Sympathetic and parasympathetic modulation of pupillary unrest

Pupillary unrest is an established indicator of drowsiness or sleepiness. How sympathetic and parasympathetic activity contribute to pupillary unrest is not entirely unclear. In this study, we investigated 83 young healthy volunteers to assess the relationship of pupillary unrest to other markers of the autonomic nervous system. Sample entropy (SE) and the established pupillary unrest index (PUI) were calculated to characterize pupil size variability. Autonomic indices were derived from heart rate, blood pressure, respiration, and skin conductance. Additionally, we assessed individual levels of calmness, vigilance, and mood. In an independent sample of 26 healthy participants, we stimulated the cardiovagal system by a deep breathing test. PUI was related to parasympathetic cardiac indices and sleepiness. A linear combination of vagal heart rate variability [root mean square of heart beat interval differences (RMSSD)] and skin conductance fluctuations (SCFs) was suited best to explain interindividual variance of PUI. Complexity of pupil diameter (PD) variations correlated to indices of sympathetic skin conductance. Furthermore, we found that spontaneous fluctuations of skin conductance are accompanied by increases of pupil size. In an independent sample, we were able to corroborate the relation of PUI with RMSSD and skin conductance. A slow breathing test enhanced RMSSD and PUI proportionally to each other, while complexity of PD dynamics decreased. Our data suggest that the slow PD oscillations ( f < 0.15 Hz) quantified by PUI are related to the parasympathetic modulation. Sympathetic arousal as detected by SCFs is associated to transient pupil size increases that increase non-linear pupillary dynamics

Modeling startle eyeblink electromyogram to assess fear learning

Pavlovian fear conditioning is widely used as a laboratory model of associative learning in human and nonhuman species. In this model, an organism is trained to predict an aversive unconditioned stimulus from initially neutral events (conditioned stimuli, CS). In humans, fear memory is typically measured via conditioned autonomic responses or fear-potentiated startle. For the latter, various analysis approaches have been developed, but a systematic comparison of competing methodologies is lacking. Here, we investigate the suitability of a model-based approach to startle eyeblink analysis for assessment of fear memory, and compare this to extant analysis strategies. First, we build a psychophysiological model (PsPM) on a generic startle response. Then, we optimize and validate this PsPM on three independent fear-conditioning data sets. We demonstrate that our model can robustly distinguish aversive (CS+) from nonaversive stimuli (CS-, i.e., has high predictive validity). Importantly, our model-based approach captures fear-potentiated startle during fear retention as well as fear acquisition. Our results establish a PsPM-based approach to assessment of fear-potentiated startle, and qualify previous peak-scoring methods. Our proposed model represents a generic startle response and can potentially be used beyond fear conditioning, for example, to quantify affective startle modulation or prepulse inhibition of the acoustic startle response

The psychophysiology primer : A guide to methods and a broad review with a focus on human-computer interaction

Publisher Copyright: © 2016 B. Cowley, M. Filetti, K. Lukander.Peer reviewe

Mental-State Estimation, 1987

Reports on the measurement and evaluation of the physiological and mental state of operators are presented

Design and Development of a Real-Time Bio-Sensing System Assessing Student Mental Workload and Engagement

