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• Neurological manifestations are increasingly being recognized in patients with COVID-19. • The ExactMechanism of seizure is unkown. • Managing Patients With Epilepsy (PWE) infected with COVID-19 can be challenging. • Important to Consider drug-drug interactions between AEDs(Anti-epileptic drugs) and anti-COVID therapies. Since the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Wuhan, China, this highly transmissible virus has since spread rapidly around the world. Though respiratory complication is the primarily reported manifestation though rare, yet serious neurological complications are being frequently reported in the literature. In selected coronavirus disease-2019 (COVID-19) cases neurologic complications may manifest as seizures. In this paper, we have reviewed current literature on seizures linked with SARS- COV 2 infection including published or pre-print original articles, review articles, and case reports. We have discussed the electroencephalogram (EEG), imaging, and Cerebrospinal fluid (CSF) findings in patients with COVID-19 presenting with seizure. We will be concluding the paper by briefly discussing the three mechanisms by which seizures can develop in patients infected with SARS- COV 2 - (a) Direct Mechanism (b) Indirect Mechanism and (c) Exacerbation of Seizure in Patients with Epilepsy (PWE). Our aim is to update the physicians working with COVID-19 patients about this potential complication and hope that understanding of these proposed mechanisms can provide an opportunity for the physicians for early diagnosis or even better, help prevent this complication. [ABSTRACT FROM AUTHOR]

Project Management is one of the main supply chains today. The project manager's responsibility is to develop a project team but he is focused on the application of methods and tools and doesn't take into consideration the fact that the human factor has a 70% impact on project success. On the other hand, Neuroleadership is a powerful tool that can be used by Project Management Professionals to understand better the human factors that impact on the project lifecycle. The purpose of that paper is to explore the application of the RELISH Model as team development tool by Project Managers in Bulgaria. The research is done among project management professionals in Bulgaria, project managers and project team members. The survey method with questions for quantitative analysis is used and the primary data is obtained directly from the respondents by a web-based questionnaire created in Google Forms and distributed by the Researcher's LinkedIn and Facebook personal profiles, and PMI Bulgaria Chapter communication channels. This study reports Seeing the facts as the most satisfied need with mean 3.8, followed by Leading the pack (3.76) and Hope for the future (3.66). Relatedness and Interpersonal Connection means are almost equal around 3.5. Expression is the need with the lowest mean level of 3.33. [ABSTRACT FROM AUTHOR]

Physics is a field of science that has traditionally used the scientific method to answer questions about why natural phenomena occur and to make testable models that explain the phenomena. Discovering equations, laws, and principles that are invariant, robust, and causal has been fundamental in physical sciences throughout the centuries. Discoveries emerge from observing the world and, when possible, performing interventions on the system under study. With the advent of big data and data-driven methods, the fields of causal and equation discovery have developed and accelerated progress in computer science, physics, statistics, philosophy, and many applied fields. This paper reviews the concepts, methods, and relevant works on causal and equation discovery in the broad field of physics and outlines the most important challenges and promising future lines of research. We also provide a taxonomy for data-driven causal and equation discovery, point out connections, and showcase comprehensive case studies in Earth and climate sciences, fluid dynamics and mechanics, and the neurosciences. This review demonstrates that discovering fundamental laws and causal relations by observing natural phenomena is revolutionised with the efficient exploitation of observational data and simulations, modern machine learning algorithms and the combination with domain knowledge. Exciting times are ahead with many challenges and opportunities to improve our understanding of complex systems. [ABSTRACT FROM AUTHOR]

The current paper aimed to analyze quantitatively the trends of four major schools of thought in scientific Psychology (neuroscience, cognitivism, behaviorism, psychoanalysis) and their intersections, covering the period from 1979 to 2020. We utilized a rigorous methodology across three distinct sources: Mainstream Psychology (MP), Highly Influential Journal (HIJ), and non-English papers (NEP). Our findings align with previous research in two aspects: psychoanalysis and behaviorism have significantly declined, cognitivism remains a prominent trend. However, we deviate from prior studies by recognizing that neuroscience may be considered the most influential trend and that trends exhibit less linearity than previously assumed. We also acknowledge the significance of NEP, which manifest an independent pattern as compared to the other sources and that may reveal what happens in the "periphery" of Psychology. It is noteworthy that NEP demonstrated a still lively contribution from psychoanalysis. Our study also highlights the insularity and lack of cross-fertilization among psychological subdisciplines, despite the widespread claims to the contrary. It eventually supports the inference that scientific Psychology is a non-paradigmatic or pre-paradigmatic discipline, pointing out the dominance of applied psychology and confuting the notion of overarching "grand theories". • Methodological Advancement : The paper introduces an improved methodology for analyzing trends in four major schools of thought in psychology. • Dominance of Neuroscience : Our findings suggest that neuroscience can be considered the most influential trend in scientific psychology. • Decline in Psychoanalysis and Behaviorism : Psychoanalysis and behaviorism exhibit significant declines in prominence. • Prominence of Cognitivism : Cognitivism continues to maintain a prominent position in psychology. • Stability and Dispersion : Trends in psychology exhibit more stability and dispersion than previously assumed. [ABSTRACT FROM AUTHOR]

The neuroscience of volition is an emerging subfield of the brain sciences, with hundreds of papers on the role of consciousness in action formation published each year. This makes the state-of-the-art in the discipline poorly accessible to newcomers and difficult to follow even for experts in the field. Here we provide a comprehensive summary of research in this field since its inception that will be useful to both groups. We also discuss important ideas that have received little coverage in the literature so far. We systematically reviewed a set of 2220 publications, with detailed consideration of almost 500 of the most relevant papers. We provide a thorough introduction to the seminal work of Benjamin Libet from the 1960s to 1980s. We also discuss common criticisms of Libet's method, including temporal introspection, the interpretation of the assumed physiological correlates of volition, and various conceptual issues. We conclude with recent advances and potential future directions in the field, highlighting modern methodological approaches to volition, as well as important recent findings. • We systematically reviewed 2220 papers related to the neuroscience of volition. • We detail Libet's work including the seminal "free will" experiments. • We discuss and categorize the extensive criticisms of Libet's experiments. • We present key philosophical issues relevant to the neuroscience of volition. • We examine contemporary techniques, methods, and findings related to volition. [ABSTRACT FROM AUTHOR]

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications. • The most groundbreaking advances in theoretical and applied Artificial Intelligence. • Deep Learning in real-world tasks, such as clinical diagnostics or robotics. • Several applications are presented, reviewed and discussed. • State-of-the-art in AI methods, models and applications. • New scientific discoveries successfully transferred to real-life applications. [ABSTRACT FROM AUTHOR]

