30 Hippocampal Internal Architecture Subfield Volumes Associated with Systematic Inflammatory Biomarkers in Multiple Sclerosis

Abstract

Objective:

Multiple Sclerosis (MS) affects up to 500,000 adults in the United States, with cognitive impairment present in 45%-65% of people. Studies showed hippocampal atrophy in MS, but the underlying mechanisms remain unknown. Inflammation has been proposed to play a significant role, and associations between systemic inflammatory biomarkers and hippocampal atrophy have been shown in other neurological conditions. However, research exploring serum biomarker and volumetric associations in MS are lacking. Given that conventional imaging methods lack resolution for hippocampal internal architecture (HIA), new protocols were developed. We used the High-Resolution Multiple Image Co-Registration and Averaging (HR-MICRA) method to visualize the HIA subfields. We investigated the relationship between subfield volumes generated from HR-MICRA scans and systemic serum biomarkers in MS.

Participants and Methods:

Patients with MS were recruited (N= 34, mean age= 54.6, 35.3% Black) underwent Magnetic Resonance Imaging (MRI), and serum biomarkers were obtained, specifically chosen for their potential role in MS. Inflammatory biomarkers included; granulocyte colony stimulating factor (G-CSF), interleukin-10 (IL-10), matrix metalloproteinase-9 (MMP-9), tumor necrosis factor- a (TNF- a), and growth factors; vascular endothelial growth factor (VEGF); insulin-like growth factor-1 (IGF-1), and brain derived growth factor (BDNF). Imaging was performed in a Siemens Prisma 3T scanner with a 64-channel head coil using the HR-MICRA method. Hippocampal subfields were calculated using the Automated Segmentation of Hippocampal Subfields (ASHS) package. We used the Magdeburg Young Adult 7T Atlas for sub-hippocampal structures and Penn Temporal Lobe Epilepsy T1-MRI Whole Hippocampus ASHS Atlas for general hippocampal structure and segmentation. Pearson's product-moment analyses provided correlations between biomarkers and hippocampal subfield volumes for each cerebral hemisphere. A statistical significance level of p < 0.05 was used for all analyses.

Results:

Correlations emerged between left hemisphere Cornu Ammonis (CA) 2 and G-CSF (r = -.384; p = .025); IL-10 (r = -.342; p = .048); VEGF (r = -.371; p= .031); and CA3 with IL-10 (r = -.488, p = .003); G-CSF (r = -.386; p= .024); VEGF (r = -.352; p= .041). Dentate gyrus correlated with MMP-9 (r =.416; p=.014); IL-10 (r = -.365; p =.034). BDNF was correlated with right hemisphere CA1 (r = -.417, p = .014), CA2 (r = -.497; p= .003) and CA3 (r = -.451; p=.007).

Conclusions:

In our sample of persons with MS, left hemisphere hippocampal subfield volumes were negatively correlated with inflammatory biomarkers, supporting previous reports linking inflammation to reduced brain volumes in other neurological conditions. In the right hemisphere, we found negative correlations between HIA and BDNF, suggesting a neuroprotective function for BDNF in this neurodegenerative disease. These findings in a representative sample of patients with MS highlight the need for further research exploring the relationship between HIA and systemic serum biomarkers in MS.