Respiratory modulation of the heart rate: A potential biomarker of cardiorespiratory function in human.

In recent decade, wearable digital devices have shown potentials for the discovery of novel biomarkers of humans' physiology and behavior. Heart rate (HR) and respiration rate (RR) are most crucial bio-signals in humans' digital phenotyping research. HR is a continuous and non-invasive proxy to autonomic nervous system and ample evidence pinpoints the critical role of respiratory modulation of cardiac function. In the present study, we recorded longitudinal (7 days, 4.63 ± 1.52) HR and RR of 89 freely behaving human subjects (Female: 39, age 57.28 ± 5.67, Male: 50, age 58.48 ± 6.32) and analyzed their dynamics using linear models and information theoretic measures. While HR's linear and nonlinear characteristics were expressed within the plane of the HR-RR directed flow of information (HR→RR - RR→HR), their dynamics were determined by its RR→HR axis. More importantly, RR→HR quantified the effect of alcohol consumption on individuals' cardiorespiratory function independent of their consumed amount of alcohol, thereby signifying the presence of this habit in their daily life activities. The present findings provided evidence for the critical role of the respiratory modulation of HR, which was previously only studied in non-human animals. These results can contribute to humans' phenotyping research by presenting RR→HR as a digital diagnosis/prognosis marker of humans' cardiorespiratory pathology.

Perception of Social Odor and Gender-Related Differences Investigated Through the Use of Transfer Entropy and Embodied Medium.

The perception of putative pheromones or social odors (PPSO) in humans is a widely debated topic because the published results seem ambiguous. Our research aimed to evaluate how cross-modal processing of PPSO and gender voice can affect the behavioral and psychophysiological states of the subject during a listening task with a bodily contact medium, and how these effects could be gender related. Before the experimental session, three embodied media, were exposed to volatilized estratetraenol (Estr), 5α-androst-16-en-3 α-ol (Andr), and Vaseline oil. The experimental session consisted in listening to a story that were transmitted, with a male or female voice, by the communicative medium via a Bluetooth system during a listening task, recorded through 64-active channel electroencephalography (EEG). The sense of co-presence and social presence, elicited by the medium, showed how the established relationship with the medium was gender dependent and modulated by the PPSO. In particular, Andr induced greater responses related to co-presence. The gender of the participants was related to the co-presence desire, where women imagined higher medium co-presence than men. EEG findings seemed to be more responsive to the PPSO-gender voice interaction, than behavioral results. The mismatch between female PPSO and male voice elicited the greatest cortical flow of information. In the case of the Andr-male voice condition, the trained model appeared to assign more relevance to the flow of information to the right frontotemporal regions (involved in odor recognition memory and social behavior). The Estr-male voice condition showed activation of the bilateral frontoparietal network, which is linked to cognitive control, cognitive flexibility, and auditory consciousness. The model appears to distinguish the dissonance condition linked to Andr matched with a female voice: it highlights a flow of information to the right occipital lobe and to the frontal pole. The PPSO could influence the co-presence judgements and EEG response. The results seem suggest that could be an implicit pattern linked to PPSO-related gender differences and gender voice.

Shrunken Social Brains? A Minimal Model of the Role of Social Interaction in Neural Complexity.

The social brain hypothesis proposes that enlarged brains have evolved in response to the increasing cognitive demands that complex social life in larger groups places on primates and other mammals. However, this reasoning can be challenged by evidence that brain size has decreased in the evolutionary transitions from solitary to social larger groups in the case of Neolithic humans and some eusocial insects. Different hypotheses can be identified in the literature to explain this reduction in brain size. We evaluate some of them from the perspective of recent approaches to cognitive science, which support the idea that the basis of cognition can span over brain, body, and environment. Here we show through a minimal cognitive model using an evolutionary robotics methodology that the neural complexity, in terms of neural entropy and degrees of freedom of neural activity, of smaller-brained agents evolved in social interaction is comparable to the neural complexity of larger-brained agents evolved in solitary conditions. The nonlinear time series analysis of agents' neural activity reveals that the decoupled smaller neural network is intrinsically lower dimensional than the decoupled larger neural network. However, when smaller-brained agents are interacting, their actual neural complexity goes beyond its intrinsic limits achieving results comparable to those obtained by larger-brained solitary agents. This suggests that the smaller-brained agents are able to enhance their neural complexity through social interaction, thereby offsetting the reduced brain size.

Conditional Entropy: A Potential Digital Marker for Stress.

