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Raji CA, Meysami S, Porter VR, Merrill DA, Mendez MF. Diagnostic utility of brain MRI volumetry in comparing traumatic brain injury, Alzheimer disease and behavioral variant frontotemporal dementia. BMC Neurol 2024; 24:337. [PMID: 39261753 PMCID: PMC11389120 DOI: 10.1186/s12883-024-03844-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2024] [Accepted: 09/03/2024] [Indexed: 09/13/2024] Open
Abstract
BACKGROUND Brain MRI with volumetric quantification, MRI volumetry, can improve diagnostic delineation of patients with neurocognitive disorders by identifying brain atrophy that may not be evident on visual assessments. OBJECTIVE To investigate diagnostic utility of MRI volumetry in traumatic brain injury (TBI), early-onset Alzheimer disease (EOAD), late-onset Alzheimer disease, and behavioral variant frontotemporal dementia (bvFTD). METHOD We utilized 137 participants of TBI (n = 40), EOAD (n = 45), LOAD (n = 32), and bvFTD (n = 20). Participants had 3D T1 brain MRI imaging amendable to MRI volumetry. Scan volumes were analyzed with Neuroreader. One-way ANOVA compared brain volumes across diagnostic groups. Discriminant analysis was done with leave-one-out cross validation on Neuroreader metrics to determine diagnostic delineation across groups. RESULT LOAD was the oldest compared to other groups (F = 27.5, p < .001). There were no statistically significant differences in sex (p = .58) with women comprising 54.7% of the entire cohort. EOAD and LOAD had the lowest Mini-Mental State Exam (MMSE) scores compared to TBI (p = .04 for EOAD and p = .01 for LOAD). LOAD had lowest hippocampal volumes (Left Hippocampus F = 13.1, Right Hippocampus F = 7.3, p < .001), low white matter volume in TBI (F = 5.9, p < .001), lower left parietal lobe volume in EOAD (F = 9.4, p < .001), and lower total gray matter volume in bvFTD (F = 32.8, p < .001) and caudate atrophy (F = 1737.5, p < .001). Areas under the curve ranged from 92.3 to 100%, sensitivity between 82.2 and 100%, specificity of 78.1-100%. TBI was the most accurately delineated diagnosis. Predictive features included caudate, frontal, parietal, temporal lobar and total white matter volumes. CONCLUSION We identified the diagnostic utility of regional volumetric differences across multiple neurocognitive disorders. Brain MRI volumetry is widely available and can be applied in distinguishing these disorders.
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Affiliation(s)
- Cyrus A Raji
- Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO, USA.
- Department of Neurology, Washington University, St. Louis, MO, USA.
| | - Somayeh Meysami
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA
- Saint John's Cancer Institute at Providence Saint John's Health Center, Santa Monica, CA, USA
| | - Verna R Porter
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA
- Saint John's Cancer Institute at Providence Saint John's Health Center, Santa Monica, CA, USA
- Providence Saint John's Health Center, Santa Monica, CA, USA
| | - David A Merrill
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA
- Saint John's Cancer Institute at Providence Saint John's Health Center, Santa Monica, CA, USA
- Providence Saint John's Health Center, Santa Monica, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Mario F Mendez
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Greater Los Angeles VA Healthcare System, Los Angeles, CA, USA
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Kinnunen KM, Mullin AP, Pustina D, Turner EC, Burton J, Gordon MF, Scahill RI, Gantman EC, Noble S, Romero K, Georgiou-Karistianis N, Schwarz AJ. Recommendations to Optimize the Use of Volumetric MRI in Huntington's Disease Clinical Trials. Front Neurol 2021; 12:712565. [PMID: 34744964 PMCID: PMC8569234 DOI: 10.3389/fneur.2021.712565] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 09/21/2021] [Indexed: 12/12/2022] Open
Abstract
Volumetric magnetic resonance imaging (vMRI) has been widely studied in Huntington's disease (HD) and is commonly used to assess treatment effects on brain atrophy in interventional trials. Global and regional trajectories of brain atrophy in HD, with early involvement of striatal regions, are becoming increasingly understood. However, there remains heterogeneity in the methods used and a lack of widely-accessible multisite, longitudinal, normative datasets in HD. Consensus for standardized practices for data acquisition, analysis, sharing, and reporting will strengthen the interpretation of vMRI results and facilitate their adoption as part of a pathobiological disease staging system. The Huntington's Disease Regulatory Science Consortium (HD-RSC) currently comprises 37 member organizations and is dedicated to building a regulatory science strategy to expedite the approval of HD therapeutics. Here, we propose four recommendations to address vMRI standardization in HD research: (1) a checklist of standardized practices for the use of vMRI in clinical research and for reporting results; (2) targeted research projects to evaluate advanced vMRI methodologies in HD; (3) the definition of standard MRI-based anatomical boundaries for key brain structures in HD, plus the creation of a standard reference dataset to benchmark vMRI data analysis methods; and (4) broad access to raw images and derived data from both observational studies and interventional trials, coded to protect participant identity. In concert, these recommendations will enable a better understanding of disease progression and increase confidence in the use of vMRI for drug development.
