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 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.

Results The 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.

Conclusions Our 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.

The authors have declared no competing interest.

This research was supported by the National Institutes of Health under the grant numbers: R01 AG063525, R01 MH111265, T32 MH119168, T32 HL007560, P01 AG025204, and U19 AG068054. This research was also supported in part by the University of Pittsburgh Center for Research Computing and Data, RRID:SCR_022735, through the resources provided. Specifically, this work used the H2P cluster, which is supported by NSF award number OAC-2117681.

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↵Ψ Senior author

Funding: This work was supported by the National Institutes of Health grants R01 AG063525, R01 MH111265, T32 MH119168, T32 HL007560, P01 AG025204, and U19 AG068054.

All data produced in the present study are available upon reasonable request to the authors.