This multicenter study examined (18)F-FDG PET measures in the differential diagnosis of Alzheimer's disease (AD), frontotemporal dementia (FTD), and dementia with Lewy bodies (DLB) from normal aging and from each other and the relation of disease-specific patterns to mild cognitive impairment (MCI).We examined the (18)F-FDG PET scans of 548 subjects, including 110 healthy elderly individuals ("normals" or NLs), 114 MCI, 199 AD, 98 FTD, and 27 DLB patients, collected at 7 participating centers. Individual PET scans were Z scored using automated voxel-based comparison with generation of disease-specific patterns of cortical and hippocampal (18)F-FDG uptake that were then applied to characterize MCI.Standardized disease-specific PET patterns were developed that correctly classified 95% AD, 92% DLB, 94% FTD, and 94% NL. MCI patients showed primarily posterior cingulate cortex and hippocampal hypometabolism (81%), whereas neocortical abnormalities varied according to neuropsychological profiles. An AD PET pattern was observed in 79% MCI with deficits in multiple cognitive domains and 31% amnesic MCI. (18)F-FDG PET heterogeneity in MCI with nonmemory deficits ranged from absent hypometabolism to FTD and DLB PET patterns.Standardized automated analysis of (18)F-FDG PET scans may provide an objective and sensitive support to the clinical diagnosis in early dementia.
The development of prevention therapies for Alzheimer's disease (AD) would greatly benefit from biomarkers that are sensitive to subtle brain changes occurring prior to the onset of clinical symptoms, when the potential for preservation of function is at the greatest. In vivo brain imaging is a promising tool for the early detection of AD through visualization of abnormalities in brain structure, function and histopathology. Currently, positron emission tomography (PET) imaging with amyloid-beta (Aβ) tracers and 2-[(18)F]fluoro-2-Deoxy-D-glucose (FDG) is largely utilized in the diagnosis of AD. This paper reviews brain Aβ- and FDG-PET studies in AD patients as well as in non-demented individuals at risk for AD. We then discuss the potential of combining symptoms-sensitive FDG-PET measures with pathology-specific Aβ-PET to improve the early detection of AD.
The variability of cognitive status and clinical progression in AD patient populations poses a major confound in clinical trials. While regional cerebral glucose metabolism (rCMglc) has been found to correlate with and predict clinical worsening in AD, the complexity of multi-region involvement and lack of consistent correlation across accepted cognitive metrics have limited its use as a biomarker. We show that a multivariate classifier approach can provide a single, interpretable biomarker of rCMglc that allows baseline stratification and prediction of subsequent cognitive deterioration. The FDG-PET scans, MMSE, CDR sum of box (CDR-sb), and ADAS-11 scores of 55 AD patients from the ADNI database (76+7 yrs, 33M/22F) having 24 months of data were analyzed to assess relationships between baseline status and longitudinal progression. FDG-PET scans were sampled using automated regions of interest (ROI) for parietal cortex, posterior cingulate/precuneus, medial temporal cortex, hippocampal subregion (HIP), prefrontal cortex, lateral temporal cortex (LTL), and occipital cortex. Additionally, we applied an FDG-PET multivariate canonical variates (CV) classifier developed to characterize the progression from Normal to AD as a single pattern-based score (Strother et al, OHBM, Quebec, 2011), and evaluated the relationship between CV scores, ROI rCMglc, and MMSE, CDR-sb, and ADAS-11 performance. AD subjects worsened on average over 24 months by 4 points (SD=5) on MMSE, 3 points (SD=3) on CDR-sb and 8 points (SD=7.6) on ADAS-11. Baseline rCMglc CV score was significantly correlated with baseline MMSE (P<0.011), CDR-sb (P<0.0004), and ADAS-11 (P<.0002) and predicted 24 month change (24mchg) in MMSE (P<.005), CDR-sb (P<.010), and ADAS-11 (P<.001). Additionally, 12mchg in CV scores predicted 24mchg in MMSE (P<0.02), CDR-sb (P<0.004), and ADAS-11 (P<0.001). The 24mchg in CV scores was significantly correlated with 24mchg in MMSE (P<.005), CDR-sb (P<.010), and ADAS-11 (P<.0001). On a regional basis, significant correlations were found between LTL and MMSE baseline and 24mchg scores (both P<0.0001), and between HIP and ADAS-11 baseline (P<0.010), ADAS-11 24mchg (P<0.072), CDR-sb baseline (P<0.003), and CDR-sb 24mchg (P<0.051). Measurement of glucose metabolism using a multivariate classifier approach, and with ROI measures, provides a valuable biomarker to predict cognitive status and subsequent longitudinal outcome. Change in the CV1 pattern-based classifier score over 24 months vs. change in ADAS-11 score over 24 months. Decreasing CV1 scores correspond to increasing disease severity.
