In neuroimaging it is helpful and useful to obtain robust and accurate estimates of relationships between the image derived data and separately derived covariates such as clinical and demographic measures. Due to the high dimensionality of brain images, complex image analysis is typically used to extract certain image features, which may or may not relate to the covariates. These correlations which explain variance within the image data are frequently of interest. Principal component analysis (PCA) is used to extract image features from a sample of 42 FDG PET brain images (19 normal controls (NCs), 23 Alzheimer's disease (AD) patients). For the first three most robust PCs, the correlation of the PC scores with: i) the Mini Mental Status Exam (MMSE) score and ii) age is examined. The key aspects of this work is the assessment of: i) the robustness and significance of the correlations using bootstrap resampling; ii) the influence of the PCA on the robustness of the correlations; iii) the impact of two intensity normalization methods (global and cerebellum). Results show that: i) Pearson's statistics can lead to overoptimistic results. ii) The robustness of the correlations deteriorate with the number of PCs. iii) The correlations are hugely influenced by the method of intensity normalization: the correlation of cognitive impairment with PC1 are stronger and more significant for global normalization; whereas the correlations with age were strongest and more robust with PC2 and cerebellar normalization.
[Show abstract][Hide abstract] 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.
[Show abstract][Hide abstract] ABSTRACT: Bootstrap resampling has been successfully used for estimation of statistical uncertainty of parameters such as tissue metabolism, blood flow or displacement fields for image registration. The performance of bootstrap resampling as applied to PET list-mode data of the human brain and dedicated phantoms is assessed in a novel and systematic way such that: (1) the assessment is carried out in two resampling stages: the 'real world' stage where multiple reference datasets of varying statistical level are generated and the 'bootstrap world' stage where corresponding bootstrap replicates are generated from the reference datasets. (2) All resampled datasets were reconstructed yielding images from which multiple voxel and regions of interest (ROI) values were extracted to form corresponding distributions between the two stages. (3) The difference between the distributions from both stages was quantified using the Jensen-Shannon divergence and the first four moments.It was found that the bootstrap distributions are consistently different to the real world distributions across the statistical levels. The difference was explained by a shift in the mean (up to 33% for voxels and 14% for ROIs) being proportional to the inverse square root of the statistical level (number of counts). Other moments were well replicated by the bootstrap although for very low statistical levels the estimation of the variance was poor. Therefore, the bootstrap method should be used with care when estimating systematic errors (bias) and variance when very low statistical levels are present such as in early time frames of dynamic acquisitions, when the underlying population may not be sufficiently represented.
Physics in Medicine and Biology 12/2014; 60(1):279-299. DOI:10.1088/0031-9155/60/1/279 · 2.76 Impact Factor
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