• Neurophysiological correlates of positive emotions contribute to wellbeing. • Brain networks that implement positive emotions are flexible and modifiable. • Developmental, social, and environmental factors impact positive emotions. • Meditation, contemplative practices, and flow cultivate positive emotions. • Linguistic dimensions contribute to advancing the neuroscience of positive emotions. This review paper provides an integrative account regarding neurophysiological correlates of positive emotions and affect that cumulatively contribute to the scaffolding for happiness and wellbeing in humans and other animals. This paper reviews the associations among neurotransmitters, hormones, brain networks, and cognitive functions in the context of positive emotions and affect. Consideration of lifespan developmental perspectives are incorporated, and we also examine the impact of healthy social relationships and environmental contexts on the modulation of positive emotions and affect. The neurophysiological processes that implement positive emotions are dynamic and modifiable, and meditative practices as well as flow states that change patterns of brain function and ultimately support wellbeing are also discussed. This review is part of "The Human Affectome Project" (http://neuroqualia.org/background.php), and in order to advance a primary aim of the Human Affectome Project, we also reviewed relevant linguistic dimensions and terminology that characterizes positive emotions and wellbeing. These linguistic dimensions are discussed within the context of the neuroscience literature with the overarching goal of generating novel recommendations for advancing neuroscience research on positive emotions and wellbeing. [ABSTRACT FROM AUTHOR]

Artificial intelligence and all its supporting tools, e.g. machine and deep learning in computational intelligence-based systems, are rebuilding our society (economy, education, life-style, etc.) and promising a new era for the social welfare state. In this paper we summarize recent advances in data science and artificial intelligence within the interplay between natural and artificial computation. A review of recent works published in the latter field and the state the art are summarized in a comprehensive and self-contained way to provide a baseline framework for the international community in artificial intelligence. Moreover, this paper aims to provide a complete analysis and some relevant discussions of the current trends and insights within several theoretical and application fields covered in the essay, from theoretical models in artificial intelligence and machine learning to the most prospective applications in robotics, neuroscience, brain computer interfaces, medicine and society, in general. [ABSTRACT FROM AUTHOR]

The primary objective of the paper is to present the essence of the new paradigm in the management science, which is neuroentrepreneurship. This objective determined the paper's layout. In the first part, the theories of entrepreneurship are presented, including the context of research on entrepreneurship. In the second part, the techniques of cognitive neuroscience are described, which are used to determine the impact of thought processes on the recognition and use of a business opportunity by an entrepreneur. This part also analyses the new paradigm of management sciences, i.e. neuroentrepreneurship. In this respect, the most important achievements of science in diagnosing the impact of neuronal impulses on the entrepreneurial process are presented. [ABSTRACT FROM AUTHOR]

• The present paper hypothesizes how research in personality psychology will change with the recent emergence of the Internet of Things (IoT). In a society, where humankind is digitally interconnected and all machines in one's own household or at work are linked to the Internet, manifold data from human-machine-interaction can be used to predict psychological traits and states. • In the present theoretical paper, we first explain why it is (still) important to study personality and we also give a short (historical) overview on different theoretical and methodological approaches to study personality. Second, this work will focus on a recent addition to the toolbox of a personality psychologist, namely digital phenotyping via methods from Psychoinformatics , which falls into the realm of the aforementioned IoT. Aside from an introduction into these new concepts, we discuss strengths and limitations of this new data layer of interest for personality psychologists. • Finally, it is argued that in these modern times personality psychology research is conducted in many scattered scientific areas with researchers sometimes unaware of each other. Therefore, tremendous collaborative efforts need to be invested in order to reunify the psychological/neuroscientific oriented personality science to ultimately understand why we are the creatures we are. [ABSTRACT FROM AUTHOR]

Over the past few years, the computer vision domain has evolved and made a revolutionary transition from human-engineered features to automated features to address challenging tasks. Computer vision is an ever-evolving domain with its roots deeply correlated with neuroscience; any new findings that trigger a more intuitive understanding and working of the human visual system generally impact the design strategy of computer vision algorithms. The convolutional neural network is one such algorithm that is currently the de facto standard for most computer vision tasks such as image classification, object detection, image segmentation, etc. As convolutional neural networks are associated with inherent constraints such as the requirement for an immense amount of labeled data and an inefficient data routing policy, capsule networks could be a viable alternative. Upheld by the backpropagation and the dynamic routing algorithm, the capsule network has set the new paradigm for developing reliable computer vision algorithms. Despite the phenomenal theoretical backing from neuroscience and the groundbreaking performance on benchmark datasets, the lack of information concerning the conception and working of capsule networks becomes the major impediment to adopting them for computer vision algorithms. This paper presents a concise overview of capsule network-based classification architectures, routing algorithms, performance analysis, limitations, and future scope, helping the research community to adopt capsule networks at the forefront of modern computer vision research. [ABSTRACT FROM AUTHOR]

Time series prediction is one of the most challenging topics in real-world applications. Most studies undertook advanced mathematics and computer techniques above benchmark methods but often unable to guarantee forecasting models' generalization and reliability. This paper proposed a biologically motivated recurrent unit based on neuronal synaptic activity mechanism and chaotic behaviors in deep learning domain called Excitatory and Inhibitory Neural Synapse unit (EINS). The major contribution of this research is to create a flexible and robust recurrent unit based on neuroscience theory. It conducted experiments on three real-world time series forecast examples including finance, household power, weather for generalization and robustness evaluation using eight state-of-the-art recurrent architecture units as baseline models to compare convergence rate and forecasting accuracy. Experimental results showed that EINS achieved satisfactory performances in time series prediction reliability and effectiveness. [ABSTRACT FROM AUTHOR]

Recent advances in neuro-imaging enable scientists to create brain network data that can lead to novel insights into neurocircuitry, and a better understanding of the brain's organization. These networks inherently involve a spatial component, depicting which brain regions are structurally, functionally or genetically related. Their visualization in 3D suffers from occlusion and clutter, especially with increasing number of nodes and connections, while 2D representations such as connectograms, connectivity matrices, and node-link diagrams neglect the spatio-anatomical context. Approaches to arrange 2D-graphs manually are tedious, species-dependent, and require the knowledge of domain experts. In this paper, we present a spatial-data-driven approach for layouting 3D brain networks in 2D node-link diagrams, while maintaining their spatial organization. The produced graphs do not need manual positioning of nodes, are consistent (even for sub-graphs), and provide a perspective-dependent arrangement for orientation. Furthermore, we provide a visual design for highlighting anatomical context, including the shape of the brain, and the size of brain regions. We present in several case-studies the applicability of our approach for different neuroscience-relevant species, including the mouse, human, and Drosophila larvae. In a user study conducted with several domain experts, we demonstrate its relevance and validity, as well as its potential for neuroscientific publications, presentations, and education. [Display omitted] • A novel method for generating Spatial-Data-Driven Layouts for neural networks of multiple species and perspectives. • No need to manually define brain region related constraints to generate anatomically feasible layouts. • Visual designs providing a consistent spatial context to the user to ease orientation and visual comparison of different brain networks. [ABSTRACT FROM AUTHOR]