Ο εντοπισμός του επακριβούς επιπέδου προσήλωσης και εμπλοκής των μαθητών με το περιεχόμενο διδασκαλίας στην τάξη είναι ένας από τους πιο μεγαλεπήβολους στόχους των ερευνητών της εκπαιδευτικής και επιστημονικής κοινότητας. (Lang, 1995, Grossberg, 1987). Σχετικές διεπιστημονικές ερευνητικές προσπάθειες προσαύξησης ενδιαφέροντος και εντοπισμού της αποτελεσματικότητας των διδακτικών πρακτικών βασίζονται σε τυπικές μελέτες από τον χώρο της ψυχολογίας, της παιδαγωγικής, της παιδοψυχολογίας και της ψυχοφυσιολογίας. Νέες τεχνολογίες έχουν εισάγει διαγνωστικές συσκευές δανεισμένες από τον χώρο της ιατρικής με σκοπό να εκμεταλλευτούν τις δυνατότητες μετρήσεων βιολογικών σημάτων τα οποία αποτελούν επιβεβαιωμένες εκφράσεις ψυχοφυσιολογικών καταστάσεων οι οποίες μπορούν να μεταφραστούν σε εκδηλώσεις διέγερσης και διάθεσης. Οι ιατρικές συσκευές απαιτούν εργαστηριακό περιβάλλον λόγω των αναγκών χρήσης ηλεκτροδίων, κινητικών περιορισμών, συγχρονισμού και ομοιομορφίας των στοιχείων που προκύπτουν και γι’ αυτό τον λόγο δεν μπόρεσαν ποτέ να αποδόσουν μια προσιτή λύση εφαρμόσιμη ευρύτερα σε εκπαιδευτικό περιβάλλον. Στην παρούσα μελέτη, αναλύονται οι επιδόσεις μιας ειδικά κατασκευασμένης ηλεκτρονικής συσκευής, σχεδιασμένης ώστε να εξεταστούν οι δυνατότητες να εξαχθούν δείκτες ψυχοσωματικών εκφράσεων του χρήστη, με την δυνατότητα να χρησιμοποιείται εύχρηστα στην τάξη χωρίς ηλεκτρόδια και επηρεασμούς από προσαρτήσεις. Το ολοκληρωμένο σύστημα μέτρησης και αποτύπωσης συμπερασμάτων είναι βασισμένο σε μοντελοποίηση συμπεριφορών αλλαγής του καρδιακού παλμού και της ειδικής διηλεκτρικής αγωγιμότητας του δέρματος σε πραγματικό χρόνο. Η συσκευή χρησιμοποιεί οπτικούς και διηλεκτρικούς αισθητήρες επαφής και έχει μελετηθεί σε αντιπαραβολή με διαβαθμισμένα περιβάλλοντα προκλητών καταστάσεων νοητικής φόρτισης. Σειρές πειραματικών διαδικασιών εφαρμοσμένες σε διαβαθμισμένα σενάρια πρόκλησης ψυχοσωματικών διεγέρσεων έχουν ολοκληρωθεί για επικύρωση, μελέτη επιδόσεων και λειτουργία του συστήματος ακόμη και σε σύγκριση με εμπορικό προϊόν. Πειραματικά αποτελέσματα δείχνουν αξιόλογους συσχετισμούς του μοντέλου και των επιδόσεων του συστήματος με τις αναμενόμενες αποκρίσεις με ενθαρρυντικά ποσοστά ακρίβειας.Facing the challenge of improving adaptive interaction in educational technologies scientists and educators have turned their focal point in diverse areas ranging from educational, teaching and behavioural psychology to cognitive, affective and perceptual neuroscience. The introduction of digital technologies and interactive media tools in education has shown improved learning efficiency, much higher memory activation and assimilation than verbal teaching, notably due to enhancing motivation achieved by employing approaches attracting student’s attention. Excelling aspects of audio visual presentation proved highly valuable particularly in classes with multi ethnic groups of students, as for example consistency between definitions and objects which were verbally and visually defined, eliminating possible misconceptions caused by mishearing or misinterpretation by the learner. Taking it all one step further as to how an educational system could be even more efficient, a new element would be needed revealing a credible judgment of learning scores and effectiveness of the learning process instantaneously as for example inner levels of activation and satisfaction. In fact, this could be made possible using existing technologies if subconscious neurophysiological responses of a learner could be ascertained and inferred to psycho-somatic conditions as they occur. A system including bio-sensing, data analysis and processing in real time able to provide quantified markers of psychosomatic states of a learner would help enormously in next generations of educational practice. Incorporating data of student engagement and active involvement could help to deduce the interest of a learner, which is known to improve sensitisation in implicit, incidental and also in classical learning. Experimental settings used in previous studies attempting to incorporate physiological responses and interpretations into responsive educational settings have faced major obstacles. Operational issues caused by the requirements of the devices used for the acquisition of physiological signals such as electrodes and movement restrictions have reduced the progress of such settings to laboratory environments. In such settings as described above, the effects of wiring harnesses and sensory components produced an additional psychological burden on the participants. Consequently, the need to approach the physiological data acquisition from a new angle with seamless and unnoticeable operation is apparent. The challenge to design, develop and validate a system that being minimally obstructive and literally unnoticed by the user would uncover combined subconscious expressions of a learner was the primary objective of this research. Physiological data of Heart Rate and Skin Trans-Conductance (Electro-dermal Response) elected as vitally important and highly appropriate to produce the input of data required to evaluate a behavioural concept model. The behavioural assessment model entailed vector classifiers producing directional interpretations of measurements. Directional information (Gradient response) has been derived by comparison of measurements to previously measured values in real time. Assessing the effectiveness and accuracy of the adopted model to deduce attention and engagement of a learner in real time formed the second major objective. For this purpose, a series of relevant experimental methodologies have been employed. Data produced using formal personality assessments have also been investigated in conjunction with those derived from physiological responses in order to identify personality related particularities. The final part of this work has been supplemented by propositions and suggestions with regards to various applications of the system in accomplishment of the initial aims

