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B N A, Li K, Honnorat N, Rashid T, Wang D, Li J, Fadaee E, Charisis S, Walker JM, Richardson TE, Wolk DA, Fox PT, Cavazos JE, Seshadri S, Wisse LEM, Habes M. Convolutional Neural Networks for the segmentation of hippocampal structures in postmortem MRI scans. J Neurosci Methods 2025; 415:110359. [PMID: 39755177 DOI: 10.1016/j.jneumeth.2024.110359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2024] [Revised: 12/06/2024] [Accepted: 12/26/2024] [Indexed: 01/06/2025]
Abstract
BACKGROUND The hippocampus plays a crucial role in memory and is one of the first structures affected by Alzheimer's disease. Postmortem MRI offers a way to quantify the alterations by measuring the atrophy of the inner structures of the hippocampus. Unfortunately, the manual segmentation of hippocampal subregions required to carry out these measures is very time-consuming. NEW METHOD In this study, we explore the use of fully automated methods relying on state-of-the-art Deep Learning approaches to produce these annotations. More specifically, we propose a new segmentation framework made of a set of encoder-decoder blocks embedding self-attention mechanisms and atrous spatial pyramidal pooling to produce better maps of the hippocampus and identify four hippocampal regions: the dentate gyrus, the hippocampal head, the hippocampal body, and the hippocampal tail. RESULTS Trained using slices extracted from 15 postmortem T1-weighted, T2-weighted, and susceptibility-weighted MRI scans, our new approach produces hippocampus parcellations that are better aligned with the manually delineated parcellations provided by neuroradiologists. COMPARISON WITH EXISTING METHODS Four standard deep learning segmentation architectures: UNet, Double UNet, Attention UNet, and Multi-resolution UNet have been utilized for the qualitative and quantitative comparison of the proposed hippocampal region segmentation model. CONCLUSIONS Postmortem MRI serves as a highly valuable neuroimaging technique for examining the effects of neurodegenerative diseases on the intricate structures within the hippocampus. This study opens the way to large sample-size postmortem studies of the hippocampal substructures.
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Affiliation(s)
- Anoop B N
- Neuroimage 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 Information and Communication Technology, Manipal Institute of Technology, Manipal, Manipal Academy of Higher Education (MAHE), Manipal, Karnaaka, 576104, India
| | - Karl Li
- Neuroimage 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
| | - Nicolas Honnorat
- Neuroimage 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
| | - Tanweer Rashid
- Neuroimage 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
| | - Di Wang
- Neuroimage 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
| | - Jinqi Li
- Neuroimage 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
| | - Elyas Fadaee
- Neuroimage 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
| | - Sokratis Charisis
- Neuroimage 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
| | - Jamie M Walker
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Peter T Fox
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - José E Cavazos
- Neuroimage 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 Neurology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Sudha Seshadri
- Neuroimage 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
| | - Laura E M Wisse
- Department of Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Mohamad Habes
- Neuroimage 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; Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.
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Wang Y, Chen L, Wu Z, Hung SC, Smith JK, Wang L, Li T, Lin W, Li G. Surface Expansion Regionalization of the Hippocampus in Early Brain Development. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2025.02.22.639699. [PMID: 40060560 PMCID: PMC11888342 DOI: 10.1101/2025.02.22.639699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 03/20/2025]
Abstract
The hippocampal formation is implicated in a myriad of crucial functions, particularly centered around memory and emotion, with distinct subdivisions fulfilling specific roles. However, there is no consensus on the spatial organization of these subdivisions, given that the functional connectivity and gene expression-based parcellation along its longitudinal axis differs from the histology-based parcellation along its medial-lateral axis. The dynamic nonuniform surface expansion of the hippocampus during early development reflects the underlying changes of microstructure and functional connectivity, providing important clues on hippocampal subdivisions. Moreover, the thin and convoluted properties bring out the hippocampal maturity largely in the form of expanding surface area. We thus unprecedentedly explore the development-based surface area regionalization and patterns of the hippocampus by leveraging 513 high-quality longitudinal MRI scans during the first two postnatal years. Our findings imply two discrete hippocampal developmental patterns, featuring one pattern of subdivisions along the anterior-posterior axis (head, regions 1 and 5; body, regions 2, 4, 6, and 7; tail, region 3) and the other one along the medial-lateral axis (subiculum, regions 4, 5, and 6; CA fields, regions 1, 2, and 7). Most of the resulting 7 subdivisions exhibit region-specific and nonlinear spatiotemporal surface area expansion patterns with an initial high growth, followed by a transition to low increase. Each subregion displays bilaterally symmetric pattern. The medial portion of the hippocampal head experiences the most rapid surface area expansion. These results provide important references for exploring the fine-grained organization and development of the hippocampus and its intricate cognitions.
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Affiliation(s)
- Ya Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, 27599, USA
| | - Liangjun Chen
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, 27599, USA
| | - Zhengwang Wu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, 27599, USA
| | - Sheng-Che Hung
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, 27599, USA
| | - J Keith Smith
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, 27599, USA
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, 27599, USA
| | - Tengfei Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, 27599, USA
| | - Weili Lin
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, 27599, USA
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, 27599, USA
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Sadeghpour N, Lim SA, Wuestefeld A, Denning AE, Ittyerah R, Trotman W, Chung E, Sadaghiani S, Prabhakaran K, Bedard ML, Ohm DT, Artacho-Pérula E, Iñiguez de Onzoño Martin MM, Muñoz M, Molina Romero FJ, Delgado González JC, Jiménez MDMA, Rabal MDPM, Insausti Serrano AM, González NV, Sánchez SC, de la Rosa Prieto C, Insausti R, McMillan C, Lee EB, Detre JA, Das SR, Xie L, Tisdall MD, Irwin DJ, Wolk DA, Yushkevich PA, Wisse LEM. Developing an anatomically valid segmentation protocol for anterior regions of the medial temporal lobe for neurodegenerative diseases. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2025.02.11.637506. [PMID: 39990318 PMCID: PMC11844510 DOI: 10.1101/2025.02.11.637506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 02/25/2025]
Abstract
Background The anterior portion of the medial temporal lobe (MTL) is one of the first regions targeted by pathology in sporadic Alzheimer's disease (AD) and Limbic-predominant Age-related TDP-43 Encephalopathy (LATE) indicating a potential for metrics from this region to serve as imaging biomarkers. Leveraging a unique post-mortem dataset of histology and magnetic resonance imaging (MRI) scans we aimed to 1) develop an anatomically valid segmentation protocol for anterior entorhinal cortex (ERC), Brodmann Area (BA) 35, and BA36 for in vivo 3 tesla (T) MRI and 2) incorporate this protocol in an automated approach. Methods We included 20 cases (61-97 years old, 50% females) with and without neurodegenerative diseases (11 vs. 9 cases) to ensure generalizability of the developed protocol. Digitized MTL Nissl-stained coronal histology sections from these cases were annotated and registered to same-subject post-mortem MRI. The protocol was developed by determining the location of histological borders of the MTL cortices in relation to anatomical landmarks. Subsequently the protocol was applied to 15 cases twice, with a 2-week interval, to assess intra-rater reliability with the Dice Similarity Index (DSI). Thereafter it was implemented in our in-house Automatic Segmentation of Hippocampal Subfields (ASHS)-T1 approach and evaluated with DSIs. Results The anterior histological border distances of ERC, BA35 and BA36 were evaluated with respect to various anatomical landmarks and the distance relative to the beginning of the hippocampus was chosen. To formulate segmentation rules, we examined the histological sections for the location of borders in relationship to anatomical landmarks in the coronal sections. The DSI for the anterior MTL cortices for the intra-rater reliability was 0.85-0.88 and for the ASHS-T1 against the manual segmentation was 0.62-0.65. Discussion We developed a reliable segmentation protocol and incorporated it in an automated approach. Given the vulnerability of the anterior MTL cortices to tau deposition in AD and LATE, the updated approach is expected to improve imaging biomarkers for these diseases.