Acceleration of the applications of cutting edge science is often most likely in situations involving conflict and warfare between different groups. That is the focus of this paper. It asks what we know of the past, present and possible future applications of advances in our understanding of the brain in the development of chemical weapons, and what might best be done to moderate the most dangerous aspects of that process at the present time, particularly by members of civil society. The paper therefore begins by examining this possibility of hostile manipulation of the brain in some detail. It then briefly reviews the current state of the chemical and biological arms control and disarmament regime as embodied in the Biological and Toxin Weapons Convention (BTWC) and the Chemical Weapons Convention (CWC) following the 2016 8th Five Year Review Conference of the BTWC and in the lead up to the 2018 4th Five Year Review Conference of the CWC. Finally, our civil society initiative to assist in achieving a successful outcome of the 2018 CWC Review Conference is described. This initiative involves the production of a multi-authored edited book, to be published in advance of the Review Conference by the UK Royal Society of Chemistry that explores many of the critical issues facing the CWC States Parties as they seek to agree effective measures to prevent the development and use of chemical weapons, as the life and chemical sciences advance. [ABSTRACT FROM AUTHOR]

Persistence is an important characteristic of many complex systems in nature, related to how long the system remains at a certain state before changing to a different one. The study of complex systems' persistence involves different definitions and uses different techniques, depending on whether short-term or long-term persistence is considered. In this paper we discuss the most important definitions, concepts, methods, literature and latest results on persistence in complex systems. Firstly, the most used definitions of persistence in short-term and long-term cases are presented. The most relevant methods to characterize persistence are then discussed in both cases. A complete literature review is also carried out. We also present and discuss some relevant results on persistence, and give empirical evidence of performance in different detailed case studies, for both short-term and long-term persistence. A perspective on the future of persistence concludes the work. [ABSTRACT FROM AUTHOR]

This paper presents a data-driven model identification strategy that characterizes the behavior of a general dynamical system relative to a set of limit cycles that emerge in response to periodic forcing. Using time series data to infer the phase–amplitude dynamics associated with the underlying forced limit cycles, a low-order model can be obtained that accurately captures the dynamical behavior in response to arbitrary external inputs. The proposed strategy can be readily implemented in situations where full state measurements are unavailable and does not require any prior knowledge of the underlying model equations. This technique is applied to a model of coupled planar oscillators and to a model that considers the spike rate in a population of coupled conductance-based neurons where it outperforms two other commonly used data-driven model identification techniques. • Forcing-induced limit cycles are considered in the context of data-driven model identification for dynamical systems. • A general procedure is developed for the proposed model identification strategy. • The proposed approach does not require a priori knowledge about the underlying dynamical system. • Direct comparisons are provided between other well-established model-identification strategies. [ABSTRACT FROM AUTHOR]

Reinforcement learning methods have recently been very successful at performing complex sequential tasks like playing Atari games, Go and Poker. These algorithms have outperformed humans in several tasks by learning from scratch, using only scalar rewards obtained through interaction with their environment. While there certainly has been considerable independent innovation to produce such results, many core ideas in reinforcement learning are inspired by phenomena in animal learning, psychology and neuroscience. In this paper, we comprehensively review a large number of findings in both neuroscience and psychology that evidence reinforcement learning as a promising candidate for modeling learning and decision making in the brain. In doing so, we construct a mapping between various classes of modern RL algorithms and specific findings in both neurophysiological and behavioral literature. We then discuss the implications of this observed relationship between RL, neuroscience and psychology and its role in advancing research in both AI and brain science. [ABSTRACT FROM AUTHOR]

• Mood disorders can feature disturbances in lipid metabolism, oxidative stress and inflammation. • Statins are extensively prescribed for cardiovascular disease for their lipid-lowering properties. • Pleiotropic effects of statins include anti-inflammatory and anti-oxidative action. • Growing evidence suggests statins may have potential as adjunctive psychotropic agents. • Larger scale clinical trials are needed to better assess the utility of statins in mood disorders. Statins (3-hydroxy-3-methylglutaryl-coenzyme A reductase inhibitors) treat dyslipidaemia and cardiovascular disease by inhibiting cholesterol biosynthesis. They also have immunomodulatory and anti-inflammatory properties. Beyond cardiovascular disease, cholesterol and inflammation appear to be components of the pathogenesis and pathophysiology of neuropsychiatric disorders. Statins may therefore afford some therapeutic benefit in mood disorders. In this paper, we review the pathophysiology of mood disorders with a focus on pharmacologically relevant pathways, using major depressive disorder and bipolar disorder as exemplars. Statins are discussed in the context of these disorders, with particular focus on the putative mechanisms involved in their anti-inflammatory and immunomodulatory effects. Recent clinical data suggest that statins may have antidepressant properties, however given their interactions with many known biological pathways, it has not been fully elucidated which of these are the major determinants of clinical outcomes in mood disorders. Moreover, it remains unclear what the appropriate dose, or appropriate patient phenotype for adjunctive treatment may be. High quality randomised control trials in concert with complementary biological investigations are needed if the potential clinical effects of statins on mood disorders, as well as their biological correlates, are to be better understood. [ABSTRACT FROM AUTHOR]

The use of ‘brain training’ games is often regarded as relating to wider ideals of self-improvement and youthfulness. Hence, use is intertwined with discourses of ‘active’ ageing. This paper analyzes how the use and users of brain training games were configured in the UK media, from 2005 to 2015, and examines how notions of active ageing relate to these representations. Game users were rarely constructed solely as gamers, and were more often presented as prudent individuals focused on a serious goal. This configuration related to assumed and enjoined motivations for brain training; specifically, users were commonly framed as seeking to enhance cognition and limit/delay cognitive decline. Scientific evidence about brain training was often deployed to explain how games might work; sometimes, however, it was used to undermine the utility of games and assert the significance and cognitive health-benefits of other activities. A minority of texts explicitly critiqued ideals of self-improvement, arguing that game playing was important for its own sake. Yet, even the pleasure associated with gaming was occasionally instrumentalized as a mechanism for ensuring prudent life choices. The analysis casts fresh light on how debates around health, ageing, and science correspond to configurations of technology uses and users. It presents evidence of the widespread cultural circulation of enjoiners regarding self-care and healthy ageing within British society. However, the paper also provides indications of the limits to such imperatives: discourses of pleasure co-exist with and perhaps supplant logics of prudence in (accounts of) practices ostensibly aimed at ageing ‘well’. [ABSTRACT FROM AUTHOR]

• Panksepp's Affective Neuroscience Theory belongs to the most important emotion theories. • The present work provides a comprehensive review on studies using the Affective Neuroscience Personality Scales (ANPS). • The ANPS aims to assess individual differences in primary emotional systems. • The current review covers 17 years of ANPS-research after the first publication of the ANPS in 2003. • The ANPS 3.1 is presented in the Appendix of the paper. Jaak Panksepp's Affective Neuroscience Theory (ANT) belongs to the most prominent emotion theories in the psychological and psychiatric sciences. ANT proposes the existence of seven primary emotional systems deeply anchored in the mammalian brain. These emotional/motivational systems have been shaped by evolutionary processes and function as tools for survival in mammalian species. The systems are called SEEKING, LUST, CARE, and PLAY, as well as ANGER, FEAR, and SADNESS. Panksepp carved out these emotional systems via means of deep brain stimulation, brain lesion and pharmacological manipulation studies. Davis et al. (2003) designed the Affective Neuroscience Personality Scales (ANPS) against the background of findings from ANT. This self-report inventory is meant to enable researchers to assess individual differences in primary emotional systems. Seventeen years have passed since the first version of the ANPS has been published. Therefore, we now provide a comprehensive overview on studies using the ANPS including work from personality science, psychiatry and the neurosciences. [ABSTRACT FROM AUTHOR]

