Imaging cerebral glucose metabolism with positron emission tomography (PET) in Alzheimer's disease (AD) has allowed for improved characterisation of this pathology. Such patterns are typically analysed using either univariate or multivariate statistical techniques. In this work we combined voxel-based group analysis and independent component analysis to extract differential characteristic patterns from PET data of glucose metabolism in a large cohort of normal elderly controls and patients with AD. The patterns were used in conjunction with a support vector machine to discriminate between subjects with mild cognitive impairment (MCI) at risk or not of converting to AD. The method was applied to baseline fluoro-deoxyglucose (FDG)-PET images of subjects from the ADNI database. Our approach achieved improved early detection and differentiation of typical versus pathological metabolic patterns in the MCI population, reaching 80% accuracy (85% sensitivity and 75% specificity) when using selected regions. The method has the potential to assist in the advance diagnosis of Alzheimer's disease, and to identify early in the development of the disease those individuals at high risk of rapid cognitive decline who could be candidates for new therapeutic approaches.
"Most of these studies were conducted in large cohorts of patients from the Alzheimer3s Disease Neuromaging Initiative (ADNI) database and reached a statistical accuracy in discriminating AD patients from controls of 93% when using three biomarkers (MRI, FDG-PET and CSF) (Zhang et al., 2011) and higher than 90% with FDG-PET only (Arbizu et al., 2013; Gray et al., 2012; Illan et al., 2011). However accuracies were substantially lower in studies in which MCI (later converted to AD) was considered instead of patients with full-blown AD dementia (Cuingnet et al., 2011; Toussaint et al., 2012; Zhang et al., 2011). Though FDG-PET has been shown to correlate with the severity of cognitive impairment (Chen et al., 2010), subtle abnormalities at the earliest (prodromal ) stages of the disease may be more difficult to be detected. "
[Show abstract][Hide abstract] ABSTRACT: An emerging issue in neuroimaging is to assess the diagnostic reliability of PET and its application in clinical practice. We aimed at assessing the accuracy of brain FDG-PET in discriminating patients with MCI due to Alzheimer's disease and healthy controls. Sixty-two patients with amnestic MCI and 109 healthy subjects recruited in five centers of the European AD Consortium were enrolled. Group analysis was performed by SPM8 to confirm metabolic differences. Discriminant analyses were then carried out using the mean FDG uptake values normalized to the cerebellum computed in 45 anatomical volumes of interest (VOIs) in each hemisphere (90 VOIs) as defined in the Automated Anatomical Labeling (AAL) Atlas and on 12 meta-VOIs, bilaterally, obtained merging VOIs with similar anatomo-functional characteristics. Further, asymmetry indexes were calculated for both datasets. Accuracy of discrimination by a Support Vector Machine and the AAL VOIs was tested against a validated method (PALZ). At the voxel level SMP8 showed a relative hypometabolism in the bilateral precuneus, and posterior cingulate, temporo-parietal and frontal cortices. Discriminant analysis classified subjects with an accuracy ranging between .91 and .83 as a function of data organization. The best values were obtained from a subset of 6 meta-VOIs plus 6 asymmetry values reaching an area under the ROC curve of .947, significantly larger than the one obtained by the PALZ score. High accuracy in discriminating MCI converters from healthy controls was reached by a non-linear classifier based on SVM applied on predefined anatomo-functional regions and inter-hemispheric asymmetries. Data pre-processing was automated and simplified by an in-house created Matlab-based script encouraging its routine clinical use. Further validation toward nonconverter MCI patients with adequately long follow-up is needed.
[Show abstract][Hide abstract] ABSTRACT: Studies of functional connectivity suggest that the default mode network (DMN) might be relevant for cognitive functions. Here, we examined metabolic and structural connectivity between major DMN nodes, the posterior cingulate (PCC) and medial prefrontal cortex (MPFC), in relation to normal working memory (WM). DMN was captured using independent component analysis of [18F]fluorodeoxyglucose positron emission tomography (FDG-PET) data from 35 young healthy adults (27.1±5.1 years). Metabolic connectivity, a correlation between FDG uptake in PCC and MPFC, was examined in groups of subjects with (relative to median) low (n=18) and high (n=17) performance on digit span backward test as an index of verbal WM. In addition, fiber tractography based on PCC and MPFC nodes as way points was performed in a subset of subjects. FDG uptake in the DMN nodes did not differ between high and low performers. However, significantly (p=0.01) lower metabolic connectivity was found in the group of low performers. Furthermore, as compared to high performers, low performers showed lower density of the left superior cingulate bundle. Verbal WM performance is related to metabolic and structural connectivity within the DMN in young healthy adults. Metabolic connectivity as quantified with FDG-PET might be a sensitive marker of the normal variability in some cognitive functions.
[Show abstract][Hide abstract] ABSTRACT: 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.
Subjects with AD dementia from the Alzheimer's Disease Neuroimaging Initiative (ADNI) with at least 1 amyloid biomarker (n = 211) were included in this report. Biomarker data from CSF Aβ42, 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.
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.
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.
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