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Motion perception is an essential ability for animals and artificially intelligent systems interacting effectively, safely with surrounding objects and environments. Biological visual systems, that have naturally evolved over hundreds-million years, are quite efficient and robust for motion perception, whereas artificial vision systems are far from such capability. This paper argues that the gap can be significantly reduced by formulation of ON/OFF channels in motion perception models encoding luminance increment (ON) and decrement (OFF) responses within receptive field, separately. Such signal-bifurcating structure has been found in neural systems of many animal species articulating early motion is split and processed in segregated pathways. However, the corresponding biological substrates, and the necessity for artificial vision systems have never been elucidated together, leaving concerns on uniqueness and advantages of ON/OFF channels upon building dynamic vision systems to address real world challenges. This paper highlights the importance of ON/OFF channels in motion perception through surveying current progress covering both neuroscience and computationally modelling works with applications. Compared to related literature, this paper for the first time provides insights into implementation of different selectivity to directional motion of looming, translating, and small-sized target movement based on ON/OFF channels in keeping with soundness and robustness of biological principles. Existing challenges and future trends of such bio-plausible computational structure for visual perception in connection with hotspots of machine learning, advanced vision sensors like event-driven camera finally are discussed. [ABSTRACT FROM AUTHOR]

Le but de ce travail est d'explorer les multiples aspects du « Praecox Feeling » tels qu'il a été décrit par le psychiatre néerlandais H.C. Rümke et développé dans les suites par ses pairs. Nous proposons une présentation historique sur ce sujet allant des premières descriptions aux études les plus récentes. Nous proposons d'y associer des notions voisines telles que les processus de typification et le jugement expert (Gut feeling) en y intégrant des aspects épistémologiques. Le Praecox Feeling a été présenté par Rümke comme le moyen le plus sensible pour diagnostiquer une schizophrénie. Phénomène singulier de la rencontre clinique, il peut être décrit comme une expérience de bizarrerie ou d'étrangeté vécue par le clinicien au contact de son patient. Rümke conclut que le diagnostic de schizophrénie ne se fait pas strictement par une description précise des symptômes, mais en examinant son propre ressenti. De nombreux auteurs et chercheurs ont travaillé sur ce sujet et sont pour la plupart sensibles à une approche phénoménologique. Ainsi, Minkowski, Binswanger ou Tellenbach, parmi d'autres, ont permis d'affiner cette notion en parlant de « diagnostic par intuition », « diagnostic par pénétration » ou encore « diagnostic atmosphérique ». Malgré un abandon progressif de la subjectivité dans le processus diagnostique en psychiatrie, on retrouve dans la littérature contemporaine un regain d'intérêt de telles approches. On peut ainsi citer les processus de typifications ou encore le jugement expert (Gut feeling) qui proposent d'éclairer les mécanismes en jeu via une approche issue en partie des sciences cognitives et sociales. Ainsi, il apparaît que la question du diagnostic et de ses processus dépasse le strict champ psychiatrique pour rejoindre celui de la médecine au sens large. The aim of this review is to explore the multiple aspects of the « Praecox Feeling » as introduced and developed by the Dutch Psychiatrist H.C Rümke in 1941 and re-assessed over the following years by its peers. We present a historical review of this subject from the first description to the most recent research and proposed related notions like typification processes and gut feeling. The Praecox Feeling, as presented by Rümke, may be considered as the most sensible tool in order to diagnose schizophrenia. This phenomenon appears in the in-between of the clinical encounter (i.e. the intersubjective space). It may be felt by the clinician as an experience of bizarreness during the interaction with a patient suffering from schizophrenia. Those feelings suggest fundamental aspects of schizophrenia that can be recognized as a peculiar form of embodiment. Rümke finally concludes that schizophrenia is not diagnosed by strictly examining patients' symptoms, but psychiatrists' own feelings. The recognition of such feelings remains hard to describe explicitly, since it takes place in a very basic (« pre reflexive ») state. However, numbers of researchers tried to explore praecox feeling more accurately. A phenomenological approach, with Minkowski, Binswanger or Tellenbach among others, developed several related terms: "Diagnostic by intuition", "Diagnostic by penetration" or "Atmospheric diagnosis". Although the precocity of the diagnosis is mentioned in Rümke's original paper, it does not precisely define the Praecox Feeling, contributing to the misunderstanding of the Rümke's original formulation. The very notion of Praecox Feeling in terms of psychiatric diagnoses fell into disuse with the development and the widespread diffusion of criteriological approaches, with the risk of an impoverishment of the clinical diagnosis. However, the study of specific subjective perspectives showed a renewed interest over the last decade, especially with the contribution of cognitive and social neurosciences. Furthermore, studies on psychiatrists' daily-practice suggest that the Praecox Feeling is still relevant. Moreover, "the subjective tool" represented by the Praecox Feeling remains as used today as it was fifty years ago, in a pre-DSM III era. Temporal aspects have, nevertheless, lead to several studies suggesting that many clinical indices are identified during the first minutes of a clinical interview, especially for schizophrenia. In order to specify this very intuitive pathway, authors developed and worked on the typifications processes that lead to such feelings, involving diagnosis through prototypes. The passive synthesis of such subtle clinical clues may induce for the clinician a "pattern recognition" phenomenon that reminds a "gestaltic" diagnosis. This specific aspect of typification may not be specific of psychiatry. The "Guts Feeling" is a notion first developed by Dutchs physicians, particularly in General medicine but also among other medicals disciplines, theoretically very close from the notion of intuitive typification. It may allow benefits in term of diagnoses, reassurance or alarm sense. It suggests that clinical experience added to criteriology provides risks reductions. The crucial role of clinical experience in the diagnosis decision making tends to be underexplored, but still represents a crucial aspect of the evidence-based medicine as firstly described. [ABSTRACT FROM AUTHOR]

