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Li J, Huang Q, Qi N, He K, Li S, Huang L, Pan F, Ren S, Hua F, Huang Y, Guan Y, Guo Q, Zhao J, Xie F. The associations between synaptic density and "A/T/N" biomarkers in Alzheimer's disease: An 18F-SynVesT-1 PET/MR study. J Cereb Blood Flow Metab 2024; 44:1199-1207. [PMID: 38295871 PMCID: PMC11179616 DOI: 10.1177/0271678x241230733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 12/08/2023] [Accepted: 12/26/2023] [Indexed: 06/13/2024]
Abstract
A newly developed SV2A radiotracer, 18F-SynVesT-1, was used in this study to investigate synaptic density and its association with Alzheimer's disease (AD) "A/T/N" biomarkers. The study included a cohort of 97 subjects, consisting of 64 patients with cognitive impairment (CI) and 33 individuals with normal cognition (CU). All subjects underwent 18F-SynVesT-1 PET/MR and 18F-florbetapir PET/CT scans. Additionally, a subgroup of individuals also underwent 18F-MK-6240, 18F-FDG PET/CT, plasma Aβ42/Aβ40 and p-tau181 tests. The differences in synaptic density between the groups and the correlations between synaptic density and AD "A/T/N" biomarkers were analyzed. The results showed that compared to the CU group, the CI with Aβ+ (CI+) group exhibited the most pronounced synapse loss in the hippocampus, with some loss also observed in the neocortex. Furthermore, synaptic density in the hippocampus and parahippocampal gyrus showed associations with AD biomarkers detected by both imaging and plasma tests in the CI group. The associations between synaptic density and FDG uptake and hippocampal volume were also observed in the CI+ group. In conclusion, the study demonstrated significant synaptic density loss, as measured by the promising tracer 18F-SynVesT-1, and its close correlation with "A/T/N" biomarkers in patients with both Alzheimer's clinical syndrome and pathological changes.
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Affiliation(s)
- Junpeng Li
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China
- Department of Nuclear Medicine, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Qi Huang
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Na Qi
- Department of Nuclear Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Kun He
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Songye Li
- PET Center, Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Lin Huang
- Department of Gerontology, Shanghai Jiaotong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Fengfeng Pan
- Department of Gerontology, Shanghai Jiaotong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Shuhua Ren
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Fengchun Hua
- Department of Nuclear Medicine, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yiyun Huang
- PET Center, Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Yihui Guan
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Qihao Guo
- Department of Gerontology, Shanghai Jiaotong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Jun Zhao
- Department of Nuclear Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Fang Xie
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China
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2
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Wang D, Xu K, Dang M, Sang F, Chen K, Zhang Z, Li X. Multi-domain cognition dysfunction accompanies frontoparietal and temporal amyloid accumulation in the elderly. Cereb Cortex 2023; 33:11329-11338. [PMID: 37859548 DOI: 10.1093/cercor/bhad369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 09/18/2023] [Accepted: 09/19/2023] [Indexed: 10/21/2023] Open
Abstract
It is helpful to understand the pathology of Alzheimer's disease by exploring the relationship between amyloid-β accumulation and cognition. The study explored the relationship between regional amyloid-β accumulation and multiple cognitions and study their application value in the Alzheimer's disease diagnosis. 135 participants completed 18F-florbetapir Positron Emission Tomography (PET), structural MRI, and a cognitive battery. Partial correlation was used to examine the relationship between global and regional amyloid-β accumulation and cognitions. Then, a support vector machine was applied to determine whether cognition-related accumulation regions can adequately distinguish the cognitively normal controls (76 participants) and mild cognitive impairment (30 participants) groups or mild cognitive impairment and Alzheimer's disease (29 participants) groups. The result showed that amyloid-β accumulation regions were mainly located in the frontoparietal cortex, calcarine fissure, and surrounding cortex and temporal pole regions. Episodic memory-related regions included the frontoparietal cortices; executive function-related regions included the frontoparietal, temporal, and occipital cortices; and processing speed-related regions included the frontal and occipital cortices. Support vector machine analysis showed that only episodic memory-related amyloid-β accumulation regions had better classification performance during the progression of Alzheimer's disease. Assessing regional changes in amyloid, particularly in frontoparietal regions, can aid in the early detection of amyloid-related decline in cognitive function.
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Affiliation(s)
- Dandan Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing, 100875, P.R. China
- Beijing Aging Brain Rejuvenation Initiative (BABRI) Center, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing, 100875, P.R. China
| | - Kai Xu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing, 100875, P.R. China
- Beijing Aging Brain Rejuvenation Initiative (BABRI) Center, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing, 100875, P.R. China
- School of Artificial Intelligence, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing, 100875, P.R. China
| | - Mingxi Dang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing, 100875, P.R. China
- Beijing Aging Brain Rejuvenation Initiative (BABRI) Center, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing, 100875, P.R. China
| | - Feng Sang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing, 100875, P.R. China
- Beijing Aging Brain Rejuvenation Initiative (BABRI) Center, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing, 100875, P.R. China
| | - Kewei Chen
- Beijing Aging Brain Rejuvenation Initiative (BABRI) Center, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing, 100875, P.R. China
- Banner Alzheimer's Institute, Phoenix, AZ 85006, United States
| | - Zhanjun Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing, 100875, P.R. China
- Beijing Aging Brain Rejuvenation Initiative (BABRI) Center, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing, 100875, P.R. China
| | - Xin Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing, 100875, P.R. China
- Beijing Aging Brain Rejuvenation Initiative (BABRI) Center, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing, 100875, P.R. China
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3
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Watson CM, Dammer EB, Ping L, Duong DM, Modeste E, Carter EK, Johnson ECB, Levey AI, Lah JJ, Roberts BR, Seyfried NT. Quantitative Mass Spectrometry Analysis of Cerebrospinal Fluid Protein Biomarkers in Alzheimer's Disease. Sci Data 2023; 10:261. [PMID: 37160957 PMCID: PMC10170100 DOI: 10.1038/s41597-023-02158-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 04/17/2023] [Indexed: 05/11/2023] Open
Abstract
Alzheimer's disease (AD) is the most common form of dementia, with cerebrospinal fluid (CSF) β-amyloid (Aβ), total Tau, and phosphorylated Tau (pTau) providing the most sensitive and specific biomarkers for diagnosis. However, these diagnostic biomarkers do not reflect the complex changes in AD brain beyond amyloid (A) and Tau (T) pathologies. Here, we report a selected reaction monitoring mass spectrometry (SRM-MS) method with isotopically labeled standards for relative protein quantification in CSF. Biomarker positive (AT+) and negative (AT-) CSF pools were used as quality controls (QCs) to assess assay precision. We detected 62 peptides (51 proteins) with an average coefficient of variation (CV) of ~13% across 30 QCs and 133 controls (cognitively normal, AT-), 127 asymptomatic (cognitively normal, AT+) and 130 symptomatic AD (cognitively impaired, AT+). Proteins that could distinguish AT+ from AT- individuals included SMOC1, GDA, 14-3-3 proteins, and those involved in glycolysis. Proteins that could distinguish cognitive impairment were mainly neuronal proteins (VGF, NPTX2, NPTXR, and SCG2). This demonstrates the utility of SRM-MS to quantify CSF protein biomarkers across stages of AD.
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Affiliation(s)
- Caroline M Watson
- Department of Neurology, Emory University School of Medicine, Atlanta, USA
| | - Eric B Dammer
- Department of Neurology, Emory University School of Medicine, Atlanta, USA
| | - Lingyan Ping
- Department of Neurology, Emory University School of Medicine, Atlanta, USA
| | - Duc M Duong
- Department of Biochemistry, Emory University School of Medicine, Atlanta, USA
| | - Erica Modeste
- Department of Neurology, Emory University School of Medicine, Atlanta, USA
| | - E Kathleen Carter
- Department of Biochemistry, Emory University School of Medicine, Atlanta, USA
| | - Erik C B Johnson
- Department of Neurology, Emory University School of Medicine, Atlanta, USA
| | - Allan I Levey
- Department of Neurology, Emory University School of Medicine, Atlanta, USA.
| | - James J Lah
- Department of Neurology, Emory University School of Medicine, Atlanta, USA.
| | - Blaine R Roberts
- Department of Neurology, Emory University School of Medicine, Atlanta, USA
- Department of Biochemistry, Emory University School of Medicine, Atlanta, USA
| | - Nicholas T Seyfried
- Department of Neurology, Emory University School of Medicine, Atlanta, USA.
- Department of Biochemistry, Emory University School of Medicine, Atlanta, USA.
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4
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Smith AM, Obuchowski NA, Foster NL, Klein G, Mozley PD, Lammertsma AA, Wahl RL, Sunderland JJ, Vanderheyden JL, Benzinger TLS, Kinahan PE, Wong DF, Perlman ES, Minoshima S, Matthews D. The RSNA QIBA Profile for Amyloid PET as an Imaging Biomarker for Cerebral Amyloid Quantification. J Nucl Med 2023; 64:294-303. [PMID: 36137760 PMCID: PMC9902844 DOI: 10.2967/jnumed.122.264031] [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: 02/16/2022] [Revised: 08/05/2022] [Accepted: 08/05/2022] [Indexed: 02/04/2023] Open
Abstract
A standardized approach to acquiring amyloid PET images increases their value as disease and drug response biomarkers. Most 18F PET amyloid brain scans often are assessed only visually (per regulatory labels), with a binary decision indicating the presence or absence of Alzheimer disease amyloid pathology. Minimizing technical variance allows precise, quantitative SUV ratios (SUVRs) for early detection of β-amyloid plaques and allows the effectiveness of antiamyloid treatments to be assessed with serial studies. Methods: The Quantitative Imaging Biomarkers Alliance amyloid PET biomarker committee developed and validated a profile to characterize and reduce the variability of SUVRs, increasing statistical power for these assessments. Results: On achieving conformance, sites can justify a claim that brain amyloid burden reflected by the SUVR is measurable to a within-subject coefficient of variation of no more than 1.94% when the same radiopharmaceutical, scanner, acquisition, and analysis protocols are used. Conclusion: This overview explains the claim, requirements, barriers, and potential future developments of the profile to achieve precision in clinical and research amyloid PET imaging.
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Affiliation(s)
- Anne M Smith
- Siemens Medical Solutions USA, Inc., Knoxville, Tennessee;
| | | | - Norman L Foster
- Department of Neurology, University of Utah, Salt Lake City, Utah
| | | | - P David Mozley
- Weill Medical College of Cornell University, New York, New York
| | - Adriaan A Lammertsma
- Amsterdam Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Location VUmc, Amsterdam, The Netherlands
- Medical Imaging Center, Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Richard L Wahl
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri
- Department of Radiation Oncology, Washington University in Saint Louis, St. Louis, Missouri
| | - John J Sunderland
- Division of Nuclear Medicine, Department of Radiology, University of Iowa, Iowa City, Iowa
| | | | - Tammie L S Benzinger
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri
- Department of Radiation Oncology, Washington University in Saint Louis, St. Louis, Missouri
| | - Paul E Kinahan
- Department of Radiology, School of Medicine, University of Washington, Seattle, Washington
| | - Dean F Wong
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri
| | | | - Satoshi Minoshima
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah; and
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5
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Zeydan B, Schwarz CG, Przybelski SA, Lesnick TG, Kremers WK, Senjem ML, Kantarci OH, Min PH, Kemp BJ, Jack CR, Kantarci K, Lowe VJ. Comparison of 11C-Pittsburgh Compound B and 18F-Flutemetamol White Matter Binding in PET. J Nucl Med 2022; 63:1239-1244. [PMID: 34916245 PMCID: PMC9364341 DOI: 10.2967/jnumed.121.263281] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 11/30/2021] [Indexed: 02/03/2023] Open
Abstract
PET imaging with β-amyloid ligands is emerging as a molecular imaging technique targeting white matter integrity and demyelination. β-amyloid PET ligands such as 11C-Pittsburgh compound B (11C-PiB) have been considered for quantitative measurement of myelin content changes in multiple sclerosis, but 11C-PiB is not commercially available given its short half-life. A 18F PET ligand such as flutemetamol with a longer half-life may be an alternative, but its ability to differentiate white matter hyperintensities (WMH) from normal-appearing white matter (NAWM) and its relationship with age remains to be investigated. Methods: Cognitively unimpaired (CU) older and younger adults (n = 61) were recruited from the community responding to a study advertisement for β-amyloid PET. Participants prospectively underwent MRI, 11C-PiB, and 18F-flutemetamol PET scans. MRI fluid-attenuated inversion recovery images were segmented into WMH and NAWM and registered to the T1-weighted MRI. 11C-PiB and 18F-flutemetamol PET images were also registered to the T1-weighted MRI. 11C-PiB and 18F-flutemetamol SUV ratios (SUVrs) from the WMH and NAWM were calculated using cerebellar crus uptake as a reference for both 11C-PiB and 18F-flutemetamol. Results: The median age was 38 y (range, 30-48 y) in younger adults and 67 y (range, 61-83 y) in older adults. WMH and NAWM SUVrs were higher with 18F-flutemetamol than with 11C-PiB in both older (P < 0.001) and younger (P < 0.001) CU adults. 11C-PiB and 18F-flutemetamol SUVrs were higher in older than in younger CU adults in both WMH (P < 0.001) and NAWM (P < 0.001). 11C-PiB and 18F-flutemetamol SUVrs were higher in NAWM than WMH in both older (P < 0.001) and younger (P < 0.001) CU adults. There was no apparent difference between 11C-PiB and 18F-flutemetamol SUVrs in differentiating WMH from NAWM in older and in younger adults. Conclusion:11C-PiB and 18F-flutemetamol show a similar topographic pattern of uptake in white matter with a similar association with age in WMH and NAWM. 11C-PiB and 18F-flutemetamol can also effectively distinguish between WMH and NAWM. However, given its longer half-life, commercial availability, and higher binding potential, 18F-flutemetamol can be an alternative to 11C-PiB in molecular imaging studies specifically targeting multiple sclerosis to evaluate white matter integrity.
