NeuroImage
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Functional hemispheric differences for speech and language processing have been traditionally studied by using verbal dichotic-listening paradigms. The commonly observed right-ear preference for the report of dichotically presented syllables is taken to reflect the left hemispheric dominance for speech processing. However, the results of recent functional imaging studies also show that both hemispheres - not only the left - are engaged by dichotic listening, suggesting a more complex relationship between behavioral laterality and functional hemispheric activation asymmetries. ⋯ Absolute leftward asymmetry was also found in the post-central and medial superior frontal gyri, while rightward asymmetries were found in middle temporal and middle frontal gyri. We conclude that dichotic listening engages a bihemispheric cortical network, showing a symmetrical and mostly leftward asymmetrical pattern. The here obtained functional (a)symmetry map might serve as a basis for future studies which - by studying the relevance of the here identified regions - clarify the relationship between behavioral laterality measures and hemispheric asymmetry.
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The segmentation of neonatal brain MR image into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF), is challenging due to the low spatial resolution, severe partial volume effect, high image noise, and dynamic myelination and maturation processes. Atlas-based methods have been widely used for guiding neonatal brain segmentation. Existing brain atlases were generally constructed by equally averaging all the aligned template images from a population. ⋯ Finally, the probability maps are integrated into a coupled level set framework for more accurate segmentation. The proposed method has been extensively evaluated on 20 training subjects using leave-one-out cross validation, and also on 132 additional testing subjects. Our method achieved a high accuracy of 0.919±0.008 for white matter and 0.901±0.005 for gray matter, respectively, measured by Dice ratio for the overlap between the automated and manual segmentations in the cortical region.
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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.