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Antiviral Treatment Improves Hepatocellular Cancers Survival.
The cerebellum plays a central role in sensory input, voluntary motor action, and many neuropsychological functions and is involved in many brain diseases and neurological disorders. Cerebellar parcellation from magnetic resonance images provides a way to study regional cerebellar atrophy and also provides an anatomical map for functional imaging. In a recent comparison, a multi-atlas approach proved to be superior to other parcellation methods including some based on convolutional neural networks (CNNs) which have a considerable speed advantage. In this work, we developed an alternative CNN design for cerebellar parcellation, yielding a method that achieves the leading performance to date. The proposed method was evaluated on multiple data sets to show its broad applicability, and a Singularity container has been made publicly available.Brain age prediction models using diffusion magnetic resonance imaging (dMRI) and machine learning techniques enable individual assessment of brain aging status in healthy people and patients with brain disorders. However, dMRI data are notorious for high intersite variability, prohibiting direct application of a model to the datasets obtained from other sites. In this study, we generalized the dMRI-based brain age model to different dMRI datasets acquired under different imaging conditions. Specifically, we adopted a transfer learning approach to achieve domain adaptation. To evaluate the performance of transferred models, brain age prediction models were constructed using a large dMRI dataset as the source domain, and the models were transferred to three target domains with distinct acquisition scenarios. The experiments were performed to investigate (1) the tuning data size needed to achieve satisfactory performance for brain age prediction, (2) the feature types suitable for different dMRI acquisition sces then 0.001); the predicted age difference of the transferred model was 4.63 and 0.26 years for patients and controls, respectively, and that of the reference model was 4.39 and -0.09 years, respectively. In conclusion, transfer learning approach is an efficient way to generalize the dMRI-based brain age prediction model. Appropriate transfer learning approach and suitable tuning data size should be chosen according to different dMRI acquisition scenarios.Whereas impaired multisensory processing of bodily stimuli and distorted body representation are well-established in various chronic pain disorders, such research has focused on exteroceptive bodily cues and neglected bodily signals from the inside of the body (or interoceptive signals). Extending existing basic and clinical research, we investigated for the first time interoception and its neurophysiological correlates in patients with complex regional pain syndrome (CRPS). In three different experiments, including a total of 36 patients with CRPS and 42 aged-gender matched healthy controls, we measured interoceptive sensitivity (heart beat counting task, HBC) and neural responses to heartbeats (heartbeat evoked potentials, HEPs). As hypothesized, we observed reduced sensitivity in perceiving interoceptive bodily stimuli, i.e. their heartbeat, in two independent samples of CRPS patients (studies 1 and 2). Moreover, the cortical processing of their heartbeat, i.e. the HEP, was reduced compared to controls (study 3) and reduced interoceptive sensitivity and HEPs were related to CRPS patients' motor impairment and pain duration. By providing consistent evidence for impaired processing of interoceptive bodily cues in CRPS, this study shows that the perceptual changes occurring in chronic pain include signals originating from the visceral organs, suggesting changes in the neural body representation, that includes next to exteroceptive, also interoceptive bodily signals. By showing that impaired interoceptive processing is associated with clinical symptoms, our findings also encourage the use of interoceptive-related information in future rehabilitation for chronic pain.Magnetoencephalography and electroencephalography (M/EEG) are non-invasive modalities that measure the weak electromagnetic fields generated by neural activity. Estimating the location and magnitude of the current sources that generated these electromagnetic fields is an inverse problem. Although it can be cast as a linear regression, this problem is severely ill-posed as the number of observations, which equals the number of sensors, is small. When considering a group study, a common approach consists in carrying out the regression tasks independently for each subject using techniques such as MNE or sLORETA. An alternative is to jointly localize sources for all subjects taken together, while enforcing some similarity between them. By pooling S subjects in a single joint regression, the number of observations is S times larger, potentially making the problem better posed and offering the ability to identify more sources with greater precision. Selleck Lys05 Here we show how the coupling of the different regression problems can be done through a multi-task regularization that promotes focal source estimates. To take into account intersubject variabilities, we propose the Minimum Wasserstein Estimates (MWE). Thanks to a new joint regression method based on optimal transport (OT) metrics, MWE does not enforce perfect overlap of activation foci for all subjects but rather promotes spatial proximity on the cortical mantle. Besides, by estimating the noise level of each subject, MWE copes with the subject-specific signal-to-noise ratios with only one regularization parameter. On realistic simulations, MWE decreases the localization error by up to 4 mm per source compared to individual solutions. Experiments on the Cam-CAN dataset show improvements in spatial specificity in population imaging compared to individual models such as dSPM as well as a state-of-the-art Bayesian group level model. Our analysis of a multimodal dataset shows how multi-subject source localization reduces the gap between MEG and fMRI for brain mapping.The P. binpinnatifidus complex included most of the Panax species distributed in Sino-Himalaya regions except for P. pseudoginseng, P. stipuleanatus and P. notoginseng. However, the delimitation and identification of these taxa within the species complex are very difficult due to the existence of morphological intermediates, and their evolutionary relationships remain unresolved despite several studies have been carried out based on traditional DNA markers. The taxonomic uncertainty hinders the identification, conservation and exploration of these wild populations of Panax. To study this species complex, we employed ddRAD-seq data of these taxa from 18 different localities of southwestern China, using two RAD analysis pipelines, STACKS and pyRAD. Based on the results of phylogenetic analysis, the species complex was divided into four clades with high supports, which largely agreed with morphologically described species. Two clades, corresponding to P. vietnamensis and P. zingiberensis, respectively, were sister groups, indicating that these two species had a closer genetic relationship; the third clade was consisted of samples with bamboo-like rhizomes named as P.
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