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Affiliation(s)
- Burcu Zeydan
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
- Department of Neurology, Mayo Clinic, Rochester, Minnesota
| | | | - Scott A Przybelski
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota; and
| | - Timothy G Lesnick
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota; and
| | - Walter K Kremers
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota; and
| | - Matthew L Senjem
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
- Department of Information Technology, Mayo Clinic, Rochester, Minnesota
| | | | - Paul H Min
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
| | - Bradley J Kemp
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
| | | | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
| | - Val J Lowe
- Department of Radiology, Mayo Clinic, Rochester, Minnesota;
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6
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Li Y, Ng YL, Paranjpe MD, Ge Q, Gu F, Li P, Yan S, Lu J, Wang X, Zhou Y. Tracer-specific reference tissues selection improves detection of 18 F-FDG, 18 F-florbetapir, and 18 F-flortaucipir PET SUVR changes in Alzheimer's disease. Hum Brain Mapp 2022; 43:2121-2133. [PMID: 35165964 PMCID: PMC8996354 DOI: 10.1002/hbm.25774] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 12/17/2021] [Accepted: 12/30/2021] [Indexed: 01/05/2023] Open
Abstract
This study sought to identify a reference tissue‐based quantification approach for improving the statistical power in detecting changes in brain glucose metabolism, amyloid, and tau deposition in Alzheimer's disease studies. A total of 794, 906, and 903 scans were included for 18F‐FDG, 18F‐florbetapir, and 18F‐flortaucipir, respectively. Positron emission tomography (PET) and T1‐weighted images of participants were collected from the Alzheimer's disease Neuroimaging Initiative database, followed by partial volume correction. The standardized uptake value ratios (SUVRs) calculated from the cerebellum gray matter, centrum semiovale, and pons were evaluated at both region of interest (ROI) and voxelwise levels. The statistical power of reference tissues in detecting longitudinal SUVR changes was assessed via paired t‐test. In cross‐sectional analysis, the impact of reference tissue‐based SUVR differences between cognitively normal and cognitively impaired groups was evaluated by effect sizes Cohen's d and two sample t‐test adjusted by age, sex, and education levels. The average ROI t values of pons were 86.62 and 38.40% higher than that of centrum semiovale and cerebellum gray matter in detecting glucose metabolism decreases, while the centrum semiovale reference tissue‐based SUVR provided higher t values for the detection of amyloid and tau deposition increases. The three reference tissues generated comparable d images for 18F‐FDG, 18F‐florbetapir, and 18F‐flortaucipir and comparable t maps for 18F‐florbetapir and 18F‐flortaucipir, but pons‐based t map showed superior performance in 18F‐FDG. In conclusion, the tracer‐specific reference tissue improved the detection of 18F‐FDG, 18F‐florbetapir, and 18F‐flortaucipir PET SUVR changes, which helps the early diagnosis, monitoring of disease progression, and therapeutic response in Alzheimer's disease.
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Affiliation(s)
- Yanxiao Li
- Central Research Institute, United Imaging Healthcare Group Co., Ltd, Shanghai, China.,School of Computer Science, The University of Sydney, Sydney, New South Wales, Australia
| | - Yee Ling Ng
- Central Research Institute, United Imaging Healthcare Group Co., Ltd, Shanghai, China
| | - Manish D Paranjpe
- Harvard-MIT Health Sciences and Technology Program, Harvard Medical School, Boston, Massachusetts, USA
| | - Qi Ge
- Central Research Institute, United Imaging Healthcare Group Co., Ltd, Shanghai, China
| | - Fengyun Gu
- Central Research Institute, United Imaging Healthcare Group Co., Ltd, Shanghai, China.,Department of Statistics, University College Cork, Cork, Ireland
| | - Panlong Li
- School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, Henan, China
| | - Shaozhen Yan
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Jie Lu
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Xiuying Wang
- School of Computer Science, The University of Sydney, Sydney, New South Wales, Australia
| | - Yun Zhou
- Central Research Institute, United Imaging Healthcare Group Co., Ltd, Shanghai, China
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Zhou Y, Tagare HD. Self-normalized Classification of Parkinson's Disease DaTscan Images. PROCEEDINGS. IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE 2021; 2021:1205-1212. [PMID: 35425663 PMCID: PMC9006242 DOI: 10.1109/bibm52615.2021.9669820] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Classifying SPECT images requires a preprocessing step which normalizes the images using a normalization region. The choice of the normalization region is not standard, and using different normalization regions introduces normalization region-dependent variability. This paper mathematically analyzes the effect of the normalization region to show that normalized-classification is exactly equivalent to a subspace separation of the half rays of the images under multiplicative equivalence. Using this geometry, a new self-normalized classification strategy is proposed. This strategy eliminates the normalizing region altogether. The theory is used to classify DaTscan images of 365 Parkinson's disease (PD) subjects and 208 healthy control (HC) subjects from the Parkinson's Progression Marker Initiative (PPMI). The theory is also used to understand PD progression from baseline to year 4.
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Affiliation(s)
- Yuan Zhou
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
| | - Hemant D Tagare
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
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8
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Zhou Y, Flores S, Mansor S, Hornbeck RC, Tu Z, Perlmutter JS, Ances B, Morris JC, Gropler RJ, Benzinger TLS. Spatially constrained kinetic modeling with dual reference tissues improves 18F-flortaucipir PET in studies of Alzheimer disease. Eur J Nucl Med Mol Imaging 2021; 48:3172-3186. [PMID: 33599811 PMCID: PMC8371062 DOI: 10.1007/s00259-020-05134-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2020] [Accepted: 11/20/2020] [Indexed: 01/03/2023]
Abstract
PURPOSE Recent studies have shown that standard compartmental models using plasma input or the cerebellum reference tissue input are generally not reliable for quantifying tau burden in dynamic 18F-flortaucipir PET studies of Alzheimer disease. So far, the optimal reference region for estimating 18F-flortaucipir delivery and specific tau binding has yet to be determined. The objective of the study is to improve 18F-flortaucipir brain tau PET quantification using a spatially constrained kinetic model with dual reference tissues. METHODS Participants were classified as either cognitively normal (CN) or cognitively impaired (CI) based on clinical assessment. T1-weighted structural MRI and 105-min dynamic 18F-flortaucipir PET scans were acquired for each participant. Using both a simplified reference tissue model (SRTM2) and Logan plot with either cerebellum gray matter or centrum semiovale (CS) white matter as the reference tissue, we estimated distribution volume ratios (DVRs) and the relative transport rate constant R1 for region of interest-based (ROI) and voxelwise-based analyses. Conventional linear regression (LR) and LR with spatially constrained (LRSC) parametric imaging algorithms were then evaluated. Noise-induced bias in the parametric images was compared to estimates from ROI time activity curve-based kinetic modeling. We finally evaluated standardized uptake value ratios at early phase (SUVREP, 0.7-2.9 min) and late phase (SUVRLP, 80-105 min) to approximate R1 and DVR, respectively. RESULTS The percent coefficients of variation of R1 and DVR estimates from SRTM2 with spatially constrained modeling were comparable to those from the Logan plot and SUVRs. The SRTM2 using CS reference tissue with LRSC reduced noise-induced underestimation in the LR generated DVR images to negligible levels (< 1%). Inconsistent overestimation of DVR in the SUVRLP only occurred using the cerebellum reference tissue-based measurements. The CS reference tissue-based DVR and SUVRLP, and cerebellum-based SUVREP and R1 provided higher Cohen's effect size d to detect increased tau deposition and reduced relative tracer transport rate in CI individuals. CONCLUSION Using a spatially constrained kinetic model with dual reference tissues significantly improved quantification of relative perfusion and tau binding. Cerebellum and CS are the suggested reference tissues to estimate R1 and DVR, respectively, for dynamic 18F-flortaucipir PET studies. Cerebellum-based SUVREP and CS-based SUVRLP may be used to simplify 18F-flortaucipir PET study.
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Affiliation(s)
- Yun Zhou
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, Campus Box 8225, 510 S. Kingshighway Blvd, St Louis, MO, 63110, USA.
| | - Shaney Flores
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, Campus Box 8225, 510 S. Kingshighway Blvd, St Louis, MO, 63110, USA
| | - Syahir Mansor
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, Campus Box 8225, 510 S. Kingshighway Blvd, St Louis, MO, 63110, USA
| | - Russ C Hornbeck
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, Campus Box 8225, 510 S. Kingshighway Blvd, St Louis, MO, 63110, USA
| | - Zhude Tu
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, Campus Box 8225, 510 S. Kingshighway Blvd, St Louis, MO, 63110, USA
| | - Joel S Perlmutter
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, Campus Box 8225, 510 S. Kingshighway Blvd, St Louis, MO, 63110, USA
- Departments of Neurology and Neuroscience, Programs of Physical Therapy and Occupational Therapy, Washington University School of Medicine, Saint Louis, MO, USA
| | - Beau Ances
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA
| | - John C Morris
- Departments of Neurology and Neuroscience, Programs of Physical Therapy and Occupational Therapy, Washington University School of Medicine, Saint Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA
| | - Robert J Gropler
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, Campus Box 8225, 510 S. Kingshighway Blvd, St Louis, MO, 63110, USA
| | - Tammie L S Benzinger
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, Campus Box 8225, 510 S. Kingshighway Blvd, St Louis, MO, 63110, USA
- Departments of Neurology and Neuroscience, Programs of Physical Therapy and Occupational Therapy, Washington University School of Medicine, Saint Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA
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Pegueroles J, Montal V, Bejanin A, Vilaplana E, Aranha M, Santos‐Santos MA, Alcolea D, Carrió I, Camacho V, Blesa R, Lleó A, Fortea J. AMYQ: An index to standardize quantitative amyloid load across PET tracers. Alzheimers Dement 2021; 17:1499-1508. [PMID: 33797846 PMCID: PMC8519100 DOI: 10.1002/alz.12317] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 01/21/2021] [Accepted: 01/31/2021] [Indexed: 12/17/2022]
Abstract
INTRODUCTION Positron emission tomography (PET) amyloid quantification methods require magnetic resonance imaging (MRI) for spatial registration and a priori reference region to scale the images. Furthermore, different tracers have distinct thresholds for positivity. We propose the AMYQ index, a new measure of amyloid burden, to overcome these limitations. METHODS We selected 18F-amyloid scans from ADNI and Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL) with the corresponding T1-MRI. A subset also had neuropathological data. PET images were normalized, and the AMYQ was calculated based on an adaptive template. We compared AMYQ with the Centiloid scale on clinical and neuropathological diagnostic performance. RESULTS AMYQ was related with amyloid neuropathological burden and had excellent diagnostic performance to discriminate controls from patients with Alzheimer's disease (AD) (area under the curve [AUC] = 0.86). AMYQ had a high agreement with the Centiloid scale (intraclass correlation coefficient [ICC] = 0.88) and AUC between 0.94 and 0.99 to discriminate PET positivity when using different Centiloid cutoffs. DISCUSSION AMYQ is a new MRI-independent index for standardizing and quantifying amyloid load across tracers.
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Affiliation(s)
- Jordi Pegueroles
- Sant Pau Memory Unit, Department of NeurologyHospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de BarcelonaBarcelonaSpain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED)MadridSpain
| | - Victor Montal
- Sant Pau Memory Unit, Department of NeurologyHospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de BarcelonaBarcelonaSpain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED)MadridSpain
| | - Alexandre Bejanin
- Sant Pau Memory Unit, Department of NeurologyHospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de BarcelonaBarcelonaSpain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED)MadridSpain
| | - Eduard Vilaplana
- Sant Pau Memory Unit, Department of NeurologyHospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de BarcelonaBarcelonaSpain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED)MadridSpain
| | - Mateus Aranha
- Sant Pau Memory Unit, Department of NeurologyHospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de BarcelonaBarcelonaSpain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED)MadridSpain
| | - Miguel Angel Santos‐Santos
- Sant Pau Memory Unit, Department of NeurologyHospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de BarcelonaBarcelonaSpain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED)MadridSpain
| | - Daniel Alcolea
- Sant Pau Memory Unit, Department of NeurologyHospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de BarcelonaBarcelonaSpain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED)MadridSpain
| | - Ignasi Carrió
- Department of Nuclear MedicineHospital de la Santa Creu i Sant Pau, Universitat Autònoma de BarcelonaBarcelonaSpain
| | - Valle Camacho
- Department of Nuclear MedicineHospital de la Santa Creu i Sant Pau, Universitat Autònoma de BarcelonaBarcelonaSpain
| | - Rafael Blesa
- Sant Pau Memory Unit, Department of NeurologyHospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de BarcelonaBarcelonaSpain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED)MadridSpain
| | - Alberto Lleó
- Sant Pau Memory Unit, Department of NeurologyHospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de BarcelonaBarcelonaSpain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED)MadridSpain
| | - Juan Fortea
- Sant Pau Memory Unit, Department of NeurologyHospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de BarcelonaBarcelonaSpain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED)MadridSpain
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10
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Vanhoutte M, Landeau B, Sherif S, de la Sayette V, Dautricourt S, Abbas A, Manrique A, Chocat A, Chételat G. Evaluation of the early-phase [ 18F]AV45 PET as an optimal surrogate of [ 18F]FDG PET in ageing and Alzheimer's clinical syndrome. Neuroimage Clin 2021; 31:102750. [PMID: 34247116 PMCID: PMC8274342 DOI: 10.1016/j.nicl.2021.102750] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 06/27/2021] [Accepted: 06/28/2021] [Indexed: 12/05/2022]
Abstract
Dual-phase [18F]AV45 positron emission tomography (PET) is highly promising in the assessment of neurodegenerative diseases, allowing to obtain information on both neurodegeneration (early-phase; eAV45) and amyloid deposition (late-phase; lAV45) which are highly complementary; yet eAV45 needs further evaluation. This study aims at validating eAV45 as an optimal proxy of [18F]FDG PET in a large mixed-population of healthy ageing and Alzheimer's clinical syndrome participants (n = 191) who had [18F]FDG PET, eAV45 and lAV45 scans. We found early time frame 0-4 min to give maximal correlation with [18F]FDG PET and minimal correlation with lAV45. Moreover, maximal overlap of [18F]FDG PET versus eAV45 associations with clinical diagnosis and cognition was obtained with pons scaling. Across reference regions, classification performance between clinical subgroups was similar for both eAV45 and [18F]FDG PET. These findings highlight the optimal use of eAV45 to assess neurodegeneration as a validated proxy of [18F]FDG PET. On top of this purpose, this study showed that combined [18F]AV45 PET dual-biomarker even outperformed [18F]FDG PET or lAV45 alone.
