NeuroImage
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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.
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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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In our daily life we look at many scenes. Some are rapidly forgotten, but others we recognize later. We accurately predicted recognition success with natural scene photographs using single trial magnetoencephalography (MEG) measures of brain activation. ⋯ A permutation test confirmed that all lSVM based prediction rates were significantly better than "guessing". More generally, we present four approaches to analyzing brain function using lSVMs. (1) We show that lSVMs can be used to extract spatio-temporal patterns of brain activation from MEG-data. (2) We show lSVM classification can demonstrate significant correlations between comparatively early and late processes predictive of scene recognition, indicating dependencies between these processes over time. (3) We use lSVM classification to compare the information content of oscillatory and event-related MEG-activations and show they contain a similar amount of and largely overlapping information. (4) A more detailed analysis of single-trial predictiveness of different frequency bands revealed that theta band activity around 5 Hz allowed for highest prediction rates, and these rates are indistinguishable from those obtained with a full dataset. In sum our results clearly demonstrate that lSVMs can reliably predict natural scene recognition from single trial MEG-activation measures and can be a useful tool for analyzing predictive brain function.
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An important step in perceptual processing is the integration of information from different sensory modalities into a coherent percept. It has been suggested that such crossmodal binding might be achieved by transient synchronization of neurons from different modalities in the gamma-frequency range (>30 Hz). Here we employed a crossmodal priming paradigm, modulating the semantic congruency between visual-auditory natural object stimulus pairs, during the recording of the high density electroencephalogram (EEG). ⋯ Early gamma-band activity (40-50 Hz) was increased between 120 ms and 180 ms following auditory stimulus onset for semantically congruent stimulus pairs. Source reconstruction for this gamma-band response revealed a maximal increase in left middle temporal gyrus (BA 21), an area known to be related to the processing of both complex auditory stimuli and multisensory processing. The data support the hypothesis that oscillatory activity in the gamma-band reflects crossmodal semantic-matching processes in multisensory convergence sites.
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It is increasingly recognized that pain-induced plasticity may provoke secondary sensory decline, i.e. centrally-mediated hypoesthesia and hypoalgesia. We investigated perceptual changes induced by conditioning electrical stimulation of C-nociceptors differing in stimulation frequencies and duty cycles provoking either sensory gain (i.e. mechanical hyperalgesia; Stim1) or sensory decline (i.e. hypoesthesia and hypoalgesia; Stim2). Underlying brain processing was investigated using functional magnetic resonance imaging. ⋯ In contrast, after induction of hypoesthesia and hypoalgesia (Stim2) the degree of sensory decline for touch and mechanical pain was directly correlated with deactivations within S1, whereas networks associated with attentional and cognitive processing showed increased activation. Therefore, our results demonstrate that brain processing underlying pain-induced sensory gain substantially differs from pain-induced sensory decline. A potential neurobiological mechanism of secondary CNS-mediated hypoesthesia and hypoalgesia may involve modification of local inhibitory networks within somatosensory cortices.