Skip to main content
eScholarship
Open Access Publications from the University of California

UCSF

UC San Francisco Previously Published Works bannerUCSF

Detection of altered hippocampal morphology in multiple sclerosis‐associated depression using automated surface mesh modeling

Published Web Location

https://doi.org/10.1002/hbm.22154
Abstract

Depression is very common in multiple sclerosis (MS) but the underlying biological mechanisms are poorly understood. The hippocampus plays a key role in mood regulation and is implicated in the pathogenesis of depression. This study utilizes volumetric and shape analyses of the hippocampus to characterize neuroanatomical correlates of depression in MS. A cross-section of 109 female patients with MS was evaluated. Bilateral hippocampi were segmented from MRI scans (volumetric T1 -weighted, 1 mm(3) ) using automated tools. Shape analysis was performed using surface mesh modeling. Depression was assessed using the Center for Epidemiologic Studies-Depression (CES-D) scale. Eighty-three subjects were classified as low depression (CES-D 0-20) versus 26 subjects with high depression (CES-D ≥ 21). Right hippocampal volumes (P = 0.04) were smaller in the high depression versus the low depression groups, but there was no significant difference in left hippocampal volumes. Surface rendering analysis revealed that hippocampal shape changes in depressed patients with MS were clustered in the right hippocampus. Significant associations were found between right hippocampal shape and affective symptoms but not vegetative symptoms of depression. Our results suggested that regionally clustered reductions in hippocampal thickness can be detected by automated surface mesh modeling and may be a biological substrate of MS depression in female patients.

Many UC-authored scholarly publications are freely available on this site because of the UC's open access policies. Let us know how this access is important for you.

Main Content
For improved accessibility of PDF content, download the file to your device.
Current View