| Summary: | 碩士 === 國立陽明大學 === 腦科學研究所 === 101 === Background: For major depressive disorder (MDD), drugs are the major treatment approach, but there are ineffective for drug-resistant patients. Repetitive transcranial magnetic stimulation (rTMS) provides another alternative therapy to these drug-resistant patients.
Materials and Methods: Thirty MDD patients were accepted high frequency (10Hz) rTMS over the left dorsolateral prefrontal cortex during two weeks, and to determined as responder by 50% decreased Hamilton Depression Rating Scale (HDRS). T1-weighted magnetic resonance images (two time points per patient, pre-treatment,week-0, and after treatment, week-2) will be compared to detect brain grey matter differences by voxel-based morphometric analysis. And there are forty subjects for normal control. Secondly, to calculate the correlation between that the grey matter difference between week-0, and week-2 and patients’clinical data before and after rTMS treatment difference. Finally, to find the significant different regions between week-0 and week-2 as cluster of interest (COI), the predicted model would be constructed by Binary Logistic Regression whose variables were computed from patient's week-0 COI volumes.
Results: During two weeks rTMS therapy, seventeen patients were as responder, thirteen patients were as non-responder. In responder, the significant volume increased regions were bilateral orbitofrontal cortex, right middle frontal gyrus, right inferior temporal gyrus, and right precuneus. The positive correlation was observed between increased bilateral orbitofrontal cortex volumes and decreased HDRS. The significant volume decreased regions were right cerebelum, right superior temporal pole, left inferior frontal gyrus, and bilateral insula. Finally, in predicted model of rTMS therapy, the percentage of correction was 93.3%.
Conclusions: Two weeks rTMS therapy for responder, the increased bilateral orbitofrontal cortex volumes are high relationship with rTMS treatment effect. The parts of gray matter volume could be variables in predicted model.
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