The medicalization of ethics or ethicalization of neuroscience: Toward a conceptual re-examination.

Thinking With a growing body of brain science, the research and technological interventions in neuroscience have led to the rise of some ethical, moral, legal, conceptual, and socioeconomic problems. These problems and the need to establish an intellectual framework to approach them framed the base of Neuroethics. Most conveniently, the normative definition of Neuroethics is declared as ethics of neuroscience and neuroscience of ethics. However, there are more critical issues to define and frame the conceptual structure of the field. The current naturalist-positivist vision in neuroscience will extend the concept that human behavior, such as decision-making, consciousness, character, and moral intuitions, are mechanical features of a machine. Arguments from philosophical and anthropological views arose around this definition, focusing on the reductionist nature of merely a positive view of the human mind and behavior. Thinking through the pearls of such an approach and what would be at stake if we fail to recognize the importance of the philosophical-anthropological aspect of neuroscience, we first review different definitions and critics of the field, then proceed to discuss two concepts of Ethicalization and Medicalization. These concepts clearly show the established positivist-naturalist view in bioethics and the issues it caused. To better understand these two concepts, we use existing discussions and literature around them in bioethics. By reviewing the existing literature and adding a philosophical view of the field, we aim to add a new approach to the field of Neuroethics. We focus on adopting an interdisciplinary approach to Neuroethics to provide the needed background vision and theory to discuss interdisciplinary issues and enable scholars and theorists to reframe the fundamental issues of the field, such as the nature and scope of Neuroethics.

Dynamic analysis of the discrete fractional-order Rulkov neuron map.

Human evolution is carried out by two genetic systems based on DNA and another based on the transmission of information through the functions of the nervous system. In computational neuroscience, mathematical neural models are used to describe the biological function of the brain. Discrete-time neural models have received particular attention due to their simple analysis and low computational costs. From the concept of neuroscience, discrete fractional order neuron models incorporate the memory in a dynamic model. This paper introduces the fractional order discrete Rulkov neuron map. The presented model is analyzed dynamically and also in terms of synchronization ability. First, the Rulkov neuron map is examined in terms of phase plane, bifurcation diagram, and Lyapunov exponent. The biological behaviors of the Rulkov neuron map, such as silence, bursting, and chaotic firing, also exist in its discrete fractional-order version. The bifurcation diagrams of the proposed model are investigated under the effect of the neuron model's parameters and the fractional order. The stability regions of the system are theoretically and numerically obtained, and it is shown that increasing the order of the fractional order decreases the stable areas. Finally, the synchronization behavior of two fractional-order models is investigated. The results represent that the fractional-order systems cannot reach complete synchronization.

Decoding the correlation between heart activation and walking path by information-based analysis.

BACKGROND: One of the important areas of heart research is to analyze heart rate variability during (HRV) walking. OBJECTIVE: In this research, we investigated the correction between heart activation and the variations of walking paths. METHOD: We employed Shannon entropy to analyze how the information content of walking paths affects the information content of HRV. Eight healthy students walked on three designed walking paths with different information contents while we recorded their ECG signals. We computed and analyzed the Shannon entropy of the R-R interval time series (as an indicator of HRV) versus the Shannon entropy of different walking paths and accordingly evaluated their relation. RESULTS: According to the obtained results, walking on the path that contains more information leads to less information in the R-R time series. CONCLUSION: The analysis method employed in this research can be extended to analyze the relation between other physiological signals (such as brain or muscle reactions) and the walking path.

COVID-19 anti-vaccine attitude and hesitancy.

