Study Protocol: Sleep Effects on Poststroke Rehabilitation Study.

BACKGROUND: A range of sleep disturbances and disorders are problematic in people after stroke; they interfere with recovery of function during poststroke rehabilitation. However, studies to date have focused primarily on the effects of one sleep disorder-obstructive sleep apnea (OSA)-on stroke recovery. OBJECTIVES: The study protocol for the SLEep Effects on Poststroke Rehabilitation (SLEEPR) Study is presented with aims of characterizing proportion of non-OSA sleep disorders in the first 90 days after stroke, evaluating the effect of non-OSA sleep disorders on poststroke recovery, and exploring the complex relationships between stroke, sleep, and recovery in the community setting. METHODS: SLEEPR is a prospective cohort observational study across multiple study sites following individuals from inpatient rehabilitation through 90 days poststroke, with three measurement time points (inpatient rehabilitation; i.e., ~15 days poststroke, 60 days poststroke, and 90 days poststroke). Measures of sleep, function, activity, cognition, emotion, disability, and participation will be obtained for 200 people without OSA at the study's start through self-report, capacity assessments, and performance measures. Key measures of sleep include wrist actigraphy, sleep diaries, overnight oximetry, and several sleep disorders screening questionnaires (Insomnia Severity Index, Cambridge-Hopkins Restless Legs Questionnaire, Epworth Sleepiness Scale, and Sleep Disorders Screening Checklist). Key measures of function and capacity include the 10-meter walk test, Stroke Impact Scale, Barthel index, and modified Rankin scale. Key performance measures include leg accelerometry (e.g., steps/day, sedentary time, upright time, and sit-to-stand transitions) and community trips via GPS data and activity logs. DISCUSSION: The results of this study will contribute to understanding the complex interplay between non-OSA sleep disorders and poststroke rehabilitation; they provide insight regarding barriers to participation in the community and return to normal activities after stroke. Such results could lead to strategies for developing new stroke recovery interventions.

A Game of Hide and Seek: Expectations of Clumpy Resources Influence Hiding and Searching Patterns.

Resources are often distributed in clumps or patches in space, unless an agent is trying to protect them from discovery and theft using a dispersed distribution. We uncover human expectations of such spatial resource patterns in collaborative and competitive settings via a sequential multi-person game in which participants hid resources for the next participant to seek. When collaborating, resources were mostly hidden in clumpy distributions, but when competing, resources were hidden in more dispersed (random or hyperdispersed) patterns to increase the searching difficulty for the other player. More dispersed resource distributions came at the cost of higher overall hiding (as well as searching) times, decreased payoffs, and an increased difficulty when the hider had to recall earlier hiding locations at the end of the experiment. Participants' search strategies were also affected by their underlying expectations, using a win-stay lose-shift strategy appropriate for clumpy resources when searching for collaboratively-hidden items, but moving equally far after finding or not finding an item in competitive settings, as appropriate for dispersed resources. Thus participants showed expectations for clumpy versus dispersed spatial resources that matched the distributions commonly found in collaborative versus competitive foraging settings.

Machine learning, medical diagnosis, and biomedical engineering research - commentary.

A large number of papers are appearing in the biomedical engineering literature that describe the use of machine learning techniques to develop classifiers for detection or diagnosis of disease. However, the usefulness of this approach in developing clinically validated diagnostic techniques so far has been limited and the methods are prone to overfitting and other problems which may not be immediately apparent to the investigators. This commentary is intended to help sensitize investigators as well as readers and reviewers of papers to some potential pitfalls in the development of classifiers, and suggests steps that researchers can take to help avoid these problems. Building classifiers should be viewed not simply as an add-on statistical analysis, but as part and parcel of the experimental process. Validation of classifiers for diagnostic applications should be considered as part of a much larger process of establishing the clinical validity of the diagnostic technique.

Heart rate variability as determinism with jump stochastic parameters.

We use measured heart rate information (RR intervals) to develop a one-dimensional nonlinear map that describes short term deterministic behavior in the data. Our study suggests that there is a stochastic parameter with persistence which causes the heart rate and rhythm system to wander about a bifurcation point. We propose a modified circle map with a jump process noise term as a model which can qualitatively capture such this behavior of low dimensional transient determinism with occasional (stochastically defined) jumps from one deterministic system to another within a one parameter family of deterministic systems.

A statistical approach to the use of control entropy identifies differences in constraints of gait in highly trained versus untrained runners.

