On the variability and dependence of human leg stiffness across strides during running and some consequences for the analysis of locomotion data.

Typically, animal locomotion studies involve consecutive strides, which are frequently assumed to be independent with parameters that do not vary across strides. This assumption is often not tested. However, failing in particular to account for dependence across strides may cause an incorrect estimate of the uncertainty of the measurements and thereby lead to either missing (overestimating variance) or over-evaluating (underestimating variance) biological signals. In turn, this impacts replicability of the results because variability is accounted for differently across experiments. In this paper, we analyse the changes of a couple of measures of human leg stiffness across strides during running experiments, using a publicly available dataset. A major finding of this analysis is that the time series of these measurements of stiffness show autocorrelation even at large lags and so there is dependence between individual strides, even when separated by many intervening strides. Our results question the practice in biomechanics research of using each stride as an independent observation or of sub-selecting strides at small lags. Following the outcome of our analysis, we strongly recommend caution in doing so without first confirming the independence of the measurements across strides and without confirming that sub-selection does not produce spurious results.

The spring-mass model and other reductionist models of bipedal locomotion on inclines.

The spring-mass model is a model of locomotion aimed at giving the essential mathematical laws of the trajectory of the center of mass of an animal during bouncing gaits, such as hopping (one-dimensional) and running (two-dimensional). This reductionist mechanical system has been extensively investigated for locomotion over horizontal surfaces, whereas it has been largely neglected on other ecologically relevant surfaces, including inclines. For example, how the degree of inclination impacts the dynamics of the center of mass of the spring-mass model has not been investigated thoroughly. In this work, we derive a mathematical model which extends the spring-mass model to inclined surfaces. Among our results, we derive an approximate solution of the system, assuming a small angular sweep of the limb and a small spring compression during stance, and show that this approximation is very accurate, especially for small inclinations of the ground. Furthermore, we derive theoretical bounds on the difference between the Lagrangian and Lagrange equations of the true and approximate system, and discuss locomotor stability questions of the approximate solutions. We test our models through a sensitivity analysis using parameters relevant to the locomotion of bipedal animals (quail, pheasant, guinea fowl, turkey, ostrich, and humans) and compare our approximate solution to the numerically derived solution of the exact system. We compare the two-dimensional spring-mass model on inclines with the one-dimensional spring-mass model to which it reduces under the limit of no horizontal velocity; we compare the two-dimensional spring-mass model on inclines with the inverted-pendulum model on inclines towards which it converges in the case of high stiffness-to-mass ratio. We include comparisons with historically prevalent no-gravity approximations of these models, as well. The insights we have gleaned through all these comparisons and the ability of our approximation to replicate some of the kinematic changes observed in animals moving on different inclines (e.g. reduction in vertical oscillation of the center of mass and decreased stride length) underlines the valuable and reasonable contributions that very simple, reductionist models, like the spring-mass model, can provide.

Transfer of Anolis locomotor behavior across environments and species.

Arboreal animals must learn to modulate their movements to overcome the challenges posed by the complexity of their heterogeneous environment, reduce performance failure, and survive. Anolis lizards are remarkable in the apparent ease with which they conquer this heterogeneity, demonstrating an impressive ability to modulate their locomotor behavior to maintain stable locomotion on widely disparate surfaces. Significant progress has been made towards understanding the impact of substrate structure on the behavioral plasticity of arboreal species, but it is unclear whether the same strategies employed to shift between substrates in one context can be employed to shift between those same substrates in a new context. Is the kinematic shift between broad and narrow perches achieved in a similar way on different inclines? Do all species within an ecomorph make similar adjustments? Here, we analyze the limb movements of two trunk-crown Anolis ecomorphs, A. carolinensis and A. evermanni, running on 6 different surfaces (3 inclinations × 2 perch diameters), from the perspective of Transfer Learning. Transfer learning is that field of machine learning which aims at exploiting the knowledge gained from one task to improve generalization about another, related task. In our setting, we use transfer learning to show that the strategies employed to improve locomotor stability on narrow perches are transferred across environments with different inclines. Further, behaviors used on vertical inclines are shared, and thus transfer well, across perch diameters whereas the relationship between horizontal and intermediate inclines change on different perch diameters, leading to lower transfer learning of shallow inclines across perch diameters. Interestingly, the best incline for transfer of behavior differs between limbs: forelimb models learn best from the vertical incline and hind limb models learn best from horizontal and intermediate inclines. Finally, our results suggest both that subtle differences exist in how A. carolinensis and A. evermanni adjust their behaviors in typical trunk-crown environments and that they may have converged on similar strategies for modulating forelimb behavior on vertical surfaces and hind limb behavior on shallow surfaces. The transfer of behavior is analogous to phenotypic plasticity, which likely plays a key role in the rapid adaptive evolution characteristic of Anolis lizards. This work is an example of how modern statistical methodology can provide an interesting perspective on new biological questions, such as on the role and nuances of behavioral plasticity and the key behaviors that help shape the versatility and rapid evolution of Anolis lizards.

