Predicting the Conversion From Mild Cognitive Impairment to Alzheimer's Disease Using Graph Frequency Bands and Functional Connectivity-based Features.

Introduction: Accurate prediction of the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is crucial for disease management. Machine learning techniques have demonstrated success in classifying AD and MCI cases, particularly using resting-state functional magnetic resonance imaging (rs-fMRI) data. Methods: This study utilized rs-fMRI data from the ADNI, involving 142 patients with stable MCI (sMCI) and 136 with progressive MCI (pMCI). Graph signal processing was applied to filter rs-fMRI data into low-, middle-, and high-frequency bands. Connectivity-based features were derived from both filtered and unfiltered data, resulting in a comprehensive set of 100 features, including global graph metrics, minimum spanning tree (MST) metrics, triadic interaction metrics, hub tendency metrics: and number of links. Feature selection was enhanced using particle swarm optimization (PSO) and simulated annealing (SA). A support vector machine (SVM) with a radial basis function (RBF) kernel and a 10-fold cross-validation setup were employed for classification. Results: The proposed approach achieved high accuracy with a reduced number of features selected via PSO, specifically five features. With these features: the SVM achieved 77% accuracy, 70% specificity, and 83% sensitivity. The identified features were as follows, (mean of clustering coefficient, mean of strength)/radius/(mean eccentricity, and modularity) from low/middle/high frequency bands of the graph. Conclusion: This study highlights the efficacy of the proposed framework in identifying individuals at risk of developing AD using a parsimonious feature set. This approach holds promise for advancing the precision of MCI-to-AD progression prediction, aiding early diagnosis and intervention strategies.