Preclinical diagnosis of Alzheimer’s disease (AD) is one of the major challenges for the prevention of AD. AD biomarkers are needed not only to reveal preclinical pathologic changes, but also to monitor progression and therapeutics. PET neuroimaging can reliably assess aspects of the molecular biology and neuropathology of AD. The aim of this article is to review the use of FDG-PET and amyloid PET imaging in the early detection of AD.
The normative reference sample is crucial for the diagnosis of Alzheimer's disease (AD) with automated (18)F-FDG PET analysis. We tested whether an (18)F-FDG PET database of longitudinally confirmed healthy elderly individuals ("normals," or NLs) would improve diagnosis of AD and mild cognitive impairment (MCI).Two (18)F-FDG PET databases of 55 NLs with 4-y clinical follow-up examinations were created: one of NLs who remained NL, and the other including a fraction of NLs who declined to MCI at follow-up. Each (18)F-FDG PET scan of 19 NLs, 37 MCI patients, and 33 AD patients was z scored using automated voxel-based comparison to both databases and examined for AD-related abnormalities.Our database of longitudinally confirmed NLs yielded 1.4- to 2-fold higher z scores than did the mixed database in detecting (18)F-FDG PET abnormalities in both the MCI and the AD groups. (18)F-FDG PET diagnosis using the longitudinal NL database identified 100% NLs, 100% MCI patients, and 100% AD patients, which was significantly more accurate for MCI patients than with the mixed database (100% NLs, 68% MCI patients, and 94% AD patients identified).Our longitudinally confirmed NL database constitutes reliable (18)F-FDG PET normative values for MCI and AD.
The role that genetics plays in the development of late-onset Alzheimer's disease (AD) has been widely studied, with one study estimating genetics to account for more than 50% of the phenotypic variance. This chapter reviews examples of a universal "one-size-fits-all" prevention strategy without any distinction that is based on genetics or other personalized risk factors versus a clinical precision medicine approach. Randomized studies in AD prevention have traditionally used either single or multiple interventions to determine efficacy across a host of clinical outcome measures. A precision medicine approach to AD prevention will need to fully utilize the genome in order to make personalized recommendations. The chapter discusses some of the genetic influencers on late-onset AD that can be ordered by a practicing physician and provides examples of a targeted precision medicine approach based on these genetic factors. The genomic-centered foundation that forms the core of precision medicine reduces diseases to their molecular and cellular processes.
A great deal of current research is aiming to better understand what happens to brains affected by Alzheimer's disease, both structurally and metabolically. However, little is known about how the brain ages in the cognitively normal adult population. There is also a limited understanding as to how glucose metabolism and brain atrophy interact in the normal human brain. To our knowledge, this study is the first to longitudinally analyze measures of atrophy and metabolism in both MRI and FDG-PET in cognitively normal subjects. 45 cognitively normal subjects were studied longitudinally over a span of at least 1.5 years (average = 5.99 years). Each had at least two structural MRIs and 2 FDG-PET scans. Free Surfer was used to analyze ventricle and intracranial volume. This was turned into an intracranial volume ratio to get a normalized picture of brain atrophy. A hippocampal masking technique (HipMask) was used to analyze the FDG-PET scans for changes in metabolic rates in regions normally affected by Alzheimer's disease (precuneus/posterior cingulate, hippocampus, inferior parietal lobe), as well as regions affected by normal aging (prefrontal cortex and cerebellum). Mixed model analysis indicates that ventricle enlargement occurs in the cognitively normal adult brain as early as adult middle age (t = 7.050, p < .001), with a particular acceleration after the age of 65 (t = 2.878, p = .004). However, the mixed model results did not show any evidence of a concomitant decrease in rate with age, nor any acceleration, in regional metabolism after controlling for brain atrophy. Our results show that brain atrophy increases over the adult lifespan. These changes appear to accelerate over time. However, metabolically, we did not find any significant results with regard to change over time. This is in contrast to Alzheimer's disease, where literature has shown metabolic decreases in frontal and parietal regions, particularly in precuneus/posterior cingulate. Our results also illustrate the importance of controlling for structural atrophy in FDG-PET.
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