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Affiliation(s)
| | - Ariana P Mullin
- Critical Path Institute, Tucson, AZ, United States.,Wave Life Sciences, Ltd., Cambridge, MA, United States
| | - Dorian Pustina
- CHDI Management/CHDI Foundation, Princeton, NJ, United States
| | | | | | - Mark F Gordon
- Teva Pharmaceuticals, West Chester, PA, United States
| | - Rachael I Scahill
- Huntington's Disease Research Centre, UCL Institute of Neurology, London, United Kingdom
| | - Emily C Gantman
- CHDI Management/CHDI Foundation, Princeton, NJ, United States
| | - Simon Noble
- CHDI Management/CHDI Foundation, Princeton, NJ, United States
| | - Klaus Romero
- Critical Path Institute, Tucson, AZ, United States
| | - Nellie Georgiou-Karistianis
- School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC, Australia
| | - Adam J Schwarz
- Takeda Pharmaceuticals, Ltd., Cambridge, MA, United States
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Nobakht S, Schaeffer M, Forkert ND, Nestor S, E. Black S, Barber P. Combined Atlas and Convolutional Neural Network-Based Segmentation of the Hippocampus from MRI According to the ADNI Harmonized Protocol. SENSORS (BASEL, SWITZERLAND) 2021; 21:2427. [PMID: 33915960 PMCID: PMC8036492 DOI: 10.3390/s21072427] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 03/21/2021] [Accepted: 03/23/2021] [Indexed: 11/17/2022]
Abstract
Hippocampus atrophy is an early structural feature that can be measured from magnetic resonance imaging (MRI) to improve the diagnosis of neurological diseases. An accurate and robust standardized hippocampus segmentation method is required for reliable atrophy assessment. The aim of this work was to develop and evaluate an automatic segmentation tool (DeepHarp) for hippocampus delineation according to the ADNI harmonized hippocampal protocol (HarP). DeepHarp utilizes a two-step process. First, the approximate location of the hippocampus is identified in T1-weighted MRI datasets using an atlas-based approach, which is used to crop the images to a region-of-interest (ROI) containing the hippocampus. In the second step, a convolutional neural network trained using datasets with corresponding manual hippocampus annotations is used to segment the hippocampus from the cropped ROI. The proposed method was developed and validated using 107 datasets with manually segmented hippocampi according to the ADNI-HarP standard as well as 114 multi-center datasets of patients with Alzheimer's disease, mild cognitive impairment, cerebrovascular disease, and healthy controls. Twenty-three independent datasets manually segmented according to the ADNI-HarP protocol were used for testing to assess the accuracy, while an independent test-retest dataset was used to assess precision. The proposed DeepHarp method achieved a mean Dice similarity score of 0.88, which was significantly better than four other established hippocampus segmentation methods used for comparison. At the same time, the proposed method also achieved a high test-retest precision (mean Dice score: 0.95). In conclusion, DeepHarp can automatically segment the hippocampus from T1-weighted MRI datasets according to the ADNI-HarP protocol with high accuracy and robustness, which can aid atrophy measurements in a variety of pathologies.
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Affiliation(s)
- Samaneh Nobakht
- Medical Sciences Graduate Program, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Morgan Schaeffer
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB T2N 1N4, Canada; (M.S.); (N.D.F.); (P.B.)
| | - Nils D. Forkert
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB T2N 1N4, Canada; (M.S.); (N.D.F.); (P.B.)
- Department of Radiology, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Sean Nestor
- Department of Psychiatry, University of Toronto, Toronto, ON M5S, Canada; (S.N.); (S.E.B.)
- Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, ON M4N 3M5, Canada
| | - Sandra E. Black
- Department of Psychiatry, University of Toronto, Toronto, ON M5S, Canada; (S.N.); (S.E.B.)
| | - Philip Barber
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB T2N 1N4, Canada; (M.S.); (N.D.F.); (P.B.)
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Raji CA, Ly M, Benzinger TLS. Overview of MR Imaging Volumetric Quantification in Neurocognitive Disorders. Top Magn Reson Imaging 2019; 28:311-315. [PMID: 31794503 DOI: 10.1097/rmr.0000000000000224] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
This review article provides a general overview on the various methodologies for quantifying brain structure on magnetic resonance images of the human brain. This overview is followed by examples of applications in Alzheimer dementia and mild cognitive impairment. Other examples will include traumatic brain injury and other neurodegenerative dementias. Finally, an overview of general principles for protocol acquisition of magnetic resonance imaging for volumetric quantification will be discussed along with the current choices of FDA cleared algorithms for use in clinical practice.
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Affiliation(s)
- Cyrus A Raji
- Division of Neuroradiology, Department of Radiology, Mallinckrodt Institute of Radiology at Washington University, St. Louis, MO
| | - Maria Ly
- University of Pittsburgh Medical Scientist Training Program, Pittsburgh, PA
| | - Tammie L S Benzinger
- Division of Neuroradiology, Department of Radiology, Mallinckrodt Institute of Radiology at Washington University, St. Louis, MO
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The Comprehensive Assessment of Neurodegeneration and Dementia: Canadian Cohort Study. Can J Neurol Sci 2019; 46:499-511. [PMID: 31309917 DOI: 10.1017/cjn.2019.27] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
BACKGROUND The Comprehensive Assessment of Neurodegeneration and Dementia (COMPASS-ND) cohort study of the Canadian Consortium on Neurodegeneration in Aging (CCNA) is a national initiative to catalyze research on dementia, set up to support the research agendas of CCNA teams. This cross-country longitudinal cohort of 2310 deeply phenotyped subjects with various forms of dementia and mild memory loss or concerns, along with cognitively intact elderly subjects, will test hypotheses generated by these teams. METHODS The COMPASS-ND protocol, initial grant proposal for funding, fifth semi-annual CCNA Progress Report submitted to the Canadian Institutes of Health Research December 2017, and other documents supplemented by modifications made and lessons learned after implementation were used by the authors to create the description of the study provided here. RESULTS The CCNA COMPASS-ND cohort includes participants from across Canada with various cognitive conditions associated with or at risk of neurodegenerative diseases. They will undergo a wide range of experimental, clinical, imaging, and genetic investigation to specifically address the causes, diagnosis, treatment, and prevention of these conditions in the aging population. Data derived from clinical and cognitive assessments, biospecimens, brain imaging, genetics, and brain donations will be used to test hypotheses generated by CCNA research teams and other Canadian researchers. The study is the most comprehensive and ambitious Canadian study of dementia. Initial data posting occurred in 2018, with the full cohort to be accrued by 2020. CONCLUSION Availability of data from the COMPASS-ND study will provide a major stimulus for dementia research in Canada in the coming years.
