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During the socialization process in family and school contexts, children display a wide variety of social behaviors with parents and peers. Yet the developmental trajectory, the predictors and outcomes, and the neural basis of those social behaviors are largely under-investigated. To address these problems, we invited experts in the field to submit their latest findings to tell this story. The current Special Issue is a collection of papers highlighting the complexity for various social behaviors, with a focus on the complex mechanisms that link social behaviors to child socio-emotional adjustment and mediating/moderating factors among the associations. Thirteen papers illustrate empirical work in the field, two papers present new methodological concerns, and one paper that provides a comprehensive review of the literature. [ABSTRACT FROM AUTHOR]

Near-death experiences (NDEs) and psychedelic drug experiences (YDEs) enable access to dimensions of non-ordinary sensation, perception, and insight beyond typical day-to-day phenomenology. Both are associated with a dissolution of conventional spatio-temporal conceptual distinctions, and a corresponding sense of connectedness and unity. Moreover, NDEs and YDEs have shown a remarkable ability to reduce the anxiety that people associate with death. In two recent papers, we showed that multi-person virtual reality experiences (VREs) designed within the 'numadelic' aesthetic (where bodies are represented as light energy rather than material objects) can elicit psychometric results comparable to YDEs. It nevertheless remains an open question why numadelic aesthetics achieve the observed results, especially given that the vast majority of VREs represent bodies as typically perceived in the 'real-world'. This article describes the origins of the numadelic aesthetic from subjective accounts of NDE phenomenology, and attempts to unravel mechanistic aspects of the numadelic aesthetic by embedding it within a more general theoretical framework. Specifically, we elaborate a 2-axis schematic grounded in predictive coding models of cognition and matter-energy ideas from physics. One axis tracks 'structural specificity', and the other tracks 'symbolic rigidity'. The majority of VREs, which emphasize photorealistic fidelity to content derived from 'day-to-day' phenomenology, are characterized by high structural specificity and high symbolic rigidity. Such approaches collapse imaginative potential into a limited low-entropy space of 'exogenous' possibility, unlike the high-entropy brain states associated with YDEs. In contrast, aesthetic domains characterized by low structural specificity and low symbolic rigidity are less concerned with fidelity to phenomenological priors, offering an expansive, 'uncollapsed' high-entropy possibility space into which participants can project meaning and corresponding endogenous insights can arise (e.g., as occurs in NDEs and YDEs). Situated within this theoretical framing, the numadelic aesthetic emerges as a practical example of an un-collapsed approach to representation, helping to explain the experimental observations within previous papers. Moreover, the theoretical framing suggests various experimental tests, and lays the groundwork for applying numadelic aesthetics to model NDEs, to help address the anxiety often associated with death. [ABSTRACT FROM AUTHOR]

Organizations that embrace innovative business strategies position themselves well for success. In this evolving technological landscape, businesses are increasingly focused on understanding consumer behaviors and preferences to tailor their products and services effectively. Notably, integrating neuroscience has become pivotal in achieving these goals, requiring its incorporation throughout various business processes. This comprehensive research review conducts a meta-analysis, synthesizing findings from numerous studies on applying neuroscience across diverse business domains. The primary objective is to identify innovative neuroscience approaches that can significantly revolutionize businesses. Through a meticulous literature exploration using PRISMA analysis, we discerned broad themes and further refined them into critical sub-themes. This thematic categorization enables a focused examination of each area of business research. The findings in this study uncovered vital intersections between neuroscience and business, offering valuable insights for corporations and researchers. In essence, the integration of neuroscientific techniques and artificial intelligence (AI)-based softwares into business practices is no longer optional, but a necessity for businesses to stay competitive in today's fast-paced world. This study, therefore, plays a crucial role in connecting the state-of-the-art tools and techniques in neuroscience and traditional business practices. It paves the way for a new era of business operations, where science and technology work hand in hand with commerce to create a more prosperous and sustainable future. In conclusion, the paper emphasizes the importance of incorporating neuroscience techniques and newly developed AI-based softwares into business practices. This integration can yield precise and favorable outcomes, offering businesses the tools to thrive in an ever-changing market environment. [ABSTRACT FROM AUTHOR]

In recent decades, the debate on consciousness has been conditioned by the idea of bottom-up emergence, which has influenced scientific research and raised a few obstacles to any attempt to bridge the explanatory gap. The analysis and explanation of vision conducted according to the accredited methodologies of scientific research in terms of physical stimuli, objectivity, methods, and explanation has encountered the resistance of subjective experience. Moreover, original Gestalt research into vision has generally been merged with cognitive neuroscience. Experimental phenomenology, building on the legacy of Gestalt psychology, has obtained new results in the fields of amodal contours and color stratifications, light perception, figurality, space, so-called perceptual illusions, and subjective space and time. Notwithstanding the outcomes and the impulse given to neuroscientific analyses, the research carried out around these phenomena has never directly confronted the issue of what it means to be conscious or, in other words, the nature of consciousness as self-referentiality. Research has tended to focus on the percept. Therefore, explaining the non-detachability of parts in subjective experience risks becoming a sort of impossible achievement, similar to that of Baron Munchausen, who succeeds in escaping unharmed from this quicksand by pulling himself out by his hair. This paper addresses how to analyze seeing as an undivided whole by discussing several basic dimensions of phenomenal consciousness on an experimental basis and suggesting an alternative way of escaping this quicksand. This mind-set reversal also sheds light on the organization and dependence relationships between phenomenology, psychophysics, and neuroscience. [ABSTRACT FROM AUTHOR]