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Affiliation(s)
- Niyousha Sadeghpour
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sydney A. Lim
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | | | - Amanda E. Denning
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ranjit Ittyerah
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Winifred Trotman
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Eunice Chung
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Shokufeh Sadaghiani
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Karthik Prabhakaran
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Madigan L. Bedard
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Daniel T. Ohm
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Emilio Artacho-Pérula
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, University of Castilla-La Mancha and Institute for Biomedicine, Albacete, Spain
| | | | - Monica Muñoz
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, University of Castilla-La Mancha and Institute for Biomedicine, Albacete, Spain
| | - Francisco Javier Molina Romero
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, University of Castilla-La Mancha and Institute for Biomedicine, Albacete, Spain
| | - José Carlos Delgado González
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, University of Castilla-La Mancha and Institute for Biomedicine, Albacete, Spain
| | - María del Mar Arroyo Jiménez
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, University of Castilla-La Mancha and Institute for Biomedicine, Albacete, Spain
| | - Maria del Pilar Marcos Rabal
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, University of Castilla-La Mancha and Institute for Biomedicine, Albacete, Spain
| | - Ana María Insausti Serrano
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, University of Castilla-La Mancha and Institute for Biomedicine, Albacete, Spain
| | - Noemí Vilaseca González
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, University of Castilla-La Mancha and Institute for Biomedicine, Albacete, Spain
| | - Sandra Cebada Sánchez
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, University of Castilla-La Mancha and Institute for Biomedicine, Albacete, Spain
| | - Carlos de la Rosa Prieto
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, University of Castilla-La Mancha and Institute for Biomedicine, Albacete, Spain
| | - Ricardo Insausti
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, University of Castilla-La Mancha and Institute for Biomedicine, Albacete, Spain
| | - Corey McMillan
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Edward B. Lee
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - John A. Detre
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sandhitsu R. Das
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Long Xie
- Department of Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ 08540, USA
| | - M. Dylan Tisdall
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - David J. Irwin
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - David A. Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Paul A. Yushkevich
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Laura EM. Wisse
- Department of Clinical Sciences, Lund University, Lund, Sweden
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4
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Denning AE, Ittyerah R, Levorse LM, Sadeghpour N, Athalye C, Chung E, Ravikumar S, Dong M, Duong MT, Li Y, Ilesanmi A, Sreepada LP, Sabatini P, Lowe M, Bahena A, Zablah J, Spencer BE, Watanabe R, Kim B, Sørensen MH, Khandelwal P, Brown C, Hrybouski S, Xie SX, de Flores R, Robinson JL, Schuck T, Ohm DT, Arezoumandan S, Porta S, Detre JA, Insausti R, Wisse LEM, Das SR, Irwin DJ, Lee EB, Wolk DA, Yushkevich PA. Association of quantitative histopathology measurements with antemortem medial temporal lobe cortical thickness in the Alzheimer's disease continuum. Acta Neuropathol 2024; 148:37. [PMID: 39227502 PMCID: PMC11371872 DOI: 10.1007/s00401-024-02789-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Revised: 08/07/2024] [Accepted: 08/15/2024] [Indexed: 09/05/2024]
Abstract
The medial temporal lobe (MTL) is a hotspot for neuropathology, and measurements of MTL atrophy are often used as a biomarker for cognitive decline associated with neurodegenerative disease. Due to the aggregation of multiple proteinopathies in this region, the specific relationship of MTL atrophy to distinct neuropathologies is not well understood. Here, we develop two quantitative algorithms using deep learning to measure phosphorylated tau (p-tau) and TDP-43 (pTDP-43) pathology, which are both known to accumulate in the MTL and are associated with MTL neurodegeneration. We focus on these pathologies in the context of Alzheimer's disease (AD) and limbic predominant age-related TDP-43 encephalopathy (LATE) and apply our deep learning algorithms to distinct histology sections, on which MTL subregions were digitally annotated. We demonstrate that both quantitative pathology measures show high agreement with expert visual ratings of pathology and discriminate well between pathology stages. In 140 cases with antemortem MR imaging, we compare the association of semi-quantitative and quantitative postmortem measures of these pathologies in the hippocampus with in vivo structural measures of the MTL and its subregions. We find widespread associations of p-tau pathology with MTL subregional structural measures, whereas pTDP-43 pathology had more limited associations with the hippocampus and entorhinal cortex. Quantitative measurements of p-tau pathology resulted in a significantly better model of antemortem structural measures than semi-quantitative ratings and showed strong associations with cortical thickness and volume. By providing a more granular measure of pathology, the quantitative p-tau measures also showed a significant negative association with structure in a severe AD subgroup where semi-quantitative ratings displayed a ceiling effect. Our findings demonstrate the advantages of using quantitative neuropathology to understand the relationship of pathology to structure, particularly for p-tau, and motivate the use of quantitative pathology measurements in future studies.
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Affiliation(s)
- Amanda E Denning
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.
| | - Ranjit Ittyerah
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Lisa M Levorse
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Chinmayee Athalye
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Eunice Chung
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Sadhana Ravikumar
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Mengjin Dong
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael Tran Duong
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Yue Li
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Ademola Ilesanmi
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Lasya P Sreepada
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Philip Sabatini
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - MaKayla Lowe
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Alejandra Bahena
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Jamila Zablah
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Barbara E Spencer
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Ryohei Watanabe
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Neurodegenerative Disease Research, Institute On Aging, University of Pennsylvania, Philadelphia, PA, USA
| | - Boram Kim
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Neurodegenerative Disease Research, Institute On Aging, University of Pennsylvania, Philadelphia, PA, USA
| | - Maja Højvang Sørensen
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Neurodegenerative Disease Research, Institute On Aging, University of Pennsylvania, Philadelphia, PA, USA
| | - Pulkit Khandelwal
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Christopher Brown
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Sharon X Xie
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Robin de Flores
- UMR-S U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, INSERM, Caen-Normandie University, GIP Cyceron, Caen, France
| | - John L Robinson
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Neurodegenerative Disease Research, Institute On Aging, University of Pennsylvania, Philadelphia, PA, USA
| | - Theresa Schuck
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Neurodegenerative Disease Research, Institute On Aging, University of Pennsylvania, Philadelphia, PA, USA
| | - Daniel T Ohm
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Sanaz Arezoumandan
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Sílvia Porta
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Neurodegenerative Disease Research, Institute On Aging, University of Pennsylvania, Philadelphia, PA, USA
| | - John A Detre
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Ricardo Insausti
- Human Neuroanatomy Lab, University of Castilla La Mancha, Albacete, Spain
| | - Laura E M Wisse
- Department of Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Sandhitsu R Das
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - David J Irwin
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Edward B Lee
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Neurodegenerative Disease Research, Institute On Aging, University of Pennsylvania, Philadelphia, PA, USA
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Paul A Yushkevich
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
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5
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Ravikumar S, Denning AE, Lim S, Chung E, Sadeghpour N, Ittyerah R, Wisse LEM, Das SR, Xie L, Robinson JL, Schuck T, Lee EB, Detre JA, Tisdall MD, Prabhakaran K, Mizsei G, de Onzono Martin MMI, Arroyo Jiménez MDM, Mũnoz M, Marcos Rabal MDP, Cebada Sánchez S, Delgado González JC, de la Rosa Prieto C, Irwin DJ, Wolk DA, Insausti R, Yushkevich PA. Postmortem imaging reveals patterns of medial temporal lobe vulnerability to tau pathology in Alzheimer's disease. Nat Commun 2024; 15:4803. [PMID: 38839876 PMCID: PMC11153494 DOI: 10.1038/s41467-024-49205-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 05/28/2024] [Indexed: 06/07/2024] Open
Abstract
Our current understanding of the spread and neurodegenerative effects of tau neurofibrillary tangles (NFTs) within the medial temporal lobe (MTL) during the early stages of Alzheimer's Disease (AD) is limited by the presence of confounding non-AD pathologies and the two-dimensional (2-D) nature of conventional histology studies. Here, we combine ex vivo MRI and serial histological imaging from 25 human MTL specimens to present a detailed, 3-D characterization of quantitative NFT burden measures in the space of a high-resolution, ex vivo atlas with cytoarchitecturally-defined subregion labels, that can be used to inform future in vivo neuroimaging studies. Average maps show a clear anterior to poster gradient in NFT distribution and a precise, spatial pattern with highest levels of NFTs found not just within the transentorhinal region but also the cornu ammonis (CA1) subfield. Additionally, we identify granular MTL regions where measures of neurodegeneration are likely to be linked to NFTs specifically, and thus potentially more sensitive as early AD biomarkers.
