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

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\u27 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

Occupational Health Problems and Safety Conditions among Small and Medium-Sized Enterprises: A Cross-sectional Study in Shiraz, Iran

Background: Small and medium-sized enterprises (SMEs) include a large part of manufacturing jobs and play an important role in developing national economics and employment. Objectives: The present study aimed to investigate occupational health problems and safety conditions among SMEs in Shiraz, Iran. Methods: This cross-sectional study was carried out on 711 SMEs, including 371 small enterprises (fewer than 25 workers) and 340 medium enterprises (25–99 workers), in Shiraz, Iran. The participants were selected randomly among the workplaces under the coverage of social security insurance. The researcher-made questionnaire, which consisted of demographic characteristics, the frequency rate of occupational accidents, and exposure to workplace harmful agents, were distributed among participants. Findings: The results showed there were significantly more physical and chemical harmful agents in medium enterprises compared to small ones (P < 0.001). However, the frequency rate of accidents in small enterprises was significantly higher than in medium enterprises (P < 0.001). Also, there was no significant difference between the studied enterprises in ergonomic hazards, except for awkward posture, whose frequency rate was significantly higher in small enterprises (P < 0.05). Finally, among the reported symptoms, the prevalence of eye, skin, ear, and respiratory symptoms was significantly higher in medium enterprises compared to small enterprises (P < 0.05). Conclusions: Occupational health and safety (OHS) regulations in medium enterprises have led to improved OHS conditions compared to small enterprises. Therefore, small enterprises should be included in OHS regulations

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

Multi-robot coordination through mediation of independent decisions

This thesis contributes to the understanding of the intentional cooperation in multi-robot systems. We show that in a complex multi-robot system cooperation is achieved implicitly through the mediation of the independent decisions of the individual agents that are made autonomously. We assert that in order for a multi-robot system to accomplish a mission cooperatively, it is necessary for the individuals to proactively contribute to planning, to incremental refinement, as well as to adaptation at the group-level as the state of the mission progresses. We show how the decomposition of a mission delegated to a multi-robot system provides the agents with the ability to make decisions in a distributed fashion. While formulating decision mechanism, we show how the state of a robotic agent with respect to the delegated mission is viewed as two independent internal and external states. Furthermore, we demonstrate the requirement of a priori knowledge in decision process is prevented via incorporation of a simple sub-ranking module into the external state component of the decision mechanism of the robotic agents. While this independent involvement of the robotic agents in decision-making process preserves the autonomy of the individual agents, we show the mediation of these independent decisions does not only ensure proper execution of the plan but serves as a basis for the evolution of the intentional cooperation among the robotic agents

Critical Examination of the Parametric Approaches to Analysis of the Non-Verbal Human Behavior: A Case Study in Facial Pre-Touch Interaction

A prevailing assumption in many behavioral studies is the underlying normal distribution of the data under investigation. In this regard, although it appears plausible to presume a certain degree of similarity among individuals, this presumption does not necessarily warrant such simplifying assumptions as average or normally distributed human behavioral responses. In the present study, we examine the extent of such assumptions by considering the case of human&ndash;human touch interaction in which individuals signal their face area pre-touch distance boundaries. We then use these pre-touch distances along with their respective azimuth and elevation angles around the face area and perform three types of regression-based analyses to estimate a generalized facial pre-touch distance boundary. First, we use a Gaussian processes regression to evaluate whether assumption of normal distribution in participants&rsquo; reactions warrants a reliable estimate of this boundary. Second, we apply a support vector regression (SVR) to determine whether estimating this space by minimizing the orthogonal distance between participants&rsquo; pre-touch data and its corresponding pre-touch boundary can yield a better result. Third, we use ordinary regression to validate the utility of a non-parametric regressor with a simple regularization criterion in estimating such a pre-touch space. In addition, we compare these models with the scenarios in which a fixed boundary distance (i.e., a spherical boundary) is adopted. We show that within the context of facial pre-touch interaction, normal distribution does not capture the variability that is exhibited by human subjects during such non-verbal interaction. We also provide evidence that such interactions can be more adequately estimated by considering the individuals&rsquo; variable behavior and preferences through such estimation strategies as ordinary regression that solely relies on the distribution of their observed behavior which may not necessarily follow a parametric distribution

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&rsquo; 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&rsquo; 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&rsquo;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

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 of the Multi-Channel EEG Recordings Identifies the Distributed Signatures of Negative, Neutral and Positive Affect in Whole-Brain Variability

Individuals&rsquo; ability to express their subjective experiences in terms of such attributes as pleasant/unpleasant or positive/negative feelings forms a fundamental property of their affect and emotion. However, neuroscientific findings on the underlying neural substrates of the affect appear to be inconclusive with some reporting the presence of distinct and independent brain systems and others identifying flexible and distributed brain regions. A common theme among these studies is the focus on the change in brain activation. As a result, they do not take into account the findings that indicate the brain activation and its information content does not necessarily modulate and that the stimuli with equivalent sensory and behavioural processing demands may not necessarily result in differential brain activation. In this article, we take a different stance on the analysis of the differential effect of the negative, neutral and positive affect on the brain functioning in which we look into the whole-brain variability: that is the change in the brain information processing measured in multiple distributed regions. For this purpose, we compute the entropy of individuals&rsquo; muti-channel EEG recordings who watched movie clips with differing affect. Our results suggest that the whole-brain variability significantly differentiates between the negative, neutral and positive affect. They also indicate that although some brain regions contribute more to such differences, it is the whole-brain variational pattern that results in their significantly above chance level prediction. These results imply that although the underlying brain substrates for negative, neutral and positive affect exhibit quantitatively differing degrees of variability, their differences are rather subtly encoded in the whole-brain variational patterns that are distributed across its entire activity