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Herz C, Vergnet N, Tian S, Aly AH, Jolley MA, Tran N, Arenas G, Lasso A, Schwartz N, O’Neill KE, Yushkevich PA, Pouch AM. Open-source graphical user interface for the creation of synthetic skeletons for medical image analysis. J Med Imaging (Bellingham) 2024; 11:036001. [PMID: 38751729 PMCID: PMC11092146 DOI: 10.1117/1.jmi.11.3.036001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 04/01/2024] [Accepted: 04/18/2024] [Indexed: 05/18/2024] Open
Abstract
Purpose Deformable medial modeling is an inverse skeletonization approach to representing anatomy in medical images, which can be used for statistical shape analysis and assessment of patient-specific anatomical features such as locally varying thickness. It involves deforming a pre-defined synthetic skeleton, or template, to anatomical structures of the same class. The lack of software for creating such skeletons has been a limitation to more widespread use of deformable medial modeling. Therefore, the objective of this work is to present an open-source user interface (UI) for the creation of synthetic skeletons for a range of medial modeling applications in medical imaging. Approach A UI for interactive design of synthetic skeletons was implemented in 3D Slicer, an open-source medical image analysis application. The steps in synthetic skeleton design include importation and skeletonization of a 3D segmentation, followed by interactive 3D point placement and triangulation of the medial surface such that the desired branching configuration of the anatomical structure's medial axis is achieved. Synthetic skeleton design was evaluated in five clinical applications. Compatibility of the synthetic skeletons with open-source software for deformable medial modeling was tested, and representational accuracy of the deformed medial models was evaluated. Results Three users designed synthetic skeletons of anatomies with various topologies: the placenta, aortic root wall, mitral valve, cardiac ventricles, and the uterus. The skeletons were compatible with skeleton-first and boundary-first software for deformable medial modeling. The fitted medial models achieved good representational accuracy with respect to the 3D segmentations from which the synthetic skeletons were generated. Conclusions Synthetic skeleton design has been a practical challenge in leveraging deformable medial modeling for new clinical applications. This work demonstrates an open-source UI for user-friendly design of synthetic skeletons for anatomies with a wide range of topologies.
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Affiliation(s)
- Christian Herz
- Children’s Hospital of Philadelphia, Department of Anesthesiology and Critical Care Medicine, Philadelphia, Pennsylvania, United States
| | - Nicolas Vergnet
- University of Pennsylvania, Penn Image Computing and Science Laboratory, Department of Radiology, Philadelphia, Pennsylvania, United States
| | - Sijie Tian
- University of Pennsylvania, Penn Image Computing and Science Laboratory, Department of Radiology, Philadelphia, Pennsylvania, United States
| | - Abdullah H. Aly
- University of Pennsylvania, Penn Image Computing and Science Laboratory, Department of Radiology, Philadelphia, Pennsylvania, United States
| | - Matthew A. Jolley
- Children’s Hospital of Philadelphia, Department of Anesthesiology and Critical Care Medicine, Philadelphia, Pennsylvania, United States
- Children’s Hospital of Philadelphia, Division of Cardiology, Philadelphia, Pennsylvania, United States
| | - Nathanael Tran
- Jefferson Einstein Hospital, Division of Cardiovascular Diseases, Philadelphia, Pennsylvania, United States
| | - Gabriel Arenas
- University of Pennsylvania, Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, Philadelphia, Pennsylvania, United States
| | - Andras Lasso
- Queen’s University, School of Computing, Laboratory for Percutaneous Surgery, Kingston, Ontario, Canada
| | - Nadav Schwartz
- University of Pennsylvania, Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, Philadelphia, Pennsylvania, United States
| | - Kathleen E. O’Neill
- University of Pennsylvania, Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Philadelphia, Pennsylvania, United States
| | - Paul A. Yushkevich
- University of Pennsylvania, Penn Image Computing and Science Laboratory, Department of Radiology, Philadelphia, Pennsylvania, United States
| | - Alison M. Pouch
- University of Pennsylvania, Penn Image Computing and Science Laboratory, Department of Radiology, Philadelphia, Pennsylvania, United States
- University of Pennsylvania, Department of Bioengineering, Philadelphia, Pennsylvania, United States
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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, 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. Alzheimers Dement 2024; 20:1586-1600. [PMID: 38050662 PMCID: PMC10984442 DOI: 10.1002/alz.13559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 10/23/2023] [Accepted: 10/24/2023] [Indexed: 12/06/2023]
Abstract
INTRODUCTION Variability in relationship of tau-based neurofibrillary tangles (T) and neurodegeneration (N) in Alzheimer's disease (AD) arises from non-specific nature of N, modulated by non-AD co-pathologies, age-related changes, and resilience factors. METHODS We used regional T-N residual patterns to partition 184 patients within the Alzheimer's continuum into data-driven groups. These were compared with groups from 159 non-AD (amyloid "negative") patients partitioned using cortical thickness, and groups in 98 patients with ante mortem MRI and post mortem tissue for measuring N and T, respectively. We applied the initial T-N residual model to classify 71 patients in an independent cohort into predefined groups. RESULTS AD groups displayed spatial T-N mismatch patterns resembling neurodegeneration patterns in non-AD groups, similarly associated with non-AD factors and diverging cognitive outcomes. In the autopsy cohort, limbic T-N mismatch correlated with TDP-43 co-pathology. DISCUSSION T-N mismatch may provide a personalized approach for determining non-AD factors associated with resilience/vulnerability in AD.
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Affiliation(s)
- Xueying Lyu
- Departments of BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Michael Tran Duong
- Departments of BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Long Xie
- Departments of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | | | - Hayley Richardson
- Department of Biostatistics, Epidemiology and InformaticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Gyujoon Hwang
- Departments of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | | | - Michael DiCalogero
- Departments of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Corey T. McMillan
- Departments of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - John L. Robinson
- Departments of Pathology and Laboratory MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Sharon X. Xie
- Department of Biostatistics, Epidemiology and InformaticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Edward B. Lee
- Departments of Pathology and Laboratory MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - David J. Irwin
- Departments of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | | | - Christos Davatzikos
- Departments of BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Ilya M. Nasrallah
- Departments of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Paul A. Yushkevich
- Departments of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - David A. Wolk
- Departments of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Sandhitsu R. Das
- Departments of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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Villavisanis DF, Khandelwal P, Zapatero ZD, Wagner CS, Blum JD, Cho DY, Swanson JW, Taylor JA, Yushkevich PA, Bartlett SP. Craniofacial Soft-Tissue Anthropomorphic Database with Magnetic Resonance Imaging and Unbiased Diffeomorphic Registration. Plast Reconstr Surg 2024; 153:667-677. [PMID: 37036329 DOI: 10.1097/prs.0000000000010526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2023]
Abstract
BACKGROUND Objective assessment of craniofacial surgery outcomes in a pediatric population is challenging because of the complexity of patient presentations, diversity of procedures performed, and rapid craniofacial growth. There is a paucity of robust methods to quantify anatomical measurements by age and objectively compare craniofacial dysmorphology and postoperative outcomes. Here, the authors present data in developing a racially and ethnically sensitive anthropomorphic database, providing plastic and craniofacial surgeons with "normal" three-dimensional anatomical parameters with which to appraise and optimize aesthetic and reconstructive outcomes. METHODS Patients with normal craniofacial anatomy undergoing head magnetic resonance imaging (MRI) scans from 2008 to 2021 were included in this retrospective study. Images were used to construct composite (template) images with diffeomorphic image registration method using the Advanced Normalization Tools package. Composites were thresholded to generate binary three-dimensional segmentations used for anatomical measurements in Materalise Mimics. RESULTS High-resolution MRI scans from 130 patients generated 12 composites from an average of 10 MRI sequences each: four 3-year-olds, four 4-year-olds, and four 5-year-olds (two male, two female, two Black, and two White). The average head circumference of 3-, 4-, and 5-year-old composites was 50.3, 51.5, and 51.7 cm, respectively, comparable to normative data published by the World Health Organization. CONCLUSIONS Application of diffeomorphic registration-based image template algorithm to MRI is effective in creating composite templates to represent "normal" three-dimensional craniofacial and soft-tissue anatomy. Future research will focus on development of automated computational tools to characterize anatomical normality, generation of indices to grade preoperative severity, and quantification of postoperative results to reduce subjectivity bias.
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Affiliation(s)
- Dillan F Villavisanis
- From the Division of Plastic and Reconstructive Surgery, Children's Hospital of Philadelphia
| | - Pulkit Khandelwal
- Department of Radiology, Penn Image Computing and Science Laboratory, University of Pennsylvania
| | - Zachary D Zapatero
- From the Division of Plastic and Reconstructive Surgery, Children's Hospital of Philadelphia
| | - Connor S Wagner
- From the Division of Plastic and Reconstructive Surgery, Children's Hospital of Philadelphia
| | - Jessica D Blum
- From the Division of Plastic and Reconstructive Surgery, Children's Hospital of Philadelphia
| | - Daniel Y Cho
- From the Division of Plastic and Reconstructive Surgery, Children's Hospital of Philadelphia
| | - Jordan W Swanson
- From the Division of Plastic and Reconstructive Surgery, Children's Hospital of Philadelphia
| | - Jesse A Taylor
- From the Division of Plastic and Reconstructive Surgery, Children's Hospital of Philadelphia
| | - Paul A Yushkevich
- Department of Radiology, Penn Image Computing and Science Laboratory, University of Pennsylvania
| | - Scott P Bartlett
- From the Division of Plastic and Reconstructive Surgery, Children's Hospital of Philadelphia
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Xie L, Das SR, Wisse LEM, Ittyerah R, de Flores R, Shaw LM, Yushkevich PA, Wolk DA. Correction: Baseline structural MRI and plasma biomarkers predict longitudinal structural atrophy and cognitive decline in early Alzheimer's disease. Alzheimers Res Ther 2024; 16:11. [PMID: 38217025 PMCID: PMC10785540 DOI: 10.1186/s13195-023-01374-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2024]
Affiliation(s)
- Long Xie
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Suite D600, Richards Building 6 Floor, Philadelphia, PA, 19104, USA.
| | - Sandhitsu R Das
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Suite D600, Richards Building 6 Floor, Philadelphia, PA, 19104, USA
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Laura E M Wisse
- Department of Diagnostic Radiology, Lund University, Lund, Sweden
| | - Ranjit Ittyerah
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Suite D600, Richards Building 6 Floor, Philadelphia, PA, 19104, USA
| | - Robin de Flores
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Suite D600, Richards Building 6 Floor, Philadelphia, PA, 19104, USA
| | - Leslie M Shaw
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Paul A Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Suite D600, Richards Building 6 Floor, Philadelphia, PA, 19104, USA
| | - David A Wolk
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA
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Ohm DT, Rhodes E, Bahena A, Capp N, Lowe M, Sabatini P, Trotman W, Olm CA, Phillips J, Prabhakaran K, Rascovsky K, Massimo L, McMillan C, Gee J, Tisdall MD, Yushkevich PA, Lee EB, Grossman M, Irwin DJ. Neuroanatomical and cellular degeneration associated with a social disorder characterized by new ritualistic belief systems in a TDP-C patient vs. a Pick patient. Front Neurol 2023; 14:1245886. [PMID: 37900607 PMCID: PMC10600461 DOI: 10.3389/fneur.2023.1245886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 08/15/2023] [Indexed: 10/31/2023] Open
Abstract
Frontotemporal dementia (FTD) is a spectrum of clinically and pathologically heterogenous neurodegenerative dementias. Clinical and anatomical variants of FTD have been described and associated with underlying frontotemporal lobar degeneration (FTLD) pathology, including tauopathies (FTLD-tau) or TDP-43 proteinopathies (FTLD-TDP). FTD patients with predominant degeneration of anterior temporal cortices often develop a language disorder of semantic knowledge loss and/or a social disorder often characterized by compulsive rituals and belief systems corresponding to predominant left or right hemisphere involvement, respectively. The neural substrates of these complex social disorders remain unclear. Here, we present a comparative imaging and postmortem study of two patients, one with FTLD-TDP (subtype C) and one with FTLD-tau (subtype Pick disease), who both developed new rigid belief systems. The FTLD-TDP patient developed a complex set of values centered on positivity and associated with specific physical and behavioral features of pigs, while the FTLD-tau patient developed compulsive, goal-directed behaviors related to general themes of positivity and spirituality. Neuroimaging showed left-predominant temporal atrophy in the FTLD-TDP patient and right-predominant frontotemporal atrophy in the FTLD-tau patient. Consistent with antemortem cortical atrophy, histopathologic examinations revealed severe loss of neurons and myelin predominantly in the anterior temporal lobes of both patients, but the FTLD-tau patient showed more bilateral, dorsolateral involvement featuring greater pathology and loss of projection neurons and deep white matter. These findings highlight that the regions within and connected to anterior temporal lobes may have differential vulnerability to distinct FTLD proteinopathies and serve important roles in human belief systems.
