Leveraging LLMs for Early Alzheimer's Prediction

TL;DR

This paper introduces a connectome-informed LLM framework that encodes dynamic fMRI data for early Alzheimer's prediction, achieving highly sensitive results suitable for clinical use.

Contribution

It presents a novel method combining connectome data with large language models for improved early Alzheimer's detection.

Findings

Achieves error rates below clinical thresholds

Effective encoding of dynamic fMRI data

Potential for timely Alzheimer's intervention

Abstract

We present a connectome-informed LLM framework that encodes dynamic fMRI connectivity as temporal sequences, applies robust normalization, and maps these data into a representation suitable for a frozen pre-trained LLM for clinical prediction. Applied to early Alzheimer's detection, our method achieves sensitive prediction with error rates well below clinically recognized margins, with implications for timely Alzheimer's intervention.