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Bartel F, van Herk M, Vrenken H, Vandaele F, Sunaert S, de Jaeger K, Dollekamp NJ, Carbaat C, Lamers E, Dieleman EMT, Lievens Y, de Ruysscher D, Schagen SB, de Ruiter MB, de Munck JC, Belderbos J. Inter-observer variation of hippocampus delineation in hippocampal avoidance prophylactic cranial irradiation. Clin Transl Oncol 2018; 21:178-186. [PMID: 29876759 DOI: 10.1007/s12094-018-1903-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2018] [Accepted: 05/24/2018] [Indexed: 01/22/2023]
Abstract
BACKGROUND Hippocampal avoidance prophylactic cranial irradiation (HA-PCI) techniques have been developed to reduce radiation damage to the hippocampus. An inter-observer hippocampus delineation analysis was performed and the influence of the delineation variability on dose to the hippocampus was studied. MATERIALS AND METHODS For five patients, seven observers delineated both hippocampi on brain MRI. The intra-class correlation (ICC) with absolute agreement and the generalized conformity index (CIgen) were computed. Median surfaces over all observers' delineations were created for each patient and regional outlining differences were analysed. HA-PCI dose plans were made from the median surfaces and we investigated whether dose constraints in the hippocampus could be met for all delineations. RESULTS The ICC for the left and right hippocampus was 0.56 and 0.69, respectively, while the CIgen ranged from 0.55 to 0.70. The posterior and anterior-medial hippocampal regions had most variation with SDs ranging from approximately 1 to 2.5 mm. The mean dose (Dmean) constraint was met for all delineations, but for the dose received by 1% of the hippocampal volume (D1%) violations were observed. CONCLUSION The relatively low ICC and CIgen indicate that delineation variability among observers for both left and right hippocampus was large. The posterior and anterior-medial border have the largest delineation inaccuracy. The hippocampus Dmean constraint was not violated.
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Affiliation(s)
- F Bartel
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - M van Herk
- Department of Cancer Sciences, University of Manchester, Manchester, UK
| | - H Vrenken
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - F Vandaele
- Department of Radiotherapy, Iridium Cancer Network, Antwerp, Belgium
| | - S Sunaert
- Department of Radiology, University Hospitals Leuven, Louvain, Belgium
| | - K de Jaeger
- Department of Radiotherapy, Catharina Hospital, Eindhoven, The Netherlands
| | - N J Dollekamp
- Department of Radiotherapy, The University Medical Center Groningen, Groningen, The Netherlands
| | - C Carbaat
- Department of Radiotherapy, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - E Lamers
- Department of Radiotherapy, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - E M T Dieleman
- Department of Radiotherapy, Academic Medical Center, Amsterdam, The Netherlands
| | - Y Lievens
- Department of Radiation Oncology, Ghent University Hospital, Ghent, Belgium
| | - D de Ruysscher
- Department of Radiotherapy, Maastricht University Medical Center, Maastricht, The Netherlands
| | - S B Schagen
- Division of Psychosocial Research and Epidemiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - M B de Ruiter
- Division of Psychosocial Research and Epidemiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - J C de Munck
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - J Belderbos
- Department of Radiotherapy, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.
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Blanken AE, Hurtz S, Zarow C, Biado K, Honarpisheh H, Somme J, Brook J, Tung S, Kraft E, Lo D, Ng DW, Vinters HV, Apostolova LG. Associations between hippocampal morphometry and neuropathologic markers of Alzheimer's disease using 7 T MRI. NEUROIMAGE-CLINICAL 2017; 15:56-61. [PMID: 28491492 PMCID: PMC5412112 DOI: 10.1016/j.nicl.2017.04.020] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/01/2016] [Revised: 04/17/2017] [Accepted: 04/19/2017] [Indexed: 11/19/2022]
Abstract
Hippocampal atrophy, amyloid plaques, and neurofibrillary tangles are established pathologic markers of Alzheimer's disease. We analyzed the temporal lobes of 9 Alzheimer's dementia (AD) and 7 cognitively normal (NC) subjects. Brains were scanned post-mortem at 7 Tesla. We extracted hippocampal volumes and radial distances using automated segmentation techniques. Hippocampal slices were stained for amyloid beta (Aβ), tau, and cresyl violet to evaluate neuronal counts. The hippocampal subfields, CA1, CA2, CA3, CA4, and subiculum were manually traced so that the neuronal counts, Aβ, and tau burden could be obtained for each region. We used linear regression to detect associations between hippocampal atrophy in 3D, clinical diagnosis and total as well as subfield pathology burden measures. As expected, we found significant correlations between hippocampal radial distance and mean neuronal count, as well as diagnosis. There were subfield specific associations between hippocampal radial distance and tau in CA2, and cresyl violet neuronal counts in CA1 and subiculum. These results provide further validation for the European Alzheimer's Disease Consortium Alzheimer's Disease Neuroimaging Initiative Center Harmonized Hippocampal Segmentation Protocol (HarP). We researched the correlations of hippocampal radial distance with Alzheimer's pathology. Hippocampal radial distance was associated with mean neuronal count and diagnosis. Our findings support for the use of hippocampal surface mapping in AD research.
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Affiliation(s)
- Anna E Blanken
- Department of Psychology, University of Southern California, Los Angeles, CA, USA
| | - Sona Hurtz
- Drexel University College of Medicine, Philadelphia, PA, USA
| | - Chris Zarow
- Department of Neurology, Keck School of Medicine at the University of Southern California, Los Angeles, CA, USA
| | - Kristina Biado
- Department of Pathology & Laboratory Medicine, UCLA, Los Angeles, CA, USA
| | - Hedieh Honarpisheh
- Department of Pathology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Johanne Somme
- Department of Neurology, Barakaldo, Basque Country, Spain
| | - Jenny Brook
- Department of Medicine - Statistics Core, UCLA, Los Angeles, CA, USA
| | - Spencer Tung
- Department of Pathology & Laboratory Medicine, UCLA, Los Angeles, CA, USA
| | - Emily Kraft
- University of Rochester, Rochester, N.Y, USA
| | - Darrick Lo
- Department of Pathology & Laboratory Medicine, UCLA, Los Angeles, CA, USA
| | - Denise W Ng
- Department of Pathology & Laboratory Medicine, UCLA, Los Angeles, CA, USA
| | - Harry V Vinters
- Department of Pathology & Laboratory Medicine, UCLA, Los Angeles, CA, USA
| | - Liana G Apostolova
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA; Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA.
