The association between electrodermal activity (EDA), depression and suicidal behaviour: A systematic review and narrative synthesis

From Social Stress and Isolation to Autonomic Nervous System Dysregulation in Suicidal Behavior

PURPOSE OF REVIEW

In this narrative review we wanted to describe the relationship of autonomic nervous system activity with social environment and suicidal spectrum behaviors.

RECENT FINDINGS

Patients with suicidal ideation/suicide attempt have higher sympathetic nervous system (SNS) and lower parasympathetic nervous system (PNS) activity in resting conditions and during acute stress tasks compared with patients without suicidal ideation/suicide attempt. Death by suicide and violent suicide attempt also are related to SNS hyperactivation. Similarly, a SNS/PNS imbalance has been observed in people with childhood trauma, stressful life events or feelings of loneliness and isolation. Social support seems to increase PNS control and resilience. Due to the importance of the social context and stressful life events in suicidal behavior, SNS/PNS imbalance could act as a mediator in this relationship and be a source of relevant biomarkers. Childhood trauma and stressful life events may impair the autonomic nervous system response in suicidal patients. Loneliness, isolation and social support may act as moderators in acute stress situations.

Automated mood disorder symptoms monitoring from multivariate time-series sensory data: getting the full picture beyond a single number

Mood disorders (MDs) are among the leading causes of disease burden worldwide. Limited specialized care availability remains a major bottleneck thus hindering pre-emptive interventions. MDs manifest with changes in mood, sleep, and motor activity, observable in ecological physiological recordings thanks to recent advances in wearable technology. Therefore, near-continuous and passive collection of physiological data from wearables in daily life, analyzable with machine learning (ML), could mitigate this problem, bringing MDs monitoring outside the clinician's office. Previous works predict a single label, either the disease state or a psychometric scale total score. However, clinical practice suggests that the same label may underlie different symptom profiles, requiring specific treatments. Here we bridge this gap by proposing a new task: inferring all items in HDRS and YMRS, the two most widely used standardized scales for assessing MDs symptoms, using physiological data from wearables. To that end, we develop a deep learning pipeline to score the symptoms of a large cohort of MD patients and show that agreement between predictions and assessments by an expert clinician is clinically significant (quadratic Cohen's κ and macro-average F1 score both of 0.609). While doing so, we investigate several solutions to the ML challenges associated with this task, including multi-task learning, class imbalance, ordinal target variables, and subject-invariant representations. Lastly, we illustrate the importance of testing on out-of-distribution samples.

Task-state skin potential abnormalities can distinguish major depressive disorder and bipolar depression from healthy controls

Early detection of bipolar depression (BPD) and major depressive disorder (MDD) has been challenging due to the lack of reliable and easily measurable biological markers. This study aimed to investigate the accuracy of discriminating patients with mood disorders from healthy controls based on task state skin potential characteristics and their correlation with individual indicators of oxidative stress. A total of 77 patients with BPD, 53 patients with MDD, and 79 healthy controls were recruited. A custom-made device, previously shown to be sufficiently accurate, was used to collect skin potential data during six emotion-inducing tasks involving video, pictorial, or textual stimuli. Blood indicators reflecting individual levels of oxidative stress were collected. A discriminant model based on the support vector machine (SVM) algorithm was constructed for discriminant analysis. MDD and BPD patients were found to have abnormal skin potential characteristics on most tasks. The accuracy of the SVM model built with SP features to discriminate MDD patients from healthy controls was 78% (sensitivity 78%, specificity 82%). The SVM model gave an accuracy of 59% (sensitivity 59%, specificity 79%) in classifying BPD patients, MDD patients, and healthy controls into three groups. Significant correlations were also found between oxidative stress indicators in the blood of patients and certain SP features. Patients with depression and bipolar depression have abnormalities in task-state skin potential that partially reflect the pathological mechanism of the illness, and the abnormalities are potential biological markers of affective disorders.

Elevated body temperature is associated with depressive symptoms: results from the TemPredict Study

Correlations between altered body temperature and depression have been reported in small samples; greater confidence in these associations would provide a rationale for further examining potential mechanisms of depression related to body temperature regulation. We sought to test the hypotheses that greater depression symptom severity is associated with (1) higher body temperature, (2) smaller differences between body temperature when awake versus asleep, and (3) lower diurnal body temperature amplitude. Data collected included both self-reported body temperature (using standard thermometers), wearable sensor-assessed distal body temperature (using an off-the-shelf wearable sensor that collected minute-level physiological data), and self-reported depressive symptoms from > 20,000 participants over the course of ~ 7 months as part of the TemPredict Study. Higher self-reported and wearable sensor-assessed body temperatures when awake were associated with greater depression symptom severity. Lower diurnal body temperature amplitude, computed using wearable sensor-assessed distal body temperature data, tended to be associated with greater depression symptom severity, though this association did not achieve statistical significance. These findings, drawn from a large sample, replicate and expand upon prior data pointing to body temperature alterations as potentially relevant factors in depression etiology and may hold implications for development of novel approaches to the treatment of major depressive disorder.

Electrodermal activity in bipolar disorder: Differences between mood episodes and clinical remission using a wearable device in a real-world clinical setting

BACKGROUND

Bipolar disorder (BD) lacks objective measures for illness activity and treatment response. Electrodermal activity (EDA) is a quantitative measure of autonomic function, which is altered in manic and depressive episodes. We aimed to explore differences in EDA (1) inter-individually: between patients with BD on acute mood episodes, euthymic states and healthy controls (HC), and (2) intra-individually: longitudinally within patients during acute mood episodes of BD and after clinical remission.

METHODS

A longitudinal observational study. EDA was recorded using a research-grade wearable in patients with BD during acute manic and depressive episodes and at clinical remission. Euthymic BD patients and HC were recorded during a single session. We compared EDA parameters derived from the tonic (mean EDA, mEDA) and phasic components (EDA peaks per minute, pmEDA, and EDA peaks mean amplitude, pmaEDA). Inter- and intra-individual comparisons were computed respectively with ANOVA and paired t-tests.

RESULTS

49 patients with BD (15 manic, 9 depressed, and 25 euthymic), and 19 HC were included. Patients with bipolar depression showed significantly reduced mEDA (p = 0.003) and pmEDA (p = 0.001), which increased to levels similar to euthymia or HC after clinical remission (mEDA, p = 0.011; pmEDA, p < 0.001; pmaEDA, p < 0.001). Manic patients showed no differences compared to euthymic patients and HCs, but a significant reduction of tonic and phasic EDA parameters after clinical remission (mEDA, p = 0.035; pmEDA, p = 0.004).

LIMITATIONS

Limited sample size, high inter-individual variability of EDA parameters, limited comparability to previous studies and non-adjustment for medication.

CONCLUSION

EDA ecological monitoring might provide several opportunities for early detection of depressive symptoms, and might aid at assessing early response to treatments in mania and bipolar depression.

A review of clinical studies of electrodermal activity and transcranial magnetic stimulation

There is a growing body of evidence indicative of changes in autonomic nervous system (ANS) activity in patients with disorders of the central nervous system (CNS). Non-invasive measures of the ANS, including heart rate variability (HRV), electrodermal activity (EDA), and pupillary light reflex (PLR) may have value as markers of symptom severity, subtype, risk profile, and/or treatment response. In this paper we provide an introduction into the anatomy and physiology of EDA and review the literature published after 2007 in which EDA was an outcome measure of cortical stimulation with transcranial magnetic stimulation (TMS). Eleven studies were included and considered regarding the potential of EDA as an outcome measure reflecting ANS activity in TMS research and treatment. These studies are summarized according to study population, experimental methodology, cortical region targeted, and correlation with other measures of ANS activity. Results indicate that EDA changes vary with the frequency and target of TMS. Inhibitory TMS to the dorsolateral prefrontal cortex (dlPFC) was the most common paradigm in these studies, consistently resulting in decreased EDA.