Corrigendum to: Refining evidence-based retinopathy of prematurity screening guidelines: The SCREENROP study.

[This corrects the article DOI: 10.1093/pch/pxz085.].

Differential power of placebo across major psychiatric disorders: a preliminary meta-analysis and machine learning study.

The placebo effect across psychiatric disorders is still not well understood. In the present study, we conducted meta-analyses including meta-regression, and machine learning analyses to investigate whether the power of placebo effect depends on the types of psychiatric disorders. We included 108 clinical trials (32,035 participants) investigating pharmacological intervention effects on major depressive disorder (MDD), bipolar disorder (BD) and schizophrenia (SCZ). We developed measures based on clinical rating scales and Clinical Global Impression scores to compare placebo effects across these disorders. We performed meta-analysis including meta-regression using sample-size weighted bootstrapping techniques, and machine learning analysis to identify the disorder type included in a trial based on the placebo response. Consistently through multiple measures and analyses, we found differential placebo effects across the three disorders, and found lower placebo effect in SCZ compared to mood disorders. The differential placebo effects could also distinguish the condition involved in each trial between SCZ and mood disorders with machine learning. Our study indicates differential placebo effect across MDD, BD, and SCZ, which is important for future neurobiological studies of placebo effects across psychiatric disorders and may lead to potential therapeutic applications of placebo on disorders more responsive to placebo compared to other conditions.

On the relationship between COVID-19 reported fatalities early in the pandemic and national socio-economic status predating the pandemic.

This study investigates the relationship between socio-economic determinants pre-dating the pandemic and the reported number of cases, deaths, and the ratio of deaths/cases in 199 countries/regions during the first months of the COVID-19 pandemic. The analysis is performed by means of machine learning methods. It involves a portfolio/ensemble of 32 interpretable models and considers the case in which the outcome variables (number of cases, deaths, and their ratio) are independent and the case in which their dependence is weighted based on geographical proximity. We build two measures of variable importance, the Absolute Importance Index (AII) and the Signed Importance Index (SII) whose roles are to identify the most contributing socio-economic factors to the variability of the COVID-19 pandemic. Our results suggest that, together with the established influence on cases and deaths of the level of mobility, the specific features of the health care system (smart/poor allocation of resources), the economy of a country (equity/non-equity), and the society (religious/not religious or community-based vs not) might contribute to the number of COVID-19 cases and deaths heterogeneously across countries.

Predicting pediatric anxiety from the temporal pole using neural responses to emotional faces.

A prominent cognitive aspect of anxiety is dysregulation of emotional interpretation of facial expressions, associated with neural activity from the amygdala and prefrontal cortex. We report machine learning analysis of fMRI results supporting a key role for a third area, the temporal pole (TP) for childhood anxiety in this context. This finding is based on differential fMRI responses to emotional faces (angry versus fearful faces) in children with one or more of generalized anxiety, separation anxiety, and social phobia (n = 22) compared with matched controls (n = 23). In our machine learning (Adaptive Boosting) model, the right TP distinguished anxious from control children (accuracy = 81%). Involvement of the TP as significant for neurocognitive aspects of pediatric anxiety is a novel finding worthy of further investigation.