The web was conceived in 1997 with the goal of universal accessibility. However, it still presents obstacles for users with diverse abilities. Although interactions in digital environments have improved the quality of life for many people, they remain a source of limitations and frustrations, up to twice as much for users with total visual impairment, which is commonly known as blindness. This article emphasizes the need to provide designers and developers with methodologies and tools that consider users with different abilities in all stages and from the beginning of the design, redesign, and development of digital products and services on the web, collecting their needs and goals and delving into their reactions and emotions. While an approach based on persona archetypes can optimally represent each group of users with disabilities throughout website design processes, serving as a communication and empathy tool for development teams and stakeholders, it is essential that this tool considers all dimensions involved in the experiences of people with disabilities. This conceptual paper develops a methodology that provides designers with a persona tool that effectively captures the goals and emotions of these users in their interaction with the web. This methodology proposal is multidimensional and holistic. It addresses conscious and subconscious dimensions, combines traditional user experience and neuroscientific techniques, focuses on total visual impairment, the most challenging in digital environments, and makes it adaptable to other disabilities. [ABSTRACT FROM AUTHOR]

Immersive Virtual Reality (I-VR) is becoming increasingly prevalent in today's society and is a technology that is being used more and more in educational contexts and whose potential for learning has been widely discussed. Taking into account the latest research in neuroscience, we want to explore the potential of using immersive virtual environments to facilitate deep learning in educational contexts that invoke the value of experience, imitation and repetition. Neurodidactics seems to offer the most interesting insights into how participation in a virtual reality session can positively affect learning processes: this suggests that it is worth focusing on the learning processes that can be generated by this emerging technology. The paper concludes with some practical suggestions for design, addressing both the didactic experience and the learning objects. [ABSTRACT FROM AUTHOR]

The neuroscience of creativity is built on a tacit and near universal assumption that is false. Paradoxically, this is not contentious; once made explicit, the assumption is readily conceded as false. Psychology regards creativity as made up of many complex, multifaceted, and varied cognitive and emotional processes deployed across many different domains. But we instead think of, and treat, creativity as if it were a single, separate, cohesive, and discrete thing--as in, Einstein had it. In a straightforward extension of this fallacy, cognitive neuroscientists have looked for uniquely creative cognition that (1) is distinct from all other kinds of cognition and (2) has a proprietary neural substrate. In other words, a standalone and monolithic creativity faculty in the brain that manages only creativity and all creativity. First, this paper brings into sharp focus the nature and ubiquity of this fallacy. It then outlines the alternative theoretical position that is (1) based on fundamental neural principles and (2) predicated on taking seriously the concept of creativity as complex and diverse. Like morality or secretiveness, it holds that creativity does not exist as its own, specialized entity in the brain. Instead, its neurocognitive mechanisms are distributed, embedded, and varied; that is, creativity is everywhere and multiply realizable. [ABSTRACT FROM AUTHOR]

This paper presents an enhanced version of our previously developed bio-optical transceiver, presenting a significant advancement in nanosensor technology. Using self-assembled polymers, this nanodevice is capable of electron detection while maintaining biocompatibility, an essential feature for in vivo medical biosensors. This enhancement finds significance in the field of infectious disease control, particularly in the early detection of respiratory viruses, including high-threat pathogens such as SARS-CoV-2. The proposed system harnesses bioluminescence by converting electric signaling to visible blue light, effectively opening the path of linking nano-sized mechanisms to larger-scale systems, thereby pushing the boundaries of in vivo biomedical sensing. The performance evaluation of our technology is analytical and is based on the use of Markov chains, through which we assess the bit error probability. The calculated improvements indicate that this technology qualifies as a forerunner in terms of supporting the communication needs of smaller, safer, and more efficient manufactured sensor technologies for in vivo medical applications. [ABSTRACT FROM AUTHOR]

The use of bioluminescence as a reporter for physiology in neuroscience is as old as the discovery of the calcium-dependent photon emission of aequorin. Over the years, luciferases have been largely replaced by fluorescent reporters, but recently, the field has seen a renaissance of bioluminescent probes, catalyzed by unique developments in imaging technology, bioengineering, and biochemistry to produce luciferases with previously unseen colors and intensity. This is not surprising as the advantages of bioluminescence make luciferases very attractive for noninvasive, longitudinal in vivo observations without the need of an excitation light source. Here, we review how the development of dedicated and specific sensor-luciferases afforded, among others, transcranial imaging of calcium and neurotransmitters, or cellular metabolites and physical quantities such as forces and membrane voltage. Further, the increased versatility and light output of luciferases have paved the way for a new field of functional bioluminescence optogenetics, in which the photon emission of the luciferase is coupled to the gating of a photosensor, e.g., a channelrhodopsin and we review how they have been successfully used to engineer synthetic neuronal connections. Finally, we provide a primer to consider important factors in setting up functional bioluminescence experiments, with a particular focus on the genetic model Caenorhabditis elegans, and discuss the leading challenges that the field needs to overcome to regain a competitive advantage over fluorescence modalities. Together, our paper caters to experienced users of bioluminescence as well as novices who would like to experience the advantages of luciferases in their own hand. [ABSTRACT FROM AUTHOR]

