Quaternion Multi-Band rs-fMRI Features for Predicting Mild Cognitive Impairment to Alzheimer's Disease Conversion

Mild cognitive impairment (MCI) is a high-risk state for Alzheimer's disease (AD), and reliable prediction of MCI-to-AD conversion is crucial for early intervention. This study proposes a quaternion-based functional connectivity (QFC) framework for multi-band analysis of resting-state fMRI (rs-fMRI) signals to predict conversion from MCI to AD over a 5-year period. In contrast to conventional independent band or simple concatenation approaches, the proposed QFC representation preserves the multidimensional structure of the signal and enables a holistic characterization of cross-frequency relationships. QFC features were extracted from multi-band rsfMRI data and used as input to machine learning classifier. The proposed method achieved superior performance compared with single-band features and a traditional multi-band concatenation strategy, yielding an accuracy of 87.8%, a sensitivity of 90.7%, a specificity of 84.4%, a mean F1-score of 0.877, and an area under the ROC curve of 0.937 using the Naïve Bayes classifier. These results correspond to relative improvements of 16%, 20%, 11%, 16%, and 14% in accuracy, sensitivity, specificity, F1-score, and AUC, respectively, over the traditional multi-band approach. The findings highlight the effectiveness of QFC for capturing frequency-dependent alterations in functional connectivity and underscore its potential for early and accurate prediction of MCI-to-AD conversion from a clinical perspective.