Foundation-Model-Boosted Multimodal Learning for fMRI-based Neuropathic Pain Drug Response Prediction
Journal:
arXiv
Published Date:
Feb 28, 2025
Abstract
Neuropathic pain, affecting up to 10% of adults, remains difficult to treat
due to limited therapeutic efficacy and tolerability. Although resting-state
functional MRI (rs-fMRI) is a promising non-invasive measurement of brain
biomarkers to predict drug response in therapeutic development, the complexity
of fMRI demands machine learning models with substantial capacity. However,
extreme data scarcity in neuropathic pain research limits the application of
high-capacity models. To address the challenge of data scarcity, we propose
FMM$_{TC}$, a Foundation-Model-boosted Multimodal learning framework for
fMRI-based neuropathic pain drug response prediction, which leverages both
internal multimodal information in pain-specific data and external knowledge
from large pain-agnostic data. Specifically, to maximize the value of limited
pain-specific data, FMM$_{TC}$ integrates complementary information from two
rs-fMRI modalities: Time series and functional Connectivity. FMM$_{TC}$ is
further boosted by an fMRI foundation model with its external knowledge from
extensive pain-agnostic fMRI datasets enriching limited pain-specific
information. Evaluations with an in-house dataset and a public dataset from
OpenNeuro demonstrate FMM$_{TC}$'s superior representation ability,
generalizability, and cross-dataset adaptability over existing unimodal fMRI
models that only consider one of the rs-fMRI modalities. The ablation study
validates the effectiveness of multimodal learning and foundation-model-powered
external knowledge transfer in FMM$_{TC}$. An integrated gradient-based
interpretation study explains how FMM$_{TC}$'s cross-dataset dynamic behaviors
enhance its adaptability. In conclusion, FMM$_{TC}$ boosts clinical trials in
neuropathic pain therapeutic development by accurately predicting drug
responses to improve the participant stratification efficiency.
