Postmortem human brain at high-resolution 7 tesla MRI

Pulkit Khandelwal et al.,

University of Pennsylvania
Paper arXiv Video Code Docker
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Abstract

To discover AD-specific structural patterns associated with neuropathology/neurodegeneration, we introduce and leverage a postmortem whole-hemisphere MRI dataset to link neuropathological markers derived from gold-standard histopathology examination with morphometry measurements derived from structural MRI. We present a set of computational image analysis methods and pipelines to analyze high-resolution postmortem MRI consistently. Specifically, we present a fast, robust, and automated voxel-based tissue segmentation and surface-based anatomical parcellation pipeline for whole cerebral hemisphere 7 tesla t2w postmortem MRI in diseased populations. Next, by constructing a population-level postmortem ex vivo MRI template, we conduct both voxel based and surface-based point-wise statistical studies within a common coordinate system to help discover disease-specific patterns of neurodegeneration by linking morphometry measurements with neuropathology markers derived from MRI and histopathology, respectively. We then present a framework to align within-subject postmortem MRI to its corresponding antemortem MRI, which would enable the exchange of information from postmortem to corresponding antemortem MRI. This dissertation presents a one-of-a-kind analysis that compares morphometry/pathology in matched postmortem and antemortem MRI specimens. Our results suggest that high-resolution postmortem 7 tesla MRI yields localized atrophy measures that are more sensitive to tau pathology and neuronal loss in Alzheimer’s disease than corresponding measures on antemortem 3 tesla MRI.

List of publications

  • Khandelwal, P. (2025). Postmortem Image Analysis of the Human Brain to Characterize Alzheimer’s Disease and Related Dementias (Doctoral dissertation, University of Pennsylvania).
  • Khandelwal, P., Duong, M. T., Sadaghiani, S., Lim, S., Denning, A. E., Chung, E., ... & Yushkevich, P. A. (2024). Automated deep learning segmentation of high-resolution 7 tesla postmortem MRI for quantitative analysis of structure-pathology correlations in neurodegenerative diseases. Imaging Neuroscience, 2, 1–30.
  • Khandelwal, P., et al. (2024). Surface-Based Parcellation and Vertex-wise Analysis of Ultra High-resolution ex vivo 7 tesla MRI in Alzheimer’s disease and related dementias. In International Workshop on Machine Learning in Clinical Neuroimaging. Springer, Cham (MICCAI 2024).
  • Khandelwal, P., et al. (2025). VIOLET: Volumetric Image registration via Optimization and Learning for Efficient image Translation. In International Workshop on Simulation and Synthesis in Medical Imaging. Springer Nature Switzerland, Cham (MICCAI 2025).
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Teaser video

Surface-based parcelaltion and vertex-wise analysis

We adapted and developed a fast and easy-to-use automated surface-based pipeline to parcellate, for the first time, ultra high-resolution ex vivo brain tissue at the native subjectspace resolution using the Desikan-Killiany-Tourville (DKT) brain atlas. This allows us to perform vertex-wise analysis in the template space and thereby link morphometry measures with pathology measurements derived from histology.

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Schematic of the developed pipeline based on deep learning volumetric segmentations and surface-based modeling for parcellations of ex vivo whole hemisphere 0.3 mm3 7T MRI.

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Ex vivo MRI segmentations and parcellations. Axial, coronal and sagittal viewing planes of ex vivo MRI at 0.3 mm3 resolution for three subjects (A, B and C) with corresponding DKT volumetric segmentations and surface-based parcellations on pial and inflated surfaces for the medial and lateral views in native subject space resolution. Our method is able to correctly delineate the brain even in regions where the MR signal contrast is low in the anterior and the posterior brain MRI due to artifacts in acquisition protocol.

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Spearman’s correlation plots between mean ROI thickness (mm) and neuropathological ratings in native subject-space. We observe significant negative correlation with global ratings of amyloid-β, Braak staging, CERAD, and the semi-quantitative ratings of the medial temporal lobe (MTL) neuronal loss and tau pathology. All the analysis were covaried for age, sex and postmortem interval (PMI) for the entire cohort of 82 subjects.

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Template-space vertex-wise morphometry-pathology correlations. Vertex-wise group analysis was performed to fit a generalized linear model (GLM). Shown are the statistical map (t-statistics) of the correlation between cortical thickness (mm) and with global ratings of amyloid-β, Braak staging, CERAD, and semiquantitative ratings of the medial temporal lobe (MTL) neuronal loss and tau pathology, with age, sex and postmortem interval (PMI) as covariates across all 82 subjects. The clusters outlined in black indicate regions significant correlations (p<0.05) were observed after FWER correction for multiple comparisons.

Acknowledgements

We gratefully acknowledge the tissue donors and their families. We also thank all the staff at the Center for Neurodegenerative Research (University of Pennsylvania) for performing the autopsies and making the tissue available for this project. This work was supported in part by the National Institute of Health Grants: P30 AG072979, R01 AG056014, RF1 AG069474, R01 AG054519, P01 AG017586, U19 AG062418.

BibTeX

@article{khandelwal2024automated,
  title={Automated deep learning segmentation of high-resolution 7 tesla postmortem MRI for quantitative analysis of structure-pathology correlations in neurodegenerative diseases},
  author={Khandelwal, Pulkit and Duong, Michael Tran and Sadaghiani, Shokufeh and Lim, Sydney and Denning, Amanda E and Chung, Eunice and Ravikumar, Sadhana and Arezoumandan, Sanaz and Peterson, Claire and Bedard, Madigan and others},
  journal={Imaging Neuroscience},
  year={2024},
  publisher={MIT Press One Broadway, 12th Floor, Cambridge, Massachusetts 02142, USA}
}