BayeSuites is the first web framework for learning, visualizing, and interpreting Bayesian networks (BNs) that can scale to tens of thousands of nodes while providing fast and friendly user experience. All the necessary features that enable this are reviewed in this paper; these features include scalability, extensibility, interoperability, ease of use, and interpretability. Scalability is the key factor in learning and processing massive networks within reasonable time; for a maintainable software open to new functionalities, extensibility and interoperability are necessary. Ease of use and interpretability are fundamental aspects of model interpretation, fairly similar to the case of the recent explainable artificial intelligence trend. We present the capabilities of our proposed framework by highlighting a real example of a BN learned from genomic data obtained from Allen Institute for Brain Science. The extensibility properties of the software are also demonstrated with the help of our BN-based probabilistic clustering implementation, together with another genomic-data example. [ABSTRACT FROM AUTHOR]

This paper provides a critical review of several possible mechanisms at different levels of analysis underlying the effects and therapeutic potential of psychedelics. At the (1) biochemical level, psychedelics primarily affect the 5-HT 2A receptor, increase neuroplasticity, offer a critical period for social reward learning, and have anti-inflammatory properties. At the (2) neural level, psychedelics have been associated with reduced efficacy of thalamo-cortical filtering, the loosening of top-down predictive signaling and an increased sensitivity to bottom-up prediction errors, and activation of the claustro-cortical-circuit. At the (3) psychological level, psychedelics have been shown to induce altered and affective states, they affect cognition, induce belief change, exert social effects, and can result in lasting changes in behavior. We outline the potential for a unifying account of the mechanisms underlying psychedelics and contrast this with a model of pluralistic causation. Ultimately, a better understanding of the specific mechanisms underlying the effects of psychedelics could allow for a more targeted therapeutic approach. We highlight current challenges for psychedelic research and provide a research agenda to foster insight in the causal-mechanistic pathways underlying the efficacy of psychedelic research and therapy. • This paper comprehensively reviews the pharmacological, neural and psychological mechanisms underlying psychedelics. • We critically evaluate the merits and pitfalls of each explanatory mechanism. • We provide a research agenda for psychedelic research, including a theory of pluralistic causation. [ABSTRACT FROM AUTHOR]

Neuroscientific evidence is increasingly being used in criminal trials as part of psychiatric testimony. Up to now, "neurolaw" literature remained focused on the use of neuroscience for assessments of criminal responsibility. However, in the field of forensic psychiatry, responsibility assessments are progressively being weakened, whereas dangerousness and risk assessment gain increasing importance. In this paper, we argue that the introduction of neuroscientific data by forensic experts in criminal trials will be mostly be used in the future as a means to evaluate or as an indication of an offender's dangerousness, rather than their responsibility. Judges confronted with the pressure to ensure public security may tend to interpret neuroscientific knowledge and data as an objective and reliable way of evaluating one's risk of reoffending. First, we aim to show how the current socio-legal context has reshaped the task of the forensic psychiatrist, with dangerousness assessments prevailing. In the second part, we examine from a critical point of view the promise of neuroscience to serve a better criminal justice system by offering new tools for risk assessment. Then we aim to explain why neuroscientific evidence is likely to be used as evidence of dangerousness of the defendants. On a theoretical level, the current tendency in criminal policies to focus on prognostics of dangerousness seems to be "justified" by a utilitarian approach to punishment, supposedly revealed by new neuroscientific discoveries that challenge the notions of free will and responsibility. Although often promoted as progressive and humane, we believe that this approach could lead to an instrumentalization of neuroscience in the interest of public safety and give rise to interventions which could entail ethical caveats and run counter to the interests of the offenders. The last part of this paper deals with some of these issues-the danger of stigmatization for brain damaged offenders because of adopting a purely therapeutic approach to crime, and the impact on their sentencing, in particular. [ABSTRACT FROM AUTHOR]

Twenty academic psychologists and neuroscientists, with an interest in autism and based within the United Kingdom, were interviewed between 2012 and 2013 on a variety of topics related to the condition. Within these qualitative interviews researchers often argued that there had been a ‘turn to infancy’ since the beginning of the 21st century with focus moving away from the high functioning adolescent and towards the pre-diagnostic infant deemed to be ‘at risk’ of autism. The archetypal research of this type is the ‘infant sibs’ study whereby infants with an elder sibling already diagnosed with autism are subjected to a range of tests, the results of which are examined only once it becomes apparent whether that infant has autism. It is claimed in this paper that the turn to infancy has been facilitated by two phenomena; the autism epidemic of the 1990s and the emergence of various methodological techniques, largely although not exclusively based within neuroscience, which seek to examine social disorder in the absence of comprehension or engagement on the part of the participant: these are experiments done to participants rather than with them. Interviewees claimed that these novel methods allowed researchers to see a ‘real’ autism that lay ‘behind’ methodology. That claim is disputed here and instead it is argued that these emerging methodologies other various phenomena, reorienting the social abnormality believed typical of autism away from language and meaning and towards the body. The paper concludes by suggesting that an attempt to draw comparisons between the symptoms of autism in infant populations and adults with the condition inevitably leads to a somaticisation of autism. [ABSTRACT FROM AUTHOR]

• The mouse and rat are the most popular model species in neuroscience. • Many studies on mice and rats do not translate to effective therapies in humans. • The selection of a valid animal species facilitates understanding of the brain. The fact that neurobiological research is reliant upon laboratory-reared rodents is well known. The following paper discusses this topic broadly, but also aims to highlight other species used in the study of the nervous system and the evolution of animal species usage from the end of World War II through recent investigations. Attention is drawn to the dramatic reduction in the diversity of species used in neuroscience, with a significant shift toward two species, the mouse (Mus musculus) and rat (Rattus norvegicus). Such a limitation in animal species causes many difficulties in the development of new therapies for various neuropsychiatric diseases. Based on numerous scientific publications, the advantages of using a greater diversity of species in neuroscience and the disadvantages of focusing on mice and rats are presented. [ABSTRACT FROM AUTHOR]

