Large Language Model-based FMRI Encoding of Language Functions for Subjects with Neurocognitive Disorder

TL;DR

This study uses large language model-based fMRI encoding to analyze language-related brain changes in older adults with Neurocognitive Disorders, revealing correlations between brain scores and cognitive abilities.

Contribution

It introduces LLM-based fMRI encoding tailored for NCD populations and explores brain-cognition correlations, addressing gaps in existing research.

Findings

Higher cognitive scores correlate with better brain scores.

Correlations peak in the middle temporal gyrus.

fMRI encoding models can detect early functional changes in NCD.

Abstract

Functional magnetic resonance imaging (fMRI) is essential for developing encoding models that identify functional changes in language-related brain areas of individuals with Neurocognitive Disorders (NCD). While large language model (LLM)-based fMRI encoding has shown promise, existing studies predominantly focus on healthy, young adults, overlooking older NCD populations and cognitive level correlations. This paper explores language-related functional changes in older NCD adults using LLM-based fMRI encoding and brain scores, addressing current limitations. We analyze the correlation between brain scores and cognitive scores at both whole-brain and language-related ROI levels. Our findings reveal that higher cognitive abilities correspond to better brain scores, with correlations peaking in the middle temporal gyrus. This study highlights the potential of fMRI encoding models and brain scores for detecting early functional changes in NCD patients.