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Affiliation(s)
- Sadhana Ravikumar
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.
| | - Amanda E Denning
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Sydney Lim
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Eunice Chung
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Ranjit Ittyerah
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Laura E M Wisse
- Institute for Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Sandhitsu R Das
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Long Xie
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - John L Robinson
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Theresa Schuck
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Edward B Lee
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - John A Detre
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - M Dylan Tisdall
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Gabor Mizsei
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | - Monica Mũnoz
- Human Neuroanatomy Laboratory, University of Castilla La Mancha, Albacete, Spain
| | | | | | | | | | - David J Irwin
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Ricardo Insausti
- Human Neuroanatomy Laboratory, University of Castilla La Mancha, Albacete, Spain
| | - Paul A Yushkevich
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.
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6
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Khandelwal P, Duong MT, Sadaghiani S, Lim S, Denning AE, Chung E, Ravikumar S, Arezoumandan S, Peterson C, Bedard M, Capp N, Ittyerah R, Migdal E, Choi G, Kopp E, Loja B, Hasan E, Li J, Bahena A, Prabhakaran K, Mizsei G, Gabrielyan M, Schuck T, Trotman W, Robinson J, Ohm DT, Lee EB, Trojanowski JQ, McMillan C, Grossman M, Irwin DJ, Detre JA, Tisdall MD, Das SR, Wisse LEM, Wolk DA, Yushkevich PA. Automated deep learning segmentation of high-resolution 7 Tesla postmortem MRI for quantitative analysis of structure-pathology correlations in neurodegenerative diseases. IMAGING NEUROSCIENCE (CAMBRIDGE, MASS.) 2024; 2:1-30. [PMID: 39301426 PMCID: PMC11409836 DOI: 10.1162/imag_a_00171] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 04/01/2024] [Accepted: 04/15/2024] [Indexed: 09/22/2024]
Abstract
Postmortem MRI allows brain anatomy to be examined at high resolution and to link pathology measures with morphometric measurements. However, automated segmentation methods for brain mapping in postmortem MRI are not well developed, primarily due to limited availability of labeled datasets, and heterogeneity in scanner hardware and acquisition protocols. In this work, we present a high-resolution dataset of 135 postmortem human brain tissue specimens imaged at 0.3 mm3 isotropic using a T2w sequence on a 7T whole-body MRI scanner. We developed a deep learning pipeline to segment the cortical mantle by benchmarking the performance of nine deep neural architectures, followed by post-hoc topological correction. We evaluate the reliability of this pipeline via overlap metrics with manual segmentation in 6 specimens, and intra-class correlation between cortical thickness measures extracted from the automatic segmentation and expert-generated reference measures in 36 specimens. We also segment four subcortical structures (caudate, putamen, globus pallidus, and thalamus), white matter hyperintensities, and the normal appearing white matter, providing a limited evaluation of accuracy. We show generalizing capabilities across whole-brain hemispheres in different specimens, and also on unseen images acquired at 0.28 mm3 and 0.16 mm3 isotropic T2*w fast low angle shot (FLASH) sequence at 7T. We report associations between localized cortical thickness and volumetric measurements across key regions, and semi-quantitative neuropathological ratings in a subset of 82 individuals with Alzheimer's disease (AD) continuum diagnoses. Our code, Jupyter notebooks, and the containerized executables are publicly available at the project webpage (https://pulkit-khandelwal.github.io/exvivo-brain-upenn/).
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Affiliation(s)
- Pulkit Khandelwal
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
- Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA, United States
| | - Michael Tran Duong
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
| | - Shokufeh Sadaghiani
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Sydney Lim
- Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA, United States
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Amanda E. Denning
- Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA, United States
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Eunice Chung
- Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA, United States
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Sadhana Ravikumar
- Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA, United States
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Sanaz Arezoumandan
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Claire Peterson
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Madigan Bedard
- Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA, United States
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Noah Capp
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Ranjit Ittyerah
- Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA, United States
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Elyse Migdal
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Grace Choi
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Emily Kopp
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Bridget Loja
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Eusha Hasan
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Jiacheng Li
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Alejandra Bahena
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Karthik Prabhakaran
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Gabor Mizsei
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Marianna Gabrielyan
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Theresa Schuck
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Winifred Trotman
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - John Robinson
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Daniel T. Ohm
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Edward B. Lee
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - John Q. Trojanowski
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Corey McMillan
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Murray Grossman
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - David J. Irwin
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - John A. Detre
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - M. Dylan Tisdall
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Sandhitsu R. Das
- Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | | | - David A. Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Paul A. Yushkevich
- Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA, United States
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
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7
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Liu D, Lu J, Wei L, Yao M, Yang H, Lv P, Wang H, Zhu Y, Zhu Z, Zhang X, Chen J, Yang QX, Zhang B. Olfactory deficit: a potential functional marker across the Alzheimer's disease continuum. Front Neurosci 2024; 18:1309482. [PMID: 38435057 PMCID: PMC10907997 DOI: 10.3389/fnins.2024.1309482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Accepted: 02/02/2024] [Indexed: 03/05/2024] Open
Abstract
Alzheimer's disease (AD) is a prevalent form of dementia that affects an estimated 32 million individuals globally. Identifying early indicators is vital for screening at-risk populations and implementing timely interventions. At present, there is an urgent need for early and sensitive biomarkers to screen individuals at risk of AD. Among all sensory biomarkers, olfaction is currently one of the most promising indicators for AD. Olfactory dysfunction signifies a decline in the ability to detect, identify, or remember odors. Within the spectrum of AD, impairment in olfactory identification precedes detectable cognitive impairments, including mild cognitive impairment (MCI) and even the stage of subjective cognitive decline (SCD), by several years. Olfactory impairment is closely linked to the clinical symptoms and neuropathological biomarkers of AD, accompanied by significant structural and functional abnormalities in the brain. Olfactory behavior examination can subjectively evaluate the abilities of olfactory identification, threshold, and discrimination. Olfactory functional magnetic resonance imaging (fMRI) can provide a relatively objective assessment of olfactory capabilities, with the potential to become a promising tool for exploring the neural mechanisms of olfactory damage in AD. Here, we provide a timely review of recent literature on the characteristics, neuropathology, and examination of olfactory dysfunction in the AD continuum. We focus on the early changes in olfactory indicators detected by behavioral and fMRI assessments and discuss the potential of these techniques in MCI and preclinical AD. Despite the challenges and limitations of existing research, olfactory dysfunction has demonstrated its value in assessing neurodegenerative diseases and may serve as an early indicator of AD in the future.
