NeuroImage
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Echo-planar imaging (EPI) is a standard procedure in functional magnetic resonance imaging (fMRI) for measuring changes in the blood oxygen level-dependent (BOLD) signal associated with neuronal activity. The images obtained from fMRI with EPI, however, exhibit signal dropouts and geometric distortions. Parallel imaging (PI), due to its short readout, accelerates image acquisition and might reduce dephasing in phase-encoding direction. ⋯ In single echoes, SNR and BOLD sensitivity followed the predicted dependency on echo time (TE) and were reduced under PI. However, the combination of echoes with mEPI recovered the quality parameters and increased BOLD signal changes at circumscribed fronto-polar and deep brain structures. We suggest applying PI only in combination with mEPI to reduce imaging artifacts and conserve BOLD sensitivity.
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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.
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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.
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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.
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Event-related desynchronization (ERD) and synchronization (ERS) of electrocortical signals (e.g., electroencephalogram [EEG] and magnetoencephalogram) reflect important aspects of sensory, motor, and cognitive cortical processing. The detection of ERD and ERS relies on time-frequency decomposition of single-trial electrocortical signals, to identify significant stimulus-induced changes in power within specific frequency bands. Typically, these changes are quantified by expressing post-stimulus EEG power as a percentage of change relative to pre-stimulus EEG power. ⋯ Furthermore, given that the variability in ERD/ERS is not only dependent on the variability in post-stimulus power but also on the variability in pre-stimulus power, an estimation of the respective contribution of pre- and post-stimulus EEG variability is needed. This can be achieved using a multivariate linear regression (MVLR) model, which could be optimally estimated using partial least square (PLS) regression, to dissect and quantify the relationship between behavioral variables and pre- and post-stimulus EEG activities. In summary, combining single-trial baseline subtraction approach with PLS regression can be used to achieve a correct detection and quantification of ERD/ERS.