Abstract #0702

First Application of Automated Hippocampal Subfield Segmentation using 7T MRI in Patients with Major Depressive Disorder

Judy Alper^1,2, Rui Feng³, Hadrien Dyvorne¹, Long Xie⁴, Marin Kautz⁵, Hung-Mo Lin⁶, Bradley N Delman⁷, Patrick Hof⁸, James Murrough^5,8, and Priti Balchandani¹

¹Radiology, Icahn School of Medicine At Mount Sinai, New York, NY, United States, ²Biomedical Engineering, City College of New York, New York, NY, United States, ³Neurosurgery, Icahn School of Medicine At Mount Sinai, New York, NY, United States, ⁴Biomedical Engineering, University of Pennsylvania, Philadelphia, PA, United States, ⁵Psychiatry, Icahn School of Medicine At Mount Sinai, New York, NY, United States, ⁶Population Health Science and Policy Department, Icahn School of Medicine At Mount Sinai, New York, NY, United States, ⁷Radiology, Mount Sinai Medical Center, New York, NY, United States, ⁸Neuroscience, Icahn School of Medicine At Mount Sinai, New York, NY, United States

Major depressive disorder (MDD) is a debilitating illness, which is widely prevalent. There is a need to elucidate MDD pathophysiology to better target treatment. Studies have shown association between hippocampal subfield volumes and MDD, making the subfields potential biomarkers. We use high-resolution 7T-MRI to perform effective subfield delineations and evaluate subfield volume differences between sixteen patients and sixteen controls. Using automatic segmentation of hippocampal subfields software revealed a trend towards reduced right-CA1 and right-DG subfield volumes in patients compared to controls. Identifying hippocampal subfield volumes as imaging biomarkers for MDD may help design more targeted treatments for the disease.

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