Emotional state as a modulator of autonomic and somatic nervous system activity in postural control: a review

Advances in our understanding of postural control have highlighted the need to examine the influence of higher brain centers in the modulation of this complex function. There is strong evidence of a link between emotional state, autonomic nervous system (ANS) activity and somatic nervous system (somatic NS) activity in postural control. For example, relationships have been demonstrated between postural threat, anxiety, fear of falling, balance confidence, and physiological arousal. Behaviorally, increased arousal has been associated with changes in velocity and amplitude of postural sway during quiet standing. The potential links between ANS and somatic NS, observed in control of posture, are associated with shared neuroanatomical connections within the central nervous system (CNS). The influence of emotional state on postural control likely reflects the important influence the limbic system has on these ANS/somatic NS control networks. This narrative review will highlight several examples of behaviors which routinely require coordination between the ANS and somatic NS, highlighting the importance of the neurofunctional link between these systems. Furthermore, we will extend beyond the more historical focus on threat models and examine how disordered/altered emotional state and ANS processing may influence postural control and assessment. Finally, this paper will discuss studies that have been important in uncovering the modulatory effect of emotional state on postural control including links that may inform our understanding of disordered control, such as that observed in individuals living with Parkinson's disease and discuss methodological tools that have the potential to advance understanding of this complex relationship.

Assessing effectiveness of heart rate variability biofeedback to mitigate mental health symptoms: a pilot study

Introduction: The increasing burden on mental health has become a worldwide concern especially due to its substantial negative social and economic impact. The implementation of prevention actions and psychological interventions is crucial to mitigate these consequences, and evidence supporting its effectiveness would facilitate a more assertive response. Heart rate variability biofeedback (HRV-BF) has been proposed as a potential intervention to improve mental wellbeing through mechanisms in autonomic functioning. The aim of this study is to propose and evaluate the validity of an objective procedure to assess the effectiveness of a HRV-BF protocol in mitigating mental health symptoms in a sample of frontline HCWs (healthcare workers) who worked in the COVID-19 pandemic. Methods: A prospective experimental study applying a HRV-BF protocol was conducted with 21 frontline healthcare workers in 5 weekly sessions. For PRE-POST intervention comparisons, two different approaches were used to evaluate mental health status: applying (a) gold-standard psychometric questionnaires and (b) electrophysiological multiparametric models for chronic and acute stress assessment. Results: After HRV-BF intervention, psychometric questionnaires showed a reduction in mental health symptoms and stress perception. The electrophysiological multiparametric also showed a reduction in chronic stress levels, while the acute stress levels were similar in PRE and POST conditions. A significant reduction in respiratory rate and an increase in some heart rate variability parameters, such as SDNN, LFn, and LF/HF ratio, were also observed after intervention. Conclusion: Our findings suggest that a 5-session HRV-BF protocol is an effective intervention for reducing stress and other mental health symptoms among frontline HCWs who worked during the COVID-19 pandemic. The electrophysiological multiparametric models provide relevant information about the current mental health state, being useful for objectively evaluating the effectiveness of stress-reducing interventions. Further research could replicate the proposed procedure to confirm its feasibility for different samples and specific interventions.

Self-Regulatory Neuronal Mechanisms and Long-Term Challenges in Schizophrenia Treatment

Schizophrenia is a chronic and relapsing disorder that is characterized not only by delusions and hallucinations but also mainly by the progressive development of cognitive and social deficits. These deficits are related to impaired synaptic plasticity and impaired neurotransmission in the nervous system. Currently, technological innovations and medical advances make it possible to use various self-regulatory methods to improve impaired synaptic plasticity. To evaluate the therapeutic effect of various rehabilitation methods, we reviewed methods that modify synaptic plasticity and improve the cognitive and executive processes of patients with a diagnosis of schizophrenia. PubMed, Scopus, and Google Scholar bibliographic databases were searched with the keywords mentioned below. A total of 555 records were identified. Modern methods of schizophrenia therapy with neuroplastic potential, including neurofeedback, transcranial magnetic stimulation, transcranial direct current stimulation, vagus nerve stimulation, virtual reality therapy, and cognitive remediation therapy, were reviewed and analyzed. Since randomized controlled studies of long-term schizophrenia treatment do not exceed 2-3 years, and the pharmacological treatment itself has an incompletely estimated benefit-risk ratio, treatment methods based on other paradigms, including neuronal self-regulatory and neural plasticity mechanisms, should be considered. Methods available for monitoring neuroplastic effects in vivo (e.g., fMRI, neuropeptides in serum), as well as unfavorable parameters (e.g., features of the metabolic syndrome), enable individualized monitoring of the effectiveness of long-term treatment of schizophrenia.

Exploring digital biomarkers of illness activity in mood episodes: hypotheses generating and model development study

BACKGROUND

Depressive and manic episodes within bipolar disorder (BD) and major depressive disorder (MDD) involve altered mood, sleep, and activity alongside physiological alterations that wearables can capture.

OBJECTIVE

We explored whether physiological wearable data could predict: (aim 1) the severity of an acute affective episode at the intra-individual level, (aim 2) the polarity of an acute affective episode and euthymia among different individuals. Secondarily, we explored which physiological data were related to the prior predictions, generalization across patients, and associations between affective symptoms and physiological data.

METHODS

We conducted a prospective exploratory observational study including patients with BD and MDD on acute affective episodes (manic, depressed, and mixed) whose physiological data were recorded with a research-grade wearable (Empatica E4) across three consecutive timepoints (acute, response, and remission of episode). Euthymic patients and healthy controls (HC) were recorded during a single session (∼48 hours). Manic and depressive symptoms were assessed with standardized psychometric scales. Physiological wearable data included the following channels: acceleration (ACC), temperature (TEMP), blood volume pulse (BVP), heart rate (HR), and electrodermal activity (EDA). For data pre-processing, invalid physiological data were removed using a rule-based filter, channels were time-aligned at 1 second time units and then segmented window lengths of 32 seconds, since those parameters showed the best performances. We developed deep learning predictive models, assessed channels' individual contribution using permutation feature importance analysis, and computed physiological data to psychometric scales' items normalized mutual information (NMI). We present a novel fully automated method for analysis of physiological data from a research-grade wearable device, including a rule-based filter for invalid data and a viable supervised learning pipeline for time-series analyses.

RESULTS

35 sessions (1,512 hours) from 12 patients (manic, depressed, mixed, and euthymic) and 7 HC (age 39.7±12.6; 31.6% female) were analyzed. (aim 1) The severity of mood episodes was predicted with moderate (62%-85%) accuracies. (aim 2) The polarity of episodes was predicted with moderate (70%) accuracy. The most relevant features for the former tasks were ACC, EDA, and HR. Kendall W showed fair agreement (0.383) in feature importance across classification tasks. Generalization of the former models were of overall low accuracy, with better results for the intra-individual models. "Increased motor activity" was associated with ACC (NMI>0.55), "aggressive behavior" with EDA (NMI=1.0), "insomnia" with ACC (NMI∼0.6), "motor inhibition" with ACC (NMI∼0.75), and "psychic anxiety" with EDA (NMI=0.52).

CONCLUSIONS

Physiological data from wearables show potential to identify mood episodes and specific symptoms of mania and depression quantitatively, both in BD and MDD. Motor activity and stress-related physiological data (EDA and HR) stand out as potential digital biomarkers for predicting mania and depression respectively. These findings represent a promising pathway towards personalized psychiatry, in which physiological wearable data could allow early identification and intervention of mood episodes.

CLINICALTRIAL

Genome-Wide Genetic Structure of Henan Indigenous Chicken Breeds

There are five indigenous chicken breeds in Henan Province, China. These breeds have their own unique phenotypic characteristics in terms of morphology, behavior, skin and feather color, and productive performance, but their genetic basis is not well understood. Therefore, we analyzed the genetic structure, genomic diversity, and migration history of Henan indigenous chicken populations and the selection signals and genes responsible for Henan gamecock unique phenotypes using whole genome resequencing. The results indicate that Henan native chickens clustered most closely with the chicken populations in neighboring provinces. Compared to other breeds, Henan gamecock's inbreeding and selection intensity were more stringent. TreeMix analysis revealed the gene flow from southern chicken breeds into the Zhengyang sanhuang chicken and from the Xichuan black-bone chicken into the Gushi chicken. Selective sweep analysis identified several genes and biological processes/pathways that were related to body size, head control, muscle development, reproduction, and aggression control. Additionally, we confirmed the association between genotypes of SNPs in the strong selective gene LCORL and body size and muscle development in the Gushi-Anka F2 resource population. These findings made it easier to understand the traits of the germplasm and the potential for using the Henan indigenous chicken.

Physiological reactivity at rest and in response to social or emotional stimuli after a traumatic brain injury: A systematic review

Numerous studies have shown that alterations in physiological reactivity (PR) after traumatic brain injury (TBI) are possibly associated with emotional deficits. We conducted a systematic review of these studies that evaluated PR in adults with moderate-to-severe TBI, either at rest or in response to emotional, stressful, or social stimuli. We focused on the most common measures of physiological response, including heart rate (HR), heart rate variability (HRV), respiratory sinus arrhythmia (RSA), electrodermal activity (EDA), salivary cortisol, facial electromyography (EMG), and blink reflex.

Methods

A systematic literature search was conducted across six databases (PsycINFO, Psycarticles, SciencDirect, Cochrane Library, PubMed, and Scopus). The search returned 286 articles and 18 studies met the inclusion criteria.

Results

Discrepancies were observed according to the type of physiological measure. Reduced physiological responses in patients with TBI have been reported in most EDA studies, which were also overrepresented in the review. In terms of facial EMG, patients with TBI appear to exhibit reduced activity of the corrugator muscle and diminished blink reflex, while in most studies, zygomaticus contraction did not show significant differences between TBI and controls. Interestingly, most studies measuring cardiac activity did not find significant differences between TBI and controls. Finally, one study measured salivary cortisol levels and reported no difference between patients with TBI and controls.

Conclusion

Although disturbed EDA responses were frequently reported in patients with TBI, other measures did not consistently indicate an impairment in PR. These discrepancies could be due to the lesion pattern resulting from TBI, which could affect the PR to aversive stimuli. In addition, methodological differences concerning the measurements and their standardization as well as the characteristics of the patients may also be involved in these discrepancies. We propose methodological recommendations for the use of multiple and simultaneous PR measurements and standardization. Future research should converge toward a common methodology in terms of physiological data analysis to improve inter-study comparisons.

Racial differences in sympathetic nervous system indicators: Implications and challenges for research

Growing evidence indicates the presence of racial differences in sympathetic nervous system (SNS) functioning, yet the nature of these differences is unclear and appears to vary across different indices of SNS activity. Moreover, racial differences among commonly used indices of SNS activity are under-investigated. This systematic review examines racial differences among widely used resting SNS indices, such as electrodermal activity (EDA), pre-ejection period (PEP), and salivary alpha-amylase (sAA). Our review reveals that Black participants have consistently been found to display lower resting EDA compared to White participants. The few studies that have investigated or reported racial differences in PEP and sAA yield mixed findings about whether racial differences exist. We discuss potential reasons for racial differences in SNS activity, such as index-specific factors, lab confounds, psychosocial environmental factors, and their interactions. We outline a framework characterizing possible contributors to racial differences in SNS functioning. Lastly, we highlight the implications of several definitional, analytic, and interpretive issues concerning the treatment of group differences in psychophysiological activity and provide future recommendations.

Digital Phenotyping for Differential Diagnosis of Major Depressive Episode: Narrative Review

BACKGROUND

Major depressive episode (MDE) is a common clinical syndrome. It can be found in different pathologies such as major depressive disorder (MDD), bipolar disorder (BD), posttraumatic stress disorder (PTSD), or even occur in the context of psychological trauma. However, only 1 syndrome is described in international classifications (Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition [DSM-5]/International Classification of Diseases 11th Revision [ICD-11]), which do not take into account the underlying pathology at the origin of the MDE. Clinical interviews are currently the best source of information to obtain the etiological diagnosis of MDE. Nevertheless, it does not allow an early diagnosis and there are no objective measures of extracted clinical information. To remedy this, the use of digital tools and their correlation with clinical symptomatology could be useful.

OBJECTIVE

We aimed to review the current application of digital tools for MDE diagnosis while highlighting shortcomings for further research. In addition, our work was focused on digital devices easy to use during clinical interview and mental health issues where depression is common.

METHODS

We conducted a narrative review of the use of digital tools during clinical interviews for MDE by searching papers published in PubMed/MEDLINE, Web of Science, and Google Scholar databases since February 2010. The search was conducted from June to September 2021. Potentially relevant papers were then compared against a checklist for relevance and reviewed independently for inclusion, with focus on 4 allocated topics of (1) automated voice analysis, behavior analysis by (2) video and physiological measures, (3) heart rate variability (HRV), and (4) electrodermal activity (EDA). For this purpose, we were interested in 4 frequently found clinical conditions in which MDE can occur: (1) MDD, (2) BD, (3) PTSD, and (4) psychological trauma.

RESULTS

A total of 74 relevant papers on the subject were qualitatively analyzed and the information was synthesized. Thus, a digital phenotype of MDE seems to emerge consisting of modifications in speech features (namely, temporal, prosodic, spectral, source, and formants) and in speech content, modifications in nonverbal behavior (head, hand, body and eyes movement, facial expressivity, and gaze), and a decrease in physiological measurements (HRV and EDA). We not only found similarities but also differences when MDE occurs in MDD, BD, PTSD, or psychological trauma. However, comparative studies were rare in BD or PTSD conditions, which does not allow us to identify clear and distinct digital phenotypes.

CONCLUSIONS

Our search identified markers from several modalities that hold promise for helping with a more objective diagnosis of MDE. To validate their potential, further longitudinal and prospective studies are needed.

Real-time digital monitoring of a suicide attempt by a hospital patient

Suicide is among the most devastating problems facing clinicians, who currently have limited tools to predict and prevent suicidal behavior. Here we report on real-time, continuous smartphone and sensor data collected before, during, and after a suicide attempt made by a patient during a psychiatric inpatient hospitalization. We observed elevated and persistent sympathetic nervous system arousal and suicidal thinking leading up to the suicide attempt. This case provides the highest resolution data to date on the psychological, psychophysiological, and behavioral markers of imminent suicidal behavior and highlights new directions for prediction and prevention efforts.

Galvanic Skin Response Features in Psychiatry and Mental Disorders: A Narrative Review

This narrative review is aimed at presenting the galvanic skin response (GSR) Biofeedback method and possibilities for its application in persons with mental disorders as a modern form of neurorehabilitation. In the treatment of mental disorders of various backgrounds and courses, attention is focused on methods that would combine pharmacological treatment with therapies improving functioning. Currently, the focus is on neuronal mechanisms which, being physiological markers, offer opportunities for correction of existing deficits. One such indicator is electrodermal activity (EDA), providing information about emotions, cognitive processes, and behavior, and thus, about the function of various brain regions. Measurement of the galvanic skin response (GSR), both skin conductance level (SCL) and skin conductance responses (SCR), is used in diagnostics and treatment of mental disorders, and the training method itself, based on GSR Biofeedback, allows for modulation of the emotional state depending on needs occurring. Summary: It is relatively probable that neurorehabilitation based on GSR-BF is a method worth noticing, which-in the future-can represent an interesting area of rehabilitation supplementing a comprehensive treatment for people with mental disorders.

Toward Mental Effort Measurement Using Electrodermal Activity Features

The ability to monitor mental effort during a task using a wearable sensor may improve productivity for both work and study. The use of the electrodermal activity (EDA) signal for tracking mental effort is an emerging area of research. Through analysis of over 92 h of data collected with the Empatica E4 on a single participant across 91 different activities, we report on the efficacy of using EDA features getting at signal intensity, signal dispersion, and peak intensity for prediction of the participant's self-reported mental effort. We implemented the logistic regression algorithm as an interpretable machine learning approach and found that features related to signal intensity and peak intensity were most useful for the prediction of whether the participant was in a self-reported high mental effort state; increased signal and peak intensity were indicative of high mental effort. When cross-validated by activity moderate predictive efficacy was achieved (AUC = 0.63, F1 = 0.63, precision = 0.64, recall = 0.63) which was significantly stronger than using the model bias alone. Predicting mental effort using physiological data is a complex problem, and our findings add to research from other contexts showing that EDA may be a promising physiological indicator to use for sensor-based self-monitoring of mental effort throughout the day. Integration of other physiological features related to heart rate, respiration, and circulation may be necessary to obtain more accurate predictions.

Evaluation of the Relationship Between Head Posture, Mandibular Movements and Emotional Tension

Introduction: The head, due to its structure and assigned functions, is a unique part of our body. In a number of studies, an association has been confirmed between the base of the head, the cervical spine, and temporomandibular joint disorders. Research objective: The aim of the study was to evaluate the correlation of spatial head position, temporomandibular joint mobility and emotional tension. Material and methods: The study comprised32 participants, aged 20 to 30 years. The position of the head in the sagittal and frontal planes was evaluated via the photogrammetric method. The mobility of the temporomandibular joints was assessed by measurements made with a ruler. Electrodermal activity was measured with the "Bitalino 3DP by BEEVERYCREATIVE" device, and stress intensity assessment was estimated using the PSS-10 scale. Results: A significant (p<0.01) linear correlation was found between electrodermal activity (EDA Min) and the values of angles describing head tilt (FHT1 and HTA). Higher EDA values are associated with higher angle values. No significant correlations (p>0.05) were found between mandibular mobility and EDA scores, or between mandibular mobility measurements and head position. Conclusions: The study revealed a relationship between head positioning and electrodermal EDA activity.

Fear-potentiated startle predicts longitudinal change in transdiagnostic symptom dimensions of anxiety and depression

BACKGROUND

Elevated defensive responding, through startle reflex (SR) and skin conductance response (SCR), may contribute to onset and maintenance of depression and anxiety. Most work examining SR and SCR has predicted psychiatric diagnoses. There is a paucity of research examining links between SR or SCR and dimensional measures of psychopathology.

METHODS

We used latent growth curve modeling to predict longitudinal change in three symptom factors (i.e., General Distress, Fears, Anhedonia-Apprehension) from SR and SCR measured during a fear-potentiated startle paradigm among adolescents oversampled for neuroticism (N = 129).

RESULTS

Elevated SCR in danger phases before and after an unpleasant muscle contraction predicted increasing Fears over time. Elevated SR in safe phases post-contraction also predicted increasing Fears over time. Attenuated SR in safe phases post-contraction predicted elevated General Distress longitudinally. Attenuated SCR pre-contraction in danger phases predicted elevated Anhedonia-Apprehension over time.

LIMITATIONS

Our non-clinical sample may limit generalizability of results. Additionally, we did not assess change in SR and SCR over time.

CONCLUSIONS

The present study demonstrates that SR and SCR during a fear-potentiated startle paradigm predict longitudinal change in dimensional anxiety and depression symptom factors and relatedly, that SR and SCR may represent risk factors for the exacerbation of symptomatology.

Linear, High Dynamic Range Isolated Skin Resistance Transducer Circuit for Neurophysiological Research in Individuals after Spinal Cord Injury

The quantification of skin resistance in individuals after spinal cord injury for the purpose of neurophysiological research is difficult, mainly as a consequence of decreased activity of sweat glands in the injured human organism. In this original work, we propose a custom electrical skin resistance transducer, featuring extremely low patient auxiliary current, linear response and high dynamic range. After the design and fabrication of the prototype device, we conducted preliminary benchmark tests. We found that our prototype transducer was able to linearly report a broad range of resistance presented to its input terminals, which is not usually found in skin resistance research instrumentation. The basic design idea is viable and, following further research, an improved version of presented prototype device may be used for the purpose of neurophysiological research in individuals after spinal cord injury.

Skin conductance while facing emotional pictures at day 7 helps predicting antidepressant response at three months in patients with a major depressive episode

There are currently no reliable biological markers to identify antidepressant responders in patients suffering from major depressive disorder. In this longitudinal pilot study, we measured skin conductance response (SCR) to assess patients' emotional reactivity after antidepressant treatment initiation. Fifty-four adult patients with a major depressive episode were recruited and followed up for 3 months. After one day of antidepressant treatment (D1) and then at day 7 (D7), emotional stimuli were presented on a computer screen while SCR and subjective emotional response were recorded. Three months later, we used Montgomery and Åsberg Depression Rating Scale (MADRS) to screen patients for treatment response, and distinguished responders (N = 28) from non-responders (N = 15). While SCR at D1 did not differ between responders and non-responders, SCR at D7 was higher in responders for both positive, negative and neutral stimuli. Skin conductance rates at D7 had a relatively poor negative predictive value (38%) but a strong positive predictive value (95%). Further studies are needed to replicate in a larger sample, and validate, these preliminary results which suggest that electrodermal activity after treatment initiation could help predict antidepressant efficacy.

Quantifying Occupational Stress in Intensive Care Unit Nurses: An Applied Naturalistic Study of Correlations Among Stress, Heart Rate, Electrodermal Activity, and Skin Temperature

OBJECTIVE

To identify physiological correlates to stress in intensive care unit nurses.

BACKGROUND

Most research on stress correlates are done in laboratory environments; naturalistic investigation of stress remains a general gap.

METHOD

Electrodermal activity, heart rate, and skin temperatures were recorded continuously for 12-hr nursing shifts (23 participants) using a wrist-worn wearable technology (Empatica E4).

RESULTS

Positive correlations included stress and heart rate (ρ = .35, p < .001), stress and skin temperature (ρ = .49, p < .05), and heart rate and skin temperatures (ρ = .54, p = .0008).

DISCUSSION

The presence and direction of some correlations found in this study differ from those anticipated from prior literature, illustrating the importance of complementing laboratory research with naturalistic studies. Further work is warranted to recognize nursing activities associated with a high level of stress and the underlying reasons associated with changes in physiological responses.

APPLICATION

Heart rate and skin temperature may be used for real-time detection of stress, but more work is needed to validate such surrogate measures.

Electrodermal activity for measuring cognitive and emotional stress level

Stress can lead to harmful conditions in the body, such as anxiety disorders and depression. One of the promising noninvasive methods, which has been widely used in detecting stress and emotion, is electrodermal activity (EDA). EDA has a tonic and phasic component called skin conductance level and skin conductance response (SCR). However, the components of the EDA cannot be directly extracted and need to be deconvolved to obtain it. The EDA signals were collected from 18 healthy subjects that underwent three sessions – Stroop test with increasing stress levels. The EDA signals were then deconvoluted by using continuous deconvolution analysis (CDA) and convex optimization approach to electrodermal activity (cvxEDA). Four features from the result of the deconvolution process were collected, namely sample average, standard deviation, first absolute difference, and normalized first absolute difference. Those features were used as the input of the classification process using the extreme learning machine (ELM). The output of classification was the stress level; mild, moderate, and severe. The visual of the phasic component using cvxEDA is more precise or smoother than the CDA's result. However, both methods could separate SCR from the original skin conductivity raw and indicate the small peaks from the SCR. The classification process results showed that both CDA and cvxEDA methods with 50 hidden layers in ELM had a high accuracy in classifying the stress level, which was 95.56% and 94.45%, respectively. This study developed a stress level classification method using ELM and the statistical features of SCR. The result showed that EDA could classify the stress level with over 94% accuracy. This system could help people monitor their mental health during overworking, leading to anxiety and depression because of untreated stress.

The Processing of Visual Signals in Major Depressive Disorder

The balanced processing of the internal mental world and the external world is a crucial aspect of everyday well-being. An extensive control of the internal emotional and cognitive world that often results in an internal expression of distress is a common feature of internalizing disorders. However, how depression affects the processing of the external world is still an open question. We, therefore, tested the processing of visual signals in major depressive disorder (MDD). To this end, we recorded the electroencephalogram of 38 MDD patients and 38 controls, while they performed a response-choice task with informative feedback and a passive viewing task. MDD patients differed significantly from controls in the early information processing of visual stimuli. The vertex positive potential (VPP) evoked by feedback in the response-choice task and pictures in the passive viewing task were smaller in MDD patients than in controls. This outcome suggests that depression might subtract attentional resources from external signal processing, with potential consequences in various cognitive domains.

Development of Autonomic Nervous System Assays as Point-of-Care Tests to Supplement Clinical Judgment in Risk Assessment for Suicidal Behavior: A Review

PURPOSE OF REVIEW

A biomarker point-of-care (POC) test that supplements the psychiatric interview and improves detection of patients at risk for suicide would be of value, and assays of autonomic nervous system (ANS) activity would satisfy the logistical requirements for a POC test. We performed a selective review of the available literature of ANS assays related to risk for suicide.

RECENT FINDINGS

We searched PubMed and Web of Science with the strategy: "suicide OR suicidal" AND "electrodermal OR heart rate variability OR pupillometry OR pupillography." The search produced 119 items, 21 of which provided original data regarding ANS methods and suicide. These 21 studies included 6 for electrodermal activity, 14 for heart rate variability, and 1 for the pupillary light reflex. The 21 papers showed associations between ANS assays and suicide risk in a direction suggesting underlying hyperarousal in patients at risk for suicide. ANS assays show promise for future development as POC tests to supplement clinical decision making in estimating risk for suicide.

Digital Technologies for Emotion-Regulation Assessment and Intervention: A Conceptual Review

The ability to regulate emotions in response to stress is central to healthy development. While early research in emotion regulation predominantly employed static, self-report measurement, the past decade has seen a shift in focus toward understanding the dynamic nature of regulation processes. This is reflected in recent refinements in the definition of emotion regulation, which emphasize the importance of the ability to flexibly adapt regulation efforts across contexts. The latest proliferation of digital technologies employed in mental health research offers the opportunity to capture the state- and context-sensitive nature of emotion regulation. In this conceptual review, we examine the use of digital technologies (ecological momentary assessment; wearable and smartphone technology, physical activity, acoustic data, visual data, and geo-location; smart home technology; virtual reality; social media) in the assessment of emotion regulation and describe their application to interventions. We also discuss challenges and ethical considerations, and outline areas for future research.

Effect of Exposure Time on Thermal Behaviour: A Psychophysiological Approach

This paper presents the findings of a 6-week long, five-participant experiment in a controlled climate chamber. The experiment was designed to understand the effect of time on thermal behaviour, electrodermal activity (EDA) and the adaptive behavior of occupants in response to a thermal non-uniform indoor environment were continuously logged. The results of the 150 h-long longitudinal study suggested a significant difference in tonic EDA levels between “morning” and “afternoon” clusters although the environmental parameters were the same, suggesting a change in the human body’s thermal reception over time. The correlation of the EDA and temperature was greater for the afternoon cluster (r = 0.449, p < 0.001) in relation to the morning cluster (r = 0.332, p < 0.001). These findings showed a strong temporal dependency of the skin conductance level of the EDA to the operative temperature, following the person’s circadian rhythm. Even further, based on the person’s chronotype, the beginning of the “afternoon” cluster was observed to have shifted according to the person’s circadian rhythm. Furthermore, the study is able to show how the body reacts differently under the same PMV values, both within and between subjects; pointing to the lack of temporal parameter in the PMV model.

Can passive measurement of physiological distress help better predict suicidal thinking?

There has been growing interest in using wearable physiological monitors to passively detect the signals of distress (i.e., increases in autonomic arousal measured through increased electrodermal activity [EDA]) that may be imminently associated with suicidal thoughts. Before using these monitors in advanced applications such as creating suicide risk detection algorithms or just-in-time interventions, several preliminary questions must be answered. Specifically, we lack information about whether: (1) EDA concurrently and prospectively predicts suicidal thinking and (2) data on EDA adds to the ability to predict the presence and severity of suicidal thinking over and above self-reports of emotional distress. Participants were suicidal psychiatric inpatients (n = 25, 56% female, M age = 33.48 years) who completed six daily assessments of negative affect and suicidal thinking duration of their psychiatric inpatient stay and 28 days post-discharge, and wore on their wrist a physiological monitor (Empatica Embrace) that passively detects autonomic activity. We found that physiological data alone both concurrently and prospectively predicted periods of suicidal thinking, but models with physiological data alone had the poorest fit. Adding physiological data to self-report models improved fit when the outcome variable was severity of suicidal thinking, but worsened model fit when the outcome was presence of suicidal thinking. When predicting severity of suicidal thinking, physiological data improved model fit more for models with non-overlapping self-report data (i.e., low arousal negative affect) than for overlapping self-report data (i.e., high arousal negative affect). These findings suggest that physiological data, under certain contexts (e.g., when combined with self-report data), may be useful in better predicting-and ultimately, preventing-acute increases in suicide risk. However, some cautious optimism is warranted since physiological data do not always improve our ability to predict suicidal thinking.

Prenatal maternal transdiagnostic, RDoC-informed predictors of newborn neurobehavior: Differences by sex

We examined whether Research Domain Criteria (RDoC)-informed measures of prenatal stress predicted newborn neurobehavior and whether these effects differed by newborn sex. Multilevel, prenatal markers of prenatal stress were obtained from 162 pregnant women. Markers of the Negative Valence System included physiological functioning (respiratory sinus arrhythmia [RSA] and electrodermal [EDA] reactivity to a speech task, hair cortisol), self-reported stress (state anxiety, pregnancy-specific anxiety, daily stress, childhood trauma, economic hardship, and family resources), and interviewer-rated stress (episodic stress, chronic stress). Markers of the Arousal/Regulatory System included physiological functioning (baseline RSA, RSA, and EDA responses to infant cries) and self-reported affect intensity, urgency, emotion regulation strategies, and dispositional mindfulness. Newborns' arousal and attention were assessed via the Neonatal Intensive Care Unit (NICU) Network Neurobehavioral Scale. Path analyses showed that high maternal episodic and daily stress, low economic hardship, few emotion regulation strategies, and high baseline RSA predicted female newborns' low attention; maternal mindfulness predicted female newborns' high arousal. As for male newborns, high episodic stress predicted low arousal, and high pregnancy-specific anxiety predicted high attention. Findings suggest that RDoC-informed markers of prenatal stress could aid detection of variance in newborn neurobehavioral outcomes within hours after birth. Implications for intergenerational transmission of risk for psychopathology are discussed.

Affective and Autonomic Reactivity During Parent-Child Interactions in Depressed and Non-Depressed Mothers and Their Adolescent Offspring

Depression presents risks that are profound and intergenerational, yet research on the association of depression with the physiological processes that might be associated with impaired mental and physical health has only recently been contextualized within the family environment. Participants in this multi-method case-control study were 180 mother-adolescent dyads (50% mothers with a history of depression treatment and current depressive symptoms). In order to examine the association between maternal depression and affective and autonomic reactivity amongst these mothers and their adolescent offspring we collected self-reported measures of positive and negative affect, as well as measures of cardiovascular and electrodermal autonomic activity, during mother-adolescent interaction tasks. Findings indicated that depressed mothers and their adolescent offspring exhibited greater self-reported negative affect reactivity during a problem-solving interaction and blunted (i.e., low) sympathetic activity as measured via skin conductance level across both interaction tasks. These effects remained significant after controlling for a range of potential covariates, including medication use, sex, age, adolescents own mental health symptoms, and behavior of the other interactant, along with correcting for multiple comparisons. Findings indicate that depressed mothers and their adolescent offspring both exhibit patterns of affect and physiology during interactions that are different from those of non-depressed mothers and their offspring, including increased negative affect reactivity during negative interactions and blunted sympathetic activity across both positive and negative interactions. These findings have potential implications for understanding the role of family processes in the intergenerational transmission of risk for depressive disorders.

