Stroke recovery phenotyping through network trajectory approaches and graph neural networks

Stroke is a leading cause of neurological injury characterized by impairments in multiple neurological domains including cognition, language, sensory and motor functions. Clinical recovery in these domains is tracked using a wide range of measures that may be continuous, ordinal, interval or categorical in nature, which can present challenges for multivariate regression approaches. This has hindered stroke researchers\u27 ability to achieve an integrated picture of the complex time-evolving interactions among symptoms. Here, we use tools from network science and machine learning that are particularly well-suited to extracting underlying patterns in such data, and may assist in prediction of recovery patterns. To demonstrate the utility of this approach, we analyzed data from the NINDS tPA trial using the Trajectory Profile Clustering (TPC) method to identify distinct stroke recovery patterns for 11 different neurological domains at 5 discrete time points. Our analysis identified 3 distinct stroke trajectory profiles that align with clinically relevant stroke syndromes, characterized both by distinct clusters of symptoms, as well as differing degrees of symptom severity. We then validated our approach using graph neural networks to determine how well our model performed predictively for stratifying patients into these trajectory profiles at early vs. later time points post-stroke. We demonstrate that trajectory profile clustering is an effective method for identifying clinically relevant recovery subtypes in multidimensional longitudinal datasets, and for early prediction of symptom progression subtypes in individual patients. This paper is the first work introducing network trajectory approaches for stroke recovery phenotyping, and is aimed at enhancing the translation of such novel computational approaches for practical clinical application

Stroke recovery phenotyping through network trajectory approaches and graph neural networks

Stroke is a leading cause of neurological injury characterized by impairments in multiple neurological domains including cognition, language, sensory and motor functions. Clinical recovery in these domains is tracked using a wide range of measures that may be continuous, ordinal, interval or categorical in nature, which can present challenges for multivariate regression approaches. This has hindered stroke researchers\u27 ability to achieve an integrated picture of the complex time-evolving interactions among symptoms. Here, we use tools from network science and machine learning that are particularly well-suited to extracting underlying patterns in such data, and may assist in prediction of recovery patterns. To demonstrate the utility of this approach, we analyzed data from the NINDS tPA trial using the Trajectory Profile Clustering (TPC) method to identify distinct stroke recovery patterns for 11 different neurological domains at 5 discrete time points. Our analysis identified 3 distinct stroke trajectory profiles that align with clinically relevant stroke syndromes, characterized both by distinct clusters of symptoms, as well as differing degrees of symptom severity. We then validated our approach using graph neural networks to determine how well our model performed predictively for stratifying patients into these trajectory profiles at early vs. later time points post-stroke. We demonstrate that trajectory profile clustering is an effective method for identifying clinically relevant recovery subtypes in multidimensional longitudinal datasets, and for early prediction of symptom progression subtypes in individual patients. This paper is the first work introducing network trajectory approaches for stroke recovery phenotyping, and is aimed at enhancing the translation of such novel computational approaches for practical clinical application

Defining the Focus of Attention: Effects of Attention on Perceived Exertion and Fatigue

This manuscript presents two experiments designed to explore the effects of attention on perceived exertion and time to failure in a fatiguing athletic task. There were two major motivating factors for these experiments. First, there are few studies evaluating attentional focus effects in endurance tasks and, second, there is a lack of integration between studies of attentional focus as external/internal (e.g., Wulf, 2007a) compared to associative/dissociative (e.g., Stevinson and Biddle, 1998). In Experiment 1, we used a fatiguing wall-sit posture (essentially a complex, isometric task) to compare two different types of external attention with an internal focus on the position of the legs. An external focus (regardless of type) increased the time taken to failure and reduced perceived exertion. In Experiment 2, we manipulated subjects’ expectancy of fatigue to test the interaction of attention and expectancy (both top-down factors) in this highly fatiguing task. Previous theories of attention during endurance tasks have suggested that as fatigue/pain increase, bottom-up factors begin to dominate subjects’ attention. While this may be true, Experiment 2 showed that even in a highly fatiguing task, attentional strategies, and expectancies affected the time to failure and perceived exertion

Modeling Longitudinal Outcomes: A Contrast of Two Methods

journal articleBackground: Repeated measures analysis of variance (ANOVA) is frequently used to model longitudinal data but does not appropriately account for within-person correlations over time, does not explicitly model time, and cannot flexibly handle missing data. In contrast, mixed-effects regression addresses these limitations. In this commentary, we compare these two methods using openly available tools. Methods: We emulated a real developmental study of elite skiers, tracking national rankings from 2011 to 2018. We constructed unconditional models of time (establishing the "pattern" of change), conditional models (identifying factors that affect change over time) and contrasted these models against comparable repeated measures ANOVAs. Results: Mixed-effects regression allowed for linear and non-linear modeling of the skiers' longitudinal trajectories despite missing data. Missing data is still a concern in mixed-effects regression models, but in the present dataset missingness could be accounted for by skiers' ages, satisfying the missing at random assumption. Discussion: Although ANOVA and mixed-effects regression are both suitable for time-series data, their applications differ. ANOVA will be most parsimonious when the research question focuses on group-level mean differences at arbitrary time points. However, mixed-effects regression is more suitable where time is inherently important to the outcome, and where individual differences are of interest