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Affiliation(s)
- Dongming Liu
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Jiaming Lu
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Liangpeng Wei
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Mei Yao
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Huiquan Yang
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Pin Lv
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Haoyao Wang
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Yajing Zhu
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Zhengyang Zhu
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Xin Zhang
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Jiu Chen
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Qing X. Yang
- Department of Radiology, Center for NMR Research, Penn State University College of Medicine, Hershey, PA, United States
| | - Bing Zhang
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
- State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, China
- Jiangsu Key Laboratory of Molecular Medicine, Nanjing, China
- Institute of Brain Science, Nanjing University, Nanjing, China
- Jiangsu Provincial Medical Key Discipline (Laboratory), Nanjing, China
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8
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Nelson PT, Schneider JA, Jicha GA, Duong MT, Wolk DA. When Alzheimer's is LATE: Why Does it Matter? Ann Neurol 2023; 94:211-222. [PMID: 37245084 PMCID: PMC10516307 DOI: 10.1002/ana.26711] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Revised: 05/07/2023] [Accepted: 05/18/2023] [Indexed: 05/29/2023]
Abstract
Recent therapeutic advances provide heightened motivation for accurate diagnosis of the underlying biologic causes of dementia. This review focuses on the importance of clinical recognition of limbic-predominant age-related TDP-43 encephalopathy (LATE). LATE affects approximately one-quarter of older adults and produces an amnestic syndrome that is commonly mistaken for Alzheimer's disease (AD). Although AD and LATE often co-occur in the same patients, these diseases differ in the protein aggregates driving neuropathology (Aβ amyloid/tau vs TDP-43). This review discusses signs and symptoms, relevant diagnostic testing, and potential treatment implications for LATE that may be helpful for physicians, patients, and families. ANN NEUROL 2023;94:211-222.
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Affiliation(s)
| | | | | | | | - David A. Wolk
- University of Pennsylvania Alzheimer’s Disease Research Center
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9
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Georgiadis M, Menzel M, Reuter JA, Born DE, Kovacevich SR, Alvarez D, Taghavi HM, Schroeter A, Rudin M, Gao Z, Guizar-Sicairos M, Weiss TM, Axer M, Rajkovic I, Zeineh MM. Imaging crossing fibers in mouse, pig, monkey, and human brain using small-angle X-ray scattering. Acta Biomater 2023; 164:317-331. [PMID: 37098400 PMCID: PMC10811447 DOI: 10.1016/j.actbio.2023.04.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 04/15/2023] [Accepted: 04/18/2023] [Indexed: 04/27/2023]
Abstract
Myelinated axons (nerve fibers) efficiently transmit signals throughout the brain via action potentials. Multiple methods that are sensitive to axon orientations, from microscopy to magnetic resonance imaging, aim to reconstruct the brain's structural connectome. As billions of nerve fibers traverse the brain with various possible geometries at each point, resolving fiber crossings is necessary to generate accurate structural connectivity maps. However, doing so with specificity is a challenging task because signals originating from oriented fibers can be influenced by brain (micro)structures unrelated to myelinated axons. X-ray scattering can specifically probe myelinated axons due to the periodicity of the myelin sheath, which yields distinct peaks in the scattering pattern. Here, we show that small-angle X-ray scattering (SAXS) can be used to detect myelinated, axon-specific fiber crossings. We first demonstrate the capability using strips of human corpus callosum to create artificial double- and triple-crossing fiber geometries, and we then apply the method in mouse, pig, vervet monkey, and human brains. We compare results to polarized light imaging (3D-PLI), tracer experiments, and to outputs from diffusion MRI that sometimes fails to detect crossings. Given its specificity, capability of 3-dimensional sampling and high resolution, SAXS could serve as a ground truth for validating fiber orientations derived using diffusion MRI as well as microscopy-based methods. STATEMENT OF SIGNIFICANCE: To study how the nerve fibers in our brain are interconnected, scientists need to visualize their trajectories, which often cross one another. Here, we show the unique capacity of small-angle X-ray scattering (SAXS) to study these fiber crossings without use of labeling, taking advantage of SAXS's specificity to myelin - the insulating sheath that is wrapped around nerve fibers. We use SAXS to detect double and triple crossing fibers and unveil intricate crossings in mouse, pig, vervet monkey, and human brains. This non-destructive method can uncover complex fiber trajectories and validate other less specific imaging methods (e.g., MRI or microscopy), towards accurate mapping of neuronal connectivity in the animal and human brain.
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Affiliation(s)
- Marios Georgiadis
- Department of Radiology, Stanford School of Medicine, Stanford, CA, USA; Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland.