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Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Green RC, Harvey D, Jack CR, Jagust W, Morris JC, Petersen RC, Saykin AJ, Shaw LM, Toga AW, Trojanowski JQ. Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials. Alzheimers Dement 2017; 13:e1-e85. [PMID: 28342697 DOI: 10.1016/j.jalz.2016.11.007] [Citation(s) in RCA: 172] [Impact Index Per Article: 24.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Revised: 11/21/2016] [Accepted: 11/28/2016] [Indexed: 01/31/2023]
Abstract
INTRODUCTION The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015. METHODS We used standard searches to find publications using ADNI data. RESULTS (1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β-amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion-like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by "classic" AD pathology but also by normal biomarkers, accelerated decline, and suspected non-Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers. DISCUSSION Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial design.
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Affiliation(s)
- Michael W Weiner
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA; Department of Radiology, University of California, San Francisco, CA, USA; Department of Medicine, University of California, San Francisco, CA, USA; Department of Psychiatry, University of California, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, CA, USA.
| | - Dallas P Veitch
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA
| | - Paul S Aisen
- Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, CA, USA
| | - Laurel A Beckett
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | - Nigel J Cairns
- Knight Alzheimer's Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA; Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Robert C Green
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Danielle Harvey
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | | | - William Jagust
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA
| | - John C Morris
- Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, CA, USA
| | | | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Leslie M Shaw
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Arthur W Toga
- Laboratory of Neuroimaging, Institute of Neuroimaging and Informatics, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
| | - John Q Trojanowski
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Institute on Aging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Alzheimer's Disease Core Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Udall Parkinson's Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Wolf D, Bocchetta M, Preboske GM, Boccardi M, Grothe MJ. Reference standard space hippocampus labels according to the European Alzheimer's Disease Consortium-Alzheimer's Disease Neuroimaging Initiative harmonized protocol: Utility in automated volumetry. Alzheimers Dement 2017; 13:893-902. [PMID: 28238738 DOI: 10.1016/j.jalz.2017.01.009] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2016] [Revised: 11/14/2016] [Accepted: 01/02/2017] [Indexed: 01/24/2023]
Abstract
INTRODUCTION A harmonized protocol (HarP) for manual hippocampal segmentation on magnetic resonance imaging (MRI) has recently been developed by an international European Alzheimer's Disease Consortium-Alzheimer's Disease Neuroimaging Initiative project. We aimed at providing consensual certified HarP hippocampal labels in Montreal Neurological Institute (MNI) standard space to serve as reference in automated image analyses. METHODS Manual HarP tracings on the high-resolution MNI152 standard space template of four expert certified HarP tracers were combined to obtain consensual bilateral hippocampus labels. Utility and validity of these reference labels is demonstrated in a simple atlas-based morphometry approach for automated calculation of HarP-compliant hippocampal volumes within SPM software. RESULTS Individual tracings showed very high agreement among the four expert tracers (pairwise Jaccard indices 0.82-0.87). Automatically calculated hippocampal volumes were highly correlated (rL/R = 0.89/0.91) with gold standard volumes in the HarP benchmark data set (N = 135 MRIs), with a mean volume difference of 9% (standard deviation 7%). CONCLUSION The consensual HarP hippocampus labels in the MNI152 template can serve as a reference standard for automated image analyses involving MNI standard space normalization.
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Affiliation(s)
- Dominik Wolf
- Department of Psychiatry and Psychotherapy, University Medical Center Mainz, Mainz, Germany.
| | - Martina Bocchetta
- Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy; Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
| | | | - Marina Boccardi
- Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy; LANVIE-Laboratory of Neuroimaging of Aging, Department of Psychiatry, University of Geneva, Switzerland
| | - Michel J Grothe
- German Center for Neurodegenerative Diseases (DZNE), Clinical Dementia Research Group, Rostock, Germany.
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Pini L, Pievani M, Bocchetta M, Altomare D, Bosco P, Cavedo E, Galluzzi S, Marizzoni M, Frisoni GB. Brain atrophy in Alzheimer's Disease and aging. Ageing Res Rev 2016; 30:25-48. [PMID: 26827786 DOI: 10.1016/j.arr.2016.01.002] [Citation(s) in RCA: 445] [Impact Index Per Article: 55.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2015] [Revised: 01/15/2016] [Accepted: 01/20/2016] [Indexed: 01/22/2023]
Abstract
Thanks to its safety and accessibility, magnetic resonance imaging (MRI) is extensively used in clinical routine and research field, largely contributing to our understanding of the pathophysiology of neurodegenerative disorders such as Alzheimer's disease (AD). This review aims to provide a comprehensive overview of the main findings in AD and normal aging over the past twenty years, focusing on the patterns of gray and white matter changes assessed in vivo using MRI. Major progresses in the field concern the segmentation of the hippocampus with novel manual and automatic segmentation approaches, which might soon enable to assess also hippocampal subfields. Advancements in quantification of hippocampal volumetry might pave the way to its broader use as outcome marker in AD clinical trials. Patterns of cortical atrophy have been shown to accurately track disease progression and seem promising in distinguishing among AD subtypes. Disease progression has also been associated with changes in white matter tracts. Recent studies have investigated two areas often overlooked in AD, such as the striatum and basal forebrain, reporting significant atrophy, although the impact of these changes on cognition is still unclear. Future integration of different MRI modalities may further advance the field by providing more powerful biomarkers of disease onset and progression.