• Friedrich Bonhoeffer and his team discovered Roger Sperry's molecular labels. • He was pioneer in developing powerful in vitro assays for axon guidance. • He showed these labels were graded, membrane bound cues in retina and targets. • Throughout the 80′s-90′s, his lab was an epicenter of scientific innovation. • The fixed tissue DiI labeling method was discovered in his lab. The main focus of research for which Friedrich Bonhoeffer's work is known in the Neuroscience community was pioneer experiments on how axonal projections could organize into "maps", what mechanisms are involved in axon guidance and involve gradients of guiding molecules, and isolation of the first such molecules, e.g. RAGS (ephrin A5) and RGM (repulsive guidance molecule). Other papers have described in detail these contributions as well as Friedrich Bonhoeffer's personality. In the mid-eighties, I made a 2-year stay in his lab and initiated a line of research on development of binocular connections in Mammals, particularly the guidance of retinal fibers to one or the other side of the brain. In this paper I recall these circumstances as they pertain to Neuroscience as it stood at the time, and explain as best as I can how his lab was a conducive setting for the discoveries made there and how Friedrich Bonhoeffer acted for me as a scientist and a tutor. [ABSTRACT FROM AUTHOR]

Rainbow color scheme is popularly used across the board in many scientific disciplines for visualizing data, yet heavily debated in visualization literature. In this paper, we first report an interdisciplinary visualization survey examining the prevalence rainbow color scheme use in scientific publications including a temporal analysis. Then we consolidate findings from empirical studies on the subject to better understand why and precisely when the rainbow color scheme might impair human performance with visualizations. Consequently, we systematically document and analyze the consequences of using the rainbow color scheme based on over 37,000 figures in 11,808 papers in information visualization, neuroscience, hydrology, geography, remote sensing and planetary science. Our findings reveal that while the rainbow scheme appears less and less in visualization-related publications, it remains popular in other scientific domains including remote sensing and planetary sciences (strikingly fourteen times more frequent in remote sensing and planetary sciences than in information visualization outlets). We also find that conflicting findings about human performance with the rainbow color scheme is most likely explained by what the users are asked to do with it (i.e., task type). We detail and typify tasks used in the related empirical work in an effort to organize the current understanding on the subject, and translate it to practicable recommendations. We believe our review and analyses bring clarity and nuance to "the rainbow debate" and enable better-informed visualization advice. [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]

Experimental psychology and neuroscience reveal that decision-behavior plays a dominant role in human-selective-attention when it comes to reading, object and scene detection. Difficulties in reading are easily reflected by eye-movement patterns. Hence, modelling eye-gaze behaviour for normal readers and people with reading impairments can greatly help in contrasting reading strategies used, which can in turn help in early identification and diagnosis of impairments such as dyslexia. This paper introduces a novel method of formulating a reinforcement learning model that is explainable, and can obtain the sequence of gaze targets based on recorded observations of dyslexic and non-dyslexic children. Results reveal that despite being a less sophisticated model, it is able to obtain the optimal reading policy of the ideal reader, from a set of good and poor readers with the help of a strong reward system and Q-Learning agent. [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]

This paper delves into the concept of archetypes, universal patterns of behavior and cognition, and proposes a novel tripartite model distinguishing between structural, regulatory, and representational archetypes. Drawing on insights from code biology, neuroscience, genetics, and epigenetics, the model provides a nuanced framework for understanding archetypes and their role in shaping cognition and behavior. The paper also explores the interplay between these elements to express representational archetypes. Furthermore, it addresses the informational capacity of the genome and its influence on post-natal development and the psyche. The paper concludes by discussing the future trajectory of psychology, emphasizing the need for an integrative approach that combines our understanding of social constructs with insights into our inherent organizational propensities or archetypes. This exploration holds the potential to advance our understanding of the human condition. [ABSTRACT FROM AUTHOR]

The model of complex dynamical network (CDN) can be represented as the mathematic graph, in which some characteristics may emerge from the dynamic nodes group (NG) and links group (LG). This paper primarily focuses on the feature appearing from the dynamic links. The average link weight (ALW), as a novel quantitative index to describe the characteristic of dynamic links is introduced. Inspired by the Hebb's neuroscience theory, the Hebb complex dynamical network (HCDN) is constructed. The ALW of the HCDN can track a given target via external stimulus signals with adaptive amplifiers' proportional coefficients. In other words, the stationary network implies the ALW is a constant in time. Finally, two simulation examples are performed to validate the proposed adaptive update law's effectiveness. • The index of the average values-weighted of the links (ALW) is constructed by using Hebb's neural theory, which is novel. • We introduce the ALW in place of the mean value to describe the overall change of links weights among many nodes in time. In addition, the stationary network implies the ALW is a constant in time, which is novel. • Using the principle of amplifiers, the ALW of Hebb complex dynamical network (HCDN) is controlled by adjusting the adaptive update law of the amplifier's proportional coefficients to track a given reference signal. [ABSTRACT FROM AUTHOR]

O'Malley et al. (2009) and Haufe (2013) suggest that the philosophical idea of science as hypothesis testing generates a pernicious bias towards hypothesis-driven research and against exploratory research in the review process of research proposals and the allocation of resources. This paper addresses a conceptual objection to the argument by O'Malley et al. (2009) and Haufe (2013). We argue that the funding agencies' concepts of good science do not belong to epistemological or philosophical contexts but to political and institutional contexts. This means that correcting (potential) biases in research funding does not entail correcting funding agencies' (supposed) philosophies of science. To illustrate this point, we provide an in-depth historical case study: the granting of funds to neuroscientist Pedro Maldonado by the Chilean funding programme FONDECYT. This is a relevant comparison as FONDECYT's guidelines explicitly promote hypothesis-driven research and endorse a view of "good science" as hypothesis testing. However, we will see that the overall influence of the philosophical idea of science as hypothesis testing over this funding programme, the research project, and the actual practice of hypothesis testing is somewhat limited. The concept of science as hypothesis testing seems to play a crucial institutional or political (not philosophical) role in allowing the conceptual articulation of social expectations and researchers' expectations. [ABSTRACT FROM AUTHOR]

