Impact of Pilates suspension with self-awareness on gait and metacognition in multiple sclerosis: Randomized, single-blinded and parallel-group trial.

BACKGROUND: Exercise and self-awareness are popular in the management of people with MS (pwMS). The combination of these techniques for diminishing mental and cognitive imparements doesn't apply. Since the capacity to monitor one's mind and maintain balance and efficient mobility is fundamental for carrying out the daily affairs of pwMS, in this study we assess the effect of Pilates Suspension with Self-awareness on Gait and Metacognition of pwMS. We also evaluate whether metacognition is trainable and, if so, which component of self-awareness (mental and physical) would be instrumental in this improvement. METHOD: Twenty-four female PwMS who scored 2-6.5 on the EDSS were homogeneously divided into two intervention groups [one received pilates suspension training (PST) with Benson relaxation (PSBR), and the other received PST with Jacobson's progressive muscle relaxation (PSJR)] and one control group for 7 consecutive weeks. Relaxation training was used as a means to self-awareness. Due to the coronavirus pandemic around the world during the research process, baseline and postintervention tests and training sessions were held online. Dynamic Gait Index (DGI) and Metacognition Questionnaire-30 (MCQ-30), outcome measures were collected before and after the intervention. RESULTS: Analysis of group data revealed significant improvement between baseline and intervention phases for Dynamic Gate Index (p = 0.002 for Benson relaxation and p = 0.001 for Jacobson's progressive muscle) and Metacognition Questionnaire-30 (p = 0.02 for Benson relaxation and p = 0.002 for Jacobson's progressive muscle). CONCLUSIONS: With regard to multidimensional disorders of pwMS, a combined training protocol is recommended for pwMS.

Investigating the EEG Profile of Elite and Non-Elite Players in the Basketball Free Throw Task.

This study aimed to investigate the electroencephalographic profile of elite and non-elite basketball players seconds before and during the basketball free throw. Sixteen male subjects in the elite group (national team/premier league players with an average age of 22.06 ± 1.56) and 16 male non-elite subjects (university players with an average age of 22.37 ± 1.45) voluntarily participated in this research. Electroencephalographic data were measured from 28 cortical areas using a mobile wireless device. ANOVA with repeated measures were also performed to investigate the characteristics of theta, alpha, and beta frequency bands. The findings showed the higher cortical activity of the elite group. Different frequency bands exhibited similar asymmetry patterns, suggesting the higher activity of the left hemisphere in most of the homologous sites. Moreover, the activity of frequency bands in the left hemisphere rose by approaching the moment of throw. Furthermore, the activity of a limited number of right hemisphere sites increased by getting closer to the moment of action. In general, hemispheric asymmetry in favor of the left hemisphere has a cortical pattern, reflecting high-performance activities. In addition, the characteristics of different frequency bands of hemispheres are directed toward increasing cognitive processing, attention focusing, and inhibiting irrelevant information.

A Scalable FPGA Architecture for Randomly Connected Networks of Hodgkin-Huxley Neurons.

Human intelligence relies on the vast number of neurons and their interconnections that form a parallel computing engine. If we tend to design a brain-like machine, we will have no choice but to employ many spiking neurons, each one has a large number of synapses. Such a neuronal network is not only compute-intensive but also memory-intensive. The performance and the configurability of the modern FPGAs make them suitable hardware solutions to deal with these challenges. This paper presents a scalable architecture to simulate a randomly connected network of Hodgkin-Huxley neurons. To demonstrate that our architecture eliminates the need to use a high-end device, we employ the XC7A200T, a member of the mid-range Xilinx Artix®-7 family, as our target device. A set of techniques are proposed to reduce the memory usage and computational requirements. Here we introduce a multi-core architecture in which each core can update the states of a group of neurons stored in its corresponding memory bank. The proposed system uses a novel method to generate the connectivity vectors on the fly instead of storing them in a huge memory. This technique is based on a cyclic permutation of a single prestored connectivity vector per core. Moreover, to reduce both the resource usage and the computational latency even more, a novel approximate two-level counter is introduced to count the number of the spikes at the synapse for the sparse network. The first level is a low cost saturated counter implemented on FPGA lookup tables that reduces the number of inputs to the second level exact adder tree. It, therefore, results in much lower hardware cost for the counter circuit. These techniques along with pipelining make it possible to have a high-performance, scalable architecture, which could be configured for either a real-time simulation of up to 5120 neurons or a large-scale simulation of up to 65536 neurons in an appropriate execution time on a cost-optimized FPGA.