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Affiliation(s)
- Lorenzo Pini
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy; Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Michela Pievani
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy
| | - Martina Bocchetta
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy; Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, University College London, London, UK
| | - Daniele Altomare
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy; Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Paolo Bosco
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy
| | - Enrica Cavedo
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy; Sorbonne Universités, Université Pierre et Marie Curie, Paris 06, Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A) Hôpital de la Pitié-Salpétrière & Institut du Cerveau et de la Moelle épinière (ICM), UMR S 1127, Hôpital de la Pitié-Salpétrière Paris & CATI Multicenter Neuroimaging Platform, France
| | - Samantha Galluzzi
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy
| | - Moira Marizzoni
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy
| | - Giovanni B Frisoni
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy; Memory Clinic and LANVIE-Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland.
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11
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Ahdidan J, Raji CA, DeYoe EA, Mathis J, Noe KØ, Rimestad J, Kjeldsen TK, Mosegaard J, Becker JT, Lopez O. Quantitative Neuroimaging Software for Clinical Assessment of Hippocampal Volumes on MR Imaging. J Alzheimers Dis 2016; 49:723-32. [PMID: 26484924 PMCID: PMC4718601 DOI: 10.3233/jad-150559] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/31/2015] [Indexed: 01/01/2023]
Abstract
BACKGROUND Multiple neurological disorders including Alzheimer's disease (AD), mesial temporal sclerosis, and mild traumatic brain injury manifest with volume loss on brain MRI. Subtle volume loss is particularly seen early in AD. While prior research has demonstrated the value of this additional information from quantitative neuroimaging, very few applications have been approved for clinical use. Here we describe a US FDA cleared software program, NeuroreaderTM, for assessment of clinical hippocampal volume on brain MRI. OBJECTIVE To present the validation of hippocampal volumetrics on a clinical software program. METHOD Subjects were drawn (n = 99) from the Alzheimer Disease Neuroimaging Initiative study. Volumetric brain MR imaging was acquired in both 1.5 T (n = 59) and 3.0 T (n = 40) scanners in participants with manual hippocampal segmentation. Fully automated hippocampal segmentation and measurement was done using a multiple atlas approach. The Dice Similarity Coefficient (DSC) measured the level of spatial overlap between NeuroreaderTM and gold standard manual segmentation from 0 to 1 with 0 denoting no overlap and 1 representing complete agreement. DSC comparisons between 1.5 T and 3.0 T scanners were done using standard independent samples T-tests. RESULTS In the bilateral hippocampus, mean DSC was 0.87 with a range of 0.78-0.91 (right hippocampus) and 0.76-0.91 (left hippocampus). Automated segmentation agreement with manual segmentation was essentially equivalent at 1.5 T (DSC = 0.879) versus 3.0 T (DSC = 0.872). CONCLUSION This work provides a description and validation of a software program that can be applied in measuring hippocampal volume, a biomarker that is frequently abnormal in AD and other neurological disorders.
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Affiliation(s)
| | | | - Edgar A. DeYoe
- Department of Radiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Jedidiah Mathis
- Department of Radiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | | | | | | | | | - James T. Becker
- Departments of Psychology, Psychiatry, and Neurology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Oscar Lopez
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
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12
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Frisoni GB, Jack CR, Bocchetta M, Bauer C, Frederiksen KS, Liu Y, Preboske G, Swihart T, Blair M, Cavedo E, Grothe MJ, Lanfredi M, Martinez O, Nishikawa M, Portegies M, Stoub T, Ward C, Apostolova LG, Ganzola R, Wolf D, Barkhof F, Bartzokis G, DeCarli C, Csernansky JG, deToledo-Morrell L, Geerlings MI, Kaye J, Killiany RJ, Lehéricy S, Matsuda H, O'Brien J, Silbert LC, Scheltens P, Soininen H, Teipel S, Waldemar G, Fellgiebel A, Barnes J, Firbank M, Gerritsen L, Henneman W, Malykhin N, Pruessner JC, Wang L, Watson C, Wolf H, deLeon M, Pantel J, Ferrari C, Bosco P, Pasqualetti P, Duchesne S, Duvernoy H, Boccardi M. The EADC-ADNI Harmonized Protocol for manual hippocampal segmentation on magnetic resonance: evidence of validity. Alzheimers Dement 2015; 11:111-25. [PMID: 25267715 PMCID: PMC4422168 DOI: 10.1016/j.jalz.2014.05.1756] [Citation(s) in RCA: 134] [Impact Index Per Article: 14.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2013] [Revised: 05/05/2014] [Accepted: 05/29/2014] [Indexed: 10/24/2022]
Abstract
BACKGROUND An international Delphi panel has defined a harmonized protocol (HarP) for the manual segmentation of the hippocampus on MR. The aim of this study is to study the concurrent validity of the HarP toward local protocols, and its major sources of variance. METHODS Fourteen tracers segmented 10 Alzheimer's Disease Neuroimaging Initiative (ADNI) cases scanned at 1.5 T and 3T following local protocols, qualified for segmentation based on the HarP through a standard web-platform and resegmented following the HarP. The five most accurate tracers followed the HarP to segment 15 ADNI cases acquired at three time points on both 1.5 T and 3T. RESULTS The agreement among tracers was relatively low with the local protocols (absolute left/right ICC 0.44/0.43) and much higher with the HarP (absolute left/right ICC 0.88/0.89). On the larger set of 15 cases, the HarP agreement within (left/right ICC range: 0.94/0.95 to 0.99/0.99) and among tracers (left/right ICC range: 0.89/0.90) was very high. The volume variance due to different tracers was 0.9% of the total, comparing favorably to variance due to scanner manufacturer (1.2), atrophy rates (3.5), hemispheric asymmetry (3.7), field strength (4.4), and significantly smaller than the variance due to atrophy (33.5%, P < .001), and physiological variability (49.2%, P < .001). CONCLUSIONS The HarP has high measurement stability compared with local segmentation protocols, and good reproducibility within and among human tracers. Hippocampi segmented with the HarP can be used as a reference for the qualification of human tracers and automated segmentation algorithms.
