Leveraging LLMs for Early Alzheimer's Prediction
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 24 references that are in the map. First posted on arXiv 27 Oct 2025.
Builds on
7 of 24 references.
References · 7
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
- 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 2,039
- 2023Time-LLM: Time Series Forecasting by Reprogramming Large Language Modelscited by 700
- 2020Topological Learning for Brain Networkscited by 6
- 2025Augmenting Bias Detection in LLMs Using Topological Data Analysiscited by 1
Built on by
Not cited yet.