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Carbonell F, McNicoll C, Zijdenbos AP, Bedell BJ. Spatial association between distributed β-amyloid and tau varies with cognition. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.27.559737. [PMID: 37808643 PMCID: PMC10557646 DOI: 10.1101/2023.09.27.559737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Several PET studies have explored the relationship between β-amyloid load and tau uptake at the early stages of Alzheimer's disease (AD) progression. Most of these studies have focused on the linear relationship between β-amyloid and tau at the local level and their synergistic effect on different AD biomarkers. We hypothesize that patterns of spatial association between β-amyloid and tau might be uncovered using alternative association metrics that account for linear as well as more complex, possible nonlinear dependencies. In the present study, we propose a new Canonical Distance Correlation Analysis (CDCA) to generate distinctive spatial patterns of the cross-correlation structure between tau, as measured by [18F]flortaucipir PET, and β-amyloid, as measured by [18F]florbetapir PET, from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. We found that the CDCA-based β-amyloid scores were not only maximally distance-correlated to tau in cognitively normal (CN) controls and mild cognitive impairment (MCI), but also differentiated between low and high levels of β-amyloid uptake. The most distinctive spatial association pattern was characterized by a spread of β-amyloid covering large areas of the cortex and localized tau in the entorhinal cortex. More importantly, this spatial dependency varies according to cognition, which cannot be explained by the uptake differences in β-amyloid or tau between CN and MCI subjects. Hence, the CDCA-based scores might be more accurate than the amyloid or tau SUVR for the enrollment in clinical trials of those individuals on the path of cognitive deterioration.
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Gao F. Integrated Positron Emission Tomography/Magnetic Resonance Imaging in clinical diagnosis of Alzheimer's disease. Eur J Radiol 2021; 145:110017. [PMID: 34826792 DOI: 10.1016/j.ejrad.2021.110017] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 08/30/2021] [Accepted: 10/31/2021] [Indexed: 12/01/2022]
Abstract
Alzheimer's disease (AD), a progressive neurodegenerative disease which seriously endangers the health of the aged, is the most common etiology of senile dementia. With the increasing progress of neuroimaging technology, more and more imaging methods have been applied to study Alzheimer's disease. The emergence of integrated PET/MRI (Positron Emission Tomography/Magnetic Resonance Imaging) is a major advance in multimodal molecular imaging with many advantages on the structure of resolution and contrast of image over computed tomography (CT), PET and MRI. PET/MRI is now used stepwise in neurodegenerative diseases, and also has broad prospect of application in the early diagnosis of AD. In this review, we emphatically introduce the imaging advances of AD including functional imaging and molecular imaging, the advantages of PET/MRI over other imaging methods and prospects of PET/MRI in AD clinical diagnosis, especially in early diagnosis, clinical assessment and prediction on AD.
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Affiliation(s)
- Feng Gao
- Key Laboratory for Experimental Teratology of the Ministry of Education and Center for Experimental Nuclear Medicine, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China.
