Quantitative Analysis of Multiparametric MRI on Ablation of STIM1 and STIM2 in a Murine Stroke Model

POSTER

Abstract

The purpose of this study is to investigate different MRI contrast mechanisms for characterizing strokes induced in mice with and without the STIM1 and STIM2 genes in their brains' microglias. Our approach integrates T2-weighted imaging, Diffusion Weighted Imaging (DWI), and Arterial Spin Labeling (ASL) to examine stroke pathophysiology. T2-weighted imaging visualizes edematous regions indicative of post-stroke inflammatory responses, DWI detects regions of necrosis or dead tissue, and ASL identifies regions that lack cerebral blood flow. Because the STIM subunits are responsible for calcium signaling in the microglia, we hypothesize that the lesion characteristics will differ between the control and experimental group. A total of 40 mice, both male and female, had a cerebral artery cauterized and were imaged 48 hours afterwards. We found that T2-weighted maps were clearly sensitive to edema, apparent diffusion coefficient (ADC) maps correctly responded to dead tissue, and perfusion maps showed the lack of blood flow to the lesion. Preliminary quantitative results suggest a statistically significant difference (p-value < 0.05) between the two groups in their lesion diffusion, pending more analysis. By detecting these small variations, these MRI methods provide a sensitive tool for probing the subtle physiological changes underlying stroke pathophysiology.

*This work was supported in part by NIH R21NS116431 funding and grants from the Focused Ultrasound Foundation (FUSF) to P. Tvrdik

Presenters

  • Anran Zhao

    • University of Virginia

Authors

  • Anran Zhao

    • University of Virginia
  • Wilson Miller

    • University of Virginia
  • Matthew Hoch

    • University of Virginia
  • Ziyuan Wang

    • University of Virginia
  • Misha Padigala

    • University of Virginia
  • Carissa Vergeres

    • University of Virginia
  • Khadijeh Sharifi

    • University of Virginia
  • Pedro Norat

    • University of Virginia
  • Nina Jannatifar

    • University of Virginia
  • Petr Tvrdik

    • University of Virginia