Back

Bridging Neuroimaging and Neuropathology: A Comprehensive Workflow for Targeted Sampling of White Matter Lesions

Farhat, N.; Li, J.; Berardinelli, J. P.; Stauffer, M.; Sajewski, A. N.; Alkhateeb, S. K.; Schweitzer, N.; Jin, H.; Ikonomovic, M. D.; Liou, J.-J.; Aizenstein, H. J.; Mettenburg, J.; Santini, T.; Wu, M.; Kofler, J.; Ibrahim, T. S.

2025-06-09 pathology
10.1101/2025.06.08.25329217 medRxiv
Show abstract

Background and PurposeWhite 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. MethodsWe establish a workflow integrating agarose-saccharose brain embedding, ultra-high field 7T MRI acquisition, reusable 3D-printed cutting guides, and semi-automated MRI-blockface alignment. Postmortem brains are stabilized in the embedding medium and scanned using optimized MRI protocols. Coronal sectioning is guided by standardized 3D-printed cutting guides, and knife traces are digitally matched to MRI planes. White matter lesions are segmented on MRI and aligned for histopathological sampling. This approach is validated in over 100 postmortem human brains. ResultsThe workflow enables reproducible brain sectioning, minimizes imaging artifacts, and achieves precise spatial alignment between MRI and histology. Consistent, high-resolution MRI data facilitated accurate lesion detection and sampling. The use of standardized cutting guides and alignment protocols reduce variability and improve efficiency. ConclusionsOur cost-effective, scalable workflow reliably links neuroimaging findings with histological analysis, enhancing the understanding of white matter lesion pathology. This framework holds significant potential for advancing translational research in aging and neurodegenerative diseases.

Published in Journal of Neuroimaging · not in our set (fewer than 10 published preprints to learn from) · training set

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.