The academic discourse surrounding tourism's interdisciplinary approach has long piqued the interest of the scholarly community. More recently, attention has pivoted towards the intersection of neuroscience and neuromarketing within the realm of tourism, giving rise to the concept of "neurotourism". This emergence requires a comprehensive elucidation of the relevance of neuromarketing and neuroscience to the field of tourism, thereby addressing the imperative to bridge existing knowledge gaps. In light of this, the present paper endeavours to fulfil this objective by synthesizing global research in this domain. The study adopts an interdisciplinary approach, employing a hybrid systematic review methodology that encompasses a literature review encompassing nine key indicators and a bibliometric analysis through co-word analysis of author keywords. In total, our investigation unearthed 45 papers from Scopus, each exploring the application of neuroscience and neuromarketing theories and methods within the context of tourism. Among these, three papers delve into the concept of neurotourism. In this paper, we underscore the deep connection between neuroscience, neuroscientific methodologies, and neuromarketing within the realm of tourism research. The outcomes of this research significantly enhance our comprehension of the current state of neurotourism research, revealing both existing voids and emerging areas of interest. Furthermore, this study introduces a pioneering methodological approach, fusing Scival topic prominence and hybrid systematic review techniques into bibliometric analysis. Ultimately, our findings illuminate a notable research lacuna, presenting a fertile terrain for prospective investigations. Additionally, we deliberate on current trends and propose directions for future research within the neurotourism landscape. [ABSTRACT FROM AUTHOR]

Three studies of the human dopamine transporter reveal how it binds to molecules such as dopamine and cocaine. Dopamine imbalances underlie some brain conditions and these data will aid targeted drug design.Insights into how molecules bind to the human dopamine transporter. [ABSTRACT FROM AUTHOR]

In the domain of radiological diagnostics, accurately detecting and classifying brain tumors from magnetic resonance imaging (MRI) scans presents significant challenges, primarily due to the complex and diverse manifestations of tumors in these scans. In this paper, a convolutional-block-based architecture has been proposed for the detection of multiclass brain tumors using MRI scans. Leveraging the strengths of CNNs, our proposed framework demonstrates robustness and efficiency in distinguishing between different tumor types. Extensive evaluations on three diverse datasets underscore the model's exceptional diagnostic accuracy, with an average accuracy rate of 97.52%, precision of 97.63%, recall of 97.18%, specificity of 98.32%, and F1-score of 97.36%. These results outperform contemporary methods, including state-of-the-art (SOTA) models such as VGG16, VGG19, MobileNet, EfficientNet, ResNet50, Xception, and DenseNet121. Furthermore, its adaptability across different MRI modalities underlines its potential for broad clinical application, offering a significant advancement in the field of radiological diagnostics and brain tumor detection. [ABSTRACT FROM AUTHOR]

We present motivation and practical steps necessary to find parameter estimates of joint models of behavior and neural electrophysiological data. This tutorial is written for researchers wishing to build joint models of human behavior and scalp and intracranial electroencephalographic (EEG) or magnetoencephalographic (MEG) data, and more specifically those researchers who seek to understand human cognition. Although these techniques could easily be applied to animal models, the focus of this tutorial is on human participants. Joint modeling of M/EEG and behavior requires some knowledge of existing computational and cognitive theories, M/EEG artifact correction, M/EEG analysis techniques, cognitive modeling, and programming for statistical modeling implementation. This paper seeks to give an introduction to these techniques as they apply to estimating parameters from neurocognitive models of M/EEG and human behavior, and to evaluate model results and compare models. Due to our research and knowledge on the subject matter, our examples in this paper will focus on testing specific hypotheses in human decision-making theory. However, most of the motivation and discussion of this paper applies across many modeling procedures and applications. We provide Python (and linked R) code examples in the tutorial and appendix. Readers are encouraged to try the exercises at the end of the document.

The field is taking a leap forward thanks to innovative technologies, such as artificial intelligence. Researchers must improve consent procedures and public involvement. [ABSTRACT FROM AUTHOR]

This document is an editorial introducing a collection of articles that explore the intersection of cultural psychology and neuroscience. The articles cover various topics, such as the impact of digital technologies on brain function, the role of culture in donation behavior, and the use of machine learning to predict cognitive functions. The authors emphasize the importance of understanding cross-cultural differences in cognition and perception in a rapidly changing world. The aim of this research topic is to provide new insights and theoretical frameworks for understanding these differences in a post-pandemic world. [Extracted from the article]

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]

Active neurons can stimulate the clearance of their own metabolic waste by driving changes to ion gradients in the surrounding fluid and by promoting the pulsation of nearby blood vessels.Neurons stimulate the brain's waste disposal system. [ABSTRACT FROM AUTHOR]

Would the neuroscientist who does not feel pain know what he is studying? In this paper, the author analyzes the subjective ontology of conscious mental states and its origin – the subject’s first-person perspective. The ontology of the mental is irreducibly first-person ontology. In the thought experiment with neuroscientist Peter, it will be shown that a comprehensive science of consciousness requires a combined approach, as the phenomena of neuroscience are internally defined. The asymmetry between first-person and third-person perspectives regarding mental states will be strongly emphasized. The third part of the paper will offer a perspectival approach to the hard problem of consciousness. In conclusion, a truly objective science of subjectivity, a genuine science of consciousness, would have to engage scientifically with the subject’s egocentric first-person perspective.