High vaccination rates are required around the world to create herd immunity and terminate the current COVID-19 pandemic growth. With the steady rise in COVID-19 vaccine supplies, hesitancy and rejection to be vaccinated has become a problem worldwide for large vaccine coverage. Understanding the causes of vaccine avoidance or hesitancy can help to increase vaccination intentions in the general population. A number of factors contributed to increasing hesitancy. Some causes of COVID-19 vaccine hesitancy include anti-vaccine myths and confusing messages about some severe side effects of few vaccines, confusion over protection levels, poor health literacy (lack of accurate knowledge about vaccines and virus), deficient legal liability from the vaccine manufacturers, political and economic intentions, mistrust and suspicion of medical companies, concern of efficacy against to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) variants, safety concerns (elderly, people with preexisting comorbidities) and some socio-demographic factors. Urgent interventions and policies targeting the corresponding factors are needed. Recognizing obstacles to vaccine uptake helps in the development of effective solutions to solve them. Evidence-based and behaviorally guided approaches should be used to achieve high acceptance and uptake. Supplementary Information: The online version contains supplementary material available at 10.1007/s40200-022-01018-y.

Outstanding qualities of a successful role model in medical education: Students and professors' points of view.

Introduction: Role models play a crucial role in determining the professional development of medical trainees. The purpose of this study was to gain in-depth understanding of the outstanding qualities of successful role models. Material and methods: In this qualitative-descriptive study, data were collected through conducting interviews, and the inductive content analysis was performed. The study was done at the school of medicine of Tehran University of Medical Sciences in Iran. Twenty-five medical students, medical graduates, and faculty members participated in 25 interviews from April to December 2019. The participants were recruited via purposive sampling. Results: five categories and 13 subcategories were extracted from the data. The results of this study illustrated five crucial qualities of a good clinical role model including an excellent character, effective coach and mentor, inspiring medical leader, expert clinical teacher, and a professional physician. Our results also highlighted the lasting effects of positive role modeling of clinical teachers on medical trainees' professional careers. Another category in the present study was connected to future consequences of being a role model in a clinical setting. Conclusion: The result of this study illustrated crucial qualities of a good medical role model. This result can assist the leaders to engage with medical educators to better understand the process of role modeling and design the best plan for development of role modeling in medical education.

COVID-19 related stigma among the general population in Iran.

BACKGROUND: COVID-19 related stigma has been identified as a critical issue since the beginning of the pandemic. We developed a valid and reliable questionnaire to measure COVID-19 related enacted stigma, inflicted by the non-infected general population. We applied the questionnaire to measure COVID-19 related enacted stigma among Tehran citizens from 27 to 30 September 2020. METHODS: A preliminary questionnaire with 18 items was developed. The total score ranged from 18 to 54; a higher score indicated a higher level of COVID-19 related stigma. An expert panel assessed the face and content validity. Of 1637 randomly recruited Tehran citizens without a history of COVID-19 infection, 1064 participants consented and were interviewed by trained interviewers by phone. RESULTS: Item content validity index (I-CVI), Item content validity ratio (I-CVR), and Item face validity index (I-FVI) were higher than 0.78 for all 18 items. The content and face validity were established with a scale content validity index (S-CVI) of 0.90 and a scale face validity index (S-CVI) of 93.9%, respectively. Internal consistency of the questionnaire with 18 items was confirmed with Cronbach's alpha of 0.625. Exploratory factor analysis revealed five latent variables, including "blaming", "social discrimination", "dishonor label", "interpersonal contact", and "retribution and requital attitude". The median of the stigma score was 24 [25th percentile: 22, 75the percentile: 28]. A large majority (86.8%) of participants reported a low level of stigma with a score below 31. None of the participants showed a high level of stigma with a score above 43. We found that the higher the educational level the lower the participant's stigma score. CONCLUSION: We found a low level of stigmatizing thoughts and behavior among the non-infected general population in Tehran, which may be due to the social desirability effect, to the widespread nature of COVID-19, or to the adaptation to sociocultural diversity of the large city.

Media codes of ethics for health professionals and media professionals: a qualitative study.

