Classification of Alzheimer's Dementia vs. Healthy subjects by studying structural disparities in fMRI Time-Series of DMN

TL;DR

This study uses a novel signal structure metric and autoencoder representations to distinguish Alzheimer's patients from healthy individuals with high accuracy based on fMRI data.

Contribution

It introduces the use of the deviation from stochasticity measure combined with autoencoder features for classifying Alzheimer's disease from fMRI time series.

Findings

DS measure differs significantly between healthy and AD groups

Achieved 95% classification accuracy with Gradient Boosting classifier

Demonstrates the effectiveness of structure-based features in neuroimaging analysis

Abstract

Time series from different regions of interest (ROI) of default mode network (DMN) from Functional Magnetic Resonance Imaging (fMRI) can reveal significant differences between healthy and unhealthy people. Here, we propose the utility of an existing metric quantifying the lack/presence of structure in a signal called, "deviation from stochasticity" (DS) measure to characterize resting-state fMRI time series. The hypothesis is that differences in the level of structure in the time series can lead to discrimination between the subject groups. In this work, an autoencoder-based model is utilized to learn efficient representations of data by training the network to reconstruct its input data. The proposed methodology is applied on fMRI time series of 50 healthy individuals and 50 subjects with Alzheimer's Disease (AD), obtained from publicly available ADNI database. DS measure for healthy fMRI as expected turns out to be different compared to that of AD. Peak classification accuracy of 95% was obtained using Gradient Boosting classifier, using the DS measure applied on 100 subjects.