ABSTRACT
Background and Purpose
White matter lesions are common imaging biomarkers associated with aging and neurodegenerative diseases, yet their underlying pathology remains unclear due to limitations in imaging-based characterization. We aim to develop and validate a comprehensive workflow enabling precise MRI-guided histological sampling of white matter lesions to bridge neuroimaging and neuropathology.
Methods
We established a workflow integrating agar-sucrose brain embedding, ultrahigh field 7 Tesla (7T) MRI acquisition, reusable three-dimensional (3D) printed cutting guides, and semiautomated MRI-blockface alignment. Left hemispheric postmortem brains were stabilized in the embedding medium and scanned using optimized MRI protocols. Coronal sectioning was guided by standardized 3D-printed cutting guides, and knife traces were digitally matched to MRI planes. White matter lesions were segmented on MRI and aligned for histopathological sampling.
Results
The workflow enabled reproducible brain sectioning, minimized imaging artifacts, and achieved precise spatial alignment between MRI and histology. For demonstration, detailed results from two representative brains were presented in this article. Consistent, high-resolution MRI data facilitated accurate lesion detection and sampling. The use of standardized cutting guides and alignment protocols reduced variability and improved efficiency.
Conclusions
Our cost-effective, scalable workflow reliably linked neuroimaging findings with histological analysis, enhancing the understanding of white matter lesion pathology. This framework held significant potential for advancing translational research in aging and neurodegenerative diseases.
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This post is Copyright: Nadim Farhat,
Jinghang Li,
Jacob Berardinelli,
Mark Stauffer,
Andrea Sajewski,
Salem Alkhateeb,
Noah Schweitzer,
Hecheng Jin,
Sossena Wood,
Milos D. Ikonomovic,
Jr‐Jiun Liou,
Howard J. Aizenstein,
Joseph M. Mettenburg,
Tales Santini,
Minjie Wu,
Julia K. Kofler,
Tamer S. Ibrahim | October 23, 2025
Wiley: Journal of Neuroimaging: Table of Contents