With the increased sophistication of AI techniques, the application of these systems has been expanding to ever newer fields. Increasingly, these systems are being used in modeling of human aesthetics and creativity, e.g. how humans create artworks and design products. Our lab has developed one such AI creativity deep learning system that can be used to create artworks in the form of images and videos. In this paper, we describe this system and its use in studying the human visual system and the formation of aesthetic experiences. Specifically, we show how time-based AI created media can be used to explore the nature of the dual-pathway neuro-architecture of the human visual system and how this relates to higher cognitive judgments such as aesthetic experiences that rely on these divergent information streams. We propose a theoretical framework for how the movement within percepts such as video clips, causes the engagement of reflexive attention and a subsequent focus on visual information that are primarily processed via the dorsal stream, thereby modulating aesthetic experiences that rely on information relayed via the ventral stream. We outline our recent study in support of our proposed framework, which serves as the first study that investigates the relationship between the two visual streams and aesthetic experiences. [ABSTRACT FROM AUTHOR]

• Prediction of customer satisfaction has been proposed using fusion of EEG and sentiments. • EEG response and sentiments from product review have been fused using ABC algorithm. • Thus, framework proposed a multimodal scheme for rating prediction. • Rating prediction has been performed using Random Forest. This paper proposes a novel multimodal framework for rating prediction of consumer products by fusing different data sources, namely physiological signals, global reviews obtained separately for the product and its brand. The reviews posted by global viewers are retrieved and processed using Natural Language Processing (NLP) technique to compute compound score considered as global rating. Also, electroencephalogram (EEG) signals of the participants were recorded simultaneously while watching different products on computer's screen. From EEG, valence scores in terms of product rating are obtained using self-report towards each viewed product for acquiring local rating. A higher valence score corresponds to intrinsic attractiveness of the participant towards a product. Random forest based regression techniques is used to model EEG data to build a rating prediction framework considered as local rating. Furthermore, Artificial Bee Colony (ABC) based optimization algorithm is used to boost the overall performance of the framework by fusing global and local ratings. EEG dataset of 40 participants including 25 male and 15 female is recorded while viewing 42 different products available on e-commerce website. Experiment results are encouraging and suggest that the proposed ABC optimization approach can achieve lower Root Mean Square Error (RMSE) in rating prediction as compared to individual unimodal schemes. [ABSTRACT FROM AUTHOR]

Despite extensive research and prodigious advances in neuroscience, our comprehension of the nature of schizophrenia remains rudimentary. Our failure to make progress is attributed to the extreme heterogeneity of this condition, enormous complexity of the human brain, limitations of extant research paradigms, and inadequacy of traditional statistical methods to integrate or interpret increasingly large amounts of multidimensional information relevant to unravelling brain function. Fortunately, the rapidly developing science of machine learning appears to provide tools capable of addressing each of these impediments. Enthusiasm about the potential of machine learning methods to break the current impasse is reflected in the steep increase in the number of scientific publication about the application of machine learning to the study of schizophrenia. Machine learning approaches are, however, poorly understood by schizophrenia researchers and clinicians alike. In this paper, we provide a simple description of the nature and techniques of machine learning and their application to the study of schizophrenia. We then summarize its potential and constraints with illustrations from six studies of machine learning in schizophrenia and address some common misconceptions about machine learning. We suggest some guidelines for researchers, readers, science editors and reviewers of the burgeoning machine learning literature in schizophrenia. In order to realize its enormous promise, we suggest the need for the disciplined application of machine learning methods to the study of schizophrenia with a clear recognition of its capability and challenges accompanied by a concurrent effort to improve machine learning literacy among neuroscientists and mental health professionals. [ABSTRACT FROM AUTHOR]

• Provide quick sorting of patients using Alzheimer Disease Intensity Number (ADIN). • Enable estimation of the progression of disease and within a patient's context. • Achieve sufficient expressiveness in understanding patients' disease information. • Knowing the context suggest related treatment(s) or an estimated life expectancy. Modern neuroscience imaging technologies considerably affect diagnostic and prognostic accuracy and facilitate progress towards the cure of brain diseases. The benefits largely depend on the practicalities by which the large-scale imaging and clinical data can be integrated, examined and understood. In EU neuGRID4You (N4U) project, many datasets were generated from research centres and hospitals. In order to perform effective analyses, these datasets and their metadata along with a number of pre-computed parameters are stored in a big data repository. This paper focuses on the patient identification using big data and Fuzzy Logic, which has been achieved through fuzzy processing where a reference number called Alzheimers Disease Identification Number (ADIN) is calculated. It has enabled patients sorting for a particular intensity of Alzheimers disease, short-term estimation of the progression of that disease and context of individual patients with respect to other patients such as appropriate treatment, estimated life expectancy etc. The generated rules define the necessary knowledge base for the inference engine to generate output sets and an aggregate membership function of each rule is formed. Using this function, a most representative value of the total output set is obtained which represents the disease intensity. The implemented system and its evaluation are based on realistic datasets, demonstrators and making use of real-life neuroscience case studies. The presented results of four selected case studies show that this approach have provided sufficient expressiveness in understanding patients' disease information. Finally, a discussion and conclusions are presented on the opportunities offered by the calculation of ADIN to manage Alzheimers disease along with potential future extensions or applications of this work. [ABSTRACT FROM AUTHOR]

Education, in epistemological terms, presents an arena in which the development of knowledge and its transmission frame fundamentally distinct assignments: in social contexts knowledge exchange is prioritised to equip individuals as knowers; in theoretical contexts, an understanding of knowing as a state of mind is the primary objective. Distinguishing between education and its institutionalised product – the school –, this paper reviews the notion that today's modern classroom settings limit the applicability of neuroscience research by focusing on equipping individuals as knowers. In contrast, educational research has become increasingly grounded in brain sciences, with future premises examining artificial intelligence technologies (AI) targeted at enhancing learning and memory based on an understanding of the nature of knowing. Taking into account this bifurcation between education and school, this paper examines how the paths of educational, neuroscience, and AI research cross beyond school-enabled learning. This is contemplated across three sections. The first section examines the historical basis of the bifurcation between education and schooling. The second reviews the potential impacts of current neuroscience knowledge on educational implementations. The third discusses how and why neuroscience and AI technologies could be pertinent to educational discourse in the future. [ABSTRACT FROM AUTHOR]

• Web-based framework for iterative exploration of multiscale brain data. • Exploring brain networks with billions of edges. • Fusing heterogeneous network data on different scales. • Identification of higher order connectivity in dense network. • Genetic analysis of brain networks based on genome-wide gene expression data. In recent years, big brain-initiatives and consortia have created vast resources of publicly available brain data that can be used by neuroscientists for their own research experiments. This includes microscale connectivity data—brain-network graphs with billions of edges—and vast spatial gene expression resources—the representation of tens of thousands genes in brain space. Their joint analysis for higher order relations in structural or functional neuroanatomy would enable the genetic dissection of brain networks on a genome-wide scale. Current experimental workflows involve only time-consuming manual aggregation and extensive graph theoretical analysis of data from different sources, which rarely provide spatial context to operate continuously on different scales. In this paper, we propose BrainTrawler , a task-driven, web-based framework that incorporates visual analytics methods to explore heterogeneous neurobiological data. It facilitates spatial indexing to query large-scale voxel-level connectivity data and gene expression collections in real-time. Relating data to the hierarchical structure of common anatomical atlases enables the retrieval on different anatomical levels. Together with intuitive network visualization, iterative visual queries, and quantitative information this allows the genetic dissection of multimodal networks on local/global scales in a spatial context. We demonstrate the relevance of our approach for neuroscience by exploring social-behavior and memory/learning related functional neuroanatomy in mice. [ABSTRACT FROM AUTHOR]