This special issue attempts to integrate personality, psychopathology, and neuroscience as means to improve understanding of specific traits and trait structures in humans. The key strategy is to dive into comparative research using a range of species to provide simple models. This strategy has, as its foundation, the fact that the most basic functions, and their supporting neural systems, are highly conserved in evolution. The papers collected in the issue show that, from fish, through rats, to primates, the homologies in brain systems and underlying functions (despite species-specific forms of expression) allow simpler cases to provide insights into the neurobiology behind more complex ones including human. Our introductory editorial paper to this special issue took a bottom-up approach, starting with the genetics of conserved brain systems and working up to cognition. Here, we deconstruct the different aspects of personality, progressing from more complex ones in primates to least complex in fish. With the primate section, we summarize papers that discuss the factors that contribute to sociability in primates and how they apply to healthy and pathological human personality traits. In the rat section, the focus is driven by psychopathology and the way that “high” strains selected for extreme behaviors can illuminate the neurobiology of motivated responses to environmental cues. The section on fish summarizes papers that look into the most fundamental emotional reactions to the environment that are governed by primitive and conserved brain structures. This raises metatheoretical questions on the nature of traits and to a section that asks “which animals have personalities.” We believe that the issue as a whole provides a nuanced answer to this question and shines a new, comparative, light on the interpretation of personality structure and the effects on it of evolution.

Near-death experiences (NDEs) and psychedelic drug experiences (YDEs) enable access to dimensions of non-ordinary sensation, perception, and insight beyond typical day-to-day phenomenology. Both are associated with a dissolution of conventional spatio-temporal conceptual distinctions, and a corresponding sense of connectedness and unity. Moreover, NDEs and YDEs have shown a remarkable ability to reduce the anxiety that people associate with death. In two recent papers, we showed that multi-person virtual reality experiences (VREs) designed within the ‘numadelic’ aesthetic (where bodies are represented as light energy rather than material objects) can elicit psychometric results comparable to YDEs. It nevertheless remains an open question why numadelic aesthetics achieve the observed results, especially given that the vast majority of VREs represent bodies as typically perceived in the ‘real-world’. This article describes the origins of the numadelic aesthetic from subjective accounts of NDE phenomenology, and attempts to unravel mechanistic aspects of the numadelic aesthetic by embedding it within a more general theoretical framework. Specifically, we elaborate a 2-axis schematic grounded in predictive coding models of cognition and matter-energy ideas from physics. One axis tracks ‘structural specificity’, and the other tracks ‘symbolic rigidity’. The majority of VREs, which emphasize photorealistic fidelity to content derived from ‘day-to-day’ phenomenology, are characterized by high structural specificity and high symbolic rigidity. Such approaches collapse imaginative potential into a limited low-entropy space of ‘exogenous’ possibility, unlike the high-entropy brain states associated with YDEs. In contrast, aesthetic domains characterized by low structural specificity and low symbolic rigidity are less concerned with fidelity to phenomenological priors, offering an expansive, ‘uncollapsed’ high-entropy possibility space into which participants can project meaning and corresponding endogenous insights can arise (e.g., as occurs in NDEs and YDEs). Situated within this theoretical framing, the numadelic aesthetic emerges as a practical example of an un-collapsed approach to representation, helping to explain the experimental observations within previous papers. Moreover, the theoretical framing suggests various experimental tests, and lays the groundwork for applying numadelic aesthetics to model NDEs, to help address the anxiety often associated with death.

Studies reveal how neuronal populations in the fruit fly brain work together to compare the direction of a goal with the direction that the fly is facing, and convert this into a signal that steers the fly towards its target.Fly brains compare directional information while steering to a goal. [ABSTRACT FROM AUTHOR]

A new wave of research is unpicking the relationship between cancer and neurons — and looking for ways to stop the crosstalk. [ABSTRACT FROM AUTHOR]

During the socialization process in family and school contexts, children display a wide variety of social behaviors with parents and peers. Yet the developmental trajectory, the predictors and outcomes, and the neural basis of those social behaviors are largely under-investigated. To address these problems, we invited experts in the field to submit their latest findings to tell this story. The current Special Issue is a collection of papers highlighting the complexity for various social behaviors, with a focus on the complex mechanisms that link social behaviors to child socio-emotional adjustment and mediating/moderating factors among the associations. Thirteen papers illustrate empirical work in the field, two papers present new methodological concerns, and one paper that provides a comprehensive review of the literature.