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Affiliation(s)
- Matthieu Vanhoutte
- Inserm UMR-S U1237, Caen-Normandie University, GIP Cyceron, Caen, France.
| | - Brigitte Landeau
- Inserm UMR-S U1237, Caen-Normandie University, GIP Cyceron, Caen, France
| | - Siya Sherif
- Inserm UMR-S U1237, Caen-Normandie University, GIP Cyceron, Caen, France
| | - Vincent de la Sayette
- Inserm U1077, Caen-Normandie University, École Pratique des Hautes Études, Caen, France; University Hospital, Neurology Department, Caen, France
| | - Sophie Dautricourt
- Inserm UMR-S U1237, Caen-Normandie University, GIP Cyceron, Caen, France; University Hospital, Neurology Department, Caen, France
| | - Ahmed Abbas
- Inserm U1077, Caen-Normandie University, École Pratique des Hautes Études, Caen, France
| | - Alain Manrique
- University Hospital, Nuclear Medicine Department, Caen, France
| | - Anne Chocat
- Inserm UMR-S U1237, Caen-Normandie University, GIP Cyceron, Caen, France
| | - Gaël Chételat
- Inserm UMR-S U1237, Caen-Normandie University, GIP Cyceron, Caen, France; Inserm U1077, Caen-Normandie University, École Pratique des Hautes Études, Caen, France.
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11
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Wang YJ, Hu H, Yang YX, Zuo CT, Tan L, Yu JT. Regional Amyloid Accumulation and White Matter Integrity in Cognitively Normal Individuals. J Alzheimers Dis 2021; 74:1261-1270. [PMID: 32176644 DOI: 10.3233/jad-191350] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Recent studies have shown that amyloid-β (Aβ) burden influenced white matter (WM) integrity before the onset of dementia. OBJECTIVE To assess whether the effects of Aβ burden on WM integrity in cognitively normal (CN) individuals were regionally specific. METHODS Our cohort consisted of 71 CNs from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database who underwent both AV45 amyloid-PET and diffusion tensor imaging. Standardized uptake value ratio (SUVR) was computed across four bilateral regions of interest (ROIs) corresponding to four stages of in vivo amyloid staging model (Amyloid stages I-IV). Linear regression models were conducted in entire CN group and between APOEɛ4 carriers and non-carriers. RESULTS Our results indicated that higher global Aβ-SUVR was associated with higher mean diffusivity (MD) in the entire CN group (p = 0.023), and with both higher MD (p = 0.015) and lower fractional anisotropy (FA) (p = 0.026) in APOEɛ4 carriers. Subregion analysis showed that higher Amyloid stage I-II Aβ-SUVRs were associated with higher MD (Stage-1: p = 0.030; Stage-2: p = 0.016) in the entire CN group, and with both higher MD (Stage-1: p = 0.004; Stage-2: p = 0.010) and lower FA (Stage-1: p = 0.022; Stage-2: p = 0.014) in APOEɛ4 carriers. No associations were found in APOEɛ4 non-carriers and in Amyloid stage III-IV ROIs. CONCLUSIONS Our results indicated that the effects of Aβ burden on WM integrity in CNs might be regionally specific, particularly in Amyloid stage I-II ROIs, and modulated by APOEɛ4 status.
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Affiliation(s)
- Ya-Juan Wang
- Department of Neurology, Qingdao Municipal Hospital, Dalian Medical University, China
| | - Hao Hu
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, China
| | - Yu-Xiang Yang
- Department of Neurology and Institute of Neurology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Chuan-Tao Zuo
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Lan Tan
- Department of Neurology, Qingdao Municipal Hospital, Dalian Medical University, China.,Department of Neurology, Qingdao Municipal Hospital, Qingdao University, China
| | - Jin-Tai Yu
- Department of Neurology and Institute of Neurology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
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12
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van Oostveen WM, de Lange ECM. Imaging Techniques in Alzheimer's Disease: A Review of Applications in Early Diagnosis and Longitudinal Monitoring. Int J Mol Sci 2021; 22:ijms22042110. [PMID: 33672696 PMCID: PMC7924338 DOI: 10.3390/ijms22042110] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 02/15/2021] [Accepted: 02/16/2021] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is a progressive neurodegenerative disorder affecting many individuals worldwide with no effective treatment to date. AD is characterized by the formation of senile plaques and neurofibrillary tangles, followed by neurodegeneration, which leads to cognitive decline and eventually death. INTRODUCTION In AD, pathological changes occur many years before disease onset. Since disease-modifying therapies may be the most beneficial in the early stages of AD, biomarkers for the early diagnosis and longitudinal monitoring of disease progression are essential. Multiple imaging techniques with associated biomarkers are used to identify and monitor AD. AIM In this review, we discuss the contemporary early diagnosis and longitudinal monitoring of AD with imaging techniques regarding their diagnostic utility, benefits and limitations. Additionally, novel techniques, applications and biomarkers for AD research are assessed. FINDINGS Reduced hippocampal volume is a biomarker for neurodegeneration, but atrophy is not an AD-specific measure. Hypometabolism in temporoparietal regions is seen as a biomarker for AD. However, glucose uptake reflects astrocyte function rather than neuronal function. Amyloid-β (Aβ) is the earliest hallmark of AD and can be measured with positron emission tomography (PET), but Aβ accumulation stagnates as disease progresses. Therefore, Aβ may not be a suitable biomarker for monitoring disease progression. The measurement of tau accumulation with PET radiotracers exhibited promising results in both early diagnosis and longitudinal monitoring, but large-scale validation of these radiotracers is required. The implementation of new processing techniques, applications of other imaging techniques and novel biomarkers can contribute to understanding AD and finding a cure. CONCLUSIONS Several biomarkers are proposed for the early diagnosis and longitudinal monitoring of AD with imaging techniques, but all these biomarkers have their limitations regarding specificity, reliability and sensitivity. Future perspectives. Future research should focus on expanding the employment of imaging techniques and identifying novel biomarkers that reflect AD pathology in the earliest stages.
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Affiliation(s)
- Wieke M. van Oostveen
- Faculty of Science, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands;
| | - Elizabeth C. M. de Lange
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre of Drug Research, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands
- Correspondence: ; Tel.: +31-71-527-6330
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13
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Wang M, Yan Z, Zhang H, Lu J, Li L, Yu J, Wang J, Matsuda H, Zuo C, Jiang J. Parametric estimation of reference signal intensity in the quantification of amyloid-beta deposition: an 18F-AV-45 study. Quant Imaging Med Surg 2021; 11:249-263. [PMID: 33392026 PMCID: PMC7719939 DOI: 10.21037/qims-20-110] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 09/07/2020] [Indexed: 12/23/2022]
Abstract
BACKGROUND Positron emission tomography (PET) with the radiotracer florbetapir (18F-AV-45) allows the pathophysiology of Alzheimer's disease (AD) to be tracked in vivo. The semi-quantification of amyloid-beta (Aβ) has been extensively evaluated with the standardized uptake value ratio (SUVR) but is susceptible to disturbance from the candidate reference region and the partial volume effect (PVE). In the present study, we applied the parametric estimation of reference signal intensity (PERSI) method to 18F-AV-45 PET images for intensity normalization. METHODS We enrolled 479 people with 18F-AV-45 images from the Alzheimer's Disease Neuroimaging Initiative database: 261 healthy controls (HCs), 102 patients with mild cognitive impairment (MCI), and 116 AD patients. We used white matter post-processed by PERSI (PERSI-WM) as the reference region and compared our proposed method with the traditional method for semi-quantification. SUVRs were calculated for eight regions of interest: the frontal lobe, the parietal lobe, the temporal lobe, the occipital lobe, the anterior cingulate cortex, the posterior cingulate cortex, the precuneus, and the global cortex. The SUVRs derived from PERSI-WM and other reference regions were evaluated by effect size and receiver-operator characteristic curve analyses. RESULTS The SUVRs derived from PERSI-WM showed significantly higher trace retention in the frontal, parietal, temporal, and occipital lobes, as well as in the anterior cingulate, posterior cingulate, precuneus, and global cortex in the AD Aβ-positive (+) group (mean: +43.3%±5.4%, P<0.01) and MCI Aβ+ group (mean: +29.6%±5.3%, P<0.01). For the global cortex, PERSI-WM had the greatest Cohen's d effect size compared with the HC Aβ-negative (-) group (AD Aβ+ and MCI Aβ+: 3.02, AD Aβ+: 3.56, MCI Aβ+: 2.34), and the highest area under the curve (AUC) between the HC Aβ- and AD Aβ+ groups (AUC: 0.983, 95% confidence interval: 0.978-0.998). CONCLUSIONS PERSI-WM could mitigate the influence of PVE and improve the semi-quantification of 18F-AV-45 images; therefore, it could be used for large-scale clinical application in the nuclear medicine domain.
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Affiliation(s)
- Min Wang
- Shanghai Institute for Advanced Communication and Data Science, School of Communication and Information Engineering, Shanghai University, Shanghai, China
- Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - Zhuangzhi Yan
- Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - Huiwei Zhang
- PET Center, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jiaying Lu
- PET Center, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ling Li
- PET Center, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jintai Yu
- Department of Neurology and Institute of Neurology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jian Wang
- Department of Neurology and Institute of Neurology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Hiroshi Matsuda
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Kodaira, Tokyo, Japan
| | - Chuantao Zuo
- PET Center, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jiehui Jiang
- Shanghai Institute for Advanced Communication and Data Science, School of Communication and Information Engineering, Shanghai University, Shanghai, China
- Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - the Alzheimer’s Disease Neuroimaging Initiative
- Shanghai Institute for Advanced Communication and Data Science, School of Communication and Information Engineering, Shanghai University, Shanghai, China
- Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai, China
- PET Center, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
- Department of Neurology and Institute of Neurology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Kodaira, Tokyo, Japan
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14
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Ma D, Yee E, Stocks JK, Jenkins LM, Popuri K, Chausse G, Wang L, Probst S, Beg MF. Blinded Clinical Evaluation for Dementia of Alzheimer's Type Classification Using FDG-PET: A Comparison Between Feature-Engineered and Non-Feature-Engineered Machine Learning Methods. J Alzheimers Dis 2021; 80:715-726. [PMID: 33579858 PMCID: PMC8978589 DOI: 10.3233/jad-201591] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
BACKGROUND Advanced machine learning methods can aid in the identification of dementia risk using neuroimaging-derived features including FDG-PET. However, to enable the translation of these methods and test their usefulness in clinical practice, it is crucial to conduct independent validation on real clinical samples, which has yet to be properly delineated in the current literature. OBJECTIVE In this paper, we present our efforts to enable such clinical translational through the evaluation and comparison of two machine-learning methods for discrimination between dementia of Alzheimer's type (DAT) and Non-DAT controls. METHODS FDG-PET-based dementia scores were generated on an independent clinical sample whose clinical diagnosis was blinded to the algorithm designers. A feature-engineered approach (multi-kernel probability classifier) and a non-feature-engineered approach (3D convolutional neural network) were analyzed. Both classifiers were pre-trained on cognitively normal subjects as well as subjects with DAT. These two methods provided a probabilistic dementia score for this previously unseen clinical data. Performance of the algorithms were compared against ground-truth dementia rating assessed by experienced nuclear physicians. RESULTS Blinded clinical evaluation on both classifiers showed good separation between the cognitively normal subjects and the patients diagnosed with DAT. The non-feature-engineered dementia score showed higher sensitivity among subjects whose diagnosis was in agreement between the machine-learning models, while the feature-engineered approach showed higher specificity in non-consensus cases. CONCLUSION In this study, we demonstrated blinded evaluation using data from an independent clinical sample for assessing the performance in DAT classification models in a clinical setting. Our results showed good generalizability for two machine-learning approaches, marking an important step for the translation of pre-trained machine-learning models into clinical practice.