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Otani T, Otsuka H, Matsushita K, Otomi Y, Kunikane Y, Azane S, Amano M, Harada M, Miyoshi H. Effect of different examination conditions on image quality and quantitative value of amyloid positron emission tomography using 18F-flutemetamol. Ann Nucl Med 2021; 35:1004-1014. [PMID: 34046870 DOI: 10.1007/s12149-021-01634-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 05/20/2021] [Indexed: 10/21/2022]
Abstract
OBJECTIVE The recommended start time for 18F-flutemetamol amyloid positron emission tomography (PET) examination is 60-120 min after 18F-flutemetamol injection, while an acquisition time of 10-30 min is generally recommended. We aimed to elucidate the effects of different examination conditions on image quality, diagnostic ability, and quantitative value of amyloid PET using 18F-flutemetamol. METHODS We acquired data on a Discovery PET/computed tomography 710 scanner using Hoffman brain and pillar phantoms with 20 MBq of 18F for 30 min. The images were reconstructed into 10-, 20-, and 30-min periods. The ordered subset-expectation maximization algorithm was used for image reconstruction, which uses a 2- or 4-mm Gaussian filter and a combination of iteration and subset numbers. The percentage contrast and coefficient of variation (CV; as the image noise) were used as physical evaluation indices for reconstructed images, and images with superior contrast and low image noise were selected for clinical evaluation. The imaging data of 15 symptomatic patients (n = 7 and n = 8 for positive and negative diagnoses of Alzheimer's disease, respectively) were reconstructed under the phantom study conditions. Radiographers visually evaluated and ranked the clinical images based on the overall contrast and image noise, and nuclear medicine specialists diagnosed Alzheimer's disease. We compared the standardized uptake value ratio (SUVR) obtained with different acquisition conditions. RESULTS The basic study using the phantom revealed high convergence of contrast and image noise in five patterns of acquisition time and filter strengths. Regarding visual evaluation, the use of a 2-mm Gaussian filter caused difficulties in diagnosis because the brain parenchymal accumulation was mottled with high image noise. Differences in image quality and diagnostic ability due to different examination times were not significant. Differences in the SUVR were not significant in patients with a negative Alzheimer's disease diagnosis; in patients with a positive diagnosis, the SUVR showed significant fluctuation depending on the acquisition conditions. CONCLUSION The differences in image quality and diagnostic performance due to the differences in 10-min acquisition time were not significant; however, of note, SUVR showed significant fluctuation depending on the acquisition conditions in patients diagnosed with Alzheimer's disease.
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Affiliation(s)
- Tamaki Otani
- Advance Radiation Research, Education, and Management Center, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan.
| | - Hideki Otsuka
- Department of Medical Imaging/Nuclear Medicine, Tokushima University Graduate School of Biomedical Sciences, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Kou Matsushita
- Faculty of Health Science, Tokushima University Graduate School of Medicine, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Yoichi Otomi
- Department of Radiology and Radiation Oncology, Tokushima University Graduate School of Biomedical Sciences, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Yamato Kunikane
- Department of Radiology, Tokushima University Hospital, 2-50-1 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Shota Azane
- Department of Radiology, Tokushima University Hospital, 2-50-1 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Masafumi Amano
- Department of Radiology, Tokushima University Hospital, 2-50-1 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Masafumi Harada
- Department of Radiology and Radiation Oncology, Tokushima University Graduate School of Biomedical Sciences, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Hirokazu Miyoshi
- Advance Radiation Research, Education, and Management Center, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
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Grey zone amyloid burden affects memory function: the SCIENCe project. Eur J Nucl Med Mol Imaging 2020; 48:747-756. [PMID: 32888039 PMCID: PMC8036199 DOI: 10.1007/s00259-020-05012-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Accepted: 08/20/2020] [Indexed: 12/24/2022]
Abstract
Purpose To determine thresholds for amyloid beta pathology and evaluate associations with longitudinal memory performance with the aim to identify a grey zone of early amyloid beta accumulation and investigate its clinical relevance. Methods We included 162 cognitively normal participants with subjective cognitive decline from the SCIENCe cohort (64 ± 8 years, 38% F, MMSE 29 ± 1). Each underwent a dynamic [18F] florbetapir PET scan, a T1-weighted MRI scan and longitudinal memory assessments (RAVLT delayed recall, n = 655 examinations). PET scans were visually assessed as amyloid positive/negative. Additionally, we calculated the mean binding potential (BPND) and standardized uptake value ratio (SUVr50–70) for an a priori defined composite region of interest. We determined six amyloid positivity thresholds using various data-driven methods (resulting thresholds: BPND 0.19/0.23/0.29; SUVr 1.28/1.34/1.43). We used Cohen’s kappa to analyse concordance between thresholds and visual assessment. Next, we used quantiles to divide the sample into two to five subgroups of equal numbers (median, tertiles, quartiles, quintiles), and operationalized a grey zone as the range between the thresholds (0.19–0.29 BPND/1.28–1.43 SUVr). We used linear mixed models to determine associations between thresholds and memory slope. Results As determined by visual assessment, 24% of 162 individuals were amyloid positive. Concordance with visual assessment was comparable but slightly higher for BPND thresholds (range kappa 0.65–0.70 versus 0.60–0.63). All thresholds predicted memory decline (range beta − 0.29 to − 0.21, all p < 0.05). Analyses in subgroups showed memory slopes gradually became steeper with higher amyloid load (all p for trend < 0.05). Participants with a low amyloid burden benefited from a practice effect (i.e. increase in memory), whilst high amyloid burden was associated with memory decline. Memory slopes of individuals in the grey zone were intermediate. Conclusion We provide evidence that not only high but also grey zone amyloid burden subtly impacts memory function. Therefore, in case a binary classification is required, we suggest using a relatively low threshold which includes grey zone amyloid pathology.