Magnitude-Based Inference is Not Bayesian and is Not a Valid Method of Inference

journal articl

The Role of Attention in Motor Learning and Control

Research on the focus of attention (FOA) has been ongoing since initial experiments over 15 years ago (see Wulf, 2007a; 2007b). Since that time, research on the FOA has evolved considerably and experimental data has revealed physiological changes underlying behavioral effects of attention. These experimental developments integrate the FOA with other robust findings in motor learning and control (e.g., choking under pressure and implicit learning). This dissertation investigates how an external FOA (e.g., on the goal of a movement) leads to better performance than an internal FOA (e.g., on the body\u27s own movement). Previous research has measured movement outcomes (e.g., accuracy) but not the quality of movement itself (e.g., neuromuscular efficiency, movement variability, movement kinematics). Thus, the current experiments use biomechanical analysis and surface electromyography to explore the role of attention in coordinating movement at the neuromuscular level up to the level of the movement outcome. Data from five experimental studies in this dissertation suggest that attention changes the control structure of the motor system: an external focus of attention reduces variation in the nominal, goal-dimension of the task, whereas an internal focus of attention reduces variation in the pattern of the movement itself (at the expense of task performance; Chapter 4). Underlying the effects of an internal focus of attention are increases in cocontraction of the muscles around a given joint (Chapters 2 and 3). Increasing mechanical impedance in the limb by increasing cocontraction is useful for reducing variability in the movement pattern, but has ancillary consequences of reducing movement efficiency and, often, effectiveness. Furthermore, the FOA appears to affect the performance of learned motor skills rather than the rate of learning itself (Chapter 5). These findings suggest significant expansion of current theories on the focus of attention in order to predict these physiological changes that mediate the attention-performance relationship. A neurophysiological framework for the role of attention in motor control is present in Chapter 6, based on these findings. This framework posits that an internal focus of attention invokes motor control structures that were active early in the learning process (e.g., dorsolateral prefrontal cortex) impairing coordination, efficiency, and effectiveness

Manipulating target size influences perceptions of success when learning a dart-throwing skill but does not impact retention

Positive feedback or experiences of success during skill acquisition have been shown to benefit motor skill learning. In this study, our aim was to manipulate learners’ success perceptions through a minor adjustment to goal criterion (target size) in a dart-throwing task. Two groups of novice participants practiced throwing at a large (easy) or a small (difficult) target from the same distance. In reference to the origin/centre of the target, the practice targets were alike in objective difficulty and indeed participants in both groups were not different in their objective practice performance (i.e. radial error from the centre). Although the groups experienced markedly different success rates, with the large target group experiencing more hits and reporting greater confidence (or self-efficacy) than the small target group, these practice effects were not carried into longer-term retention, which was assessed after a one-week delay. For success perceptions to moderate or benefit motor learning, we argue that unambiguous indicators of positive performance are necessary, especially for tasks where intrinsic feedback about objective error is salient

Model Specification in Mixed-Effects Models: A Focus on Random Effects

Mixed-effect models are flexible tools for researchers in a myriad of fields, but that flexibility comes at the cost of complexity and if users are not careful in how their model is specified, they could be making faulty inferences from their data. We argue that there is significant confusion around appropriate random effects to be included in a model given the study design, with researchers generally being better at specifying the fixed effects of a model, which map onto to their research hypotheses. To that end, we present an instructive framework for evaluating the random effects of a model in three different situations: (1) longitudinal designs; (2) factorial repeated measures; and (3) when dealing with multiple sources of variance. We provide worked examples with open-access code and data in an online repository. We think this framework will be helpful for students and researchers who are new to mixed effect models, and to reviewers who may have to evaluate a novel model as part of their review.Comment: ~8,000 words body text, 7 figures, 4 tables. Currently posting version 3 responding to comments on previous draft

Predicting later categories of upper limb activity from earlier clinical assessments following stroke: An exploratory analysis

BACKGROUND: Accelerometers allow for direct measurement of upper limb (UL) activity. Recently, multi-dimensional categories of UL performance have been formed to provide a more complete measure of UL use in daily life. Prediction of motor outcomes after stroke have tremendous clinical utility and a next step is to explore what factors might predict someone\u27s subsequent UL performance category. PURPOSE: To explore how different machine learning techniques can be used to understand how clinical measures and participant demographics captured early after stroke are associated with the subsequent UL performance categories. METHODS: This study analyzed data from two time points from a previous cohort (n = 54). Data used was participant characteristics and clinical measures from early after stroke and a previously established category of UL performance at a later post stroke time point. Different machine learning techniques (a single decision tree, bagged trees, and random forests) were used to build predictive models with different input variables. Model performance was quantified with the explanatory power (in-sample accuracy), predictive power (out-of-bag estimate of error), and variable importance. RESULTS: A total of seven models were built, including one single decision tree, three bagged trees, and three random forests. Measures of UL impairment and capacity were the most important predictors of the subsequent UL performance category, regardless of the machine learning algorithm used. Other non-motor clinical measures emerged as key predictors, while participant demographics predictors (with the exception of age) were generally less important across the models. Models built with the bagging algorithms outperformed the single decision tree for in-sample accuracy (26-30% better classification) but had only modest cross-validation accuracy (48-55% out of bag classification). CONCLUSIONS: UL clinical measures were the most important predictors of the subsequent UL performance category in this exploratory analysis regardless of the machine learning algorithm used. Interestingly, cognitive and affective measures emerged as important predictors when the number of input variables was expanded. These results reinforce that UL performance, in vivo, is not a simple product of body functions nor the capacity for movement, instead being a complex phenomenon dependent on many physiological and psychological factors. Utilizing machine learning, this exploratory analysis is a productive step toward the prediction of UL performance. Trial registration NA