A Standardized [18F]-FDG-PET Template for Spatial Normalization in Statistical Parametric Mapping of Dementia

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Simultaneous PET-MRI Studies of the Concordance of Atrophy and Hypometabolism in Syndromic Variants of Alzheimer's Disease and Frontotemporal Dementia: An Extended Case Series

Background: Simultaneous PET-MRI is used to compare patterns of cerebral hypometabolism and atrophy in six different dementia syndromes. Objectives: The primary objective was to conduct an initial exploratory study regarding the concordance of atrophy and hypometabolism in syndromic variants of Alzheimer’s disease (AD) and frontotemporal dementia (FTD). The secondary objective was to determine the effect of image analysis methods on determination of atrophy and hypometabolism. Method: PET and MRI data were acquired simultaneously on 24 subjects with six variants of AD and FTD (n = 4 per group). Atrophy was rated visually and also quantified with measures of cortical thickness. Hypometabolism was rated visually and also quantified using atlas- and SPM-based approaches. Concordance was measured using weighted Cohen’s kappa. Results: Atrophy-hypometabolism concordance differed markedly between patient groups; kappa scores ranged from 0.13 (nonfluent/agrammatic variant of primary progressive aphasia, nfvPPA) to 0.49 (posterior cortical variant of AD, PCA). Heterogeneity was also observed within groups; the confidence intervals of kappa scores ranging from 0–0.25 for PCA to 0.29–0.61 for nfvPPA. More widespread MRI and PET changes were identified using quantitative methods than on visual rating. Conclusion: The marked differences in concordance identified in this initial study may reflect differences in the molecular pathologies underlying AD and FTD syndromic variants but also operational differences in the methods used to diagnose these syndromes. The superior ability of quantitative methodologies to detect changes on PET and MRI, if confirmed on larger cohorts, may favor their usage over qualitative visual inspection in future clinical diagnostic practic

Gaussian Mixture Models and Model Selection for [18F] Fluorodeoxyglucose Positron Emission Tomography Classification in Alzheimer’s Disease

We present a method to discover discriminative brain metabolism patterns in [18F] fluorodeoxyglucose positron emission tomography (PET) scans, facilitating the clinical diagnosis of Alzheimer’s disease. In the work, the term “pattern” stands for a certain brain region that characterizes a target group of patients and can be used for a classification as well as interpretation purposes. Thus, it can be understood as a so-called “region of interest (ROI)”. In the literature, an ROI is often found by a given brain atlas that defines a number of brain regions, which corresponds to an anatomical approach. The present work introduces a semi-data-driven approach that is based on learning the characteristics of the given data, given some prior anatomical knowledge. A Gaussian Mixture Model (GMM) and model selection are combined to return a clustering of voxels that may serve for the definition of ROIs. Experiments on both an in-house dataset and data of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) suggest that the proposed approach arrives at a better diagnosis than a merely anatomical approach or conventional statistical hypothesis testing

Contribution of FDG-PET and MRI to improve Understanding, Detection and Differentiation of Dementia

Progression and pattern of changes in different biomarkers of Alzheimer’s disease (AD) and frontotemporal lobar degeneration (FTLD) like [18F]fluorodeoxyglucose positron emission tomography (FDG-PET) and magnetic resonance imaging (MRI) have been carefully investigated over the past decades. However, there have been substantially less studies investigating the potential of combining these imaging modalities to make use of multimodal information to further improve understanding, detection and differentiation of various dementia syndromes. Further the role of preprocessing has been rarely addressed in previous research although different preprocessing algorithms have been shown to substantially affect diagnostic accuracy of dementia. In the present work common preprocessing procedures used to scale FDG-PET data were compared to each other. Further, FDG-PET and MRI information were jointly analyzed using univariate and multivariate techniques. The results suggest a highly differential effect of different scaling procedures of FDG-PET data onto detection and differentiation of various dementia syndromes. Additionally, it has been shown that combining multimodal information does further improve automatic detection and differentiation of AD and FTLD

Development of a simulation platform for the evaluation of PET neuroimaging protocols in epilepsy

