Automated analysis of FDG PET as a tool for single-subject probabilistic prediction and detection of Alzheimer’s disease dementia

Arbizu, Javier; Prieto, Elena; Martínez-Lage, Pablo; Martí-Climent, Josep M.; Garcı́a-Granero, Marta; Lamet, Isabel; Pástor, Pau; Riverol, Mario; Gómez-Isla, M. T.; Peñuelas, Iván; Richter, José A.; Weiner, Michael W.

doi:10.1007/s00259-013-2458-z

Cited by 43 publications

(41 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This observation confirms the utility of such data-driven methodology to uncover correlations that have pathophysiologic meaning (Table 3). Several studies have implemented automated image-based classification methods to differentiate AD and MCI patients from controls and have found such methods to have a statistical accuracy of 90% in discriminating AD patients from controls (25)(26)(27). When MCI patients who later converted to AD were investigated by unimodal biomarkers, the reported ability to discriminate from controls ranged from 80% (28) to 91% (29).…”

Section: Discussionmentioning

confidence: 99%

Progressive Disintegration of Brain Networking from Normal Aging to Alzheimer Disease: Analysis of Independent Components of ¹⁸F-FDG PET Data

et al. 2017

View full text Add to dashboard Cite

Brain connectivity has been assessed in several neurodegenerative disorders investigating the mutual correlations between predetermined regions or nodes. Selective breakdown of brain networks during progression from normal aging to Alzheimer disease dementia (AD) has also been observed. Methods: We implemented independentcomponent analysis of 18 F-FDG PET data in 5 groups of subjects with cognitive states ranging from normal aging to AD-including mild cognitive impairment (MCI) not converting or converting to ADto disclose the spatial distribution of the independent components in each cognitive state and their accuracy in discriminating the groups. Results: We could identify spatially distinct independent components in each group, with generation of local circuits increasing proportionally to the severity of the disease. AD-specific independent components first appeared in the late-MCI stage and could discriminate converting MCI and AD from nonconverting MCI with an accuracy of 83.5%. Progressive disintegration of the intrinsic networks from normal aging to MCI to AD was inversely proportional to the conversion time. Conclusion: Independent-component analysis of 18 F-FDG PET data showed a gradual disruption of functional brain connectivity with progression of cognitive decline in AD. This information might be useful as a prognostic aid for individual patients and as a surrogate biomarker in intervention trials.

show abstract

Section: Discussionmentioning

confidence: 99%

Progressive Disintegration of Brain Networking from Normal Aging to Alzheimer Disease: Analysis of Independent Components of ¹⁸F-FDG PET Data

et al. 2017

View full text Add to dashboard Cite

show abstract

“…A similar approach was used by Chen et al [85], who developed an automatically generated hypometabolic convergence index (HCI) reflective of the degree to which the patient’s pattern and magnitude of cerebral hypometabolism corresponded to that of probable AD patients. Arbizu et al [397] combined the HCI with age and gender in a multivariate model to produce an AD score in an automated analysis method. To reflect the developing idea of AD as a continuum of disease rather than a progression of discrete states, they categorized patients into sixtile groups that had a progressive monotonic increase in AD scores.…”

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

284

224

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 ...

show abstract

“…With respect to early diagnosis of AD, namely the capability to distinguish a MCI subject who will convert to AD from an individual with stable MCI, CSF biomarkers [8,[65][66][67] and FDG-PET [68][69][70] have the largest body of evidence in terms of sensitivity and specificity. In monocenter studies, CSF biomarkers represent the most reliable tool, with the strongest level of evidence and the most advanced program for the standardization of procedures [43,71] and have shown their usefulness applying the new research criteria in clinical practice [72].…”

Section: State-of-the-art Initiatives For Decreasing Intercenter Diffmentioning

confidence: 99%

The clinical use of cerebrospinal fluid biomarker testing for Alzheimer's disease diagnosis: A consensus paper from the Alzheimer's Biomarkers Standardization Initiative

Molinuevo

Blennow

Dubois³

et al. 2014

Alzheimer's & Dementia

176

136

View full text Add to dashboard Cite

show abstract

Automated analysis of FDG PET as a tool for single-subject probabilistic prediction and detection of Alzheimer’s disease dementia

Cited by 43 publications

References 37 publications

Progressive Disintegration of Brain Networking from Normal Aging to Alzheimer Disease: Analysis of Independent Components of ¹⁸F-FDG PET Data

Progressive Disintegration of Brain Networking from Normal Aging to Alzheimer Disease: Analysis of Independent Components of ¹⁸F-FDG PET Data

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

The clinical use of cerebrospinal fluid biomarker testing for Alzheimer's disease diagnosis: A consensus paper from the Alzheimer's Biomarkers Standardization Initiative

Contact Info

Product

Resources

About

Automated analysis of FDG PET as a tool for single-subject probabilistic prediction and detection of Alzheimer’s disease dementia

Cited by 43 publications

References 37 publications

Progressive Disintegration of Brain Networking from Normal Aging to Alzheimer Disease: Analysis of Independent Components of 18F-FDG PET Data

Progressive Disintegration of Brain Networking from Normal Aging to Alzheimer Disease: Analysis of Independent Components of 18F-FDG PET Data

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

The clinical use of cerebrospinal fluid biomarker testing for Alzheimer's disease diagnosis: A consensus paper from the Alzheimer's Biomarkers Standardization Initiative

Contact Info

Product

Resources

About

Progressive Disintegration of Brain Networking from Normal Aging to Alzheimer Disease: Analysis of Independent Components of ¹⁸F-FDG PET Data

Progressive Disintegration of Brain Networking from Normal Aging to Alzheimer Disease: Analysis of Independent Components of ¹⁸F-FDG PET Data