| | - Miriam Menzel
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich GmbH, Jülich 52425, Germany; Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands
| | - Jan A Reuter
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich GmbH, Jülich 52425, Germany
| | - Donald E Born
- Department of Pathology, Stanford School of Medicine, Stanford, CA, USA
| | | | - Dario Alvarez
- Department of Radiology, Stanford School of Medicine, Stanford, CA, USA
| | | | - Aileen Schroeter
- Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland
| | - Markus Rudin
- Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland
| | - Zirui Gao
- Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland
| | | | - Thomas M Weiss
- SLAC National Accelerator Laboratory, Stanford Synchrotron Radiation Lightsource, USA
| | - Markus Axer
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich GmbH, Jülich 52425, Germany
| | - Ivan Rajkovic
- SLAC National Accelerator Laboratory, Stanford Synchrotron Radiation Lightsource, USA
| | - Michael M Zeineh
- Department of Radiology, Stanford School of Medicine, Stanford, CA, USA
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10
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Sadaghiani S, Trotman W, Lim SA, Chung E, Ittyerah R, Ravikumar S, Khandelwal P, Prabhakaran K, Lavery ML, Ohm DT, Gabrielyan M, Das SR, Schuck T, Capp N, Peterson CS, Migdal E, Artacho-Pérula E, del Mar Arroyo Jiménez M, del Pilar Marcos Rabal M, Sánchez SC, de la Rosa Prieto C, Parada MC, Insausti R, Robinson JL, McMillan C, Grossman M, Lee EB, Detre JA, Xie SX, Trojanowski JQ, Tisdall MD, Wisse LEM, Irwin DJ, Wolk DA, Yushkevich PA. Associations of phosphorylated tau pathology with whole-hemisphere ex vivo morphometry in 7 tesla MRI. Alzheimers Dement 2023; 19:2355-2364. [PMID: 36464907 PMCID: PMC10239526 DOI: 10.1002/alz.12884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 09/29/2022] [Accepted: 10/27/2022] [Indexed: 12/12/2022]
Abstract
INTRODUCTION Neurodegenerative disorders are associated with different pathologies that often co-occur but cannot be measured specifically with in vivo methods. METHODS Thirty-three brain hemispheres from donors with an Alzheimer's disease (AD) spectrum diagnosis underwent T2-weighted magnetic resonance imaging (MRI). Gray matter thickness was paired with histopathology from the closest anatomic region in the contralateral hemisphere. RESULTS Partial Spearman correlation of phosphorylated tau and cortical thickness with TAR DNA-binding protein 43 (TDP-43) and α-synuclein scores, age, sex, and postmortem interval as covariates showed significant relationships in entorhinal and primary visual cortices, temporal pole, and insular and posterior cingulate gyri. Linear models including Braak stages, TDP-43 and α-synuclein scores, age, sex, and postmortem interval showed significant correlation between Braak stage and thickness in the parahippocampal gyrus, entorhinal cortex, and Broadman area 35. CONCLUSION We demonstrated an association of measures of AD pathology with tissue loss in several AD regions despite a limited range of pathology in these cases. HIGHLIGHTS Neurodegenerative disorders are associated with co-occurring pathologies that cannot be measured specifically with in vivo methods. Identification of the topographic patterns of these pathologies in structural magnetic resonance imaging (MRI) may provide probabilistic biomarkers. We demonstrated the correlation of the specific patterns of tissue loss from ex vivo brain MRI with underlying pathologies detected in postmortem brain hemispheres in patients with Alzheimer's disease (AD) spectrum disorders. The results provide insight into the interpretation of in vivo structural MRI studies in patients with AD spectrum disorders.
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Affiliation(s)
- Shokufeh Sadaghiani
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Winifred Trotman
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sydney A Lim
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Eunice Chung
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ranjit Ittyerah
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sadhana Ravikumar
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Pulkit Khandelwal
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Karthik Prabhakaran
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Madigan L Lavery
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Daniel T Ohm
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Marianna Gabrielyan
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sandhitsu R. Das
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Theresa Schuck
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Noah Capp
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Claire S Peterson
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Elyse Migdal
- College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Emilio Artacho-Pérula
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, University of Castilla-La Mancha, Albacete, Spain
| | | | | | - Sandra Cebada Sánchez
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, University of Castilla-La Mancha, Albacete, Spain
| | - Carlos de la Rosa Prieto
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, University of Castilla-La Mancha, Albacete, Spain
| | - Marta Córcoles Parada
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, University of Castilla-La Mancha, Albacete, Spain
| | - Ricardo Insausti
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, University of Castilla-La Mancha, Albacete, Spain
| | - John L Robinson
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Corey McMillan
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Murray Grossman
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Edward B Lee
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - John A. Detre
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sharon X. Xie
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - John Q Trojanowski
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - M Dylan Tisdall
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Laura EM Wisse
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Diagnostic Radiology, Lund University, 22242 Lund, Sweden
| | - David J Irwin
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - David A. Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Paul A. Yushkevich
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
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11
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Llamas-Rodríguez J, Oltmer J, Marshall M, Champion S, Frosch MP, Augustinack JC. TDP-43 and tau concurrence in the entorhinal subfields in primary age-related tauopathy and preclinical Alzheimer's disease. Brain Pathol 2023:e13159. [PMID: 37037195 DOI: 10.1111/bpa.13159] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 03/24/2023] [Indexed: 04/12/2023] Open
Abstract
Phosphorylated tau (p-tau) pathology correlates strongly with cognitive decline and is a pathological hallmark of Alzheimer's Disease (AD). In recent years, phosphorylated transactive response DNA-binding protein (pTDP-43) has emerged as a common comorbidity, found in up to 70% of all AD cases (Josephs et al., Acta Neuropathol, 131(4), 571-585; Josephs, Whitwell, et al., Acta Neuropathol, 127(6), 811-824). Current staging schemes for pTDP-43 in AD and primary age-related tauopathy (PART) track its progression throughout the brain, but the distribution of pTDP-43 within the entorhinal cortex (EC) at the earliest stages has not been studied. Moreover, the exact nature of p-tau and pTDP-43 co-localization is debated. We investigated the selective vulnerability of the entorhinal subfields to phosphorylated pTDP-43 pathology in preclinical AD and PART postmortem tissue. Within the EC, posterior-lateral subfields showed the highest semi-quantitative pTDP-43 density scores, while the anterior-medial subfields had the lowest. On the rostrocaudal axis, pTDP-43 scores were higher posteriorly than anteriorly (p < 0.010), peaking at the posterior-most level (p < 0.050). Further, we showed the relationship between pTDP-43 and p-tau in these regions at pathology-positive but clinically silent stages. P-tau and pTDP-43 presented a similar pattern of affected subregions (p < 0.0001) but differed in density magnitude (p < 0.0001). P-tau burden was consistently higher than pTDP-43 at every anterior-posterior level and in most EC subfields. These findings highlight pTDP-43 burden heterogeneity within the EC and the posterior-lateral subfields as the most vulnerable regions within stage II of the current pTDP-43 staging schemes for AD and PART. The EC is a point of convergence for p-tau and pTDP-43 and identifying its most vulnerable neuronal populations will prove key for early diagnosis and disease intervention.
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Affiliation(s)
- Josué Llamas-Rodríguez
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
| | - Jan Oltmer
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
| | - Michael Marshall
- Department of Neuropathology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Samantha Champion
- Department of Neuropathology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Matthew P Frosch
- Department of Neuropathology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Jean C Augustinack
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
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12
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Insausti R, Insausti AM, Muñoz López M, Medina Lorenzo I, Arroyo-Jiménez MDM, Marcos Rabal MP, de la Rosa-Prieto C, Delgado-González JC, Montón Etxeberria J, Cebada-Sánchez S, Raspeño-García JF, Iñiguez de Onzoño MM, Molina Romero FJ, Benavides-Piccione R, Tapia-González S, Wisse LEM, Ravikumar S, Wolk DA, DeFelipe J, Yushkevich P, Artacho-Pérula E. Ex vivo, in situ perfusion protocol for human brain fixation compatible with microscopy, MRI techniques, and anatomical studies. Front Neuroanat 2023; 17:1149674. [PMID: 37034833 PMCID: PMC10076536 DOI: 10.3389/fnana.2023.1149674] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Accepted: 02/28/2023] [Indexed: 04/11/2023] Open
Abstract
We present a method for human brain fixation based on simultaneous perfusion of 4% paraformaldehyde through carotids after a flush with saline. The left carotid cannula is used to perfuse the body with 10% formalin, to allow further use of the body for anatomical research or teaching. The aim of our method is to develop a vascular fixation protocol for the human brain, by adapting protocols that are commonly used in experimental animal studies. We show that a variety of histological procedures can be carried out (cyto- and myeloarchitectonics, histochemistry, immunohistochemistry, intracellular cell injection, and electron microscopy). In addition, ex vivo, ex situ high-resolution MRI (9.4T) can be obtained in the same specimens. This procedure resulted in similar morphological features to those obtained by intravascular perfusion in experimental animals, provided that the postmortem interval was under 10 h for several of the techniques used and under 4 h in the case of intracellular injections and electron microscopy. The use of intravascular fixation of the brain inside the skull provides a fixed whole human brain, perfectly fitted to the skull, with negligible deformation compared to conventional techniques. Given this characteristic of ex vivo, in situ fixation, this procedure can probably be considered the most suitable one available for ex vivo MRI scans of the brain. We describe the compatibility of the method proposed for intravascular fixation of the human brain and fixation of the donor's body for anatomical purposes. Thus, body donor programs can provide human brain tissue, while the remainder of the body can also be fixed for anatomical studies. Therefore, this method of human brain fixation through the carotid system optimizes the procurement of human brain tissue, allowing a greater understanding of human neurological diseases, while benefiting anatomy departments by making the remainder of the body available for teaching purposes.