Bereavement is a common human experience that often involves significant impacts on psychological, emotional and even cognitive functioning. Though various psychological theories have been proposed to conceptualize the grief process, our current understanding of the underlying neurocognitive mechanisms of grief is limited. The present paper proposes a neurocognitive model to understand phenomena in typical grief, which links loss-related reactions to underlying learning and executive processes. We posit that the competitive relationship between the basal ganglia (BG) and circuitry involving the medial temporal lobe (MTL) underlies common cognitive experiences in grief such as a sense of "brain fog." Due to the intense stressor of bereavement, we suggest that these two systems' usually flexible interactive relationship become imbalanced. The resulting temporary dominance of either the BG or the MTL system is then manifested in perceived cognitive changes. Understanding the underlying neurocognitive mechanism in grief could inform ways to best support bereaved individuals. • Bereavement has significant impacts on psychological, emotional, and cognitive functioning, however, our current understanding of the underlying neurocognitive mechanisms of grief is limited. • A neurocognitive model, which links loss-related reactions to underlying learning and executive processes is proposed to understand phenomena in typical grief. • The basal ganglia and circuitry involving the medial temporal lobe underlie common cognitive experiences in grief. • The competitive relationship between these two systems becomes imbalanced due to the intense stressor of bereavement. • Understanding the underlying neurocognitive mechanism in grief could inform ways to best support bereaved individuals. [ABSTRACT FROM AUTHOR]

Modern neuroscience methods, such as functional Magnetic Resonance Imaging (fMRI), offer the unparalleled opportunity to monitor the human brain in vivo − a revolution that has had a tremendous impact on many disciplines (economics, culture, health). Thus, the paper by Tang et al. (2017) is an exciting epistemological step introducing this technology and the possibilities for those studying the health effects of landscapes. Yet, as in similar interdisciplinary efforts there are always risks: questions are very general or results are misinterpreted. The present commentary aims, using examples and a simple but rigorous language, to help the audience of Landscape and Urban Planning understand the basic principles of fMRI and neuroscience methods We end up with a call to landscape researchers and others studying how environments can affect people’s mental health and well-being to boldly be involved in this exciting interdisciplinary effort to help neuroscientists understand how the brain works. [ABSTRACT FROM AUTHOR]

This ECNP meeting was designed to build bridges between different constituencies of mental illness treatment researchers from a range of backgrounds with a specific focus on enhancing the development of novel, evidence based, psychological treatments. In particular we wished to explore the potential for basic neuroscience to support the development of more effective psychological treatments, just as this approach is starting to illuminate the actions of drugs. To fulfil this aim, a selection of clinical psychologists, psychiatrists and neuroscientists were invited to sit at the same table. The starting point of the meeting was the proposition that we know certain psychological treatments work, but we have only an approximate understanding of why they work. The first task in developing a coherent mental health science would therefore be to uncover the mechanisms (at all levels of analysis) of effective psychological treatments. Delineating these mechanisms, a task that will require input from both the clinic and the laboratory, will provide a key foundation for the rational optimisation of psychological treatments. As reviewed in this paper, the speakers at the meeting reviewed recent advances in the understanding of clinical and cognitive psychology, neuroscience, experimental psychopathology, and treatment delivery technology focussed primarily on anxiety disorders and depression. We started by asking three rhetorical questions: What has psychology done for treatment? What has technology done for psychology? What has neuroscience done for psychology? We then addressed how research in five broad research areas could inform the future development of better treatments: Attention, Conditioning, Compulsions and addiction, Emotional Memory, and Reward and emotional bias. Research in all these areas (and more) can be harnessed to neuroscience since psychological therapies are a learning process with a biological basis in the brain. Because current treatment approaches are not fully satisfactory, there is an imperative to understand why not. And when psychological therapies do work we need to understand why this is the case, and how we can improve them. We may be able to improve accessibility to treatment without understanding mechanisms. But for treatment innovation and improvement, mechanistic insights may actually help. Applying neuroscience in this way will become an additional mission for ECNP. [ABSTRACT FROM AUTHOR]

Public perception, fueled not only by popular and news media but also by expert claims that psychopaths are archetypes of evil: incorrigible, remorseless, cold-blooded criminals, whose crimes manifest in the most extreme levels of violence. But is there empirical evidence that psychopaths truly are what they are portrayed to be? If so, should the law respond, and adjust its treatment of psychopaths in court - permitting psychopathy to be admitted under an insanity defense and/or resulting in mitigation? In this paper, we demonstrate that fundamental questions from the law to science remain unanswered and must be addressed before any alternative treatment of psychopathy can be considered. As it stands, psychopaths cannot be reliably defined or diagnosed and, as we will demonstrate, even the presumed link with criminal dangerousness is problematic. We conclude that the current legal approach should not be modified, however, if preliminary findings regarding impairments in impulsivity/self-control are confirmed, some, but not all individuals who fall under one definition of psychopathy may merit different treatment in future. [ABSTRACT FROM AUTHOR]

Over the last two decades statistical learning (SL) has evolved into a key explanatory mechanism underlying the incidental learning of regularities across different domains of cognition, such as language, visual and auditory perception, and memory. Yet SL has mainly been investigated as an independent research area, separated from the primary study of the relevant cognitive domains. The aim of this special issue is to foster a bilateral integration of SL research with cognitive science: not only should domain-relevant evidence about the complexity of real-world input become more tightly integrated into SL research, but non-SL studies should also carefully consider the nature and range of statistical regularities that may affect learning and processing in a given domain. Four papers on reading in this volume demonstrate that such integration can lead to a better understanding of reading, while also revealing the complexity and abundance of different statistical patterns present in printed text. Moving beyond disciplinary boundaries has the promise to broaden the focus of SL research beyond simple artificial patterns, to examine the rich and subtle intricacies of real-world cognition. A final paper on the neurobiological underpinnings of SL and the consolidation of learned statistical regularities further illustrates what might be gained from a better integration of SL and memory research. We conclude by discussing possible directions for taking an integrative approach to SL forward. [ABSTRACT FROM AUTHOR]

In this paper, we propose metric Hashing Forests (mHF) which is a supervised variant of random forests tailored for the task of nearest neighbor retrieval through hashing. This is achieved by training independent hashing trees that parse and encode the feature space such that local class neighborhoods are preserved and encoded with similar compact binary codes. At the level of each internal node, locality preserving projections are employed to project data to a latent subspace, where separability between dissimilar points is enhanced. Following which, we define an oblique split that maximally preserves this separability and facilitates defining local neighborhoods of similar points. By incorporating the inverse-lookup search scheme within the mHF, we can then effectively mitigate pairwise neuron similarity comparisons, which allows for scalability to massive databases with little additional time overhead. Exhaustive experimental validations on 22,265 neurons curated from over 120 different archives demonstrate the superior efficacy of mHF in terms of its retrieval performance and precision of classification in contrast to state-of-the-art hashing and metric learning based methods. We conclude that the proposed method can be utilized effectively for similarity-preserving retrieval and categorization in large neuron databases. [ABSTRACT FROM AUTHOR]

