Bondengel4414

We tested both protocols with healthy participants.A unique virtual reality platform for multisensory integration studies is presented. It allows to provide multimodal sensory stimuli (i.e. auditory, visual, tactile, etc.) ensuring temporal coherence, key factor in cross-modal integration. Four infrared cameras allow to real-time track the human motion and correspondingly control a virtual avatar. A user-friendly interface allows to manipulate a great variety of features (i.e. stimulus type, duration and distance from the participants' body, as well as avatar gender, height, arm pose, perspective, etc.) and to real-time provide quantitative measures of all the parameters. The platform has been validated on two healthy participants testing a reaction time task which combines tactile and visual stimuli, for the investigation of peripersonal space. Results proved the effectiveness of the proposed platform, showing a significant correlation (p=0.013) between the participant's hand distance from the visual stimulus and the reaction time to the tactile stimulus. More participants will be recruited to further investigate the other measures provided by the platform.Post-stroke rehabilitation, occupational and physical therapy, and training for use of assistive prosthetics leverages our current understanding of bilateral motor control to better train individuals. In this study, we examine upper limb lateralization and model transference using a bimanual joystick cursor task with orthogonal controls. Two groups of healthy subjects are recruited into a 2-session study spaced seven days apart. One group uses their left and right hands to control cursor position and rotation respectively, while the other uses their right and left hands. The groups switch control methods in the second session, and a rotational perturbation is applied to the positional controls in the latter half of each session. We find agreement with current lateralization theories when comparing robustness to feedforward perturbations in feedback and feedforward measures. We find no evidence of a transferable model after seven days, and evidence that the brain does not synchronize task completion between the hands.Identification of causal relationships of neural activity is one of the most important problems in neuroscience and neural engineering. We show that a novel deep learning approach using a convolutional neural network to model output neural spike activity from input neural spike activity is able to achieve high correlation between the predicted probability of spiking in the output neuron and the true probability of spiking in the output neuron for data generated with a generalized linear model. The convolutional neural network is also able to recover the true model variables (kernels) used to generate the probability of spiking in the output neuron. Based on the convolutional neural network model's validation via a generalized linear model, future work will include validation with non-linear models that use higher-order kernels.Movement control process can be considered to take place on at least two different levels a high, more cognitive level and a low, sensorimotor level. On a high level processing a motor command is planned accordingly to the desired goal and the sensory afference, mainly proprioception, is used to determine the necessary adjustments in order to minimize any discrepancy between predicted and executed action. On a lower level processing, the proprioceptive feedback later employed in high level regulations, is generated by Ia sensory fibers positioned in muscle main proprioceptors muscle spindles. By entraining the activity of these spindle fibers through 80Hz vibration of triceps distal tendon, we show the intriguing possibility of inducing kinematics adjustments due to negative feedback corrections, during a lifting task.Stroke survivors often experience unilateral sensorimotor impairment. The restoration of upper limb function is an important determinant of quality of life after stroke. Wearable technologies that can measure hand function at home are needed to assess the impact of new interventions. Egocentric cameras combined with computer vision algorithms have been proposed as a means to capture hand use in unconstrained environments, and have shown promising results in this application for individuals with cervical spinal cord injury (cSCI). The objective of this study was to examine the generalizability of this approach to individuals who have experienced a stroke. An egocentric camera was used to capture the hand use (hand-object interactions) of 6 stroke survivors performing daily tasks in a home simulation laboratory. The interaction detection classifier previously trained on 9 individuals with cSCI was applied to detect hand use in the stroke survivors. The processing pipeline consisted of hand detection, hand segmentation, feature extraction, and interaction detection. The resulting average F1 scores for affected and unaffected hands were 0.66 ± 0.25 and 0.80 ± 0.15, respectively, indicating that the approach is feasible and has the potential to generalize to stroke survivors. Using stroke-specific training data may further increase the accuracy obtained for the affected hand.Traumatic brain injury (TBI), is one of the leading causes of motor deficits in children and adults, affecting motor control, coordination, and acuity. AT7519 in vitro This results in reduced functional ambulation and quality of life. Robotic exoskeletons (REs) are quickly becoming an effective method for gait neurorehabilitation in individuals with TBI. Neurorehabilitation is based on the principle that the human brain is capable of reorganization due to high dose motor training. Understanding the underlying mechanisms of cortical reorganization will help improve current rehabilitation. The objective of the study is to understand the cortical activity differences due to RE training and recovery of functional ambulation for individuals with chronic TBI, using functional near-infrared spectroscopy. There was an increase in cortical activation in the prefrontal cortex (PFC), bilateral premotor cortex (PMC) and motor cortex (M1) while walking with RE versus without RE at follow-up. Furthermore, decreased activation was observed in PFC, bilateral PMC and M1 from baseline to follow-up while walking without RE with a corresponding improvement in functional ambulation.