Feature Learning and Classification in Neuroimaging: Predicting Cognitive Impairment from Magnetic Resonance Imaging

TL;DR

This paper reviews feature learning and selection techniques in neuroimaging, comparing their effectiveness in predicting Alzheimer's disease from MRI data to improve classifier performance and biomarker discovery.

Contribution

It provides a comparative analysis of various feature learning methods applied to neuroimaging data for disease prediction.

Findings

Lasso-based methods effectively select relevant features.

PCA reduces dimensionality while preserving important information.

Auto-encoders capture complex patterns in MRI data.

Abstract

Due to the rapid innovation of technology and the desire to find and employ biomarkers for neurodegenerative disease, high-dimensional data classification problems are routinely encountered in neuroimaging studies. To avoid over-fitting and to explore relationships between disease and potential biomarkers, feature learning and selection plays an important role in classifier construction and is an important area in machine learning. In this article, we review several important feature learning and selection techniques including lasso-based methods, PCA, the two-sample t-test, and stacked auto-encoders. We compare these approaches using a numerical study involving the prediction of Alzheimer's disease from Magnetic Resonance Imaging.