Résumé La compréhension de l’anorexie de l’adolescent évolue. Certaines données de la médecine par la preuve permettent de remettre en question les pratiques de terrain centrées sur la séparation avec le milieu familial. Le but de cet article est de retracer l’expérience d’une équipe pluridisciplinaire française de pédopsychiatrie dans la construction des modalités de soins pour la prise en charge de l’anorexie de l’adolescent, de la séparation complète avec le milieu familial vers un cadre de soin sans séparation. La séparation induit des difficultés dans les équipes de soin, chez les adolescents et leurs familles qui sont analysées. La construction progressive d’un parcours de soin sans séparation familiale est décrite et explicitée. Understanding adolescent anorexia evolves. Some evidence based data question the practices centered on the separation with the family used in the field in France. The purpose of this paper is to trace the experience of a French adolescent psychiatry department throughout the construction of changing care conditions of anorexia in adolescents. The treatment evolved from complete separation with the family environment to a structured care without separation. The difficulties faced by teams, families and teenagers at the time of using the device of separation are analyzed. The gradual construction of a care course involving families is described and explained. [ABSTRACT FROM AUTHOR]

Constantly increasing number of information items (i.e. published or digital subjects) set the necessary goal to develop new approaches for filtering and structuring data. This paper describes agent-based approach that is used in Multi-agent information and analytical system (MIAS) developed at the National Research Nuclear University “MEPhI” in order to form a dynamic profile of the Human Brain Project, which includes general information, identification of funding, interrelation of subprojects and new technologies that are being developed in the course of the project. The MIAS support of object-oriented approach and international standards of linked data contributes to the development of future information systems that will enable forecasting on specific scientific field. [ABSTRACT FROM AUTHOR]

Résumé Objectifs Nous souhaitons examiner la façon dont des techniques et connaissances neuroscientifiques ont été utilisées dans quelques tribunaux européens, afin de discuter leur impact sur l’évaluation de la responsabilité pénale et d’anticiper les mésusages potentiels de l’utilisation des neurosciences dans les tribunaux. Méthode Nous avons effectué une recherche jurisprudentielle et examiné deux arrêts européens, où des données neuroscientifiques ont été utilisées dans le but de prouver une responsabilité réduite ou l’irresponsabilité. Résultats À l’issue de l’examen de ces cas nous avons constaté qu’il y a des difficultés et limitations importantes techniques et/ou scientifiques, mais aussi conceptuelles et juridiques concernant l’utilisation des neurosciences pour l’évaluation de la responsabilité pénale. Discussion Les données de neurosciences, aussi précises et fiables deviendront-elles, n’auront de sens dans la quête de l’évaluation de la responsabilité pénale, qu’à la condition qu’elles soient contextualisées et complétées à des données collectées à d’autres niveaux d’analyse. Conclusion Malgré l’intérêt de l’apport des neurosciences à la pratique expertale par une compréhension plus raffinée de l’interaction complexe entre le cerveau, l’état mental et le comportement, l’utilisation des preuves neuroscientifiques ne nous dispensera pas de la nécessité de définir les limites de la responsabilité et de l’irresponsabilité des prévenus. Il s’agit d’une question d’ordre social, moral, politique et, en fin de compte, juridique. Objectives This paper sets out to examine the way in which neuroscientific knowledge and techniques have been used in some European courts, in order to assess their impact on the assessment of criminal responsibility and to anticipate potential misuse of neuroscience in the courts. Method We conducted a case-law search and examined two European cases in which neuroscientific techniques were used in order to prove the diminished responsibility or irresponsibility of the accused. Results After reviewing these cases we concluded that there were significant difficulties and limitations of technical/scientific, conceptual and legal nature, concerning the use of neuroscience for the assessment of responsibility. Discussion Neuroscientific data, no matter how accurate and reliable may become, will only make sense in the quest for the assessment of criminal responsibility if they are contextualized and supplemented with data collected from other levels of analysis. Conclusion Despite the importance of the contribution of neuroscience to forensic assessments for a more refined understanding of the complex interaction between the brain, mental states and behavior, the use of neuroscientific evidence in legal contexts will not dispense with the need to define the limits of responsibility and irresponsibility of the accused. It is a social, moral, political and, ultimately, legal question. [ABSTRACT FROM AUTHOR]

There is a long-standing debate in the philosophy of mind and philosophy of science regarding how best to interpret the relationship between neuroscience and psychology. It has traditionally been argued that either the two domains will evolve and change over time until they converge on a single unified account of human behaviour, or else that they will continue to work in isolation given that they identify properties and states that exist autonomously from one another (due to the multiple-realizability of psychological states). In this paper, I argue that progress in psychology and neuroscience is contingent on the fact that both of these positions are false. Contra the convergence position, I argue that the theories of psychology and the theories of neuroscience are scientifically valuable as representational tools precisely because they cannot be integrated into a single account. However, contra the autonomy position, I propose that the theories of psychology and neuroscience are deeply dependent on one another for further refinement and improvement. In this respect, there is an irreconcilable codependence between psychology and neuroscience that is necessary for both domains to improve and progress. The two domains are forever linked while simultaneously being unable to integrate. [ABSTRACT FROM AUTHOR]

The scientific study of the biological basis of intelligence has been contributing to our understanding of individual differences in cognitive abilities for decades. In particular, the ongoing development of electrophysiological, neuroimaging, and genetic methods has created new opportunities to gain insights into pressing questions, allowing the field to come closer towards a comprehensive theory that explains how genotypes exert their influence on human intelligence through intermediate biological and cognitive endophenotypes. The aim of this article is to provide a focused overview of empirical benchmark findings on biological correlates of intelligence. Specifically, we summarize benchmark findings from electrophysiological, neuroimaging, and genetic research. Moreover, we discuss four open questions: (1) The robustness of research findings; (2) the relation between neural parameters and cognitive processes; (3) promising methodological developments; and (4) theory development. The aim of this paper is to assemble the most important and robust findings on the biological basis of intelligence to stimulate future research and to contribute to theory development. [Display omitted] • Focused overview of research on the biological basis of intelligence. • Benchmark findings from EEG, neuroimaging, and genetic research. • Critical open questions and future directions. [ABSTRACT FROM AUTHOR]

