Representative Functional Connectivity Learning for Multiple Clinical groups in Alzheimer's Disease

TL;DR

This paper introduces a deep learning model that learns representative functional connectivity patterns for different clinical groups in Alzheimer's disease, achieving high classification accuracy between healthy, MCI, and AD groups.

Contribution

It proposes an integrated autoencoder and multi-class classifier model to identify group-specific functional connectivity features in Alzheimer's disease progression.

Findings

Achieved 68% accuracy in classifying five clinical groups.

Learned representative FC patterns for each clinical group.

Demonstrated the model's effectiveness in distinguishing MCI subtypes.

Abstract

Mild cognitive impairment (MCI) is a high-risk dementia condition which progresses to probable Alzheimer's disease (AD) at approximately 10% to 15% per year. Characterization of group-level differences between two subtypes of MCI - stable MCI (sMCI) and progressive MCI (pMCI) is the key step to understand the mechanisms of MCI progression and enable possible delay of transition from MCI to AD. Functional connectivity (FC) is considered as a promising way to study MCI progression since which may show alterations even in preclinical stages and provide substrates for AD progression. However, the representative FC patterns during AD development for different clinical groups, especially for sMCI and pMCI, have been understudied. In this work, we integrated autoencoder and multi-class classification into a single deep model and successfully learned a set of clinical group related feature vectors. Specifically, we trained two non-linear mappings which realized the mutual transformations between original FC space and the feature space. By mapping the learned clinical group related feature vectors to the original FC space, representative FCs were constructed for each group. Moreover, based on these feature vectors, our model achieves a high classification accuracy - 68% for multi-class classification (NC vs SMC vs sMCI vs pMCI vs AD).