Decoding pain: uncovering the factors that affect the performance of neuroimaging-based pain models.

ABSTRACT:

Abstract

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 the results from a comprehensive literature survey and benchmark 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, ali

SUBMITTER: Lee DH 

PROVIDER: S-EPMC11726494 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature