Locomotion in virtual environments predicts cardiovascular responsiveness to subsequent stressful challenges

When virtual reality becomes psychoneuroendocrine reality: A stress(or) review

This review article was awarded the Dirk Hellhammer award from ISPNE in 2023. It explores the dynamic relationship between stressors and stress from a historical view as well as a vision towards the future of stress research via virtual reality (VR). We introduce the concept of a "syncytium," a permeable boundary that blurs the distinction between stress and stressor, in order to understand why the field of stress biology continues to inadequately measure stress alone as a proxy for the force of external stressors. Using Virtual Reality (VR) as an illustrative example to explicate the black box of stressors, we examine the distinction between 'immersion' and 'presence' as analogous terms for stressor and stress, respectively. We argue that the conventional psychological approaches to stress measurement and appraisal theory unfortunately fall short in quantifying the force of the stressor, leading to reverse causality fallacies. Further, the concept of affordances is introduced as an ecological or holistic tool to measure and design a stressor's force, bridging the gap between the external environment and an individual's physiological response to stress. Affordances also serve to ameliorate shortcomings in stress appraisal by integrating ecological interdependencies. By combining VR and psychobiological measures, this paper aims to unravel the complexity of the stressor-stress syncytium, highlighting the necessity of assessing both the internal and external facets to gain a holistic understanding of stress physiology and shift away from reverse causality reasoning. We find that the utility of VR extends beyond presence to include affordance-based measures of immersion, which can effectively model stressor force. Future research should prioritize the development of tools that can measure both immersion and presence, thereby providing a more comprehensive understanding of how external stressors interact with individual psychological states.

Virtual Reality Environmental Enrichment Effects on Craving for Cigarette in Smokers

Background: Preclinical studies suggested the exposure to environmental enrichment (EE) as an intervention able to prevent or reduce nicotine-taking and nicotine-seeking behaviors. Virtual reality (VR) may help to test the effects of EE in smokers in a reproducible and feasible manner. Materials and Methods: In the present study, 31 smokers (14 women) were divided into two groups: (1) exposure to a virtual EE (VR-EE) and (2) exposure to a virtual neutral environment (VR-NoEE). Cigarette craving was assessed as basal and evoked, at different timepoints during the session. Behavior activity during VR exposure, mood, and subjective measures were also collected. Results: EE exposure in VR significantly reduced craving scores from basal timepoint. This was not observed in the VR-NoEE group, which significantly increased craving compared with values at neutral scenario. When both groups were exposed to smoking-related VR scenario, the VR-EE group showed an increased craving compared with previous timepoint up to score values not different from those in the VR-NoEE group. A significant positive correlation between basal craving scores and interactive behavior with virtual smoking cues was observed in the VR-NoEE but not in the VR-EE group. Conclusion: These findings suggest that virtual EE might have an inhibitory effect in smokers on basal, but not on evoked cigarette craving. Noteworthily, the interactive activity correlation to craving scores in the VR-NoEE participants was not observed in the VR-EE group, adding further evidence that the enrichment simulation was nonetheless able to modify behavior in the smoking-related scenario.

Can virtual reality have effects on cardiac rehabilitation? An overview of systematic reviews

OBJECTIVE

This paper aims to provide a review of the use of virtual reality in cardiac rehabilitation.

BACKGROUND

Can virtual reality technology improve outcomes in patients with cardiovascular disease? The question is still open.

DESIGN

Systematic review and meta-analyses.

METHODS

A literature search was conducted in the Embase, the Cochrane Library, PubMed, Web of Science, China National Knowledge Infrastructure Database, Wanfang Database, and China Biological Medicine Database. Databases were searched to July 2023. The inclusion criteria were as follows: the nature of the studies was set as a systematic review; the research participants were patients with cardiovascular diseases undergoing cardiac rehabilitation; the research content was a comparison of virtual reality effects between other care approaches. A Measurement Tool to Assess Systematic Reviews was employed to evaluate the quality of included studies and judge the overall certainty of evidence by using the Grading of Recommendations, Assessment, Development, and Evaluation methodology. When there were differences between the outcomes, we used the RevMan 5.3 to recalculate.

RESULTS

A total of 7 reviews were included in our synthesis, including 3 low-quality articles and 4 very low-quality articles. Virtual reality was effective in improving patients' depression symptoms, anxiety, stress, and improving athletic ability, but it remains unknown whether virtual reality is effective for other outcomes or not.

CONCLUSIONS

Virtual reality can effectively improve the mental health of patients with cardiovascular disease. However, its role in improving other health indicators such as adherence, satisfaction, and quality of life has not been shown.