Despite the recent success of neural network models in mimicking animal performance on various tasks, critics worry that these models fail to illuminate brain function. We take it that a central approach to explanation in systems neuroscience is that of mechanistic modeling, where understanding the system requires us to characterize its parts, organization, and activities, and how those give rise to behaviors of interest. However, it remains controversial what it takes for a model to be mechanistic, and whether computational models such as neural networks qualify as explanatory on this approach. We argue that certain kinds of neural network models are actually good examples of mechanistic models, when an appropriate notion of mechanistic mapping is deployed. Building on existing work on model-to-mechanism mapping (3M), we describe criteria delineating such a notion, which we call 3M++. These criteria require us, first, to identify an abstract level of description that is still detailed enough to be "runnable", and then, to construct model-to-brain mappings using the same principles as those employed for brain-to-brain mapping across individuals. Perhaps surprisingly, the abstractions required are just those already in use in experimental neuroscience and deployed in the construction of more familiar computational models — just as the principles of inter-brain mappings are very much in the spirit of those already employed in the collection and analysis of data across animals. In a companion paper, we address the relationship between optimization and intelligibility, in the context of functional evolutionary explanations. Taken together, mechanistic interpretations of computational models and the dependencies between form and function illuminated by optimization processes can help us to understand why brain systems are built they way they are. [ABSTRACT FROM AUTHOR]

The process of reading a specific text is considered complex and little known in neuroscience, since it involves the vision, memory, motor control, learning, among others. In this sense, an excellent possibility to study the brain activity in the reading task can be achieved by the analysis of the multi-channel Electroencephalogram (EEG) and also with new statistical methods, like the detrended fluctuation analysis method (DFA). In this paper it will be proposed a model to analyze the brain activity in the reading task, performed by two subjects using a 22-channels EEG (NEUROMAP® model E Q S A 260). In order to test our model, two adults subjects (graduates) were tested here. These subjects were arranged in a chair facing a panel with the specific text, excluding involuntary movements that activated regions of the brain that were not being stimulated by reading. For the first subject, chosen at random, the text was presented before the task for understanding and some memorization. For the other subject the text was presented at the time of task. For the signal processing we chose 11 bio-electrodes located at the frontal, parietal, temporal and occipital regions of the brain. Therefore, to treat these non-stationary bio-signals we must apply robust and modern statistical techniques. With this objective, DFA method was applied in order to analyze the F D F A (n) fluctuation function in multi-channel EEG bio-sensors, more specifically the difference of its logarithm, i.e., Δ l o g F D F A. The results show that the use of this new function can be useful for brain activities. This paper, as we shall see here, is an initial contribution for EEG data analyze, that would be of medical interest, mainly in neuroscience area. • A model to analyze the brain activity (EEG signal) in the reading task is proposed. • In order to test our model, two adults subjects (graduate) were tested. • DFA method was applied in order to analyze the F D F A (n) fluctuation function. • For brain activities studies, new possibilities in the area of neuroscience is now open. [ABSTRACT FROM AUTHOR]

Assess the impact the force-plate actometer, invented by Stephen C. Fowler, has had on behavioral neuroscience so far and what may be possible for future progress. The web service Scopus was queried on April 28, 2021 for articles that cited the Journal of Neuroscience Methods paper titled "A force-plate actometer for quantitating rodent behaviors: illustrative data on locomotion, rotation, spatial patterning, stereotypies, and tremor" resulting in 134 articles. Articles were coded by the author for type (e.g., research, review, book chapter), phenomenon (e.g., stress, addiction), intervention (e.g., pharmacological), and measure (e.g., distance traveled, tremor). Of the 134 citations, 116 were research articles, 10 were review articles, 7 were book chapters and one was an advertisement. The force-plate actometer has been used to study a variety of phenomena and its measurement capabilities were expanded. While primarily used for rats and mice, other species have been used. [ABSTRACT FROM AUTHOR]

Highlights • The overarching aim of this study is to create a theoretical pedagogical framework within which to develop competences for students so they will be able to encounter hitherto unknown challenges related to renewable energy education and thus to apply different aspects of this rapidly developing technology. • The paper aims to make an approach to include solar energy education within the framework of an upgraded version of the socio-cultural theory for learning. • The intention is to connect this educational domain to educational neuroscience and to contemporary research on creativity. Abstract The aim in this paper is to upgrade experiences from traditional studies on solar energy education to include social, sociological and pedagogical aspects. This is done in order to develop insights beyond production of artifacts. The aim is promotion of educational means for utilization of the artifacts developed. This includes upgrading knowledge about hardware as well as about software, and in this process also to upgrade our ways of thinking. This implies changing our neurological mindset in accordance with continuous development of knowledge within these domains. The scope of this paper is thus to discuss development of curricula and pedagogical means for teaching and learning within the domain of renewable energy education. The paper also aims to make a tentative approach to include solar energy education within the framework of an upgraded version of the socio-cultural theory for learning. A tentative approach is also made, with the intention, to connect this educational domain to educational neuroscience and to contemporary research on creativity. [ABSTRACT FROM AUTHOR]

In their recent paper, Alkoby et al. (2017) provide the readership with an extensive and very insightful review of the factors influencing NeuroFeedback (NF) performance. These factors are drawn from both the NF literature and the Brain–Computer Interface (BCI) literature. Our short review aims to complement Alkoby et al.’s review by reporting recent additions to the BCI literature. The object of this paper is to highlight this literature and discuss its potential relevance and usefulness to better understand the processes underlying NF and further improve the design of clinical trials assessing NF efficacy. Indeed, we are convinced that while NF and BCI are fundamentally different in many ways, both the BCI and NF communities could reach compelling achievements by building upon one another. By reviewing the recent BCI literature, we identified three types of factors that influence BCI performance: task-specific, cognitive/motivational and technology-acceptance-related factors. Since BCIs and NF share a common goal (i.e., learning to modulate specific neurophysiological patterns), similar cognitive and neurophysiological processes are likely to be involved during the training process. Thus, the literature on BCI training may help (1) to deepen our understanding of neurofeedback training processes and (2) to understand the variables that influence the clinical efficacy of NF. This may help to properly assess and/or control the influence of these variables during randomized controlled trials. [ABSTRACT FROM AUTHOR]