Inferring implicit relevance from physiological signals

Ongoing growth in data availability and consumption has meant users are increasingly faced with the challenge of distilling relevant information from an abundance of noise. Overcoming this information overload can be particularly difficult in situations such as intelligence analysis, which involves subjectivity, ambiguity, or risky social implications. Highly automated solutions are often inadequate, therefore new methods are needed for augmenting existing analysis techniques to support user decision making. This project investigated the potential for deep learning to infer the occurrence of implicit relevance assessments from users' biometrics. Internal cognitive processes manifest involuntarily within physiological signals, and are often accompanied by 'gut feelings' of intuition. Quantifying unconscious mental processes during relevance appraisal may be a useful tool during decision making by offering an element of objectivity to an inherently subjective situation. Advances in wearable or non-contact sensors have made recording these signals more accessible, whilst advances in artificial intelligence and deep learning have enhanced the discovery of latent patterns within complex data. Together, these techniques might make it possible to transform tacit knowledge into codified knowledge which can be shared. A series of user studies recorded eye gaze movements, pupillary responses, electrodermal activity, heart rate variability, and skin temperature data from participants as they completed a binary relevance assessment task. Participants were asked to explicitly identify which of 40 short-text documents were relevant to an assigned topic. Investigations found this physiological data to contain detectable cues corresponding with relevance judgements. Random forests and artificial neural networks trained on features derived from the signals were able to produce inferences with moderate correlations with the participants' explicit relevance decisions. Several deep learning algorithms trained on the entire physiological time series data were generally unable to surpass the performance of feature-based methods, and instead produced inferences with low correlations with participants' explicit personal truths. Overall, pupillary responses, eye gaze movements, and electrodermal activity offered the most discriminative power, with additional physiological data providing diminishing or adverse returns. Finally, a conceptual design for a decision support system is used to discuss social implications and practicalities of quantifying implicit relevance using deep learning techniques. Potential benefits included assisting with introspection and collaborative assessment, however quantifying intrinsically unknowable concepts using personal data and abstruse artificial intelligence techniques were argued to pose incommensurate risks and challenges. Deep learning techniques therefore have the potential for inferring implicit relevance in information-rich environments, but are not yet fit for purpose. Several avenues worthy of further research are outlined

Electrophysiological Signatures of Fear Conditioning: From Methodological Considerations to Catecholaminergic Mechanisms and Translational Perspectives

Fear conditioning describes a learning mechanism during which a specific stimulus gets associated with an aversive event (i.e., an unconditioned stimulus; US). Thereby, this initially neutral or arbitrary stimulus becomes a so-called “conditioned” stimulus (CS), which elicits a conditioned threat response. Fear extinction refers to the decrease in conditioned threat responses as soon as the CS is repeatedly presented in the absence of the US. While fear conditioning is an important learning model for understanding the etiology and maintenance of anxiety and fear-related disorders, extinction learning is considered to reflect the most important learning process of exposure therapy. Neurophysiological signatures of fear conditioning have been widely studied in rodents, leading to the development of groundbreaking neurobiological models, including brain regions such as the amygdala, insula, and prefrontal areas. These models aim to explain neural mechanisms of threat processing, with the ultimate goal to improve treatment strategies for pathological fear. Recording intracranial electrical activity of single units in animals offers the opportunity to uncover neural processes involved in threat processing with excellent spatial and temporal resolution. A large body of functional magnetic resonance imaging (fMRI) studies have helped to translate this knowledge about the anatomy of fear conditioning into the human realm. fMRI is an imaging technique with a high spatial resolution that is well suited to study slower brain processes. However, the temporal resolution of fMRI is relatively poor. By contrast, electroencephalography (EEG) is a neuroscientific method to capture fast and transient cortical processes. While EEG offers promising opportunities to unravel the speed of neural threat processing, it also provides the possibility to study oscillatory brain activity (e.g., prefrontal theta oscillations). The present thesis contains six research manuscripts, describing fear conditioning studies that mainly applied EEG methods in combination with other central (fMRI) and peripheral (skin conductance, heart rate, and fear-potentiated startle) measures. A special focus of this thesis lies in methodological considerations for EEG fear conditioning research. In addition, catecholaminergic mechanisms are studied, with the ultimate goal of opening up new translational perspectives. Taken together, the present thesis addresses several methodological challenges for neuroscientific (in particular, EEG) fear conditioning research (e.g., appropriate US types and experimental designs, signal-to-noise ratio, simultaneous EEG-fMRI). Furthermore, this thesis gives critical insight into catecholaminergic (noradrenaline and dopamine) mechanisms. A variety of neuroscientific methods (e.g., EEG, fMRI, peripheral physiology, pharmacological manipulation, genetic associations) have been combined, an approach that allowed us (a) to translate knowledge from animal studies to human research, and (b) to stimulate novel clinical directions

