Multifaceted Analysis of Migraine Brain MRI and Machine Learning

• Main Authors: Yung-Lin Chen, 陳永霖
• Other Authors: Yu-Te Wu
• Format: Others
• Language: en_US
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/8d45vr

碩士 === 國立陽明大學 === 生醫光電研究所 === 107 === Background Computational Analysis of MRI has been developed for a long time. With the purpose of automation, rapidity, precision and low inter-subject variability, it offers feature quantification for scientific research, and clinically assists to diagnosis and medical treatment. The algorithms also include machine learning (ML) and deep learning, and they can apply on image segmentation and classification. Migraine is a primary headache that the etiology is still unclear, and its pathology is diverse also complex. Therefore, in this study, we aimed to analyze clinical migraine data, using different algorithms to make an integrated investigation to complete three missions: 1. Applying computational analysis to T1W image and rsfMRI, and compare binary sub-groups in four groups: migraine without depression (MwoD) versus with depression (MwD), migraine without aura (MwoA) versus with aura (MwA), control versus migraine with severe headache above 5 days per month, and control versus migraine occurred over 10 years 2. To perform ML and deep learning binary classification by using structural and functional features and the knowledge based on previous studies. Materials and methods In this study, we collected 46 normal control MRI and 251 migraine MRI with clinical data, and MRI sequences contain structural T1W image and rsfMRI. T1W analysis were using both VBM and SBM method. VBM calculates the spatial standardized GM distribution, and SBM calculates the numeric features including volume, thickness and surface area in each GM parcellation, and volume of WM and ventricles. In rsfMRI, we calculated fALFF map, ReHo map and functional connectivity in each subject. We use permutation test for functional connectivity statistics (correlation coefficient and dynamic variance), and two sample T-test for others. Finally, we apply ML by using numeric or volumetric feature to perform binary classification between two sub-groups. Results We found from structural MRI that migraine occurring above 5 years would decrease GM volume in right parietal and frontal lobe, and migraine with headache above 10 days per month would have similar pattern in frontal and temporal lobe. Aura would not affect GM volume, while depression would decrease GM volume in several regions. In fALFF and ReHo results, headache, aura and depression had distinct activation and deactivation regions. In the group of control versus migraine occurring above 5 years, and control versus migraine with headache above 10 days per month, the differences appeared in many places and both had similar patterns in occipital lobe. Among these 4 groups, the group MwoA versus MwA was found with the strongest and highest amount of difference in functional connectivity correlation coefficient and variance. In depression group, the differences were mainly in bilateral temporal lobe, and in two headache groups, the differences were more fragmentary. Performances for classifying migraine and migraine subgroup were not ideal. The best result was classifying aura in migraine using ML and functional connectivity features, and the testing accuracy is 73.3%. Conclusion The depression, aura and headache symptoms would affect in brain microstructure and functions respectively. Therefore, the structural and functional MRI may find various individual differences, and the individual differences affects directly the ML performance.