Neural activity in human brains rapidly restructures to reflect hidden relationships needed to adapt to a changing environment. Surprisingly, trial-and-error learning and verbal instruction induce similar changes.Mental maps of associated items are rapidly assembled by the human brain. [ABSTRACT FROM AUTHOR]

To conceptualise biological and mental processes, often a dynamical systems perspective is suggested. In addition to dynamics, the structure of the contextual makeup or world configuration (of an organism or brain) plays a crucial role too, as well as adaptivity of the processes. This paper provides a conceptual perspective where the structure, dynamics, and adaptivity of these processes are distinguished and related to each other via adaptive dynamical systems. Moreover, it is shown how networks can be used to represent this conceptual perspective. Here an adaptive dynamical system of any order of adaptivity can be covered where any level can exert control over the level below. The approach is illustrated by case studies for higher-order adaptive evolutionary processes. One of these case studies shows a fifth-order adaptive dynamical system that models how due to bad environmental influences at a young age, epigenetic effects can lead to a lifelong mental disorder.

This paper tests the similarity in neural responses across repeated words and morphosyntactic features both within and between two languages. Prior work using priming has revealed robust cross-linguistic lexical effects and effects for shared grammatical form, such as argument structure; these methods have been less successful when applied to morphosyntactic features. Combining machine-learning based neural decoding with EEG data collected from Korean-English bilinguals we, first, replicate prior work showing successful classification of lexical items from EEG signals. We then extend this to demonstrate successful classification of morphosyntactic features of number and tense. Finally, we find that EEG decoding in one language does not successfully generalize to another, even when temporal differences are considered. Taken together, these results point to stable EEG representations for lexical items and morphosyntactic features, but suggest that these representations are different between the two languages investigated here.

Google scientists have modelled a fragment of the human brain at nanoscale resolution, revealing cells with previously undiscovered features. [ABSTRACT FROM AUTHOR]

A single gene in astrocytes can constrain repetitive behaviours, indicating that these cells are regulators of behavioural disruption in conditions such as Huntington’s disease and obsessive–compulsive disorder.Astrocytes temper neuronal excitability to control repetitive behaviours. [ABSTRACT FROM AUTHOR]

There is such a vast proliferation of scientific theories of consciousness that it is worrying some scholars. There are even competitions to test different theories, and the results are inconclusive. Consciousness research, far from converging toward a unifying framework, is becoming more discordant than ever, especially with respect to theoretical elements that do not have a clear neurobiological basis. Rather than dueling theories, an integration across theories is needed to facilitate a comprehensive view on consciousness and on how normal nervous system dynamics can develop into pathological states. In dealing with what is considered an extremely complex matter, we try to adopt a perspective from which the subject appears in relative simplicity. Grounded in experimental and theoretical observations, we advance an encompassing biophysical theory, MaxCon, which incorporates aspects of several of the main existing neuroscientific consciousness theories, finding convergence points in an attempt to simplify and to understand how cellular collective activity is organized to fulfill the dynamic requirements of the diverse theories our proposal comprises. Moreover, a computable index indicating consciousness level is presented. Derived from the level of description of the interactions among cell networks, our proposal highlights the association of consciousness with maximization of the number of configurations of neural network connections --constrained by neuroanatomy, biophysics and the environment-- that is common to all consciousness theories. [ABSTRACT FROM AUTHOR]

Introduction: Exploring gender differences in cognitive abilities offers vital insights into human brain functioning. Methods: Our study utilized advanced techniques like magnetic resonance thermometry, standard working memory n-back tasks, and functional MRI to investigate if gender-based variations in brain temperature correlate with distinct neuronal responses and working memory capabilities. Results: We observed a significant decrease in average brain temperature in males during working memory tasks, a phenomenon not seen in females. Although changes in female brain temperature were significantly lower than in males, we found an inverse relationship between the absolute temperature change (ATC) and cognitive performance, alongside a correlation with blood oxygen level dependent (BOLD) signal change induced by neural activity. This suggests that in females, ATC is a crucial determinant for the link between cognitive performance and BOLD responses, a linkage not evident in males. However, we also observed additional female specific BOLD responses aligned with comparable task performance to that of males. Discussion: Our results suggest that females compensate for their brain's heightened temperature sensitivity by activating additional neuronal networks to support working memory. This study not only underscores the complexity of gender differences in cognitive processing but also opens new avenues for understanding how temperature fluctuations influence brain functionality. [ABSTRACT FROM AUTHOR]

Objective biomarkers have been a critical challenge for the field of psychiatry, where diagnostic, prognostic, and theranostic assessments are still based on subjective narratives. Psychopathology operates with idiographic knowledge and subjective evaluations incorporated into clinical assessment inventories, but is considered to be a medical discipline and, as such, uses medical intervention methods (e.g., pharmacological, ECT; rTMS; tDCS) and, therefore, is supposed to operate with the language and methods of nomothetic networks. The idiographic assessments are provisionally "quantified" into "structured clinical scales" to in some way resemble nomothetic measures. Instead of fostering data merging and integration, this approach further encapsulates the clinical psychiatric methods, as all other biological tests (molecular, neuroimaging) are performed separately, only after the clinical assessment has provided diagnosis. Translational cross-validation of clinical assessment instruments and fMRI is an attempt to address the gap. The aim of this approach is to investigate whether there exist common and specific neural circuits, which underpin differential item responses to clinical self-rating scales during fMRI sessions in patients suffering from the two main spectra of mental disorders: schizophrenia and major depression. The current status of this research program and future implications to promote the development of psychiatry as a medical discipline are discussed. [ABSTRACT FROM AUTHOR]