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Nogay HS, Adeli H. Machine learning (ML) for the diagnosis of autism spectrum disorder (ASD) using brain imaging. Rev Neurosci 2020; 31:/j/revneuro.ahead-of-print/revneuro-2020-0043/revneuro-2020-0043.xml. [PMID: 32866134 DOI: 10.1515/revneuro-2020-0043] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Accepted: 07/25/2020] [Indexed: 02/24/2024]
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental incurable disorder with a long diagnostic period encountered in the early years of life. If diagnosed early, the negative effects of this disease can be reduced by starting special education early. Machine learning (ML), an increasingly ubiquitous technology, can be applied for the early diagnosis of ASD. The aim of this study is to examine and provide a comprehensive state-of-the-art review of ML research for the diagnosis of ASD based on (a) structural magnetic resonance image (MRI), (b) functional MRI and (c) hybrid imaging techniques over the past decade. The accuracy of the studies with a large number of participants is in general lower than those with fewer participants leading to the conclusion that further large-scale studies are needed. An examination of the age of the participants shows that the accuracy of the automated diagnosis of ASD is higher at a younger age range. ML technology is expected to contribute significantly to the early and rapid diagnosis of ASD in the coming years and become available to clinicians in the near future. This review is aimed to facilitate that.
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Affiliation(s)
- Hidir Selcuk Nogay
- Department of Electrical and Energy, Kayseri University, Kayseri, Turkey
- The Ohio State University, Mathematical Bioscience Institute, Columbus, OH, USA
| | - Hojjat Adeli
- Departments of Biomedical Informatics and Neuroscience, The Ohio State University, Columbus, US
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6
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A new Centiloid method for 18F-florbetaben and 18F-flutemetamol PET without conversion to PiB. Eur J Nucl Med Mol Imaging 2019; 47:1938-1948. [DOI: 10.1007/s00259-019-04596-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Accepted: 11/04/2019] [Indexed: 10/25/2022]
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7
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Tahmi M, Bou-Zeid W, Razlighi QR. A Fully Automatic Technique for Precise Localization and Quantification of Amyloid-β PET Scans. J Nucl Med 2019; 60:1771-1779. [PMID: 31171596 DOI: 10.2967/jnumed.119.228510] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Accepted: 05/29/2019] [Indexed: 11/16/2022] Open
Abstract
Spatial heterogeneity in the accumulation of amyloid-β plaques throughout the brain during asymptomatic as well as clinical stages of Alzheimer disease calls for precise localization and quantification of this protein using PET imaging. To address this need, we have developed and evaluated a technique that quantifies the extent of amyloid-β pathology on a millimeter-by-millimeter scale in the brain with unprecedented precision using data from PET scans. Methods: An intermodal and intrasubject registration with normalized mutual information as the cost function was used to transform all FreeSurfer neuroanatomic labels into PET image space, which were subsequently used to compute regional SUV ratio (SUVR). We have evaluated our technique using postmortem histopathologic staining data from 52 older participants as the standard-of-truth measurement. Results: Our method resulted in consistently and significantly higher SUVRs in comparison to the conventional method in almost all regions of interest. A 2-way ANOVA revealed a significant main effect of method as well as a significant interaction effect of method on the relationship between computed SUVR and histopathologic staining score. Conclusion: These findings suggest that processing the amyloid-β PET data in subjects' native space can improve the accuracy of the computed SUVRs, as they are more closely associated with the histopathologic staining data than are the results of the conventional approach.