Computational modeling plays an increasingly important role in neuroscience, highlighting the philosophical question of how computational models explain. In the particular case of neural network models, concerns have been raised about their intelligibility, and how these models relate (if at all) to what is found in the brain. We claim that what makes a system intelligible is an understanding of the dependencies between its behavior and the factors that are responsible for that behavior. In biology, many of these dependencies are naturally "top-down", as ethological imperatives interact with evolutionary and developmental constraints under natural selection to produce systems with capabilities and behaviors appropriate to their evolutionary needs. We describe how the optimization techniques used to construct neural network models capture some key aspects of these dependencies, and thus help explain why brain systems are as they are — because when a challenging ecologically-relevant goal is shared by a neural network and the brain, it places constraints on the possible mechanisms exhibited in both kinds of systems. The presence and strength of these constraints explain why some outcomes are more likely than others. By combining two familiar modes of explanation — one based on bottom-up mechanistic description (whose relation to neural network models we address in a companion paper) and the other based on top-down constraints, these models have the potential to illuminate brain function. [ABSTRACT FROM AUTHOR]

The nature of play in animals has been long debated, but progress is being made in characterizing play and its variants, documenting its distribution across vertebrate and invertebrate taxa, describing its mechanisms and development, and proposing testable theories about its origins, evolution, and adaptive functions. To achieve a deeper understanding of the functions and evolution of play, integrative and conceptual advances are needed in neuroscience, computer modeling, phylogenetics, experimental techniques, behavior development, and inter- and intra-specific variation. The special issue contains papers documenting many of these advances. Here, we describe seven timely areas where further research is needed to understand this still enigmatic class of phenomena more fully. Growing empirical and theoretical evidence reveals that play has been crucial in the evolution of behavior and psychology but has been underestimated, if not ignored, in both empirical and theoretical areas of evolutionary biology and neuroscience. Play research has important ramifications for understanding the evolution of cognition, emotion, and culture, and research on animals can be both informative and transformative. • Play is important for understanding biological and cultural evolution but has largely been ignored by biological researchers. • We identify critical issues that can advance our understanding of the evolutionary processes and physiological bases of play • We describe seven timely areas where further research is needed to fully understand this enigmatic class of phenomena. • Integrating neuroscience, computer modeling, and phylogenetics will help achieve a deeper evolutionary understanding of play • The special issue documents many of these advances. [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]

Le concept de schéma corporel (SC) est porteur d'une histoire très complexe. Proposé par Bonnier en tant que « sens des attitudes » et par Head et Holmes comme « schéma postural », il a donné lieu à des débats confus jusque dans les années 1980. Il fut clarifié en France par Dolto et dans le monde anglo-saxon par Gallagher. Ces deux auteurs proposent de cantonner le SC à un « équipement physiologique ». Dès lors, la conceptualisation deviendra plus claire mais devra attendre l'essor considérable des neurosciences – et des techniques d'imagerie modernes – pour identifier les structures neuro-anatomiques et les systèmes sensoriels impliqués et trouver une définition stabilisée. L'image du corps, en France, intéressera d'abord plutôt les psychanalystes. Ce texte est le premier de deux. Il raconte les vicissitudes presque séculaires du concept de SC. Le deuxième fera état des données les plus récentes et en proposera une nouvelle définition. The history of the body schema concept derives from a long and complex story. First proposed by Bonnier as "the sense of attitudes" and by Head and Holmes as "postural schemata", it gave rise to several confusing debates until the 1980's. Dolto in France and Gallagher in the Anglo-Saxon world, cleared that notion by letting it go to the physiological equipment. Since then, conceptualization has become clearer but yet needed the neurosciences and the imagery technologies to become modern enough. Thus, neuro-anatomical structures and sensorial systems involved have been identified. The definition of the body schema is now much more stable than near a century ago. Body image, as far as it is concerned, has been a very attractive field for psychoanalysts in France. This paper is the first of two and concerns the complex history of the body schema. The second one will give rise to the most recent data in neurosciences. It will propose a new definition of the body schema. [ABSTRACT FROM AUTHOR]

Ce texte est le deuxième sur deux et décrit de nouvelles hypothèses sur la notion de schéma corporel (SC). Clarifié par Dolto et Gallagher qui l'ont conceptuellement cantonné à l'équipement neuro-anatomique et neurophysiologique, le SC est devenu un champ de recherche pour les neurosciences. Distinct de l'image du corps, donnée affective, il concerne, en plus des systèmes moteurs, la proprioception principalement, la vision périphérique–c'est-à-dire la vision des mouvements – et le système vestibulaire. La vision périphérique est un soutien. Le système vestibulaire (antigravitaire) est concerné par les constants et nécessaires réajustements posturaux. Intéroception et toucher ne sont pas impliqués dans le SC, contrairement à l'espace péri-personnel. Différentes zones du système nerveux central sont impliquées. This paper is the second of two about the relationship between the body schema concept and the neurosciences. The first one concerned the history of the body schema concept. Dolto and Gallagher let the body schema go to the neurological and neurophysiological equipment. Since then, body schema has become a very good research field in neurosciences. Body image is an affective concept about the relationship between mind and body. Body schema is a physiological notion with proprioceptive, visual (including peripheral retina which involves the vision of movements and is located on the peripheral retina) and vestibular information. Motor systems are engaged too. The vestibular system is engaged for the postural modifications which change the weight of gravity. Interoception and touch are not supposed to interfere with body schema. Neurosciences have taught us to take into account a new kind of space to include in the body schema : the peri-personal space. Differents regions of the brain are involved in the body schema notion and are described here. [ABSTRACT FROM AUTHOR]

• Introducing a united framework to bridge the gap between network neuroscience and psychopathological networks. • Showcasing three methodological avenues to introduce networks of brain and behavioral data. • Creating a common language that allows to exploit synergies. In recent years, there has been an increase in applications of network science in many different fields. In clinical neuroscience and psychopathology, the developments and applications of network science have occurred mostly simultaneously, but without much collaboration between the two fields. The promise of integrating these network applications lies in a united framework to tackle one of the fundamental questions of our time: how to understand the link between brain and behavior. In the current overview, we bridge this gap by introducing conventions in both fields, highlighting similarities, and creating a common language that enables the exploitation of synergies. We provide research examples in autism research, as it accurately represents research lines in both network neuroscience and psychological networks. We integrate brain and behavior not only semantically, but also practically, by showcasing three methodological avenues that allow to combine networks of brain and behavioral data. As such, the current paper offers a stepping stone to further develop multi-modal networks and to integrate brain and behavior. [ABSTRACT FROM AUTHOR]