The tree shrew (Tupaia belangeri) has long been proposed as a suitable alternative to non-human primates (NHPs) in biomedical and laboratory research due to its close evolutionary relationship with primates. In recent years, significant advances have facilitated tree shrew studies, including the determination of the tree shrew genome, genetic manipulation using spermatogonial stem cells, viral vector-mediated gene delivery, and mapping of the tree shrew brain atlas. However, the limited availability of tree shrews globally remains a substantial challenge in the field. Additionally, determining the key questions best answered using tree shrews constitutes another difficulty. Tree shrew models have historically been used to study hepatitis B virus (HBV) and hepatitis C virus (HCV) infection, myopia, and psychosocial stress-induced depression, with more recent studies focusing on developing animal models for infectious and neurodegenerative diseases. Despite these efforts, the impact of tree shrew models has not yet matched that of rodent or NHP models in biomedical research. This review summarizes the prominent advancements in tree shrew research and reflects on the key biological questions addressed using this model. We emphasize that intensive dedication and robust international collaboration are essential for achieving breakthroughs in tree shrew studies. The use of tree shrews as a unique resource is expected to gain considerable attention with the application of advanced techniques and the development of viable animal models, meeting the increasing demands of life science and biomedical research. [ABSTRACT FROM AUTHOR]

Copyright of Human Sciences Journal / Revue des Sciences Humaines is the property of Universite des Freres Mentouri Constantine and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: In incomplete spinal cord injury (SCI), there is a partial decrease in motor or sensory or autonomic function. Mainly due to the motor impairment in SCI, a muscle–machine interface is a tool that can bring functional benefits to this population. Objective: To investigate the feasibility of the non-invasive myoelectric signal–functional electrical stimulation (MES-FES) interface on the response of the quadriceps muscle in an individual with incomplete SCI. Methods: This is a quasi-experimental, uncontrolled, longitudinal case report study carried out with an individual with incomplete SCI in the chronic phase. The assessments performed before (pre) and after eight (post8) interventions were neuromuscular assessment (surface electromyography (EMG) in rectus femoris (RF) and vastus lateralis (VL) muscles); muscle strength (load cell); knee extension range of motion (goniometry); spasticity (Modified Ashworth Scale); and quality of life (Spinal Cord Injury Quality-of-Life Questionnaire (SCI-QoL.Br)). The MES-FES interface was associated with physical therapy exercises on the extension knee joint muscle group. Results: Improvement in neuromuscular activation (normalized increase in EMGRMS of 2% (RF) and 3.3% (VL)) and synchronism of the motor units (normalized reduction in EMGMDF of 22.8% (RF) and 5.9% (VL)); 1.4 kgf increase in quadriceps strength; 10.6° increase in knee joint extension amplitude; 1 point spasticity reduction; improved quality of life, confirmed by a 12-point reduction in the SCI-QoL.Br score. Moreover, along with interventions, the participant increased the correct FES activation rate, indicating a user learning curve (ρ = 0.78, p-value = 0.04). Conclusions: The MES-FES interface associated with physical therapy promotes neuromuscular and quality of life improvements in the SCI participant. [ABSTRACT FROM AUTHOR]

Abstract Organizations that embrace innovative business strategies position themselves well for success. In this evolving technological landscape, businesses are increasingly focused on understanding consumer behaviors and preferences to tailor their products and services effectively. Notably, integrating neuroscience has become pivotal in achieving these goals, requiring its incorporation throughout various business processes. This comprehensive research review conducts a meta-analysis, synthesizing findings from numerous studies on applying neuroscience across diverse business domains. The primary objective is to identify innovative neuroscience approaches that can significantly revolutionize businesses. Through a meticulous literature exploration using PRISMA analysis, we discerned broad themes and further refined them into critical sub-themes. This thematic categorization enables a focused examination of each area of business research. The findings in this study uncovered vital intersections between neuroscience and business, offering valuable insights for corporations and researchers. In essence, the integration of neuroscientific techniques and artificial intelligence (AI)-based softwares into business practices is no longer optional, but a necessity for businesses to stay competitive in today’s fast-paced world. This study, therefore, plays a crucial role in connecting the state-of-the-art tools and techniques in neuroscience and traditional business practices. It paves the way for a new era of business operations, where science and technology work hand in hand with commerce to create a more prosperous and sustainable future. In conclusion, the paper emphasizes the importance of incorporating neuroscience techniques and newly developed AI-based softwares into business practices. This integration can yield precise and favorable outcomes, offering businesses the tools to thrive in an ever-changing market environment.

A neural probe has been used to capture the activity of large populations of single neurons as people are speaking or listening, providing detailed insights into how the brain encodes specific features of speech.Probing the neurons that encode features of speech in the human brain. [ABSTRACT FROM AUTHOR]

Abstract In recent years, Artificial Intelligence (AI) shows a spectacular ability of insertion inside a variety of disciplines which use it for scientific advancements and which sometimes improve it for their conceptual and methodological needs. According to the transverse science framework originally conceived by Shinn and Joerges, AI can be seen as an instrument which is progressively acquiring a universal character through its diffusion across science. In this paper we address empirically one aspect of this diffusion, namely the penetration of AI into a specific field of research. Taking neuroscience as a case study, we conduct a scientometric analysis of the development of AI in this field. We especially study the temporal egocentric citation network around the articles included in this literature, their represented journals and their authors linked together by a temporal collaboration network. We find that AI is driving the constitution of a particular disciplinary ecosystem in neuroscience which is distinct from other subfields, and which is gathering atypical scientific profiles who are coming from neuroscience or outside it. Moreover we observe that this AI community in neuroscience is socially confined in a specific subspace of the neuroscience collaboration network, which also publishes in a small set of dedicated journals that are mostly active in AI research. According to these results, the diffusion of AI in a discipline such as neuroscience didn’t really challenge its disciplinary orientations but rather induced the constitution of a dedicated socio-cognitive environment inside this field.

