Predictive value of preoperative white and gray matter microstructural alterations for microvascular decompression outcome in classical trigeminal neuralgia.

BackgroundClassical trigeminal neuralgia (CTN) is a severe chronic pain disorder. While microvascular decompression (MVD) is an effective treatment, outcomes are variable, and preoperative predictive biomarkers are needed. Furthermore, the relationship between brain microstructural alterations and neurotransmitter systems in CTN is not well understood. This study aimed to characterize these alterations and assess their prognostic value for MVD.MethodsWe evaluated microstructural alterations in both white matter (WM) and gray matter (GM) in 101 CTN patients and 84 healthy controls (HCs) using Neurite Orientation Dispersion and Density Imaging (NODDI). Tract-Based Spatial Statistics (TBSS) and Gray Matter-Based Spatial Statistics (GBSS) were used to identify microstructural differences. Spatial correlations between WM and GM alterations and neurotransmitter distributions were analyzed using JuSpace. Univariate and multivariate logistic regression models with bootstrap validation were employed to identify predictors of pain recurrence after MVD in 58 patients who underwent the surgery.ResultsCompared to HCs, patients with CTN exhibited widespread microstructural disruptions in both WM and GM. These alterations, characterized by neurite injury and elevated free water, were spatially correlated with distributions of key neurotransmitter systems including serotonergic, dopaminergic, and GABAergic pathways. Critically, preoperative free-water accumulation in a specific GM cluster was identified as an independent predictor of MVD failure (AUC = 0.847). This neuroimaging biomarker provided a specificity of 97.92% for predicting poor surgical outcome.ConclusionOur findings indicate neurite injury in both WM and GM of CTN patients that is linked to specific neurotransmitter systems. Critically, preoperative free-water accumulation in GM emerges as a specific neuroimaging biomarker for predicting MVD failure, potentially guiding personalized therapeutic strategies.