Personalized Brain-Based Analgesia Detection with Portable fNIRS and AI

Neuroimaging-based pain decoding faces two underappreciated challenges: between-subject variability that prevents classifiers from generalizing across patients, and within-session cross-validation designs that inflate reported accuracy by conflating within- and between-person variance. Here we address both using portable functional near-infrared spectroscopy (fNIRS) during pharmacologically verified local nerve anesthesia. Twenty-five patients with clinically painful teeth underwent 36-channel bilateral fNIRS during percussion before ("Pre") and after ("Post") local nerve anesthesia. In 13 block-success patients, a paired Pre-versus-Post comparison with healthy-tooth control identified three temporal hemodynamic response function (HRF) features – late slope, mean first derivative, and baseline-normalized amplitude – whose analgesia interaction effects (d = 0.63–0.79) exceeded that of raw general linear model (GLM) amplitude (d = 0.56), with a significant difference-in-differences interaction (p = 0.011). Per-patient calibration with these features yielded leave-one-subject-out (LOSO) AUC = 0.68–0.76 for nonlinear classifiers (permutation p = 0.002), with HbO-specific feature selection achieving the best performance (RF AUC = 0.760); a healthy-tooth negative control was non-significant. End-to-end deep learning on raw time series (CNN-LSTM AUC = 0.719) was competitive with feature-based classifiers, while linear models did not reach significance. Critically, head-to-head comparison of within-session CV and LOSO on the same data revealed mean inflation of +0.13 AUC across all model types, including deep learning – demonstrating that high within-session accuracy alone does not establish subject-independent validity. Exploratory analyses suggested complementary roles for oxyhemoglobin (HbO; within-patient analgesia detection) and deoxyhemoglobin (HbR; cross-patient information), and that trial-to-trial response variability may complement amplitude for cross-patient pain detection. These results show that per-patient calibration with temporal HRF features supports subject-independent analgesic-state detection under strict LOSO evaluation, and that within-session validation – standard in the fNIRS pain-decoding literature – can substantially overestimate performance.