Multi-atlas multi-modality morphometry analysis of the South Texas Alzheimer’s Disease Research Center postmortem repository
Histopathology provides critical insights into the neurological processes inducing neurodegenerative diseases and their impact on the brain, but brain banks combining histology and neuroimaging data are difficult to create. As part of an ongoing global effort to establish new brain banks providing b...
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| Language: | English |
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Elsevier
2025-01-01
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| Series: | NeuroImage: Clinical |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2213158225000221 |
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| author | Nicolas Honnorat Mariam Mojtabai Karl Li Jinqi Li David Michael Martinez Tanweer Rashid Morgan Smith Margaret E Flanagan Elyas Fadaee Morgan Fox Torres Mallory Keating Kevin Bieniek Sudha Seshadri Mohamad Habes |
| author_facet | Nicolas Honnorat Mariam Mojtabai Karl Li Jinqi Li David Michael Martinez Tanweer Rashid Morgan Smith Margaret E Flanagan Elyas Fadaee Morgan Fox Torres Mallory Keating Kevin Bieniek Sudha Seshadri Mohamad Habes |
| author_sort | Nicolas Honnorat |
| collection | DOAJ |
| description | Histopathology provides critical insights into the neurological processes inducing neurodegenerative diseases and their impact on the brain, but brain banks combining histology and neuroimaging data are difficult to create. As part of an ongoing global effort to establish new brain banks providing both high-quality neuroimaging scans and detailed histopathology examinations, the South Texas Alzheimer’s Disease Re- search Center postmortem repository was recently created with the specific purpose of studying comorbid dementias. As the repository is reaching a milestone of two hundred brain donations and a hundred curated MRI sessions are ready for processing, robust statistical analyses can now be conducted. In this work, we report the very first morphometry analysis conducted with this new data set. We describe the processing pipelines that were specifically developed to exploit the available MRI sequences, and we explain how we addressed several postmortem neuroimaging challenges, such as the separation of brain tissues from fixative fluids, the need for updated brain atlases, and the tissue contrast changes induced by brain fixation. In general, our results establish that a combination of structural MRI sequences can provide enough informa- tion for state-of-the-art Deep Learning algorithms to almost perfectly separate brain tissues from a formalin buffered solution. Regional brain volumes are challenging to measure in postmortem scans, but robust estimates sensitive to sex differences and age trends, reflecting clinical diagnosis, neuropathology findings, and the shrinkage induced by tissue fixation can be obtained. We hope that the new processing methods developed in this work, such as the lightweight Deep Networks we used to identify the formalin signal in multimodal MRI scans and the MRI synthesis tools we used to fix our anisotropic resolution brain scans, will inspire other research teams working with postmortem MRI scans. |
| format | Article |
| id | doaj-art-cd35aceeb5c84fedb5a1b379c76fbd03 |
| institution | DOAJ |
| issn | 2213-1582 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage: Clinical |
| spelling | doaj-art-cd35aceeb5c84fedb5a1b379c76fbd032025-08-20T02:57:32ZengElsevierNeuroImage: Clinical2213-15822025-01-014510375210.1016/j.nicl.2025.103752Multi-atlas multi-modality morphometry analysis of the South Texas Alzheimer’s Disease Research Center postmortem repositoryNicolas Honnorat0Mariam Mojtabai1Karl Li2Jinqi Li3David Michael Martinez4Tanweer Rashid5Morgan Smith6Margaret E Flanagan7Elyas Fadaee8Morgan Fox Torres9Mallory Keating10Kevin Bieniek11Sudha Seshadri12Mohamad Habes13Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USANeuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USANeuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USAResearch Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USANeuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USANeuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USADepartment of Pathology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USADepartment of Pathology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USANeuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USADepartment of Pathology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USADepartment of Pathology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USANeuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA; Department of Pathology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USANeuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USANeuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA; Corresponding author.Histopathology provides critical insights into the neurological processes inducing neurodegenerative diseases and their impact on the brain, but brain banks combining histology and neuroimaging data are difficult to create. As part of an ongoing global effort to establish new brain banks providing both high-quality neuroimaging scans and detailed histopathology examinations, the South Texas Alzheimer’s Disease Re- search Center postmortem repository was recently created with the specific purpose of studying comorbid dementias. As the repository is reaching a milestone of two hundred brain donations and a hundred curated MRI sessions are ready for processing, robust statistical analyses can now be conducted. In this work, we report the very first morphometry analysis conducted with this new data set. We describe the processing pipelines that were specifically developed to exploit the available MRI sequences, and we explain how we addressed several postmortem neuroimaging challenges, such as the separation of brain tissues from fixative fluids, the need for updated brain atlases, and the tissue contrast changes induced by brain fixation. In general, our results establish that a combination of structural MRI sequences can provide enough informa- tion for state-of-the-art Deep Learning algorithms to almost perfectly separate brain tissues from a formalin buffered solution. Regional brain volumes are challenging to measure in postmortem scans, but robust estimates sensitive to sex differences and age trends, reflecting clinical diagnosis, neuropathology findings, and the shrinkage induced by tissue fixation can be obtained. We hope that the new processing methods developed in this work, such as the lightweight Deep Networks we used to identify the formalin signal in multimodal MRI scans and the MRI synthesis tools we used to fix our anisotropic resolution brain scans, will inspire other research teams working with postmortem MRI scans.http://www.sciencedirect.com/science/article/pii/S2213158225000221Postmortem MRIRegistrationDeep learning |
| spellingShingle | Nicolas Honnorat Mariam Mojtabai Karl Li Jinqi Li David Michael Martinez Tanweer Rashid Morgan Smith Margaret E Flanagan Elyas Fadaee Morgan Fox Torres Mallory Keating Kevin Bieniek Sudha Seshadri Mohamad Habes Multi-atlas multi-modality morphometry analysis of the South Texas Alzheimer’s Disease Research Center postmortem repository NeuroImage: Clinical Postmortem MRI Registration Deep learning |
| title | Multi-atlas multi-modality morphometry analysis of the South Texas Alzheimer’s Disease Research Center postmortem repository |
| title_full | Multi-atlas multi-modality morphometry analysis of the South Texas Alzheimer’s Disease Research Center postmortem repository |
| title_fullStr | Multi-atlas multi-modality morphometry analysis of the South Texas Alzheimer’s Disease Research Center postmortem repository |
| title_full_unstemmed | Multi-atlas multi-modality morphometry analysis of the South Texas Alzheimer’s Disease Research Center postmortem repository |
| title_short | Multi-atlas multi-modality morphometry analysis of the South Texas Alzheimer’s Disease Research Center postmortem repository |
| title_sort | multi atlas multi modality morphometry analysis of the south texas alzheimer s disease research center postmortem repository |
| topic | Postmortem MRI Registration Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S2213158225000221 |
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