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Affiliation(s)
- Mouna Tahmi
- Department of Neurology, Columbia University Medical Center, New York, New York; and
| | - Wassim Bou-Zeid
- Department of Neurology, Columbia University Medical Center, New York, New York; and
| | - Qolamreza R Razlighi
- Department of Neurology, Columbia University Medical Center, New York, New York; and.,Department of Biomedical Engineering, Columbia University, New York, New York
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Mateos-Pérez JM, Dadar M, Lacalle-Aurioles M, Iturria-Medina Y, Zeighami Y, Evans AC. Structural neuroimaging as clinical predictor: A review of machine learning applications. NEUROIMAGE-CLINICAL 2018; 20:506-522. [PMID: 30167371 PMCID: PMC6108077 DOI: 10.1016/j.nicl.2018.08.019] [Citation(s) in RCA: 101] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/16/2017] [Revised: 01/22/2018] [Accepted: 08/09/2018] [Indexed: 11/26/2022]
Abstract
In this paper, we provide an extensive overview of machine learning techniques applied to structural magnetic resonance imaging (MRI) data to obtain clinical classifiers. We specifically address practical problems commonly encountered in the literature, with the aim of helping researchers improve the application of these techniques in future works. Additionally, we survey how these algorithms are applied to a wide range of diseases and disorders (e.g. Alzheimer's disease (AD), Parkinson's disease (PD), autism, multiple sclerosis, traumatic brain injury, etc.) in order to provide a comprehensive view of the state of the art in different fields.
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Affiliation(s)
| | - Mahsa Dadar
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | | | | | - Yashar Zeighami
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Alan C Evans
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
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9
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Chiao P, Bedell BJ, Avants B, Zijdenbos AP, Grand'Maison M, O’Neill P, O’Gorman J, Chen T, Koeppe R. Impact of Reference and Target Region Selection on Amyloid PET SUV Ratios in the Phase 1b PRIME Study of Aducanumab. J Nucl Med 2018; 60:100-106. [DOI: 10.2967/jnumed.118.209130] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2018] [Accepted: 05/07/2018] [Indexed: 01/30/2023] Open
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10
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Schwarz CG, Jones DT, Gunter JL, Lowe VJ, Vemuri P, Senjem ML, Petersen RC, Knopman DS, Jack CR. Contributions of imprecision in PET-MRI rigid registration to imprecision in amyloid PET SUVR measurements. Hum Brain Mapp 2017; 38:3323-3336. [PMID: 28432784 PMCID: PMC5518286 DOI: 10.1002/hbm.23622] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2016] [Revised: 04/06/2017] [Accepted: 04/09/2017] [Indexed: 11/05/2022] Open
Abstract
Quantitative measurement of β-amyloid from amyloid PET scans typically relies on localizing target and reference regions by image registration to MRI. In this work, we present a series of simulations where 50 small random perturbations of starting location and orientation were applied to each subject's PET scan, and rigid registration using spm_coreg was performed between each perturbed PET scan and its corresponding MRI. We then measured variation in the output PET-MRI registrations and how this variation affected the resulting SUVR measurements. We performed these experiments using scans of 1196 participants, half using 18F florbetapir and half using 11C PiB. From these experiments, we measured the magnitude of the imprecision in the rigid registration steps used to localize measurement regions, and how this contributes to the overall imprecision in SUVR measurements. Unexpectedly, we found for both tracers that the imprecision in these measurements depends on the degree of amyloid tracer uptake, and thus also indirectly on Alzheimer's disease clinical status. We then examined common choices of reference regions, and we show that SUVR measurements using supratentorial white matter references are relatively resistant to this source of error. We also show that the use of partial volume correction further magnifies the effects of registration imprecision on SUVR measurements. Together, these results suggest that this rigid registration step is an attractive target for future work in improving measurement techniques. Hum Brain Mapp 38:3323-3336, 2017. © 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.