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Affiliation(s)
- Daniel T. Ohm
- Penn Digital Neuropathology Laboratory, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
- Penn Frontotemporal Degeneration Center, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Emma Rhodes
- Penn Frontotemporal Degeneration Center, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Alejandra Bahena
- Penn Digital Neuropathology Laboratory, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Noah Capp
- Penn Digital Neuropathology Laboratory, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - MaKayla Lowe
- Penn Digital Neuropathology Laboratory, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Philip Sabatini
- Penn Digital Neuropathology Laboratory, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Winifred Trotman
- Penn Digital Neuropathology Laboratory, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Christopher A. Olm
- Penn Frontotemporal Degeneration Center, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Jeffrey Phillips
- Penn Frontotemporal Degeneration Center, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Karthik Prabhakaran
- Penn Image Computing and Science Lab, Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Katya Rascovsky
- Penn Frontotemporal Degeneration Center, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Lauren Massimo
- Penn Frontotemporal Degeneration Center, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Corey McMillan
- Penn Frontotemporal Degeneration Center, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - James Gee
- Penn Image Computing and Science Lab, Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - M. Dylan Tisdall
- Center for Advanced Magnetic Resonance Imaging and Spectroscopy, Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Paul A. Yushkevich
- Penn Image Computing and Science Lab, Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Edward B. Lee
- Center for Neurodegenerative Disease Research, Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Murray Grossman
- Penn Frontotemporal Degeneration Center, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - David J. Irwin
- Penn Digital Neuropathology Laboratory, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
- Penn Frontotemporal Degeneration Center, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
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Hrybouski S, Das SR, Xie L, Wisse LEM, Kelley M, Lane J, Sherin M, DiCalogero M, Nasrallah I, Detre J, Yushkevich PA, Wolk DA. Aging and Alzheimer's disease have dissociable effects on local and regional medial temporal lobe connectivity. Brain Commun 2023; 5:fcad245. [PMID: 37767219 PMCID: PMC10521906 DOI: 10.1093/braincomms/fcad245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 08/06/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023] Open
Abstract
Functional disruption of the medial temporal lobe-dependent networks is thought to underlie episodic memory deficits in aging and Alzheimer's disease. Previous studies revealed that the anterior medial temporal lobe is more vulnerable to pathological and neurodegenerative processes in Alzheimer's disease. In contrast, cognitive and structural imaging literature indicates posterior, as opposed to anterior, medial temporal lobe vulnerability in normal aging. However, the extent to which Alzheimer's and aging-related pathological processes relate to functional disruption of the medial temporal lobe-dependent brain networks is poorly understood. To address this knowledge gap, we examined functional connectivity alterations in the medial temporal lobe and its immediate functional neighbourhood-the Anterior-Temporal and Posterior-Medial brain networks-in normal agers, individuals with preclinical Alzheimer's disease and patients with Mild Cognitive Impairment or mild dementia due to Alzheimer's disease. In the Anterior-Temporal network and in the perirhinal cortex, in particular, we observed an inverted 'U-shaped' relationship between functional connectivity and Alzheimer's stage. According to our results, the preclinical phase of Alzheimer's disease is characterized by increased functional connectivity between the perirhinal cortex and other regions of the medial temporal lobe, as well as between the anterior medial temporal lobe and its one-hop neighbours in the Anterior-Temporal system. This effect is no longer present in symptomatic Alzheimer's disease. Instead, patients with symptomatic Alzheimer's disease displayed reduced hippocampal connectivity within the medial temporal lobe as well as hypoconnectivity within the Posterior-Medial system. For normal aging, our results led to three main conclusions: (i) intra-network connectivity of both the Anterior-Temporal and Posterior-Medial networks declines with age; (ii) the anterior and posterior segments of the medial temporal lobe become increasingly decoupled from each other with advancing age; and (iii) the posterior subregions of the medial temporal lobe, especially the parahippocampal cortex, are more vulnerable to age-associated loss of function than their anterior counterparts. Together, the current results highlight evolving medial temporal lobe dysfunction in Alzheimer's disease and indicate different neurobiological mechanisms of the medial temporal lobe network disruption in aging versus Alzheimer's disease.
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Affiliation(s)
- Stanislau Hrybouski
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sandhitsu R Das
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Alzheimer’s Disease Research Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Long Xie
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Laura E M Wisse
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Diagnostic Radiology, Lund University, 221 00 Lund, Sweden
| | - Melissa Kelley
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jacqueline Lane
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Monica Sherin
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Michael DiCalogero
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ilya Nasrallah
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Alzheimer’s Disease Research Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - John Detre
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Paul A Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Alzheimer’s Disease Research Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Alzheimer’s Disease Research Center, University of Pennsylvania, Philadelphia, PA 19104, USA
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Xie L, Das SR, Wisse LEM, Ittyerah R, de Flores R, Shaw LM, Yushkevich PA, Wolk DA. Baseline structural MRI and plasma biomarkers predict longitudinal structural atrophy and cognitive decline in early Alzheimer's disease. Alzheimers Res Ther 2023; 15:79. [PMID: 37041649 PMCID: PMC10088234 DOI: 10.1186/s13195-023-01210-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 03/13/2023] [Indexed: 04/13/2023]
Abstract
BACKGROUND Crucial to the success of clinical trials targeting early Alzheimer's disease (AD) is recruiting participants who are more likely to progress over the course of the trials. We hypothesize that a combination of plasma and structural MRI biomarkers, which are less costly and non-invasive, is predictive of longitudinal progression measured by atrophy and cognitive decline in early AD, providing a practical alternative to PET or cerebrospinal fluid biomarkers. METHODS Longitudinal T1-weighted MRI, cognitive (memory-related test scores and clinical dementia rating scale), and plasma measurements of 245 cognitively normal (CN) and 361 mild cognitive impairment (MCI) patients from ADNI were included. Subjects were further divided into β-amyloid positive/negative (Aβ+/Aβ-)] subgroups. Baseline plasma (p-tau181 and neurofilament light chain) and MRI-based structural medial temporal lobe subregional measurements and their association with longitudinal measures of atrophy and cognitive decline were tested using stepwise linear mixed effect modeling in CN and MCI, as well as separately in the Aβ+/Aβ- subgroups. Receiver operating characteristic (ROC) analyses were performed to investigate the discriminative power of each model in separating fast and slow progressors (first and last terciles) of each longitudinal measurement. RESULTS A total of 245 CN (35.0% Aβ+) and 361 MCI (53.2% Aβ+) participants were included. In the CN and MCI groups, both baseline plasma and structural MRI biomarkers were included in most models. These relationships were maintained when limited to the Aβ+ and Aβ- subgroups, including Aβ- CN (normal aging). ROC analyses demonstrated reliable discriminative power in identifying fast from slow progressors in MCI [area under the curve (AUC): 0.78-0.93] and more modestly in CN (0.65-0.73). CONCLUSIONS The present data support the notion that plasma and MRI biomarkers, which are relatively easy to obtain, provide a prediction for the rate of future cognitive and neurodegenerative progression that may be particularly useful in clinical trial stratification and prognosis. Additionally, the effect in Aβ- CN indicates the potential use of these biomarkers in predicting a normal age-related decline.
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Affiliation(s)
- Long Xie
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Suite D600, Richards Building 6th floor, Philadelphia, PA, 19104, USA.
| | - Sandhitsu R Das
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Suite D600, Richards Building 6th floor, Philadelphia, PA, 19104, USA
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Laura E M Wisse
- Department of Diagnostic Radiology, Lund University, Lund, Sweden
| | - Ranjit Ittyerah
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Suite D600, Richards Building 6th floor, Philadelphia, PA, 19104, USA
| | - Robin de Flores
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Suite D600, Richards Building 6th floor, Philadelphia, PA, 19104, USA
| | - Leslie M Shaw
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Paul A Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Suite D600, Richards Building 6th floor, Philadelphia, PA, 19104, USA
| | - David A Wolk
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA
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8
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Dong M, Xie L, Das SR, Wang J, Wisse LEM, deFlores R, Wolk DA, Yushkevich PA. Regional Deep Atrophy: a Self-Supervised Learning Method to Automatically Identify Regions Associated With Alzheimer's Disease Progression From Longitudinal MRI. ArXiv 2023:arXiv:2304.04673v1. [PMID: 37090239 PMCID: PMC10120742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Longitudinal assessment of brain atrophy, particularly in the hippocampus, is a well-studied biomarker for neurodegenerative diseases, such as Alzheimer's disease (AD). In clinical trials, estimation of brain progressive rates can be applied to track therapeutic efficacy of disease modifying treatments. However, most state-of-the-art measurements calculate changes directly by segmentation and/or deformable registration of MRI images, and may misreport head motion or MRI artifacts as neurodegeneration, impacting their accuracy. In our previous study, we developed a deep learning method DeepAtrophy that uses a convolutional neural network to quantify differences between longitudinal MRI scan pairs that are associated with time. DeepAtrophy has high accuracy in inferring temporal information from longitudinal MRI scans, such as temporal order or relative inter-scan interval. DeepAtrophy also provides an overall atrophy score that was shown to perform well as a potential biomarker of disease progression and treatment efficacy. However, DeepAtrophy is not interpretable, and it is unclear what changes in the MRI contribute to progression measurements. In this paper, we propose Regional Deep Atrophy (RDA), which combines the temporal inference approach from DeepAtrophy with a deformable registration neural network and attention mechanism that highlights regions in the MRI image where longitudinal changes are contributing to temporal inference. RDA has similar prediction accuracy as DeepAtrophy, but its additional interpretability makes it more acceptable for use in clinical settings, and may lead to more sensitive biomarkers for disease monitoring in clinical trials of early AD.
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Affiliation(s)
- Mengjin Dong
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Long Xie
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - Sandhitsu R Das
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, United States
- Penn Memory Center, University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - Jiancong Wang
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Laura E M Wisse
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, United States
- Department of Diagnostic Radiology, Lund University, Lund, Sweden
| | - Robin deFlores
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, United States
- Penn Memory Center, University of Pennsylvania, Philadelphia, Pennsylvania, United States
- Institut National de la Santé et de la Recherche Médicale (INSERM), Caen, France
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, United States
- Penn Memory Center, University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - Paul A Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, United States
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9
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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 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] [What about the content of this article? (0)] [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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10
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Hrybouski S, Das SR, Xie L, Wisse LEM, Kelley M, Lane J, Sherin M, DiCalogero M, Nasrallah I, Detre JA, Yushkevich PA, Wolk DA. Aging and Alzheimer's Disease Have Dissociable Effects on Medial Temporal Lobe Connectivity. medRxiv 2023:2023.01.18.23284749. [PMID: 36711782 PMCID: PMC9882834 DOI: 10.1101/2023.01.18.23284749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Functional disruption of the medial temporal lobe-dependent networks is thought to underlie episodic memory deficits in aging and Alzheimer's disease. Previous studies revealed that the anterior medial temporal lobe is more vulnerable to pathological and neurodegenerative processes in Alzheimer's disease. In contrast, cognitive and structural imaging literature indicates posterior, as opposed to anterior, medial temporal lobe vulnerability in normal aging. However, the extent to which Alzheimer's and aging-related pathological processes relate to functional disruption of the medial temporal lobe-dependent brain networks is poorly understood. To address this knowledge gap, we examined functional connectivity alterations in the medial temporal lobe and its immediate functional neighborhood - the Anterior-Temporal and Posterior-Medial brain networks - in normal agers, individuals with preclinical Alzheimer's disease, and patients with Mild Cognitive Impairment or mild dementia due to Alzheimer's disease. In the Anterior-Temporal network and in the perirhinal cortex, in particular, we observed an inverted 'U-shaped' relationship between functional connectivity and Alzheimer's stage. According to our results, the preclinical phase of Alzheimer's disease is characterized by increased functional connectivity between the perirhinal cortex and other regions of the medial temporal lobe, as well as between the anterior medial temporal lobe and its one-hop neighbors in the Anterior-Temporal system. This effect is no longer present in symptomatic Alzheimer's disease. Instead, patients with symptomatic Alzheimer's disease displayed reduced hippocampal connectivity within the medial temporal lobe as well as hypoconnectivity within the Posterior-Medial system. For normal aging, our results led to three main conclusions: (1) intra-network connectivity of both the Anterior-Temporal and Posterior-Medial networks declines with age; (2) the anterior and posterior segments of the medial temporal lobe become increasingly decoupled from each other with advancing age; and, (3) the posterior subregions of the medial temporal lobe, especially the parahippocampal cortex, are more vulnerable to age-associated loss of function than their anterior counterparts. Together, the current results highlight evolving medial temporal lobe dysfunction in Alzheimer's disease and indicate different neurobiological mechanisms of the medial temporal lobe network disruption in aging vs. Alzheimer's disease.
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11
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Xie L, Wisse LEM, Wang J, Ravikumar S, Khandelwal P, Glenn T, Luther A, Lim S, Wolk DA, Yushkevich PA. Deep label fusion: A generalizable hybrid multi-atlas and deep convolutional neural network for medical image segmentation. Med Image Anal 2023; 83:102683. [PMID: 36379194 PMCID: PMC10009820 DOI: 10.1016/j.media.2022.102683] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 10/18/2022] [Accepted: 11/03/2022] [Indexed: 11/07/2022]
Abstract
Deep convolutional neural networks (DCNN) achieve very high accuracy in segmenting various anatomical structures in medical images but often suffer from relatively poor generalizability. Multi-atlas segmentation (MAS), while less accurate than DCNN in many applications, tends to generalize well to unseen datasets with different characteristics from the training dataset. Several groups have attempted to integrate the power of DCNN to learn complex data representations and the robustness of MAS to changes in image characteristics. However, these studies primarily focused on replacing individual components of MAS with DCNN models and reported marginal improvements in accuracy. In this study we describe and evaluate a 3D end-to-end hybrid MAS and DCNN segmentation pipeline, called Deep Label Fusion (DLF). The DLF pipeline consists of two main components with learnable weights, including a weighted voting subnet that mimics the MAS algorithm and a fine-tuning subnet that corrects residual segmentation errors to improve final segmentation accuracy. We evaluate DLF on five datasets that represent a diversity of anatomical structures (medial temporal lobe subregions and lumbar vertebrae) and imaging modalities (multi-modality, multi-field-strength MRI and Computational Tomography). These experiments show that DLF achieves comparable segmentation accuracy to nnU-Net (Isensee et al., 2020), the state-of-the-art DCNN pipeline, when evaluated on a dataset with similar characteristics to the training datasets, while outperforming nnU-Net on tasks that involve generalization to datasets with different characteristics (different MRI field strength or different patient population). DLF is also shown to consistently improve upon conventional MAS methods. In addition, a modality augmentation strategy tailored for multimodal imaging is proposed and demonstrated to be beneficial in improving the segmentation accuracy of learning-based methods, including DLF and DCNN, in missing data scenarios in test time as well as increasing the interpretability of the contribution of each individual modality.
