| dc.description.abstract | Migraine is associated with grey matter changes in the human brain, but the interrelationships
between voxels and naturally grouped patterns of structural variations need to
be identi ed to understand how migraine a ects the brain at the network level. Three
dimensional, T1 weighted images of 45 migraine patients and 46 healthy controls who
underwent brain MR scanning in a 3 Tesla scanner were selected. The data were analyzed
using Computational Anatomy Toolbox-CAT12. Group independent component analysis
(Group ICA) was performed on grey matter volumes using GIFT toolbox. Two sample
t-tests were performed using component loadings of each component to nd out the
signi cant independent components (ICs). Nine maximally independent components (ICs)
were resulted using group ICA and labeled according to the Resting State Network (RSN)
atlas. Among them 6 ICs were found to be signi cant (p<0.05) based on two-sample ttests
representing the attention, sensorimotor, frontal, and visual networks. Further, mask
based multivariate pattern analyses (MVPA) were performed to distinguish patients with
migraine and healthy controls. MVPA revealed that component 4 (sensorimotor, classi
cation accuracy=61.53%), component 5 (sensorimotor, classi cation accuracy=73.62%),
component 7 (sensorimotor, classi cation accuracy=69.23%) and component 8 (visual,
classi cation accuracy=68.13%) show di erent potentials to correctly classify patients with
migraine and healthy subjects (p<0.05, number of permutations, n=1000). Source Based
Morphometry can detect the structural covariance networks of brains with migraine,
which exhibit signi cant di erences when compared with healthy controls. Further, the
above structural network changes can be used to develop an e ective biomarker for
objective diagnosis of migraine. | en_US |