Recent decades have witnessed a substantial progress in the utilization of brain activity for the identification of stress digital markers. In particular, the success of entropic measures for this purpose is very appealing, considering (1) their suitability for capturing both linear and non-linear characteristics of brain activity recordings and (2) their direct association with the brain signal variability. These findings rely on external stimuli to induce the brain stress response. On the other hand, research suggests that the use of different types of experimentally induced psychological and physical stressors could potentially yield differential impacts on the brain response to stress and therefore should be dissociated from more general patterns. The present study takes a step toward addressing this issue by introducing conditional entropy (CE) as a potential electroencephalography (EEG)-based resting-state digital marker of stress. For this purpose, we use the resting-state multi-channel EEG recordings of 20 individuals whose responses to stress-related questionnaires show significantly higher and lower level of stress. Through the application of representational similarity analysis (RSA) and K-nearest-neighbor (KNN) classification, we verify the potential that the use of CE can offer to the solution concept of finding an effective digital marker for stress.

Entropy and the Brain: An Overview.

Entropy is a powerful tool for quantification of the brain function and its information processing capacity. This is evident in its broad domain of applications that range from functional interactivity between the brain regions to quantification of the state of consciousness. A number of previous reviews summarized the use of entropic measures in neuroscience. However, these studies either focused on the overall use of nonlinear analytical methodologies for quantification of the brain activity or their contents pertained to a particular area of neuroscientific research. The present study aims at complementing these previous reviews in two ways. First, by covering the literature that specifically makes use of entropy for studying the brain function. Second, by highlighting the three fields of research in which the use of entropy has yielded highly promising results: the (altered) state of consciousness, the ageing brain, and the quantification of the brain networks' information processing. In so doing, the present overview identifies that the use of entropic measures for the study of consciousness and its (altered) states led the field to substantially advance the previous findings. Moreover, it realizes that the use of these measures for the study of the ageing brain resulted in significant insights on various ways that the process of ageing may affect the dynamics and information processing capacity of the brain. It further reveals that their utilization for analysis of the brain regional interactivity formed a bridge between the previous two research areas, thereby providing further evidence in support of their results. It concludes by highlighting some potential considerations that may help future research to refine the use of entropic measures for the study of brain complexity and its function. The present study helps realize that (despite their seemingly differing lines of inquiry) the study of consciousness, the ageing brain, and the brain networks' information processing are highly interrelated. Specifically, it identifies that the complexity, as quantified by entropy, is a fundamental property of conscious experience, which also plays a vital role in the brain's capacity for adaptation and therefore whose loss by ageing constitutes a basis for diseases and disorders. Interestingly, these two perspectives neatly come together through the association of entropy and the brain capacity for information processing.

Comparative Analysis of the Permutation and Multiscale Entropies for Quantification of the Brain Signal Variability in Naturalistic Scenarios.

As alternative entropy estimators, multiscale entropy (MSE) and permutation entropy (PE) are utilized for quantification of the brain function and its signal variability. In this context, their applications are primarily focused on two specific domains: (1) the effect of brain pathology on its function (2) the study of altered states of consciousness. As a result, there is a paucity of research on applicability of these measures in more naturalistic scenarios. In addition, the utility of these measures for quantification of the brain function and with respect to its signal entropy is not well studied. These shortcomings limit the interpretability of the measures when used for quantification of the brain signal entropy. The present study addresses these limitations by comparing MSE and PE with entropy of human subjects' EEG recordings, who watched short movie clips with negative, neutral, and positive content. The contribution of the present study is threefold. First, it identifies a significant anti-correlation between MSE and entropy. In this regard, it also verifies that such an anti-correlation is stronger in the case of negative rather than positive or neutral affects. Second, it finds that MSE significantly differentiates between these three affective states. Third, it observes that the use of PE does not warrant such significant differences. These results highlight the level of association between brain's entropy in response to affective stimuli on the one hand and its quantification in terms of MSE and PE on the other hand. This, in turn, allows for more informed conclusions on the utility of MSE and PE for the study and analysis of the brain signal variability in naturalistic scenarios.

Stress Changes the Resting-State Cortical Flow of Information from Distributed to Frontally Directed Patterns.