Although a growing body of evidence suggests that giving to (helping) others is linked reliably to better health and longevity for the helper, little is known about causal mechanisms. In the present paper we use a recently developed model of caregiving motivation to identify possible neurophysiological mechanisms. The model describes a mammalian neurohormonal system that evolved to regulate maternal care, but over time may have been recruited to support a wide variety of helping behaviors in humans and other social animals. According to the model, perception of need or distress in others activates caregiving motivation, which in turn, can facilitate helping behavior. Motivational regulation is governed by the medial preoptic area of the hypothalamus, interacting with certain other brain regions, hormones, and neuromodulators (especially oxytocin and progesterone). Consideration of neurohormonal circuitry and related evidence raises the possibility that it is these hormones, known to have stress-buffering and restorative properties, that are responsible, at least in part, for health and longevity benefits associated with helping others. [ABSTRACT FROM AUTHOR]

Learning and reproducing temporal sequences is a fundamental ability used by living beings to adapt behaviour repertoire to environmental constraints. This paper is focused on the description of a model based on spiking neurons, able to learn and autonomously generate a sequence of events. The neural architecture is inspired by the insect Mushroom Bodies (MBs) that are a crucial centre for multimodal sensory integration and behaviour modulation. The sequence learning capability coexists, within the insect brain computational model, with all the other features already addressed like attention, expectation, learning classification and others. This is a clear example that a unique neural structure is able to cope concurrently with a plethora of behaviours. Simulation results and robotic experiments are reported and discussed. [ABSTRACT FROM AUTHOR]

Over the last decade a convergent body of evidence has emerged from epidemiology, animal experiments and clinical trials which links low vitamin D status with a range of adverse neuropsychiatric outcomes. This research demonstrates that the timing of exposure to low vitamin D influences the nature of brain phenotypes, as exposures during gestation versus adulthood result in different phenotypes. With respect to early life exposures, there is robust evidence from rodent experiments indicating that transient developmental vitamin D (DVD) deficiency is associated with changes in brain structure, neurochemistry, gene and protein expression and behavior. In particular, DVD deficiency is associated with alterations in the dopaminergic neurotransmitter systems. In contrast, recently published animal experiments indicate that adult vitamin D (AVD) deficiency is associated with more subtle neurochemical and behavioral phenotypes. This paper explores key issues that need to be addressed in future research. There is a need to define the timing and duration of the ‘critical window’ during which low vitamin D status is associated with differential and adverse brain outcomes. We discuss the role for ‘two-hit hypotheses’, which propose that adult vitamin D deficiency leaves the brain more vulnerable to secondary adverse exposures, and thus may exacerbate disease progression. Finally, we explore the evidence implicating a role for vitamin D in rapid, non-genomic mechanisms that may involve L-type calcium channels and brain function. This article is part of a Special Issue entitled ‘17th Vitamin D Workshop’. [ABSTRACT FROM AUTHOR]

Summary This paper aims to describe current questions concerning conversive disorders among children and adolescents. We first describe prevalence and clinical characteristics of these. Many unresolved questions remain. Why do patients show excess, or loss of function? Attachment theory offers a relevant framework to answer this question. Does neurobiology of conversion disorders shed light on conversive processes? Current neurobiological research paradigms focus on the symptom, trying to infer processes, instead of proposing paradigms that test theoretical hypotheses. The most convincing theoretical framework that has already proposed a coherent theory of conversion is a psychodynamic one, which has not yet been tested with neurobiological paradigms. The interest of studying child and adolescent conversive disorders is to provide a means to more deeply investigate the two challenges we face: theoretical, and clinical ones. It provides the opportunity to access a pathopsychological process at its roots, not yet hidden by many defensive, rationalizing attitudes, and to better explore environmental features. We propose a “complementarist” model, which allows the combination of different approaches (neural, cognitive, environmental, attachment, intra-psychic) and permits proposal of different levels of therapeutic targets and means. [ABSTRACT FROM AUTHOR]

It has been observed experimentally that the neural tissues generate highly variable and scale-free distributed outbursts of activity both in vivo and in vitro. Understanding whether these heterogeneous patterns of activity come from operation of the brain at the edge of a phase transition is an interesting possibility. Therefore, constructing a simple model that exhibits such behavior is of great interest. Additionally, the presence of both critical behavior and oscillatory patterns in brain dynamics is a very interesting phenomenon: Oscillatory patterns define a temporal scale, while criticality imposes scale-free characteristics. In this paper, we consider a model for a neuronal population where each neuron is modeled by an over-damped rotator. We find that there are some regions in the space of external parameters where the system shows synchronization. Interestingly, just at the transition point, the avalanche statistics show power-law behavior. Also, in the case of small systems, the (partial) synchronization and power-law behavior can occur simultaneously. [ABSTRACT FROM AUTHOR]

The integration of neuroscience with multiple disciplines dealing with cognition, behavior and contextual influences holds potential to create new avenues for the application of process oriented interventions and guidelines for clinical psychological practice. In this paper, the main avenues by which neuroscience may readily be used for the clinical practice of Cognitive Behavior Therapy (CBT) are outlined: (1) the selection and optimal use of CBT procedures; (2) the combination of CBT with neurocognitive and neurobiological interventions; (3) tailoring CBT to the neurocognitive characteristics of patients; and (4) the use of neuroscience in psychoeducation. This translational view may facilitate multidisciplinary collaboration in case conceptualization. Moreover, it emphasizes that CBT course programs would benefit from neuroscience training and that continued education to keep track with the latest developments in neuroscience are helpful for good CBT practice. • The integration of neuroscience has proven to hold potential for the development of CBT. • Neuroscience can already be instrumental to the practice of CBT. • CBT programs would benefit from neuroscience training. [ABSTRACT FROM AUTHOR]

This paper examines the entanglement of medicine, brain injury, and subjectivity within newspaper discourse and through the case of ex-American footballer Aaron Hernandez. In 2017, two years after being found guilty of murder and five years after scoring in the Super Bowl, Aaron Hernandez died by suicide in his prison cell. Hernandez was posthumously diagnosed with Chronic Traumatic Encephalopathy (CTE), a neurodegenerative disease associated with violence, depression, and dementia-like symptoms. I examine newspaper coverage of the Hernandez case, focusing upon the murder, arrests, conviction, suicide, and diagnosis of CTE in order to examine understandings of Hernandez's subjectivity. I make three conclusions: First, the disease is not mentioned prior to diagnosis with family instability, friendship groups, individual psychology, and the entitlement of celebrity foregrounded. Second, CTE is foregrounded after the diagnosis and is used to explain much of Hernandez's behaviour. Third, the diagnosis of CTE goes someway to normalizing the behaviour of Hernandez, rendering his behaviours comprehensible. I conclude by considering how the specific narrative of CTE-as-acquired-dementia shapes depictions of Hernandez's subjectivity and discuss how this case troubles existing literatures on the neurologization of selfhood. • CTE is a neurodegenerative disease popularly associated with contact sport. • Aaron Hernandez was convicted of murder, died by suicide, and diagnosed with CTE. • Through media analysis, I ask if Hernandez's actions were seen as shaped by CTE. • CTE is not mentioned prior to suicide and rarely prior to formal diagnosis. • After diagnosis CTE is used to explain and normalize much of Hernandez's behaviour. [ABSTRACT FROM AUTHOR]