Towards Monitoring Parkinson's Disease Following Drug Treatment: CGP Classification of rs-MRI Data

TL;DR

This study introduces a novel evolutionary algorithm-based method, CGP, for classifying rs-fMRI data to monitor Parkinson's disease post-treatment, achieving comparable accuracy to traditional methods and offering better interpretability.

Contribution

It presents CGP, an evolutionary algorithm, as a new data-driven classification tool for brain imaging, with advantages in interpretability and causal analysis over traditional statistical models.

Findings

CGP achieved up to 74.57% accuracy in classifying rs-fMRI data.

CGP's performance was comparable to ANN and SVM methods.

EAs facilitate easier decoding and understanding of data inputs than traditional classifiers.

Abstract

Background and Objective: It is commonly accepted that accurate monitoring of neurodegenerative diseases is crucial for effective disease management and delivery of medication and treatment. This research develops automatic clinical monitoring techniques for PD, following treatment, using the novel application of EAs. Specifically, the research question addressed was: Can accurate monitoring of PD be achieved using EAs on rs-fMRI data for patients prescribed Modafinil (typically prescribed for PD patients to relieve physical fatigue)? Methods: This research develops novel clinical monitoring tools using data from a controlled experiment where participants were administered Modafinil versus placebo, examining the novel application of EAs to both map and predict the functional connectivity in participants using rs-fMRI data. Specifically, CGP was used to classify DCM analysis and timeseries data. Results were validated with two other commonly used classification methods (ANN and SVM) and via k-fold cross-validation. Results: Findings revealed a maximum accuracy of 74.57% for CGP. Furthermore, CGP provided comparable performance accuracy relative to ANN and SVM. Nevertheless, EAs enable us to decode the classifier, in terms of understanding the data inputs that are used, more easily than in ANN and SVM. Conclusions: These findings underscore the applicability of both DCM analyses for classification and CGP as a novel classification technique for brain imaging data with medical implications for medication monitoring. Furthermore, classification of fMRI data for research typically involves statistical modelling techniques being often hypothesis driven, whereas EAs use data-driven explanatory modelling methods resulting in numerous benefits. DCM analysis is novel for classification and advantageous as it provides information on the causal links between different brain regions.