Despite converging evidence on the involvement of large-scale distributed brain networks in response to stress, the effect of stress on the components of these networks is less clear. Although some studies identify higher regional activities in response to stress, others observe an opposite effect in the similar regions. Studies based on synchronized activities and coactivation of these components also yield similar differing results. However, these differences are not necessarily contradictory once we observe the effect of stress on these functional networks in terms of the change in information processing capacity of their components. In the present study, we investigate the utility of such a shift in the analysis of the effect of stress on distributed cortical regions through quantification of the flow of information among them. For this purpose, we use the self-assessed responses of 216 individuals to stress-related questionnaires and systematically select 20 of them whose responses showed significantly higher and lower susceptibility to stress. We then use these 20 individuals' resting-state multi-channel electroencephalography (EEG) recordings (both Eyes-Closed (EC) and Eyes-Open (EO) settings) and compute the distributed flow of information among their cortical regions using transfer entropy (TE). The contribution of the present study is three-fold. First, it identifies that the stress-susceptibility is characterized by the change in flow of information in fronto-parietal brain network. Second, it shows that these regions are distributed bi-hemispherically and are sufficient to significantly differentiate between the individuals with high versus low stress-susceptibility. Third, it verifies that the high stress-susceptibility is markedly associated with a higher parietal-to-frontal flow of information. These results provide further evidence for the viewpoint in which the brain's modulation of information is not necessarily accompanied by the change in its regional activity. They further construe the effect of stress in terms of a disturbance that disrupts the flow of information among the brain's distributed cortical regions. These observations, in turn, suggest that some of the differences in the previous findings perhaps reflect different aspects of impaired distributed brain information processing in response to stress. From a broader perspective, these results posit the use of TE as a potential diagnostic/prognostic tool in identification of the effect of stress on distributed brain networks that are involved in stress-response.

Gentle Versus Strong Touch Classification: Preliminary Results, Challenges, and Potentials.

Touch plays a crucial role in humans' nonverbal social and affective communication. It then comes as no surprise to observe a considerable effort that has been placed on devising methodologies for automated touch classification. For instance, such an ability allows for the use of smart touch sensors in such real-life application domains as socially-assistive robots and embodied telecommunication. In fact, touch classification literature represents an undeniably progressive result. However, these results are limited in two important ways. First, they are mostly based on overall (i.e., average) accuracy of different classifiers. As a result, they fall short in providing an insight on performance of these approaches as per different types of touch. Second, they do not consider the same type of touch with different level of strength (e.g., gentle versus strong touch). This is certainly an important factor that deserves investigating since the intensity of a touch can utterly transform its meaning (e.g., from an affectionate gesture to a sign of punishment). The current study provides a preliminary investigation of these shortcomings by considering the accuracy of a number of classifiers for both, within- (i.e., same type of touch with differing strengths) and between-touch (i.e., different types of touch) classifications. Our results help verify the strength and shortcoming of different machine learning algorithms for touch classification. They also highlight some of the challenges whose solution concepts can pave the path for integration of touch sensors in such application domains as human-robot interaction (HRI).

Higher hypnotic suggestibility is associated with the lower EEG signal variability in theta, alpha, and beta frequency bands.

Variation of information in the firing rate of neural population, as reflected in different frequency bands of electroencephalographic (EEG) time series, provides direct evidence for change in neural responses of the brain to hypnotic suggestibility. However, realization of an effective biomarker for spiking behaviour of neural population proves to be an elusive subject matter with its impact evident in highly contrasting results in the literature. In this article, we took an information-theoretic stance on analysis of the EEG time series of the brain activity during hypnotic suggestions, thereby capturing the variability in pattern of brain neural activity in terms of its information content. For this purpose, we utilized differential entropy (DE, i.e., the average information content in a continuous time series) of theta, alpha, and beta frequency bands of fourteen-channel EEG time series recordings that pertain to the brain neural responses of twelve carefully selected high and low hypnotically suggestible individuals. Our results show that the higher hypnotic suggestibility is associated with a significantly lower variability in information content of theta, alpha, and beta frequencies. Moreover, they indicate that such a lower variability is accompanied by a significantly higher functional connectivity (FC, a measure of spatiotemporal synchronization) in the parietal and the parieto-occipital regions in the case of theta and alpha frequency bands and a non-significantly lower FC in the central region's beta frequency band. Our results contribute to the field in two ways. First, they identify the applicability of DE as a unifying measure to reproduce the similar observations that are separately reported through adaptation of different hypnotic biomarkers in the literature. Second, they extend these previous findings that were based on neutral hypnosis (i.e., a hypnotic procedure that involves no specific suggestions other than those for becoming hypnotized) to the case of hypnotic suggestions, thereby identifying their presence as a potential signature of hypnotic experience.

Emergence of the Affect from the Variation in the Whole-Brain Flow of Information.