Synchronous monitoring of brain-heart electrophysiology using heart rate variability coupled with rapid quantitative electroencephalography in orthostatic hypotension patients with α-synucleinopathies: Rapid prediction of orthostatic hypotension and preliminary exploration of brain stimulation therapy

BACKGROUND

In α-synucleinopathies, the dysfunction of the autonomic nervous system which typically manifests as orthostatic hypotension (OH) often leads to severe consequences and poses therapeutic challenges. This study aims to discover the brain-cardiac electrophysiological changes in OH patients with α-synucleinopathies using the rapid quantitative electroencephalography (qEEG) coupled with heart rate variability (HRV) technique to identify rapid, noninvasive biomarkers for early warning and diagnosis, as well as shed new light on complementary treatment approaches such as brain stimulation targets.

METHODS

In this study, 26 subjects of α-synucleinopathies with OH (α-OH group), 21 subjects of α-synucleinopathies without OH (α-NOH group), and 34 healthy controls (control group) were included from September 2021 to August 2023 (NCT05527067). The heart rate-blood pressure variations in supine and standing positions were monitored, and synchronization parameters of seated resting-state HRV coupled with qEEG were collected. Time-domain and frequency-domain of HRV measures as well as peak frequency and power of the brainwaves were extracted. Differences between these three groups were compared, and correlations between brain-heart parameters were analyzed.

RESULTS

The research results showed that the time-domain parameters such as MxDMn, pNN50, RMSSD, and SDSD of seated resting-state HRV exhibited a significant decrease only in the α-OH group compared to the healthy control group (p < 0.05), while there was no significant difference between the α-NOH group and the healthy control group. Several time-domain and frequency-domain parameters of seated resting-state HRV were found to be correlated with the blood pressure changes within the first 5 min of transitioning from supine to standing position (p < 0.05). Differences were observed in the power of beta1 waves (F4 and Fp2) and beta2 waves (Fp2 and F4) in the seated resting-state qEEG between the α-OH and α-NOH groups (p < 0.05). The peak frequency of theta waves in the Cz region also showed a difference (p < 0.05). The power of beta2 waves in the Fp2 and F4 brain regions correlated with frequency-domain parameters of HRV (p < 0.05). Additionally, abnormal electrical activity in the alpha, theta, and beta1 waves was associated with changes in heart rate and blood pressure within the first 5 min of transitioning from supine to standing position (p < 0.05).

CONCLUSION

Rapid resting-state HRV with certain time-domain parameters below normal levels may serve as a predictive indicator for the occurrence of orthostatic hypotension (OH) in patients with α-synucleinopathies. Additionally, the deterioration of HRV parameters correlates with synchronous abnormal qEEG patterns, which can provide insights into the brain stimulation target areas for OH in α-synucleinopathy patients.

The effects of virtual reality environmental enrichments on craving to food in healthy volunteers

RATIONALE

Environmental enrichment (EE) is a non-pharmacological approach that has been shown to be effective in reducing food-taking in rats. Studies in human volunteers are still in their infancy, given the difficulty to translate the complexity of EE in clinical practice. Virtual reality (VR) is a promising methodological approach, but no study has yet applied it to model and test EE in humans.

OBJECTIVES

The present study is the first to assess the effects of virtual EE on craving for palatable food.

METHODS

Eighty-one healthy volunteers (43 women) were divided into three groups: (i) exposure to a virtual EE (VR-EE), (ii) exposure to a virtual neutral environment (VR-NoEE), and (iii) without exposure to VR (No VR). Craving for palatable food at basal level and evoked by neutral and palatable food images was assessed before and after the VR simulation. Behavior during VR exposure and subjective measures related to the experience were also collected.

RESULTS

VR-EE group showed a significantly greater decrease in pre-post craving difference compared to No VR for all assessments and at basal level compared to VR-NoEE. Interestingly, an inverse correlation between craving and deambulation in the VR simulation emerged in VR-EE group only.

CONCLUSIONS

The study highlighted the feasibility of exposing human subjects to an EE as a virtual simulation. Virtual EE induced effects on basal craving for food that suggest the potential for further improvements of the protocol to extend its efficacy to palatable food cues.

Physiological sensor data cleaning with autoencoders

Objective.Physiological sensor data (e.g. photoplethysmograph) is important for remotely monitoring patients' vital signals, but is often affected by measurement noise. Existing feature-based models for signal cleaning can be limited as they might not capture the full signal characteristics.Approach.In this work we present a deep learning framework for sensor signal cleaning based on dilated convolutions which capture the coarse- and fine-grained structure in order to classify whether a signal is noisy or clean. However, since obtaining annotated physiological data is costly and time-consuming we propose an autoencoder-based semi-supervised model which is able to learn a representation of the sensor signal characteristics, also adding an element of interpretability.Main results.Our proposed models are over 8% more accurate than existing feature-based approaches with half the false positive/negative rates. Finally, we show that with careful tuning (that can be improved further), the semi-supervised model outperforms supervised approaches suggesting that incorporating the large amounts of available unlabeled data can be advantageous for achieving high accuracy (over 90%) and minimizing the false positive/negative rates.Significance.Our approach enables us to reliably separate clean from noisy physiological sensor signal that can pave the development of reliable features and eventually support decisions regarding drug efficacy in clinical trials.

The connection between heart rate variability (HRV), neurological health, and cognition: A literature review

The heart and brain have bi-directional influences on each other, including autonomic regulation and hemodynamic connections. Heart rate variability (HRV) measures variation in beat-to-beat intervals. New findings about disorganized sinus rhythm (erratic rhythm, quantified as heart rate fragmentation, HRF) are discussed and suggest overestimation of autonomic activities in HRV changes, especially during aging or cardiovascular events. When excluding HRF, HRV is regulated via the central autonomic network (CAN). HRV acts as a proxy of autonomic activity and is associated with executive functions, decision-making, and emotional regulation in our health and wellbeing. Abnormal changes of HRV (e.g., decreased vagal functioning) are observed in various neurological conditions including mild cognitive impairments, dementia, mild traumatic brain injury, migraine, COVID-19, stroke, epilepsy, and psychological conditions (e.g., anxiety, stress, and schizophrenia). Efforts are needed to improve the dynamic and intriguing heart-brain interactions.

Evoking stress reactivity in virtual reality: A systematic review and meta-analysis

BACKGROUND

Virtual reality (VR) research probes stress environments that are infeasible to create in the real world. However, because research simulations are applied to narrow populations, it remains unclear if VR simulations can stimulate a broadly applicable stress-response. This systematic review and meta-analysis was conducted on studies using VR stress tasks and biomarkers.

METHODS

Included papers (N = 52) measured cortisol, heart rate (HR), galvanic skin response (GSR), systolic blood pressure (SBP), diastolic blood pressure (DBP), respiratory sinus arrhythmia (RSA), parasympathetic activity (RMSSD), sympathovagal balance (LF/HF), and/or salivary alpha-amylase (sAA). Effect sizes (ES) and confidence intervals (CI) were calculated based on standardized mean change of baseline-to-peak biomarker levels.

RESULTS

From baseline-to-peak (ES, CI), analyses showed a statistically significant change in cortisol (0.56, 0.28-0.83), HR (0.68, 0.53-0.82), GSR (0.59, 0.36-0.82), SBP (.55, 0.19-0.90), DBP (.64, 0.23-1.05), RSA (-0.59, -0.88 to -0.30), and sAA (0.27, 0.092-0.45). There was no effect for RMSSD and LF/HF.

CONCLUSION

VR stress tasks elicited a varied magnitude of physiological stress reactivity. VR may be an effective tool in stress research.

Recentering neuroscience on behavior: The interface between brain and environment is a privileged level of control of neural activity

Despite the huge and constant progress in the molecular and cellular neuroscience fields, our capability to understand brain alterations and treat mental illness is still limited. Therefore, a paradigm shift able to overcome such limitation is warranted. Behavior and the associated mental states are the interface between the central nervous system and the living environment. Since, in any system, the interface is a key regulator of system organization, behavior is proposed here as a unique and privileged level of control and orchestration of brain structure and activity. This view has relevant scientific and clinical implications. First, the study of behavior represents a singular starting point for the investigation of neural activity in an integrated and comprehensive fashion. Second, behavioral changes, accomplished through psychotherapy or environmental interventions, are expected to have the highest impact to specifically reorganize the complexity of the human mind and thus achieve a solid and long-lasting improvement in mental health.

Cross-species anxiety tests in psychiatry: pitfalls and promises

Behavioural anxiety tests in non-human animals are used for anxiolytic drug discovery, and to investigate the neurobiology of threat avoidance. Over the past decade, several of them were translated to humans with three clinically relevant goals: to assess potential efficacy of candidate treatments in healthy humans; to develop diagnostic tests or biomarkers; and to elucidate the pathophysiology of anxiety disorders. In this review, we scrutinise these promises and compare seven anxiety tests that are validated across species: five approach-avoidance conflict tests, unpredictable shock anticipation, and the social intrusion test in children. Regarding the first goal, three tests appear suitable for anxiolytic drug screening in humans. However, they have not become part of the drug development pipeline and achieving this may require independent confirmation of predictive validity and cost-effectiveness. Secondly, two tests have shown potential to measure clinically relevant individual differences, but their psychometric properties, predictive value, and clinical applicability need to be clarified. Finally, cross-species research has not yet revealed new evidence that the physiology of healthy human behaviour in anxiety tests relates to the physiology of anxiety symptoms in patients. To summarise, cross-species anxiety tests could be rendered useful for drug screening and for development of diagnostic instruments. Using these tests for aetiology research in healthy humans or animals needs to be queried and may turn out to be unrealistic.