Background: Huntington disease (HD) is a neurodegenerative disorder with complex motor and behavioural manifestations. The Q175 knock-in mouse model of HD has gained recent popularity as a genetically accurate model of the human disease. However, behavioural phenotypes are often subtle and progress slowly in this model. Here, we have implemented machine-learning algorithms to investigate behaviour in the Q175 model and compare differences between sexes and disease stages. We explore distinct behavioural patterns and motor functions in open field, rotarod, water T-maze, and home cage lever-pulling tasks. Results: In the open field, we observed habituation deficits in two versions of the Q175 model (zQ175dn and Q175FDN, on two different background strains), and using B-SOiD, an advanced machine learning approach, we found altered performance of rearing in male manifest zQ175dn mice. Notably, we found that weight had a considerable effect on performance of accelerating rotarod and water T-maze tasks and controlled for this by normalizing for weight. Manifest zQ175dn mice displayed a deficit in accelerating rotarod (after weight normalization), as well as changes to paw kinematics specific to males. Our water T-maze experiments revealed response learning deficits in manifest zQ175dn mice and reversal learning deficits in premanifest male zQ175dn mice; further analysis using PyMouseTracks software allowed us to characterize new behavioural features in this task, including time at decision point and number of accelerations. In a home cage-based lever-pulling assessment, we found significant learning deficits in male manifest zQ175dn mice. A subset of mice also underwent electrophysiology slice experiments, revealing a reduced spontaneous excitatory event frequency in male manifest zQ175dn mice. Conclusions: Our study uncovered several behavioural changes in Q175 mice that differed by sex, age, and strain. Our results highlight the impact of weight and experimental protocol on behavioural results, and the utility of machine learning tools to examine behaviour in more detailed ways than was previously possible. Specifically, this work provides the field with an updated overview of behavioural impairments in this model of HD, as well as novel techniques for dissecting behaviour in the open field, accelerating rotarod, and T-maze tasks. [ABSTRACT FROM AUTHOR]

Over several decades, motivated behavior has emerged as a crucial study area within neuroscience. Understanding the neural substrates and mechanisms driving behaviors related to reward, addiction, and other motivation forms is pivotal for novel therapeutic interventions. This review provides a bibliometric analysis of the literature, highlighting the main trends, influential authors, and the potential future direction of the field. Utilizing a dataset comprised by 3,150 publications from the Web of Science and Scopus databases ("motivated behavior as query), we delve into key metrics like publication trends, keyword prevalence, author collaborations, citation impacts, and employed an unsupervised natural language processing technique - Latent Dirichlet Allocation - for topic modeling. From early investigations focusing on basic neural mechanism and behaviors in animal models to more recent studies exploring the complex interplay of neurobiological, psychological, and social factors in humans, the field had undergone a remarkable transformation. The last century has seen a proliferation of research dedicated to uncovering the intricacies of motivation, significantly enriching our understanding of its myriad implications for human behavior and mental health. This bibliometric analysis aims to offer comprehensive insights into this dynamic research area, highlighting the field's key contributions and potential future directions, thereby serving as a valuable resource for researchers, and hopefully give a more thorough understanding of the research area. [ABSTRACT FROM AUTHOR]

Over the past decade, microbiome research has significantly expanded in both scope and volume, leading to the development of new models and treatments targeting the gut–brain axis to mitigate the effects of various disorders. Related research suggests that interventions during the critical period from birth to three years old may yield the greatest benefits. Investigating the substantial link between the gut and brain during this crucial developmental phase raises fundamental issues about the role of microorganisms in human health and brain development. This underscores the importance of focusing on the prevention rather than the treatment of neurodevelopmental and neuropsychiatric disorders. The present review examines the gut microbiota from birth to age 3, with a particular focus on its potential relationship with neurodevelopment. This review emphasizes the immunological mechanisms underlying this relationship. Additionally, the study investigates the impact of the microbiome on cognitive development and neurobehavioral issues such as anxiety and autism. Importantly, it highlights the need to integrate mechanistic studies of animal models with epidemiological research across diverse cultures to better understand the role of a healthy microbiome in early life and the implications of dysbiosis. Furthermore, this review summarizes factors contributing to the transmission of gut microbiome-targeted therapies and their effects on neurodevelopment. Recent studies on environmental toxins known to impact neurodevelopment are also reviewed, exploring whether the microbiota may mitigate or modulate these effects. [ABSTRACT FROM AUTHOR]

