Machine Learning Approach to Find Brain white matter connectivity Clasifying People with Trigeminal Neuralgia
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.
To use the multivariate pattern classification method to distinguish specific patterns of abnormal WM connectivity of classic TN from healthy controls (HCs).
Subjects & Methods
- 23 right-sided TN, 23 age and sex matched controls
- Diffusion-weighted scans and T1-weighted MRI at 3 T
- Whole-brain interregional streamline extraction
- linear support vector machine (SVM) algorithm to differentiate interregional normalized streamline count between TN and HC.
- LOOCV is used to calculate the model performance
Results & Conlcusion
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.
This study is published in Pain, October 2018 (DOI: 10.1097/j.pain.0000000000001312): “Multivariate pattern classification of brain white matter connectivity predicts classic trigeminal neuralgia”. This study has been highlighted as Editor’s Choice and has an accompanied video. For more information please head over to the paper or the video.