Grant: Doctoral Research Award: Canada Graduate Scholarships

BACKGROUND: Major depressive disorder (MDD) is a debilitating mental health condition that affects brain structure and function, accelerating the brain's age. Repetitive transcranial magnetic stimulation (rTMS) is a treatment option for MDD patients unresponsive to standard treatments, termed treatment-resistant depression (TRD). This non-invasive therapy alters brain structure and function, but existing studies on how the brain changes in response to rTMS treatment are not conclusive and have limitations such as small sample sizes. AIM 1: My first research aim is to determine if rTMS can reduce the "brain age" of patients with TRD. Using established machine learning methods, I will estimate brain age from brain images and compare this with their chronological age. By analyzing scans before and after rTMS treatment, I aim to see if rTMS makes the brain appear younger, indicating improved brain health. AIM 2: I will identify which brain regions change the most after successful rTMS treatment by comparing the size of different brain regions before and after rTMS treatment. This will help identify critical brain areas for treatment effectiveness. AIM 3: I will use a computational brain simulation tool called The Virtual Brain (TVB) to study how large-scale brain networks change with rTMS. TVB allows for personalized brain models that can be used to study the effects of rTMS treatment on neural activity and connectivity, helping to uncover biomarkers for treatment response. SIGNIFICANCE: Major depression is common in Canada; about 11% of adults will meet criteria for MDD in their lifetime. My research aims to enhance understanding of how rTMS changes the brain to identify potential brain markers that signify clinical improvement. These insights could lead to improvements in treatment strategies for a condition that significantly impacts Canadian lives.