Influence Factors for Decision-Making Performance of Suicide Attempters and Suicide Ideators: The Roles of Somatic Markers and Explicit Knowledge

Impaired decision-making has been observed in suicide attempters during the Iowa Gambling Task (IGT). Decision-making performance is influenced by somatic markers and explicit knowledge, but it is still unclear of the influencing role on decision-making performance in suicidal individuals. We aimed to investigate whether there is a decision-making deficit in suicide attempters, suicide ideators, as well as the distinct roles of somatic markers and explicit knowledge wherein. Thirteen suicide attempters, 23 suicide ideators, and 19 healthy controls performed the IGT. Both somatic markers (by the skin conductance responses, SCRs) and explicit knowledge (by the subjective experience rating and a list of questions) were recorded. No significant differences were found among the three groups on IGT performance, explicit knowledge, and anticipatory SCRs. IGT Performance of suicide attempters was positively correlated with explicit knowledge index while behavior performance was positively associated with the SCRs in healthy controls. These results indicate that the suicide attempters seem to apply a compensatory strategy by mostly utilizing explicit knowledge to perform normally as healthy controls in the IGT.

Virtual Reality in Home Palliative Care: Brief Report on the Effect on Cancer-Related Symptomatology

Virtual reality (VR) has been used as a complementary therapy for managing psychological and physical symptoms in cancer patients. In palliative care, the evidence about the use of VR is still inadequate. This study aims to assess the effect of an immersive VR-based intervention conducted at home on anxiety, depression, and pain over 4days and to evaluate the short-term effect of VR sessions on cancer-related symptomatology. Participants were advanced cancer patients assisted at home who were provided with a VR headset for 4days. On days one and four, anxiety and depression were measured by the Hospital Anxiety and Depression Scale (HADS) and pain by the Brief Pain Inventory (BPI). Before and after each VR session, symptoms were collected by the Edmonton Symptom Assessment Scale (ESAS). Participants wore a smart wristband measuring physiological signals associated with pain, anxiety, and depression. Fourteen patients (mean age 47.2±14.2years) were recruited. Anxiety, depression (HADS), and pain (BPI) did not change significantly between days one and four. However, the ESAS items related to pain, depression, anxiety, well-being, and shortness of breath collected immediately after the VR sessions showed a significant improvement (p<0.01). A progressive reduction in electrodermal activity has been observed comparing the recordings before, during, and after the VR sessions, although these changes were not statistically significant. This brief research report supports the idea that VR could represent a suitable complementary tool for psychological treatment in advanced cancer patients assisted at home.

Electrodermal Activity and Stress Assessment

Stress, as a kind of emotion, is inevitable in everyday life. In psychosomatic medicine stress represents a powerful link in the pathophysiological chain of disorder. Having evidence about the power of stress on the body, the interest in medicine was how to measure it in appropriate, fast way and with minimal cost. Electrodermal activity seems to be available for this purpose. The galvanic skin response (GSR) is an objective, transient indication of autonomic nervous system arousal in response to a stimulus. It refers to changes in sweat gland activity that are reflective of the intensity of our emotional arousal. In this article we discuss physiological specifics of skin conductance/resistance and how it is measured in practice. The most used application of GSR is in biofeedback methodology. Biofeedback assessment and training exactly uses skin reaction to different stimuli and aims to gain voluntary control over this autonomic response. The aim of this article is to show effectiveness of this method in paediatric practice.

Heart Rate Variability Changes in Patients With Major Depressive Disorder: Related to Confounding Factors, Not to Symptom Severity?

Background

The aim of this study was to assess the electrophysiological and other influencing factors correlating with symptom severity in patients with major depressive disorder (MDD) under three different conditions: baseline, stress exposure, and relaxation following stress exposure.

Methods

Symptom severity was assessed using the Beck Depression Inventory (BDI-II) in 89 inpatients (37 women; mean age 51 years) with MDD. Resting heart rate (RHR), heart rate variability (HRV), respiration rate (RR), skin conductance (SC), and skin temperature (ST) were recorded at baseline for 300 s, under stress exposure for 60 s, and under self-induced relaxation for 300 s. Age, nicotine consumption, body mass index, and blood pressure were evaluated as influencing factors.

Results

The BDI-II mean score was 29.7 points. Disease severity correlated positively with SC elevation under stress exposure and with a higher RR in the relaxed state, but no association was found between HRV and symptom severity. Age and higher blood pressure were both associated with lower HRV and higher RHR.

Conclusion

The results indicate that, in patients with MDD, changes in the autonomic nervous system (ANS) are complex; and the assessment of ANS reactivity to stressors is useful. Elevated blood pressure might be underdiagnosed, although it is already relevant in patients with MDD in their early 50s.

Machine Learning Methods for Fear Classification Based on Physiological Features

This paper focuses on the binary classification of the emotion of fear, based on the physiological data and subjective responses stored in the DEAP dataset. We performed a mapping between the discrete and dimensional emotional information considering the participants’ ratings and extracted a substantial set of 40 types of features from the physiological data, which represented the input to various machine learning algorithms—Decision Trees, k-Nearest Neighbors, Support Vector Machine and artificial networks—accompanied by dimensionality reduction, feature selection and the tuning of the most relevant hyperparameters, boosting classification accuracy. The methodology we approached included tackling different situations, such as resolving the problem of having an imbalanced dataset through data augmentation, reducing overfitting, computing various metrics in order to obtain the most reliable classification scores and applying the Local Interpretable Model-Agnostic Explanations method for interpretation and for explaining predictions in a human-understandable manner. The results show that fear can be predicted very well (accuracies ranging from 91.7% using Gradient Boosting Trees to 93.5% using dimensionality reduction and Support Vector Machine) by extracting the most relevant features from the physiological data and by searching for the best parameters which maximize the machine learning algorithms’ classification scores.

Fathers' Heightened Stress Responses to Recounting their NICU Experiences Months after Discharge: A Mixed Methods Pilot Study

OBJECTIVE

 The acute and traumatic events associated with having a newborn who requires admission to the neonatal intensive care unit (NICU) may elicit long-term concerns for parents postdischarge. Cognitive processing of taxing events influences recurring stress responses, which can be inferred via biomarkers such as salivary cortisol (sCort) and skin conductance (SC). In addition, personal narratives provide an important insight into individual perceptions and coping strategies. The current pilot study aimed to (1) test the hypotheses that fathers' sCort and SC would peak in response to stress induction and decrease during recovery, (2) examine associations among stress biomarkers and stress perceptions, (3) explore fathers' narratives using thematic analysis, and (4) integrate fathers' narrative themes with their stress responsivity.

STUDY DESIGN

 Using a convergent mixed methods approach, we enrolled 10 fathers of infants formerly cared for in NICU who underwent a Trier Social Stress Test including recounting their NICU experience months postdischarge. Stress responsivity was measured via sCort and SC, while stress perceptions were identified by using the Perceived Stress Scale and Distress Thermometer-Parent. Personal narratives were explored by using thematic analysis.

RESULTS

 The significant rise in fathers' sCort and SC in response to stress induction was reflected in narrative themes including loss, worry, and role strain. Subsequently, fathers' sCort and SC returned to baseline, which was illustrated by themes such as role strength, coping, and medical staff interactions. Fathers' stress measured by PSS was lower than that required for mental health referral, and did not correlate with stress biomarkers.

CONCLUSION

 Salivary cortisol and skin conductance are useful biomarkers of paternal stress responsivity and recovery. Thematic analysis identified fathers' NICU stressors and coping strategies that mirrored their stress responsivity patterns. Further studies are needed to more broadly examine the sociodemographic variables that influence stress reactivity and perceptions in parents of infants formerly cared for in NICU.