Individualized identification of first-episode bipolar disorder using machine learning and cognitive tests.

Identifying cognitive dysfunction in the early stages of Bipolar Disorder (BD) can allow for early intervention. Previous studies have shown a strong correlation between cognitive dysfunction and number of manic episodes. The objective of this study was to apply machine learning (ML) techniques on a battery of cognitive tests to identify first-episode BD patients (FE-BD). Two cohorts of participants were used for this study. Cohort #1 included 74 chronic BD patients (CHR-BD) and 53 healthy controls (HC), while the Cohort #2 included 37 FE-BD and 18 age- and sex-matched HC. Cognitive functioning was assessed using the Cambridge Neuropsychological Test Automated Battery (CANTAB). The tests examined domains of visual processing, spatial memory, attention and executive function. We trained an ML model to distinguish between chronic BD patients (CHR-BD) and HC at the individual level. We used linear Support Vector Machines (SVM) and were able to identify individual CHR-BD patients at 77% accuracy. We then applied the model to Cohort #2 (FE-BD patients) and achieved an accuracy of 76% (AUC = 0.77). These results reveal that cognitive impairments may appear in early stages of BD and persist into later stages. This suggests that the same deficits may exist for both CHR-BD and FE-BD. These cognitive deficits may serve as markers for early BD. Our study provides a tool that these early markers can be used for detection of BD.

Deep Representational Similarity Learning for Analyzing Neural Signatures in Task-based fMRI Dataset.

Similarity analysis is one of the crucial steps in most fMRI studies. Representational Similarity Analysis (RSA) can measure similarities of neural signatures generated by different cognitive states. This paper develops Deep Representational Similarity Learning (DRSL), a deep extension of RSA that is appropriate for analyzing similarities between various cognitive tasks in fMRI datasets with a large number of subjects, and high-dimensionality - such as whole-brain images. Unlike the previous methods, DRSL is not limited by a linear transformation or a restricted fixed nonlinear kernel function - such as Gaussian kernel. DRSL utilizes a multi-layer neural network for mapping neural responses to linear space, where this network can implement a customized nonlinear transformation for each subject separately. Furthermore, utilizing a gradient-based optimization in DRSL can significantly reduce runtime of analysis on large datasets because it uses a batch of samples in each iteration rather than all neural responses to find an optimal solution. Empirical studies on multi-subject fMRI datasets with various tasks - including visual stimuli, decision making, flavor, and working memory - confirm that the proposed method achieves superior performance to other state-of-the-art RSA algorithms.

Refining evidence-based retinopathy of prematurity screening guidelines: The SCREENROP study.

PURPOSE: Retinopathy of prematurity (ROP) is a potentially blinding condition affecting premature infants for which less than 10% of babies undergoing screening require treatment. This study assessed and validated predictors of developing clinically significant ROP (type 2 or worse) and ROP requiring treatment. DESIGN: Nationwide retrospective cohort study. METHODS: This study included infants born between January 2014 and June 2016, admitted to level 3 neonatal intensive care units across Canada who underwent ROP screening. Data were derived from the Canadian Neonatal Network database. Predefined ≥ 1% risk for clinically significant retinopathy or prematurity and ROP requiring treatment was set as threshold for screening. Thirty-two potential predictors were analyzed, to identify and validate the most important ones for predicting clinically significant ROP. The predictors were determined on a derivation cohort and tested on a validation cohort. Multivariable logistic regression modeling was used for analysis. RESULTS: Using a sample of 4,888 babies and analyzing 32 potential predictors, capturing babies with ≥1% risk of developing clinically significant ROP equated to screening babies with birth weight (BW) <1,300 g or gestational age (GA) <30 weeks while capturing babies with ≥1% risk of requiring ROP treatment equated to screening babies with BW <1,200 g or GA <29 weeks. CONCLUSIONS: The Canadian ROP screening criteria can be modified to screen babies with BW <1,200 g or GA <30 weeks. Using these criteria, babies requiring treatment would be identified while reducing the number of babies screened unnecessarily.