When subjects practice reaching movements in a force field, they learn a new sensorimotor map that associates desired trajectories to motor commands. The map is formed in the brain with elements that allow for generalization beyond the region of training. We quantified spatial generalization properties of these elements by training in one extreme of the reachable space and testing near another. Training resulted in rotations in the preferred direction (PD) of activation of some arm muscles. We designed force fields that maintained a constant rotation in muscle PDs as the shoulder joint rotated in the horizontal plane. In such fields, training in a small region resulted in generalization to near and far work spaces (80 cm). In one such field, the forces on the hand reversed directions for a given hand velocity with respect to the location of original training. Despite this, there was generalization. However, if the field was such that the change in the muscle PDs reversed as the work spaces changed, then performance was worse than performance of naive subjects. We suggest that the sensorimotor map of arm dynamics is represented in the brain by elements that globally encode the position of the arm but locally encode its velocity. The elements have preferred directions of movement but are modulated globally by the position of the shoulder joint. We suggest that tuning properties of cells in the motor system influence behavior and that this influence is reflected in the way that we learn dynamics of reaching movements.

Dynamic balance exercises on fixed and compliant sponge surfaces were feasibly coupled to interactive game-based exercise. This coupling, in turn, resulted in a greater improvement in dynamic standing balance control compared with the typical exercise program. However, there was no transfer of effect to gait function.

No abstract

In this case study, a man at the onset of Alzheimer’s disease (AD) was enrolled in a cognitive treatment program based upon spatial navigation in a virtual reality (VR) environment. We trained him to navigate to targets in a symmetric, landmark-less virtual building. Our research goals were to determine whether an individual with AD could learn to navigate in a simple VR navigation (VRN) environment and whether that training could also bring real-life cognitive benefits. The results show that our participant learned to perfectly navigate to desired targets in the VRN environment over the course of the training program. Furthermore, subjective feedback from his primary caregiver (his wife) indicated that his skill at navigating while driving improved noticeably and that he enjoyed cognitive improvement in his daily life at home. These results suggest that VRN treatments might benefit other people with AD.

Repetitive transcranial magnetic stimulation (rTMS) uses a magnetic coil to induce an electric field in brain tissue. As a pilot study, we investigated the effect of rTMS treatment on 10 volunteers with Alzheimer’s disease (AD) in a two-stage study. The first stage consisted of a double-blind crossover study with real and sham treatments. Each treatment block consisted of 13 sessions over 4 weeks. During each session, 2000 TMS pulses at 90%–100% of resting motor threshold were applied to dorsolateral prefrontal cortex bilaterally, and the patients were kept cognitively active by object/action naming during the treatment. The second stage was an open-label study, in which the same treatments were performed in 2-week blocks (10 sessions) approximately every 3 months as follow-up treatments on six of the volunteers, who completed the first stage of the study. Primary outcome measures were the Montreal Cognitive Assessment (MOCA) and the Alzheimer’s Disease Assessment Scale-cognitive subscale. The secondary outcome measures were the Revised Memory and Behavior Checklist as well as our team’s custom-designed cognitive assessments. The results showed a noticeably stronger improvement on all assessments during the real treatment as compared to the sham treatment. The changes in MOCA scores as well as our designed cognitive assessment were found to be statistically significant, with particularly strong results in the six volunteers who were in the early stages of the disease. The long-term trends observed in the second stage of the study also showed generally less decline than would be expected for their condition. It appears that rTMS can be an effective tool for improving the cognitive abilities of patients with early to moderate stages of AD. However, the positive effects of rTMS may persist for only up to a few weeks. Specific skills being practiced during rTMS treatment may retain their improvement for longer periods.

ABSTRACT

In this paper, an automatic and unsupervised snore detection algorithm is proposed. The respiratory sound signals of 30 patients with different levels of airway obstruction were recorded by two microphones: one placed over the trachea (the tracheal microphone), and the other was a freestanding microphone (the ambient microphone). All the recordings were done simultaneously with full-night polysomnography during sleep. The sound activity episodes were identified using the vertical box (V-Box) algorithm. The 500-Hz subband energy distribution and principal component analysis were used to extract discriminative features from sound episodes. An unsupervised fuzzy C-means clustering algorithm was then deployed to label the sound episodes as either snore or no-snore class, which could be breath sound, swallowing sound, or any other noise. The algorithm was evaluated using manual annotation of the sound signals. The overall accuracy of the proposed algorithm was found to be 98.6% for tracheal sounds recordings, and 93.1% for the sounds recorded by the ambient microphone.

The relationship between respiratory sounds and flow is of great interest for researchers and physicians due to its diagnostic potentials. Due to difficulties and inaccuracy of most of the flow measurement techniques, several researchers have attempted to estimate flow from respiratory sounds. However, all of the proposed methods heavily depend on the availability of different rates of flow for calibrating the model, which makes their use limited by a large degree. In this paper, a robust and novel method for estimating flow using entropy of the band pass filtered tracheal sounds is proposed. The proposed method is novel in terms of being independent of the flow rate chosen for calibration; it requires only one breath for calibration and can estimate any flow rate even out of the range of calibration flow. After removing the effects of heart sounds (which distort the low-frequency components of tracheal sounds) on the calculated entropy of the tracheal sounds, the performance of the method at different frequency ranges were investigated. Also, the performance of the proposed method was tested using 6 different segment sizes for entropy calculation and the best segment sizes during inspiration and expiration were found. The method was tested on data of 10 healthy subjects at five different flow rates. The overall estimation error was found to be 8.3 +/- 2.8% and 9.6 +/- 2.8% for inspiration and expiration phases, respectively.