Resting state FDG-PET functional connectivity as an early biomarker of Alzheimer's disease using conjoint univariate and independent component analyses

Laboratoire d'Imagerie Fonctionnelle, UMR-S 678, INSERM-UPMC, Paris, France
NeuroImage (Impact Factor: 6.13). 04/2012; 63(2):936-46. DOI: 10.1016/j.neuroimage.2012.03.091
Source: PubMed

ABSTRACT 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.

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    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.
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