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Affiliation(s)
- Long Xie
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, USA.
| | - Laura E M Wisse
- Department of Diagnostic Radiology, Lund University, Lund, Sweden
| | - Jiancong Wang
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Sadhana Ravikumar
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Pulkit Khandelwal
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Trevor Glenn
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Anica Luther
- Department of Diagnostic Radiology, Lund University, Lund, Sweden
| | - Sydney Lim
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - David A Wolk
- Penn Memory Center, University of Pennsylvania, Philadelphia, USA; Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - Paul A Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, USA
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12
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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, Jiménez MDMA, Rabal MDPM, Sánchez SC, Prieto CDLR, 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 2022. [PMID: 36464907 DOI: 10.1002/alz.12884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [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, Pennsylvania, USA
| | - Winifred Trotman
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sydney A Lim
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Eunice Chung
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ranjit Ittyerah
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sadhana Ravikumar
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Pulkit Khandelwal
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Karthik Prabhakaran
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Madigan L Lavery
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Daniel T Ohm
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Marianna Gabrielyan
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sandhitsu R Das
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Theresa Schuck
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Noah Capp
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Claire S Peterson
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Elyse Migdal
- College of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania, 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, Pennsylvania, USA
| | - Corey McMillan
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Murray Grossman
- 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 Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sharon X Xie
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - John Q Trojanowski
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - M Dylan Tisdall
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Laura E M Wisse
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Diagnostic Radiology, Lund University, Lund, Sweden
| | - 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
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13
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Khandelwal P, Duong MT, Chung E, Sadaghiani S, Lim SA, Ravikumar S, Arezoumandan S, Peterson C, Bedard ML, Capp N, Ittyerah R, Migdal E, Choi G, Kopp E, Patino BL, Hasan E, Li J, Prabhakaran K, Mizsei G, Gabrielyan M, Schuck T, Robinson JL, Ohm DT, Nasrallah IM, Lee EB, Trojanowski JQ, McMillan CT, Grossman M, Irwin DJ, Tisdall DM, Das SR, Wisse LEM, Wolk DA, Yushkevich PA. Deep Learning for Ultra High Resolution T2‐weighted 7 Tesla
Ex vivo
Magnetic Resonance Imaging Reveals Differential Subcortical Atrophy across Neurodegenerative Diseases. Alzheimers Dement 2022. [DOI: 10.1002/alz.062628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Pulkit Khandelwal
- University of Pennsylvania Philadelphia PA USA
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania Philadelphia PA USA
| | | | | | | | | | | | - Sanaz Arezoumandan
- Penn FTD Center, Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
- Digital Neuropathology Laboratory, Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
| | - Claire Peterson
- Penn FTD Center, Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
- Digital Neuropathology Laboratory, Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
| | - Madigan L Bedard
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania Philadelphia PA USA
| | - Noah Capp
- University of Pennsylvania Philadelphia PA USA
| | - Ranjit Ittyerah
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania Philadelphia PA USA
- Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
| | | | - Grace Choi
- University of Pennsylvania Philadelphia PA USA
| | - Emily Kopp
- University of Pennsylvania Philadelphia PA USA
| | | | - Eusha Hasan
- University of Pennsylvania Philadelphia PA USA
| | - Jiacheng Li
- University of Pennsylvania Philadelphia PA USA
| | | | | | | | | | | | - Daniel T Ohm
- Penn FTD Center, Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
- Digital Neuropathology Laboratory, Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
| | - Ilya M. Nasrallah
- University of Pennsylvania Philadelphia PA USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
| | - Eddie B Lee
- Perelman School of Medicine at the University of Pennsylvania Philadelphia PA USA
- Translational Neuropathology Research Laboratory, Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
- Center for Neurodegenerative Disease Research, Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
| | - John Q Trojanowski
- Center for Neurodegenerative Disease Research, Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
- Department of Pathology and Laboratory Medicine Philadelphia PA USA
| | - Corey T McMillan
- University of Pennsylvania Philadelphia PA USA
- Penn Alzheimer’s Disease Research Center, Perelman School of Medicine Philadelphia PA USA
| | - Murray Grossman
- Penn FTD Center, Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
- Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
| | - David J. Irwin
- Penn FTD Center, Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
- Digital Neuropathology Laboratory, Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
| | - Dylan M Tisdall
- Perelman School of Medicine, University og Pennsylvania Philadelphia PA USA
| | - Sandhitsu R. Das
- University of Pennsylvania Philadelphia PA USA
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania Philadelphia PA USA
| | - Laura EM Wisse
- Department of Diagnostic Radiology, Clinical Sciences, Lund University Lund Sweden
- Lund University Lund Sweden
| | - David A. Wolk
- Department of Neurology, University of Pennsylvania School of Medicine Philadelphia PA USA
- Penn Alzheimer’s Disease Research Center, University of Pennsylvania Philadelphia PA USA
| | - Paul A. Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania Philadelphia PA USA
- Penn Alzheimer’s Disease Research Center, Perelman School of Medicine Philadelphia PA USA
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14
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Sadaghiani S, Ravikumar S, Ittyerah R, Lim SA, Chung E, Bedard ML, Xie L, Das SR, Schuck T, Grossman M, Lee EB, Tisdall DM, Prabhakaran K, Detre JA, Mizsei G, Trojanowski JQ, Irwin DJ, Wisse LEM, Wolk DA, Yushkevich PA. Linking histology to post‐mortem 7T MRI measures of neurodegeneration. Alzheimers Dement 2022. [DOI: 10.1002/alz.066155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
| | | | | | | | | | | | - Long Xie
- University of Pennsylvania Philadelphia PA USA
| | | | | | | | - Eddie B Lee
- University of Pennsylvania Philadelphia PA USA
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15
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Ravikumar S, Denning A, Lim SA, Chung E, Ittyerah R, Wisse LEM, Xie L, Das SR, Robinson J, Schuck T, Grossman M, Lee EB, Tisdall DM, Mizsei G, Artacho‐Perula E, Martin MMIDO, Jimenez MA, Munoz M, Romero FJM, Rabal PM, Sanchez SC, Gonzalez JCD, Prieto R, Parada MC, Irwin DJ, Trojanowski JQ, Wolk DA, Insausti R, Yushkevich PA. Combining high‐resolution ex vivo MRI and histopathology to identify medial temporal lobe atrophy patterns specific to tau pathology in Alzheimer’s Disease. Alzheimers Dement 2022. [DOI: 10.1002/alz.065784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
| | | | | | | | | | | | - Long Xie
- University of Pennsylvania Philadelphia PA USA
| | | | | | | | | | - Eddie B Lee
- University of Pennsylvania Philadelphia PA USA
| | | | | | | | | | | | | | | | | | | | | | - Rosa Prieto
- University of Castilla‐La Mancha Albacete Spain
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16
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Khandelwal P, Sadaghiani S, Chung E, Lim SA, Duong MT, Ravikumar S, Arezoumandan S, Peterson C, Bedard ML, Capp N, Ittyerah R, Migdal E, Choi G, Kopp E, Patino BL, Hasan E, Li J, Prabhakaran K, Mizsei G, Gabrielyan M, Schuck T, Robinson J, Ohm DT, Lee EB, Trojanowski JQ, McMillan CT, Grossman M, Irwin DJ, Tisdall DM, Das SR, Wisse LEM, Wolk DA, Yushkevich PA. Deep learning pipeline for cortical gray matter segmentation and thickness analysis in Ultra High Resolution T2w 7 Tesla
Ex vivo
MRI across neurodegenerative diseases reveals associations with underlying neuropathology. Alzheimers Dement 2022. [DOI: 10.1002/alz.065737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Pulkit Khandelwal
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania Philadelphia PA USA
| | | | | | | | | | | | - Sanaz Arezoumandan
- Penn FTD Center, Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
- Digital Neuropathology Laboratory, Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
| | - Claire Peterson
- Penn FTD Center, Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
- Digital Neuropathology Laboratory, Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
| | - Madigan L Bedard
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania Philadelphia PA USA
| | - Noah Capp
- University of Pennsylvania Philadelphia PA USA
| | - Ranjit Ittyerah
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania Philadelphia PA USA
- Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
| | | | - Grace Choi
- University of Pennsylvania Philadelphia PA USA
| | - Emily Kopp
- University of Pennsylvania Philadelphia PA USA
| | | | - Eusha Hasan
- University of Pennsylvania Philadelphia PA USA
| | - Jiacheng Li
- University of Pennsylvania Philadelphia PA USA
| | | | | | | | | | | | - Daniel T Ohm
- Penn FTD Center, Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
- Digital Neuropathology Laboratory, Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
| | - Eddie B Lee
- Perelman School of Medicine at the University of Pennsylvania Philadelphia PA USA
- Translational Neuropathology Research Laboratory, Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
- Center for Neurodegenerative Disease Research, Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
| | - John Q Trojanowski
- Center for Neurodegenerative Disease Research, Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
- Department of Pathology and Laboratory Medicine Philadelphia PA USA
| | - Corey T McMillan
- University of Pennsylvania Philadelphia PA USA
- Penn Alzheimer’s Disease Research Center, Perelman School of Medicine Philadelphia PA USA
| | - Murray Grossman
- Penn FTD Center, Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
- Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
| | - David J. Irwin
- Penn FTD Center, Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
- Digital Neuropathology Laboratory, Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
| | | | - Sandhitsu R. Das
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania Philadelphia PA USA
- University of Pennsylvania Philadelphia PA USA
| | - Laura EM Wisse
- Department of Diagnostic Radiology, Clinical Sciences, Lund University Lund Sweden
- Lund University Lund Sweden
| | - David A. Wolk
- Department of Neurology, University of Pennsylvania School of Medicine Philadelphia PA USA
- Penn Alzheimer’s Disease Research Center, University of Pennsylvania Philadelphia PA USA
| | - Paul A. Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania Philadelphia PA USA
- Penn Alzheimer’s Disease Research Center, Perelman School of Medicine Philadelphia PA USA
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17
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Dong M, Xie L, Wang J, Das SR, Wolk DA, Yushkevich PA. Deep Learning Registration with Scan‐temporal Consistency May Improve Detection of Alzheimer’s Disease Progression. Alzheimers Dement 2022. [DOI: 10.1002/alz.068343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Mengjin Dong
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania Philadelphia PA USA
| | - Long Xie
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania Philadelphia PA USA
| | | | - Sandhitsu R. Das
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania Philadelphia PA USA
| | - David A. Wolk
- Penn Alzheimer’s Disease Research Center, University of Pennsylvania Philadelphia PA USA
| | - Paul A. Yushkevich
- University of Pennsylvania Philadelphia PA USA
- Penn Alzheimer’s Disease Research Center, Perelman School of Medicine Philadelphia PA USA
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18
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Wisse LEM, Xie L, Lyu X, Das SR, de Flores R, Lane J, Yushkevich PA, Wolk DA, Initiative DN. PART is part of SNAP‐MCI. Alzheimers Dement 2022. [DOI: 10.1002/alz.064439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
| | - Long Xie
- University of Pennsylvania Philadelphia PA USA
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania Philadelphia PA USA
| | - Xueying Lyu
- University of Pennsylvania Philadelphia PA USA
| | - Sandhitsu R. Das
- University of Pennsylvania Philadelphia PA USA
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania Philadelphia PA USA
| | - Robin de Flores
- Normandie University, UNICAEN, INSERM, U, , PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen‐Normandie, Cyceron Caen 1237 France
| | - Jacqueline Lane
- Penn Memory Center, University of Pennsylvania Philadelphia PA USA
| | - Paul A. Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania Philadelphia PA USA
| | - David A. Wolk
- Department of Neurology, University of Pennsylvania School of Medicine Philadelphia PA USA
- Penn Alzheimer’s Disease Research Center, University of Pennsylvania Philadelphia PA USA
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19
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Lyu X, Duong MT, Xie L, Richardson H, de Flores R, DiCalogero M, McMillan CT, Robinson J, Trojanowski JQ, Grossman M, Lee EB, Irwin DJ, Dickerson BC, Xie SX, Nasrallah IM, Yushkevich PA, Wolk DA, Das SR. Tau‐Neurodegeneration mismatch reveals vulnerability and resilience in Alzheimer’s continuum and Non‐Alzheimer’s pathophysiology. Alzheimers Dement 2022. [DOI: 10.1002/alz.062542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Xueying Lyu
- University of Pennsylvania Philadelphia PA USA
| | | | - Long Xie
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania Philadelphia PA USA
| | | | - Robin de Flores
- Normandie University, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen‐Normandie, Cyceron Caen France
| | | | | | | | - John Q Trojanowski
- Center for Neurodegenerative Disease Research, Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
| | - Murray Grossman
- Penn FTD Center, Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
| | - Eddie B Lee
- Center for Neurodegenerative Disease Research, University of Pennsylvania Philadelphia PA USA
| | - David J. Irwin
- Penn FTD Center, Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
| | | | - Sharon X Xie
- Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
| | | | - Paul A. Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania Philadelphia PA USA
| | - David A. Wolk
- Penn Alzheimer’s Disease Research Center, University of Pennsylvania Philadelphia PA USA
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20
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Xie L, Wisse LEM, Das SR, Lyu X, de Flores R, Yushkevich PA, Wolk DA. Tau burden is associated with cross‐sectional and longitudinal neurodegeneration in the medial temporal lobe in cognitively normal individuals. Alzheimers Dement 2022. [DOI: 10.1002/alz.067095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Long Xie
- University of Pennsylvania Philadelphia PA USA
| | - Laura EM Wisse
- Department of Diagnostic Radiology, Clinical Sciences, Lund University Lund Sweden
| | | | - Xueying Lyu
- University of Pennsylvania Philadelphia PA USA
| | - Robin de Flores
- Normandie University, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen‐Normandie, Cyceron Caen France
| | | | - David A. Wolk
- Penn Alzheimer’s Disease Research Center, University of Pennsylvania Philadelphia PA USA
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21
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Perez SD, Phillips JS, Norise C, Kinney NG, Vaddi P, Halpin A, Rascovsky K, Irwin DJ, McMillan CT, Xie L, Wisse LE, Yushkevich PA, Kallogjeri D, Grossman M, Cousins KA. Neuropsychological and Neuroanatomical Features of Patients with Behavioral/Dysexecutive Variant Alzheimer’s disease (AD): A Comparison to Behavioral Variant Frontotemporal Dementia and Amnestic AD Groups. J Alzheimers Dis 2022; 89:641-658. [PMID: 35938245 PMCID: PMC10117623 DOI: 10.3233/jad-215728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: An understudied variant of Alzheimer’s disease (AD), the behavioral/dysexecutive variant of AD (bvAD), is associated with progressive personality, behavior, and/or executive dysfunction and frontal atrophy. Objective: This study characterizes the neuropsychological and neuroanatomical features associated with bvAD by comparing it to behavioral variant frontotemporal dementia (bvFTD), amnestic AD (aAD), and subjects with normal cognition. Methods: Subjects included 16 bvAD, 67 bvFTD, and 18 aAD patients, and 26 healthy controls. Neuropsychological assessment and MRI data were compared between these groups. Results: Compared to bvFTD, bvAD showed more significant visuospatial impairments (Rey Figure copy and recall), more irritability (Neuropsychological Inventory), and equivalent verbal memory (Philadelphia Verbal Learning Test). Compared to aAD, bvAD indicated more executive dysfunction (F-letter fluency) and better visuospatial performance. Neuroimaging analysis found that bvAD showed cortical thinning relative to bvFTD posteriorly in left temporal-occipital regions; bvFTD had cortical thinning relative to bvAD in left inferior frontal cortex. bvAD had cortical thinning relative to aAD in prefrontal and anterior temporal regions. All patient groups had lower volumes than controls in both anterior and posterior hippocampus. However, bvAD patients had higher average volume than aAD patients in posterior hippocampus and higher volume than bvFTD patients in anterior hippocampus after adjustment for age and intracranial volume. Conclusion: Findings demonstrated that underlying pathology mediates disease presentation in bvAD and bvFTD.
