NeuroImage
-
MRI-based human brain atlases, which serve as a common coordinate system for image analysis, play an increasingly important role in our understanding of brain anatomy, image registration, and segmentation. Study-specific brain atlases are often obtained from one of the subjects in a study or by averaging the images of all participants after linear or non-linear registration. The latter approach has the advantage of providing an unbiased anatomical representation of the study population. ⋯ In addition to the volume-based quantitative analysis, the preserved brain topology of the VTE allows surface-based analysis within the same atlas framework. This property was demonstrated by analyzing the registration accuracy of the pre- and post-central gyri. The proposed method achieved registration accuracy within 1mm for these population-preserved cortical structures in an elderly population.
-
Interest has recently grown in multi-center studies, which have more power than smaller studies in conducting sophisticated evaluations of basic neuroanatomy and neurodegenerative disorders. The large number of subjects that result from pooling multi-center datasets increases sensitivity, but also introduces a between-center variance component. Taking sex differences as an example, we examined the effects of different ratios of cases to controls (males to females) between scanners in multi-scanner morphometric studies, using voxel-based morphometry and data obtained on two scanners of the exact same model. ⋯ When the ratio of males to females was balanced, the inclusion of scanner as a covariate in the statistical analysis had almost no influence on the results of analyses of sex differences. When the ratio of males to females was ill-balanced, the inclusion of scanner as a covariate suppressed scanner effects on the results, but made sex differences less likely to become significant. The present results suggest that as long as the ratio of cases to controls is well-balanced across different scanners, it is not always necessary to include scanner as a covariate in the statistical analysis, and that when the ratio of cases to controls is ill-balanced across scanners, the inclusion of scanner as a covariate in the statistical analysis can suppress scanner effects, but may make differences less likely to be detected.
-
One of the cellular markers of neuroinflammation is increased microglia activation, characterized by overexpression of mitochondrial 18kDa Translocator Protein (TSPO). TSPO expression can be quantified in-vivo using the positron emission tomography (PET) radioligand [(18)F]-FEPPA. This study examined microglial activation as measured with [(18)F]-FEPPA PET across the adult lifespan in a group of healthy volunteers. ⋯ We found no significant effect of age on [(18)F]-FEPPA VT (F (1,30)=0.918; p=0.346), and a significant effect of genetic polymorphism (F (1,30)=8.767; p=0.006). This is the first in-vivo study to evaluate age-related changes in TSPO binding, using the new generation TSPO radioligands. Increased neuroinflammation, as measured with [(18)F]-FEPPA PET was not associated with normal aging, suggesting that healthy elderly individuals may serve as useful benchmark against patients with neurodegenerative disorders where neuroinflammation may be present.
-
Modern machine learning algorithms are increasingly being used in neuroimaging studies, such as the prediction of Alzheimer's disease (AD) from structural MRI. However, finding a good representation for multivariate brain MRI features in which their essential structure is revealed and easily extractable has been difficult. We report a successful application of a machine learning framework that significantly improved the use of brain MRI for predictions. ⋯ In contrast, predictions using the original features performed not better than by chance (accuracy/sensitivity/specificity: =0.56/0.65/0.46). In conclusion, LLE is a very effective tool for classification studies of AD using multivariate MRI data. The improvement in predicting conversion to AD in MCI could have important implications for health management and for powering therapeutic trials by targeting non-demented subjects who later convert to AD.
-
Cerebrovascular reactivity (CVR) can be mapped using BOLD fMRI to provide a clinical insight into vascular health that can be used to diagnose cerebrovascular disease. Breath-holds are a readily accessible method for producing the required arterial CO2 increases but their implementation into clinical studies is limited by concerns that patients will demonstrate highly variable performance of breath-hold challenges. This study assesses the repeatability of CVR measurements despite poor task performance, to determine if and how robust results could be achieved with breath-holds in patients. ⋯ In contrast, the end-tidal CO2 regressors resulted in "excellent" repeatability (ICC=0.82) in the average grey matter data, and resulted in acceptable repeatability in all smaller regions tested (ICC>0.4). Further analysis of intra-subject CVR variability across the brain (ICCspatial and voxelwise correlation) supported the use of end-tidal CO2 data to extract robust whole-brain CVR maps, despite variability in breath-hold performance. We conclude that the incorporation of end-tidal CO2 monitoring into scanning enables robust, repeatable measurement of CVR that makes breath-hold challenges suitable for routine clinical practice.