This review concentrates on the issue of acquisition of abstract words in a cognitive robot with the grounding principle, from relevant theories to practical models of agents and robots. Most cognitive robotics models developed for grounding of language take inspiration from the findings of neuroscience and psychology to get the theoretical skeleton of these models. To better understand these modelling approaches, it is indispensable to work from the base (theoretical accounts) to the top (computational models). Therefore in this paper, succinct definition of abstract words is presented first, and then the symbol grounding issue and accounts of grounded cognition for abstract words are given. The next section discusses the computational modelling approaches for abstract words grounding phenomenon. Finally, important cognitive robotics models are reviewed. This paper also points out the strengths and weaknesses of relevant hypotheses and models for the representation of abstract words in the grounded cognition framework and helps the understanding of issues such as where and why modelling efforts stand to address this problem in comparison with theoretical findings. [ABSTRACT FROM AUTHOR]

In 1899 a landmark paper entitled “On the musical centers of the brain” was published in Pflügers Archiv , based on work carried out in the Anatomo-Physiological Laboratory of the Neuropsychiatric Clinic of Vladimir M. Bekhterev (1857–1927) in St. Petersburg, Imperial Russia. The author of that paper was Vladimir E. Larionov (1857–1929), a military doctor and devoted brain scientist, who pursued the problem of the localization of function in the canine and human auditory cortex. His data detailed the existence of tonotopy in the temporal lobe and further demonstrated centrifugal auditory pathways emanating from the auditory cortex and directed to the opposite hemisphere and lower brain centers. Larionov’s discoveries have been largely considered as findings of the Bekhterev school. Perhaps this is why there are limited resources on Larionov, especially keeping in mind his military medical career and the fact that after 1917 he just seems to have practiced otorhinolaryngology in Odessa. Larionov died two years after Bekhterev’s mysterious death of 1927. The present study highlights the pioneering contributions of Larionov to auditory neuroscience, trusting that the life and work of Vladimir Efimovich will finally, and deservedly, emerge from the shadow of his celebrated master, Vladimir Mikhailovich. [ABSTRACT FROM AUTHOR]

In this paper, the concept of context-dependent realisation of mental models is introduced and discussed. Literature from neuroscience is discussed showing that different types of mental models can use different types of brain areas. Moreover, it is discussed that the same occurs for the formation and adaptation of mental models and the control of these processes. This makes that it is hard to claim that all mental models use the same brain mechanisms and areas. Instead, the notion of context-dependent realisation is proposed here as a better manner to relate neural correlates to mental models. It is shown in some formal detail how this context-dependent realisation approach can be related to well-known perspectives based on bridge principle realisation and interpretation mapping realisation. [ABSTRACT FROM AUTHOR]

Optical coherence tomography angiography (OCTA) is a novel imaging modality that has been widely utilized in ophthalmology and neuroscience studies to observe retinal vessels and microvascular systems. However, publicly available OCTA datasets remain scarce. In this paper, we introduce the largest and most comprehensive OCTA dataset dubbed OCTA-500, which contains OCTA imaging under two fields of view (FOVs) from 500 subjects. The dataset provides rich images and annotations including two modalities (OCT/OCTA volumes), six types of projections, four types of text labels (age/gender/eye/disease) and seven types of segmentation labels (large vessel/capillary/artery/vein/2D FAZ/3D FAZ/retinal layers). Then, we propose a multi-object segmentation task called CAVF, which integrates capillary segmentation, artery segmentation, vein segmentation, and FAZ segmentation under a unified framework. In addition, we optimize the 3D-to-2D image projection network (IPN) to IPN-V2 to serve as one of the segmentation baselines. Experimental results demonstrate that IPN-V2 achieves an about 10% mIoU improvement over IPN on CAVF task. Finally, we further study the impact of several dataset characteristics: the training set size, the model input (OCT/OCTA, 3D volume/2D projection), the baseline networks, and the diseases. The dataset and code are publicly available at: https://ieee-dataport.org/open-access/octa-500. • Proposed OCTA-500, which is the largest and comprehensive OCTA dataset. • The OCTA-500 includes OCTA imaging from 500 subjects and rich annotation information. • Proposed a CAVF task, which integrates multiple key segmentation tasks. • Optimized the IPN to IPN-V2 to serve as one of the competitive baselines. • The OCTA-500 dataset has great potential to promote other researches in OCTA. [ABSTRACT FROM AUTHOR]

Verbal interaction is one of the most frequent social interactions humans encounter on a daily basis. In the current paper, we zoom in on what the multi-brain approach has contributed, and can contribute in the future, to our understanding of the neural mechanisms supporting verbal interaction. Indeed, since verbal interaction can only exist between individuals, it seems intuitive to focus analyses on inter-individual neural markers, i.e. between-brain neural coupling. To date, however, there is a severe lack of theoretically-driven, testable hypotheses about what between-brain neural coupling actually reflects. In this paper, we develop a testable hypothesis in which between-pair variation in between-brain neural coupling is of key importance. Based on theoretical frameworks and empirical data, we argue that the level of between-brain neural coupling reflects speaker-listener alignment at different levels of linguistic and extra-linguistic representation. We discuss the possibility that between-brain neural coupling could inform us about the highest level of inter-speaker alignment: mutual understanding. [ABSTRACT FROM AUTHOR]