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Affiliation(s)
- Giovanni B Frisoni
- LENITEM (Laboratory of Epidemiology, Neuroimaging and Telemedicine) IRCCS - Istituto Centro S. Giovanni di Dio - Fatebenefratelli, Brescia, Italy; Memory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland
| | - Clifford R Jack
- Department of Diagnostic Radiology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Martina Bocchetta
- LENITEM (Laboratory of Epidemiology, Neuroimaging and Telemedicine) IRCCS - Istituto Centro S. Giovanni di Dio - Fatebenefratelli, Brescia, Italy; Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Corinna Bauer
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, USA
| | - Kristian S Frederiksen
- Memory Disorders Research Group, Department of Neurology, Rigshospitalet, Copenhagen, Denmark
| | - Yawu Liu
- Department of Neurology, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Gregory Preboske
- Department of Diagnostic Radiology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Tim Swihart
- Department of Neurology, Oregon Health & Science University, Portland, OR, USA
| | - Melanie Blair
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, National Hospital for Neurology and Neurosurgery, London, UK
| | - Enrica Cavedo
- LENITEM (Laboratory of Epidemiology, Neuroimaging and Telemedicine) IRCCS - Istituto Centro S. Giovanni di Dio - Fatebenefratelli, Brescia, Italy
| | - Michel J Grothe
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
| | - Mariangela Lanfredi
- Unit of Psychiatry, IRCCS - Centro S. Giovanni di Dio - Fatebenefratelli, Brescia, Italy
| | - Oliver Martinez
- Department of Neurology, University of California, Davis, CA, USA
| | | | - Marileen Portegies
- University Medical Center Utrecht, Julius Center for Health Sciences and Primary Care, Utrecht, The Netherlands
| | - Travis Stoub
- Department of Neurological Sciences, Rush University, Chicago, IL, USA
| | - Chadwich Ward
- Department of Diagnostic Radiology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Liana G Apostolova
- Mary S. Easton Center for Alzheimer's Disease Research and Laboratory of NeuroImaging, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Rossana Ganzola
- Department of Radiology, Université Laval and Centre de Recherche de l'Institut universitaire de santé mentale de Québec, Quebec City, Canada
| | - Dominik Wolf
- Klinik für Psychiatrie und Psychotherapie, Johannes Gutenberg-Universität Mainz, Mainz, Germany
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Image Analysis Center, VU University Medical Center, Amsterdam, The Netherlands
| | - George Bartzokis
- Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Charles DeCarli
- Department of Neurology, University of California, Davis, CA, USA
| | | | | | - Mirjam I Geerlings
- University Medical Center Utrecht, Julius Center for Health Sciences and Primary Care, Utrecht, The Netherlands
| | - Jeffrey Kaye
- Department of Neurology, Oregon Health & Science University, Portland, OR, USA
| | - Ronald J Killiany
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, USA
| | - Stephane Lehéricy
- Service de Neuroradiologie, Hopital de la Pitie-Salpetriere, Paris, France
| | | | - John O'Brien
- Institute for Ageing and Health, Newcastle University, Newcastle upon Tyne, UK
| | - Lisa C Silbert
- Department of Neurology, Oregon Health & Science University, Portland, OR, USA
| | - Philip Scheltens
- Department of Neurology and Alzheimer Center, VU University Medical Cente and Neuroscience Campus Amsterdam, Amsterdam, The Netherlands
| | - Hilkka Soininen
- Department of Neurology, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Stefan Teipel
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany; Department of Psychosomatic Medicine, University of Rostock, Rostock, Germany
| | - Gunhild Waldemar
- Memory Disorders Research Group, Department of Neurology, Rigshospitalet, Copenhagen, Denmark
| | - Andreas Fellgiebel
- Klinik für Psychiatrie und Psychotherapie, Johannes Gutenberg-Universität Mainz, Mainz, Germany
| | - Josephine Barnes
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, National Hospital for Neurology and Neurosurgery, London, UK
| | - Michael Firbank
- Institute for Ageing and Health, Newcastle University, Newcastle upon Tyne, UK
| | - Lotte Gerritsen
- University Medical Center Utrecht, Julius Center for Health Sciences and Primary Care, Utrecht, The Netherlands; Department of Medical epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Wouter Henneman
- Department of Radiology and Nuclear Medicine, Image Analysis Center, VU University Medical Center, Amsterdam, The Netherlands
| | - Nikolai Malykhin
- Department of Biomedical Engineering, Centre for Neuroscience, University of Alberta, Edmonton, Alberta, Canada
| | - Jens C Pruessner
- Department of Psychiatry, McGill Centre for Studies in Aging, McGill University, Montreal, Quebec, Canada
| | - Lei Wang
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Craig Watson
- Department of Neurology, University of California, Davis, CA, USA
| | - Henrike Wolf
- Department of Psychiatry Research and Geriatric Psychiatry, Psychiatric University Hospitals, University of Zurich, Zurich, Switzerland; German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Mony deLeon
- New York University School of Medicine, Center for Brain Health, New York, NY, USA
| | - Johannes Pantel
- Institute of General Practice, Goethe-University Frankfurt, Frankfurt, Germany
| | - Clarissa Ferrari
- Unit of Psychiatry, IRCCS - Centro S. Giovanni di Dio - Fatebenefratelli, Brescia, Italy
| | - Paolo Bosco
- LENITEM (Laboratory of Epidemiology, Neuroimaging and Telemedicine) IRCCS - Istituto Centro S. Giovanni di Dio - Fatebenefratelli, Brescia, Italy
| | - Patrizio Pasqualetti
- SeSMIT (Service for Medical Statistics and Information Technology), AFaR (Fatebenefratelli Association for Research), Fatebenefratelli Hospital, Rome, Italy; Unit of Clinical and Molecular Epidemiology, IRCCS "San Raffaele Pisana", Rome, Italy
| | - Simon Duchesne
- Department of Radiology, Université Laval and Centre de Recherche de l'Institut universitaire de santé mentale de Québec, Quebec City, Canada
| | | | - Marina Boccardi
- LENITEM (Laboratory of Epidemiology, Neuroimaging and Telemedicine) IRCCS - Istituto Centro S. Giovanni di Dio - Fatebenefratelli, Brescia, Italy.
