Trigeminal neuralgia (TN) is a severe form of chronic facial neuropathic pain. Increasing interest in the neuroimaging of pain has highlighted changes in the root entry zone in TN, but also group-level central nervous system gray and white matter (WM) abnormalities. Group differences in neuroimaging data are frequently evaluated with univariate statistics; however, this approach is limited because it is based on single, or clusters of, voxels. By contrast, multivariate pattern analyses consider all the model's neuroanatomical features to capture a specific distributed spatial pattern. This approach has potential use as a prediction tool at the individual level. We hypothesized that a multivariate pattern classification method can distinguish specific patterns of abnormal WM connectivity of classic TN from healthy controls (HCs). Diffusion-weighted scans in 23 right-sided TN and matched controls were processed to extract whole-brain interregional streamlines. We used a linear support vector machine algorithm to differentiate interregional normalized streamline count between TN and HC. This algorithm successfully differentiated between TN and HC with an accuracy of 88%. The structural pattern emphasized WM connectivity of regions that subserve sensory, affective, and cognitive dimensions of pain, including the insula, precuneus, inferior and superior parietal lobules, and inferior and medial orbital frontal gyri. Normalized streamline counts were associated with longer pain duration and WM metric abnormality between the connections. This study demonstrates that machine-learning algorithms can detect characteristic patterns of structural alterations in TN and highlights the role of structural brain imaging for identification of neuroanatomical features associated with neuropathic pain disorders.
A white matter connectivity pattern involving areas implicated in the sensory, affective, and cognitive dimensions of pain predicts trigeminal neuralgia with an 88% accuracy.
aDivision of Brain, Imaging and Behaviour, Systems Neuroscience, Krembil Brain Institute, University Health Network, Toronto, ON, Canada
bDepartment of Surgery, Institute of Medical Science, University of Toronto, Toronto, ON, Canada
cDivision of Neurosurgery, Department of Surgery, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, ON, Canada
Corresponding author. Address: Division of Neurosurgery, Toronto Western Hospital, 399 Bathurst Street, 4WW-443, Toronto, Ontario, M5T 2S8. Tel.: 416 603-6441; fax: 416 603-5298. E-mail address: email@example.com (M. Hodaie).
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Received January 06, 2018
Received in revised form March 19, 2018
Accepted April 17, 2018