Background Generative models of neuroimaging data, such as dynamic causal models (DCMs), are commonly used for inferring effective connectivity from individual subject data. Recently introduced “generative embedding” approaches have used DCM-based connectivity parameters for supervised classification of individual patients or to find unknown subgroups in heterogeneous groups using unsupervised clustering methods. New method We present a novel framework which combines DCMs with finite mixture models into a single hierarchical model. This approach unifies the inference of connectivity parameters in individual subjects with inference on population structure, i.e. the existence of subgroups defined by model parameters, and allows for empirical Bayesian estimates of a subject’s connectivity based on subgroup-specific prior distributions. We introduce a Markov chain Monte Carlo sampling method for inversion of this hierarchical generative model. Results This paper formally introduces the idea behind our novel concept and demonstrates the face validity of the model in application to both simulated data as well as an empirical fMRI dataset from healthy controls and patients with schizophrenia. Comparison with existing method(s) The analysis of our empirical fMRI data demonstrates that our approach results in superior model evidence than the conventional non-hierarchical inversion of DCMs. Conclusions In this paper, we have presented a novel unified framework to jointly infer the effective connectivity parameters in DCMs for multiple subjects and, at the same time, discover connectivity-defined cluster structure of the whole population, using a mixture model approach. [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]

The concept of a rat-robot was initially introduced in 2002, bringing to the field, a novel area of research using modern research into neuroscience and robotics. This paper brings to the table, a study into the method best used for navigation systems in a rat-robot. Current research is epitomized by the use of reward-based spatial navigation, combining the concept of an induced reward sensation as well as a ‘virtual touch’ sensation to control the movement of the rat-robot. However, such methods are plagued by limitations affecting the success rate as well as preparation procedures which may have varying effects on different rats, even under similar conditions. Hence, this paper studies the stimulation of two different portions of the brain to induce a turning motion within the rat, namely the Ventral Posteromedial (VPM) thalamic nucleus as well as the Barrel-Field (BF) cortex and demonstrates the preferential usage of VPM as the choice use of navigational control in a rat-robot. [ABSTRACT FROM AUTHOR]

This paper discusses a Boolean method for establishing constitutive regularity statements which, according to the regularity theory of mechanistic constitution, form the core of any mechanistic explanation in neuroscience. After presenting the regularity definition for the constitution relation, the paper develops a set of inference rules allowing one to establish constitutive hypotheses in light of certain kinds of empirical evidence. The general methodology consisting of these rules is characterized as having formed the basis of many successful explanatory projects in neuroscience. [ABSTRACT FROM AUTHOR]

Phenomenology since Husserl has always had a problematic relationship with empirical science. In its early articulations, there was Husserl's rejection of ‘the scientific attitude’, Merleau-Ponty's distancing of the scientifically-objectified self, and Heidegger's critique of modern science. These suggest an antipathy to science and to its methods of explaining the natural world. Recent developments in neuroscience have opened new opportunities for an engagement between phenomenology and cognitive science and through this, a re-thinking of science and its hidden assumptions more generally. This is so partly because of the shortcomings of conventional mechanistically-conceived science in dealing with complex and dynamic phenomena such as climate change, brain plasticity, the behaviour of collectives, the dynamics of various microbiological processes, etc. But it is also due to recent phenomenological scholarship focussed on the ‘embodied’ phenomenology of Husserl's Ideen II and Merleau Ponty's later ontology of nature which have helped to extend the insights of phenomenology beyond the narrowly ‘human’ to an understanding of nature (which includes the human) more generally. Thus re-contextualised, phenomenology is well placed to examine some of the assumptions that give rise to the reductionism and associated scientism which has characterised conventional science in its approach to the study of natural processes. In light of this, it might be suggested that the ‘anti-science’ of early articulations of phenomenology is more a hostility to the underlying assumptions of science as conventionally understood than to science itself – that it is scientism rather than science that is targeted. In this paper, I aim to show how a phenomenological naturalism might be seen as a necessary step towards the development of a non-reductionist and non-scientistic approach to scientific inquiry. A key to this is a reconceptualization of nature as inclusive of meanings and of mind. It is a conception developed by Merleau-Ponty, especially in his later ontology of nature, and one that is shared by American pragmatist philosopher of science, C.S. Peirce (1839–1914). For both philosophers, meaning must be understood in terms of an ontology which is relational rather than atomistic, and dynamic or processual rather than static and substance-based. For Merleau-Ponty this is an experientially-derived ontology; for Peirce it is a more conceptually-based one. In this paper, I explore this connection between these two philosophers in two stages. The first is by reference to Peirce's theory of signs or semiotics. More specifically, I look at the application of this theory to the study of biological processes as developed in Peirce-inspired biosemiotics. In the light of this, I suggest that Merleau-Ponty's account of intentional relations in nature might be articulated as semiotic relations, and can serve as a philosophical basis for a non-reductive biological science. I then turn to questions relating to the ontology of nature. I explore Merleau-Ponty's experientially-based “ontology of flesh” and Peirce's distinctive form of naturalism to show affinities at this ontological level. These affinities consist in commitments to a reality that includes possibility, meaning, temporality, and final causation – that is, an ontology which is far more inclusive than that of conventional positivistic science. Peirce's broader scientific metaphysics enables us to extend Merleau-Ponty's phenomenological naturalism beyond the biological to the physical sciences. Whilst Merleau-Ponty's ontology of nature provides the experiential basis necessary for a critique of scientism, Peirce establishes the relevance of that ontology for a re-conceived empirical science. [ABSTRACT FROM AUTHOR]

Neuroscience study verifies that synchronized audiovisual stimuli would make a stronger response of visual perception than an independent stimulus. Many researches show that audio signals would affect human gaze behavior in the viewing of natural video scenes. Thus in this paper, we propose a multi-sensory framework of audio and visual signals for video saliency prediction. It mainly includes four modules: auditory feature extraction, visual feature extraction, semantic interaction between auditory feature and visual feature, and feature fusion. With the inputs of audio and visual signals, we present a network architecture of deep learning to undertake the tasks of these four modules. It is an end-to-end architecture that could interact the semantics from its learned features of audio and visual stimuli. The numerical and visual results show our method achieves a significant improvement over eleven recent saliency models that are regardless of the audio stimuli, even some of them are state-of-the-art deep learning models. [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 time- and frequency-varying dynamics of how brain regions interact is one of the fundamental mysteries of neuroscience. In electrophysiological data, functional connectivity is often measured through the consistency of oscillatory phase angles between two electrodes placed in or over different brain regions. However, due to volume conduction, the results of such analyses can be difficult to interpret, because mathematical estimates of connectivity can be driven both by true inter-regional connectivity, and by volume conduction from the same neural source. Generally, there are two approaches to attenuate artifacts due to volume conduction: spatial filtering in combination with standard connectivity methods, or connectivity methods such as the weighted phase lag index that are blind to instantaneous connectivity that may reflect volume conduction artifacts. The purpose of this paper is to compare these two approaches directly in the presence of different connectivity time lags (5 or 25 ms) and physiologically realistic frequency non-stationarities. The results show that standard connectivity methods in combination with Laplacian spatial filtering correctly identified simulated connectivity regardless of time lag or changes in frequency, although residual volume conduction artifacts were seen in the vicinity of the “seed” electrode. Weighted phase lag index under-estimated connectivity strength at small time lags and failed to identify connectivity in the presence of frequency mismatches or non-stationarities, but did not misidentify volume conduction as “connectivity.” Both approaches have strengths and limitations, and this paper concludes with practical advice for when to use which approach in context of hypothesis testing and exploratory data analyses. [ABSTRACT FROM AUTHOR]

