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Postural instability as a symptom of progressing Parkinson's disease (PD) greatly reduces quality of life. Hence, early detection of postural impairments is crucial to facilitate interventions. Our aim was to use a convolutional neural network (CNN) to differentiate people with early to mid-stage PD from healthy age-matched individuals based on spectrogram images obtained from their body movement. We hypothesized the time-frequency content of body sway to be predictive of PD, even when impairments are not yet manifested in day-to-day postural control. We tracked their center of pressure (COP) using a Wii Balance Board and their full-body motion using a Microsoft Kinect, out of which we calculated the trajectory of their center of mass (COM). We used 30 s-snippets of motion data from which we acquired wavelet-based time-frequency spectrograms that were fed into a custom-built CNN as labeled images. We used binary classification to have the network differentiate between individuals with PD and controls (n=15, respectively). Classification performance was best when the medio-lateral motion of the COM was considered. Here, our network reached an average predictive accuracy of 98.45 % with a receiver operating characteristic area under the curve of 1.0. Moreover, an explainable AI approach revealed high frequencies in the postural sway data to be most distinct between both groups. Our findings suggest a CNN classifier based on cost-effective and conveniently obtainable posturographic data to be a promising approach to detect postural impairments in early to mid-stage PD and to gain novel insight into the subtle characteristics of impairments at this stage of the disease.

Understanding the effect of task compared to rest on detecting stroke-related network abnormalities will inform efforts to optimize detection of such abnormalities. The goal of this work was to determine whether connectivity measures obtained during an overt task are more effective than connectivity obtained during a "resting" state for detecting stroke-related changes in network function of the brain. This study examined working memory, discrete pedaling, continuous pedaling and language tasks. Functional magnetic resonance imaging was used to examine regional and inter-regional brain network function in 14 stroke and 16 control participants. Independent component analysis was used to identify 149 regions of interest (ROI). Using the inter-regional connectivity measurements, the weighted sum was calculated across only regions associated with a given task. Both inter-regional connectivity and regional connectivity were greater during each of the tasks as compared to the resting state. The working memory and discrete pedaling tasks allowed for detection of stroke-related decreases in inter-regional connectivity, while the continuous pedaling and language tasks allowed for detection of stroke-related enhancements in regional connectivity. These observations illustrate that task-based functional connectivity allows for detection of stroke-related changes not seen during resting states. In addition, this work provides evidence that tasks emphasizing different cognitive domains reveal different aspects of stroke-related reorganization. We also illustrate that within the motor domain, different tasks can reveal inter-regional or regional stroke-related changes, in this case suggesting that discrete pedaling required more central drive than continuous pedaling.

Understanding the effect of task compared to rest on detecting stroke-related network abnormalities will inform efforts to optimize detection of such abnormalities. The goal of this work was to determine whether connectivity measures obtained during an overt task are more effective than connectivity obtained during a "resting" state for detecting stroke-related changes in network function of the brain. This study examined working memory, discrete pedaling, continuous pedaling and language tasks. Functional magnetic resonance imaging was used to examine regional and inter-regional brain network function in 14 stroke and 16 control participants. Independent component analysis was used to identify 149 regions of interest (ROI). Using the inter-regional connectivity measurements, the weighted sum was calculated across only regions associated with a given task. Both inter-regional connectivity and regional connectivity were greater during each of the tasks as compared to the resting state. The working memory and discrete pedaling tasks allowed for detection of stroke-related decreases in inter-regional connectivity, while the continuous pedaling and language tasks allowed for detection of stroke-related enhancements in regional connectivity. These observations illustrate that task-based functional connectivity allows for detection of stroke-related changes not seen during resting states. In addition, this work provides evidence that tasks emphasizing different cognitive domains reveal different aspects of stroke-related reorganization. We also illustrate that within the motor domain, different tasks can reveal inter-regional or regional stroke-related changes, in this case suggesting that discrete pedaling required more central drive than continuous pedaling.

The goal of this work was to examine task-dependent functional connectivity of the brain in people with stroke. The work was motivated by prior observations indicating that, during pedaling, cortical activation volume is lower in people with stroke than controls. During paretic foot tapping, activation volume tends to be higher in people with stroke than controls. This study asked whether these differences could be explained by altered network function of the brain. Functional magnetic resonance imaging was used to examine local and global network function of the brain during tapping and pedaling in 15 stroke and 8 control participants. Independent component analysis was used to identify six task regions of interest (ROIs) in the primary sensorimotor cortex (M1S1), anterior lobe of cerebellum (AlCb), and secondary sensory cortex (S2) on the lesioned and non-lesioned sides of the brain (left, right for controls). Global connectivity was calculated as the correlation between mean time series for each ROI. Local connectivity was calculated as the mean correlation between voxels within each ROI. Local efficiency, weighted sum, and clustering coefficient were also calculated. Results suggested that local and global networks of the brain were altered in stroke, but not in the same direction. Detection of both global and local network changes was task-dependent. We found that global network function of the brain was reduced in stroke participants as compared with controls. This effect was detected during pedaling and nonparetic tapping, but not during paretic tapping. Local network function of the brain was elevated in stroke participants during paretic tapping and reduced during pedaling. No between-group differences in local connectivity were seen during nonparetic tapping. Connections involving S2, M1S1, and AlCb were significantly affected. Reduced global connectivity of the brain might contribute to reduced brain activation volume during pedaling poststroke.

Pathologic movement patterns are characterized by abnormal kinematics that alter how muscles support the body during walking. Individual muscles are often the target of interventions with physical therapy and surgery alike, yet the tools to assess individual muscles clinically remain limited. The aim of this study is to assess OpenSim as a clinical tool for individualized rehabilitative evaluation of children using orthotics. This anatomic and kinematic modeling study was focused on pre- and post-treatment assessment of gait characteristics in fourteen children using orthotic devices. A range of four to twelve acceptable gait capture trials was collected for each child before therapy began and again after four weeks of treatment. The effects of therapy were significant in four of the lower extremity muscle analyses, three of the temporal parameters, and eighteen of the spatial parameters. All muscle lengths showed less deviation from normal values after physical therapy across all subjects. Results of this study support the further evaluation of OpenSim as a tool to improve quantitative assessment of musculoskeletal dynamics during the course of rehabilitative therapy in children using orthotics.

A quantitative description of the deterministic properties of a CT system is necessary when evaluating image quality. The most common such metric is the modulation transfer function (MTF), usually calculated from a line spread function (LSF) or point spread function (PSF). Currently, there exist many test objects used to measure the LSF or PSF. In this paper we report on a comparison of these measures using: a thin foil slit test object, a teflon cube edge test object and a novel "negative" cube test object. Images were acquired using a custom-built bench-top flat-panel-based cone-beam CT scanner and a cylindrical water-filled PMMA phantom with the test objects embedded in the middle. From the 3-dimensional reconstructed volumes, we estimated the LSF either directly or as estimated from the edge spread function. From these, a modulation transfer function can be estimated, and the frequency dependent image transfer of each object can be reported.