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Affiliation(s)
- Sophia Dominguez Perez
- Penn Frontotemporal Degeneration Center (FTDC), University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA
| | - Jeffrey S. Phillips
- Penn Frontotemporal Degeneration Center (FTDC), University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Catherine Norise
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Nikolas G. Kinney
- Penn Frontotemporal Degeneration Center (FTDC), University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Prerana Vaddi
- Penn Frontotemporal Degeneration Center (FTDC), University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Amy Halpin
- Penn Frontotemporal Degeneration Center (FTDC), University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychology, University of Maine, Orono, ME, USA
| | - Katya Rascovsky
- Penn Frontotemporal Degeneration Center (FTDC), University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - David J. Irwin
- Penn Frontotemporal Degeneration Center (FTDC), University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Disease Research, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Corey T. McMillan
- Penn Frontotemporal Degeneration Center (FTDC), University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Long Xie
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Image Computing and Science Lab & Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Laura E.M. Wisse
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Image Computing and Science Lab & Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Diagnostic Radiology, Lund University, Lund, Sweden
| | - Paul A. Yushkevich
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Image Computing and Science Lab & Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dorina Kallogjeri
- Department of Otolaryngology, Washington University, St. Louis, MO, USA
| | - Murray Grossman
- Penn Frontotemporal Degeneration Center (FTDC), University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Katheryn A.Q. Cousins
- Penn Frontotemporal Degeneration Center (FTDC), University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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22
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Aly AH, Khandelwal P, Aly AH, Kawashima T, Mori K, Saito Y, Hung J, Gorman JH, Pouch AM, Gorman RC, Yushkevich PA. Fully Automated 3D Segmentation and Diffeomorphic Medial Modeling of the Left Ventricle Mitral Valve Complex in Ischemic Mitral Regurgitation. Med Image Anal 2022; 80:102513. [PMID: 35772323 DOI: 10.1016/j.media.2022.102513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 05/30/2022] [Accepted: 06/10/2022] [Indexed: 10/18/2022]
Abstract
There is an urgent unmet need to develop a fully-automated image-based left ventricle mitral valve analysis tool to support surgical decision making for ischemic mitral regurgitation patients. This requires an automated tool for segmentation and modeling of the left ventricle and mitral valve from immediate pre-operative 3D transesophageal echocardiography. Previous works have presented methods for semi-automatically segmenting and modeling the mitral valve, but do not include the left ventricle and do not avoid self-intersection of the mitral valve leaflets during shape modeling. In this study, we develop and validate a fully automated algorithm for segmentation and shape modeling of the left ventricular mitral valve complex from pre-operative 3D transesophageal echocardiography. We performed a 3-fold nested cross validation study on two datasets from separate institutions to evaluate automated segmentations generated by nnU-net with the expert manual segmentation which yielded average overall Dice scores of 0.82±0.03 (set A), 0.87±0.08 (set B) respectively. A deformable medial template was subsequently fitted to the segmentation to generate shape models. Comparison of shape models to the manual and automatically generated segmentations resulted in an average Dice score of 0.93-0.94 and 0.75-0.81 for the left ventricle and mitral valve, respectively. This is a substantial step towards automatically analyzing the left ventricle mitral valve complex in the operating room.
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Affiliation(s)
- Ahmed H Aly
- Division of Cardiothoracic Surgery, The Ohio State University, Columbus, OH, USA; Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA; Gorman Cardiovascular Research Group, University of Pennsylvania, Philadelphia, PA, USA.
| | - Pulkit Khandelwal
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Abdullah H Aly
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; The Ohio State College of Medicine, Columbus, OH, USA
| | - Takayuki Kawashima
- Gorman Cardiovascular Research Group, University of Pennsylvania, Philadelphia, PA, USA
| | - Kazuki Mori
- Gorman Cardiovascular Research Group, University of Pennsylvania, Philadelphia, PA, USA
| | - Yoshiaki Saito
- Gorman Cardiovascular Research Group, University of Pennsylvania, Philadelphia, PA, USA
| | - Judy Hung
- Department of Cardiology at Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Joseph H Gorman
- Gorman Cardiovascular Research Group, University of Pennsylvania, Philadelphia, PA, USA; Department of Surgery, University of Pennsylvania, Philadelphia, PA, USA
| | - Alison M Pouch
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Robert C Gorman
- Gorman Cardiovascular Research Group, University of Pennsylvania, Philadelphia, PA, USA; Department of Surgery, University of Pennsylvania, Philadelphia, PA, USA
| | - Paul A Yushkevich
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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23
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Duong MT, Das SR, Lyu X, Xie L, Richardson H, Xie SX, Yushkevich PA, Wolk DA, Nasrallah IM. Dissociation of tau pathology and neuronal hypometabolism within the ATN framework of Alzheimer's disease. Nat Commun 2022; 13:1495. [PMID: 35314672 PMCID: PMC8938426 DOI: 10.1038/s41467-022-28941-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 02/11/2022] [Indexed: 11/08/2022] Open
Abstract
Alzheimer's disease (AD) is defined by amyloid (A) and tau (T) pathologies, with T better correlated to neurodegeneration (N). However, T and N have complex regional relationships in part related to non-AD factors that influence N. With machine learning, we assessed heterogeneity in 18F-flortaucipir vs. 18F-fluorodeoxyglucose positron emission tomography as markers of T and neuronal hypometabolism (NM) in 289 symptomatic patients from the Alzheimer's Disease Neuroimaging Initiative. We identified six T/NM clusters with differing limbic and cortical patterns. The canonical group was defined as the T/NM pattern with lowest regression residuals. Groups resilient to T had less hypometabolism than expected relative to T and displayed better cognition than the canonical group. Groups susceptible to T had more hypometabolism than expected given T and exhibited worse cognitive decline, with imaging and clinical measures concordant with non-AD copathologies. Together, T/NM mismatch reveals distinct imaging signatures with pathobiological and prognostic implications for AD.
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Affiliation(s)
- Michael Tran Duong
- 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
| | - Sandhitsu R Das
- 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
| | - Xueying Lyu
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Long Xie
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Hayley Richardson
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sharon X Xie
- Alzheimer's Disease Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Paul A Yushkevich
- 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
- Alzheimer's Disease Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - David A Wolk
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA.
- 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.
| | - Ilya M Nasrallah
- 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.
- Alzheimer's Disease Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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24
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de Flores R, Das SR, Xie L, Wisse LEM, Lyu X, Shah P, Yushkevich PA, Wolk DA. Medial Temporal Lobe Networks in Alzheimer's Disease: Structural and Molecular Vulnerabilities. J Neurosci 2022; 42:2131-2141. [PMID: 35086906 PMCID: PMC8916768 DOI: 10.1523/jneurosci.0949-21.2021] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 11/30/2021] [Accepted: 12/04/2021] [Indexed: 11/21/2022] Open
Abstract
The medial temporal lobe (MTL) is connected to the rest of the brain through two main networks: the anterior-temporal (AT) and the posterior-medial (PM) systems. Given the crucial role of the MTL and networks in the physiopathology of Alzheimer's disease (AD), the present study aimed at (1) investigating whether MTL atrophy propagates specifically within the AT and PM networks, and (2) evaluating the vulnerability of these networks to AD proteinopathies. To do that, we used neuroimaging data acquired in human male and female in three distinct cohorts: (1) resting-state functional MRI (rs-fMRI) from the aging brain cohort (ABC) to define the AT and PM networks (n = 68); (2) longitudinal structural MRI from Alzheimer's disease neuroimaging initiative (ADNI)GO/2 to highlight structural covariance patterns (n = 349); and (3) positron emission tomography (PET) data from ADNI3 to evaluate the networks' vulnerability to amyloid and tau (n = 186). Our results suggest that the atrophy of distinct MTL subregions propagates within the AT and PM networks in a dissociable manner. Brodmann area (BA)35 structurally covaried within the AT network while the parahippocampal cortex (PHC) covaried within the PM network. In addition, these networks are differentially associated with relative tau and amyloid burden, with higher tau levels in AT than in PM and higher amyloid levels in PM than in AT. Our results also suggest differences in the relative burden of tau species. The current results provide further support for the notion that two distinct MTL networks display differential alterations in the context of AD. These findings have important implications for disease spread and the cognitive manifestations of AD.SIGNIFICANCE STATEMENT The current study provides further support for the notion that two distinct medial temporal lobe (MTL) networks, i.e., anterior-temporal (AT) and the posterior-medial (PM), display differential alterations in the context of Alzheimer's disease (AD). Importantly, neurodegeneration appears to occur within these networks in a dissociable manner marked by their covariance patterns. In addition, the AT and PM networks are also differentially associated with relative tau and amyloid burden, and perhaps differences in the relative burden of tau species [e.g., neurofibriliary tangles (NFTs) vs tau in neuritic plaques]. These findings, in the context of a growing literature consistent with the present results, have important implications for disease spread and the cognitive manifestations of AD in light of the differential cognitive processes ascribed to them.
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Affiliation(s)
- Robin de Flores
- Department of Neurology, University of Pennsylvania, Philadelphia 19104, Pennsylvania
- Université de Caen Normandie, Institut National de la Santé et de la Recherche Médicale Unité Mixte de Recherche Scientifique (UMRS) Unité 1237, Caen 14000, France
| | - Sandhitsu R Das
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia 19104, Pennsylvania
| | - Long Xie
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia 19104, Pennsylvania
- Department of Radiology, University of Pennsylvania, Philadelphia 19104, Pennsylvania
| | - Laura E M Wisse
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia 19104, Pennsylvania
- Department of Diagnostic Radiology, Lund University, Lund 22185, Sweden
| | - Xueying Lyu
- Department of Bioengineering, University of Pennsylvania, Philadelphia 19104, Pennsylvania
| | - Preya Shah
- Department of Bioengineering, University of Pennsylvania, Philadelphia 19104, Pennsylvania
| | - Paul A Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia 19104, Pennsylvania
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia 19104, Pennsylvania
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25
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Khandelwal P, Zimmerman CE, Xie L, Lee H, Song HK, Yushkevich PA, Vossough A, Bartlett SP, Wehrli FW. Automatic Segmentation of Bone Selective MR Images for Visualization and Craniometry of the Cranial Vault. Acad Radiol 2022; 29 Suppl 3:S98-S106. [PMID: 33903011 PMCID: PMC8536795 DOI: 10.1016/j.acra.2021.03.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 03/11/2021] [Accepted: 03/11/2021] [Indexed: 11/24/2022]
Abstract
RATIONALE AND OBJECTIVES Solid-state MRI has been shown to provide a radiation-free alternative imaging strategy to CT. However, manual image segmentation to produce bone-selective MR-based 3D renderings is time and labor intensive, thereby acting as a bottleneck in clinical practice. The objective of this study was to evaluate an automatic multi-atlas segmentation pipeline for use on cranial vault images entirely circumventing prior manual intervention, and to assess concordance of craniometric measurements between pipeline produced MRI and CT-based 3D skull renderings. MATERIALS AND METHODS Dual-RF, dual-echo, 3D UTE pulse sequence MR data were obtained at 3T on 30 healthy subjects along with low-dose CT images between December 2018 to January 2020 for this prospective study. The four-point MRI datasets (two RF pulse widths and two echo times) were combined to produce bone-specific images. CT images were thresholded and manually corrected to segment the cranial vault. CT images were then rigidly registered to MRI using mutual information. The corresponding cranial vault segmentations were then transformed to MRI. The "ground truth" segmentations served as reference for the MR images. Subsequently, an automated multi-atlas pipeline was used to segment the bone-selective images. To compare manually and automatically segmented MR images, the Dice similarity coefficient (DSC) and Hausdorff distance (HD) were computed, and craniometric measurements between CT and automated-pipeline MRI-based segmentations were examined via Lin's concordance coefficient (LCC). RESULTS Automated segmentation reduced the need for an expert to obtain segmentation. Average DSC was 90.86 ± 1.94%, and average 95th percentile HD was 1.65 ± 0.44 mm between ground truth and automated segmentations. MR-based measurements differed from CT-based measurements by 0.73-1.2 mm on key craniometric measurements. LCC for distances between CT and MR-based landmarks were vertex-basion: 0.906, left-right frontozygomatic suture: 0.780, and glabella-opisthocranium: 0.956 for the three measurements. CONCLUSION Good agreement between CT and automated MR-based 3D cranial vault renderings has been achieved, thereby eliminating the laborious manual segmentation process. Target applications comprise craniofacial surgery as well as imaging of traumatic injuries and masses involving both bone and soft tissue.
