No abstract

Sports-related concussion in adolescents is a major public health issue; however, little is known about the underlying changes in functional brain connectivity. We evaluated connectivity of resting-state brain networks to determine whether alterations in specific networks distinguish adolescents with sports-related concussion from a group of healthy, active control adolescents. Twelve adolescents with a clinical diagnosis of subacute concussion and ten healthy adolescents matched for age, gender, and physical activity completed functional magnetic resonance imaging (fMRI) scanning. Functional connectivity of resting-state brain networks was evaluated in both groups using probabilistic independent component analysis (ICA). Altered functional connectivity was found within three resting-state networks in adolescents with concussion. Specifically, we noted: a) alterations within the default mode network; b) increased connectivity in the right frontal pole in the executive function network; and c) increased connectivity in the left frontal operculum cortex associated with the ventral attention network. This preliminary report shows that whole-brain functional connectivity is altered in networks related to cognition and attention in adolescents in the subacute phase following sports-related concussion. This first report in adolescents should be used to inform future studies in larger cohorts of adolescents with sports-related concussion. Increased knowledge of these changes may lead to improvements in clinical management and help to develop rehabilitation programs.

The purpose of this study was to characterize postural sway in quiet standing under eyes-open and eyes-closed conditions, and to obtain a measure of postural stiffness during quiet standing in adults with Down syndrome (DS) versus control subjects. We obtained descriptive measures from centre-of-pressure (COP) data and analysed and compared COP trajectories and postural stiffness estimates from two stochastic models, the "pinned polymer" (PP) and "inverted pendulum" (IP) models. These estimates were correlated with clinical measures of muscle tone. Our results showed that overall, estimated values for postural stiffness from both models were larger for the DS group than for normal controls. In addition, average stiffness measures were greater under the eyes-closed condition than under the eyes-open condition for the DS group. The IP model detected significant trends over trials whereas the PP model did not. Clinical assessment of muscle tone for the DS group ranged from low to high-normal and there was no significant correlation with the postural stiffness measures obtained from either model. These results suggest that individuals with DS have the ability to modulate their underlying "stiffness" under conditions of quiet standing. Furthermore, there appears to be no strong relationship between clinical measures of muscle tone and postural stiffness measures under dynamic conditions.

Abstract. This paper presents research using full body skeletal movements captured using video-based sensor technology developed by Vicon Motion Systems, to train a machine to identify different human emotions. The Vicon system uses a series of 6 cameras to capture lightweight markers placed on various points of the body in 3D space, and digitizes movement into x, y, and z displacement data. Gestural data from five subjects was collected depicting four emotions: sadness, joy, anger, and fear. Experimental results with different machine learning techniques show that automatic classification of this data ranges from 84% to 92% depending on how it is calculated. In order to put these automatic classification results into perspective a user study on the human perception of the same data was conducted with average classification accuracy of 93%.