NeuroImage
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In humans, it is generally not possible to use invasive techniques in order to identify brain activity corresponding to activity of individual muscles. Further, it is believed that the spatial resolution of non-invasive brain imaging modalities is not sufficient to isolate neural activity related to individual muscles. However, this study shows that it is possible to reconstruct muscle activity from functional magnetic resonance imaging (fMRI). ⋯ The two voxel sets corresponding to the activity of the antagonist muscles were intermingled but disjoint. They were distributed over a wide area of pre-motor cortex and M1 and not limited to regions generally associated with wrist control. These results show that brain activity measured by fMRI in humans can be used to predict individual muscle activity through Bayesian linear models, and that our algorithm provides a novel and non-invasive tool to investigate the brain mechanisms involved in motor control and learning in humans.
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Cortical lesions (CLs) can be detected in the majority of patients with established multiple sclerosis (MS), but little is known about their evolution over time. This study was performed to investigate the short-term MRI evolution of CLs, with the ultimate aim to achieve a better in vivo understanding of their nature. Seven hundred and sixty-eight CLs from 107 MS patients (76 with relapsing-remitting [RR] and 31 with secondary progressive [SP] MS) were followed with brain MR examinations, including a double inversion recovery (DIR) sequence, every 6 months for 1 year. ⋯ At baseline, the mean number of CLs was higher in SPMS than in RRMS patients (p<0.001), whereas the mean number of new CLs per patient after 1 year did not differ between the two groups. Over a one-year period, CLs can increase their number and size in a relevant proportion of MS patients, without spreading into the subcortical white matter or showing inflammatory features similar to those of white matter lesions. The short-term rate of CLs accumulation does not seem to vary according to the clinical stage of MS.
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A hierarchical Bayesian method estimated current sources from MEG data, incorporating an fMRI constraint as a hierarchical prior whose strength is controlled by hyperparameters. A previous study [Sato, M., Yoshioka, T., Kajihara, S., Toyama, K., Goda, N., Doya, K., Kawato, M., 2004. Hierarchical Bayesian estimation for MEG inverse problem. ⋯ The false-positive effects of the noisy priors were suppressed by using appropriate hyperparameter values. The hierarchical Bayesian method also was capable of reconstructing retinotopic sequential activation in V1 with fine spatiotemporal resolution, from MEG data elicited by sequential stimulation of the four visual quadrants with the fan-shaped checker board pattern at much shorter intervals (150 and 400 ms) than the temporal resolution of fMRI. These results indicate the potential capability for the hierarchical Bayesian method combining MEG with fMRI to improve the spatiotemporal resolution of noninvasive brain activity measurement.
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The diffusion tensor is a commonly used model for diffusion-weighted MR image data. The parameters are typically estimated by ordinary or weighted least squares on log-transformed data, assuming normal or log-normal distribution of measurement errors respectively. This may not be adequate when using high b-values and or performing high-resolution scans, resulting in poor SNR, in which case the difference between the assumed and the true (Rician) noise model becomes important. ⋯ By pooling the Rician estimates of uncertainty over neighbouring voxel estimates with higher precision, but still not as high as with a Gaussian model, can be obtained. We suggest the use of a Rician estimator when it is important with truly quantitative values and when comparing different predictive models. The higher precision of the Gaussian estimates may be more important when the objective is to compare diffusion related parameters over time or across groups.
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Chronic pediatric traumatic brain injury (TBI) is associated with significant and persistent neurobehavioral deficits. Using diffusion tensor imaging (DTI), we examined area, fractional anisotropy (FA), radial diffusion, and axial diffusion from six regions of the corpus callosum (CC) in 41 children and adolescents with TBI and 31 comparison children. Midsagittal cross-sectional area of the posterior body and isthmus was similar in younger children irrespective of injury status; however, increased area was evident in the older comparison children but was obviated in older children with TBI, suggesting arrested development. ⋯ IQ, working memory, motor, and academic skills were correlated significantly with radial diffusion and/or FA from the isthmus and splenium only in the TBI group. Reduced size and microstructural changes in posterior callosal regions after TBI suggest arrested development, decreased organization, and disrupted myelination. Increased radial diffusivity was the most sensitive DTI-based surrogate marker of the extent of neuronal damage following TBI; FA was most strongly correlated with neuropsychological outcomes.