KEY POINTS

· Stress associated with NICU stay is impactful on fathers and may have long-term implications.. · Salivary cortisol and skin conductance are useful noninvasive stress biomarkers.. · Fathers' coping strategies included infant bonding, partner relationship, and trust building..

Validation of Affect-tag Affective and Cognitive Indicators

The Affect-tag solution measures physiological signals to deliver indicators derived from cognitive science. To provide the most accurate and effective results, a database of electrodermal activity (EDA) signals acquired using the Affect-tag A1 band was created. An experimental paradigm was designed to measure action-taking, autonomic regulation, cognitive load (CL), emotions, and stress, affects, and social stress. The Affect-tag emotional power (EP), emotional density (ED), and CL affective and cognitive indicators were refined based on the physiological responses of 48 participants during these tasks. Statistical significance was obtained for all indicators in tasks they were designed to measure, resulting in a total accuracy score of 89% for the combined indicators. Data obtained during this study will be further analyzed to define emotional and affective states.

Potential Applications of Mobile and Wearable Devices for Psychological Support During the COVID-19 Pandemic: A Review

The coronavirus disease 19 (COVID-19) pandemic that has been raging in 2020 does affect not only the physical state but also the mental health of the general population, particularly, that of the healthcare workers. Given the unprecedented large-scale impacts of the COVID-19 pandemic, digital technology has gained momentum as invaluable social interaction and health tracking tools in this time of great turmoil, in part due to the imposed state-wide mobilization limitations to mitigate the risk of infection that might arise from in-person socialization or hospitalization. Over the last five years, there has been a notable increase in the demand and usage of mobile and wearable devices as well as their adoption in studies of mental fitness. The purposes of this scoping review are to summarize evidence on the sweeping impact of COVID-19 on mental health as well as to evaluate the merits of the devices for remote psychological support. We conclude that the COVID-19 pandemic has inflicted a significant toll on the mental health of the population, leading to an upsurge in reports of pathological stress, depression, anxiety, and insomnia. It is also clear that mobile and wearable devices (e.g., smartwatches and fitness trackers) are well placed for identifying and targeting individuals with these psychological burdens in need of intervention. However, we found that most of the previous studies used research-grade wearable devices that are difficult to afford for the normal consumer due to their high cost. Thus, the possibility of replacing the research-grade wearable devices with the current smartwatch is also discussed.

Emotion Recognition Using Electrodermal Activity Signals and Multiscale Deep Convolutional Neural Network

In this work, an attempt has been made to classify emotional states using electrodermal activity (EDA) signals and multiscale convolutional neural networks. For this, EDA signals are considered from a publicly available "A Dataset for Emotion Analysis using Physiological Signals" (DEAP) database. These signals are decomposed into multiple-scales using the coarse-grained method. The multiscale signals are applied to the Multiscale Convolutional Neural Network (MSCNN) to automatically learn robust features directly from the raw signals. Experiments are performed with the MSCNN approach to evaluate the hypothesis (i) improved classification with electrodermal activity signals, and (ii) multiscale learning captures robust complementary features at a different scale. Results show that the proposed approach is able to differentiate various emotional states. The proposed approach yields a classification accuracy of 69.33% and 71.43% for valence and arousal states, respectively. It is observed that the number of layers and the signal length are the determinants for the classifier performance. The performance of the proposed approach outperforms the single-layer convolutional neural network. The MSCNN approach provides end-to-end learning and classification of emotional states without additional signal processing. Thus, it appears that the proposed method could be a useful tool to assess the difference in emotional states for automated decision making.

Clinical effectiveness of the electrodermal orienting reactivity test for evaluating relapse and recurrence risk in patients hospitalized for depression

BACKGROUND

Recurrence is a problem for many patients who have episodes of depression. In experimental settings, hyporeactivity in the Electrodermal Orienting Reactivity (EDOR) test has been observed to be more frequent in these patients. The aim of this study was to investigate the clinical value of this test with regard to a prognosis of episode recurrence in patients hospitalized for depression.

METHODS

The study was performed using a cohort design at a specialized psychiatric clinic in Warsaw, Poland. The primary endpoint measure was relapse or recurrence of depression. Data on electrodermal reactivity measured by the EDOR test, clinical status, and psychiatric history were collected at the clinic. Relapse and recurrence data were collected by clinical interviews 1 year after the EDOR test. The predictive (adjusting for confounders) and comparative (relative to other predictors) performance of electrodermal hyporeactivity was assessed using simple and multiple binary logistic regression.

RESULTS

The patient sample included 97 patients aged between 20 and 81 years (mean, 51.2 years). Twenty patients (20.6%) were hyporeactive in the EDOR test. The group of hyporeactive patients did not differ significantly from the reactive group with regard to background factors or clinical status on admission. Forty-seven patients (51.6%) had at least one depressive episode during the follow-up period. In the analysis including potential confounders, the likelihood of relapse or recurrence of depression was nearly five times higher among the hyporeactive patients than the reactive patients (odds ratio [OR], 4.7; 95% confidence interval (CI), 1.3-16.2; p = 0.015). In the comparative analysis, only hyporeactivity was found to be associated with recurring episodes (OR, 3.3; 95% CI, 1.1-10.2; p = 0.036).

CONCLUSIONS

Electrodermal hyporeactivity was associated with a higher risk of relapse or recurrence after discharge among patients hospitalized for depression. This finding warrants further clinical investigations that cover different types of depression and account for causal mechanisms.

TRIAL REGISTRATION

The study design was registered in the German Clinical Trials Register ( DRKS00010082 ).

Physiological responses to proposals during dyadic decision-making conversations

A novel conversation-analytically informed paradigm was used to examine how joint decision-making interaction, with its various types of proposal sequences, is reflected in the physiological responses of participants. Two types of dyads–dyads with one depressed and one non-depressed participant (N = 15) and dyads with two non-depressed participants (N = 15)–engaged in a series of conversational joint decision-making tasks, during which we measured their skin conductance (SC) responses. We found that the participants’ SC response rates were higher and more synchronized during proposal sequences than elsewhere in the conversation. Furthermore, SC response rates were higher when the participant was in the role of a proposal speaker (vs. a proposal recipient), and making a proposal was associated with higher SC response rates for participants with depression (vs. participants without depression). Moreover, the SC response rates in the proposal speaker were higher when the recipient accepted (vs. not accepted) the proposal. We interpret this finding with reference to accepting responses suggesting a commitment to future action, for which the proposal speaker may feel specifically responsible for. A better understanding of the physiological underpinnings of joint decision-making interaction may help improve democratic practices in contexts where certain individuals experience challenges in this regard.

Facial Expression Recognition With Machine Learning and Assessment of Distress in Patients With Cancer

OBJECTIVES

To estimate the effectiveness of combining facial expression recognition and machine learning for better detection of distress.

SAMPLE & SETTING

232 patients with cancer in Sichuan University West China Hospital in Chengdu, China.

METHODS & VARIABLES

The Distress Thermometer (DT) and Hospital Anxiety and Depression Scale (HADS) were used as instruments. The HADS included scores for anxiety (HADS-A), depression (HADS-D), and total score (HADS-T). Distressed patients were defined by the DT cutoff score of 4, the HADS-A cutoff score of 8 or 9, the HADS-D cutoff score of 8 or 9, or the HADS-T cutoff score of 14 or 15. The authors applied histogram of oriented gradients to extract facial expression features from face images, and used a support vector machine as the classifier.

RESULTS

The facial expression features showed feasible differentiation ability on cases classified by DT and HADS.

IMPLICATIONS FOR NURSING

Facial expression recognition could serve as a supplementary screening tool for improving the accuracy of distress assessment and guide strategies for treatment and nursing.

