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IgG4-related auto-immune expressions inside Alemtuzumab-treated ms patients.
y between different hierarchical inference levels and neurotransmitter systems, but suggest a more complex interaction between these neuromodulatory systems and hierarchical Bayesian quantities. (R,S)3,5DHPG However, our present results may have been affected by confounds inherent to pharmacological fMRI. We discuss these confounds and outline improved experimental tests for the future.Diffusion kurtosis imaging (DKI) is a diffusion MRI approach that enables the measurement of brain microstructural properties, reflecting molecular restrictions and tissue heterogeneity. DKI parameters such as mean kurtosis (MK) provide additional subtle information to that provided by popular diffusion tensor imaging (DTI) parameters, and thus have been considered useful to detect white matter abnormalities, especially in populations that are not expected to show severe brain pathologies. However, DKI parameters often yield artifactual output values that are outside of the biologically plausible range, which diminish sensitivity to identify true microstructural changes. Recently we have proposed the mean-kurtosis-curve (MK-Curve) method to correct voxels with implausible DKI parameters, and demonstrated its improved performance against other approaches that correct artifacts in DKI. In this work, we aimed to evaluate the utility of the MK-Curve method to improve the identification of white matter abnormalitierize white matter alterations in the CHR stage, demonstrating that MK and FA abnormalities are widespread, and mostly overlap. The improvement in group differences and stronger correlation with clinical variables suggest that applying MK-curve would be beneficial for the detection and characterization of subtle group differences in other experiments as well.The "language-ready" brain theory suggests that the infant brain is pre-wired for language acquisition prior to language exposure. As a potential brain marker of such a language readiness, a leftward structural brain asymmetry was found in human infants for the Planum Temporale (PT), which overlaps with Wernicke's area. In the present longitudinal in vivo MRI study conducted in 35 newborn monkeys (Papio anubis), we found a similar leftward PT surface asymmetry. Follow-up rescanning sessions on 29 juvenile baboons at 7-10 months showed that such an asymmetry increases across the two ages classes. These original findings in non-linguistic primate infants strongly question the idea that the early PT asymmetry constitutes a human infant-specific marker for language development. Such a shared early perisylvian organization provides additional support that PT asymmetry might be related to a lateralized system inherited from our last common ancestor with Old-World monkeys at least 25-35 million years ago.Developmental language disorder (DLD) is characterised by difficulties in learning one's native language for no apparent reason. These language difficulties occur in 7% of children and are known to limit future academic and social achievement. Our understanding of the brain abnormalities associated with DLD is limited. Here, we used a simple four-minute verb generation task (children saw a picture of an object and were instructed to say an action that goes with that object) to test children between the ages of 10-15 years (DLD N = 50, typically developing N = 67). We also tested 26 children with poor language ability who did not meet our criteria for DLD. Contrary to our registered predictions, we found that children with DLD did not have (i) reduced activity in language relevant regions such as the left inferior frontal cortex; (ii) dysfunctional striatal activity during overt production; or (iii) a reduction in left-lateralised activity in frontal cortex. Indeed, performance of this simple language task evoked activity in children with DLD in the same regions and to a similar level as in typically developing children. Consistent with previous reports, we found sub-threshold group differences in the left inferior frontal gyrus and caudate nuclei, but only when analysis was limited to a subsample of the DLD group (N = 14) who had the poorest performance on the task. Additionally, we used a two-factor model to capture variation in all children studied (N = 143) on a range of neuropsychological tests and found that these language and verbal memory factors correlated with activity in different brain regions. Our findings indicate a lack of support for some neurological models of atypical language learning, such as the procedural deficit hypothesis or the atypical lateralization hypothesis, at least when using simple language tasks that children can perform. These results also emphasise the importance of controlling for and monitoring task performance.The fMRI community has made great strides in decoupling neuronal activity from other physiologically induced T2* changes, using sensors that provide a ground-truth with respect to cardiac, respiratory, and head movement dynamics. However, blood oxygenation level-dependent (BOLD) time-series dynamics are also confounded by scanner artifacts, in complex ways that can vary not only between scanners but even, for the same scanner, between sessions. Unfortunately, the lack of an equivalent ground truth for BOLD time-series has thus far stymied the development of reliable methods for identification and removal of scanner-induced noise, a problem that we have previously shown to severely impact detection sensitivity of resting-state brain networks. To address this problem, we first designed and built a phantom capable of providing dynamic signals equivalent to that of the resting-state brain. Using the dynamic phantom, we then compared the ground-truth time-series with its measured fMRI data. Using these, we introdue temporal bandpass filtering and denoising using Marchenko-Pastur principal component analysis. Critically, we observed that the CNN temporal denoising pushes ST-SNR to a regime where signal power is higher than that of noise (ST-SNR > 1). Denoising human-data with ground-truth-trained CNN, in turn, showed markedly increased detection sensitivity of resting-state networks. These were visible even at the level of the single-subject, as required for clinical applications of fMRI.
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