The paper focuses on the biology of stress and resilience and their biomarkers in humans from the system science perspective. A stressor pushes the physiological system away from its baseline state toward a lower utility state. The physiological system may return toward the original state in one attractor basin but may be shifted to a state in another, lower utility attractor basin. While some physiological changes induced by stressors may benefit health, there is often a chronic wear and tear cost due to implementing changes to enable the return of the system to its baseline state and maintain itself in the high utility baseline attractor basin following repeated perturbations. This cost, also called allostatic load , is the utility reduction associated with both a change in state and with alterations in the attractor basin that affect system responses following future perturbations. This added cost can increase the time course of the return to baseline or the likelihood of moving into a different attractor basin following a perturbation. Opposite to this is the system's resilience which influences its ability to return to the high utility attractor basin following a perturbation by increasing the likelihood and/or speed of returning to the baseline state following a stressor. This review paper is a qualitative systematic review; it covers areas most relevant for moving the stress and resilience field forward from a more quantitative and neuroscientific perspective. [ABSTRACT FROM AUTHOR]

Creation of a computationally efficient and biologically plausible neuron and neural network models is an exceptionally important occupation, the results of which are much needed for the neuroscience in general or for the neurophysiology in partial. Besides, the applying neural network technological areas are always looking for novel improvements of the methodological approaches, algorithms, computational facilities. In this paper, a new (in some aspects) hybrid integrate-and-fire (HIF) neuron model, based on N -shaped Na + and K + ionic current–voltage characteristics and a double discrete reset mechanism, is proposed. In the results of computational experiments, the spike series from a single spiking to the burst and chaotic burst trains are demonstrated. In the other part of the paper, analogous FitzHugh–Nagumo neuron model as a generalized neural networks system with an expansion to an inclusion of a coupling kernel function has been considered. The mean field approximation of neuronal potentials and recovery currents inside a neuron cluster was used. The biologically more realistic nonlinear N-shaped Na + characteristic and kernel functions (one - or two-dimensional) were applied. A possibility to present the nonlinear integral differential equations with kernel functions under the Fourier transformation by partial differential equations was used that allowed us to overcome the analytical and numerical modeling difficulties. The equivalence of two kinds of solutions was additionally confirmed based on the error evaluation. The approach of the equivalent partial differential equations was successfully employed to solve the system with the heterogeneous synaptic functions. The analytical studies are corroborated by many numerical modeling experiments. [ABSTRACT FROM AUTHOR]

In this paper, we propose a biologically inspired visual integrated model for image classification, called VMVI-CNN. Motivated in part by recent neuroscience progress in revealing integrated functions of human visual system, two bio-inspired visual mechanisms (the visual memory decay mechanism and the visual interaction mechanism) are proposed and built within the VMVI-CNN to (1) control the feature information passing through, and (2) increase the richness of feature information. The proposed method is tested on three benchmark datasets (MNIST, Cifar-10, and Mini-ImageNet) and a real-world industrial dataset. The results demonstrate that the new model can extract distinctive features and exhibit a better recognition performance than the current state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

In this paper, we study the output-feedback formation tacking control problem for a group of unmanned surface vehicles (USVs) with modeling uncertainties under communication constraints. We consider a one-to-one communication topology, in which the leading vehicle is assigned a task to track a desired trajectory and each vehicle except for the last follower (tail agent) communicates only with one leader and with one follower. We assume that the information exchange among the vehicles is limited by some given communication radius. Under the limited communication range, connectivity maintenance and collision avoidance between the leader and follower are considered in the formation tracking control design. To compensate for the modeling uncertainties, we employ neural network (NN) approximators to estimate uncertain dynamics. Based on the dynamic surface control technique, backstepping procedure, NN-based observers, tan-type barrier Lyapunov functions, and control Lyapunov synthesis, a decentralized adaptive output-feedback formation tracking controller is presented to achieve the boundedness of the signals in the closed-loop system, while guaranteeing connectivity maintenance and collision avoidance between the leader and follower during whole operation. Simulation results demonstrate the effectiveness of the proposed formation controller. [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]

• 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]

• Comprehensive analysis of 20 years of research trends in the resting-state fMRI field. • Identification of key research topics, hotspots, and collaborative patterns among scholars in this area. • Use of advanced visualization techniques, including scientific collaboration network maps and co-citation maps. • Insights into the interdisciplinary nature of resting-state fMRI research and its applications to neurological conditions. Resting-state functional magnetic resonance imaging (rs-fMRI), initially proposed by Biswal et al. in 1995, has emerged as a pivotal facet of neuroimaging research. Its ability to examine brain activity during the resting state without the need for explicit tasks or stimuli has made it an integral component of brain imaging studies. In recent years, rs-fMRI has witnessed substantial growth and found widespread application in the investigation of functional connectivity within the brain. To delineate the developmental trajectory of rs-fMRI over the past two decades, we conducted a comprehensive analysis using bibliometric tool Citespace. Our analysis encompassed publication trends, authorship networks, institutional affiliations, international collaborations, as well as emergent themes in references and keywords. Our study reveals a remarkable increase in the volume of rs-fMRI publications over the past two decades, underscoring the burgeoning interest and potential within this field. Harvard University stands out as the institution with the highest number of research papers published in the realm of RS-fMRI, while the United States holds the highest overall influence in this domain. The recent emergence of keywords such as "machine learning" and "default mode," coupled with citation surges in reference to rs-fMRI, have paved new avenues for research within this field. Our study underscores the critical importance of integrating machine learning techniques into rs-fMRI investigations, offering valuable insights into brain function and disease diagnosis. These findings hold profound significance for the field of neuroscience and may furnish insights for future research employing rs-fMRI as a diagnostic tool for a wide array of neurological disorders, thus emphasizing its pivotal role and potential as a tool for investigating brain functionality. [ABSTRACT FROM AUTHOR]