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Affiliation(s)
- Da Ma
- School of Engineering Science, Simon Fraser University, Burnaby, Canada
| | - Evangeline Yee
- School of Engineering Science, Simon Fraser University, Burnaby, Canada
| | - Jane K. Stocks
- Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Lisanne M. Jenkins
- Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Karteek Popuri
- School of Engineering Science, Simon Fraser University, Burnaby, Canada
| | | | - Lei Wang
- Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | | | - Mirza Faisal Beg
- School of Engineering Science, Simon Fraser University, Burnaby, Canada
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15
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Franceschi AM, Clifton M, Naser-Tavakolian K, Ahmed O, Cruciata G, Bangiyev L, Clouston S, Franceschi D. ( 18F)-Fluorodeoxyglucose positron emission tomography/magnetic resonance imaging assessment of hypometabolism patterns in clinical phenotypes of suspected corticobasal degeneration. World J Nucl Med 2020; 20:176-184. [PMID: 34321971 PMCID: PMC8286003 DOI: 10.4103/wjnm.wjnm_62_20] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 05/22/2020] [Accepted: 06/06/2020] [Indexed: 11/30/2022] Open
Abstract
Corticobasal degeneration (CBD) is a rare neurodegenerative disorder presenting with atypical parkinsonian symptoms that characteristically involves the frontoparietal region including the primary sensorimotor cortex, ipsilateral basal ganglia, and thalamus, typically in an asymmetric pattern. We aim to evaluate the metabolic and volumetric abnormalities in patients with clinically suspected CBD phenotypes utilizing hybrid 18F-fluorodeoxyglucose (FDG) positron emission tomography–magnetic resonance (PET/ MR) brain imaging. A retrospective analysis was performed on 75 patients (mean age 74 years, 31 males and 44 females) who underwent 18F-FDG PET/MR imaging (MRI) as part of their clinical dementia workup. Images were obtained using an integrated Siemens mMR 3T PET/MRI scanner. Two board-certified neuroradiologists and a nuclear medicine physician evaluated the metabolic and volumetric data of each hemisphere to assess for symmetric or asymmetric involvement of regions of interest in the subset of patients with suspected CBD. Of the 75 patients, 12 were diagnosed with suspected CBD based on a combination of clinical symptoms, neurocognitive testing, and hybrid neuroimaging findings. Ten of 12 patients (87%) demonstrated asymmetrically decreased FDG uptake involving a single cerebral hemisphere and ipsilateral subcortical structures, whereas two of 12 patients (13%) demonstrated striking hypometabolism of the bilateral sensorimotor cortices. Our study highlights two characteristic patterns of hypometabolism in patients with clinical and neuroimaging findings suggestive of the underlying CBD. The first pattern is asymmetric hypometabolism and volume loss, particularly within the frontoparietal and occipital cortices with involvement of ipsilateral subcortical structures, including the basal ganglia and thalamus. The second, more atypical pattern, is symmetric hypometabolism with striking involvement of the bilateral sensorimotor cortices.
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Affiliation(s)
- Ana M Franceschi
- Department of Radiology, Neuroradiology Section, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell Health, Manhasset, NY, USA
| | - Michael Clifton
- Department of Radiology, Population and Preventative Medicine, SUNY Stony Brook, Stony Brook, NY, USA
| | - Kiyon Naser-Tavakolian
- Department of Radiology, Population and Preventative Medicine, SUNY Stony Brook, Stony Brook, NY, USA
| | - Osama Ahmed
- Department of Radiology, Population and Preventative Medicine, SUNY Stony Brook, Stony Brook, NY, USA
| | - Giuseppe Cruciata
- Department of Radiology, Population and Preventative Medicine, SUNY Stony Brook, Stony Brook, NY, USA
| | - Lev Bangiyev
- Department of Radiology, Population and Preventative Medicine, SUNY Stony Brook, Stony Brook, NY, USA
| | - Sean Clouston
- Department of Family, Population and Preventative Medicine, SUNY Stony Brook, Stony Brook, NY, USA
| | - Dinko Franceschi
- Department of Radiology, Population and Preventative Medicine, SUNY Stony Brook, Stony Brook, NY, USA
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16
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Beyer L, Brendel M, Scheiwein F, Sauerbeck J, Hosakawa C, Alberts I, Shi K, Bartenstein P, Ishii K, Seibyl J, Cumming P, Rominger A. Improved Risk Stratification for Progression from Mild Cognitive Impairment to Alzheimer's Disease with a Multi-Analytical Evaluation of Amyloid-β Positron Emission Tomography. J Alzheimers Dis 2020; 74:101-112. [PMID: 31985461 DOI: 10.3233/jad-190818] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Amyloid-β (Aβ) accumulation in brain of patients with suspected Alzheimer's disease (AD) can be assessed by positron emission tomography (PET) in vivo. While visual classification prevails in the clinical routine, semiquantitative PET analyses may enable more reliable evaluation of cases with a visually uncertain, borderline Aβ accumulation. OBJECTIVE We evaluated different analysis approaches (visual/semiquantitative) to find the most accurate and sensitive interpretation of Aβ-PET for predicting risk of progression from mild cognitive impairment (MCI) to AD. METHODS Based on standard uptake value (SUV) ratios of a cortical-composite volume of interest of 18F-AV45-PET from MCI subjects (n = 396, ADNI database), we compared three different reference region (cerebellar grey matter, CBL; brainstem, BST; white matter, WM) normalizations and the visual read by receiver operator characteristics for calculating a hazard ratio (HR) for progression to Alzheimer's disease dementia (ADD). RESULTS During a mean follow-up time of 45.6±13.0 months, 28% of the MCI cases (110/396) converted to ADD. Among the tested methods, the WM reference showed best discriminatory power and progression-risk stratification (HRWM of 4.4 [2.6-7.6]), but the combined results of the visual and semiquantitative analysis with all three reference regions showed an even higher discriminatory power. CONCLUSION A multi-analytical composite of visual and semiquantitative reference tissue analyses of 18F-AV45-PET gave improved risk stratification for progression from MCI to ADD relative to performance of single read-outs. This optimized approach is of special interest for prospective treatment trials, which demand a high accuracy.
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Affiliation(s)
- Leonie Beyer
- Department of Nuclear Medicine, University Hospital of Munich, LMU, Munich, Germany
| | - Matthias Brendel
- Department of Nuclear Medicine, University Hospital of Munich, LMU, Munich, Germany
| | - Franziska Scheiwein
- Department of Nuclear Medicine, University Hospital of Munich, LMU, Munich, Germany
| | - Julia Sauerbeck
- Department of Nuclear Medicine, University Hospital of Munich, LMU, Munich, Germany
| | - Chisa Hosakawa
- Department of Radiology, Kindai University, Osaka, Japan
| | - Ian Alberts
- Department of Nuclear Medicine, Inselspital, University Hospital Bern, Bern, Switzerland
| | - Kuangyu Shi
- Department of Nuclear Medicine, Inselspital, University Hospital Bern, Bern, Switzerland
| | - Peter Bartenstein
- Department of Nuclear Medicine, University Hospital of Munich, LMU, Munich, Germany
| | - Kazunari Ishii
- Department of Radiology, Kindai University, Osaka, Japan
| | | | - Paul Cumming
- Department of Nuclear Medicine, Inselspital, University Hospital Bern, Bern, Switzerland.,School of Psychology and Counseling and IHBI, Queensland University of Technology, Brisbane, Australia
| | - Axel Rominger
- Department of Nuclear Medicine, Inselspital, University Hospital Bern, Bern, Switzerland
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17
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Golla SS, Verfaillie SC, Boellaard R, Adriaanse SM, Zwan MD, Schuit RC, Timmers T, Groot C, Schober P, Scheltens P, van der Flier WM, Windhorst AD, van Berckel BN, Lammertsma AA. Quantification of [ 18F]florbetapir: A test-retest tracer kinetic modelling study. J Cereb Blood Flow Metab 2019; 39:2172-2180. [PMID: 29897009 PMCID: PMC6826855 DOI: 10.1177/0271678x18783628] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Accumulation of amyloid beta can be visualized using [18F]florbetapir positron emission tomography. The aim of this study was to identify the optimal model for quantifying [18F]florbetapir uptake and to assess test-retest reliability of corresponding outcome measures. Eight Alzheimer's disease patients (age: 67 ± 6 years, Mini-Mental State Examination (MMSE): 23 ± 3) and eight controls (age: 63 ± 4 years, MMSE: 30 ± 0) were included. Ninety-minute dynamic positron emission tomography scans, together with arterial blood sampling, were acquired immediately following a bolus injection of 294 ± 32 MBq [18F]florbetapir. Several plasma input models and the simplified reference tissue model (SRTM) were evaluated. The Akaike information criterion was used to identify the preferred kinetic model. Compared to controls, Alzheimer's disease patients had lower MMSE scores and evidence for cortical Aβ pathology. A reversible two-tissue compartment model with fitted blood volume fraction (2T4k_VB) was the preferred model for describing [18F]florbetapir kinetics. SRTM-derived non-displaceable binding potential (BPND) correlated well (r2 = 0.83, slope = 0.86) with plasma input-derived distribution volume ratio. Test-retest reliability for plasma input-derived distribution volume ratio, SRTM-derived BPND and SUVr(50-70) were r = 0.88, r = 0.91 and r = 0.86, respectively. In vivo kinetics of [18F]florbetapir could best be described by a reversible two-tissue compartmental model and [18F]florbetapir BPND can be reliably estimated using an SRTM.
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Affiliation(s)
- Sandeep Sv Golla
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands
| | - Sander Cj Verfaillie
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands.,Department of Neurology & Alzheimer Center, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands
| | - Ronald Boellaard
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands.,Department of Nuclear Medicine & Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Sofie M Adriaanse
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands
| | - Marissa D Zwan
- Department of Neurology & Alzheimer Center, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands
| | - Robert C Schuit
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands
| | - Tessa Timmers
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands.,Department of Neurology & Alzheimer Center, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands
| | - Colin Groot
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands
| | - Patrick Schober
- Department of Anaesthesiology, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands
| | - Philip Scheltens
- Department of Neurology & Alzheimer Center, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands
| | - Wiesje M van der Flier
- Department of Neurology & Alzheimer Center, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands.,Department of Epidemiology & Biostatistics, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands
| | - Albert D Windhorst
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands
| | - Bart Nm van Berckel
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands
| | - Adriaan A Lammertsma
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands
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18
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Kameyama M, Ishibash K, Wagatsuma K, Toyohara J, Ishii K. A pitfall of white matter reference regions used in [18F] florbetapir PET: a consideration of kinetics. Ann Nucl Med 2019; 33:848-854. [DOI: 10.1007/s12149-019-01397-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2019] [Accepted: 08/17/2019] [Indexed: 12/16/2022]
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19
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Meyer PF, McSweeney M, Gonneaud J, Villeneuve S. AD molecular: PET amyloid imaging across the Alzheimer's disease spectrum: From disease mechanisms to prevention. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2019; 165:63-106. [PMID: 31481172 DOI: 10.1016/bs.pmbts.2019.05.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The advent of amyloid-beta (Aβ) positron emission tomography (PET) imaging has transformed the field of Alzheimer's disease (AD) by enabling the quantification of cortical Aβ accumulation and propagation in vivo. This revolutionary tool has made it possible to measure direct associations between Aβ and other AD biomarkers, to identify factors that influence Aβ accumulation and to redefine entry criteria into clinical trials as well as measure drug target engagement. This chapter summarizes the main findings on the associations of Aβ with other biomarkers of disease progression across the AD spectrum. It discusses investigations of the timing at which Aβ pathology starts to accumulate, demonstrates the clinical utility of Aβ PET imaging and discusses some ethical implications. Finally, it presents genetic and potentially modifiable lifestyle factors that might influence Aβ accumulation and therefore be targets for AD prevention.
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Affiliation(s)
- Pierre-François Meyer
- Centre for Studies on the Prevention of Alzheimer's Disease, Douglas Mental Health University Institute, Montréal, Canada; McGill University, Montréal, Canada
| | - Melissa McSweeney
- Centre for Studies on the Prevention of Alzheimer's Disease, Douglas Mental Health University Institute, Montréal, Canada; McGill University, Montréal, Canada
| | - Julie Gonneaud
- Centre for Studies on the Prevention of Alzheimer's Disease, Douglas Mental Health University Institute, Montréal, Canada; McGill University, Montréal, Canada
| | - Sylvia Villeneuve
- Centre for Studies on the Prevention of Alzheimer's Disease, Douglas Mental Health University Institute, Montréal, Canada; McGill University, Montréal, Canada.
