NeuroImage
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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.
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Randomized Controlled Trial
Magnetoencephalographic evidence for the modulation of cortical swallowing processing by transcranial direct current stimulation.
Swallowing is a complex neuromuscular task that is processed within multiple regions of the human brain. Rehabilitative treatment options for dysphagia due to neurological diseases are limited. Because the potential for adaptive cortical changes in compensation of disturbed swallowing is recognized, neuromodulation techniques like transcranial direct current stimulation (tDCS) are currently considered as a treatment option. ⋯ No relevant behavioral effects were observed on swallow response time, but swallow precision improved after left tDCS (p<0.05). Anodal tDCS applied over the swallowing motor cortex of either hemisphere was able to increase bilateral swallow-related cortical network activation in a frequency specific manner. These neuroplastic effects were associated with subtle behavioral gains during complex swallow tasks in healthy individuals suggesting that tDCS deserves further evaluation as a treatment tool for dysphagia.
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In many neuroscience and clinical studies, accurate measurement of hippocampus is very important to reveal the inter-subject anatomical differences or the subtle intra-subject longitudinal changes due to aging or dementia. Although many automatic segmentation methods have been developed, their performances are still challenged by the poor image contrast of hippocampus in the MR images acquired especially from 1.5 or 3.0 Tesla (T) scanners. With the recent advance of imaging technology, 7.0 T scanner provides much higher image contrast and resolution for hippocampus study. ⋯ Then, under the multi-atlas segmentation framework, multiple sequences of ACM-based classifiers are trained for all atlases to incorporate the anatomical variability. In the application stage, for a new image, its hippocampus segmentation can be achieved by fusing the labeling results from all atlases, each of which is obtained by applying the atlas-specific ACM-based classifiers. Experimental results on twenty 7.0 T images with the voxel size of 0.35×0.35×0.35 mm3 show very promising hippocampus segmentations (in terms of Dice overlap ratio 89.1±0.020), indicating high applicability for the future clinical and neuroscience studies.
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There has been an increasing use of functional magnetic resonance imaging (fMRI) by the neuroscience community to examine differences in functional connectivity between normal control groups and populations of interest. Understanding the reliability of these functional connections is essential to the study of neurological development and degenerate neuropathological conditions. To date, most research assessing the reliability with which resting-state functional connectivity characterizes the brain's functional networks has been on scans between 3 and 11 min in length. ⋯ This improvement in reliability due to scan length is much greater for scans acquired during the same session. Gains in intersession reliability began to diminish after 9-12 min, while improvements in intrasession reliability plateaued around 12-16 min. Consequently, new techniques that improve reliability across sessions will be important for the interpretation of longitudinal fMRI studies.
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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.