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Affiliation(s)
- Pulkit Khandelwal
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA,Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Carrie E. Zimmerman
- Division of Plastic and Reconstructive Surgery, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Long Xie
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA,Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Hyunyeol Lee
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA,Laboratory for Structural, Physiologic and Functional Imaging, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Hee Kwon Song
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA,Laboratory for Structural, Physiologic and Functional Imaging, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Paul A. Yushkevich
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA,Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Arastoo Vossough
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA,Children’s Hospital of Philadelphia, Department of Radiology, Philadelphia, PA, USA
| | - Scott P. Bartlett
- Division of Plastic and Reconstructive Surgery, Children’s Hospital of Philadelphia, Philadelphia, PA, USA,Department of Surgery, University of Pennsylvania, Philadelphia, PA USA
| | - Felix W. Wehrli
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA,Laboratory for Structural, Physiologic and Functional Imaging, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA,Corresponding Author: University of Pennsylvania, Department of Radiology, MRI Education Center, 1 Founders Building, 3400 Spruce Street, Philadelphia, PA 19104-4283,
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26
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Wisse LEM, Xie L, Das SR, De Flores R, Hansson O, Habes M, Doshi J, Davatzikos C, Yushkevich PA, Wolk DA. Tau pathology mediates age effects on medial temporal lobe structure. Neurobiol Aging 2022; 109:135-144. [PMID: 34740075 PMCID: PMC8800343 DOI: 10.1016/j.neurobiolaging.2021.09.017] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 09/14/2021] [Accepted: 09/15/2021] [Indexed: 01/03/2023]
Abstract
Hippocampal atrophy is endemic in 'normal aging' but it is unclear what factors drive age-related changes in medial temporal lobe (MTL) structural measures. We investigated cross-sectional (n = 191) and longitudinal (n = 164) MTL atrophy patterns in cognitively normal older adults from ADNI-GO/2 with no to low cerebral β-amyloid and assessed whether white matter hyperintensities (WMHs) and cerebrospinal fluid (CSF) phospho tau (p-tau) levels can explain age-related changes in the MTL. Age was significantly associated with hippocampal volumes and Brodmann Area (BA) 35 thickness, regions affected early by neurofibrillary tangle pathology, in the cross-sectional analysis and with anterior and/or posterior hippocampus, entorhinal cortex and BA35 in the longitudinal analysis. CSF p-tau was significantly associated with hippocampal volumes and atrophy rates. Mediation analyses showed that CSF p-tau levels partially mediated age effects on hippocampal atrophy rates. No significant associations were observed for WMHs. These findings point toward a role of tau pathology, potentially reflecting Primary Age-Related Tauopathy, in age-related MTL structural changes and suggests a potential role for tau-targeted interventions in age-associated neurodegeneration and memory decline.
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Affiliation(s)
- LEM Wisse
- Department of Diagnostic Radiology, Lund University, Lund, Sweden
| | - L Xie
- Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - SR Das
- Penn Memory Center, Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - R De Flores
- Université Normandie, Inserm, Université de Caen-Normandie, Inserm UMR-S U1237, GIP Cyceron, Caen, France
| | - O Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden,Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - M Habes
- Biggs Alzheimer’s Institute, UT Health, San Antonio, USA
| | - J Doshi
- Section of Biomedical Image Analysis, University of Pennsylvania, Philadelphia, PA, USA
| | - C Davatzikos
- Section of Biomedical Image Analysis, University of Pennsylvania, Philadelphia, PA, USA
| | - PA Yushkevich
- Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - DA Wolk
- Penn Memory Center, Department of Neurology, University of Pennsylvania, Philadelphia, USA
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27
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Berron D, Vogel JW, Insel P, Pereira JB, Xie L, Wisse L, Yushkevich PA, Palmqvist S, Mattsson‐Carlgren N, Stomrud E, Smith R, Strandberg O, Hansson O. Early stages of tau pathology and its associations with functional connectivity, atrophy and memory. Alzheimers Dement 2021. [DOI: 10.1002/alz.053787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- David Berron
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University Lund Sweden
- German Center for Neurodegenerative Diseases (DZNE) Magdeburg Germany
| | - Jacob W. Vogel
- Department of Psychiatry, University of Pennsylvania Philadelphia PA USA
| | - Philip Insel
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University Malmö Sweden
| | - Joana B. Pereira
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences, and Society, Karolinska Institutet, Stockholm, Sweden Stockholm Sweden
| | - Long Xie
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania Philadelphia PA USA
| | | | - Paul A. Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania Philadelphia PA USA
- Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
| | | | | | | | | | | | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University Malmö Sweden
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28
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Ravikumar S, Wisse L, Lim SA, Ittyerah R, Xie L, Bedard ML, Das SR, Irwin DJ, Lee EB, Tisdall DM, Prabhakaran K, Detre JA, Trojanowski JQ, Robinson J, Schuck T, Grossman M, Mizsei G, Artacho‐Perula E, Martin MMIDO, del Mar Arroyo Jimenez M, Munoz M, Romero FJM, del Pilar Marcos Rabal M, Sanchez SC, Gonzalez JCD, de la Rosa Prieto C, Parada MC, Wolk DA, Insausti R, Yushkevich PA. Unfolding the medial temporal lobe to characterize neurodegeneration due to Alzheimer's disease pathology. Alzheimers Dement 2021. [DOI: 10.1002/alz.057622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Sadhana Ravikumar
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
| | | | - Sydney A. Lim
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
| | - Ranjit Ittyerah
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
| | - Long Xie
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
| | - Madigan L. Bedard
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
| | - Sandhitsu R. Das
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
| | - David J. Irwin
- Perelman School of Medicine University of Pennsylvania Philadelphia PA USA
| | - Eddie B. Lee
- Center for Neurodegenerative Disease Research University of Pennsylvania Philadelphia PA USA
| | - Dylan M. Tisdall
- Perelman School of Medicine University of Pennsylvania Philadelphia PA USA
| | | | | | - John Q. Trojanowski
- Center for Neurodegenerative Disease Research Perelman School of Medicine University of Pennsylvania Philadelphia PA USA
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Paul A. Yushkevich
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
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Xie L, Das SR, Wisse L, Ittyerah R, de Flores R, Yushkevich PA, Wolk DA. Baseline structural MRI and plasma biomarkers predict longitudinal structural atrophy and cognitive decline in early AD. Alzheimers Dement 2021. [DOI: 10.1002/alz.056164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Long Xie
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania Philadelphia PA USA
| | - Sandhitsu R. Das
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania Philadelphia PA USA
| | - Laura Wisse
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania Philadelphia PA USA
| | - Ranjit Ittyerah
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania Philadelphia PA USA
| | - Robin de Flores
- Inserm, Inserm UMR‐S U1237, Université de Caen‐Normandie, GIP Cyceron Caen France
| | - Paul A. Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania Philadelphia PA USA
| | - David A. Wolk
- Penn Memory Center, Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
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Das SR, Lyu X, Duong MT, Xie L, McCollum L, de Flores R, DiCalogero M, Irwin DJ, Dickerson BC, Nasrallah IM, Yushkevich PA, Wolk DA. Tau-Atrophy Variability Reveals Phenotypic Heterogeneity in Alzheimer's Disease. Ann Neurol 2021; 90:751-762. [PMID: 34617306 PMCID: PMC8841129 DOI: 10.1002/ana.26233] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Revised: 09/27/2021] [Accepted: 09/27/2021] [Indexed: 01/02/2023]
Abstract
OBJECTIVE Tau neurofibrillary tangles (T) are the primary driver of downstream neurodegeneration (N) and subsequent cognitive impairment in Alzheimer's disease (AD). However, there is substantial variability in the T-N relationship - manifested in higher or lower atrophy than expected for level of tau in a given brain region. The goal of this study was to determine if region-based quantitation of this variability allows for identification of underlying modulatory factors, including polypathology. METHODS Cortical thickness (N) and 18 F-Flortaucipir SUVR (T) were computed in 104 gray matter regions from a cohort of cognitively-impaired, amyloid-positive (A+) individuals. Region-specific residuals from a robust linear fit between SUVR and cortical thickness were computed as a surrogate for T-N mismatch. A summary T-N mismatch metric defined using residuals were correlated with demographic and imaging-based modulatory factors, and to partition the cohort into data-driven subgroups. RESULTS The summary T-N mismatch metric correlated with underlying factors such as age and burden of white matter hyperintensity lesions. Data-driven subgroups based on clustering of residuals appear to represent different biologically relevant phenotypes, with groups showing distinct spatial patterns of higher or lower atrophy than expected. INTERPRETATION These data support the notion that a measure of deviation from a normative relationship between tau burden and neurodegeneration across brain regions in individuals on the AD continuum captures variability due to multiple underlying factors, and can reveal phenotypes, which if validated, may help identify possible contributors to neurodegeneration in addition to tau, which may ultimately be useful for cohort selection in clinical trials. ANN NEUROL 2021;90:751-762.
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Affiliation(s)
- Sandhitsu R Das
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Xueying Lyu
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael Tran Duong
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Long Xie
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Lauren McCollum
- Department of Medicine, University of Tennessee, Knoxville, TN, USA
| | - Robin de Flores
- Université de Caen Normandie, INSERM UMRS U1237, Caen, France
| | - Michael DiCalogero
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - David J Irwin
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Ilya M Nasrallah
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Paul A Yushkevich
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
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31
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Dong M, Xie L, Das SR, Wang J, Wisse LEM, deFlores R, Wolk DA, Yushkevich PA. DeepAtrophy: Teaching a neural network to detect progressive changes in longitudinal MRI of the hippocampal region in Alzheimer's disease. Neuroimage 2021; 243:118514. [PMID: 34450261 PMCID: PMC8604562 DOI: 10.1016/j.neuroimage.2021.118514] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 08/18/2021] [Accepted: 08/23/2021] [Indexed: 11/26/2022] Open
Abstract
Measures of change in hippocampal volume derived from longitudinal MRI are a well-studied biomarker of disease progression in Alzheimer's disease (AD) and are used in clinical trials to track therapeutic efficacy of disease-modifying treatments. However, longitudinal MRI change measures based on deformable registration can be confounded by MRI artifacts, resulting in over-estimation or underestimation of hippocampal atrophy. For example, the deformation-based-morphometry method ALOHA (Das et al., 2012) finds an increase in hippocampal volume in a substantial proportion of longitudinal scan pairs from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, unexpected, given that the hippocampal gray matter is lost with age and disease progression. We propose an alternative approach to quantify disease progression in the hippocampal region: to train a deep learning network (called DeepAtrophy) to infer temporal information from longitudinal scan pairs. The underlying assumption is that by learning to derive time-related information from scan pairs, the network implicitly learns to detect progressive changes that are related to aging and disease progression. Our network is trained using two categorical loss functions: one that measures the network's ability to correctly order two scans from the same subject, input in arbitrary order; and another that measures the ability to correctly infer the ratio of inter-scan intervals between two pairs of same-subject input scans. When applied to longitudinal MRI scan pairs from subjects unseen during training, DeepAtrophy achieves greater accuracy in scan temporal ordering and interscan interval inference tasks than ALOHA (88.5% vs. 75.5% and 81.1% vs. 75.0%, respectively). A scalar measure of time-related change in a subject level derived from DeepAtrophy is then examined as a biomarker of disease progression in the context of AD clinical trials. We find that this measure performs on par with ALOHA in discriminating groups of individuals at different stages of the AD continuum. Overall, our results suggest that using deep learning to infer temporal information from longitudinal MRI of the hippocampal region has good potential as a biomarker of disease progression, and hints that combining this approach with conventional deformation-based morphometry algorithms may lead to improved biomarkers in the future.
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Affiliation(s)
- Mengjin Dong
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States.
| | - Long Xie
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States; Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - Sandhitsu R Das
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States; Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, United States; Penn Memory Center, University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - Jiancong Wang
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Laura E M Wisse
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States; Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, United States; Department of Diagnostic Radiology, Lund University, Lund, Sweden
| | - Robin deFlores
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, United States; Penn Memory Center, University of Pennsylvania, Philadelphia, Pennsylvania, United States; Institut National de la Santé et de la Recherche Médicale (INSERM), Caen, France
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, United States; Penn Memory Center, University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - Paul A Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States; Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, United States
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Ravikumar S, Wisse LEM, Lim S, Ittyerah R, Xie L, Bedard ML, Das SR, Lee EB, Tisdall MD, Prabhakaran K, Lane J, Detre JA, Mizsei G, Trojanowski JQ, Robinson JL, Schuck T, Grossman M, Artacho-Pérula E, de Onzoño Martin MMI, Del Mar Arroyo Jiménez M, Muñoz M, Romero FJM, Del Pilar Marcos Rabal M, Sánchez SC, González JCD, de la Rosa Prieto C, Parada MC, Irwin DJ, Wolk DA, Insausti R, Yushkevich PA. Ex vivo MRI atlas of the human medial temporal lobe: characterizing neurodegeneration due to tau pathology. Acta Neuropathol Commun 2021; 9:173. [PMID: 34689831 PMCID: PMC8543911 DOI: 10.1186/s40478-021-01275-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Accepted: 10/11/2021] [Indexed: 01/08/2023] Open
Abstract
Tau neurofibrillary tangle (NFT) pathology in the medial temporal lobe (MTL) is closely linked to neurodegeneration, and is the early pathological change associated with Alzheimer's disease (AD). To elucidate patterns of structural change in the MTL specifically associated with tau pathology, we compared high-resolution ex vivo MRI scans of human postmortem MTL specimens with histology-based pathological assessments of the MTL. MTL specimens were obtained from twenty-nine brain donors, including patients with AD, other dementias, and individuals with no known history of neurological disease. Ex vivo MRI scans were combined using a customized groupwise diffeomorphic registration approach to construct a 3D probabilistic atlas that captures the anatomical variability of the MTL. Using serial histology imaging in eleven specimens, we labelled the MTL subregions in the atlas based on cytoarchitecture. Leveraging the atlas and neuropathological ratings of tau and TAR DNA-binding protein 43 (TDP-43) pathology severity, morphometric analysis was performed to correlate regional MTL thickness with the severity of tau pathology, after correcting for age and TDP-43 pathology. We found significant correlations between tau pathology and thickness in the entorhinal cortex (ERC) and stratum radiatum lacunosum moleculare (SRLM). When focusing on cases with low levels of TDP-43 pathology, we found strong associations between tau pathology and thickness in the ERC, SRLM and the subiculum/cornu ammonis 1 (CA1) subfields of the hippocampus, consistent with early Braak stages.
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Affiliation(s)
- Sadhana Ravikumar
- Department of Bioengineering, University of Pennsylvania, Richards Building 6th Floor, Suite D, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA.