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Affiliation(s)
- Ricardo Insausti
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, Medical Sciences Department, School of Medicine and CRIB, University of Castilla La Mancha, Albacete, Spain
| | - Ana María Insausti
- Department of Health, School of Medicine, Public University of Navarra, Pamplona, Spain
| | - Mónica Muñoz López
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, Medical Sciences Department, School of Medicine and CRIB, University of Castilla La Mancha, Albacete, Spain
| | - Isidro Medina Lorenzo
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, Medical Sciences Department, School of Medicine and CRIB, University of Castilla La Mancha, Albacete, Spain
| | - Maria del Mar Arroyo-Jiménez
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, Medical Sciences Department, School of Medicine and CRIB, University of Castilla La Mancha, Albacete, Spain
| | - María Pilar Marcos Rabal
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, Medical Sciences Department, School of Medicine and CRIB, University of Castilla La Mancha, Albacete, Spain
| | - Carlos de la Rosa-Prieto
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, Medical Sciences Department, School of Medicine and CRIB, University of Castilla La Mancha, Albacete, Spain
| | - José Carlos Delgado-González
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, Medical Sciences Department, School of Medicine and CRIB, University of Castilla La Mancha, Albacete, Spain
| | - Javier Montón Etxeberria
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, Medical Sciences Department, School of Medicine and CRIB, University of Castilla La Mancha, Albacete, Spain
| | - Sandra Cebada-Sánchez
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, Medical Sciences Department, School of Medicine and CRIB, University of Castilla La Mancha, Albacete, Spain
| | - Juan Francisco Raspeño-García
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, Medical Sciences Department, School of Medicine and CRIB, University of Castilla La Mancha, Albacete, Spain
| | - María Mercedes Iñiguez de Onzoño
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, Medical Sciences Department, School of Medicine and CRIB, University of Castilla La Mancha, Albacete, Spain
| | - Francisco Javier Molina Romero
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, Medical Sciences Department, School of Medicine and CRIB, University of Castilla La Mancha, Albacete, Spain
| | - Ruth Benavides-Piccione
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, and Instituto Cajal, CSIC, Madrid, Spain
| | - Silvia Tapia-González
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, and Instituto Cajal, CSIC, Madrid, Spain
| | | | - Sadhana Ravikumar
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - David A. Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Javier DeFelipe
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, and Instituto Cajal, CSIC, Madrid, Spain
| | - Paul Yushkevich
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Emilio Artacho-Pérula
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, Medical Sciences Department, School of Medicine and CRIB, University of Castilla La Mancha, Albacete, Spain
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13
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Stouffer KM, Chen C, Kulason S, Xu E, Witter MP, Ceritoglu C, Albert MS, Mori S, Troncoso J, Tward DJ, Miller MI. Early amygdala and ERC atrophy linked to 3D reconstruction of rostral neurofibrillary tau tangle pathology in Alzheimer's disease. Neuroimage Clin 2023; 38:103374. [PMID: 36934675 PMCID: PMC10034129 DOI: 10.1016/j.nicl.2023.103374] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 03/07/2023] [Accepted: 03/08/2023] [Indexed: 03/17/2023]
Abstract
Previous research has emphasized the unique impact of Alzheimer's Disease (AD) pathology on the medial temporal lobe (MTL), a reflection that tau pathology is particularly striking in the entorhinal and transentorhinal cortex (ERC, TEC) early in the course of disease. However, other brain regions are affected by AD pathology during its early phases. Here, we use longitudinal diffeomorphometry to measure the atrophy rate from MRI of the amygdala compared with that in the ERC and TEC in cognitively unimpaired (CU) controls, CU individuals who progressed to mild cognitive impairment (MCI), and individuals with MCI who progressed to dementia of the AD type (DAT), using a dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Our results show significantly higher atrophy rates of the amygdala in both groups of 'converters' (CU→MCI, MCI→DAT) compared to controls, with rates of volume loss comparable to rates of thickness loss in the ERC and TEC. We localize atrophy within the amygdala within each of these groups using fixed effects modeling. Controlling for the familywise error rate highlights the medial regions of the amygdala as those with significantly higher atrophy in both groups of converters than in controls. Using our recently developed method, referred to as Projective LDDMM, we map measures of neurofibrillary tau tangles (NFTs) from digital pathology to MRI atlases and reconstruct dense 3D spatial distributions of NFT density within regions of the MTL. The distribution of NFTs is consistent with the spatial distribution of MR measured atrophy rates, revealing high densities (and atrophy) in the amygdala (particularly medial), ERC, and rostral third of the MTL. The similarity of the location of NFTs in AD and shape changes in a well-defined clinical population suggests that amygdalar atrophy rate, as measured through MRI may be a viable biomarker for AD.
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Affiliation(s)
- Kaitlin M Stouffer
- Department of Biomedical Engineering, Johns Hopkins University, 3400 N Charles St, Baltimore 21218, MD, USA.
| | - Claire Chen
- Department of Biomedical Engineering, Johns Hopkins University, 3400 N Charles St, Baltimore 21218, MD, USA
| | - Sue Kulason
- Department of Biomedical Engineering, Johns Hopkins University, 3400 N Charles St, Baltimore 21218, MD, USA
| | - Eileen Xu
- Department of Biomedical Engineering, Johns Hopkins University, 3400 N Charles St, Baltimore 21218, MD, USA
| | - Menno P Witter
- Kavli Institute for Systems Neuroscience, Norwegian University of Science and Technology, 7491 Trondheim, Norway
| | - Can Ceritoglu
- Department of Biomedical Engineering, Johns Hopkins University, 3400 N Charles St, Baltimore 21218, MD, USA
| | - Marilyn S Albert
- Departments of Neurology, Johns Hopkins School of Medicine, 733 N Broadway, Baltimore 21205, MD, USA
| | - Susumu Mori
- Department of Radiology, Johns Hopkins School of Medicine, 733 N Broadway, Baltimore 21205, MD, USA
| | - Juan Troncoso
- Department of Pathology, Johns Hopkins School of Medicine, 733 N Broadway, Baltimore 21205, MD, USA
| | - Daniel J Tward
- Departments of Computational Medicine and Neurology, University of California, Los Angeles, UCLA Brain Mapping Center, 660 Charles E. Young Drive South, Los Angeles 90095, CA, USA
| | - Michael I Miller
- Department of Biomedical Engineering, Johns Hopkins University, 3400 N Charles St, Baltimore 21218, MD, USA
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14
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Lyu X, Duong MT, Xie L, de Flores R, Richardson H, Hwang G, Wisse LEM, DiCalogero M, McMillan CT, Robinson JL, Xie SX, Grossman M, Lee EB, Irwin DJ, Dickerson BC, Davatzikos C, Nasrallah IM, Yushkevich PA, Wolk DA, Das SR. Tau-Neurodegeneration mismatch reveals vulnerability and resilience to comorbidities in Alzheimer's continuum. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.12.23285594. [PMID: 36824762 PMCID: PMC9949174 DOI: 10.1101/2023.02.12.23285594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
Abstract
Variability in the relationship of tau-based neurofibrillary tangles (T) and degree of neurodegeneration (N) in Alzheimer's Disease (AD) is likely attributable to the non-specific nature of N, which is also modulated by such factors as other co-pathologies, age-related changes, and developmental differences. We studied this variability by partitioning patients within the Alzheimer's continuum into data-driven groups based on their regional T-N dissociation, which reflects the residuals after the effect of tau pathology is "removed". We found six groups displaying distinct spatial T-N mismatch and thickness patterns despite similar tau burden. Their T-N patterns resembled the neurodegeneration patterns of non-AD groups partitioned on the basis of z-scores of cortical thickness alone and were similarly associated with surrogates of non-AD factors. In an additional sample of individuals with antemortem imaging and autopsy, T-N mismatch was associated with TDP-43 co-pathology. Finally, T-N mismatch training was then applied to a separate cohort to determine the ability to classify individual patients within these groups. These findings suggest that T-N mismatch may provide a personalized approach for determining non-AD factors associated with resilience/vulnerability to Alzheimer's disease.
