Linear predictive coding of EEG Activity for Diagnosis of Parkinson's Disease (LEAD-PD)

University Of Iowa, Iowa City IA

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Abstract

Reliable and efficient tools are needed to 1) diagnose and differentiate Parkinson’s disease (PD) from other movement disorders with similar clinical features but with different prognosis and treatment, 2) quantify and track motor and cognitive symptoms of PD over time, and 3) assess response to treatment changes for optimization of symptom control. Current tools for these purposes mainly consist of clinical scales and questionnaires; however, the results can be highly variable. Thus, there is a critical need for accurate and feasible biomarkers in PD. We propose a novel, neurophysiological, machine-learning approach to fulfill this need. We have developed Linear predictive coding (LPC) of EEG Activity for the Diagnosis of PD (LEAD-PD). Rather than focusing on frequency bands, LEAD-PD captures critical differences in the power spectra of PD patients using <5 minutes of resting data. Preliminary results show that LEAD-PD achieves >85% sensitivity/specificity in independent validation sets, surpassing other potential clinical biomarkers for PD. The overall objective of the proposed research is to develop a novel, objective biomarker for diagnosing PD and tracking its progression and response to treatment. In this proposal, we will test the overall hypothesis that that LEAD-PD captures PD diagnosis and diversity/severity of clinical features. Our specific aims are: AIM 1: Determine the diagnostic role of EEG in PD. Our working hypothesis is that the LEAD-PD diagnostic index will distinguish patients with PD from controls, patients with essential tremor, and Parkinson-plus syndromes such as multiple system atrophy (MSA) and progressive supranuclear palsy (PSP), including Alzheimer-related dementias (ADRD) such as Dementia with Lewy Bodies (DLB). AIM 2: Determine the role of EEG in predicting the severity and progression of key symptoms of PD. Our working hypotheses are that the LEAD-PD motor and cognitive indices will predict both baseline severity and longitudinal worsening of these key symptoms over 2 years. AIM 3: Determine the role of EEG in assessing the motor response to DBS treatment in PD. Our work could contribute novel biomarkers and real-time applications for PD and for Alzheimer’s disease and related dementias (ADRD) such as Alzheimer’s dementia (AD) and Lewy Body Dementias (LBD), including Parkinson’s disease dementia (PDD) and DLB.  Because the LEAD-PD index may be utilized to discern symptom severity including cognitive impairment and severity across a continuous spectrum of disease stages in synucleinopathies, our findings are directly related to ADRD such as LBD.Â