Media is an opportunity for health professionals; however, it is not free of threats. Fixing the threats requires professional systematization through developing practical guidelines, which brings us to the goal this study was designed to achieve. The study was conducted qualitatively through literature review, semi-structured interviews, and a focus group discussion with health and media experts, as a result of which 486 codes were extracted and classified into 4 groups. The first group was addressed to media professionals and contained 126 codes in 5 categories: seeking and reporting the truth, harm minimization, integrity, independence, and respect for the rights of others. The second and third groups were addressed to health professionals, the former (150 codes) dealing with formal media, and the latter (190 codes) dealing with cyberspace. These groups were both categorized into 6 categories: scientific demeanor, beneficence, harm minimization, integrity, maintaining the dignity of the profession and professionals, and respect for the rights of others. The fourth group was addressed to the public audience and contained 20 codes categorized into 2 categories: ethics of belief, and ethics of (re-)publishing. Since the study was conducted during the pandemic/infodemic, the proposed codes can help reduce possible conflicts in similar future situations.

Synchronization of the neurons coupled with sequential developing electrical and chemical synapses.

There is some evidence representing the sequential formation and elimination of electrical and chemical synapses in particular brain regions. Relying on this feature, this paper presents a purely mathematical modeling study on the synchronization among neurons connected by transient electrical synapses transformed to chemical synapses over time. This deletion and development of synapses are considered consecutive. The results represent that the transient synapses lead to burst synchronization of the neurons while the neurons are resting when both synapses exist constantly. The period of the transitions and also the time of presence of electrical synapses to chemical ones are effective on the synchronization. The larger synchronization error is obtained by increasing the transition period and the time of chemical synapses' existence.

Decoding the coupling between the brain and skin reactions in auditory stimulation by information-based analysis of EEG and GSR signals.

BACKGROUND: The analysis of brain activity in different conditions is an important research area in neuroscience. OBJECTIVE: This paper analyzed the correlation between the brain and skin activities in rest and stimulations by information-based analysis of electroencephalogram (EEG) and galvanic skin resistance (GSR) signals. METHODS: We recorded EEG and GSR signals of eleven subjects during rest and auditory stimulations using three pieces of music that were differentiated based on their complexity. Then, we calculated the Shannon entropy of these signals to quantify their information contents. RESULTS: The results showed that music with greater complexity has a more significant effect on altering the information contents of EEG and GSR signals. We also found a strong correlation (r= 0.9682) among the variations of the information contents of EEG and GSR signals. Therefore, the activities of the skin and brain are correlated in different conditions. CONCLUSION: This analysis technique can be utilized to evaluate the correlation among the activities of various organs versus brain activity in different conditions.

Information-based analysis of the coupling between brain and heart reactions to olfactory stimulation.

BACKGROUND: Analysis of the heart activity is one of the important areas of research in biomedical science and engineering. For this purpose, scientists analyze the activity of the heart in various conditions. Since the brain controls the heart's activity, a relationship should exist among their activities. OBJECTIVE: In this research, for the first time the coupling between heart and brain activities was analyzed by information-based analysis. METHODS: Considering Shannon entropy as the indicator of the information of a system, we recorded electroencephalogram (EEG) and electrocardiogram (ECG) signals of 13 participants (7 M, 6 F, 18-22 years old) in different external stimulations (using pineapple, banana, vanilla, and lemon flavors as olfactory stimuli) and evaluated how the information of EEG signals and R-R time series (as heart rate variability (HRV)) are linked. RESULTS: The results indicate that the changes in the information of the R-R time series and EEG signals are strongly correlated (ρ=-0.9566). CONCLUSION: We conclude that heart and brain activities are related.

Decoding of the coupling between the brain and facial muscle reactions in auditory stimulation.

BACKGROUND: Analysis of the reactions of different organs to external stimuli is an important area of research in physiological science. OBJECTIVE: In this paper, we investigated the correlation between the brain and facial muscle activities by information-based analysis of electroencephalogram (EEG) signals and electromyogram (EMG) signals using Shannon entropy. METHOD: The EEG and EMG signals of thirteen subjects were recorded during rest and auditory stimulations using relaxing, pop, and rock music. Accordingly, we calculated the Shannon entropy of these signals. RESULTS: The results showed that rock music has a greater effect on the information of EEG and EMG signals than pop music, which itself has a greater effect than relaxing music. Furthermore, a strong correlation (r= 0.9980) was found between the variations of the information of EEG and EMG signals. CONCLUSION: The activities of the facial muscle and brain are correlated in different conditions. This technique can be utilized to investigate the correlation between the activities of different organs versus brain activity in different situations.