Monte Carlo simulation of PET studies is a reference tool for the evaluation and standardization of PET protocols. However, current Monte Carlo software codes require a high degree of knowledge in physics, mathematics and programming languages, in addition to a high cost of time and computational resources. These drawbacks make their use difficult for a large part of the scientific community. In order to overcome these limitations, a free and an efficient web-based platform was designed, implemented and validated for the simulation of realistic brain PET studies, and specifically employed for the generation of a wellvalidated large database of brain FDG-PET studies of patients with refractory epilepsy

Why musical memory can be preserved in advanced Alzheimer's disease

Musical memory is relatively preserved in Alzheimer's disease and other dementias. In a 7 Tesla functional MRI study employing multi-voxel pattern analysis, Jacobsen et al. identify brain regions encoding long-term musical memory in young healthy controls, and show that these same regions display relatively little atrophy and hypometabolism in patients with Alzheimer's disease.See Clark and Warren (doi:10.1093/brain/awv148) for a scientific commentary on this article. Musical memory is relatively preserved in Alzheimer's disease and other dementias. In a 7 Tesla functional MRI study employing multi-voxel pattern analysis, Jacobsen et al. identify brain regions encoding long-term musical memory in young healthy controls, and show that these same regions display relatively little atrophy and hypometabolism in patients with Alzheimer's disease.See Clark and Warren (doi:10.1093/awv148) for a scientific commentary on this article

Feasibility of pharmacokinetic parametric PET images in scaled subprofile modelling using principal component analysis

Scaled subprofile model using principal component analysis (SSM/PCA) is a multivariate analysis technique used, mainly in [18F]-2-fluoro-2-deoxy-D-glucose (FDG) PET studies, for the generation of disease-specific metabolic patterns (DP) that may aid with the classification of subjects with neurological disorders, like Alzheimer's disease (AD). The aim of this study was to explore the feasibility of using quantitative parametric images for this type of analysis, with dynamic [11C]-labelled Pittsburgh Compound B (PIB) PET data as an example. Therefore, 15 AD patients and 15 healthy control subjects were included in an SSM/PCA analysis to generate four AD-DPs using relative cerebral blood flow (R1), binding potential (BPND) and SUVR images derived from dynamic PIB and static FDG-PET studies. Furthermore, 49 new subjects with a variety of neurodegenerative cognitive disorders were tested against these DPs. The AD-DP was characterized by a reduction in the frontal, parietal, and temporal lobes voxel values for R1 and SUVR-FDG DPs; and by a general increase of values in cortical areas for BPND and SUVR-PIB DPs. In conclusion, the results suggest that the combination of parametric images derived from a single dynamic scan might be a good alternative for subject classification instead of using 2 independent PET studies

Support vector machine-based classification of neuroimages in Alzheimer’s disease: direct comparison of FDG-PET, rCBF-SPECT and MRI data acquired from the same individuals

OBJECTIVE: To conduct the first support vector machine (SVM)-based study comparing the diagnostic accuracy of T1-weighted magnetic resonance imaging (T1-MRI), F-fluorodeoxyglucose-positron emission tomography (FDG-PET) and regional cerebral blood flow single-photon emission computed tomography (rCBF-SPECT) in Alzheimer's disease (AD). METHOD: Brain T1-MRI, FDG-PET and rCBF-SPECT scans were acquired from a sample of mild AD patients (n=20) and healthy elderly controls (n=18). SVM-based diagnostic accuracy indices were calculated using whole-brain information and leave-one-out cross-validation. RESULTS: The accuracy obtained using PET and SPECT data were similar. PET accuracy was 68∼71% and area under curve (AUC) 0.77∼0.81; SPECT accuracy was 68∼74% and AUC 0.75∼0.79, and both had better performance than analysis with T1-MRI data (accuracy of 58%, AUC 0.67). The addition of PET or SPECT to MRI produced higher accuracy indices (68∼74%; AUC: 0.74∼0.82) than T1-MRI alone, but these were not clearly superior to the isolated neurofunctional modalities. CONCLUSION: In line with previous evidence, FDG-PET and rCBF-SPECT more accurately identified patients with AD than T1-MRI, and the addition of either PET or SPECT to T1-MRI data yielded increased accuracy. The comparable SPECT and PET performances, directly demonstrated for the first time in the present study, support the view that rCBF-SPECT still has a role to play in AD diagnosis