Trigeminal Neuralgia Diffusivities using Gaussian Process Classification and Merged Group Tractography

David Qixiang Chen, Jidan Zhong, Powell P W Chu, Cathy Meng Fei Li, Mojgan Hodaie
Pain 2020 July 21
Imaging of trigeminal neuralgia (TN) has demonstrated key DTI based diffusivity alterations in the trigeminal nerve, however imaging has primarily focused on the peripheral nerve segment due to previous limitations in reliably segmenting small fiber bundles across multiple subjects. We used Selective Automated Group Integrated Tractography (SAGIT) to study 36 TN subjects (right sided pain) and 36 sex matched controls, to examine the trigeminal nerve (CN V), pontine decussation (TPT), and thalamocortical fibers (S1). GP classifiers were trained by scrolling a moving window over CN V, TPT, and S1 tractography centroids. Fractional anisotropy (FA), generalized FA (GFA), radial diffusivity (RD), axial diffusivity (AD), and mean diffusivity (MD) metrics were evaluated for both groups, analyzing TN vs. control groups and affected vs. unaffected sides. Classifiers that performed at greater-or-equal-to 70% accuracy were included. GP classifier consistently demonstrated bilateral trigeminal changes, differentiating them from controls with an accuracy of 80%. Affected and unaffected sides could be differentiated from each other with 75% accuracy. Bilateral TPT could be distinguished from controls with at least 85% accuracy. TPT left-right classification achieved 98% accuracy. Bilateral S1 could be differentiated from controls, where the affected S1 RD classifier achieved 87% accuracy. This is the first TN study that combines group-wise merged tractography, machine learning classification, and analysis of the complete trigeminal pathways from the peripheral fibers to S1 cortex. This analysis demonstrates that TN is characterized by bilateral abnormalities throughout the trigeminal pathway compared with controls, as well as abnormalities between affected and unaffected sides.

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