Synchronization and chimera states in the network of electrochemically coupled memristive Rulkov neuron maps.

Map-based neuronal models have received much attention due to their high speed, efficiency, flexibility, and simplicity. Therefore, they are suitable for investigating different dynamical behaviors in neuronal networks, which is one of the recent hottest topics. Recently, the memristive version of the Rulkov model, known as the m-Rulkov model, has been introduced. This paper investigates the network of the memristive version of the Rulkov neuron map to study the effect of the memristor on collective behaviors. Firstly, two m-Rulkov neuronal models are coupled in different cases, through electrical synapses, chemical synapses, and both electrical and chemical synapses. The results show that two electrically coupled memristive neurons can become synchronous, while the previous studies have shown that two non-memristive Rulkov neurons do not synchronize when they are coupled electrically. In contrast, chemical coupling does not lead to synchronization; instead, two neurons reach the same resting state. However, the presence of both types of couplings results in synchronization. The same investigations are carried out for a network of 100 m-Rulkov models locating in a ring topology. Different firing patterns, such as synchronization, lagged-phase synchronization, amplitude death, non-stationary chimera state, and traveling chimera state, are observed for various electrical and chemical coupling strengths. Furthermore, the synchronization of neurons in the electrical coupling relies on the network's size and disappears with increasing the nodes number.

Complexity-Based Analysis of the Variations of Brain and Muscle Reactions in Walking and Standing Balance While Receiving Different Perturbations.

In this article, we evaluated the variations of the brain and muscle activations while subjects are exposed to different perturbations to walking and standing balance. Since EEG and EMG signals have complex structures, we utilized the complexity-based analysis. Specifically, we analyzed the fractal dimension and sample entropy of Electroencephalogram (EEG) and Electromyogram (EMG) signals while subjects walked and stood, and received different perturbations in the form of pulling and rotation (via virtual reality). The results showed that the complexity of EEG signals was higher in walking than standing as the result of different perturbations. However, the complexity of EMG signals was higher in standing than walking as the result of different perturbations. Therefore, the alterations in the complexity of EEG and EMG signals are inversely correlated. This analysis could be extended to investigate simultaneous variations of rhythmic patterns of other physiological signals while subjects perform different activities.

Predictors of COVID-19 Preventive Behavior Adoption Intention in Malaysia.

Preventive behavior adoption is the key to reduce the possibility of getting COVID-19 infection. This paper aims to examine the determinants of intention to adopt preventive behavior by incorporating perception of e-government information and services and perception of social media into the theory of reasoned action. A cross-sectional online survey was carried out among Malaysian residents. Four hundred four valid responses were obtained and used for data analysis. A partial least-square-based path analysis revealed direct effects of attitude and subjective norm in predicting intention to adopt preventive behavior. In addition, perception of e-government information and services and perception of social media were found to be significant predictors of attitude toward preventive behavior. The findings highlight the importance of digital platforms in improving people's attitudes toward preventive behavior and in turn contain the spread of the infectious disease.

Information-based decoding of the coupling among human brain activity and movement paths.

BACKGROUND: Walking like many other actions of a human is controlled by the brain through the nervous system. In fact, if a problem occurs in our brain, we cannot walk correctly. Therefore, the analysis of the coupling of brain activity and walking is very important especially in rehabilitation science. The complexity of movement paths is one of the factors that affect human walking. For instance, if we walk on a path that is more complex, our brain activity increases to adjust our movements. OBJECTIVE: This study for the first time analyzed the coupling of walking paths and brain reaction from the information point of view. METHODS: We analyzed the Shannon entropy for electroencephalography (EEG) signals versus the walking paths in order to relate their information contents. RESULTS: According to the results, walking on a path that contains more information causes more information in EEG signals. A strong correlation (p= 0.9999) was observed between the information contents of EEG signals and walking paths. Our method of analysis can also be used to investigate the relation among other physiological signals of a human and walking paths, which has great benefits in rehabilitation science.

