MCI Detection using fMRI time series embeddings of Recurrence plots

TL;DR

This study introduces a novel method for classifying Mild Cognitive Impairment using fMRI time series embeddings derived from recurrence plots and autoencoders, achieving high accuracy on a public dataset.

Contribution

It proposes a new approach combining recurrence plots and autoencoders for MCI detection from fMRI data, demonstrating promising classification performance.

Findings

Peak classification accuracy of 93%

Mean accuracy of 89.3%

Effective differentiation between healthy and MCI subjects

Abstract

The human brain can be conceptualized as a dynamical system. Utilizing resting state fMRI time series imaging, we can study the underlying dynamics at ear-marked Regions of Interest (ROIs) to understand structure or lack thereof. This differential behavior could be key to understanding the neurodegeneration and also to classify between healthy and Mild Cognitive Impairment (MCI) subjects. In this study, we consider 6 brain networks spanning over 160 ROIs derived from Dosenbach template, where each network consists of 25-30 ROIs. Recurrence plot, extensively used to understand evolution of time series, is employed. Representative time series at each ROI is converted to its corresponding recurrence plot visualization, which is subsequently condensed to low-dimensional feature embeddings through Autoencoders. The performance of the proposed method is shown on fMRI volumes of 100 subjects (balanced data), taken from publicly available ADNI dataset. Results obtained show peak classification accuracy of 93% among the 6 brain networks, mean accuracy of 89.3% thereby illustrating promise in the proposed approach.