NeuroImage
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Multicenter Study Comparative Study
Brain morphometry reproducibility in multi-center 3T MRI studies: a comparison of cross-sectional and longitudinal segmentations.
Large-scale longitudinal multi-site MRI brain morphometry studies are becoming increasingly crucial to characterize both normal and clinical population groups using fully automated segmentation tools. The test-retest reproducibility of morphometry data acquired across multiple scanning sessions, and for different MR vendors, is an important reliability indicator since it defines the sensitivity of a protocol to detect longitudinal effects in a consortium. There is very limited knowledge about how across-session reliability of morphometry estimates might be affected by different 3T MRI systems. ⋯ The average of two MPRAGE volumes acquired within each test-retest session did not systematically improve the across-session reproducibility of morphometry estimates. Our results extend those from previous studies that showed improved reliability of the longitudinal analysis at single sites and/or with non-standard acquisition methods. The multi-site acquisition and analysis protocol presented here is promising for clinical applications since it allows for smaller sample sizes per MRI site or shorter trials in studies evaluating the role of potential biomarkers to predict disease progression or treatment effects.
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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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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.