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA.
| | - Laura E M Wisse
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Diagnostic Radiology, Lund University, 22242, Lund, Sweden
| | - Sydney Lim
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Ranjit Ittyerah
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Long Xie
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Madigan L Bedard
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Sandhitsu R Das
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Edward B Lee
- Department of Pathology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - M Dylan Tisdall
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Karthik Prabhakaran
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Jacqueline Lane
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - John A Detre
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Gabor Mizsei
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - John Q Trojanowski
- Department of Pathology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - John L Robinson
- Department of Pathology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Theresa Schuck
- Department of Pathology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Murray Grossman
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Emilio Artacho-Pérula
- Human Neuroanatomy Laboratory, CSIC Neuromax Associated Unit, University of Castilla La Mancha, 02008, Albacete, Spain
| | | | - María Del Mar Arroyo Jiménez
- Human Neuroanatomy Laboratory, CSIC Neuromax Associated Unit, University of Castilla La Mancha, 02008, Albacete, Spain
| | - Monica Muñoz
- Human Neuroanatomy Laboratory, CSIC Neuromax Associated Unit, University of Castilla La Mancha, 02008, Albacete, Spain
| | | | - Maria Del Pilar Marcos Rabal
- Human Neuroanatomy Laboratory, CSIC Neuromax Associated Unit, University of Castilla La Mancha, 02008, Albacete, Spain
| | - Sandra Cebada Sánchez
- Human Neuroanatomy Laboratory, CSIC Neuromax Associated Unit, University of Castilla La Mancha, 02008, Albacete, Spain
| | - José Carlos Delgado González
- Human Neuroanatomy Laboratory, CSIC Neuromax Associated Unit, University of Castilla La Mancha, 02008, Albacete, Spain
| | - Carlos de la Rosa Prieto
- Human Neuroanatomy Laboratory, CSIC Neuromax Associated Unit, University of Castilla La Mancha, 02008, Albacete, Spain
| | - Marta Córcoles Parada
- Human Neuroanatomy Laboratory, CSIC Neuromax Associated Unit, University of Castilla La Mancha, 02008, Albacete, Spain
| | - 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
| | - Ricardo Insausti
- Human Neuroanatomy Laboratory, CSIC Neuromax Associated Unit, University of Castilla La Mancha, 02008, Albacete, Spain
| | - Paul A Yushkevich
- Department of Bioengineering, University of Pennsylvania, Richards Building 6th Floor, Suite D, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
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Yushkevich PA, Muñoz López M, Iñiguez de Onzoño Martin M, Ittyerah R, Lim S, Ravikumar S, Bedard ML, Pickup S, Liu W, Wang J, Hung LY, Lasserve J, Vergnet N, Xie L, Dong M, Cui S, McCollum L, Robinson JL, Schuck T, de Flores R, Grossman M, Tisdall MD, Prabhakaran K, Mizsei G, Das SR, Artacho-Pérula E, Arroyo Jiménez MDM, Marcos Raba MP, Molina Romero FJ, Cebada Sánchez S, Delgado González JC, de la Rosa-Prieto C, Córcoles Parada M, Lee EB, Trojanowski JQ, Ohm DT, Wisse LEM, Wolk DA, Irwin DJ, Insausti R. Three-dimensional mapping of neurofibrillary tangle burden in the human medial temporal lobe. Brain 2021; 144:2784-2797. [PMID: 34259858 PMCID: PMC8783607 DOI: 10.1093/brain/awab262] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 05/19/2021] [Accepted: 06/21/2021] [Indexed: 11/14/2022] Open
Abstract
Tau protein neurofibrillary tangles are closely linked to neuronal/synaptic loss and cognitive decline in Alzheimer's disease and related dementias. Our knowledge of the pattern of neurofibrillary tangle progression in the human brain, critical to the development of imaging biomarkers and interpretation of in vivo imaging studies in Alzheimer's disease, is based on conventional two-dimensional histology studies that only sample the brain sparsely. To address this limitation, ex vivo MRI and dense serial histological imaging in 18 human medial temporal lobe specimens (age 75.3 ± 11.4 years, range 45 to 93) were used to construct three-dimensional quantitative maps of neurofibrillary tangle burden in the medial temporal lobe at individual and group levels. Group-level maps were obtained in the space of an in vivo brain template, and neurofibrillary tangles were measured in specific anatomical regions defined in this template. Three-dimensional maps of neurofibrillary tangle burden revealed significant variation along the anterior-posterior axis. While early neurofibrillary tangle pathology is thought to be confined to the transentorhinal region, we found similar levels of burden in this region and other medial temporal lobe subregions, including amygdala, temporopolar cortex, and subiculum/cornu ammonis 1 hippocampal subfields. Overall, the three-dimensional maps of neurofibrillary tangle burden presented here provide more complete information about the distribution of this neurodegenerative pathology in the region of the cortex where it first emerges in Alzheimer's disease, and may help inform the field about the patterns of pathology spread, as well as support development and validation of neuroimaging biomarkers.
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Affiliation(s)
- Paul A Yushkevich
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Mónica Muñoz López
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, University of Castilla-La Mancha, Albacete, Spain
| | | | - Ranjit Ittyerah
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Sydney Lim
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Sadhana Ravikumar
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Madigan L Bedard
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Stephen Pickup
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Weixia Liu
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Jiancong Wang
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Ling Yu Hung
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Jade Lasserve
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Nicolas Vergnet
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Long Xie
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Mengjin Dong
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Salena Cui
- Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - Lauren McCollum
- Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - John L Robinson
- Department of Pathology, University of Pennsylvania, Philadelphia, USA
| | - Theresa Schuck
- Department of Pathology, University of Pennsylvania, Philadelphia, USA
| | - Robin de Flores
- Institut National de la Santé et de la Recherche Médicale (INSERM), Caen, France
| | - Murray Grossman
- Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - M Dylan Tisdall
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | | | - Gabor Mizsei
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Sandhitsu R Das
- Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - Emilio Artacho-Pérula
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, University of Castilla-La Mancha, Albacete, Spain
| | | | - Marı’a Pilar Marcos Raba
- 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
| | - Edward B Lee
- Department of Pathology, University of Pennsylvania, Philadelphia, USA
| | | | - Daniel T Ohm
- Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - Laura E M Wisse
- Department of Diagnostic Radiology, University of Lund, Lund, Sweden
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - David J Irwin
- Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - Ricardo Insausti
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, University of Castilla-La Mancha, Albacete, Spain
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Wisse LEM, Ravikumar S, Ittyerah R, Lim S, Lane J, Bedard ML, Xie L, Das SR, Schuck T, Grossman M, Lee EB, Tisdall MD, Prabhakaran K, Detre JA, Mizsei G, Trojanowski JQ, Artacho-Pérula E, de Iñiguez de Onzono Martin MM, M Arroyo-Jiménez M, Muñoz Lopez M, Molina Romero FJ, P Marcos Rabal M, Cebada Sánchez S, Delgado González JC, de la Rosa Prieto C, Córcoles Parada M, Wolk DA, Irwin DJ, Insausti R, Yushkevich PA. Downstream effects of polypathology on neurodegeneration of medial temporal lobe subregions. Acta Neuropathol Commun 2021; 9:128. [PMID: 34289895 PMCID: PMC8293481 DOI: 10.1186/s40478-021-01225-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 07/06/2021] [Indexed: 12/14/2022] Open
Abstract
The medial temporal lobe (MTL) is a nidus for neurodegenerative pathologies and therefore an important region in which to study polypathology. We investigated associations between neurodegenerative pathologies and the thickness of different MTL subregions measured using high-resolution post-mortem MRI. Tau, TAR DNA-binding protein 43 (TDP-43), amyloid-β and α-synuclein pathology were rated on a scale of 0 (absent)-3 (severe) in the hippocampus and entorhinal cortex (ERC) of 58 individuals with and without neurodegenerative diseases (median age 75.0 years, 60.3% male). Thickness measurements in ERC, Brodmann Area (BA) 35 and 36, parahippocampal cortex, subiculum, cornu ammonis (CA)1 and the stratum radiatum lacunosum moleculare (SRLM) were derived from 0.2 × 0.2 × 0.2 mm3 post-mortem MRI scans of excised MTL specimens from the contralateral hemisphere using a semi-automated approach. Spearman's rank correlations were performed between neurodegenerative pathologies and thickness, correcting for age, sex and hemisphere, including all four proteinopathies in the model. We found significant associations of (1) TDP-43 with thickness in all subregions (r = - 0.27 to r = - 0.46), and (2) tau with BA35 (r = - 0.31) and SRLM thickness (r = - 0.33). In amyloid-β and TDP-43 negative cases, we found strong significant associations of tau with ERC (r = - 0.40), BA35 (r = - 0.55), subiculum (r = - 0.42) and CA1 thickness (r = - 0.47). This unique dataset shows widespread MTL atrophy in relation to TDP-43 pathology and atrophy in regions affected early in Braak stageing and tau pathology. Moreover, the strong association of tau with thickness in early Braak regions in the absence of amyloid-β suggests a role of Primary Age-Related Tauopathy in neurodegeneration.
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Affiliation(s)
- L E M Wisse
- Department of Diagnostic Radiology, Lund University, Klinikgatan 13b, Lund, Sweden.
- Department of Radiology, University of Pennsylvania, Philadelphia, USA.
| | - S Ravikumar
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - R Ittyerah
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - S Lim
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - J Lane
- Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - M L Bedard
- Department of Pharmacology, University of North Carolina At Chapel Hill, Chapel Hill, USA
| | - L Xie
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - S R Das
- Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - T Schuck
- Center for Neurodegenerative Disease Research, University of Pennsylvania, Philadelphia, USA
| | - M Grossman
- Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - E B Lee
- Center for Neurodegenerative Disease Research, University of Pennsylvania, Philadelphia, USA
| | - M D Tisdall
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - K Prabhakaran
- Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - J A Detre
- Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - G Mizsei
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - J Q Trojanowski
- Center for Neurodegenerative Disease Research, University of Pennsylvania, Philadelphia, USA
| | - E Artacho-Pérula
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, University of Castilla La Mancha, Albacete, Spain
| | | | - M M Arroyo-Jiménez
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, University of Castilla La Mancha, Albacete, Spain
| | - M Muñoz Lopez
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, University of Castilla La Mancha, Albacete, Spain
| | - F J Molina Romero
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, University of Castilla La Mancha, Albacete, Spain
| | - M P Marcos Rabal
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, University of Castilla La Mancha, Albacete, Spain
| | - S Cebada Sánchez
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, University of Castilla La Mancha, Albacete, Spain
| | - J C Delgado González
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, University of Castilla La Mancha, Albacete, Spain
| | - C de la Rosa Prieto
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, University of Castilla La Mancha, Albacete, Spain
| | - M Córcoles Parada
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, University of Castilla La Mancha, Albacete, Spain
| | - D A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - D J Irwin
- Department of Neurology, University of Pennsylvania, Philadelphia, USA
- Center for Neurodegenerative Disease Research, University of Pennsylvania, Philadelphia, USA
| | - R Insausti
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, University of Castilla La Mancha, Albacete, Spain
| | - P A Yushkevich
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
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McCollum LE, Das SR, Xie L, de Flores R, Wang J, Xie SX, Wisse LEM, Yushkevich PA, Wolk DA. Oh brother, where art tau? Amyloid, neurodegeneration, and cognitive decline without elevated tau. Neuroimage Clin 2021; 31:102717. [PMID: 34119903 PMCID: PMC8207301 DOI: 10.1016/j.nicl.2021.102717] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 05/21/2021] [Accepted: 06/02/2021] [Indexed: 12/24/2022]
Abstract
Mild cognitive impairment (MCI) can be an early manifestation of Alzheimer's disease (AD) pathology, other pathologic entities [e.g., cerebrovascular disease, Lewy body disease, LATE (limbic-predominant age-related TDP-43 encephalopathy)], or mixed pathologies, with concomitant AD- and non-AD pathology being particularly common, albeit difficult to identify, in living MCI patients. The National Institute on Aging and Alzheimer's Association (NIA-AA) A/T/(N) [β-Amyloid/Tau/(Neurodegeneration)] AD research framework, which classifies research participants according to three binary biomarkers [β-amyloid (A+/A-), tau (T+/T-), and neurodegeneration (N+/N-)], provides an indirect means of identifying such cases. Individuals with A+T-(N+) MCI are thought to have both AD pathologic change, given the presence of β-amyloid, and non-AD pathophysiology, given neurodegeneration without tau, because in typical AD it is tau accumulation that is most tightly linked to neuronal injury and cognitive decline. Thus, in A+T-(N+) MCI (hereafter referred to as "mismatch MCI" for the tau-neurodegeneration mismatch), non-AD pathology is hypothesized to drive neurodegeneration and symptoms, because β-amyloid, in the absence of tau, likely reflects a preclinical stage of AD. We compared a group of individuals with mismatch MCI to groups with A+T+(N+) MCI (or "prodromal AD") and A-T-(N+) MCI (or "neurodegeneration-only MCI") on cross-sectional and longitudinal cognition and neuroimaging characteristics. β-amyloid and tau status were determined by CSF assays, while neurodegeneration status was based on hippocampal volume on MRI. Overall, mismatch MCI was less "AD-like" than prodromal AD and generally, with some exceptions, more closely resembled the neurodegeneration-only group. At baseline, mismatch MCI had less episodic memory loss compared to prodromal AD. Longitudinally, mismatch MCI declined more slowly than prodromal AD across all included cognitive domains, while mismatch MCI and neurodegeneration-only MCI declined at comparable rates. Prodromal AD had smaller baseline posterior hippocampal volume than mismatch MCI, and whole brain analyses demonstrated cortical thinning that was widespread in prodromal AD but largely restricted to the medial temporal lobes (MTLs) for the mismatch and neurodegeneration-only MCI groups. Longitudinally, mismatch MCI had slower rates of volume loss than prodromal AD throughout the MTLs. Differences in cross-sectional and longitudinal cognitive and neuroimaging measures between mismatch MCI and prodromal AD may reflect disparate underlying pathologic processes, with the mismatch group potentially being driven by non-AD pathologies on a background of largely preclinical AD. These findings suggest that β-amyloid status alone in MCI may not reveal the underlying driver of symptoms with important implications for enrollment in clinical trials and prognosis.