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Affiliation(s)
| | - David T Jones
- Department of Neurology, Mayo Clinic and Foundation, Rochester, Minnesota
| | - Jeffrey L Gunter
- Department of Radiology, Mayo Clinic and Foundation, Rochester, Minnesota.,Department of Information Technology, Mayo Clinic and Foundation, Rochester, Minnesota
| | - Val J Lowe
- Department of Radiology, Mayo Clinic and Foundation, Rochester, Minnesota
| | - Prashanthi Vemuri
- Department of Radiology, Mayo Clinic and Foundation, Rochester, Minnesota
| | - Matthew L Senjem
- Department of Radiology, Mayo Clinic and Foundation, Rochester, Minnesota.,Department of Information Technology, Mayo Clinic and Foundation, Rochester, Minnesota
| | - Ronald C Petersen
- Department of Neurology, Mayo Clinic and Foundation, Rochester, Minnesota
| | - David S Knopman
- Department of Neurology, Mayo Clinic and Foundation, Rochester, Minnesota
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic and Foundation, Rochester, Minnesota
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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 PMCID: PMC6818723 DOI: 10.1016/j.jalz.2016.11.007] [Citation(s) in RCA: 182] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [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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12
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Carbonell F, Zijdenbos AP, McLaren DG, Iturria-Medina Y, Bedell BJ. Modulation of glucose metabolism and metabolic connectivity by β-amyloid. J Cereb Blood Flow Metab 2016; 36:2058-2071. [PMID: 27301477 PMCID: PMC5363668 DOI: 10.1177/0271678x16654492] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2016] [Accepted: 05/17/2016] [Indexed: 11/17/2022]
Abstract
Glucose hypometabolism in the pre-clinical stage of Alzheimer's disease (AD) has been primarily associated with the APOE ɛ4 genotype, rather than fibrillar β-amyloid. In contrast, aberrant patterns of metabolic connectivity are more strongly related to β-amyloid burden than APOE ɛ4 status. A major limitation of previous studies has been the dichotomous classification of subjects as amyloid-positive or amyloid-negative. Dichotomous treatment of a continuous variable, such as β-amyloid, potentially obscures the true relationship with metabolism and reduces the power to detect significant changes in connectivity. In the present work, we assessed alterations of glucose metabolism and metabolic connectivity as continuous function of β-amyloid burden using positron emission tomography scans from the Alzheimer's Disease Neuroimaging Initiative study. Modeling β-amyloid as a continuous variable resulted in better model fits and improved power compared to the dichotomous model. Using this continuous model, we found that both APOE ɛ4 genotype and β-amyloid burden are strongly associated with glucose hypometabolism at early stages of Alzheimer's disease. We also determined that the cumulative effects of β-amyloid deposition result in a particular pattern of altered metabolic connectivity, which is characterized by global, synchronized hypometabolism at early stages of the disease process, followed by regionally heterogeneous, progressive hypometabolism.
