Decoding Pain: Uncovering the Factors that Affect Performance of Neuroimaging-Based Pain Models

A bstract Neuroimaging-based pain biomarkers, when combined with machine learning techniques, have demonstrated potential in decoding pain intensity and diagnosing clinical pain conditions. However, a systematic evaluation of how different modeling options affect model performance remains unexplored. This study presents results from a comprehensive literature survey and benchmarking analysis. We conducted a survey of 57 previously published articles that included neuroimaging-based predictive modeling of pain, comparing classification and prediction performance based on the following modeling variables—the levels of data, spatial scales, idiographic vs. population models, and sample sizes. The findings revealed a preference for population-level modeling with brain-wide features, aligning with the goal of clinical translation of neuroimaging biomarkers. However, a systematic evaluation of the influence of different modeling options was hindered by a limited number of independent test results. This prompted us to conduct benchmarking analyses using a locally collected functional Magnetic Resonance Imaging (fMRI) dataset ( N = 124) involving an experimental thermal pain task. The results clearly demonstrated that data levels, spatial scales, and sample sizes significantly impact model performance. Specifically, incorporating more pain-related brain regions, increasing sample sizes, and using less data averaging in training while increasing it in testing enhanced performance. These findings provide a useful reference for decision-making in the development of neuroimaging-based biomarkers, highlighting the importance of the careful selection of modeling strategies and options to build better-performing neuroimaging pain biomarkers. These findings offer useful guidance for developing neuroimaging-based biomarkers, underscoring the importance of strategic selection of modeling approaches to build better-performing neuroimaging pain biomarkers.