s in the WT post-PTZ, while its levels did not change in KO groups. Overall, our results demonstrated the role of Fyn and tau in seizures and their impact on the mediators of early epileptogenesis in PTZ model.As COVID-19 rampages throughout the world and has a major impact on the healthcare system, non-emergency medical procedures have nearly come to a halt due to appropriate resource reallocation. However, pain never stops, particularly for patients with chronic intractable pain and implanted spinal cord stimulation (SCS) devices. The isolation required to fight this pandemic makes it impossible for such patients to adjust the parameters or configuration of the device on site. Although telemedicine has shown a great effect in many healthcare scenarios, there have been fewer applications of such technology focusing on the interaction with implanted devices. Here, we introduce the first remote and wireless programming system that enables healthcare providers to perform video-based real-time programming and palliative medicine for pain patients with a SCS implant. During the COVID-19 pandemic from January 23, 2020, the date of lockdown of Wuhan, to April 30, 2020, 34 sessions of remote programming were conducted with 16 patients. Thirteen of the 16 patients required programming for parameter optimization. Improvement was achieved with programming adjustment in 12 of 13 (92.3%) cases. Eleven of the 16 (68.8%) patients reported that the system was user-friendly and met their needs. Five patients complained of an unstable connection resulting from the low network speed initially, and three of these patients solved this problem. In summary, we demonstrated that a remote wireless programming system can deliver safe and effective programming operations of implantable SCS device, thereby providing palliative care of value to the most vulnerable chronic pain patients during a pandemic.
  www.clinicaltrials.gov, identifier NCT03858790.
 www.clinicaltrials.gov, identifier NCT03858790.We present DeepVesselNet, an architecture tailored to the challenges faced when extracting vessel trees and networks and corresponding features in 3-D angiographic volumes using deep learning. We discuss the problems of low execution speed and high memory requirements associated with full 3-D networks, high-class imbalance arising from the low percentage ( less then 3%) of vessel voxels, and unavailability of accurately annotated 3-D training data-and offer solutions as the building blocks of DeepVesselNet. First, we formulate 2-D orthogonal cross-hair filters which make use of 3-D context information at a reduced computational burden. Second, we introduce a class balancing cross-entropy loss function with false-positive rate correction to handle the high-class imbalance and high false positive rate problems associated with existing loss functions. Finally, we generate a synthetic dataset using a computational angiogenesis model capable of simulating vascular tree growth under physiological constraints on locifurcation detection. We make our synthetic training data publicly available, fostering future research, and serving as one of the first public datasets for brain vessel tree segmentation and analysis.Functional connectivity analyses are typically based on matrices containing bivariate measures of covariability, such as correlations. Although this has been a fruitful approach, it may not be the optimal strategy to fully explore the complex associations underlying brain activity. Here, we propose extending connectivity to multivariate functions relating to the temporal dynamics of a region with the rest of the brain. The main technical challenges of such an approach are multidimensionality and its associated risk of overfitting or even the non-uniqueness of model solutions. To minimize these risks, and as an alternative to the more common dimensionality reduction methods, we propose using two regularized multivariate connectivity models. On the one hand, simple linear functions of all brain nodes were fitted with ridge regression. On the other hand, a more flexible approach to avoid linearity and additivity assumptions was implemented through random forest regression. Similarities and differences between both methods and with simple averages of bivariate correlations (i.e., weighted global brain connectivity) were evaluated on a resting state sample of N = 173 healthy subjects. Results revealed distinct connectivity patterns from the two proposed methods, which were especially relevant in the age-related analyses where both ridge and random forest regressions showed significant patterns of age-related disconnection, almost completely absent from the much less sensitive global brain connectivity maps. On the other hand, the greater flexibility provided by the random forest algorithm allowed detecting sex-specific differences. The generic framework of multivariate connectivity implemented here may be easily extended to other types of regularized models.Prior research has shown that during development, there is increased segregation between, and increased integration within, prototypical resting-state functional brain networks. Functional networks are typically defined by static functional connectivity over extended periods of rest. However, little is known about how time-varying properties of functional networks change with age. Likewise, a comparison of standard approaches to functional connectivity may provide a nuanced view of how network integration and segregation are reflected across the lifespan. Therefore, this exploratory study evaluated common approaches to static and dynamic functional network connectivity in a publicly available dataset of subjects ranging from 8 to 75 years of age. Analyses evaluated relationships between age and static resting-state functional connectivity, variability (standard deviation) of connectivity, and mean dwell time of functional network states defined by recurring patterns of whole-brain connectivity. Results showed that older age was associated with decreased static connectivity between nodes of different canonical networks, particularly between the visual system and nodes in other networks. Age was not significantly related to variability of connectivity.  https://www.selleckchem.com/products/ly333531.html  Mean dwell time of a network state reflecting high connectivity between visual regions decreased with age, but older age was also associated with increased mean dwell time of a network state reflecting high connectivity within and between canonical sensorimotor and visual networks. Results support a model of increased network segregation over the lifespan and also highlight potential pathways of top-down regulation among networks.