This research proposal describes a cross-disciplinary project aimed at developing Digital Twins (DTs) of Alzheimer’s Disease (AD) using Language Models (LMs). By mimicking the functional deficits observed in individuals with AD, these DTs will serve as tools for early detection and understanding of disease progression. Several approaches to altering the LM will be explored, and the resulting effects on brain score — an evaluation of the correlation between brain activity and the LM’s internal activations — will be studied. Detection models will be trained based on each approach; these models will be compared against themselves and the state-of-the-art.Two converging lines of evidence motivate this work: LMs achieve high accuracy in classifying AD from speech transcripts, and their internal representations correlate significantly with human brain activity during language processing. If successful, this project could lead to significant advancements in the early detection and monitoring of AD, ultimately improving patient outcomes.