en Computational Neuroscience Computational Neuroscience Original Original <div style="text-align: justify;"><strong>Introduction</strong>: Accurate prediction of the progression from mild cognitive impairment (MCI) to Alzheimer&rsquo;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.<br> <strong>Methods</strong>: 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.&nbsp;<br> <strong>Results</strong>: 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.&nbsp;<br> <strong>Conclusion</strong>: 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.</div> Mild cognitive impairment (MCI), Alzheimer’s disease (AD), Graph signal processing, Connectivity-based features, Classification 1113 1130 http://bcn.iums.ac.ir/browse.php?a_code=A-10-6925-1&slc_lang=en&sid=1 Jafar Zamani zamani.jafar66@gmail.com 13700319475328460056777 13700319475328460056777 No Department of Psychiatry and Behavioral Sciences, Stanford University, California, United States. Alireza Talesh Jafadideh alireza.talesh@gmail.com 13700319475328460056778 13700319475328460056778 Yes School of Engineering Science, College of Engineering, University of Tehran, Tehran, Iran.