Control entropy (CE) is a complexity analysis suitable for dynamic, non-stationary conditions which allows the inference of the control effort of a dynamical system generating the signal. These characteristics make CE a highly relevant time varying quantity relevant to the dynamic physiological responses associated with running. Using High Resolution Accelerometry (HRA) signals we evaluate here constraints of running gait, from two different groups of runners, highly trained collegiate and untrained runners. To this end,we further develop the control entropy (CE) statistic to allow for group analysis to examine the non-linear characteristics of movement patterns in highly trained runners with those of untrained runners, to gain insight regarding gaits that are optimal for running. Specifically, CE develops response time series of individuals descriptive of the control effort; a group analysis of these shapes developed here uses Karhunen Loeve Analysis (KL) modes of these time series which are compared between groups by application of a Hotelling T² test to these group response shapes. We find that differences in the shape of the CE response exist within groups, between axes for untrained runners (vertical vs anterior-posterior and mediolateral vs anterior-posterior) and trained runners (mediolateral vs anterior-posterior). Also shape differences exist between groups by axes (vertical vs mediolateral). Further, the CE, as a whole, was higher in each axis in trained vs untrained runners. These results indicate that the approach can provide unique insight regarding the differing constraints on running gait in highly trained and untrained runners when running under dynamic conditions. Further, the final point indicates trained runners are less constrained than untrained runners across all running speeds.

Categorizing and comparing psychophysical detection strategies based on biomechanical responses to short postural perturbations.

BACKGROUND: A fundamental unsolved problem in psychophysical detection experiments is in discriminating guesses from the correct responses. This paper proposes a coherent solution to this problem by presenting a novel classification method that compares biomechanical and psychological responses. METHODS: Subjects (13) stood on a platform that was translated anteriorly 16 mm to find psychophysical detection thresholds through a Adaptive 2-Alternative-Forced-Choice (2AFC) task repeated over 30 separate sequential trials. Anterior-posterior center-of-pressure (APCoP) changes (i.e., the biomechanical response R(B)) were analyzed to determine whether sufficient biomechanical information was available to support a subject's psychophysical selection (R(Ψ)) of interval 1 or 2 as the stimulus interval. A time-series-bitmap approach was used to identify anomalies in interval 1 (a1) and interval 2 (a2) that were present in the resultant APCoP signal. If a1 > a2 then R(B) = Interval 1. If a1 < a2, then R(B)= Interval 2. If a2-a1 < 0.1, R(B) was set to 0 (no significant difference present in the anomaly scores of interval 1 and 2). RESULTS: By considering both biomechanical (R(B)) and psychophysical (R(Ψ)) responses, each trial run could be classified as a: 1) HIT (and True Negative), if R(B) and R(Ψ) both matched the stimulus interval (SI); 2) MISS, if R(B) matched SI but the subject's reported response did not; 3) PSUEDO HIT, if the subject signalled the correct SI, but R(B) was linked to the non-SI; 4) FALSE POSITIVE, if R(B) = R(Ψ), and both associated to non-SI; and 5) GUESS, if R(B) = 0, if insufficient APCoP differences existed to distinguish SI. Ensemble averaging the data for each of the above categories amplified the anomalous behavior of the APCoP response. CONCLUSIONS: The major contributions of this novel classification scheme were to define and verify by logistic models a 'GUESS' category in these psychophysical threshold detection experiments, and to add an additional descriptor, "PSEUDO HIT". This improved classification methodology potentially could be applied to psychophysical detection experiments of other sensory modalities.

A quiet standing index for testing the postural sway of healthy and diabetic adults across a range of ages.

A quiet standing index is developed for tracking the postural sway of healthy and diabetic adults over a range of ages. Several postural sway features are combined into a single composite feature C that increases with age a. Sway features are ranked based on the r(2)-values of their linear regression models, and the composite feature is a weighted sum of selected sway features with optimal weighting coefficients determined using principal component analysis. A performance index based on both reliability and sensitivity is used to determine the optimal number of features. The features used to form C include power and distance metrics. The quiet standing index is a scalar that compares the composite feature C to a linear regression model f(a) using C(')(a) = C/f(a). For a motionless subject, C(') = 0, and when the composite feature exactly matches the healthy control (HC) model, C(') = 1. Values of C(') >> 1 represent excessive postural sway and may indicate impaired postural control. Diabetic neurologically intact subjects, nondiabetic peripheral neuropathy subjects (PN), and diabetic PN subjects (DPN) were evaluated. The quiet standing indexes of the PN and DPN groups showed statistically significant increases over the HC group. Changes in the quiet standing index over time may be useful in identifying people with impaired balance who may be at an increased risk of falling.

Dynamic computation of network statistics via updating schema.

