A Machine Learning Approach for Identifying Anatomical Biomarkers of Early Mild Cognitive Impairment

TL;DR

This study evaluates machine learning techniques on MRI data to identify early biomarkers of mild cognitive impairment, aiming for early Alzheimer’s detection through a semi-automatic pipeline.

Contribution

It introduces a machine learning pipeline with data harmonization for early MCI detection, identifying key neuroanatomical biomarkers and comparing various classifiers.

Findings

Consistent biomarkers include entorhinal, hippocampus, lateral ventricle, and lateral orbitofrontal regions.

Naive Bayes with z score harmonization performs best on balanced ADNI data.

RUSBoost shows strong performance on imbalanced datasets.

Abstract

Alzheimer Disease poses a significant challenge, necessitating early detection for effective intervention. MRI is a key neuroimaging tool due to its ease of use and cost effectiveness. This study analyzes machine learning methods for MRI based biomarker selection and classification to distinguish between healthy controls and those who develop mild cognitive impairment within five years. Using 3 Tesla MRI data from ADNI and OASIS 3, we applied various machine learning techniques, including MATLAB Classification Learner app, nested cross validation, and Bayesian optimization. Data harmonization with polynomial regression improved performance. Consistent features identified were the entorhinal, hippocampus, lateral ventricle, and lateral orbitofrontal regions. For balanced ADNI data, Naive Bayes with z score harmonization performed best. For balanced OASIS 3, SVM with z score correction excelled. In imbalanced data, RUSBoost showed strong performance on ADNI and OASIS 3. Z score harmonization highlighted the potential of a semi automatic pipeline for early AD detection using MRI.