Machine learning diagnosis of mild cognitive impairment using advanced diffusion MRI and CSF biomarkers.

ABSTRACT:

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) 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, Aβ42, Aβ40), and pairwise dMRI + CSF combinations in distinguishing cognitive normality from mild cognitive impairment.

Results

MAP-RTAP (AUC = 0.78) and pTau/Aβ42 (AUC = 0.76) were the best performing individual bio

SUBMITTER: Guo AY 

PROVIDER: S-EPMC12426029 | biostudies-literature | 2025 Jul-Sep

REPOSITORIES: biostudies-literature