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20
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Zeydan B, Schwarz CG, Lowe VJ, Reid RI, Przybelski SA, Lesnick TG, Kremers WK, Senjem ML, Gunter JL, Min H, Vemuri P, Knopman DS, Petersen RC, Jack CR, Kantarci OH, Kantarci K. Investigation of white matter PiB uptake as a marker of white matter integrity. Ann Clin Transl Neurol 2019; 6:678-688. [PMID: 31019992 PMCID: PMC6469255 DOI: 10.1002/acn3.741] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2018] [Revised: 01/14/2019] [Accepted: 02/03/2019] [Indexed: 01/20/2023] Open
Abstract
OBJECTIVE To investigate the associations of Pittsburgh compound-B (PiB) uptake in white matter hyperintensities (WMH) and normal appearing white matter (NAWM) with white matter (WM) integrity measured with DTI and cognitive function in cognitively unimpaired older adults. METHODS Cognitively unimpaired older adults from the population-based Mayo Clinic Study of Aging (n = 537, age 65-95) who underwent both PiB PET and DTI were included. The associations of WM PiB standard uptake value ratio (SUVr) with fractional anisotropy (FA) and mean diffusivity (MD) in the WMH and NAWM were tested after adjusting for age. The associations of PiB SUVr with cognitive function z-scores were tested after adjusting for age and global cortical PiB SUVr. RESULTS The WMH PiB SUVr was lower than NAWM PiB SUVr (P < 0.001). In the WMH, lower PiB SUVr correlated with lower FA (r = 0.21, P < 0.001), and higher MD (r = -0.31, P < 0.001). In the NAWM, lower PiB SUVr only correlated with higher MD (r = -0.10, P = 0.02). Both in the WMH and NAWM, lower PiB SUVr was associated with lower memory, language, and global cognitive function z-scores after adjusting for age and global cortical PiB SUVr. INTERPRETATION Reduced PiB uptake in the WMH is associated with a loss of WM integrity and cognitive function after accounting for the global cortical PiB uptake, suggesting that WM PiB uptake may be an early biomarker of WM integrity that precedes cognitive impairment in older adults. When using WM as a reference region in cross-sectional analysis of PiB SUVr, individual variability in WMH volume as well as age should be considered.
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Affiliation(s)
- Burcu Zeydan
- Department of RadiologyMayo ClinicRochesterMinnesota
- Department of NeurologyMayo ClinicRochesterMinnesota
- Center for Multiple Sclerosis and Autoimmune NeurologyMayo ClinicRochesterMinnesota
| | | | - Val J. Lowe
- Department of RadiologyMayo ClinicRochesterMinnesota
| | - Robert I. Reid
- Department of Information TechnologyMayo ClinicRochesterMinnesota
| | | | | | | | - Matthew L. Senjem
- Department of RadiologyMayo ClinicRochesterMinnesota
- Department of Information TechnologyMayo ClinicRochesterMinnesota
| | - Jeffrey L. Gunter
- Department of RadiologyMayo ClinicRochesterMinnesota
- Department of Information TechnologyMayo ClinicRochesterMinnesota
| | - Hoon‐Ki Min
- Department of RadiologyMayo ClinicRochesterMinnesota
| | | | | | | | | | - Orhun H. Kantarci
- Department of NeurologyMayo ClinicRochesterMinnesota
- Center for Multiple Sclerosis and Autoimmune NeurologyMayo ClinicRochesterMinnesota
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21
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Ottoy J, Niemantsverdriet E, Verhaeghe J, De Roeck E, Struyfs H, Somers C, Wyffels L, Ceyssens S, Van Mossevelde S, Van den Bossche T, Van Broeckhoven C, Ribbens A, Bjerke M, Stroobants S, Engelborghs S, Staelens S. Association of short-term cognitive decline and MCI-to-AD dementia conversion with CSF, MRI, amyloid- and 18F-FDG-PET imaging. NEUROIMAGE-CLINICAL 2019; 22:101771. [PMID: 30927601 PMCID: PMC6444289 DOI: 10.1016/j.nicl.2019.101771] [Citation(s) in RCA: 95] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Revised: 01/08/2019] [Accepted: 03/10/2019] [Indexed: 12/31/2022]
Abstract
Disease-modifying treatment trials are increasingly advanced to the prodromal or preclinical phase of Alzheimer's disease (AD), and inclusion criteria are based on biomarkers rather than clinical symptoms. Therefore, it is of great interest to determine which biomarkers should be combined to accurately predict conversion from mild cognitive impairment (MCI) to AD dementia. However, up to date, only few studies performed a complete A/T/N subject characterization using each of the CSF and imaging markers, or they only investigated long-term (≥ 2 years) prognosis. This study aimed to investigate the association between cerebrospinal fluid (CSF), magnetic resonance imaging (MRI), amyloid- and 18F-FDG positron emission tomography (PET) measures at baseline, in relation to cognitive changes and conversion to AD dementia over a short-term (12-month) period. We included 13 healthy controls, 49 MCI and 16 AD dementia patients with a clinical-based diagnosis and a complete A/T/N characterization at baseline. Global cortical amyloid-β (Aβ) burden was quantified using the 18F-AV45 standardized uptake value ratio (SUVR) with two different reference regions (cerebellar grey and subcortical white matter), whereas metabolism was assessed based on 18F-FDG SUVR. CSF measures included Aβ1–42, Aβ1–40, T-tau, P-tau181, and their ratios, and MRI markers included hippocampal volumes (HV), white matter hyperintensities, and cortical grey matter volumes. Cognitive functioning was measured by MMSE and RBANS index scores. All statistical analyses were corrected for age, sex, education, and APOE ε4 genotype. As a result, faster cognitive decline was most strongly associated with hypometabolism (posterior cingulate) and smaller hippocampal volume (e.g., Δstory recall: β = +0.43 [p < 0.001] and + 0.37 [p = 0.005], resp.) at baseline. In addition, faster cognitive decline was significantly associated with higher baseline Aβ burden only if SUVR was referenced to the subcortical white matter (e.g., Δstory recall: β = −0.28 [p = 0.020]). Patients with MCI converted to AD dementia at an annual rate of 31%, which could be best predicted by combining neuropsychological testing (visuospatial construction skills) with either MRI-based HV or 18F-FDG-PET. Combining all three markers resulted in 96% specificity and 92% sensitivity. Neither amyloid-PET nor CSF biomarkers could discriminate short-term converters from non-converters. FDG-PET and MRI HV are the strongest predictors of cognitive decline and conversion to AD. Combination of visuospatial construction testing with FDG-PET or MRI HV present high predicting power of conversion. CSF and amyloid-PET seem less suitable markers of disease progression. Increased AV45-PET predicts short-term cognitive decline if SUVR is referenced to WM instead of CB.
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Affiliation(s)
- Julie Ottoy
- Molecular Imaging Center Antwerp, University of Antwerp, Antwerp, Belgium
| | - Ellis Niemantsverdriet
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Jeroen Verhaeghe
- Molecular Imaging Center Antwerp, University of Antwerp, Antwerp, Belgium
| | - Ellen De Roeck
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Hanne Struyfs
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Charisse Somers
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Leonie Wyffels
- Department of Nuclear Medicine, Antwerp University Hospital, Edegem, Belgium
| | - Sarah Ceyssens
- Department of Nuclear Medicine, Antwerp University Hospital, Edegem, Belgium
| | - Sara Van Mossevelde
- Neurodegenerative Brain Diseases Group, Center for Molecular Neurology, VIB, Antwerp, Belgium; Laboratory of Neurogenetics, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Tobi Van den Bossche
- Neurodegenerative Brain Diseases Group, Center for Molecular Neurology, VIB, Antwerp, Belgium
| | - Christine Van Broeckhoven
- Neurodegenerative Brain Diseases Group, Center for Molecular Neurology, VIB, Antwerp, Belgium; Laboratory of Neurogenetics, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | | | - Maria Bjerke
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Sigrid Stroobants
- Department of Nuclear Medicine, Antwerp University Hospital, Edegem, Belgium
| | - Sebastiaan Engelborghs
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Steven Staelens
- Molecular Imaging Center Antwerp, University of Antwerp, Antwerp, Belgium.
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22
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Lowe VJ, Lundt ES, Senjem ML, Schwarz CG, Min HK, Przybelski SA, Kantarci K, Knopman D, Petersen RC, Jack CR. White Matter Reference Region in PET Studies of 11C-Pittsburgh Compound B Uptake: Effects of Age and Amyloid-β Deposition. J Nucl Med 2018; 59:1583-1589. [PMID: 29674420 PMCID: PMC6167534 DOI: 10.2967/jnumed.117.204271] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2017] [Accepted: 04/04/2018] [Indexed: 02/06/2023] Open
Abstract
Amyloid-β (Aβ) deposition as seen on PET using an Aβ-binding agent is a critical diagnostic biomarker for Alzheimer disease (AD). Some reports suggest using white matter (WM) as a reference region for quantification of serial Aβ PET studies; however, nonspecific WM retention in Aβ PET in people with dementia or cognitively unimpaired (CU) has been widely reported and is poorly understood. Methods: To investigate the suitability of WM as a reference region and the factors affecting WM 11C-Pittsburgh compound B (11C-PiB) uptake variability, we conducted a retrospective study on 2 large datasets: a longitudinal study of participants (n = 577) who were CU, had mild cognitive impairment, or had dementia likely due to AD; and a cross-sectional study of single-scan PET imaging in CU subjects (n = 1,349). In the longitudinal study, annual changes in WM 11C-PiB uptake were assessed, and in the cross-sectional study, WM 11C-PiB uptake was assessed relative to subject age. Results: Overall, we found that WM 11C-PiB uptake showed age-related increases, which varied with the WM regions selected. Further, variable annual WM 11C-PiB uptake changes were seen with different gray matter (GM) 11C-PiB baseline uptake levels. Conclusion: WM binding increases with age and varies with GM 11C-PiB. These correlations should be considered when using WM for normalization in 11C-PiB PET studies. The cerebellar crus1+crus2 showed no increase with age and cerebellar GM+WM showed minimal increase, supporting their use as reference regions for cross-sectional studies comparing wide age spans. In longitudinal studies, the increase in WM uptake may be minimal in the short-term and thus using WM as a reference region in these studies seems reasonable. However, as participants age, the findings may be affected by changes in WM uptake. Changes in WM 11C-PiB uptake may relate to disease progression, warranting examination of the causes of WM 11C-PiB uptake.
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Affiliation(s)
- Val J Lowe
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
| | - Emily S Lundt
- Division of Biostatistics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | - Matthew L Senjem
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
- Department of Information Technology, Mayo Clinic, Rochester, Minnesota; and
| | | | - Hoon-Ki Min
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
| | - Scott A Przybelski
- Division of Biostatistics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
| | - David Knopman
- Department of Neurology, Mayo Clinic, Rochester, Minnesota
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23
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Gangishetti U, Christina Howell J, Perrin RJ, Louneva N, Watts KD, Kollhoff A, Grossman M, Wolk DA, Shaw LM, Morris JC, Trojanowski JQ, Fagan AM, Arnold SE, Hu WT. Non-beta-amyloid/tau cerebrospinal fluid markers inform staging and progression in Alzheimer's disease. ALZHEIMERS RESEARCH & THERAPY 2018; 10:98. [PMID: 30253800 PMCID: PMC6156847 DOI: 10.1186/s13195-018-0426-3] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Accepted: 09/03/2018] [Indexed: 11/21/2022]
Abstract
Background Alzheimer’s disease (AD) is a complex neurodegenerative disorder characterized by neuropathologic changes involving beta-amyloid (Aβ), tau, neuronal loss, and other associated biological events. While levels of cerebrospinal fluid (CSF) Aβ and tau peptides have enhanced the antemortem detection of AD-specific changes, these two markers poorly reflect the severity of cognitive and functional deficits in people with altered Aβ and tau levels. While multiple previous studies identified non-Aβ, non-tau proteins as candidate neurodegenerative markers to inform the A/T/N biomarker scheme of AD, few have advanced beyond association with clinical AD diagnosis. Here we analyzed nine promising neurodegenerative markers in a three-centered cohort using independent assays to identify candidates most likely to complement Aβ and tau in the A/T/N framework. Methods CSF samples from 125 subjects recruited at the three centers were exchanged such that each of the nine previously identified biomarkers can be measured at one of the three centers. Subjects were classified according to cognitive status and CSF AD biomarker profiles as having normal cognition and normal CSF (n = 31), normal cognition and CSF consistent with AD (n = 13), mild cognitive impairment and normal CSF (n = 13), mild cognitive impairment with CSF consistent with AD (n = 23), AD dementia (n = 32; CSF consistent with AD), and other non-AD dementia (n = 13; CSF not consistent with AD). Results Three biomarkers were identified to differ among the AD stages, including neurofilament light chain (NfL; p < 0.001), fatty acid binding protein 3 (Fabp3; p < 0.001), and interleukin (IL)-10 (p = 0.033). Increased NfL levels were most strongly associated with the dementia stage of AD, but increased Fabp3 levels were more sensitive to milder AD stages and correlated with both CSF tau markers. IL-10 levels did not correlate with tau biomarkers, but were associated with rates of longitudinal cognitive decline in mild cognitive impairment due to AD (p = 0.006). Prefreezing centrifugation did not influence measured CSF biomarker levels. Conclusion CSF proteins associated with AD clinical stages and progression can complement Aβ and tau markers to inform neurodegeneration. A validated panel inclusive of multiple biomarker features (etiology, stage, progression) can improve AD phenotyping along the A/T/N framework.
