Empirical derivation of the reference region for computing diagnostic sensitive 18fluorodeoxyglucose ratios in Alzheimer's disease based on the ADNI sample

Rasmussen, Jerod; Lakatos, Anita; Erp, Theo G.M. van; Kruggel, Frithjof; Keator, David B.; Fallon, James; Macciardi, Fabìo; Potkin, Steven G.; Initiative, Alzheimer’s Disease Neuroimaging

doi:10.1016/j.bbadis.2011.09.008

Cited by 23 publications

(19 citation statements)

References 25 publications

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“…The method described by Rasmussen et al [245] sought to improve this critical step by the selection of candidate reference regions based on heat maps of coefficients of variation of FDG ratios over time. They found that intensity normalization systematically isolated the superior portion of the cerebellum as the test reference region for detecting rates of decline and baseline deficits in AD patients.…”

Section: Methods Papersmentioning

confidence: 99%

The Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception

Weiner

Veitch

Aisen

et al. 2013

Alzheimer's & Dementia

634

423

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The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The study aimed to enroll 400 subjects with early mild cognitive impairment (MCI), 200 subjects with early AD, and 200 normal control subjects; $67 million funding was provided by both the public and private sectors, including the National Institute on Aging, 13 pharmaceutical companies, and 2 foundations that provided support through the Foundation for the National Institutes of Health. This article reviews all papers published since the inception of the initiative and summarizes the results as of February 2011. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimers Dis 2006;9(Suppl 3):151–3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers combine optimum features from multiple modalities, including MRI, [18F]-fluorodeoxyglucose-PET, CSF biomarkers, and clinical tests; (4) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects, and are leading candidates for the detection of AD in its preclinical stages; (5) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Baseline cognitive and/or MRI measures generally predicted future decline better than other modalities, whereas MRI measures of change were shown to be the most efficient outcome measures; (6) the confirmation of the AD risk loci CLU, CR1, and PICALM and the identification of novel candidate risk loci; (7) worldwide impact through the establishment of ADNI-like programs in Europe, Asia, and Australia; (8) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker data with clinical data from ADNI to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (9) the establishment of infrastructure to allow sharing of all raw and ...

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Section: Methods Papersmentioning

confidence: 99%

The Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception

Weiner

Veitch

Aisen

et al. 2013

Alzheimer's & Dementia

634

423

View full text Add to dashboard Cite

show abstract

“…A stable reference region is also critical to detecting metabolic changes with optimum sensitivity across different sites and scanners. Rasmussen et al [363] used an approach based on differences in coefficients of variation of FDG ratios over time across selected anatomic regions. They found the superior portion of the cerebellum to be the optimum reference region because of its stability over time compared with AD patients.…”

Section: Methods Papersmentioning

confidence: 99%

2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception

Weiner

Veitch

Aisen

et al. 2015

Alzheimer's & Dementia

261

221

View full text Add to dashboard Cite

The Alzheimer’s Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer’s disease (AD). The initial study, ADNI-1, enrolled 400 subjects with early mild cognitive impairment (MCI), 200 with early AD, and 200 cognitively normal elderly controls. ADNI-1 was extended by a 2-year Grand Opportunities grant in 2009 and by a competitive renewal, ADNI-2, which enrolled an additional 550 participants and will run until 2015. This article reviews all papers published since the inception of the initiative and summarizes the results to the end of 2013. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are largely consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimer’s Dis 2006;9(Suppl 3):151–3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers select and combine optimum features from multiple modalities, including MRI, [18F]-fluorodeoxyglucose-PET, amyloid PET, CSF biomarkers, and clinical tests; (4) the development of blood biomarkers for AD as potentially noninvasive and low-cost alternatives to CSF biomarkers for AD diagnosis and the assessment of α-syn as an additional biomarker; (5) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects and are leading candidates for the detection of AD in its preclinical stages; (6) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Multimodal methods incorporating APOE status and longitudinal MRI proved most highly predictive of future decline. Refinements of clinical tests used as outcome measures such as clinical dementia rating-sum of boxes further reduced sample sizes; (7) the pioneering of genome-wide association studies that leverage quantitative imaging and biomarker phenotypes, including longitudinal data, to confirm recently identified loci, CR1, CLU, and PICALM and to identify novel AD risk loci; (8) worldwide impact through the establishment of ADNI-like programs in Japan, Australia, Argentina, Taiwan, China, Korea, Europe, and Italy; (9) understanding ...

