Steelefarmer1545

Segmentation of brain tissue types from diffusion MRI (dMRI) is an important task, required for quantification of brain microstructure and for improving tractography. Current dMRI segmentation is mostly based on anatomical MRI (e.g., T1- and T2-weighted) segmentation that is registered to the dMRI space. However, such inter-modality registration is challenging due to more image distortions and lower image resolution in dMRI as compared with anatomical MRI. In this study, we present a deep learning method for diffusion MRI segmentation, which we refer to as DDSeg. Our proposed method learns tissue segmentation from high-quality imaging data from the Human Connectome Project (HCP), where registration of anatomical MRI to dMRI is more precise. The method is then able to predict a tissue segmentation directly from new dMRI data, including data collected with different acquisition protocols, without requiring anatomical data and inter-modality registration. We train a convolutional neural network (CNN) to learn a tissue segmentation model using a novel augmented target loss function designed to improve accuracy in regions of tissue boundary. To further improve accuracy, our method adds diffusion kurtosis imaging (DKI) parameters that characterize non-Gaussian water molecule diffusion to the conventional diffusion tensor imaging parameters. The DKI parameters are calculated from the recently proposed mean-kurtosis-curve method that corrects implausible DKI parameter values and provides additional features that discriminate between tissue types. We demonstrate high tissue segmentation accuracy on HCP data, and also when applying the HCP-trained model on dMRI data from other acquisitions with lower resolution and fewer gradient directions.Statistical power is key for robust, replicable science. Here, we systematically explored how numbers of trials and subjects affect statistical power in MEG sensor-level data. More specifically, we simulated "experiments" using the MEG resting-state dataset of the Human Connectome Project (HCP). We divided the data in two conditions, injected a dipolar source at a known anatomical location in the "signal condition", but not in the "noise condition", and detected significant differences at sensor level with classical paired t-tests across subjects, using amplitude, squared amplitude, and global field power (GFP) measures. Group-level detectability of these simulated effects varied drastically with anatomical origin. We thus examined in detail which spatial properties of the sources affected detectability, looking specifically at the distance from closest sensor and orientation of the source, and at the variability of these parameters across subjects. In line with previous single-subject studies, we found that the ideal number of trials and subjects for any experiment cannot be realistically provided for neurophysiological studies and should be adapted according to the brain regions under study.Face-to-face imitation is a unique social interaction wherein a shared action is executed based on the feedback of the partner. Imitation by the partner is the feedback to the imitatee's action, resulting in sharing actions. The neural mechanisms of the shared representation of action during face-to-face imitation, the core of inter-subjectivity, are not well-known. Here, based on the predictive coding account, we hypothesized that the pair-specific forward internal model is the shared representation of action which is represented by the inter-individual synchronization of some portion of the mirror neuron system. Hyperscanning functional magnetic resonance imaging was conducted during face-to-face interaction in 16 pairs of participants who completed an immediate imitation task of facial expressions. Paired participants were alternately assigned to either an imitator or an imitatee who was prompted to express a happy, sad, or non-emotional face. While neural activation elicited by imitating and being imitated were distinct with little overlap, on-line imitative interaction enhanced inter-brain synchronization in the right inferior parietal lobule that correlated with the similarity in facial movement kinematic profile. This finding indicates a critical role of the right inferior parietal lobule in sharing representation of action as a pair-specific forward internal model through imitative interaction.Evidence indicates a critical role of neuroinflammatory response as an underlying pathophysiological process in several central nervous system disorders, including neurodegenerative diseases. However, the molecular mechanisms that trigger neuroinflammatory processes are not fully known. Selleckchem PCI-34051 The discovery of bitter taste receptors in regions other than the oral cavity substantially increased research interests on their functional roles in extra-oral tissues. It is now widely accepted that bitter taste receptors, for instance, in the respiratory, intestinal, reproductive and urinary tracts, are crucial not only for sensing poisonous substances, but also, act as immune sentinels, mobilizing defense mechanisms against pathogenic aggression. The relatively recent discovery of bitter taste receptors in the brain has intensified research investigation on the functional implication of cerebral bitter taste receptor expression. Very recent data suggest that responses of bitter taste receptors to neurotoxins and microbial molecules, under normal condition, are necessary to prevent neuroinflammatory reactions. Furthermore, emerging data have revealed that downregulation of key components of the taste receptor signaling cascade leads to increased oxidative stress and inflammasome signaling in neurons that ultimately culminate in neuroinflammation. Nevertheless, the mechanisms that link taste receptor mediated surveillance of the extracellular milieu to neuroinflammatory responses are not completely understood. This review integrates new data on the molecular mechanisms that link bitter taste receptor sensing to neuroinflammatory responses. The role of bitter taste receptor-mediated sensing of toxigenic substances in brain disorders is also discussed. The therapeutic significance of targeting these receptors for potential treatment of neurodegenerative diseases is also highlighted.