Multivariate pattern classification of brain white matter connectivity predicts classic trigeminal neuralgia
| Autor: | Kevin E. Liang, Karen D. Davis, David Qixiang Chen, Mojgan Hodaie, Dave J. Hayes, Jidan Zhong, Peter Shih-Ping Hung |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Adult
Male Support Vector Machine Precuneus computer.software_genre 050105 experimental psychology White matter Young Adult 03 medical and health sciences Nerve Fibers 0302 clinical medicine Neuroimaging Trigeminal neuralgia Voxel Connectome Image Processing Computer-Assisted medicine Humans 0501 psychology and cognitive sciences Correlation of Data Aged business.industry 05 social sciences Brain Inferior parietal lobule Middle Aged Trigeminal Neuralgia medicine.disease Magnetic Resonance Imaging White Matter Anesthesiology and Pain Medicine medicine.anatomical_structure Neurology Case-Control Studies Female Neurology (clinical) business Insula Neuroscience computer 030217 neurology & neurosurgery |
| Zdroj: | Pain. 159:2076-2087 |
| ISSN: | 1872-6623 0304-3959 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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