Generative forecasting of brain activity enhances Alzheimer's classification and interpretation

TL;DR

This paper introduces a generative forecasting approach using deep learning models, including a novel Transformer-based BrainLM, to improve Alzheimer's disease classification and interpret brain network sensitivities from rs-fMRI data.

Contribution

It presents a new generative forecasting method with BrainLM, enhancing AD classification and interpretability of brain network sensitivities from rs-fMRI data.

Findings

Forecasting improves AD classification accuracy.

BrainLM reveals class-specific brain network sensitivities.

Generative models augment limited neuroimaging datasets.

Abstract

Understanding the relationship between cognition and intrinsic brain activity through purely data-driven approaches remains a significant challenge in neuroscience. Resting-state functional magnetic resonance imaging (rs-fMRI) offers a non-invasive method to monitor regional neural activity, providing a rich and complex spatiotemporal data structure. Deep learning has shown promise in capturing these intricate representations. However, the limited availability of large datasets, especially for disease-specific groups such as Alzheimer's Disease (AD), constrains the generalizability of deep learning models. In this study, we focus on multivariate time series forecasting of independent component networks derived from rs-fMRI as a form of data augmentation, using both a conventional LSTM-based model and the novel Transformer-based BrainLM model. We assess their utility in AD classification, demonstrating how generative forecasting enhances classification performance. Post-hoc interpretation of BrainLM reveals class-specific brain network sensitivities associated with AD.