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15
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Shih NC, Kurniawan ND, Cabeen RP, Korobkova L, Wong E, Chui HC, Clark KA, Miller CA, Hawes D, Jones KT, Sepehrband F. Microstructural mapping of dentate gyrus pathology in Alzheimer's disease: A 16.4 Tesla MRI study. Neuroimage Clin 2023; 37:103318. [PMID: 36630864 PMCID: PMC9841366 DOI: 10.1016/j.nicl.2023.103318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 01/03/2023] [Accepted: 01/04/2023] [Indexed: 01/07/2023]
Abstract
The dentate gyrus (DG) is an integral portion of the hippocampal formation, and it is composed of three layers. Quantitative magnetic resonance (MR) imaging has the capability to map brain tissue microstructural properties which can be exploited to investigate neurodegeneration in Alzheimer's disease (AD). However, assessing subtle pathological changes within layers requires high resolution imaging and histological validation. In this study, we utilized a 16.4 Tesla scanner to acquire ex vivo multi-parameter quantitative MRI measures in human specimens across the layers of the DG. Using quantitative diffusion tensor imaging (DTI) and multi-parameter MR measurements acquired from AD (N = 4) and cognitively normal control (N = 6) tissues, we performed correlation analyses with histological measurements. Here, we found that quantitative MRI measures were significantly correlated with neurofilament and phosphorylated Tau density, suggesting sensitivity to layer-specific changes in the DG of AD tissues.
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Affiliation(s)
- Nien-Chu Shih
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Nyoman D Kurniawan
- Center for Advanced Imaging, The University of Queensland, Brisbane 4072, Australia
| | - Ryan P Cabeen
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Laura Korobkova
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA 90089. USA
| | - Ellen Wong
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA; Department of Neurology, Rancho Los Amigos National Rehabilitation Center, Downey, CA 90242, USA
| | - Helena C Chui
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Kristi A Clark
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Carol A Miller
- Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Debra Hawes
- Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA; Department of Pathology and Laboratory Medicine, Children's Hospital of Los Angeles, Los Angeles, CA 90033, USA
| | - Kymry T Jones
- Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
| | - Farshid Sepehrband
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
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16
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Casamitjana A, Iglesias JE. High-resolution atlasing and segmentation of the subcortex: Review and perspective on challenges and opportunities created by machine learning. Neuroimage 2022; 263:119616. [PMID: 36084858 PMCID: PMC11534291 DOI: 10.1016/j.neuroimage.2022.119616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 08/30/2022] [Accepted: 09/05/2022] [Indexed: 11/17/2022] Open
Abstract
This paper reviews almost three decades of work on atlasing and segmentation methods for subcortical structures in human brain MRI. In writing this survey, we have three distinct aims. First, to document the evolution of digital subcortical atlases of the human brain, from the early MRI templates published in the nineties, to the complex multi-modal atlases at the subregion level that are available today. Second, to provide a detailed record of related efforts in the automated segmentation front, from earlier atlas-based methods to modern machine learning approaches. And third, to present a perspective on the future of high-resolution atlasing and segmentation of subcortical structures in in vivo human brain MRI, including open challenges and opportunities created by recent developments in machine learning.
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Affiliation(s)
- Adrià Casamitjana
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, UK.
| | - Juan Eugenio Iglesias
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, UK; Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, USA
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17
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Duong MT, Wolk DA. Limbic-Predominant Age-Related TDP-43 Encephalopathy: LATE-Breaking Updates in Clinicopathologic Features and Biomarkers. Curr Neurol Neurosci Rep 2022; 22:689-698. [PMID: 36190653 PMCID: PMC9633415 DOI: 10.1007/s11910-022-01232-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/21/2022] [Indexed: 01/27/2023]
Abstract
PURPOSE OF REVIEW Limbic-predominant age-related TDP-43 encephalopathy (LATE) is a recently defined neurodegenerative disease characterized by amnestic phenotype and pathological inclusions of TAR DNA-binding protein 43 (TDP-43). LATE is distinct from rarer forms of TDP-43 diseases such as frontotemporal lobar degeneration with TDP-43 but is also a common copathology with Alzheimer's disease (AD) and cerebrovascular disease and accelerates cognitive decline. LATE contributes to clinicopathologic heterogeneity in neurodegenerative diseases, so it is imperative to distinguish LATE from other etiologies. RECENT FINDINGS Novel biomarkers for LATE are being developed with magnetic resonance imaging (MRI) and positron emission tomography (PET). When cooccurring with AD, LATE exhibits identifiable patterns of limbic-predominant atrophy on MRI and hypometabolism on 18F-fluorodeoxyglucose PET that are greater than expected relative to levels of local AD pathology. Efforts are being made to develop TDP-43-specific radiotracers, molecularly specific biofluid measures, and genomic predictors of TDP-43. LATE is a highly prevalent neurodegenerative disease distinct from previously characterized cognitive disorders.
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Affiliation(s)
- Michael Tran Duong
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - David A Wolk
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Alzheimer's Disease Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Institute On Aging, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce St, Philadelphia, PA, 19104, USA.