Attitude Toward Protective Behavior Engagement During COVID-19 Pandemic in Malaysia: The Role of E-government and Social Media.

The coronavirus disease 2019 (COVID-19) pandemic is still evolving and affecting millions of lives. E-government and social media have been used widely during this unprecedented time to spread awareness and educate the public on preventive measures. However, the extent to which the 2 digital platforms bring to improve public health awareness and prevention during a health crisis is unknown. In this study, we examined the influence of e-government and social media on the public's attitude to adopt protective behavior. For this purpose, a Web survey was conducted among 404 Malaysian residents during the Recovery Movement Control Order (RMCO) period in the country. Descriptive and multiple regression analyses were conducted using IBM SPSS software. Social media was chosen by most of the respondents (n = 331 or 81.9%) as the source to get information related to COVID-19. Multiple regression analysis suggests the roles of e-government and social media to be significantly related to people's attitudes to engage in protective behavior. In conclusion, during the COVID-19 outbreak, public health decision makers may use e-government and social media platforms as effective tools to improve public engagement on protective behavior. This, in turn, will help the country to contain the transmission of the virus.

Analysis of the information transfer between brains during a conversation.

BACKGROUND: The interaction between people is one of the usual daily activities. For this purpose, people mainly connect with others, using their voice. Voices act as the auditory stimuli on the brain during a conversation. OBJECTIVE: In this research, we analyze the relationship between the brains' activities of subjects during a conversation. METHODS: Since human voice transfers information from one subject to another, we used information theory for our analysis. We investigated the alterations of Shannon entropy of electroencephalography (EEG) signals for subjects during a conversation. RESULTS: The results demonstrated that the alterations in the information contents of the EEG signals for the listeners and speakers are correlated. Therefore, we concluded that the brains' activities of both subjects are linked. CONCLUSION: Our results can be expanded to analyze the coupling among other physiological signals of subjects (such as heart rate) during the conversation.

Information-based analysis of the relationship between brain and facial muscle activities in response to static visual stimuli.

BACKGROUND: Human facial muscles react differently to different visual stimuli. It is known that the human brain controls and regulates the activity of the muscles. OBJECTIVE: In this research, for the first time, we investigate how facial muscle reaction is related to the reaction of the human brain. METHODS: Since both electromyography (EMG) and electroencephalography (EEG) signals, as the features of muscle and brain activities, contain information, we benefited from the information theory and computed the Shannon entropy of EMG and EEG signals when subjects were exposed to different static visual stimuli with different Shannon entropies (information content). RESULTS: Based on the obtained results, the variations of the information content of the EMG signal are related to the variations of the information content of the EEG signal and the visual stimuli. Statistical analysis also supported the results indicating that the visual stimuli with greater information content have a greater effect on the variation of the information content of both EEG and EMG signals. CONCLUSION: This investigation can be further continued to analyze the relationship between facial muscle and brain reactions in case of other types of stimuli.

Complexity-Based Decoding of the Coupling Among Heart Rate Variability (HRV) and Walking Path.

Walking is an everyday activity in our daily life. Because walking affects heart rate variability, in this research, for the first time, we analyzed the coupling among the alterations of the complexity of walking paths and heart rate. We benefited from the fractal theory and sample entropy to evaluate the influence of the complexity of paths on the complexity of heart rate variability (HRV) during walking. We calculated the fractal exponent and sample entropy of the R-R time series for nine participants who walked on four paths with various complexities. The findings showed a strong coupling among the alterations of fractal dimension (an indicator of complexity) of HRV and the walking paths. Besides, the result of the analysis of sample entropy also verified the obtained results from the fractal analysis. In further studies, we can analyze the coupling among the alterations of the complexities of other physiological signals and walking paths.