Machine Learning Classification of Mild Cognitive Impairment using Advanced Multi-Shell Diffusion MRI and CSF Biomarkers

INTRODUCTION: Machine learning applied to neuroimaging can help with medical diagnosis and early detection by identifying biomarkers of subtle changes in brain structure and function. The effectiveness of advanced diffusion MRI (dMRI) imaging methods for pre-dementia classification remains largely unexplored, particularly when combined with CSF biomarkers. METHODS: We implemented XGBoost machine learning models to evaluate the classification potential of dMRI parameters (derived using NODDI, C-NODDI, MAP, or SMI), CSF biomarkers of Alzheimer's pathology (Tau, pTau, AB42, AB40), and pairwise dMRI + CSF combinations in distinguishing cognitive normality from mild cognitive impairment. RESULTS: MAP-RTAP (AUC=0.78) and pTau/AB42 (AUC=0.76) were the best performing individual biomarkers. Combining C-NODDI-C-NDI and AB42/AB40 achieved the highest performance (AUC=0.84) and accuracy (0.84), while other combinations optimized either sensitivity (0.93) or specificity (0.88). DISCUSSION: dMRI biomarkers demonstrate comparable performance to CSF biomarkers, with notable improvements achieved when combined. This study highlights dMRI's potential for enhancing early AD detection. Key words: Machine Learning, Early Diagnostics, Mild Cognitive Impairment, Diffusion MRI, CSF Biomarkers, Alzheimer's Disease, Neuroimaging