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Modulating Ocular Area Soreness Via Neurokinin-1 Receptor Restriction.
Background Migraine is a chronic neurological disorder characterized by attacks of moderate or severe headache and various neurological symptoms. Migraine is typically treated by pharmacological or non-pharmacological therapies to relieve pain or prevent migraine attacks. Pharmacological therapies show limited efficacy in relieving headache and are often accompanied by adverse effects, while the benefits of acupuncture, a non-pharmacological therapy, have been well-documented in both the treatment and prevention of acute migraine attacks. However, the underlying mechanism of the effect of acupuncture on relieving migraine remains unclear. Recent advances in neuroimaging technology have offered new opportunities to explore the underlying neural mechanism of acupuncture in treating migraine. To pave the way for future research, this review provides an overview neuroimaging studies on the use of acupuncture for migraine in the last 10 years. Methods Using search terms about acupuncture, neuroimaging and migraineor descending pain modulatory system (DPMS). Conclusion The neural mechanism underlying the immediate effect of acupuncture analgesia remains unclear, and the neural mechanism of sustained acupuncture treatment for migraine might be related to the regulation of pain-related brain networks. The experimental design of neuroimaging studies that examined the effect of acupuncture in migraine also have some shortcomings, and it is necessary to standardize and optimize the experimental design. Multi-center neuroimaging studies are needed to provide a better insight into the neural mechanism underlying the effect of acupuncture on migraine. Multi-modality neuroimaging studies that integrate multiple data analysis methods are required for cross-validation of the neuroimaging results. In addition, applying machine learning methods in neuroimaging studies can help to predict acupuncture efficacy and screen for migraineurs for whom acupuncture treatment would be suitable.
Feature counting requires rapid shifts of attention in the visual field and reflects higher-level cortical functions. This process is drastically impaired in the amblyopic eye of strabismic amblyopes. In this study, we hypothesized that feature counting performance in anisometropic and strabismic amblyopes is further impaired when shifts in attention is required between the eyes.

Through a mirror stereoscope, highly visible Gabor patches were presented to the same eye within a block or randomly presented to the left eye or to the right eye with an equal probability within a block. The task was to report the number of Gabors (3 to 9) as accurately as possible. Counting performance was compared between the amblyopes and the normal-vision observers and between the viewing conditions (shifting attention between the eyes versus maintaining attention in the same eye).

When attention was maintained in the same eye, the amblyopic eye of both anisometropic and strabismic groups undercounted the number of Gabors,ometropic amblyopes, suggesting that different patterns of visual deficits are associated with amblyopia of different etiologies. The inability to count multiple features accurately reflects dysfunctions of high-level cortices in the amblyopic brain.
Our data showed that the amblyopic eye of both anisometropic and strabismic amblyopes further undercounted features when shifting attention between the eyes, compared to when maintaining attention in the same eye. This suggests that the ability to quickly redirect attention, particularly under interocular suppression, is impaired in amblyopia. The fellow eye of strabismic amblyopes also undercounted features when shifting attention between the eyes. https://www.selleckchem.com/products/Cediranib.html However, such fellow eye abnormality was not found in anisometropic amblyopes, suggesting that different patterns of visual deficits are associated with amblyopia of different etiologies. The inability to count multiple features accurately reflects dysfunctions of high-level cortices in the amblyopic brain.The development of brain-inspired neuromorphic computing architectures as a paradigm for Artificial Intelligence (AI) at the edge is a candidate solution that can meet strict energy and cost reduction constraints in the Internet of Things (IoT) application areas. Toward this goal, we present μBrain the first digital yet fully event-driven without clock architecture, with co-located memory and processing capability that exploits event-based processing to reduce an always-on system's overall energy consumption (μW dynamic operation). The chip area in a 40 nm Complementary Metal Oxide Semiconductor (CMOS) digital technology is 2.82 mm2 including pads (without pads 1.42 mm2). This small area footprint enables μBrain integration in re-trainable sensor ICs to perform various signal processing tasks, such as data preprocessing, dimensionality reduction, feature selection, and application-specific inference. We present an instantiation of the μBrain architecture in a 40 nm CMOS digital chip and demonstrate its efficiency in a radar-based gesture classification with a power consumption of 70 μW and energy consumption of 340 nJ per classification. As a digital architecture, μBrain is fully synthesizable and lends to a fast development-to-deployment cycle in Application-Specific Integrated Circuits (ASIC). To the best of our knowledge, μBrain is the first tiny-scale digital, spike-based, fully parallel, non-Von-Neumann architecture (without schedules, clocks, nor state machines). For these reasons, μBrain is ultra-low-power and offers software-to-hardware fidelity. μBrain enables always-on neuromorphic computing in IoT sensor nodes that require running on battery power for years.Traditionally, functional networks in resting-state data were investigated with linear Fourier and wavelet-related methods to characterize their frequency content by relying on pre-specified frequency bands. In this study, Empirical Mode Decomposition (EMD), an adaptive time-frequency method, is used to investigate the naturally occurring frequency bands of resting-state data obtained by Group Independent Component Analysis. Specifically, energy-period profiles of Intrinsic Mode Functions (IMFs) obtained by EMD are created and compared for different resting-state networks. These profiles have a characteristic distribution for many resting-state networks and are related to the frequency content of each network. A comparison with the linear Short-Time Fourier Transform (STFT) and the Maximal Overlap Discrete Wavelet Transform (MODWT) shows that EMD provides a more frequency-adaptive representation of different types of resting-state networks. Clustering of resting-state networks based on the energy-period profiles leads to clusters of resting-state networks that have a monotone relationship with frequency and energy.
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