Bridging Neuroimaging and Neuropathology: A Comprehensive Workflow for Targeted Sampling of White Matter Lesions Article Swipe
Nadim Farhat
,
Jinghang Li
,
Jacob Berardinelli
,
Mark Stauffer
,
Andrea Sajewski
,
Salem Alkhateeb
,
Noah Schweitzer
,
Hecheng Jin
,
Miloš D. Ikonomović
,
Jr‐Jiun Liou
,
Howard Aizenstein
,
Joseph Mettenburg
,
Tales Santini
,
Minjie Wu
,
Julia Kofler
,
Tamer S. Ibrahim
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.06.08.25329217
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.06.08.25329217
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.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2025.06.08.25329217
- https://www.medrxiv.org/content/medrxiv/early/2025/06/09/2025.06.08.25329217.full.pdf
- OA Status
- green
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4411146802
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4411146802Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2025.06.08.25329217Digital Object Identifier
- Title
-
Bridging Neuroimaging and Neuropathology: A Comprehensive Workflow for Targeted Sampling of White Matter LesionsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-06-09Full publication date if available
- Authors
-
Nadim Farhat, Jinghang Li, Jacob Berardinelli, Mark Stauffer, Andrea Sajewski, Salem Alkhateeb, Noah Schweitzer, Hecheng Jin, Miloš D. Ikonomović, Jr‐Jiun Liou, Howard Aizenstein, Joseph Mettenburg, Tales Santini, Minjie Wu, Julia Kofler, Tamer S. IbrahimList of authors in order
- Landing page
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https://doi.org/10.1101/2025.06.08.25329217Publisher landing page
- PDF URL
-
https://www.medrxiv.org/content/medrxiv/early/2025/06/09/2025.06.08.25329217.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.medrxiv.org/content/medrxiv/early/2025/06/09/2025.06.08.25329217.full.pdfDirect OA link when available
- Concepts
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Neuropathology, Neuroimaging, Workflow, Bridging (networking), White matter, Neuroscience, Medicine, Computer science, Psychology, Pathology, Magnetic resonance imaging, Radiology, Disease, Computer network, DatabaseTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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39Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| citation_normalized_percentile.is_in_top_10_percent | False |