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Affiliation(s)
| | | | | | | | - Barry J Bedell
- Biospective Inc., Montreal, Canada.,McGill University, Montreal, Canada
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13
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Schwarz CG, Senjem ML, Gunter JL, Tosakulwong N, Weigand SD, Kemp BJ, Spychalla AJ, Vemuri P, Petersen RC, Lowe VJ, Jack CR. Optimizing PiB-PET SUVR change-over-time measurement by a large-scale analysis of longitudinal reliability, plausibility, separability, and correlation with MMSE. Neuroimage 2016; 144:113-127. [PMID: 27577718 DOI: 10.1016/j.neuroimage.2016.08.056] [Citation(s) in RCA: 62] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2016] [Revised: 07/28/2016] [Accepted: 08/26/2016] [Indexed: 01/29/2023] Open
Abstract
Quantitative measurements of change in β-amyloid load from Positron Emission Tomography (PET) images play a critical role in clinical trials and longitudinal observational studies of Alzheimer's disease. These measurements are strongly affected by methodological differences between implementations, including choice of reference region and use of partial volume correction, but there is a lack of consensus for an optimal method. Previous works have examined some relevant variables under varying criteria, but interactions between them prevent choosing a method via combined meta-analysis. In this work, we present a thorough comparison of methods to measure change in β-amyloid over time using Pittsburgh Compound B (PiB) PET imaging. METHODS We compare 1,024 different automated software pipeline implementations with varying methodological choices according to four quality metrics calculated over three-timepoint longitudinal trajectories of 129 subjects: reliability (straightness/variance); plausibility (lack of negative slopes); ability to predict accumulator/non-accumulator status from baseline value; and correlation between change in β-amyloid and change in Mini Mental State Exam (MMSE) scores. RESULTS AND CONCLUSION From this analysis, we show that an optimal longitudinal measure of β-amyloid from PiB should use a reference region that includes a combination of voxels in the supratentorial white matter and those in the whole cerebellum, measured using two-class partial volume correction in the voxel space of each subject's corresponding anatomical MR image.
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Affiliation(s)
| | - Matthew L Senjem
- Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, United States; Department of Information Technology, Mayo Clinic and Foundation, Rochester, MN, United States
| | - Jeffrey L Gunter
- Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, United States; Department of Information Technology, Mayo Clinic and Foundation, Rochester, MN, United States
| | - Nirubol Tosakulwong
- Department of Health Sciences Research, Division of Biostatistics, Mayo Clinic and Foundation, Rochester, MN, United States
| | - Stephen D Weigand
- Department of Health Sciences Research, Division of Biostatistics, Mayo Clinic and Foundation, Rochester, MN, United States
| | - Bradley J Kemp
- Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, United States
| | - Anthony J Spychalla
- Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, United States
| | - Prashanthi Vemuri
- Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, United States
| | - Ronald C Petersen
- Department of Neurology, Mayo Clinic and Foundation, Rochester, MN, United States
| | - Val J Lowe
- Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, United States
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, United States
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Mathotaarachchi S, Wang S, Shin M, Pascoal TA, Benedet AL, Kang MS, Beaudry T, Fonov VS, Gauthier S, Labbe A, Rosa-Neto P. VoxelStats: A MATLAB Package for Multi-Modal Voxel-Wise Brain Image Analysis. Front Neuroinform 2016; 10:20. [PMID: 27378902 PMCID: PMC4908129 DOI: 10.3389/fninf.2016.00020] [Citation(s) in RCA: 68] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2016] [Accepted: 06/01/2016] [Indexed: 11/15/2022] Open
Abstract
In healthy individuals, behavioral outcomes are highly associated with the variability on brain regional structure or neurochemical phenotypes. Similarly, in the context of neurodegenerative conditions, neuroimaging reveals that cognitive decline is linked to the magnitude of atrophy, neurochemical declines, or concentrations of abnormal protein aggregates across brain regions. However, modeling the effects of multiple regional abnormalities as determinants of cognitive decline at the voxel level remains largely unexplored by multimodal imaging research, given the high computational cost of estimating regression models for every single voxel from various imaging modalities. VoxelStats is a voxel-wise computational framework to overcome these computational limitations and to perform statistical operations on multiple scalar variables and imaging modalities at the voxel level. VoxelStats package has been developed in Matlab(®) and supports imaging formats such as Nifti-1, ANALYZE, and MINC v2. Prebuilt functions in VoxelStats enable the user to perform voxel-wise general and generalized linear models and mixed effect models with multiple volumetric covariates. Importantly, VoxelStats can recognize scalar values or image volumes as response variables and can accommodate volumetric statistical covariates as well as their interaction effects with other variables. Furthermore, this package includes built-in functionality to perform voxel-wise receiver operating characteristic analysis and paired and unpaired group contrast analysis. Validation of VoxelStats was conducted by comparing the linear regression functionality with existing toolboxes such as glim_image and RMINC. The validation results were identical to existing methods and the additional functionality was demonstrated by generating feature case assessments (t-statistics, odds ratio, and true positive rate maps). In summary, VoxelStats expands the current methods for multimodal imaging analysis by allowing the estimation of advanced regional association metrics at the voxel level.