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“…[12][13][14][15][16] Others have derived new analysis methods for imaging biomarker data [17][18][19][20][21][22][23][24][25][26][27] or developed theories of biomarker progression patterns. 28 To our knowledge, the NIA-AA-C criteria have not yet been studied in a systematic manner.…”

Section: Introductionmentioning

confidence: 99%

Application of the National Institute on Aging-Alzheimer's Association AD criteria to ADNI

et al. 2013

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Objective: We describe the operationalization of the National Institute on Aging-Alzheimer's Association (NIA-AA) workgroup diagnostic guidelines pertaining to Alzheimer disease (AD) dementia in a large multicenter group of subjects with AD dementia.Methods: Subjects with AD dementia from the Alzheimer's Disease Neuroimaging Initiative (ADNI) with at least 1 amyloid biomarker (n 5 211) were included in this report. Biomarker data from CSF Ab42, amyloid PET, fluorodeoxyglucose-PET, and MRI were examined. The biomarker results were assessed on a per-patient basis and the subject categorization as defined in the NIA-AA workgroup guidelines was determined.Results: When using a requirement that subjects have a positive amyloid biomarker and single neuronal injury marker having an AD pattern, 87% (48% for both neuronal injury biomarkers) of the subjects could be categorized as "high probability" for AD. Amyloid status of the combined Pittsburgh compound B-PET and CSF results showed an amyloid-negative rate of 10% in the AD group. In the ADNI AD group, 5 of 92 subjects fit the category "dementia unlikely due to AD" when at least one neuronal injury marker was negative.Conclusions: A large proportion of subjects with AD dementia in ADNI may be categorized more definitively as high-probability AD using the proposed biomarker scheme in the NIA-AA criteria. A minority of subjects may be excluded from the diagnosis of AD by using biomarkers in clinically categorized AD subjects. In a well-defined AD dementia population, significant biomarker inconsistency can be seen on a per-patient basis. Recent recommendations from the National Institute on Aging-Alzheimer's Association (NIA-AA) workgroup for clinical diagnostic guidelines (NIA-AA-C) for Alzheimer disease (AD) dementia have integrated biomarkers into the diagnostic algorithm of AD.1 The diagnostic category of probable AD (pAD) dementia is modified by the results of biomarker findings.Many investigators have validated biomarkers using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Biomarkers have been used to characterize diagnostic groups within the ADNI population.2,3 AD subject fluorodeoxyglucose (FDG) categorization accuracy in ADNI was 85% 4 and had a sensitivity and specificity of 83% and 78% using a quantitative image analysis program.5 Similar reports on FDG, 6 MRI, 7 or CSF 8 performance are based on ADNI subject categorization. Investigators have evaluated the interaction of biomarker modalities in defining the ADNI subject groups.2,9-11 Other publications predict which subjects will progress to AD using MRI. [12][13][14][15][16] Others have derived new analysis methods for imaging biomarker data [17][18][19][20][21][22][23][24][25][26][27] or developed theories of biomarker progression patterns. 28 To our knowledge, the NIA-AA-C criteria have not yet been studied in a systematic manner.

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Empirical derivation of the reference region for computing diagnostic sensitive 18fluorodeoxyglucose ratios in Alzheimer's disease based on the ADNI sample

Cited by 23 publications

References 25 publications

The Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception

The Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception

2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception

Application of the National Institute on Aging-Alzheimer's Association AD criteria to ADNI

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