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13
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Apostolova LG, Zarow C, Biado K, Hurtz S, Boccardi M, Somme J, Honarpisheh H, Blanken AE, Brook J, Tung S, Lo D, Ng D, Alger JR, Vinters HV, Bocchetta M, Duvernoy H, Jack CR, Frisoni GB. Relationship between hippocampal atrophy and neuropathology markers: a 7T MRI validation study of the EADC-ADNI Harmonized Hippocampal Segmentation Protocol. Alzheimers Dement 2015; 11:139-50. [PMID: 25620800 PMCID: PMC4348340 DOI: 10.1016/j.jalz.2015.01.001] [Citation(s) in RCA: 49] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2013] [Revised: 11/15/2014] [Accepted: 01/06/2015] [Indexed: 10/24/2022]
Abstract
OBJECTIVE The pathologic validation of European Alzheimer's Disease Consortium Alzheimer's Disease Neuroimaging Initiative Center Harmonized Hippocampal Segmentation Protocol (HarP). METHODS Temporal lobes of nine Alzheimer's disease (AD) and seven cognitively normal subjects were scanned post-mortem at 7 Tesla. Hippocampal volumes were obtained with HarP. Six-micrometer-thick hippocampal slices were stained for amyloid beta (Aβ), tau, and cresyl violet. Hippocampal subfields were manually traced. Neuronal counts, Aβ, and tau burden for each hippocampal subfield were obtained. RESULTS We found significant correlations between hippocampal volume and Braak and Braak staging (ρ = -0.75, P = .001), tau (ρ = -0.53, P = .034), Aβ burden (ρ = -0.61, P = .012), and neuronal count (ρ = 0.77, P < .001). Exploratory subfield-wise significant associations were found for Aβ in Cornu Ammonis (CA)1 (ρ = -0.58, P = .019) and subiculum (ρ = -0.75, P = .001), tau in CA2 (ρ = -0.59, P = .016), and CA3 (ρ = -0.5, P = .047), and neuronal count in CA1 (ρ = 0.55, P = .028), CA3 (ρ = 0.65, P = .006), and CA4 (ρ = 0.76, P = .001). CONCLUSIONS The observed associations provide pathological confirmation of hippocampal morphometry as a valid biomarker for AD and pathologic validation of HarP.
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Affiliation(s)
| | - Chris Zarow
- Department of Neurology, USC, Los Angeles, CA, USA
| | - Kristina Biado
- Department of Pathology & Laboratory Medicine, UCLA, Los Angeles, CA, USA
| | - Sona Hurtz
- San Francisco State University, San Francisco, CA, USA
| | - Marina Boccardi
- LENITEM (Laboratory of Epidemiology, Neuroimaging and Telemedicine), IRCCS S.Giovanni di Dio- Fatebenefratelli, Brescia, Italy
| | - Johanne Somme
- Department of Neurology, Alava University Hospital, Victoria-Gasteiz, Spain
| | - Hedieh Honarpisheh
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
| | | | - Jenny Brook
- Department of Medicine Statistics Core, UCLA, Los Angeles, CA, USA
| | - Spencer Tung
- Department of Pathology & Laboratory Medicine, UCLA, Los Angeles, CA, USA
| | - Darrick Lo
- Department of Pathology & Laboratory Medicine, UCLA, Los Angeles, CA, USA
| | - Denise Ng
- Department of Pathology & Laboratory Medicine, UCLA, Los Angeles, CA, USA
| | | | - Harry V Vinters
- Department of Neurology, UCLA, Los Angeles, CA, USA; Department of Pathology & Laboratory Medicine, UCLA, Los Angeles, CA, USA
| | - Martina Bocchetta
- LENITEM (Laboratory of Epidemiology, Neuroimaging and Telemedicine), IRCCS S.Giovanni di Dio- Fatebenefratelli, Brescia, Italy; Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | | | - Clifford R Jack
- Department of Diagnostic Radiology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Giovanni B Frisoni
- LENITEM (Laboratory of Epidemiology, Neuroimaging and Telemedicine), IRCCS S.Giovanni di Dio- Fatebenefratelli, Brescia, Italy; University Hospitals and University of Geneva, Geneva, Switzerland
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14
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Boccardi M, Bocchetta M, Morency FC, Collins DL, Nishikawa M, Ganzola R, Grothe MJ, Wolf D, Redolfi A, Pievani M, Antelmi L, Fellgiebel A, Matsuda H, Teipel S, Duchesne S, Jack CR, Frisoni GB. Training labels for hippocampal segmentation based on the EADC-ADNI harmonized hippocampal protocol. Alzheimers Dement 2015; 11:175-83. [PMID: 25616957 DOI: 10.1016/j.jalz.2014.12.002] [Citation(s) in RCA: 68] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2013] [Revised: 03/25/2014] [Accepted: 12/12/2014] [Indexed: 11/26/2022]
Abstract
BACKGROUND The European Alzheimer's Disease Consortium and Alzheimer's Disease Neuroimaging Initiative (ADNI) Harmonized Protocol (HarP) is a Delphi definition of manual hippocampal segmentation from magnetic resonance imaging (MRI) that can be used as the standard of truth to train new tracers, and to validate automated segmentation algorithms. Training requires large and representative data sets of segmented hippocampi. This work aims to produce a set of HarP labels for the proper training and certification of tracers and algorithms. METHODS Sixty-eight 1.5 T and 67 3 T volumetric structural ADNI scans from different subjects, balanced by age, medial temporal atrophy, and scanner manufacturer, were segmented by five qualified HarP tracers whose absolute interrater intraclass correlation coefficients were 0.953 and 0.975 (left and right). Labels were validated as HarP compliant through centralized quality check and correction. RESULTS Hippocampal volumes (mm(3)) were as follows: controls: left = 3060 (standard deviation [SD], 502), right = 3120 (SD, 897); mild cognitive impairment (MCI): left = 2596 (SD, 447), right = 2686 (SD, 473); and Alzheimer's disease (AD): left = 2301 (SD, 492), right = 2445 (SD, 525). Volumes significantly correlated with atrophy severity at Scheltens' scale (Spearman's ρ = <-0.468, P = <.0005). Cerebrospinal fluid spaces (mm(3)) were as follows: controls: left = 23 (32), right = 25 (25); MCI: left = 15 (13), right = 22 (16); and AD: left = 11 (13), right = 20 (25). Five subjects (3.7%) presented with unusual anatomy. CONCLUSIONS This work provides reference hippocampal labels for the training and certification of automated segmentation algorithms. The publicly released labels will allow the widespread implementation of the standard segmentation protocol.