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Affiliation(s)
- Umesh Gangishetti
- Department of Neurology, Emory University, 615 Michael Street, 505F, Atlanta, GA, 30322, USA
| | - J Christina Howell
- Department of Neurology, Emory University, 615 Michael Street, 505F, Atlanta, GA, 30322, USA.,Department of Alzheimer's Disease Research Center, Emory University, Atlanta, GA, USA
| | - Richard J Perrin
- Knight Alzheimer's Disease Research Center, Washington University, St. Louis, MO, USA.,Department of Pathology, Washington University, St. Louis, MO, USA
| | - Natalia Louneva
- Department of Pathology, Washington University, St. Louis, MO, USA
| | - Kelly D Watts
- Department of Neurology, Emory University, 615 Michael Street, 505F, Atlanta, GA, 30322, USA
| | - Alexander Kollhoff
- Department of Neurology, Emory University, 615 Michael Street, 505F, Atlanta, GA, 30322, USA
| | - Murray Grossman
- Center for Neurodegenerative Disease Research, University of Pennsylvania, Philadelphia, PA, USA.,Penn FTD Center, University of Pennsylvania, Philadelphia, PA, USA.,Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - David A Wolk
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA.,Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Leslie M Shaw
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - John C Morris
- Knight Alzheimer's Disease Research Center, Washington University, St. Louis, MO, USA.,Department of Neurology, Washington University, St. Louis, MO, USA
| | - John Q Trojanowski
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA.,Center for Neurodegenerative Disease Research, University of Pennsylvania, Philadelphia, PA, USA.,Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Anne M Fagan
- Knight Alzheimer's Disease Research Center, Washington University, St. Louis, MO, USA.,Department of Neurology, Washington University, St. Louis, MO, USA
| | - Steven E Arnold
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA.,Center for Neurodegenerative Disease Research, University of Pennsylvania, Philadelphia, PA, USA.,Present Address: Massachusetts General Hospital, Boston, MA, USA
| | - William T Hu
- Department of Neurology, Emory University, 615 Michael Street, 505F, Atlanta, GA, 30322, USA. .,Department of Alzheimer's Disease Research Center, Emory University, Atlanta, GA, USA.
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24
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Salloway S, Honigberg LA, Cho W, Ward M, Friesenhahn M, Brunstein F, Quartino A, Clayton D, Mortensen D, Bittner T, Ho C, Rabe C, Schauer SP, Wildsmith KR, Fuji RN, Suliman S, Reiman EM, Chen K, Paul R. Amyloid positron emission tomography and cerebrospinal fluid results from a crenezumab anti-amyloid-beta antibody double-blind, placebo-controlled, randomized phase II study in mild-to-moderate Alzheimer's disease (BLAZE). ALZHEIMERS RESEARCH & THERAPY 2018; 10:96. [PMID: 30231896 PMCID: PMC6146627 DOI: 10.1186/s13195-018-0424-5] [Citation(s) in RCA: 90] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Accepted: 08/29/2018] [Indexed: 11/10/2022]
Abstract
BACKGROUND We investigated the effect of crenezumab, a humanized anti-amyloid-beta (Aβ) immunoglobulin (Ig)G4 monoclonal antibody, on biomarkers of amyloid pathology, neurodegeneration, and disease progression in patients with mild-to-moderate Alzheimer's disease (AD). METHODS This double-blind, placebo-controlled, randomized phase II study enrolled patients with mild-to-moderate AD and a Mini-Mental State Examination (MMSE) score of 18-26. In part 1 of the study, patients were 2:1 randomized to receive low-dose subcutaneous (SC) 300 mg crenezumab every 2 weeks (q2w) or placebo for 68 weeks; in part 2, patients were 2:1 randomized to receive high-dose intravenous (IV) 15 mg/kg crenezumab every 4 weeks (q4w) or placebo for 68 weeks. The primary endpoint was change in amyloid burden from baseline to week 69 assessed by florbetapir positron emission tomography (PET) in the modified intent-to-treat population. Secondary endpoints were change from baseline to week 69 in cerebrospinal fluid (CSF) biomarkers and fluorodeoxyglucose PET, and change from baseline to week 73 in 12-point Alzheimer's Disease Assessment Scale cognitive subscale (ADAS-Cog12) and Clinical Dementia Rating Sum of Boxes (CDR-SB). Safety was assessed in patients who received at least one dose of study treatment. RESULTS From August 2011 to September 2012, 91 patients were enrolled and randomized (low-dose SC cohort: crenezumab (n = 26) or placebo (n = 13); high-dose IV cohort: crenezumab (n = 36) or placebo (n = 16)). The primary endpoint was not met using a prespecified cerebellar reference region to calculate standard uptake value ratios (SUVRs) from florbetapir PET. Exploratory analyses using subcortical white matter reference regions showed nonsignificant trends toward slower accumulation of plaque amyloid in the high-dose IV cohort. In both cohorts, a significant mean increase from baseline in CSF Aβ(1-42) levels versus placebo was observed. Nonsignificant trends toward ADAS-Cog12 and CDR-SB benefits were identified in a mild (MMSE 20-26) subset of the high-dose IV cohort. No amyloid-related imaging abnormalities due to edema/effusion were observed. CONCLUSION The primary endpoint was not met. Exploratory findings suggest potential Aβ target engagement with crenezumab and possible slower accumulation of plaque amyloid. Studies investigating the effects of higher doses of crenezumab on amyloid load and disease progression are ongoing. TRIAL REGISTRATION ClinicalTrials.gov, NCT01397578 . Registered on 18 July 2011.
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Affiliation(s)
- Stephen Salloway
- Department of Neurology and Psychiatry, The Warren Alpert Medical School of Brown University, 345 Blackstone Boulevard, Providence, RI, 2906, USA.
| | | | - William Cho
- Genentech Inc., South San Francisco, CA, USA
| | | | | | | | | | | | | | | | - Carole Ho
- Genentech Inc., South San Francisco, CA, USA
| | | | | | | | | | | | | | - Kewei Chen
- Banner Alzheimer's Institute, Phoenix, AZ, USA
| | - Robert Paul
- Genentech Inc., South San Francisco, CA, USA
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25
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Landau SM. Optimizing Longitudinal Amyloid-β PET Measurement: The Challenges of Intensity Normalization. J Nucl Med 2018; 59:1581-1582. [PMID: 30213847 DOI: 10.2967/jnumed.118.212662] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Accepted: 09/10/2018] [Indexed: 11/16/2022] Open
Affiliation(s)
- Susan M Landau
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, California; and Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, California
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26
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Segovia F, Sánchez-Vañó R, Górriz JM, Ramírez J, Sopena-Novales P, Testart Dardel N, Rodríguez-Fernández A, Gómez-Río M. Using CT Data to Improve the Quantitative Analysis of 18F-FBB PET Neuroimages. Front Aging Neurosci 2018; 10:158. [PMID: 29930505 PMCID: PMC6001114 DOI: 10.3389/fnagi.2018.00158] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Accepted: 05/08/2018] [Indexed: 01/17/2023] Open
Abstract
18F-FBB PET is a neuroimaging modality that is been increasingly used to assess brain amyloid deposits in potential patients with Alzheimer's disease (AD). In this work, we analyze the usefulness of these data to distinguish between AD and non-AD patients. A dataset with 18F-FBB PET brain images from 94 subjects diagnosed with AD and other disorders was evaluated by means of multiple analyses based on t-test, ANOVA, Fisher Discriminant Analysis and Support Vector Machine (SVM) classification. In addition, we propose to calculate amyloid standardized uptake values (SUVs) using only gray-matter voxels, which can be estimated using Computed Tomography (CT) images. This approach allows assessing potential brain amyloid deposits along with the gray matter loss and takes advantage of the structural information provided by most of the scanners used for PET examination, which allow simultaneous PET and CT data acquisition. The results obtained in this work suggest that SUVs calculated according to the proposed method allow AD and non-AD subjects to be more accurately differentiated than using SUVs calculated with standard approaches.
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Affiliation(s)
- Fermín Segovia
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
| | - Raquel Sánchez-Vañó
- Department of Nuclear Medicine, "9 de Octubre" Hospital, Valencia, Spain.,Clinical Medicine and Public Health Doctoral Program of the University of Granada, Granada, Spain
| | - Juan M Górriz
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain.,Biosanitary Investigation Institute of Granada, Granada, Spain
| | - Javier Ramírez
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain.,Biosanitary Investigation Institute of Granada, Granada, Spain
| | | | - Nathalie Testart Dardel
- Department of Nuclear Medicine, "Virgen de las Nieves" University Hospital, Granada, Spain.,Department of Nuclear Medicine, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Antonio Rodríguez-Fernández
- Biosanitary Investigation Institute of Granada, Granada, Spain.,Department of Nuclear Medicine, "Virgen de las Nieves" University Hospital, Granada, Spain
| | - Manuel Gómez-Río
- Biosanitary Investigation Institute of Granada, Granada, Spain.,Department of Nuclear Medicine, "Virgen de las Nieves" University Hospital, Granada, Spain
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27
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Niemantsverdriet E, Ottoy J, Somers C, De Roeck E, Struyfs H, Soetewey F, Verhaeghe J, Van den Bossche T, Van Mossevelde S, Goeman J, De Deyn PP, Mariën P, Versijpt J, Sleegers K, Van Broeckhoven C, Wyffels L, Albert A, Ceyssens S, Stroobants S, Staelens S, Bjerke M, Engelborghs S. The Cerebrospinal Fluid Aβ1-42/Aβ1-40 Ratio Improves Concordance with Amyloid-PET for Diagnosing Alzheimer's Disease in a Clinical Setting. J Alzheimers Dis 2018; 60:561-576. [PMID: 28869470 PMCID: PMC5611891 DOI: 10.3233/jad-170327] [Citation(s) in RCA: 65] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Background: Evidence suggests that the concordance between amyloid-PET and cerebrospinal fluid (CSF) amyloid-β (Aβ) increases when the CSF Aβ1–42/Aβ1–40 ratio is used as compared to CSF Aβ1–42 levels alone. Objective: In order to test this hypothesis, we set up a prospective longitudinal study comparing the concordance between different amyloid biomarkers for Alzheimer’s disease (AD) in a clinical setting. Methods: Seventy-eight subjects (AD dementia (n = 17), mild cognitive impairment (MCI, n = 48), and cognitively healthy controls (n = 13)) underwent a [18F]Florbetapir ([18F]AV45) PET scan, [18F]FDG PET scan, MRI scan, and an extensive neuropsychological examination. In a large subset (n = 67), a lumbar puncture was performed and AD biomarkers were analyzed (Aβ1–42, Aβ1–40, T-tau, P-tau181). Results: We detected an increased concordance in the visual and quantitative (standardized uptake value ratio (SUVR) and total volume of distribution (VT)) [18F]AV45 PET measures when the CSF Aβ1–42/Aβ1–40 was applied compared to Aβ1–42 alone. CSF biomarkers were stronger associated to [18F]AV45 PET for SUVR values when considering the total brain white matter as reference region instead of cerebellar grey matter Conclusions: The concordance between CSF Aβ and [18F]AV45 PET increases when the CSF Aβ1–42/Aβ1–40 ratio is applied. This finding is of most importance for the biomarker-based diagnosis of AD as well as for selection of subjects for clinical trials with potential disease-modifying therapies for AD.
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Affiliation(s)
- Ellis Niemantsverdriet
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, UAntwerp, Antwerp, Belgium
| | - Julie Ottoy
- Molecular Imaging Center Antwerp (MICA), UAntwerp, Antwerp, Belgium
| | - Charisse Somers
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, UAntwerp, Antwerp, Belgium
| | - Ellen De Roeck
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, UAntwerp, Antwerp, Belgium.,Faculty of Psychology and Educational Sciences, Vrije Universiteit Brussels, Brussels, Belgium
| | - Hanne Struyfs
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, UAntwerp, Antwerp, Belgium
| | - Femke Soetewey
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, UAntwerp, Antwerp, Belgium
| | - Jeroen Verhaeghe
- Molecular Imaging Center Antwerp (MICA), UAntwerp, Antwerp, Belgium
| | - Tobi Van den Bossche
- VIB-UAntwerp Center for Molecular Neurology, Antwerp, Belgium.,Laboratory of Neurogenetics, Institute Born-Bunge, UAntwerp, Antwerp, Belgium.,Department of Neurology and Memory Clinic, Hospital Network Antwerp (ZNA) Middelheim and Hoge Beuken, Antwerp, Belgium
| | - Sara Van Mossevelde
- VIB-UAntwerp Center for Molecular Neurology, Antwerp, Belgium.,Laboratory of Neurogenetics, Institute Born-Bunge, UAntwerp, Antwerp, Belgium.,Department of Neurology and Memory Clinic, Hospital Network Antwerp (ZNA) Middelheim and Hoge Beuken, Antwerp, Belgium
| | - Johan Goeman
- Department of Neurology and Memory Clinic, Hospital Network Antwerp (ZNA) Middelheim and Hoge Beuken, Antwerp, Belgium
| | - Peter Paul De Deyn
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, UAntwerp, Antwerp, Belgium.,Department of Neurology and Memory Clinic, Hospital Network Antwerp (ZNA) Middelheim and Hoge Beuken, Antwerp, Belgium
| | - Peter Mariën
- Department of Neurology and Memory Clinic, Hospital Network Antwerp (ZNA) Middelheim and Hoge Beuken, Antwerp, Belgium.,Clinical and Experimental Neurolinguistics, CLIN, Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Jan Versijpt
- Vrije Universiteit Brussel(VUB), University Hospital Brussels (UZ Brussel), Department of Neurology, Brussels, Belgium
| | - Kristel Sleegers
- VIB-UAntwerp Center for Molecular Neurology, Antwerp, Belgium.,Laboratory of Neurogenetics, Institute Born-Bunge, UAntwerp, Antwerp, Belgium
| | - Christine Van Broeckhoven
- VIB-UAntwerp Center for Molecular Neurology, Antwerp, Belgium.,Laboratory of Neurogenetics, Institute Born-Bunge, UAntwerp, Antwerp, Belgium
| | - Leonie Wyffels
- Molecular Imaging Center Antwerp (MICA), UAntwerp, Antwerp, Belgium.,Departmentof Nuclear Medicine, Antwerp University Hospital, Antwerp, Belgium
| | - Adrien Albert
- Departmentof Nuclear Medicine, Antwerp University Hospital, Antwerp, Belgium
| | - Sarah Ceyssens
- Departmentof Nuclear Medicine, Antwerp University Hospital, Antwerp, Belgium
| | - Sigrid Stroobants
- Molecular Imaging Center Antwerp (MICA), UAntwerp, Antwerp, Belgium.,Departmentof Nuclear Medicine, Antwerp University Hospital, Antwerp, Belgium
| | - Steven Staelens
- Molecular Imaging Center Antwerp (MICA), UAntwerp, Antwerp, Belgium
| | - Maria Bjerke
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, UAntwerp, Antwerp, Belgium
| | - Sebastiaan Engelborghs
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, UAntwerp, Antwerp, Belgium.,Department of Neurology and Memory Clinic, Hospital Network Antwerp (ZNA) Middelheim and Hoge Beuken, Antwerp, Belgium
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Ben Bouallègue F, Vauchot F, Mariano-Goulart D, Payoux P. Diagnostic and prognostic value of amyloid PET textural and shape features: comparison with classical semi-quantitative rating in 760 patients from the ADNI-2 database. Brain Imaging Behav 2018; 13:111-125. [PMID: 29427064 DOI: 10.1007/s11682-018-9833-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
We evaluated the performance of amyloid PET textural and shape features in discriminating normal and Alzheimer's disease (AD) subjects, and in predicting conversion to AD in subjects with mild cognitive impairment (MCI) or significant memory concern (SMC). Subjects from the Alzheimer's Disease Neuroimaging Initiative with available baseline 18F-florbetapir and T1-MRI scans were included. The cross-sectional cohort consisted of 181 controls and 148 AD subjects. The longitudinal cohort consisted of 431 SMC/MCI subjects, 85 of whom converted to AD during follow-up. PET images were normalized to MNI space and post-processed using in-house software. Relative retention indices (SUVr) were computed with respect to pontine, cerebellar, and composite reference regions. Several textural and shape features were extracted then combined using a support vector machine (SVM) to build a predictive model of AD conversion. Diagnostic and prognostic performance was evaluated using ROC analysis and survival analysis with the Cox proportional hazard model. The three SUVr and all the tested features effectively discriminated AD subjects in cross-sectional analysis (all p < 0.001). In longitudinal analysis, the variables with the highest prognostic value were composite SUVr (AUC 0.86; accuracy 81%), skewness (0.87; 83%), local minima (0.85; 79%), Geary's index (0.86; 81%), gradient norm maximal argument (0.83; 82%), and the SVM model (0.91; 86%). The adjusted hazard ratio for AD conversion was 5.5 for the SVM model, compared with 4.0, 2.6, and 3.8 for cerebellar, pontine and composite SUVr (all p < 0.001), indicating that appropriate amyloid textural and shape features predict conversion to AD with at least as good accuracy as classical SUVr.