Over the past few decades, the quest for discovering the brain substrates of the affect to understand the underlying neural basis of the human's emotions has resulted in substantial and yet contrasting results. Whereas some point at distinct and independent brain systems for the Positive and Negative affects, others propose the presence of flexible brain regions. In this respect, there are two factors that are common among these previous studies. First, they all focused on the change in brain activation, thereby neglecting the findings that indicate that the stimuli with equivalent sensory and behavioral processing demands may not necessarily result in differential brain activation. Second, they did not take into consideration the brain regional interactivity and the findings that identify that the signals from individual cortical neurons are shared across multiple areas and thus concurrently contribute to multiple functional pathways. To address these limitations, we performed Granger causal analysis on the electroencephalography (EEG) recordings of the human subjects who watched movie clips that elicited Negative, Neutral, and Positive affects. This allowed us to look beyond the brain regional activation in isolation to investigate whether the brain regional interactivity can provide further insights for understanding the neural substrates of the affect. Our results indicated that the differential affect states emerged from subtle variation in information flow of the brain cortical regions that were in both hemispheres. They also showed that these regions that were rather common between affect states than distinct to a specific affect were characterized with both short- as well as long-range information flow. This provided evidence for the presence of simultaneous integration and differentiation in the brain functioning that leads to the emergence of different affects. These results are in line with the findings on the presence of intrinsic large-scale interacting brain networks that underlie the production of psychological events. These findings can help advance our understanding of the neural basis of the human's emotions by identifying the signatures of differential affect in subtle variation that occurs in the whole-brain cortical flow of information.

Information Content of Prefrontal Cortex Activity Quantifies the Difficulty of Narrated Stories.

The ability to realize the individuals' impressions during the verbal communication allows social robots to significantly facilitate their social interactions in such areas as child education and elderly care. However, such impressions are highly subjective and internalized and therefore cannot be easily comprehended through behavioural observations. Although brain-machine interface suggests the utility of the brain information in human-robot interaction, previous studies did not consider its potential for estimating the internal impressions during verbal communication. In this article, we introduce a novel approach to estimation of the individuals' perceived difficulty of stories using the quantified information content of their prefrontal cortex activity. We demonstrate the robustness of our approach by showing its comparable performance in face-to-face, humanoid, speaker, and video-chat settings. Our results contribute to the field of socially assistive robotics by taking a step toward enabling robots determine their human companions' perceived difficulty of conversations, thereby enabling these media to sustain their communication with humans by adapting to individuals' pace and interest in response to conversational nuances and complexity.

Facial Pre-Touch Space Differentiates the Level of Openness Among Individuals.

Social and cognitive psychology provide a rich map of our personality landscape. What appears to be unexplored is the correspondence between these findings and our behavioural responses during day-to-day life interaction. In this article, we utilize cluster analysis to show that the individuals' facial pre-touch space can be divided into three well-defined subspaces and that within the first two immediate clusters around the face area such distance information significantly correlate with their openness in the five-factor model (FFM). In these two clusters, we also identify that the individuals' facial pre-touch space can predict their level of openness that are further categorized into six distinct levels with a highly above chance accuracy. Our results suggest that such personality factors as openness are not only reflected in individuals' behavioural responses but also these responses allow for a fine-grained categorization of individuals' personality.

An Information-Theoretic Approach to Quantitative Analysis of the Correspondence Between Skin Blood Flow and Functional Near-Infrared Spectroscopy Measurement in Prefrontal Cortex Activity.

Effect of Skin blood flow (SBF) on functional near-infrared spectroscopy (fNIRS) measurement of cortical activity proves to be an illusive subject matter with divided stances in the neuroscientific literature on its extent. Whereas, some reports on its non-significant influence on fNIRS time series of cortical activity, others consider its impact misleading, even detrimental, in analysis of the brain activity as measured by fNIRS. This situation is further escalated by the fact that almost all analytical studies are based on comparison with functional Magnetic Resonance Imaging (fMRI). In this article, we pinpoint the lack of perspective in previous studies on preservation of information content of resulting fNIRS time series once the SBF is attenuated. In doing so, we propose information-theoretic criteria to quantify the necessary and sufficient conditions for SBF attenuation such that the information content of frontal brain activity in resulting fNIRS times series is preserved. We verify these criteria through evaluation of their utility in comparative analysis of principal component (PCA) and independent component (ICA) SBF attenuation algorithms. Our contributions are 2-fold. First, we show that mere reduction of SBF influence on fNIRS time series of frontal activity is insufficient to warrant preservation of cortical activity information. Second, we empirically justify a higher fidelity of PCA-based algorithm in preservation of the fontal activity's information content in comparison with ICA-based approach. Our results suggest that combination of the first two principal components of PCA-based algorithm results in most efficient SBF attenuation while preserving maximum frontal activity's information. These results contribute to the field by presenting a systematic approach to quantification of the SBF as an interfering process during fNIRS measurement, thereby drawing an informed conclusion on this debate. Furthermore, they provide evidence for a reliable choice among existing SBF attenuation algorithms and their inconclusive number of components, thereby ensuring minimum loss of cortical information during SBF attenuation process.