Amygdala Neurofeedback Training in Borderline Personality Disorder: Capturing Improvements in Emotion Regulation

The way we regulate emotions is a powerful determinant of behavior and directly impacts affect and physiology. Many mental disorders, such as borderline personality disorder, are in large part disorders of emotion dysregulation. Because of its important role in mental health, research has endeavored to understand the mechanisms and biological underpinnings of emotion regulation and to create trainings and specific clinical programs that aim to augment the ability to regulate emotions. The assessment of psychophysiological responses represents an important complementary method to quantify emotion regulation in both studies on healthy individuals and studies assessing clinical emotion regulation trainings. However, psychophysiological effects have been inconsistent across literature, which impedes informed decisions about suitable psychophysiological variables of emotion regulation experiments and clinical trainings. A new technique assumed to improve emotion regulation is amygdala neurofeedback training. Because patients with borderline personality disorder show hyperreactivity of the amygdala likely underlying the severe emotion regulation problems they suffer from, amygdala neurofeedback training may be a candidate training to improve emotion regulation in these patients. Until now, it has been unclear which aspects of psychopathology and emotion regulation may change with neurofeedback-aided amygdala downregulation in borderline personality disorder, which is urgently needed to determine a primary outcome measure for future randomized controlled trails. To fill these gaps, the present doctoral thesis identified the effects of psychophysiological responses of emotion regulation as well as important moderators and identified primary outcome measures of emotion dysregulation after neurofeedback training in patients with borderline personality disorder. In total, three studies were conducted. In Study I, a total of 1353 studies on psychophysiological responses of emotion regulation were screened through a systematic search of articles and meta-analyses were used to evaluate effect sizes of instructed downregulation strategies on common autonomic and electromyographic measures. Following this, Study II systematically tested effects of the startle probe timing on startle responses during emotion regulation in 47 healthy individuals. Study II aimed at optimizing emotion regulation assessment with the emotion-modulated startle that was then used in Study III. In Study III, a four-session amygdala neurofeedback training was tested in 24 female patients with borderline personality disorder. Before and after the neurofeedback training, as well as at a 6-week follow-up assessment, measures of emotion dysregulation and borderline personality disorder psychopathology were tested at diverse levels of analysis. Results from Study I demonstrate that effects of emotion regulation on autonomic measures, even if significant, were small and heterogeneous across studies, while electromyographic measures were more homogeneous and revealed medium effect sizes. Important study characteristics such as the study design, control instruction and trial duration moderated some autonomic effects of suppression and reappraisal. Study II demonstrated a significant inhibition of the startle response with emotion downregulation. Startle probes delivered at >7 seconds into the regulation phase were useful to quantify reappraisal effects, although earlier probes did not yield significantly smaller effects. Finally, Study III demonstrated that the inhibition of the startle with emotion downregulation increased after the training, suggesting improved emotion regulation abilities. In addition, we could demonstrate that general BPD psychopathology as well as affective instability and negative affect in daily life improved after training. However, after correction for multiple comparisons, observed effect sizes did not surpass the significance level and some effects (e.g., startle) faded to the 6-week follow-up assessment. In sum, the present thesis provides the groundwork for future randomized controlled trials of amygdala neurofeedback training and enables future laboratory and clinical studies to gain more stable effects in psychophysiological measurements of emotion regulation. In particular, the findings implicate that with regard to emotion regulation research, autonomic measures appear to be highly variable and thus should be selected carefully. In addition, we need more comparable psychophysiological set-ups in the empirical study of emotion regulation. The emotion-modulated startle not only proved to be a robust measure to quantify emotion regulation effects in general, but also appeared to be suitable to track improvements in emotion regulation in the context of a neurofeedback training targeting emotion dysregulation. With respect to emotion regulation outcome measures for future amygdala neurofeedback studies, further improvement of the specific paradigms is needed. In addition, the neurofeedback training itself should be optimized in terms of e.g. training time and booster sessions. Future placebo-controlled trials must then confirm that the treatment is effective in improving emotion regulation in those with borderline personality disorder