ReLearn: A Robust Machine Learning Framework in Presence of Missing Data for Multimodal Stress Detection from Physiological Signals. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021;2021:535-541. [PMID: 34891350 DOI: 10.1109/embc46164.2021.9630040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Continuous and multimodal stress detection has been performed recently through wearable devices and machine learning algorithms. However, a well-known and important challenge of working on physiological signals recorded by conventional monitoring devices is missing data due to sensors insufficient contact and interference by other equipment. This challenge becomes more problematic when the user/patient is mentally or physically active or stressed because of more frequent conscious or subconscious movements. In this paper, we propose ReLearn, a robust machine learning framework for stress detection from biomarkers extracted from multimodal physiological signals. ReLearn effectively copes with missing data and outliers both at training and inference phases. ReLearn, composed of machine learning models for feature selection, outlier detection, data imputation, and classification, allows us to classify all samples, including those with missing values at inference. In particular, according to our experiments and stress database, while by discarding all missing data, as a simplistic yet common approach, no prediction can be made for 34% of the data at inference, our approach can achieve accurate predictions, as high as 78%, for missing samples. Also, our experiments show that the proposed framework obtains a cross-validation accuracy of 86.8% even if more than 50% of samples within the features are missing.

CAFS: Cost-Aware Features Selection Method for Multimodal Stress Monitoring on Wearable Devices

OBJECTIVE

Today, stress monitoring on wearable devices is challenged by the tension between high-detection accuracy and battery lifetime driven by multimodal data acquisition and processing. Limited research has addressed the classification cost on multimodal wearable sensors, particularly when the features are cost-dependent. Thus, we design a Cost-Aware Feature Selection (CAFS) methodology that trades-off between prediction-power and energy-cost for multimodal stress monitoring.

METHODS

CAFS selects the most important features under different energy-constraints, which allows us to obtain energy-scalable stress monitoring models. We further propose a self-aware stress monitoring method that intelligently switches among the energy-scalable models, reducing energy consumption.

RESULTS

Using CAFS methodology on experimental data and simulation, we reduce the energy-cost of the stress model designed without energy constrains up to 94.37%. We obtain 90.98% and 95.74% as the best accuracy and confidence values, respectively, on unseen data, outperforming state-of-the-art studies. Analyzing our interpretable and energy-scalable models, we showed that simple models that use only heart rate (HR) or skin conductance level (SCL), confidently predict stress for HR >93.30 BPM and non-stress for SCL <6.42S, but, outside these values, a multimodal model using respiration and pulse waves features is needed for confident stress classification. Our self-aware stress monitoring proposal saves10x energy and provides 88.72% of ac-curacy on unseen data.

CONCLUSION

We propose a comprehensive solution for the design of cost-aware stress monitoring addressing the problem of selecting an optimal feature subset considering their cost-dependency and cost-constrains. Significant: Our design framework enables long-term, confident, and accurate stress monitoring on wearable devices.

IMVEST, an immersive multimodal virtual environment stress test for humans that adjusts challenge to individual's performance

Laboratory stressors are essential tools to study the human stress response. However, despite considerable progress in the development of stress induction procedures in recent years, the field is still missing standardization and the methods employed frequently require considerable personnel resources. Virtual reality (VR) offers flexible solutions to these problems, but available VR stress-induction tests still contain important sources of variation that challenge data interpretation. One of the major drawbacks is that tasks based on motivated performance do not adapt to individual abilities. Here, we provide open access to, and present, a novel and standardized immersive multimodal virtual environment stress test (IMVEST) in which participants are simultaneously exposed to mental -arithmetic calculations- and environmental challenges, along with intense visual and auditory stimulation. It contains critical elements of stress elicitation – perceived threat to physical self, social-evaluative threat and negative feedback, uncontrollability and unpredictability – and adjusts mathematical challenge to individual's ongoing performance. It is accompanied by a control VR scenario offering a comparable but not stressful situation. We validate and characterize the stress response to IMVEST in one-hundred-and-eighteen participants. Both cortisol and a wide range of autonomic nervous system (ANS) markers – extracted from the electrocardiogram, electrodermal activity and respiration – are significantly affected. We also show that ANS features can be used to train a stress prediction machine learning model that strongly discriminates between stress and control conditions, and indicates which aspects of IMVEST affect specific ANS components.

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Laboratory stressors are an essential tool to study the stress response in humans.

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We present a novel immersive multimodal virtual environment stress test (IMVEST).

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IMVEST adapts to individual performance.

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Induces acute increase in stress markers.

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Stress responses do not depend on performance differences.