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.

Extracting ingredients from a recipe text is a very common activity especially for data scientists and developers who want to study recipes or want to make statistical representations about nutritive values of cuisine recipes. Ingredients is not the only useful information we want to extract, the quantity used for each ingredient and how they are prepared are also interesting informations that we can extract by the same method presented in this work. Hidden Markov Models are the first idea that came in my mind because there are previous successful works that used this method for information extraction ((Freitag & McCallum, 2000),(Freitag & McCallum, 1999),(Seymore, McCallum, Rosenfeld, et al., 1999),(Bikel, Miller, Schwartz, & Weischedel, 1998),(Leek, 1997)), and also because modeling sequences of words where we have to estimate the hidden state is typically a hidden Markov procedure. In this work we are concentrating on the external knowledge part deployed in what we called a simple double agent Viterbi algorithm.