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Affiliation(s)
- Lauren E McCollum
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA; Department of Medicine, University of Tennessee Graduate School of Medicine, Knoxville, TN, USA.
| | - Sandhitsu R Das
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, PA, USA
| | - Long Xie
- Department of Radiology, Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, PA, USA
| | - Robin de Flores
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, PA, USA; INSERM UMR-S U1237, Université de Caen Normandie, Caen, Normandy, USA
| | - Jieqiong Wang
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Sharon X Xie
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Laura E M Wisse
- Department of Radiology, Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, PA, USA; Department of Diagnostic Radiology, Lund University, Lund, Sweden
| | - Paul A Yushkevich
- Department of Radiology, Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, PA, USA
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
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Wisse LEM, de Flores R, Xie L, Das SR, McMillan CT, Trojanowski JQ, Grossman M, Lee EB, Irwin D, Yushkevich PA, Wolk DA. Pathological drivers of neurodegeneration in suspected non-Alzheimer's disease pathophysiology. Alzheimers Res Ther 2021; 13:100. [PMID: 33990226 PMCID: PMC8122549 DOI: 10.1186/s13195-021-00835-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 04/26/2021] [Indexed: 11/16/2022]
Abstract
Background Little is known about the heterogeneous etiology of suspected non-Alzheimer’s pathophysiology (SNAP), a group of subjects with neurodegeneration in the absence of β-amyloid. Using antemortem MRI and pathological data, we investigated the etiology of SNAP and the association of neurodegenerative pathologies with structural medial temporal lobe (MTL) measures in β-amyloid-negative subjects. Methods Subjects with antemortem MRI and autopsy data were selected from ADNI (n=63) and the University of Pennsylvania (n=156). Pathological diagnoses and semi-quantitative scores of MTL tau, neuritic plaques, α-synuclein, and TDP-43 pathology and MTL structural MRI measures from antemortem T1-weighted MRI scans were obtained. β-amyloid status (A+/A−) was determined by CERAD score and neurodegeneration status (N+/N−) by hippocampal volume. Results SNAP reflects a heterogeneous group of pathological diagnoses. In ADNI, SNAP (A−N+) had significantly more neuropathological diagnoses than A+N+. In the A− group, tau pathology was associated with hippocampal, entorhinal cortex, and Brodmann area 35 volume/thickness and TDP-43 pathology with hippocampal volume. Conclusion SNAP had a heterogeneous profile with more mixed pathologies than A+N+. Moreover, a role for TDP-43 and tau pathology in driving MTL neurodegeneration in the absence of β-amyloid was supported. Supplementary Information The online version contains supplementary material available at 10.1186/s13195-021-00835-2.
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Affiliation(s)
- L E M Wisse
- Department of Diagnostic Radiology, Lund University, Remissgatan 4, Room 14-520, 222 42, Lund, Sweden. .,Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, Philadelphia, USA. .,Penn Memory Center, Department of Neurology, University of Pennsylvania, Philadelphia, USA.
| | - R de Flores
- Université Normandie, Inserm, Université de Caen-Normandie, Inserm UMR-S U1237, GIP Cyceron, Caen, France
| | - L Xie
- Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, Philadelphia, USA.,Penn Memory Center, Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - S R Das
- Penn Memory Center, Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - C T McMillan
- Penn FTD Center, Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - J Q Trojanowski
- Center for Neurodegenerative Disease Research, University of Pennsylvania, Philadelphia, PA, USA
| | - M Grossman
- Penn FTD Center, Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - E B Lee
- Center for Neurodegenerative Disease Research, University of Pennsylvania, Philadelphia, PA, USA
| | - D Irwin
- Center for Neurodegenerative Disease Research, University of Pennsylvania, Philadelphia, PA, USA
| | - P A Yushkevich
- Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - D A Wolk
- Penn Memory Center, Department of Neurology, University of Pennsylvania, Philadelphia, USA
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Berron D, Vogel JW, Insel PS, Pereira JB, Xie L, Wisse LEM, Yushkevich PA, Palmqvist S, Mattsson-Carlgren N, Stomrud E, Smith R, Strandberg O, Hansson O. Early stages of tau pathology and its associations with functional connectivity, atrophy and memory. Brain 2021; 144:2771-2783. [PMID: 33725124 PMCID: PMC8557349 DOI: 10.1093/brain/awab114] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 01/15/2021] [Accepted: 03/04/2021] [Indexed: 11/12/2022] Open
Abstract
In Alzheimer's disease, postmortem studies have shown that the first cortical site where neurofibrillary tangles appear is the transentorhinal region, a subregion within the medial temporal lobe that largely overlaps with area 35, and the entorhinal cortex. Here we used tau-PET imaging to investigate the sequence of tau pathology progression within the human medial temporal lobe and across regions in the posterior-medial system. Our objective was to study how medial temporal tau is related to functional connectivity, regional atrophy, and memory performance. We included 215 β-amyloid negative cognitively unimpaired, 81 β-amyloid positive cognitively unimpaired and 87 β-amyloid positive individuals with mild cognitive impairment, who each underwent [18]F-RO948 tau and [18]F-flutemetamol amyloid PET imaging, structural T1-MRI and memory assessments as part of the Swedish BioFINDER-2 study. First, event-based modelling revealed that the entorhinal cortex and area 35 show the earliest signs of tau accumulation followed by the anterior and posterior hippocampus, area 36 and the parahippocampal cortex. In later stages, tau accumulation became abnormal in neocortical temporal and finally parietal brain regions. Second, in cognitively unimpaired individuals, increased tau load was related to local atrophy in the entorhinal cortex, area 35 and the anterior hippocampus and tau load in several anterior medial temporal lobe subregions was associated with distant atrophy of the posterior hippocampus. Tau load, but not atrophy, in these regions was associated with lower memory performance. Further, tau-related reductions in functional connectivity in critical networks between the medial temporal lobe and regions in the posterior-medial system were associated with this early memory impairment. Finally, in patients with mild cognitive impairment, the association of tau load in the hippocampus with memory performance was partially mediated by posterior hippocampal atrophy. In summary, our findings highlight the progression of tau pathology across medial temporal lobe subregions and its disease-stage specific association with memory performance. While tau pathology might affect memory performance in cognitively unimpaired individuals via reduced functional connectivity in critical medial temporal lobe-cortical networks, memory impairment in mild cognitively impaired patients is associated with posterior hippocampal atrophy.
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Affiliation(s)
- David Berron
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 223 62 Lund, Sweden
| | - Jacob W Vogel
- Department of Psychiatry, University of Pennsylvania, 19104 Philadelphia, USA
| | - Philip S Insel
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 223 62 Lund, Sweden.,Department of Psychiatry and Behavioral Sciences, University of California, 94143 San Francisco, USA
| | - Joana B Pereira
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institute, 171 77 Stockholm, Sweden
| | - Long Xie
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, 19104, Philadelphia, Pennsylvania, USA.,Department of Radiology, University of Pennsylvania, 19104 Philadelphia, Pennsylvania, USA
| | - Laura E M Wisse
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, 19104, Philadelphia, Pennsylvania, USA.,Department of Radiology, University of Pennsylvania, 19104 Philadelphia, Pennsylvania, USA.,Department of Diagnostic Radiology, Lund University, 221 00 Lund, Sweden
| | - Paul A Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, 19104, Philadelphia, Pennsylvania, USA.,Department of Radiology, University of Pennsylvania, 19104 Philadelphia, Pennsylvania, USA
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 223 62 Lund, Sweden.,Memory Clinic, Skåne University Hospital, 205 02 Malmö, Sweden
| | - Niklas Mattsson-Carlgren
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 223 62 Lund, Sweden.,Department of Neurology, Skåne University Hospital, 221 00 Lund, Sweden.,Wallenberg Center for Molecular Medicine, Lund University, 221 00 Lund, Sweden
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 223 62 Lund, Sweden.,Memory Clinic, Skåne University Hospital, 205 02 Malmö, Sweden
| | - Ruben Smith
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 223 62 Lund, Sweden.,Department of Neurology, Skåne University Hospital, 221 00 Lund, Sweden
| | - Olof Strandberg
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 223 62 Lund, Sweden
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 223 62 Lund, Sweden.,Department of Psychiatry, University of Pennsylvania, 19104 Philadelphia, USA
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Aly AH, Saito Y, Bouma W, Pilla JJ, Pouch AM, Yushkevich PA, Gillespie MJ, Gorman JH, Gorman RC. Multimodal image analysis and subvalvular dynamics in ischemic mitral regurgitation. JTCVS Open 2021; 5:48-60. [PMID: 36003177 PMCID: PMC9390375 DOI: 10.1016/j.xjon.2020.10.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 10/09/2020] [Indexed: 11/15/2022]
Affiliation(s)
- Ahmed H. Aly
- Gorman Cardiovascular Research Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pa
| | - Yoshiaki Saito
- Gorman Cardiovascular Research Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa
- Department of Thoracic and Cardiovascular Surgery, Hirosaki University, Aomori, Japan
| | - Wobbe Bouma
- Department of Cardiothoracic Surgery, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - James J. Pilla
- Gorman Cardiovascular Research Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa
| | - Alison M. Pouch
- Gorman Cardiovascular Research Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa
| | - Paul A. Yushkevich
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa
| | - Matthew J. Gillespie
- Gorman Cardiovascular Research Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa
- Department of Cardiology, The Children's Hospital of Philadelphia, Philadelphia, Pa
| | - Joseph H. Gorman
- Gorman Cardiovascular Research Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa
| | - Robert C. Gorman
- Gorman Cardiovascular Research Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa
- Address for reprints: Robert C. Gorman, MD, Gorman Cardiovascular Research Group, Smilow Center for Translational Research, 3400 Civic Center Blvd, Bldg 421, 11th Floor, Room 114, Philadelphia, PA, 19104-5156.
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Wisse LEM, Ungrady MB, Ittyerah R, Lim SA, Yushkevich PA, Wolk DA, Irwin DJ, Das SR, Grossman M. Cross-sectional and longitudinal medial temporal lobe subregional atrophy patterns in semantic variant primary progressive aphasia. Neurobiol Aging 2021; 98:231-241. [PMID: 33341654 PMCID: PMC8018475 DOI: 10.1016/j.neurobiolaging.2020.11.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 11/13/2020] [Accepted: 11/16/2020] [Indexed: 10/22/2022]
Abstract
T1-magnetic resonance imaging (MRI) studies report early atrophy in the left anterior temporal lobe, especially the perirhinal cortex, in semantic variant primary progressive aphasia (svPPA). Improved segmentation protocols using high-resolution T2-MRI have enabled fine-grained medial temporal lobe (MTL) subregional measurements, which may provide novel information on the atrophy pattern and disease progression in svPPA. We aimed to investigate the MTL subregional atrophy pattern cross-sectionally and longitudinally in patients with svPPA as compared with controls and patients with Alzheimer's disease (AD). MTL subregional volumes were obtained using the Automated Segmentation for Hippocampal Subfields software from high-resolution T2-MRIs in 15 svPPA, 37 AD, and 23 healthy controls. All MTL volumes were corrected for intracranial volume and parahippocampal cortices for slice number. Longitudinal atrophy rates of all subregions were obtained using an unbiased deformation-based morphometry pipeline in 6 svPPA patients, 9 controls, and 12 AD patients. Cross-sectionally, significant volume loss was observed in svPPA compared with controls in the left MTL, right cornu ammonis 1 (CA1), Brodmann area (BA)35, and BA36 (subdivisions of the perirhinal cortex). Compared with AD patients, svPPA patients had significantly smaller left CA1, BA35, and left and right BA36 volumes. Longitudinally, svPPA patients had significantly greater atrophy rates of left and right BA36 than controls but not relative to AD patients. Fine-grained analysis of MTL atrophy patterns provides information about the evolution of atrophy in svPPA. These results indicate that MTL subregional measures might be useful markers to track disease progression or for clinical trials in svPPA.
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Affiliation(s)
- Laura E M Wisse
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA; Department of Neurology, Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA; Department of Diagnostic Radiology, Lund University, Lund, Sweden.
| | - Molly B Ungrady
- Department of Neurology, Penn Frontotemporal Degeneration Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Ranjit Ittyerah
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Sydney A Lim
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Paul A Yushkevich
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - David A Wolk
- Department of Neurology, Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA
| | - David J Irwin
- Department of Neurology, Penn Frontotemporal Degeneration Center, University of Pennsylvania, Philadelphia, PA, USA; Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine Center for Neurodegenerative Disease Research (CNDR), University of Pennsylvania, Philadelphia, PA, USA
| | - Sandhitsu R Das
- Department of Neurology, Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Murray Grossman
- Department of Neurology, Penn Frontotemporal Degeneration Center, University of Pennsylvania, Philadelphia, PA, USA
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Wisse LEM, Chételat G, Daugherty AM, de Flores R, la Joie R, Mueller SG, Stark CEL, Wang L, Yushkevich PA, Berron D, Raz N, Bakker A, Olsen RK, Carr VA. Hippocampal subfield volumetry from structural isotropic 1 mm 3 MRI scans: A note of caution. Hum Brain Mapp 2021; 42:539-550. [PMID: 33058385 PMCID: PMC7775994 DOI: 10.1002/hbm.25234] [Citation(s) in RCA: 71] [Impact Index Per Article: 23.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 09/01/2020] [Accepted: 09/29/2020] [Indexed: 01/05/2023] Open
Abstract
Spurred by availability of automatic segmentation software, in vivo MRI investigations of human hippocampal subfield volumes have proliferated in the recent years. However, a majority of these studies apply automatic segmentation to MRI scans with approximately 1 × 1 × 1 mm3 resolution, a resolution at which the internal structure of the hippocampus can rarely be visualized. Many of these studies have reported contradictory and often neurobiologically surprising results pertaining to the involvement of hippocampal subfields in normal brain function, aging, and disease. In this commentary, we first outline our concerns regarding the utility and validity of subfield segmentation on 1 × 1 × 1 mm3 MRI for volumetric studies, regardless of how images are segmented (i.e., manually or automatically). This image resolution is generally insufficient for visualizing the internal structure of the hippocampus, particularly the stratum radiatum lacunosum moleculare, which is crucial for valid and reliable subfield segmentation. Second, we discuss the fact that automatic methods that are employed most frequently to obtain hippocampal subfield volumes from 1 × 1 × 1 mm3 MRI have not been validated against manual segmentation on such images. For these reasons, we caution against using volumetric measurements of hippocampal subfields obtained from 1 × 1 × 1 mm3 images.