A Naturalistic, European Multi-Center Clinical Study of Electrodermal Reactivity and Suicide Risk Among Patients With Depression

Background: Electrodermal hyporeactivity has been proposed as a marker of suicidal risk. The EUDOR-A study investigated the prevalence of electrodermal hyporeactivity among patients with depression and its association with attempted and completed suicide. Methods: Between August 2014 and March 2016, 1,573 in- and outpatients with a primary diagnosis of depression (active or remission phase) were recruited at 15 European psychiatric centers. Each patient was followed-up for 1 year. Electrodermal activity was assessed at baseline with the ElectroDermal Orienting Reactivity Test. Data on the sociodemographic characteristics, clinical diagnoses, and treatment of the subjects were also collected. The severity of the depressive symptoms was assessed through the Montgomery-Asberg Depression Rating Scale. Information regarding number, time, and method of suicide attempts was gathered at baseline and at the end of the 1-year follow-up. The same data were collected in case of completed suicide. Results: Hyporeactive patients were shown to be significantly more at risk of suicide attempt compared to reactive patients, both at baseline and follow-up. A sensitivity of 29.86% and a positive predictive value (PPV) of 46.77% were found for attempted suicide at baseline, while a sensitivity of 35.36% and a PPV of 8.92% were found for attempted suicide at follow-up. The sensitivity and PPV for completed suicide were 25.00 and 0.61%, respectively. However, when controlled for suicide attempt at baseline, the association between hyporeactivity and follow-up suicide attempt was no longer significant. The low number of completed suicides did not allow any analysis.

Altered Experienced Thermoregulation in Depression-No Evidence for an Effect of Early Life Stress

Objectives: Accumulating evidence suggests that individuals with depression are characterised by difficulties in thermoregulatory cooling. The aim of this study was to investigate, for the first time, whether depressed individuals are aware of these alterations, what their physical consequences are and whether they may be rooted in early life stress. Methods: A total of N = 672 medically healthy individuals from the general population were recruited to participate in an online survey. Participants were divided into depressed vs. non-depressed using the Patient Health Questionnaire. Experienced autonomic and behavioural thermoregulation as well as vigilance problems in response to temperature increases were assessed by the Experienced Temperature Sensitivity and Regulation Survey. The Childhood Trauma Questionnaire was administered to assess early life stress. Results: Controlling for age, sex, body mass index, and physical activity, depressed vs. non-depressed individuals did not differ in their experienced autonomic and behavioural responses to temperature increases. However, the depressed individuals reported comparably greater difficulties in concentrating and drowsiness/fatigue in warm environments (p = 0.029), during physical exertion (p = 0.029), and during stress (p < 0.001). There were no differences in the experienced thermoregulation between depressed individuals with vs. without early life stress. Conclusions: Depressed individuals experienced more severe physical impairments (i.e., greater vigilance problems) in response to intense warmth when compared to non-depressed individuals. These differences were not attributable to comorbid illnesses, the intake of medication, or physical deconditioning. Further enquiries in clinical populations are warranted to investigate to what extent the observed alterations map onto specific symptoms of depression (e.g., sleep disturbances).

Stress Markers for Mental States and Biotypes of Depression and Anxiety: A Scoping Review and Preliminary Illustrative Analysis

Depression and anxiety disrupt daily function and their effects can be long-lasting and devastating, yet there are no established physiological indicators that can be used to predict onset, diagnose, or target treatments. In this review, we conceptualize depression and anxiety as maladaptive responses to repetitive stress. We provide an overview of the role of chronic stress in depression and anxiety and a review of current knowledge on objective stress indicators of depression and anxiety. We focused on cortisol, heart rate variability and skin conductance that have been well studied in depression and anxiety and implicated in clinical emotional states. A targeted PubMed search was undertaken prioritizing meta-analyses that have linked depression and anxiety to cortisol, heart rate variability and skin conductance. Consistent findings include reduced heart rate variability across depression and anxiety, reduced tonic and phasic skin conductance in depression, and elevated cortisol at different times of day and across the day in depression. We then provide a brief overview of neural circuit disruptions that characterize particular types of depression and anxiety. We also include an illustrative analysis using predictive models to determine how stress markers contribute to specific subgroups of symptoms and how neural circuits add meaningfully to this prediction. For this, we implemented a tree-based multi-class classification model with physiological markers of heart rate variability as predictors and four symptom subtypes, including normative mood, as target variables. We achieved 40% accuracy on the validation set. We then added the neural circuit measures into our predictor set to identify the combination of neural circuit dysfunctions and physiological markers that accurately predict each symptom subtype. Achieving 54% accuracy suggested a strong relationship between those neural-physiological predictors and the mental states that characterize each subtype. Further work to elucidate the complex relationships between physiological markers, neural circuit dysfunction and resulting symptoms would advance our understanding of the pathophysiological pathways underlying depression and anxiety.

Integrating Biosignals Measurement in Virtual Reality Environments for Anxiety Detection

This paper proposes a protocol for the acquisition and processing of biophysical signals in virtual reality applications, particularly in phobia therapy experiments. This protocol aims to ensure that the measurement and processing phases are performed effectively, to obtain clean data that can be used to estimate the users' anxiety levels. The protocol has been designed after analyzing the experimental data of seven subjects who have been exposed to heights in a virtual reality environment. The subjects' level of anxiety has been estimated based on the real-time evaluation of a nonlinear function that has as parameters various features extracted from the biophysical signals. The highest classification accuracy was obtained using a combination of seven heart rate and electrodermal activity features in the time domain and frequency domain.

Linking physical and social environments with mental health in old age: a multisensor approach for continuous real-life ecological and emotional assessment

Background

Urban stress is mentioned as a plausible mechanism leading to chronic stress, which is a risk factor of depression. Yet, an accurate assessment of urban stressors in environmental epidemiology requires new methods. This article discusses methods for the sensor-based continuous assesment of geographic environments, stress and depressive symptoms in older age. We report protocols of the promoting mental well-being and healthy ageing in cities (MINDMAP) and Healthy Aging and Networks in Cities (HANC) studies nested in the RECORD Cohort as a background for a broad discussion about the theoretical foundation and monitoring tools of mobile sensing research in older age. Specifically, these studies allow one to compare how older people with and without depression perceive, navigate and use their environment; and how the built environments, networks of social contacts, and spatial mobility patterns influence the mental health of older people.

Methods

Our research protocol combines (1) Global Positioning System (GPS) and accelerometer tracking and a GPS-based mobility survey to assess participants’ mobility patterns, activity patterns and environmental exposures; (2) proximity detection to assess whether household members are close to each other; (3) ecological momentary assessment to track momentary mood and stress and environmental perceptions; and (4) electrodermal activity for the tentative prediction of stress. Data will be compared within individuals (at different times) and between persons with and without depressive symptoms.

Conclusion

The development of mobile sensing and survey technologies opens an avenue to improve understanding of the role of momentary stressors and resourcing features of residential and non-residential environments for older populations’ mental health. However, validation, privacy and ethical aspects are important issues to consider.

A Biofeedback Enhanced Adaptive Virtual Reality Environment for Managing Surgical Pain and Anxiety

Pain and anxiety are common accompaniments of surgery, and opioids have been the mainstay of pain management for decades, with about 80% of the surgical population leaving the hospital with an opioid prescription. Moreover, patients receiving an opioid prescription after short-stay surgeries have a 44% increased risk of long-term opioid use, and about one in 16 surgical patients becomes a long-term user. Current opioid abuse and addiction now place the US in an “opioid epidemic,” and calls for alternative pain management mechanisms. To mitigate the preoperative anxiety and postoperative pain, we developed a virtual reality (VR) experience based on Attention Restoration Theory (ART) and integrated the user’s heart rate variability (HRV) biofeedback to create an adaptive environment. A randomized control trial among 16 Total Knee Arthroplasty (TKA) patients undergoing surgery at Patewood Memorial Hospital, Greenville, SC demonstrated that patients experiencing the adaptive VR environment reported a significant decrease in preoperative anxiety ([Formula: see text]) and postoperative pain ([Formula: see text]) after the VR intervention. These results were also supported by the physiological measures where there was a significant increase in RR Interval (RRI) ([Formula: see text]) and a significant decrease in the low frequency (LF)/high frequency (HF) ratio ([Formula: see text]) and respiration rate (RR) ([Formula: see text]).