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Affiliation(s)
- Fayçal Ben Bouallègue
- Nuclear Medicine Department, Montpellier University Hospital, Montpellier, France. .,Nuclear Medicine Department, Purpan University Hospital, Toulouse, France.
| | - Fabien Vauchot
- Nuclear Medicine Department, Montpellier University Hospital, Montpellier, France
| | - Denis Mariano-Goulart
- Nuclear Medicine Department, Montpellier University Hospital, Montpellier, France.,PhyMedExp, INSERM - CNRS, Montpellier University, Montpellier, France
| | - Pierre Payoux
- Nuclear Medicine Department, Purpan University Hospital, Toulouse, France.,ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, Toulouse, France
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Shokouhi S, Campbell D, Brill AB, Gwirtsman HE. Longitudinal Positron Emission Tomography in Preventive Alzheimer's Disease Drug Trials, Critical Barriers from Imaging Science Perspective. Brain Pathol 2018; 26:664-71. [PMID: 27327527 PMCID: PMC5958602 DOI: 10.1111/bpa.12399] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2016] [Accepted: 06/16/2016] [Indexed: 12/30/2022] Open
Abstract
Recent Alzheimer's trials have recruited cognitively normal people at risk for Alzheimer's dementia. Due to the lack of clinical symptoms in normal population, conventional clinical outcome measures are not suitable for these early trials. While several groups are developing new composite cognitive tests that could serve as potential outcome measures by detecting subtle cognitive changes in normal people, there is a need for longitudinal brain imaging techniques that can correlate with temporal changes in these new tests and provide additional objective measures of neuropathological changes in brain. Positron emission tomography (PET) is a nuclear medicine imaging procedure based on the measurement of annihilation photons after positron emission from radiolabeled molecules that allow tracking of biological processes in body, including the brain. PET is a well-established in vivo imaging modality in Alzheimer's disease diagnosis and research due to its capability of detecting abnormalities in three major hallmarks of this disease. These include (1) amyloid beta plaques; (2) neurofibrillary tau tangles and (3) decrease in neuronal activity due to loss of nerve cell connection and death. While semiquantitative PET imaging techniques are commonly used to set discrete cut-points to stratify abnormal levels of amyloid accumulation and neurodegeneration, they are suboptimal for detecting subtle longitudinal changes. In this study, we have identified and discussed four critical barriers in conventional longitudinal PET imaging that may be particularly relevant for early Alzheimer's disease studies. These include within and across subject heterogeneity of AD-affected brain regions, PET intensity normalization, neuronal compensations in early disease stages and cerebrovascular amyloid deposition.
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Affiliation(s)
- Sepideh Shokouhi
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center
| | - Desmond Campbell
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center
| | - Aaron B Brill
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center
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Campbell DL, Kang H, Shokouhi S. Application of Haralick texture features in brain [ 18F]-florbetapir positron emission tomography without reference region normalization. Clin Interv Aging 2017; 12:2077-2086. [PMID: 29263656 PMCID: PMC5724427 DOI: 10.2147/cia.s143307] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Objectives Semi-quantitative image analysis methods in Alzheimer’s Disease (AD) require normalization of positron emission tomography (PET) images. However, recent studies have found variabilities associated with reference region selection of amyloid PET images. Haralick features (HFs) generated from the Gray Level Co-occurrence Matrix (GLCM) quantify spatial characteristics of amyloid PET radiotracer uptake without the need for intensity normalization. The objective of this study is to calculate several HFs in different diagnostic groups and determine the group differences. Methods All image and metadata were acquired through the Alzheimer’s Disease Neuroimaging Initiative database. Subjects were grouped in three ways: by clinical diagnosis, by APOE e4 allele, and by Alzheimer’s Disease Assessment Scale-cognitive subscale (ADAS-Cog) score. Several GLCM matrices were calculated for different direction and distances (1–4 mm) from multiple regions on PET images. The HFs, contrast, correlation, dissimilarity, energy, entropy, and homogeneity, were calculated from these GLCMs. Wilcoxon tests and Student t-tests were performed on Haralick features and standardized uptake value ratio (SUVR) values, respectively, to determine group differences. In addition to statistical testing, receiver operating characteristic (ROC) curves were generated to determine the discrimination performance of the selected regional HFs and the SUVR values. Results Preliminary results from statistical testing indicate that HFs were capable of distinguishing groups at baseline and follow-up (false discovery rate corrected p<0.05) in particular regions at much higher occurrences than SUVR (81 of 252). Conversely, we observed nearly no significant differences between all groups within ROIs at baseline or follow-up utilizing SUVR. From the ROC analysis, we found that the Energy and Entropy offered the best performance to distinguish Normal versus mild cognitive impairment and ADAS-Cog negative versus ADAS-Cog positive groups. Conclusion These results suggest that this technique could improve subject stratification in AD drug trials and help to evaluate the disease progression and treatment effects longitudinally without the disadvantages associated with intensity normalization.
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Affiliation(s)
| | - Hakmook Kang
- Department of Biostatistics, Vanderbilt University Medical Center, Vanderbilt University Institute of Imaging Science, Nashville, TN, USA
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31
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Ottoy J, Verhaeghe J, Niemantsverdriet E, Engelborghs S, Stroobants S, Staelens S. A simulation study on the impact of the blood flow-dependent component in [18F]AV45 SUVR in Alzheimer's disease. PLoS One 2017; 12:e0189155. [PMID: 29211812 PMCID: PMC5718604 DOI: 10.1371/journal.pone.0189155] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Accepted: 11/20/2017] [Indexed: 01/04/2023] Open
Abstract
Background Increased brain uptake on [18F]AV45 PET is a biomarker for Alzheimer’s disease (AD). The standardised uptake value ratio (SUVR) is widely used for quantification but is subject to variability. Here we evaluate how SUVR of a cortical target region is affected by blood flow changes in the target and two frequently used reference regions. Methods Regional baseline time-activity curves (TACs) were simulated based on metabolite-corrected plasma input functions and pharmacokinetic parameters obtained from our previously acquired data in healthy control (HC; n = 10), amnestic mild cognitive impairment (aMCI; n = 15) and AD cohorts (n = 9). Blood flow changes were simulated by altering the regional tracer delivery rate K1 (and clearance rate k2) between -40% and +40% from its regional baseline value in the target region and/or cerebellar grey (CB) or subcortical white matter (WM) reference regions. The corresponding change in SUVR was calculated at 50–60 min post-injection. Results A -40% blood flow reduction in the target resulted in an increased SUVRtarget (e.g. SUVRprecuneus: +10.0±5% in HC, +2.5±2% in AD), irrespective of the used reference region. A -40% blood flow reduction in the WM reference region increased SUVRWM (+11.5±4% in HC, +13.5±3% in AD) while a blood flow reduction in CB decreased SUVRCB (-9.5±6% in HC, -5.5±2% in AD), irrespective of the used target region. A -40% flow reduction in both the precuneus and reference WM (i.e., global flow change) induced an increased SUVR (+22.5±8% in HC, +16.0±4% in AD). When considering reference CB instead, SUVR was decreased by less than -5% (both in HC and AD). Conclusion Blood flow changes introduce alterations in [18F]AV45 PET SUVR. Flow reductions in the CB and WM reference regions resulted in a decreased and increased SUVR of the target, respectively. SUVR was more affected by global blood flow changes when considering WM instead of CB normalization.
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Affiliation(s)
- Julie Ottoy
- Molecular Imaging Center Antwerp, University of Antwerp, Antwerp, Belgium
| | - Jeroen Verhaeghe
- Molecular Imaging Center Antwerp, University of Antwerp, Antwerp, Belgium
| | - Ellis Niemantsverdriet
- Reference Center for Biological Markers of Dementia (BIODEM), University of Antwerp, Antwerp, Belgium
| | - Sebastiaan Engelborghs
- Reference Center for Biological Markers of Dementia (BIODEM), University of Antwerp, Antwerp, Belgium
- Department of Neurology and Memory Clinic, Hospital Network Antwerp (ZNA) Hoge Beuken en Middelheim, Antwerp, Belgium
| | - Sigrid Stroobants
- Department of Nuclear Medicine, Antwerp University Hospital, Edegem, Belgium
| | - Steven Staelens
- Molecular Imaging Center Antwerp, University of Antwerp, Antwerp, Belgium
- * E-mail:
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Martínez G, Vernooij RWM, Fuentes Padilla P, Zamora J, Bonfill Cosp X, Flicker L. 18F PET with florbetapir for the early diagnosis of Alzheimer's disease dementia and other dementias in people with mild cognitive impairment (MCI). Cochrane Database Syst Rev 2017; 11:CD012216. [PMID: 29164603 PMCID: PMC6486090 DOI: 10.1002/14651858.cd012216.pub2] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
BACKGROUND 18F-florbetapir uptake by brain tissue measured by positron emission tomography (PET) is accepted by regulatory agencies like the Food and Drug Administration (FDA) and the European Medicine Agencies (EMA) for assessing amyloid load in people with dementia. Its added value is mainly demonstrated by excluding Alzheimer's pathology in an established dementia diagnosis. However, the National Institute on Aging and Alzheimer's Association (NIA-AA) revised the diagnostic criteria for Alzheimer's disease and confidence in the diagnosis of mild cognitive impairment (MCI) due to Alzheimer's disease may be increased when using amyloid biomarkers tests like 18F-florbetapir. These tests, added to the MCI core clinical criteria, might increase the diagnostic test accuracy (DTA) of a testing strategy. However, the DTA of 18F-florbetapir to predict the progression from MCI to Alzheimer's disease dementia (ADD) or other dementias has not yet been systematically evaluated. OBJECTIVES To determine the DTA of the 18F-florbetapir PET scan for detecting people with MCI at time of performing the test who will clinically progress to ADD, other forms of dementia (non-ADD), or any form of dementia at follow-up. SEARCH METHODS This review is current to May 2017. We searched MEDLINE (OvidSP), Embase (OvidSP), PsycINFO (OvidSP), BIOSIS Citation Index (Thomson Reuters Web of Science), Web of Science Core Collection, including the Science Citation Index (Thomson Reuters Web of Science) and the Conference Proceedings Citation Index (Thomson Reuters Web of Science), LILACS (BIREME), CINAHL (EBSCOhost), ClinicalTrials.gov (https://clinicaltrials.gov), and the World Health Organization International Clinical Trials Registry Platform (WHO ICTRP) (http://www.who.int/ictrp/search/en/). We also searched ALOIS, the Cochrane Dementia & Cognitive Improvement Group's specialised register of dementia studies (http://www.medicine.ox.ac.uk/alois/). We checked the reference lists of any relevant studies and systematic reviews, and performed citation tracking using the Science Citation Index to identify any additional relevant studies. No language or date restrictions were applied to the electronic searches. SELECTION CRITERIA We included studies that had prospectively defined cohorts with any accepted definition of MCI at time of performing the test and the use of 18F-florbetapir scan to evaluate the DTA of the progression from MCI to ADD or other forms of dementia. In addition, we only selected studies that applied a reference standard for Alzheimer's dementia diagnosis, for example, National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer's Disease and Related Disorders Association (NINCDS-ADRDA) or Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV) criteria. DATA COLLECTION AND ANALYSIS We screened all titles and abstracts identified in electronic-database searches. Two review authors independently selected studies for inclusion and extracted data to create two-by-two tables, showing the binary test results cross-classified with the binary reference standard. We used these data to calculate sensitivities, specificities, and their 95% confidence intervals. Two independent assessors performed quality assessment using the QUADAS-2 tool plus some additional items to assess the methodological quality of the included studies. MAIN RESULTS We included three studies, two of which evaluated the progression from MCI to ADD, and one evaluated the progression from MCI to any form of dementia.Progression from MCI to ADD was evaluated in 448 participants. The studies reported data on 401 participants with 1.6 years of follow-up and in 47 participants with three years of follow-up. Sixty-one (15.2%) participants converted at 1.6 years follow-up; nine (19.1%) participants converted at three years of follow-up.Progression from MCI to any form of dementia was evaluated in five participants with 1.5 years of follow-up, with three (60%) participants converting to any form of dementia.There were concerns regarding applicability in the reference standard in all three studies. Regarding the domain of flow and timing, two studies were considered at high risk of bias. MCI to ADD;Progression from MCI to ADD in those with a follow-up between two to less than four years had a sensitivity of 67% (95% CI 30 to 93) and a specificity of 71% (95% CI 54 to 85) by visual assessment (n = 47, 1 study).Progression from MCI to ADD in those with a follow-up between one to less than two years had a sensitivity of 89% (95% CI 78 to 95) and a specificity of 58% (95% CI 53 to 64) by visual assessment, and a sensitivity of 87% (95% CI 76 to 94) and a specificity of 51% (95% CI 45 to 56) by quantitative assessment by the standardised uptake value ratio (SUVR)(n = 401, 1 study). MCI to any form of dementia;Progression from MCI to any form of dementia in those with a follow-up between one to less than two years had a sensitivity of 67% (95% CI 9 to 99) and a specificity of 50% (95% CI 1 to 99) by visual assessment (n = 5, 1 study). MCI to any other forms of dementia (non-ADD);There was no information regarding the progression from MCI to any other form of dementia (non-ADD). AUTHORS' CONCLUSIONS Although sensitivity was good in one included study, considering the poor specificity and the limited data available in the literature, we cannot recommend routine use of 18F-florbetapir PET in clinical practice to predict the progression from MCI to ADD.Because of the poor sensitivity and specificity, limited number of included participants, and the limited data available in the literature, we cannot recommend its routine use in clinical practice to predict the progression from MCI to any form of dementia.Because of the high financial costs of 18F-florbetapir, clearly demonstrating the DTA and standardising the process of this modality are important prior to its wider use.
