Classification of resting-state fMRI for olfactory dysfunction in parkinson's disease using evolutionary algorithms

ccurate early diagnosis and monitoring of neurodegenerative conditions is essential for effective disease management and treatment. This research develops automatic methods for detecting brain imaging preclinical biomarkers for olfactory dysfunction in early stage Parkinson's disease (PD) by considering the novel application of evolutionary algorithms. Classification will be applied to PD patients with severe hyposmia, patients with no/mild hyposmia, and healthy controls. An additional novel element is the use of evolutionary algorithms to map and predict the functional connectivity using rs-fMRI. Cartesian Genetic Programming (CGP) will be used to classify dynamic causal modelling (DCM) data as well as timeseries data. The findings will be validated using two other commonly used classification methods (ANN and SVM) and by employing k-fold cross-validation. Developing methods for identifying early stage PD patients with hyposmia is relevant since current methods of diagnosing early stage PD have low reliability and accuracy. Furthermore, exploring the performance of CGP relative to other methods is crucial given the additional benefits it provides regarding easy classifier decoding. Hence, this research underscores the potential relevance of DCM analyses for classification and CGP as a novel classification tool for brain imaging data with medical implications for disease diagnosis, particularly in early stages.