Individual Magnetoencephalography Response Profiles to Short-Duration L-Dopa in Parkinson's Disease.

Matthew D Johnson,Jawad A Bajwa,Fedaa Almohammed,Shahpar Nahrir,Tahani Alotaibi,Edgar Peña,Tareq M Mohammad,Vahe Poghosyan,Sheraz Khan

doi:10.3389/fnhum.2021.640591

Abstract

Clinical responses to dopamine replacement therapy for individuals with Parkinson’s disease (PD) are often difficult to predict. We characterized changes in MDS-UPDRS motor factor scores resulting from a short-duration L-Dopa response (SDR), and investigated how the inter-subject clinical differences could be predicted from motor cortical magnetoencephalography (MEG). MDS-UPDRS motor factor scores and resting-state MEG recordings were collected during SDR from twenty individuals with a PD diagnosis. We used a novel subject-specific strategy based on linear support vector machines to quantify motor cortical oscillatory frequency profiles that best predicted medication state. Motor cortical profiles differed substantially across individuals and showed consistency across multiple data folds. There was a linear relationship between classification accuracy and SDR of lower limb bradykinesia, although this relationship did not persist after multiple comparison correction, suggesting that combinations of spectral power features alone are insufficient to predict clinical state. Factor score analysis of therapeutic response and novel subject-specific machine learning approaches based on subject-specific neuroimaging provide tools to predict outcomes of therapies for PD.

Highlights

Parkinson’s Disease (PD) symptoms differ across individuals (Foltynie et al, 2002), exhibiting distinct symptom profiles that include motor and non-motor components (Goetz et al, 2008)
Categorizing the MDS-UPDRS Part III scores into sub-items based on motor factors revealed a variety of motor profiles across subjects (Figure 1A)
We calculated a Spearman correlation matrix of the degree of short-duration L-Dopa response (SDR) across factor scores, showing SDRs were uncorrelated across all motor factors (Supplementary Figure 1)

Summary

Introduction

Parkinson’s Disease (PD) symptoms differ across individuals (Foltynie et al, 2002), exhibiting distinct symptom profiles that include motor and non-motor components (Goetz et al, 2008). There is limited quantitative information on symptom profile response to treatment, including for the gold standard therapy L-Dopa where. Similar to clinical symptoms, non-invasive neuroimaging metrics differ across individuals (Finn et al, 2015; Yang et al, 2017) and the therapeutic implications of such inter-subject variability in PD are currently not well characterized (Marras et al, 2020). Previous studies using non-invasive magnetoencephalography (MEG) in PD to image motor cortex reported SDR effects in a variety of neuroimaging metrics such as spectral power (Heinrichs-Graham et al, 2014; Cao et al, 2020; Vinding et al, 2020), cortico-muscular coherence (Salenius et al, 2002; Hirschmann et al, 2013), and interhemispheric coherence (Heinrichs-Graham et al, 2014). Inter-subject variability among multi-faceted neuroimaging metrics remains a challenge for identification of SDR biomarkers

Methods

Results

Conclusion