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Affiliation(s)
- Laura E. M. Wisse
- Diagnostic RadiologyLund UniversityLundSweden
- Penn Image Computing and Science Laboratory, Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Penn Memory Center, Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Gaël Chételat
- Université Normandie, InsermUniversité de Caen‐Normandie, Inserm UMR‐S U1237CaenFrance
| | - Ana M. Daugherty
- Department of PsychologyWayne State UniversityDetroitMichiganUSA
- Institute of GerontologyWayne State UniversityDetroitMichiganUSA
- Department of Psychiatry and Behavioral NeurosciencesWayne State UniversityDetroitMichiganUSA
| | - Robin de Flores
- Université Normandie, InsermUniversité de Caen‐Normandie, Inserm UMR‐S U1237CaenFrance
| | - Renaud la Joie
- Memory and Aging CenterUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Susanne G. Mueller
- Department of RadiologyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- Center for Imaging of Neurodegenerative DiseasesSan Francisco VA Medical CenterSan FranciscoCaliforniaUSA
| | - Craig E. L. Stark
- Department of Neurobiology and BehaviorUniversity of California IrvineIrvineCaliforniaUSA
| | - Lei Wang
- Department of Psychiatry and Behavioral SciencesNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
- Department of RadiologyNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Paul A. Yushkevich
- Penn Image Computing and Science Laboratory, Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - David Berron
- Clinical Memory Research Unit, Department of Clinical Sciences MalmöLund UniversityLundSweden
| | - Naftali Raz
- Department of PsychologyWayne State UniversityDetroitMichiganUSA
- Institute of GerontologyWayne State UniversityDetroitMichiganUSA
- Center for Lifespan PsychologyMax Planck Institute for Human DevelopmentBerlinGermany
| | - Arnold Bakker
- Department of Psychiatry and Behavioral SciencesJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | | | - Valerie A. Carr
- Department of PsychologySan Jose State UniversitySan JoseCaliforniaUSA
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Aly AH, Aly AH, Lai EK, Yushkevich N, Stoffers RH, Gorman JH, Cheung AT, Gorman JH, Gorman RC, Yushkevich PA, Pouch AM. In Vivo Image-Based 4D Modeling of Competent and Regurgitant Mitral Valve Dynamics. Exp Mech 2021; 61:159-169. [PMID: 33776070 PMCID: PMC7988343 DOI: 10.1007/s11340-020-00656-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 08/05/2020] [Indexed: 06/12/2023]
Abstract
BACKGROUND In vivo characterization of mitral valve dynamics relies on image analysis algorithms that accurately reconstruct valve morphology and motion from clinical images. The goal of such algorithms is to provide patient-specific descriptions of both competent and regurgitant mitral valves, which can be used as input to biomechanical analyses and provide insights into the pathophysiology of diseases like ischemic mitral regurgitation (IMR). OBJECTIVE The goal is to generate accurate image-based representations of valve dynamics that visually and quantitatively capture normal and pathological valve function. METHODS We present a novel framework for 4D segmentation and geometric modeling of the mitral valve in real-time 3D echocardiography (rt-3DE), an imaging modality used for pre-operative surgical planning of mitral interventions. The framework integrates groupwise multi-atlas label fusion and template-based medial modeling with Kalman filtering to generate quantitatively descriptive and temporally consistent models of valve dynamics. RESULTS The algorithm is evaluated on rt-3DE data series from 28 patients: 14 with normal mitral valve morphology and 14 with severe IMR. In these 28 data series that total 613 individual 3DE images, each 3D mitral valve segmentation is validated against manual tracing, and temporal consistency between segmentations is demonstrated. CONCLUSIONS Automated 4D image analysis allows for reliable non-invasive modeling of the mitral valve over the cardiac cycle for comparison of annular and leaflet dynamics in pathological and normal mitral valves. Future studies can apply this algorithm to cardiovascular mechanics applications, including patient-specific strain estimation, fluid dynamics simulation, inverse finite element analysis, and risk stratification for surgical treatment.
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Affiliation(s)
- A H Aly
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - A H Aly
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - E K Lai
- Gorman Cardiovascular Research Group, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - N Yushkevich
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | | | - J H Gorman
- Gorman Cardiovascular Research Group, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - A T Cheung
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University Medical Center, Stanford, CA, USA
| | - J H Gorman
- Gorman Cardiovascular Research Group, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - R C Gorman
- Gorman Cardiovascular Research Group, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - P A Yushkevich
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - A M Pouch
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
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42
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Wisse L, Ravikumar S, Ittyerah R, Lim SA, Lane J, Lavery M, Xie L, Robinson JL, Schuck T, Grossman M, Lee EB, Tisdall DM, Prabhakaran K, Detre JA, Das SR, Mizsei G, Artacho‐Perula E, Martin MMIDO, Jimenez MDMA, Munoz M, Romero FJM, Rabal MDPM, Sanchez SC, Gonzalez JCD, Prieto CDLR, Parada MC, Irwin DJ, Trojanowski JQ, Wolk DA, Insausti R, Yushkevich PA. High‐resolution postmortem MRI reveals TDP‐43 association with medial temporal lobe subregional atrophy. Alzheimers Dement 2020. [DOI: 10.1002/alz.045744] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Laura Wisse
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
- Lund University Lund Sweden
| | - Sadhana Ravikumar
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
| | - Ranjit Ittyerah
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
| | - Sydney A. Lim
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
| | - Jacqueline Lane
- Penn Memory Center University of Pennsylvania Philadelphia PA USA
| | - Madigan Lavery
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
| | - Long Xie
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
| | | | | | - Murray Grossman
- Penn FTD Center University of Pennsylvania Philadelphia PA USA
| | - Eddie B. Lee
- Center for Neurodegenerative Disease Research University of Pennsylvania Philadelphia PA USA
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - David J. Irwin
- Penn FTD Center University of Pennsylvania Philadelphia PA USA
| | - John Q. Trojanowski
- Center for Neurodegenerative Disease Research University of Pennsylvania Philadelphia PA USA
| | | | | | - Paul A. Yushkevich
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
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Maroni MJ, Wolk DA, Das SR, De Flores R, Wisse L, Xie L, Yushkevich PA, Lee EB, McMillan CT. Epigenetic measurement of biological age associates with tau load in normal brain aging. Alzheimers Dement 2020. [DOI: 10.1002/alz.042068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
| | | | - Sandhitsu R Das
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
| | | | - Laura Wisse
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
| | - Long Xie
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
| | - Paul A Yushkevich
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
| | - Eddie B Lee
- Center for Neurodegenerative Disease Research University of Pennsylvania Philadelphia PA USA
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44
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Lasserve J, Lim SA, Wisse L, Ittyerah R, Ravikumar S, Lavery M, Robinson JL, Schuck T, Grossman M, Lee EB, Yushkevich PA, Tisdall DM, Prabhakaran K, Mizsei G, Artacho‐Perula E, Martin MMIDO, Jimenez MDMA, Munoz M, Romero FJM, Rabal MDPM, Irwin DJ, Trojanowski JQ, Wolk DA, Insausti R. Optimized extraction of the medial temporal lobe for postmortem MRI based on custom 3D printed molds. Alzheimers Dement 2020. [DOI: 10.1002/alz.043254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Jade Lasserve
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
| | - Sydney A. Lim
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
| | - Laura Wisse
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
| | - Ranjit Ittyerah
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
| | - Sadhana Ravikumar
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
| | - Madigan Lavery
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
| | | | | | - Murray Grossman
- Penn FTD Center University of Pennsylvania Philadelphia PA USA
| | - Eddie B. Lee
- Center for Neurodegenerative Disease Research University of Pennsylvania Philadelphia PA USA
| | - Paul A. Yushkevich
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
| | | | | | | | | | | | | | | | | | | | - David J. Irwin
- Perelman School of Medicine University of Pennsylvania Philadelphia PA USA
| | - John Q. Trojanowski
- Center for Neurodegenerative Disease Research University of Pennsylvania Philadelphia PA USA
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45
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de Flores R, Das SR, Xie L, Wisse L, Shah P, Yushkevich PA, Wolk DA. Neurodegeneration in medial temporal lobe subregions propagates within distinct functional networks in the AD continuum. Alzheimers Dement 2020. [DOI: 10.1002/alz.045063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
| | | | - Long Xie
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
| | - Laura Wisse
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
| | - Preya Shah
- University of Pennsylvania Philadelphia PA USA
| | - Paul A. Yushkevich
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
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46
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La Joie R, Olsen R, Berron D, Amunts K, Augustinack J, Bakker A, Bender A, Boccardi M, Bocchetta M, Chakravarty MM, Chetelat G, de Flores R, DeKraker J, Ding S, Insausti R, Kedo O, Mueller SG, Ofen N, Palombo D, Raz N, Stark CE, Wang L, Yushkevich PA, Yu Q, Carr VA, Wisse L, Daugherty AM. The development of a valid, reliable, harmonized segmentation protocol for hippocampal subfields and medial temporal lobe cortices: A progress update. Alzheimers Dement 2020. [DOI: 10.1002/alz.046652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Renaud La Joie
- Memory and Aging Center UCSF Weill Institute for Neurosciences University of California, San Francisco San Francisco CA USA
| | | | - David Berron
- Clinical Memory Research Unit Lund University Lund Sweden
| | - Katrin Amunts
- Institute of Neuroscience and Medicine (INM‐1) Jülich Germany
| | | | | | | | | | - Martina Bocchetta
- Dementia Research Centre Queen Square Institute of Neurology University College London London United Kingdom
| | - M. Mallar Chakravarty
- Cerebral Imaging Centre ‐ Douglas Mental Health University Institute Verdun QC Canada
| | | | - Robin de Flores
- Inserm UMR‐S U1237 Université de Caen‐Normandie GIP Cyceron Caen France
| | | | | | | | - Olga Kedo
- Forschungszentrum Jülich Julich Germany
| | - Susanne G Mueller
- Center for Imaging of Neurodegenerative Diseases San Francisco CA USA
| | - Noa Ofen
- Wayne State University Detroit MI USA
| | | | | | | | - Lei Wang
- Northwestern University Chicago IL USA
| | - Paul A. Yushkevich
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
| | - Qijing Yu
- Wayne State University Detroit MI USA
| | | | - Laura Wisse
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
| | - Ana M. Daugherty
- Beckman Institute for Advanced Science and Technology Champaign IL USA
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47
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Dong M, Xie L, Das SR, Wolk DA, Yushkevich PA. Detecting biological changes in longitudinal MRI scans of Alzheimer’s disease patients in the hippocampus region with deep learning. Alzheimers Dement 2020. [DOI: 10.1002/alz.046453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Mengjin Dong
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
| | - Long Xie
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
| | - Sandhitsu R. Das
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
| | - David A. Wolk
- Penn Memory Center University of Pennsylvania Philadelphia PA USA
| | - Paul A. Yushkevich
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
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48
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Ravikumar S, Wisse L, Xie L, Ittyerah R, Das SR, Detre JA, Grossman M, Lavery M, Lim SA, Irwin DJ, Schuck T, Trojanowski JQ, Artacho‐Perula E, Martin MMIDO, Lee EB, Mizsei G, Tisdall DM, del Mar Arroyo Jimenez M, Munoz M, Gonzalez JCD, Parada MC, de la Rosa Prieto C, Sanchez SC, Prabhakaran K, del Pilar Marcos Rabal M, Romero FJM, Robinson JL, Wolk DA, Insausti R, Yushkevich PA. Novel ex vivo MRI atlas of the medial temporal lobe can be used to characterize structural changes due to Alzheimer’s disease pathology. Alzheimers Dement 2020. [DOI: 10.1002/alz.041279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Sadhana Ravikumar
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
| | - Laura Wisse
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
| | - Long Xie
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
| | - Ranjit Ittyerah
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
| | - Sandhitsu R. Das
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
| | | | - Murray Grossman
- Penn FTD Center University of Pennsylvania Philadelphia PA USA
| | - Madigan Lavery
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
| | - Sydney A. Lim
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
| | - David J. Irwin
- Penn FTD Center University of Pennsylvania Philadelphia PA USA
| | | | - John Q. Trojanowski
- Center for Neurodegenerative Disease Research University of Pennsylvania Philadelphia PA USA
| | | | | | - Eddie B. Lee
- Center for Neurodegenerative Disease Research University of Pennsylvania Philadelphia PA USA
| | | | | | | | | | | | | | | | | | | | | | | | | | - David A. Wolk
- Penn Memory Center University of Pennsylvania Philadelphia PA USA
| | | | - Paul A. Yushkevich
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
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49
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Khandelwal P, Xie L, Bassett DS, de Flores R, Wolk DA, Yushkevich PA, Das SR. Longitudinal network connectivity measurements in medial temporal lobe subregions discriminate preclinical Alzheimer’s from amyloid‐β negative controls. Alzheimers Dement 2020. [DOI: 10.1002/alz.047689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Pulkit Khandelwal
- University of Pennsylvania Philadelphia PA USA
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
| | - Long Xie
- University of Pennsylvania Philadelphia PA USA
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
| | | | - Robin de Flores
- Inserm UMR‐S U1237 Université de Caen‐Normandie, GIP Cyceron Caen France
| | - David A Wolk
- University of Pennsylvania Philadelphia PA USA
- Penn Memory Center University of Pennsylvania Philadelphia PA USA
| | - Paul A Yushkevich
- University of Pennsylvania Philadelphia PA USA
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
| | - Sandhitsu R Das
- University of Pennsylvania Philadelphia PA USA
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
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50
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Xie L, Das SR, Wisse L, Ittyerah R, Vergnet N, de Flores R, Wolk DA, Yushkevich PA. Subtle differences in the appearance of hippocampal layers differentiate preclinical AD from healthy aging. Alzheimers Dement 2020. [DOI: 10.1002/alz.045050] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Long Xie
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
| | - Sandhitsu R. Das
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
| | | | - Ranjit Ittyerah
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
| | - Nicolas Vergnet
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
| | - Robin de Flores
- Inserm UMR‐S U1237 Université de Caen‐Normandie, GIP Cyceron Caen France
| | - David A. Wolk
- Penn Memory Center University of Pennsylvania Philadelphia PA USA
| | - Paul A. Yushkevich
- Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA
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