Discovering developmental stuttering's biological explanation has been an enduring concern. Novel advances in genomics and neuroscience are making it possible to isolate and pinpoint genetic and brain differences implicated in stuttering. This is giving rise to a hope that, in the future, dysfluency could be better managed if stuttering's biological basis could be better understood. Concurrent to this, there is another hope rising: a hope of a future where differing fluencies would not be viewed through a reductive lens of biology and associated pathologies. The central aim of this paper is to edge out ethical implications of novel research into stuttering's biological explanation. In doing so, the paper proposes to look beyond the bifurcation sketched by the medical and social model of disability. The paper demonstrates how the scientific hope of discovering stuttering's biological explanation acts as an accessory of disablement due to the language of 'lack' and 'deficit' employed in reporting scientific findings and proposes participatory research with people who stutter as an antidote to manage this disablement. • Genomic and neuroscientific research on stuttering raises an ethical concern of pathologizing difference. • There is lack of research on how people who stutter ethically reflect on scientific research on stuttering. • The language of 'lack' and 'deficit' used in biological explanations of stuttering acts as an accessory of disablement. • We propose to look beyond an either/or approach to medical and social model of disability as applied to stuttering. • Views of people who stutter should be sought by incorporating insights from participatory research methodologies. [ABSTRACT FROM AUTHOR]

Clinical brain profiling is an attempt to map a descriptive nosology in psychiatry to underlying constructs in neurobiology and brain dynamics. This paper briefly reviews the motivation behind clinical brain profiling (CBP) and presents some provisional validation using clinical assessments and meta-analyses of neuroscientific publications. The paper has four sections. In the first, we review the nature and motivation for clinical brain profiling. This involves a description of the key aspects of functional anatomy that can lead to psychopathology. These features constitute the dimensions or categories for a profile of brain disorders based upon pathophysiology. The second section describes a mapping or translation matrix that maps from symptoms and signs, of a descriptive sort, to the CBP dimensions that provide a more mechanistic explanation. We will describe how this mapping engenders archetypal diagnoses, referring readers to tables and figures. The third section addresses the construct validity of clinical brain profiling by establishing correlations between profiles based on clinical ratings of symptoms and signs under classical diagnostic categories with the corresponding profiles generated automatically using archetypal diagnoses. We then provide further validation by performing a cluster analysis on the symptoms and signs and showing how they correspond to the equivalent brain profiles based upon clinical and automatic diagnosis. In the fourth section, we address the construct validity of clinical brain profiling by looking for associations between pathophysiological mechanisms (such as connectivity and plasticity) and nosological diagnoses (such as schizophrenia and depression). Based upon the mechanistic perspective offered in the first section, we test some particular hypotheses about double dissociations using a meta-analysis of PubMed searches. The final section concludes with perspectives for the future and outstanding validation issues for clinical brain profiling. [ABSTRACT FROM AUTHOR]

This paper reviews the published literature on the hyperscanning methodologies using hemodynamic or neuro-electric modalities. In particular, we describe how different brain recording devices have been employed in different experimental paradigms to gain information about the subtle nature of human interactions. This review also included papers based on single-subject recordings in which a correlation was found between the activities of different (non-simultaneously recorded) participants in the experiment. The descriptions begin with the methodological issues related to the simultaneous measurements and the descriptions of the results generated by such approaches will follow. Finally, a discussion of the possible future uses of such new approaches to explore human social interactions will be presented. [ABSTRACT FROM AUTHOR]

Abstract: This historiographical (not historical) paper explores the difference in quality that separates, on the one hand, research on the clinical usage of lithium in psychiatry and on the other, writings on its history. Whilst in general the former is of high standard the latter, with few exceptions, is repetitious and biased. This disparity is due to a corrosive asymmetry affecting our discipline, namely, that whilst professional historians and philosophers of psychiatry would not dream of writing a ‘weekend’ paper on psychopharmacology or the basic and clinical neurosciences, experts in the latter disciplines have no compunction in writing on the history and philosophy of psychiatry. The superficial and bad quality history thereby generated is then perpetuated by the fact that: a) it appears in clinical journals and textbooks and hence becomes a daily pabulum for clinicians, and b) trainees rarely if ever will consult professional historical sources. An educational and ideological shift is needed to teach psychiatric trainees that (like in the neurosciences) special training is required to research in the history and philosophy of psychiatry. This might prevent future academic trespassing and expose clinicians to good quality historical writings. [Copyright &y& Elsevier]

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Abstract: Evoked potentials (EPs) are of great interest in neuroscience, but their measurement is difficult as they are embedded in background spontaneous electroencephalographic (EEG) activity which has a much larger amplitude. The widely used averaging technique requires the delivery of a large number of identical stimuli and yields only an “average” EP which does not allow the investigation of the possible variability of single-trial EPs. In the present paper, we propose the use of a multi-task learning method (MTL) for the simultaneous extraction of both the average and the N single-trial EPs from N recorded sweeps. The technique is developed within a Bayesian estimation framework and uses flexible stochastic models to describe the average response and the N shifts between the single-trial EPs and this average. Differently from other single-trial estimation approaches proposed in the literature, MTL can provide estimates of both the average and the N single-trial EPs in a single stage. In the present paper, MTL is successfully assessed on both synthetic (100 simulated recording sessions with N =20 sweeps) and real data (11 subjects with N =20 sweeps) relative to a cognitive task carried out for the investigation of the P300 component of the EP. [Copyright &y& Elsevier]