NeuroImage
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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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Clinical Trial
Measurement of OEF and absolute CMRO2: MRI-based methods using interleaved and combined hypercapnia and hyperoxia.
Blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) is most commonly used in a semi-quantitative manner to infer changes in brain activity. Despite the basis of the image contrast lying in the cerebral venous blood oxygenation level, quantification of absolute cerebral metabolic rate of oxygen consumption (CMRO2) has only recently been demonstrated. Here we examine two approaches to the calibration of fMRI signal to measure absolute CMRO2 using hypercapnic and hyperoxic respiratory challenges. ⋯ The combined approach to oxygen and carbon dioxide modulation, as well as taking less time to acquire data, yielded whole brain grey matter estimates of CMRO2 and OEF of 184±45 μmol/100 g/min and 0.42±0.12 respectively, along with additional estimates of the vascular parameters α=0.33±0.06, the exponent relating relative increases in CBF to CBV, and β=1.35±0.13, the exponent relating deoxyhaemoglobin concentration to the relaxation rate R2*. Maps of cerebrovascular and cerebral metabolic parameters were also calculated. We show that combined modulation of oxygen and carbon dioxide can offer an experimentally more efficient approach to estimating OEF and absolute CMRO2 along with the additional vascular parameters that form an important part of the commonly used calibrated fMRI signal model.
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Functional connectivity (FC) as measured by correlation between fMRI BOLD time courses of distinct brain regions has revealed meaningful organization of spontaneous fluctuations in the resting brain. However, an increasing amount of evidence points to non-stationarity of FC; i.e., FC dynamically changes over time reflecting additional and rich information about brain organization, but representing new challenges for analysis and interpretation. Here, we propose a data-driven approach based on principal component analysis (PCA) to reveal hidden patterns of coherent FC dynamics across multiple subjects. ⋯ We then used PCA to identify FC patterns, termed "eigenconnectivities", that reflect meaningful patterns in FC fluctuations. We then assessed the contributions of these patterns to the dynamic FC at any given time point and identified a network of connections centered on the default-mode network with altered contribution in patients. Our results complement traditional stationary analyses, and reveal novel insights into brain connectivity dynamics and their modulation in a neurodegenerative disease.
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Trial-to-trial reaction time (RT) variability is consistently higher in children and older adults than in younger adults. Converging evidence also indicates that higher RT variability is (a) associated with lower behavioral performance on complex cognitive tasks, (b) distinguishes patients with neurological deficits from healthy individuals, and also (c) predicts longitudinal cognitive decline in older adults. However, so far the processes underlying increased RT variability are poorly understood. ⋯ Importantly, this effect was strongest at high performance monitoring demands and independent of motor response execution as well as theta power. Taken together, our findings reveal that lower theta inter-trial coherence is related to greater behavioral variability within and across age groups. These results hint at the possibility that more variable MFC control may be associated with greater performance fluctuations.