effective connectivity and graph theoretical analyses to identify distinct cerebello-cortical mechanisms (Aim 2), and apply machine learning approaches to integrate physiological, wearable sensor, and clinical data to refine ET subtype classification (Aim 3). This project builds upon my strong background in EEG methodology and cerebellar physiology to address a critical unmet need for objective, physiology-based stratification of ET patients, laying the foundation for precision therapeutics. The K99/R00 award will support my transition to independence by providing comprehensive training in ET physiology and clinical assessments, wearable sensor technology, network analysis, and machine learning approaches applied to multimodal data, positioning me to lead an innovative research program focused on ET subtype classification. The proposed work will be initiated in the lab of Dr. Sheng-Han Kuo (primary mentor). During the K99 phase, I will continue to be mentored by Dr. Kuo, with additional mentorship from Prof. Elan Louis on ET clinical assessments and physiology, and from Dr. Anoopum Gupta on wearable sensor technology and machine learning. This integrated training and mentorship plan will advance ET subtype classification and position me on a path to independence.
