Notes

Supplements involving antihypertensive medication routine along with vitamin e d-alpha ameliorates alterations regarding main haemodynamic variables as well as overall de-oxidizing capacity within ovariectomised subjects.
The rostromedial tegmental nucleus (RMTg) is a bilateral structure localized in the brainstem and comprise of mainly GABAergic neurons. One of the main functions of the RMTg is to regulate the activity of dopamine neurons of the mesoaccumbens pathway. Therefore, the RMTg has been proposed as a modulator of the reward system and adaptive behaviors associated to reward learning. The RMTg receives an important glutamatergic input from the lateral habenula. Also, it receives cholinergic inputs from the laterodorsal and pedunculopontine tegmental nuclei. Previously, it was reported that nicotine increases glutamate release, evoked by electric stimulation, in the RMTg nucleus. However, the mechanisms by which nicotine induces this effect were not explored. In the present work, we performed electrophysiological experiments in brainstem slices to study the effect of nicotine on spontaneous excitatory postsynaptic currents recorded from immunocytochemically identified RMTg neurons. Also, we used calcium imaging techniques to explore the effects of nicotine on multiple RMTg neurons simultaneously. We found that nicotine promotes the persistent release of glutamate through the activation of α7 nicotinic acetylcholine receptors present on glutamatergic afferents and by a mechanism involving calcium release from intracellular stores. Through these mechanisms, nicotine increases the excitability and synchronizes the activity of RMTg neurons. Our results suggest that the RMTg nucleus mediates the noxious effects of the nicotine, and it could be a potential therapeutic target against tobacco addiction.Neuromorphic computing is emerging to be a disruptive computational paradigm that attempts to emulate various facets of the underlying structure and functionalities of the brain in the algorithm and hardware design of next-generation machine learning platforms. This work goes beyond the focus of current neuromorphic computing architectures on computational models for neuron and synapse to examine other computational units of the biological brain that might contribute to cognition and especially self-repair. We draw inspiration and insights from computational neuroscience regarding functionalities of glial cells and explore their role in the fault-tolerant capacity of Spiking Neural Networks (SNNs) trained in an unsupervised fashion using Spike-Timing Dependent Plasticity (STDP). We characterize the degree of self-repair that can be enabled in such networks with varying degree of faults ranging from 50 to 90% and evaluate our proposal on the MNIST and Fashion-MNIST datasets.
To develop a non-invasive and clinically practical method for a long-term monitoring of infant sleep cycling in the intensive care unit.

Forty three infant polysomnography recordings were performed at 1-18 weeks of age, including a piezo element bed mattress sensor to record respiratory and gross-body movements. The hypnogram scored from polysomnography signals was used as the ground truth in training sleep classifiers based on 20,022 epochs of movement and/or electrocardiography signals. Three classifier designs were evaluated in the detection of deep sleep (N3 state) support vector machine (SVM), Long Short-Term Memory neural network, and convolutional neural network (CNN).

Deep sleep was accurately identified from other states with all classifier variants. The SVM classifier based on a combination of movement and electrocardiography features had the highest performance (AUC 97.6%). A SVM classifier based on only movement features had comparable accuracy (AUC 95.0%). The feature-independent CNN resulted in roughly comparable accuracy (AUC 93.3%).

Automated non-invasive tracking of sleep state cycling is technically feasible using measurements from a piezo element situated under a bed mattress.

An open source infant deep sleep detector of this kind allows quantitative, continuous bedside assessment of infant's sleep cycling.
An open source infant deep sleep detector of this kind allows quantitative, continuous bedside assessment of infant's sleep cycling.Brain connectivity plays an important role in determining the brain region's function. selleck inhibitor Previous researchers proposed that the brain region's function is characterized by that region's input and output connectivity profiles. Following this proposal, numerous studies have investigated the relationship between connectivity and function. However, this proposal only utilizes direct connectivity profiles and thus is deficient in explaining individual differences in the brain region's function. To overcome this problem, we proposed that a brain region's function is characterized by that region's multi-hops connectivity profile. To test this proposal, we used multi-hops functional connectivity to predict the individual face activation of the right fusiform face area (rFFA) via a multi-layer graph neural network and showed that the prediction performance is essentially improved. Results also indicated that the two-layer graph neural network is the best in characterizing rFFA's face activation and revealed a hierarchical network for the face processing of rFFA.Neuromuscular electrical stimulation (NMES) of the nervous system has been extensively used in neurorehabilitation due to its capacity to engage the muscle fibers, improving muscle tone, and the neural pathways, sending afferent volleys toward the brain. Although different neuroimaging tools suggested the capability of NMES to regulate the excitability of sensorimotor cortex and corticospinal circuits, how the intensity and dose of NMES can neuromodulate the brain oscillatory activity measured with electroencephalography (EEG) is still unknown to date. We quantified the effect of NMES parameters on brain oscillatory activity of 12 healthy participants who underwent stimulation of wrist extensors during rest. Three different NMES intensities were included, two below and one above the individual motor threshold, fixing the stimulation frequency to 35 Hz and the pulse width to 300 μs. Firstly, we efficiently removed stimulation artifacts from the EEG recordings. Secondly, we analyzed the effect of amplitude and dose on the sensorimotor oscillatory activity.
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