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Affiliation(s)
- Gabriel Martínez
- Iberoamerican Cochrane CentreC/ Sant Antoni Maria Claret 167Pavelló 18 Planta 0BarcelonaBarcelonaSpain08025
- Universidad de AntofagastaFaculty of Medicine and DentistryAntofagastaChile
- Institut Català de Neurociències AplicadesAlzheimer Research Center and Memory Clinic of Fundació ACEBarcelonaSpain
| | - Robin WM Vernooij
- Iberoamerican Cochrane CentreC/ Sant Antoni Maria Claret 167Pavelló 18 Planta 0BarcelonaBarcelonaSpain08025
| | - Paulina Fuentes Padilla
- Iberoamerican Cochrane CentreC/ Sant Antoni Maria Claret 167Pavelló 18 Planta 0BarcelonaBarcelonaSpain08025
- Universidad de AntofagastaFaculty of Medicine and DentistryAntofagastaChile
| | - Javier Zamora
- Ramon y Cajal Institute for Health Research (IRYCIS), CIBER Epidemiology and Public Health (CIBERESP), Madrid (Spain) and Women's Health Research Unit, Centre for Primary Care and Public Health, Queen Mary University of LondonClinical Biostatistics UnitLondonMadridUK
| | - Xavier Bonfill Cosp
- CIBER Epidemiología y Salud Pública (CIBERESP)Iberoamerican Cochrane Centre, Biomedical Research Institute Sant Pau (IIB Sant Pau)Sant Antoni Maria Claret 167Pavilion 18BarcelonaCatalunyaSpain08025
- Universitat Autònoma de BarcelonaSant Antoni Maria Claret, 167Pavilion 18 (D‐13)BarcelonaCatalunyaSpain08025
| | - Leon Flicker
- University of Western AustraliaWestern Australian Centre for Health & Ageing ‐ WACHACrawleyPerthWestern AustraliaAustralia6014
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Ben Bouallègue F, Mariano-Goulart D, Payoux P. Comparison of CSF markers and semi-quantitative amyloid PET in Alzheimer's disease diagnosis and in cognitive impairment prognosis using the ADNI-2 database. Alzheimers Res Ther 2017; 9:32. [PMID: 28441967 PMCID: PMC5405503 DOI: 10.1186/s13195-017-0260-z] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2017] [Accepted: 03/24/2017] [Indexed: 11/13/2022]
Abstract
BACKGROUND The relative performance of semi-quantitative amyloid positron emission tomography (PET) and cerebrospinal fluid (CSF) markers in diagnosing Alzheimer's disease (AD) and predicting the cognitive evolution of patients with mild cognitive impairment (MCI) is still debated. METHODS Subjects from the Alzheimer's Disease Neuroimaging Initiative 2 with complete baseline cognitive assessment (Mini Mental State Examination, Clinical Dementia Rating [CDR] and Alzheimer's Disease Assessment Scale-Cognitive Subscale [ADAS-cog] scores), CSF collection (amyloid-β1-42 [Aβ], tau and phosphorylated tau) and 18F-florbetapir scans were included in our cross-sectional cohort. Among these, patients with MCI or substantial memory complaints constituted our longitudinal cohort and were followed for 30 ± 16 months. PET amyloid deposition was quantified using relative retention indices (standardised uptake value ratio [SUVr]) with respect to pontine, cerebellar and composite reference regions. Diagnostic and prognostic performance based on PET and CSF was evaluated using ROC analysis, multivariate linear regression and survival analysis with the Cox proportional hazards model. RESULTS The cross-sectional study included 677 participants and revealed that pontine and composite SUVr values were better classifiers (AUC 0.88, diagnostic accuracy 85%) than CSF markers (AUC 0.83 and 0.85, accuracy 80% and 75%, for Aβ and tau, respectively). SUVr was a strong independent determinant of cognition in multivariate regression, whereas Aβ was not; tau was also a determinant, but to a lesser degree. Among the 396 patients from the longitudinal study, 82 (21%) converted to AD within 22 ± 13 months. Optimal SUVr thresholds to differentiate AD converters were quite similar to those of the cross-sectional study. Composite SUVr was the best AD classifier (AUC 0.86, sensitivity 88%, specificity 81%). In multivariate regression, baseline cognition (CDR and ADAS-cog) was the main predictor of subsequent cognitive decline. Pontine and composite SUVr were moderate but independent predictors of final status and CDR/ADAS-cog progression rate, whereas baseline CSF markers had a marginal influence. The adjusted HRs for AD conversion were 3.8 (p = 0.01) for PET profile, 1.2 (p = ns) for Aβ profile and 1.8 (p = 0.03) for tau profile. CONCLUSIONS Semi-quantitative amyloid PET appears more powerful than CSF markers for AD grading and MCI prognosis in terms of cognitive decline and AD conversion.
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Affiliation(s)
- Fayçal Ben Bouallègue
- Toulouse NeuroImaging Centre (ToNIC), Université de Toulouse, Inserm/UPS, Toulouse, France
- Nuclear Medicine Department, Purpan University Hospital, Toulouse, France
- Nuclear Medicine Department, Lapeyronie University Hospital, Montpellier, France
| | | | - Pierre Payoux
- Toulouse NeuroImaging Centre (ToNIC), Université de Toulouse, Inserm/UPS, Toulouse, France
- Nuclear Medicine Department, Purpan University Hospital, Toulouse, France
| | - the Alzheimer’s Disease Neuroimaging Initiative (ADNI)
- Toulouse NeuroImaging Centre (ToNIC), Université de Toulouse, Inserm/UPS, Toulouse, France
- Nuclear Medicine Department, Purpan University Hospital, Toulouse, France
- Nuclear Medicine Department, Lapeyronie University Hospital, Montpellier, France
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Ottoy J, Verhaeghe J, Niemantsverdriet E, Wyffels L, Somers C, De Roeck E, Struyfs H, Soetewey F, Deleye S, Van den Bossche T, Van Mossevelde S, Ceyssens S, Versijpt J, Stroobants S, Engelborghs S, Staelens S. Validation of the Semiquantitative Static SUVR Method for 18F-AV45 PET by Pharmacokinetic Modeling with an Arterial Input Function. J Nucl Med 2017; 58:1483-1489. [PMID: 28336779 DOI: 10.2967/jnumed.116.184481] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2016] [Accepted: 03/06/2017] [Indexed: 11/16/2022] Open
Abstract
Increased brain uptake of 18F-AV45 visualized by PET is a key biomarker for Alzheimer disease (AD). The SUV ratio (SUVR) is widely used for quantification, but is subject to variability based on choice of reference region and changes in cerebral blood flow. Here we validate the SUVR method against the gold standard volume of distribution (VT) to assess cross-sectional differences in plaque load. Methods: Dynamic 60-min 18F-AV45 (291 ± 67 MBq) and 1-min 15O-H2O (370 MBq) scans were obtained in 35 age-matched elderly subjects, including 10 probable AD, 15 amnestic mild cognitive impairment (aMCI), and 10 cognitively healthy controls (HCs). 18F-AV45 VT was determined from 2-tissue-compartment modeling using a metabolite-corrected plasma input function. Static SUVR was calculated at 50-60 min after injection, using either cerebellar gray matter (SUVRCB) or whole subcortical white matter (SUVRWM) as the reference. Additionally, whole cerebellum, pons, centrum semiovale, and a composite region were examined as alternative references. Blood flow was quantified by 15O-H2O SUV. Data are presented as mean ± SEM. Results: There was rapid metabolization of 18F-AV45, with only 35% of unchanged parent remaining at 10 min. Compared with VT, differences in cortical Aβ load between aMCI and AD were overestimated by SUVRWM (+4% ± 2%) and underestimated by SUVRCB (-10% ± 2%). VT correlated better with SUVRWM (Pearson r: from 0.63 for posterior cingulate to 0.89 for precuneus, P < 0.0001) than with SUVRCB (Pearson r: from 0.51 for temporal lobe [P = 0.002] to 0.82 for precuneus [P < 0.0001]) in all tested regions. Correlation results for the alternative references were in between those for CB and WM. 15O-H2O data showed that blood flow was decreased in AD compared with aMCI in cortical regions (-5% ± 1%) and in the reference regions (CB, -9% ± 8%; WM, -8% ± 8%). Conclusion: Increased brain uptake of 18F-AV45 assessed by the simplified static SUVR protocol does not truly reflect Aβ load. However, SUVRWM is better correlated with VT and more closely reflects VT differences between aMCI and AD than SUVRCB.
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Affiliation(s)
- Julie Ottoy
- Molecular Imaging Center Antwerp, University of Antwerp, Antwerp, Belgium
| | - Jeroen Verhaeghe
- Molecular Imaging Center Antwerp, University of Antwerp, Antwerp, Belgium
| | | | - Leonie Wyffels
- Department of Nuclear Medicine, Antwerp University Hospital, Edegem, Belgium
| | - Charisse Somers
- Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Ellen De Roeck
- Institute Born-Bunge, University of Antwerp, Antwerp, Belgium.,Developmental and Lifespan Psychology, Vrije Universiteit Brussel, Brussels, Belgium
| | - Hanne Struyfs
- Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Femke Soetewey
- Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Steven Deleye
- Molecular Imaging Center Antwerp, University of Antwerp, Antwerp, Belgium
| | - Tobi Van den Bossche
- Institute Born-Bunge, University of Antwerp, Antwerp, Belgium.,Department of Molecular Genetics, VIB, University of Antwerp, Antwerp, Belgium.,Department of Neurology, Antwerp University Hospital, Edegem, Belgium.,Department of Neurology and Memory Clinic, Hospital Network Antwerp (ZNA) Middelheim and Hoge Beuken, Antwerp, Belgium; and
| | - Sara Van Mossevelde
- Institute Born-Bunge, University of Antwerp, Antwerp, Belgium.,Department of Molecular Genetics, VIB, University of Antwerp, Antwerp, Belgium.,Department of Neurology, Antwerp University Hospital, Edegem, Belgium.,Department of Neurology and Memory Clinic, Hospital Network Antwerp (ZNA) Middelheim and Hoge Beuken, Antwerp, Belgium; and
| | - Sarah Ceyssens
- Department of Nuclear Medicine, Antwerp University Hospital, Edegem, Belgium
| | - Jan Versijpt
- Department of Neurology, University Hospital Brussels, Brussels, Belgium
| | - Sigrid Stroobants
- Department of Nuclear Medicine, Antwerp University Hospital, Edegem, Belgium
| | - Sebastiaan Engelborghs
- Institute Born-Bunge, University of Antwerp, Antwerp, Belgium.,Department of Neurology and Memory Clinic, Hospital Network Antwerp (ZNA) Middelheim and Hoge Beuken, Antwerp, Belgium; and
| | - Steven Staelens
- Molecular Imaging Center Antwerp, University of Antwerp, Antwerp, Belgium
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