Locomotion in virtual environments predicts cardiovascular responsiveness to subsequent stressful challenges

Figures

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  Fig. 1. Virtual reality scenarios designed to reveal behavioral variance in participants’ locomotor responses under nonthreatening conditions. a Scenario 1: Empty room. Illustrations and example of key features feeding location parameters (e.g., distance to walls, corners, and center area) plotted on an average exploration heatmap. b Scenario 2: Elevated alley, consisting of successive narrower patches. Illustration and example of key features feeding location parameters, such as the threshold between patches to determine time to cross or time spent at each patch. c Examples of focused and concentric trajectories displayed in scenario 1 (adapted from ref. 101). d Example of the gait cycle and its subcomponents stance and swing. e Example of the detection of bursts of movement. Consecutive gait cycles, starting at the red line, separated by immobility periods.

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  Fig. 2. Stressful virtual reality scenario and model prediction of cardiac stress responses from behavior. a Scenario 3: Dark maze, aimed at eliciting heightened physiological responses. b Heart rate (HR) and heart rate variability (HRV, the RMSSD formula) over time (three blocks of 30 s each) during dark maze exploration. N = 135 participants examined over three consecutive blocks of 30 s (S1, S2, and S3). Data are presented as mean values ± SEM (vertical lines). A repeated measures analysis of variance (rm-ANOVA) was performed for both HR and HRV data sets. Post hoc tests were performed with two-sided paired t tests, with p values corrected for multiple comparisons (three comparisons) with the Holm correction. ***p value <0.001. Exact p values and statistics are presented in Supplementary Information section “Statistics for physiology in exploration scenarios”, Tables S6–7. Correlation between the XGBoost model’s prediction and the integrated HRV index (iHRV) for the test set c and train + test set d. e SHAP values for the trained model, calculated for each subject. The nine most discriminating features (from the 18 used by the model) were the minimum distance to the corners of the…

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  Fig. 3. Correlation plots. a Correlation between the model’s prediction of PC1 (i.e., iHRV) for the entire data set with each of the cardiorespiratory variables used to create PC1. b Comparison between PC1 and the model’s prediction and the correlation between PC1 and other possible explanatory variables. Time center (er)—time in the center of the empty room; STAI-T - self-reported trait anxiety; Threat anxiety—self-reported anxiety felt during the persistent threat scenario; STAI-S—self-reported state anxiety. Black line in the center of shaded area are Pearson correlation coefficients; shaded area, 95% confidence intervals for the correlation coefficients, corrected for multiple comparisons in each panel; blue shaded area, significant correlation corrected for multiple comparisons in each panel.

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  Fig. 4. Generalization of model prediction of iHRV to other stressful or anxiogenic situations and a generalization subsample. a Schematic of a virtual reality stress test involving mental arithmetic and challenging contextual navigation. This scenario lasts 10 min and is split into 2.5-minute segments (S1–S4). After segments S1, S2, and S3, participants are presented with a prerecorded voice asking them to perform better. b Physiological (HR, HRV) responses to the stress induction task. During segment BL, baseline recordings are taken. N = 99 participants examined over five blocks of 2.5 min (Bl, S1, S2, S3, and S4). Data are presented as mean values ±SEM (vertical lines). A repeated measures analysis of variance (rm-ANOVA) was performed for HR and to HRV. Post hoc tests were performed with two-sided paired t tests, with p values corrected for multiple comparisons (10 comparisons) with the Holm correction. *p value < 0.05, **p value < 0.01, ***p < 0.001. Exact p values and statistics are presented in Supplementary Information section “Statistics for physiology in Fig. 4b“, Tables S13–14. c Spearman correlations (two-tailed p = 5.258e-5) between the model predictions for iHRV and HRV for…