Structural Neuroimaging Predictors and Correlates of Treatment Response for rTMS in Major Depressive Disorder
Keywords: Brain Cortical Thickness
Major Depressive Disorder (MDD)
Neuroimaging
Repetitive Transcranial Magnetic Stimulation (rTMS)
Structural MRI
Voxel Based Morphometry (VBM)

Major Depressive Disorder (MDD) is a multifaceted condition characterized by a complex etiology and pathology. Current classification systems, which define mental disorders based on symptom clusters and clinical course, may not adequately capture the diverse range of illnesses and their distinct pathologies. This heterogeneity becomes evident when analyzing the brain's network activity as a whole since different structural and functional networks may contribute to MDD pathology and influence repetitive transcranial magnetic stimulation (rTMS) outcomes. Understanding these neural mechanisms and MDD's neuropathophysiology is essential for enhancing rTMS remission rates and predicting patient responsiveness to treatment. This thesis focuses on identifying the structural correlates of rTMS treatment response in Major Depressive Disorder using structural MRI, a modality with greater potential for integration into clinical practice than other biomarkers. However, there is a lack of consistency among studies investigating brain structural predictors and correlates of MDD response to rTMS, and the mechanisms through which rTMS affects brain structure remain poorly understood. The objective of this thesis is to determine the structural MRI predictors and correlates of treatment response to rTMS in Major Depressive Disorder, with the ultimate goal of developing predictive tests that reduce non-response rates among treated patients and inform the development of novel treatment strategies for non-responders. In this thesis, we present what is to our knowledge the most extensive investigation to date into the brain structural effects of rTMS in patients with MDD. Our findings provide some support for earlier research suggesting that the thickness of the left rostral anterior cingulate cortex (rACC) within the prefrontal cortex might have potential as a biomarker for predicting rTMS treatment response in MDD patients. However, it is crucial to note that the observed effects were modest in scale. This observation, coupled with the failure of some studies to replicate the robust statistical findings of previous, smaller-scale research, suggests that the use of current methods to detect such changes might be insufficiently sensitive. Indeed, our results indicate that brain structure measures, such as gray matter volume (GMV) and cortical thickness in the rACC, previously thought to be associated with treatment outcomes, may not be as reliable or consistent across diverse MDD populations as once believed. This thesis underscores the need for future research to explore other potential structural biomarkers using more advanced methodologies, such as multivariate statistical approaches or higher resolution structural imaging. The prospect of developing a clinically useful structural MRI biomarker for the rACC that could inform personalized treatment decisions in MDD seems limited based on the current analysis. In conclusion, while the large-scale studies presented in this thesis did not replicate some of the results found in prior smaller investigations, they did reveal a significant association between rTMS and brain structural changes potentially relevant to clinical practice. These finding merit further exploration in future research. Despite the difficulty in reliably detecting these brain structural markers using current neuroimaging and analytical techniques, these methods pave the way for future studies incorporating higher-resolution structural neuroimaging along with other biomarkers. Collectively, these advances could aid in predicting response and non-response to rTMS, ultimately enabling a more personalized approach to treating MDD.