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Affiliation(s)
- Marina Boccardi
- LENITEM (Laboratory of Epidemiology, Neuroimaging and Telemedicine) IRCCS - Centro S. Giovanni di Dio - Fatebenefratelli, Brescia, Italy.
| | - Martina Bocchetta
- LENITEM (Laboratory of Epidemiology, Neuroimaging and Telemedicine) IRCCS - Centro S. Giovanni di Dio - Fatebenefratelli, Brescia, Italy; Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Félix C Morency
- Department of Radiology, Université Laval and Centre de Recherche de l'Institut universitaire de santé mentale de Québec, Quebec City, Canada; Imeka, Sherbrooke, Québec, Canada
| | - D Louis Collins
- McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, Canada
| | | | - Rossana Ganzola
- Department of Radiology, Université Laval and Centre de Recherche de l'Institut universitaire de santé mentale de Québec, Quebec City, Canada
| | - Michel J Grothe
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
| | - Dominik Wolf
- Klinik für Psychiatrie und Psychotherapie, Johannes Gutenberg-Universität, Mainz, Germany
| | - Alberto Redolfi
- LENITEM (Laboratory of Epidemiology, Neuroimaging and Telemedicine) IRCCS - Centro S. Giovanni di Dio - Fatebenefratelli, Brescia, Italy
| | - Michela Pievani
- LENITEM (Laboratory of Epidemiology, Neuroimaging and Telemedicine) IRCCS - Centro S. Giovanni di Dio - Fatebenefratelli, Brescia, Italy
| | - Luigi Antelmi
- LENITEM (Laboratory of Epidemiology, Neuroimaging and Telemedicine) IRCCS - Centro S. Giovanni di Dio - Fatebenefratelli, Brescia, Italy; Laboratory of Neuroimaging of Aging, Department of Psychiatry, HUG Belle-Idée, Geneva, Switzerland
| | - Andreas Fellgiebel
- Klinik für Psychiatrie und Psychotherapie, Johannes Gutenberg-Universität, Mainz, Germany
| | - Hiroshi Matsuda
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Stefan Teipel
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany; Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Simon Duchesne
- Department of Radiology, Université Laval and Centre de Recherche de l'Institut universitaire de santé mentale de Québec, Quebec City, Canada
| | - Clifford R Jack
- Department of Diagnostic Radiology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Giovanni B Frisoni
- LENITEM (Laboratory of Epidemiology, Neuroimaging and Telemedicine) IRCCS - Centro S. Giovanni di Dio - Fatebenefratelli, Brescia, Italy; Laboratory of Neuroimaging of Aging, Department of Psychiatry, HUG Belle-Idée, Geneva, Switzerland; University of Geneva, Faculty of Medicine, Geneva, Switzerland
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15
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Harmonized benchmark labels of the hippocampus on magnetic resonance: The EADC‐ADNI project. Alzheimers Dement 2014; 11:151-60.e5. [DOI: 10.1016/j.jalz.2013.12.019] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2013] [Revised: 10/21/2013] [Accepted: 12/20/2013] [Indexed: 11/24/2022]
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16
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Delphi definition of the EADC-ADNI Harmonized Protocol for hippocampal segmentation on magnetic resonance. Alzheimers Dement 2014; 11:126-38. [PMID: 25130658 DOI: 10.1016/j.jalz.2014.02.009] [Citation(s) in RCA: 100] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2013] [Revised: 12/12/2013] [Accepted: 02/16/2014] [Indexed: 11/20/2022]
Abstract
BACKGROUND This study aimed to have international experts converge on a harmonized definition of whole hippocampus boundaries and segmentation procedures, to define standard operating procedures for magnetic resonance (MR)-based manual hippocampal segmentation. METHODS The panel received a questionnaire regarding whole hippocampus boundaries and segmentation procedures. Quantitative information was supplied to allow evidence-based answers. A recursive and anonymous Delphi procedure was used to achieve convergence. Significance of agreement among panelists was assessed by exact probability on Fisher's and binomial tests. RESULTS Agreement was significant on the inclusion of alveus/fimbria (P = .021), whole hippocampal tail (P = .013), medial border of the body according to visible morphology (P = .0006), and on this combined set of features (P = .001). This definition captures 100% of hippocampal tissue, 100% of Alzheimer's disease-related atrophy, and demonstrated good reliability on preliminary intrarater (0.98) and inter-rater (0.94) estimates. DISCUSSION Consensus was achieved among international experts with respect to hippocampal segmentation using MR resulting in a harmonized segmentation protocol.
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