Abstract #0055

An optimised framework for relating microstructural MRI to multi-stain histology metrics in the mouse brain

Cristiana Tisca¹, Mohamed Tachrount¹, Adele Smart¹, Frederik J Lange¹, Amy FD Howard¹, Chaoyue Wang^1,2, Benjamin Tendler¹, Lily Qiu¹, Claire Bratley¹, Daniel Z L Kor¹, Istvan N Huszar^1,3, Javier Ballarobre-Barreiro⁴, Manuel Mayr⁴, Jason Lerch^1,5, Aurea B Martins-Bach¹, and Karla L Miller¹

¹Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom, ²SJTU-Ruijin-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, ³Athinoula A. Martinos Centre for Biomedical Imaging, Harvard University, Cambridge, MA, United States, ⁴British Heart Foundation Centre of Research Excellence, King's College London, London, UK, London, United Kingdom, ⁵Mouse Imaging Centre, Hospital for Sick Children, Toronto, ON, Canada

Synopsis

Keywords: Biology, Models, Methods, Microstructure, Validation

Motivation: Voxel-wise MRI-histology comparisons routinely rely on manual segmentations of ROIs and subjective quantitative histological metrics, an error-prone and labour-intensive process.

Goal(s): We developed an automated framework for investigating relationships between multiple MRI metrics and immunostains.

Approach: We used MRI and histology protocols optimised for ex-vivo mouse brains. We co-registered this data and derived quantitative histological metrics. We conducted voxel-wise correlations in grey and white matter between MRI (diffusion, R₂* and susceptibility) and immunohistochemistry (myelin, neurofilament and extracellular matrix proteins).

Results: Our framework successfully recapitulated known relationships for myelin and neurofilaments and, interestingly, demonstrated new relationships between MRI metrics and extracellular matrix protein stains.

Impact: Our optimised framework combines openly-shared software and MRI-histology protocols, addressing current challenges, such as obtaining high-quality histology data, MRI-to-histology registration and automatic extraction of quantitative histological metrics. This can benefit future MRI-histology studies in mouse brains prepared for ex-vivo MRI.

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