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18
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Farrell K, Iida MA, Cherry JD, Casella A, Stein TD, Bieniek KF, Walker JM, Richardson TE, White CL, Alvarez VE, Huber BR, Dickson DW, Insausti R, Dams-O'Connor K, McKee AC, Crary JF. Differential Vulnerability of Hippocampal Subfields in Primary Age-Related Tauopathy and Chronic Traumatic Encephalopathy. J Neuropathol Exp Neurol 2022; 81:781-789. [PMID: 36004533 PMCID: PMC9487677 DOI: 10.1093/jnen/nlac066] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Chronic traumatic encephalopathy (CTE) is a tauopathy associated with repetitive mild head impacts characterized by perivascular hyperphosphorylated tau (p-tau) in neurofibrillary tangles (NFTs) and neurites in the depths of the neocortical sulci. In moderate to advanced CTE, NFTs accumulate in the hippocampus, potentially overlapping neuroanatomically with primary age-related tauopathy (PART), an age-related tauopathy characterized by Alzheimer disease-like tau pathology in the hippocampus devoid of amyloid plaques. We measured p-tau burden using positive-pixel counts on immunohistochemically stained and neuroanatomically segmented hippocampal tissue. Subjects with CTE had a higher total p-tau burden than PART subjects in all sectors (p = 0.005). Within groups, PART had significantly higher total p-tau burden in CA1/subiculum compared to CA3 (p = 0.02) and CA4 (p = 0.01) and total p-tau burden in CA2 trended higher than CA4 (p = 0.06). In CTE, total p-tau burden in CA1/subiculum was significantly higher than in the dentate gyrus; and CA2 also trended higher than dentate gyrus (p = 0.01, p = 0.06). When controlling for p-tau burden across the entire hippocampus, CA3 and CA4 had significantly higher p-tau burden in CTE than PART (p < 0.0001). These data demonstrate differences in hippocampal p-tau burden and regional distribution in CTE compared to PART that might be helpful in differential diagnosis and reveal insights into disease pathogenesis.
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Affiliation(s)
- Kurt Farrell
- Departments of Pathology, Artificial Intelligence & Human Health, Nash Family Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Friedman Brain Institute, Neuropathology Brain Bank & Research CoRE, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Megan A Iida
- Departments of Pathology, Artificial Intelligence & Human Health, Nash Family Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Friedman Brain Institute, Neuropathology Brain Bank & Research CoRE, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Jonathan D Cherry
- Department of Pathology, Alzheimer's Disease and CTE Center, Boston University School of Medicine, Boston, Massachusetts, USA
- Department of Veterans Affairs Medical Center, Bedford, Massachusetts, USA
- VA Boston Healthcare System, Boston, Massachusetts, USA
| | - Alicia Casella
- Departments of Pathology, Artificial Intelligence & Human Health, Nash Family Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Friedman Brain Institute, Neuropathology Brain Bank & Research CoRE, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Thor D Stein
- Department of Pathology, Alzheimer's Disease and CTE Center, Boston University School of Medicine, Boston, Massachusetts, USA
- Department of Veterans Affairs Medical Center, Bedford, Massachusetts, USA
- VA Boston Healthcare System, Boston, Massachusetts, USA
| | - Kevin F Bieniek
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center, San Antonio, Texas, USA
- Department of Pathology, University of Texas Health Science Center, San Antonio, Texas, USA
| | - Jamie M Walker
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center, San Antonio, Texas, USA
- Department of Pathology, University of Texas Health Science Center, San Antonio, Texas, USA
| | - Timothy E Richardson
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center, San Antonio, Texas, USA
- Department of Pathology, University of Texas Health Science Center, San Antonio, Texas, USA
| | - Charles L White
- Department of Pathology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Victor E Alvarez
- Department of Pathology, Alzheimer's Disease and CTE Center, Boston University School of Medicine, Boston, Massachusetts, USA
- Department of Veterans Affairs Medical Center, Bedford, Massachusetts, USA
- VA Boston Healthcare System, Boston, Massachusetts, USA
- Department of Neurology, Boston University School of Medicine, Boston, Massachusetts, USA
| | - Bertrand R Huber
- Department of Pathology, Alzheimer's Disease and CTE Center, Boston University School of Medicine, Boston, Massachusetts, USA
- Department of Veterans Affairs Medical Center, Bedford, Massachusetts, USA
- VA Boston Healthcare System, Boston, Massachusetts, USA
- Department of Neurology, Boston University School of Medicine, Boston, Massachusetts, USA
| | - Dennis W Dickson
- Departments of Pathology and Neuroscience, Mayo Clinic, Jacksonville, Florida, USA
| | - Ricardo Insausti
- Human Neuroanatomy Laboratory, School of Medicine, University of Castilla-La Mancha, Albacete, Spain
| | - Kristen Dams-O'Connor
- Department of Rehabilitation and Human Performance, Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ann C McKee
- Department of Pathology, Alzheimer's Disease and CTE Center, Boston University School of Medicine, Boston, Massachusetts, USA
- Department of Veterans Affairs Medical Center, Bedford, Massachusetts, USA
- VA Boston Healthcare System, Boston, Massachusetts, USA
- Department of Neurology, Boston University School of Medicine, Boston, Massachusetts, USA
| | - John F Crary
- Departments of Pathology, Artificial Intelligence & Human Health, Nash Family Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Friedman Brain Institute, Neuropathology Brain Bank & Research CoRE, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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19
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Llamas-Rodríguez J, Oltmer J, Greve DN, Williams E, Slepneva N, Wang R, Champion S, Lang-Orsini M, Fischl B, Frosch MP, van der Kouwe AJ, Augustinack JC. Entorhinal Subfield Vulnerability to Neurofibrillary Tangles in Aging and the Preclinical Stage of Alzheimer's Disease. J Alzheimers Dis 2022; 87:1379-1399. [PMID: 35491780 PMCID: PMC9198759 DOI: 10.3233/jad-215567] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/25/2022] [Indexed: 12/15/2022]
Abstract
BACKGROUND Neurofibrillary tangle (NFT) accumulation in the entorhinal cortex (EC) precedes the transformation from cognitive controls to mild cognitive impairment and Alzheimer's disease (AD). While tauopathy has been described in the EC before, the order and degree to which the individual subfields within the EC are engulfed by NFTs in aging and the preclinical AD stage is unknown. OBJECTIVE We aimed to investigate substructures within the EC to map the populations of cortical neurons most vulnerable to tau pathology in aging and the preclinical AD stage. METHODS We characterized phosphorylated tau (CP13) in 10 cases at eight well-defined anterior-posterior levels and assessed NFT density within the eight entorhinal subfields (described by Insausti and colleagues) at the preclinical stages of AD. We validated with immunohistochemistry and labeled the NFT density ratings on ex vivo MRIs. We measured subfield cortical thickness and reconstructed the labels as three-dimensional isosurfaces, resulting in anatomically comprehensive, histopathologically validated tau "heat maps." RESULTS We found the lateral EC subfields ELc, ECL, and ECs (lateral portion) to have the highest tau density in semi-quantitative scores and quantitative measurements. We observed significant stepwise higher tau from anterior to posterior levels (p < 0.001). We report an age-dependent anatomically-specific vulnerability, with all cases showing posterior tau pathology, yet older individuals displaying an additional anterior tau burden. Finally, cortical thickness of each subfield negatively correlated with respective tau scores (p < 0.05). CONCLUSION Our findings indicate that posterior-lateral subfields within the EC are the most vulnerable to early NFTs and atrophy in aging and preclinical AD.
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Affiliation(s)
- Josué Llamas-Rodríguez
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Jan Oltmer
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Douglas N. Greve
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Emily Williams
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Natalya Slepneva
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Ruopeng Wang
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Samantha Champion
- Department of Neuropathology, Massachusetts General Hospital, Boston, MA, USA
| | - Melanie Lang-Orsini
- Department of Neuropathology, Massachusetts General Hospital, Boston, MA, USA
| | - Bruce Fischl
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- CSAIL/HST, MIT, Cambridge, MA, USA
| | - Matthew P. Frosch
- Department of Neuropathology, Massachusetts General Hospital, Boston, MA, USA
| | - André J.W. van der Kouwe
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Jean C. Augustinack
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
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