Multiscale Entropy Quantifies the Differential Effect of the Medium Embodiment on Older Adults Prefrontal Cortex during the Story Comprehension: A Comparative Analysis.

Todays' communication media virtually impact and transform every aspect of our daily communication and yet the extent of their embodiment on our brain is unexplored. The study of this topic becomes more crucial, considering the rapid advances in such fields as socially assistive robotics that envision the use of intelligent and interactive media for providing assistance through social means. In this article, we utilize the multiscale entropy (MSE) to investigate the effect of the physical embodiment on the older people's prefrontal cortex (PFC) activity while listening to stories. We provide evidence that physical embodiment induces a significant increase in MSE of the older people's PFC activity and that such a shift in the dynamics of their PFC activation significantly reflects their perceived feeling of fatigue. Our results benefit researchers in age-related cognitive function and rehabilitation who seek for the adaptation of these media in robot-assistive cognitive training of the older people. In addition, they offer a complementary information to the field of human-robot interaction via providing evidence that the use of MSE can enable the interactive learning algorithms to utilize the brain's activation patterns as feedbacks for improving their level of interactivity, thereby forming a stepping stone for rich and usable human mental model.

Bodily-Contact Communication Medium Induces Relaxed Mode of Brain Activity While Increasing Its Dynamical Complexity: A Pilot Study.

We present the results of the analysis of the effect of a bodily-contact communication medium on the brain activity of the individuals during verbal communication. Our results suggest that the communicated content that is mediated through such a device induces a significant effect on electroencephalogram (EEG) time series of human subjects. Precisely, we find a significant reduction of overall power of the EEG signals of the individuals. This observation that is supported by the analysis of the permutation entropy (PE) of the EEG time series of brain activity of the participants suggests the positive effect of such a medium on the stress relief and the induced sense of relaxation. Additionally, multiscale entropy (MSE) analysis of our data implies that such a medium increases the level of complexity that is exhibited by EEG time series of our participants, thereby suggesting their sustained sense of involvement in their course of communication. These findings that are in accord with the results reported by cognitive neuroscience research suggests that the use of such a medium can be beneficial as a complementary step in treatment of developmental disorders, attentiveness of schoolchildren and early child development, as well as scenarios where intimate physical interaction over distance is desirable (e.g., distance-parenting).

Differential Entropy Preserves Variational Information of Near-Infrared Spectroscopy Time Series Associated With Working Memory.

Neuroscience research shows a growing interest in the application of Near-Infrared Spectroscopy (NIRS) in analysis and decoding of the brain activity of human subjects. Given the correlation that is observed between the Blood Oxygen Dependent Level (BOLD) responses that are exhibited by the time series data of functional Magnetic Resonance Imaging (fMRI) and the hemoglobin oxy/deoxy-genation that is captured by NIRS, linear models play a central role in these applications. This, in turn, results in adaptation of the feature extraction strategies that are well-suited for discretization of data that exhibit a high degree of linearity, namely, slope and the mean as well as their combination, to summarize the informational contents of the NIRS time series. In this article, we demonstrate that these features are inefficient in capturing the variational information of NIRS data, limiting the reliability and the adequacy of the conclusion on their results. Alternatively, we propose the linear estimate of differential entropy of these time series as a natural representation of such information. We provide evidence for our claim through comparative analysis of the application of these features on NIRS data pertinent to several working memory tasks as well as naturalistic conversational stimuli.

A Non-parametric Approach to the Overall Estimate of Cognitive Load Using NIRS Time Series.

We present a non-parametric approach to prediction of the n-back n ∈ {1, 2} task as a proxy measure of mental workload using Near Infrared Spectroscopy (NIRS) data. In particular, we focus on measuring the mental workload through hemodynamic responses in the brain induced by these tasks, thereby realizing the potential that they can offer for their detection in real world scenarios (e.g., difficulty of a conversation). Our approach takes advantage of intrinsic linearity that is inherent in the components of the NIRS time series to adopt a one-step regression strategy. We demonstrate the correctness of our approach through its mathematical analysis. Furthermore, we study the performance of our model in an inter-subject setting in contrast with state-of-the-art techniques in the literature to show a significant improvement on prediction of these tasks (82.50 and 86.40% for female and male participants, respectively). Moreover, our empirical analysis suggest a gender difference effect on the performance of the classifiers (with male data exhibiting a higher non-linearity) along with the left-lateralized activation in both genders with higher specificity in females.