  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.

Neural and Evolutionary Computing;Neurons and Cognition