Given a large network, computing statistics such as clustering coefficient, or modularity, is costly for large networks. When one more edge or vertex is added, traditional methods require that the full (expensive) computation be redone on this slightly modified graph. Alternatively, we introduce here a new approach: under modification to the graph, we update the statistics instead of computing them from scratch. In this paper we provide update schemes for a number of popular statistics, to include degree distribution, clustering coefficient, assortativity, and modularity. Our primary aim is to reduce the computational complexity needed to track the evolving behavior of large networks. As an important consequence, this approach provides efficient methods which may support modeling the evolution of dynamic networks to identify and understand critical transitions. Using the updating scheme, the network statistics can be computed much faster than re-calculating each time that the network evolves. We also note that the update formula can be used to determine which edge or node will lead to the extremal change of network statistics, providing a way of predicting or designing network evolution rules that would optimize some chosen statistic. We present our evolution methods in terms of a network statistics differential notation.

Control entropy: a complexity measure for nonstationary signals.

We propose an entropy statistic designed to assess the behavior of slowly varying parameters of real systems. Based on correlation entropy, the method uses symbol dynamics and analysis of increments to achieve sufficient recurrence in a short time series to enable entropy measurements on small data sets. We analyze entropy along a moving window of a time series, the entropy statistic tracking the behavior of slow variables of the data series. We employ the technique against several physiological time series to illustrate its utility in characterizing the constraints on a physiological time series. We propose that changes in the entropy of measured physiological signal (e.g. power output) during dynamic exercise will indicate changes in underlying constraint of the system of interest. This is compelling because CE may serve as a non-invasive, objective means of determining physiological stress under non-steady state conditions such as competition or acute clinical pathologies. If so, CE could serve as a valuable tool for dynamically monitoring health status in a wide range of non-stationary systems.

A concept of homeomorphic defect for defining mostly conjugate dynamical systems.

A centerpiece of dynamical systems is comparison by an equivalence relationship called topological conjugacy. We present details of how a method to produce conjugacy functions based on a functional fixed point iteration scheme can be generalized to compare dynamical systems that are not conjugate. When applied to nonconjugate dynamical systems, we show that the fixed-point iteration scheme still has a limit point, which is a function we now call a "commuter"-a nonhomeomorphic change of coordinates translating between dissimilar systems. This translation is natural to the concepts of dynamical systems in that it matches the systems within the language of their orbit structures, meaning that orbits must be matched to orbits by some commuter function. We introduce methods to compare nonequivalent systems by quantifying how much the commuter function fails to be a homeomorphism, an approach that gives more respect to the dynamics than the traditional comparisons based on normed linear spaces, such as L(2). Our discussion addresses a fundamental issue-how does one make principled statements of the degree to which a "toy model" might be representative of a more complicated system?

Relaxing conjugacy to fit modeling in dynamical systems.

We address a fundamental modeling issue in science as related to the field of dynamical systems: when is a model of a physical system a OgoodO representation? Conjugacy provides a means to determine if two systems are dynamically equivalent. We develop mathematical technology to decide when dynamics of a OtoyO model are like (although not identical to) dynamics of the physical system, since the concept of conjugacy is too rigid for such cases. We contrast the usual methodology where model quality is measured in a Banach space to our dynamically motivated notion of matching orbits as best as possible. We highlight our methods with a lower-ordered model of a "noisy" logistic map and also a simplified model of a Lorenz system such that the usual one-dimensional map model is not exactly justified in the traditional sense.

Edge of chaos in a parallel shear flow.

We study the transition between laminar and turbulent states in a Galerkin representation of a parallel shear flow, where a stable laminar flow and a transient turbulent flow state coexist. The regions of initial conditions where the lifetimes show strong fluctuations and a sensitive dependence on initial conditions are separated from the ones with a smooth variation of lifetimes by an object in phase space which we call the "edge of chaos." We describe techniques to identify and follow the edge, and our results indicate that the edge is a surface. For low Reynolds numbers we find that the surface coincides with the stable manifold of a periodic orbit, whereas at higher Reynolds numbers it is the stable set of a higher-dimensional chaotic object.

Communication and synchronization in, disconnected networks with dynamic topology: moving neighborhood networks.

We consider systems that are well modelled as networks that evolve in time, which we call Moving Neighborhood Networks. These models are relevant in studying cooperative behavior of swarms and other phenomena where emergent interactions arise from ad hoc networks. In a natural way, the time-averaged degree distribution gives rise to a scale-free network. Simulations show that although the network may have many noncommunicating components, the recent weighted time-averaged communication is sufficient to yield robust synchronization of chaotic oscillators. In particular, we contend that such time-varying networks are important to model in the situation where each agent carries a pathogen (such as a disease) in which the pathogen's lifecycle has a natural time-scale which competes with the time-scale of movement of the agents, and thus with the networks communication channels.