| dc.description.abstract | Magnetic Resonance Imaging (MRI) based computational neuroanatomy has shown to be
an e ective approach in detecting grey matter, and white matter changes in the brains
of individuals with migraine. However, research on detecting gross volume changes in
migraine is rare. Therefore, the objective of this study is to investigate gross volume
changes associated with migraine and test the potential utility of gross volume changes
in developing a novel neuroimaging biomarker for objective diagnosis of migraine. 45
patients with migraine, and 46 healthy controls were scanned using a 3 Tesla scanner,
and 3D, T1- weighted MR images were obtained. First, Tensor-based morphometry
was performed using Computational Anatomy Toolbox (CAT 12) to generate voxel-wise
Jacobian determinant images and smoothed (HWFM = 8 mm). A group-level univariate
analysis was performed, using a two-sample t- test and the results were corrected for
multiple comparisons. Second, multivariate pattern analysis (MVPA) was conducted using
support vector machine (SVM) to classify the patients with migraine and healthy controls.
Reduced gross volume changes in patients were detected in the middle frontal, superior
frontal, inferior temporal of right cerebrum, middle temporal, angular, cuneus, calcarine
in the left cerebrum and cerebellum. Moreover, the results of the MVPA indicated
that gross volume changes can be served as a biomarker to distinguish patients with
migraine and healthy individuals (Accuracy = 73.63%). In conclusion, we propose that
the gross volume changes detected in migraineurs can be considered when developing
neuroimaging tools to facilitate the objective diagnosis of migraine. | en_US |
