![]() Children were asked to walk barefoot and while wearing a foot orthosis that modified the ankle movement pattern toward a more physiological one without blocking foot intrinsic motion. Fourteen participants were enrolled in the study and underwent instrumented gait analysis. Differences in ankle kinematics between single- (conventional gait model, PiG, or Davis) and multi-segment (Oxford foot model, OFM) foot models were investigated in children with ITW. When studying foot and ankle gait deviations, the adoption of a single-segment foot model entails a significant simplification of foot and ankle movement, and thus may potentially mask some important foot dynamics. Idiopathic toe walking (ITW) is a gait deviation characterized by forefoot contact with the ground, sometimes observed in children, that alters ankle kinematics, possibly leading to health-related issues. Overall, these findings demonstrate reliability and face validity of metastability as a candidate neuromechanistic biomarker of schizophrenia pathology. Furthermore, our analyses demonstrated that patients with early psychosis exhibit intermittent disconnectivity of subcortical regions with frontal cortex and cerebellar regions, introducing new insights about the mechanistic bases of these conditions. Additionally, our new metric showed explanatory power of between 81-92% for measures of integration and segregation. Our results show that our new approach is capable of discriminating cases from controls with elevated effect sizes relative to published literature, reflected in an up to 76% area under the curve (AUC) in out-of-sample classification analyses. We used non-parametric testing to evaluate group-level differences and a naïve Bayes classifier to discriminate cases from controls. In this work we extend Leading Eigenvector Dynamic Analysis (LEiDA) to capture specific features of dynamic functional connectivity and then implement a novel approach to estimate metastability. Group-level discrimination, individual-level classification, pathophysiological relevance, and explanatory power were assessed using two independent case-control studies of schizophrenia, the Human Connectome Project Early Psychosis (HCPEP) study (controls n = 53, non-affective psychosis n = 82) and the Cobre study (controls n = 71, cases n = 59). In this study, we investigate metastability as a candidate neuromechanistic biomarker of schizophrenia pathology, including a demonstration of reliability and face validity. The concept of metastability characterizes the coexistence of competing tendencies for functional integration and functional segregation in the brain, and is therefore well suited for the study of schizophrenia. The disconnection hypothesis of schizophrenia proposes that symptoms of the disorder arise as a result of aberrant functional integration between segregated areas of the brain. Our re-examination of some published HCI results exhibits advantages of the ART. We also provide ARTool and ARTweb, desktop and Web-based programs for aligning and ranking data. Unlike most articles on the ART, which only address two factors, we generalize the ART to N factors. The ART relies on a preprocessing step that "aligns" data before applying averaged ranks, after which point common ANOVA procedures can be used, making the ART accessible to anyone familiar with the F-test. To address these concerns, we present the Aligned Rank Transform (ART) for nonparametric factorial data analysis in HCI. While some statistical techniques exist to handle such data, these techniques are not widely available and are complex. But because multiple factors are involved, common nonparametric tests (e.g., Friedman) are inadequate, as they are unable to examine interaction effects. Examples may include error counts, Likert responses, and preference tallies. Nonparametric data from multi-factor experiments arise often in human-computer interaction (HCI).
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