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Affiliation(s)
- Sulantha Mathotaarachchi
- Translational Neuroimaging Laboratory, Departments of Neurology and Neurosurgery, McGill University Research Centre for Studies in Aging, Douglas Research Institute, McGill UniversityMontreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill UniversityMontreal, QC, Canada
| | - Seqian Wang
- Translational Neuroimaging Laboratory, Departments of Neurology and Neurosurgery, McGill University Research Centre for Studies in Aging, Douglas Research Institute, McGill UniversityMontreal, QC, Canada
| | - Monica Shin
- Translational Neuroimaging Laboratory, Departments of Neurology and Neurosurgery, McGill University Research Centre for Studies in Aging, Douglas Research Institute, McGill UniversityMontreal, QC, Canada
| | - Tharick A. Pascoal
- Translational Neuroimaging Laboratory, Departments of Neurology and Neurosurgery, McGill University Research Centre for Studies in Aging, Douglas Research Institute, McGill UniversityMontreal, QC, Canada
| | - Andrea L. Benedet
- Translational Neuroimaging Laboratory, Departments of Neurology and Neurosurgery, McGill University Research Centre for Studies in Aging, Douglas Research Institute, McGill UniversityMontreal, QC, Canada
| | - Min Su Kang
- Translational Neuroimaging Laboratory, Departments of Neurology and Neurosurgery, McGill University Research Centre for Studies in Aging, Douglas Research Institute, McGill UniversityMontreal, QC, Canada
| | - Thomas Beaudry
- Translational Neuroimaging Laboratory, Departments of Neurology and Neurosurgery, McGill University Research Centre for Studies in Aging, Douglas Research Institute, McGill UniversityMontreal, QC, Canada
| | - Vladimir S. Fonov
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill UniversityMontreal, QC, Canada
| | - Serge Gauthier
- McGill University Research Centre for Studies in Aging, Douglas Research Institute, McGill UniversityMontreal, QC, Canada
- Douglas Hospital Research Center, Douglas Research Institute, McGill UniversityMontreal, QC, Canada
- Department of Psychiatry, McGill UniversityMontreal, QC, Canada
- Department of Neurology and Neurosurgery, McGill UniversityMontreal, QC, Canada
| | - Aurélie Labbe
- Douglas Hospital Research Center, Douglas Research Institute, McGill UniversityMontreal, QC, Canada
- Department of Psychiatry, McGill UniversityMontreal, QC, Canada
- Department of Epidemiology and Biostatistics, McGill UniversityMontreal, QC, Canada
| | - Pedro Rosa-Neto
- Translational Neuroimaging Laboratory, Departments of Neurology and Neurosurgery, McGill University Research Centre for Studies in Aging, Douglas Research Institute, McGill UniversityMontreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill UniversityMontreal, QC, Canada
- McGill University Research Centre for Studies in Aging, Douglas Research Institute, McGill UniversityMontreal, QC, Canada
- Douglas Hospital Research Center, Douglas Research Institute, McGill UniversityMontreal, QC, Canada
- Department of Psychiatry, McGill UniversityMontreal, QC, Canada
- Department of Neurology and Neurosurgery, McGill UniversityMontreal, QC, Canada
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