Relationship between baseline brain metabolism measured using [18F]FDG PET and memory and executive function in prodromal and early Alzheimer's disease

Cognitive Neuroscience Division, The Taub Institute for Research on Aging and Alzheimer's Disease, Columbia University, New York, NY, USA, .
Brain Imaging and Behavior (Impact Factor: 4.6). 11/2012; 6(4). DOI: 10.1007/s11682-012-9208-x
Source: PubMed


Differences in brain metabolism as measured by FDG-PET in prodromal and early Alzheimer's disease (AD) have been consistently observed, with a characteristic parietotemporal hypometabolic pattern. However, exploration of brain metabolic correlates of more nuanced measures of cognitive function has been rare, particularly in larger samples. We analyzed the relationship between resting brain metabolism and memory and executive functioning within diagnostic group on a voxel-wise basis in 86 people with AD, 185 people with mild cognitive impairment (MCI), and 86 healthy controls (HC) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We found positive associations within AD and MCI but not in HC. For MCI and AD, impaired executive functioning was associated with reduced parietotemporal metabolism, suggesting a pattern consistent with known AD-related hypometabolism. These associations suggest that decreased metabolic activity in the parietal and temporal lobes may underlie the executive function deficits in AD and MCI. For memory, hypometabolism in similar regions of the parietal and temporal lobes were significantly associated with reduced performance in the MCI group. However, for the AD group, memory performance was significantly associated with metabolism in frontal and orbitofrontal areas, suggesting the possibility of compensatory metabolic activity in these areas. Overall, the associations between brain metabolism and cognition in this study suggest the importance of parietal and temporal lobar regions in memory and executive function in the early stages of disease and an increased importance of frontal regions for memory with increasing impairment.

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    • ") of cognitive domain measurements examined are ADNI-memory (ADNI-Mem) and ADNIexecutive function (ADNI-Exe) [15] [16].The scores are psychometrically optimized composite scores of memory and executive function, respectively, derived from items from ADNI NP tests. These measurements were validated previously, robust, and have external validity [15] [16] [17] "
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    ABSTRACT: Background It is unknown which commonly used Alzheimer disease (AD) biomarker values—baseline or progression—best predict longitudinal cognitive decline. Methods 526 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). ADNI composite memory and executive scores were the primary outcomes. Individual-specific slope of the longitudinal trajectory of each biomarker was first estimated. These estimates and observed baseline biomarker values were used as predictors of cognitive declines. Variability in cognitive declines explained by baseline biomarker values was compared with variability explained by biomarker progression values. Results About 40% of variability in memory and executive function declines was explained by ventricular volume progression among mild cognitive impairment patients. A total of 84% of memory and 65% of executive function declines were explained by fluorodeoxyglucose positron emission tomography (FDG-PET) score progression and ventricular volume progression, respectively, among AD patients. Conclusions For most biomarkers, biomarker progressions explained higher variability in cognitive decline than biomarker baseline values. This has important implications for clinical trials targeted to modify AD biomarkers.
    Alzheimer's and Dementia 11/2014; 10(6). DOI:10.1016/j.jalz.2014.04.513 · 12.41 Impact Factor
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    • "easures were better predictors of MCI conversion to AD than most biomarkers . However , we advise caution because the cognitive performance measures we used were derived , in part , from the same ADNI participants to whom we applied diagnostic classification . While other reports have also used these factors in classifiers ( Crane et al . , 2012 ; Habeck et al . , 2012 ; Nir et al . , 2013 ) , we would like to caution readers to this source of potential bias and the possibility of inflated classification accuracy based on using these cognitive factors . Nonetheless , our findings suggest that there is added accuracy gained by incorporating MRI metrics to predict diagnostic classi - fication . Specific"
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    ABSTRACT: Identifying predictors of mild cognitive impairment (MCI) and Alzheimer׳s disease (AD) can lead to more accurate diagnosis and facilitate clinical trial participation. We identified 320 participants (93 cognitively normal or CN, 162 MCI, 65 AD) with baseline magnetic resonance imaging (MRI) data, cerebrospinal fluid biomarkers, and cognition data in the Alzheimer׳s Disease Neuroimaging Initiative database. We used independent component analysis (ICA) on structural MR images to derive 30 matter covariance patterns (ICs) across all participants. These ICs were used in iterative and stepwise discriminant classifier analyses to predict diagnostic classification at 24 months for CN vs. MCI, CN vs. AD, MCI vs. AD, and stable MCI (MCI-S) vs. MCI progression to AD (MCI-P). Models were cross-validated with a "leave-10-out" procedure. For CN vs. MCI, 84.7% accuracy was achieved based on cognitive performance measures, ICs, p-tau181p, and ApoE ε4 status. For CN vs. AD, 94.8% accuracy was achieved based on cognitive performance measures, ICs, and p-tau181p. For MCI vs. AD and MCI-S vs. MCI-P, models achieved 83.1% and 80.3% accuracy, respectively, based on cognitive performance measures, ICs, and p-tau181p. ICA-derived MRI biomarkers achieve excellent diagnostic accuracy for MCI conversion, which is little improved by CSF biomarkers and ApoE ε4 status.
    Psychiatry Research: Neuroimaging 08/2014; 224(2). DOI:10.1016/j.pscychresns.2014.08.005 · 2.42 Impact Factor
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    • "PET image analysis can provide evidence of the accumulation of amyloid-beta plaques that is independent from structural brain changes. It also provides evidence of a reduction of glucose metabolism in the parietal and temporal lobe regions that are involved in memory and executive function (Habeck et al., 2012). Both structural MRI and FDG-PET imaging reflect the effects of the disease progress in symptomatic stages, however it is the diagnosis in AD’s asymptomatic stages that remains to be solved. "
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    ABSTRACT: By 2050 it is estimated that the number of worldwide Alzheimer's disease (AD) patients will quadruple from the current number of 36 million people. To date, no single test, prior to postmortem examination, can confirm that a person suffers from AD. Therefore, there is a strong need for accurate and sensitive tools for the early diagnoses of AD. The complex etiology and multiple pathogenesis of AD call for a system-level understanding of the currently available biomarkers and the study of new biomarkers via network-based modeling of heterogeneous data types. In this review, we summarize recent research on the study of AD as a connectivity syndrome. We argue that a network-based approach in biomarker discovery will provide key insights to fully understand the network degeneration hypothesis (disease starts in specific network areas and progressively spreads to connected areas of the initial loci-networks) with a potential impact for early diagnosis and disease-modifying treatments. We introduce a new framework for the quantitative study of biomarkers that can help shorten the transition between academic research and clinical diagnosis in AD.
    Frontiers in Aging Neuroscience 02/2014; 6:12. DOI:10.3389/fnagi.2014.00012 · 4.00 Impact Factor
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