Legged robot control has improved in recent years with the rise of deep reinforcement learning, however, much of the underlying neural mechanisms remain difficult to interpret. Our aim is to leverage bio-inspired methods from computational neuroscience to better understand the neural activity of robust robot locomotion controllers. Similar to past work, we observe that terrain-based curriculum learning improves agent stability. We study the biomechanical responses and neural activity within our neural network controller by simultaneously pairing physical disturbances with targeted neural ablations. We identify an agile hip reflex that enables the robot to regain its balance and recover from lateral perturbations. Model gradients are employed to quantify the relative degree that various sensory feedback channels drive this reflexive behavior. We also find recurrent dynamics are implicated in robust behavior, and utilize sampling-based ablation methods to identify these key neurons. Our framework combines model-based and sampling-based methods for drawing causal relationships between neural network activity and robust embodied robot behavior. [ABSTRACT FROM AUTHOR]

Virtual brain twins are personalized, generative and adaptive brain models based on data from an individual's brain for scientific and clinical use. After a description of the key elements of virtual brain twins, we present the standard model for personalized whole-brain network models. The personalization is accomplished using a subject's brain imaging data by three means: (1) assemble cortical and subcortical areas in the subject-specific brain space; (2) directly map connectivity into the brain models, which can be generalized to other parameters; and (3) estimate relevant parameters through model inversion, typically using probabilistic machine learning. We present the use of personalized whole-brain network models in healthy ageing and five clinical diseases: epilepsy, Alzheimer's disease, multiple sclerosis, Parkinson's disease and psychiatric disorders. Specifically, we introduce spatial masks for relevant parameters and demonstrate their use based on the physiological and pathophysiological hypotheses. Finally, we pinpoint the key challenges and future directions. [ABSTRACT FROM AUTHOR]

Objective: (1) This trial will compare the clinical and psychosocial effectiveness of in-group and individually pain neuroscience education (PNE) in patients with chronic low back pain (CLBP). In addition, (2) the influence of social determinants of health on post-treatment results will be analyzed. Methods: A three-arm randomized controlled trial will be conducted. Sixty-nine participants with CLBP will be recruited in a 1:1:1 ratio. Participants, assessor, and statistician will be blinded to group assignment. The PNE intervention will be adapted to the context of the participants. An experimental group (n = 33) will receive PNE in an in-group modality, the other experimental group (n = 33) will receive PNE in an individually modality and the control group (n = 33) will continue with usual care. Additionally, participants will be encouraged to stay active by walking for 20–30 min 3–5 times per week and will be taught an exercise to improve transversus abdominis activation (bracing or abdominal following). The outcome measures will be fear avoidance and beliefs, pressure pain threshold, pain self-efficacy, catastrophizing, pain intensity, and treatment expectation. Outcome measures will be collected at one-week before intervention, immediately post-intervention, and four-weeks post-intervention. Conclusion: The innovative approach of PNE oriented to fear beliefs proposed in this study could broaden the application strategies of this educational therapeutic modality. Impact. Contextualized PNE delivered by physical therapist could be essential to achieve a good cost-effectiveness ratio of this intervention to improve the clinical condition of people with CLBP. [ABSTRACT FROM AUTHOR]

Emotions are a series of subconscious, fleeting, and sometimes elusive manifestations of the human innate system. They play crucial roles in everyday life—influencing the way we evaluate ourselves, our surroundings, and how we interact with our world. To date, there has been an abundance of research on the domains of neuroscience and affective computing, with experimental evidence and neural network models, respectively, to elucidate the neural circuitry involved in and neural correlates for emotion recognition. Recent advances in affective computing neural network models often relate closely to evidence and perspectives gathered from neuroscience to explain the models. Specifically, there has been growing interest in the area of EEG-based emotion recognition to adopt models based on the neural underpinnings of the processing, generation, and subsequent collection of EEG data. In this respect, our review focuses on providing neuroscientific evidence and perspectives to discuss how emotions potentially come forth as the product of neural activities occurring at the level of subcortical structures within the brain's emotional circuitry and the association with current affective computing models in recognizing emotions. Furthermore, we discuss whether such biologically inspired modeling is the solution to advance the field in EEG-based emotion recognition and beyond. [ABSTRACT FROM AUTHOR]

Whilst radio, podcasts, and music streaming are considered unique audio formats that offer brands different opportunities, limited research has explored this notion. This current study analyses how the brain responds to these formats and suggests that they offer different branding opportunities. Participants' engagement, attitude, attention, memory, and physiological arousal were measured while each audio format was consumed. The results revealed that music streaming elicited more positive attitudes, higher attention, greater levels of memory encoding, and increased physiological arousal compared to either radio or podcasts. This study emphasises the importance for brands of utilising diverse audio channels for unique branding and marketing opportunities. [ABSTRACT FROM AUTHOR]

Background. Brain–machine interfaces (BMIs) offer users the ability to directly communicate with digital devices through neural signals decoded with machine learning (ML)-based algorithms. Spiking Neural Networks (SNNs) are a type of Artificial Neural Network (ANN) that operate on neural spikes instead of continuous scalar outputs. Compared to traditional ANNs, SNNs perform fewer computations, use less memory, and mimic biological neurons better. However, SNNs only retain information for short durations, limiting their ability to capture long-term dependencies in time-variant data. Here, we propose a novel spike-weighted SNN with spiking long short-term memory (swSNN-SLSTM) for a regression problem. Spike-weighting captures neuronal firing rate instead of membrane potential, and the SLSTM layer captures long-term dependencies. Methods. We compared the performance of various ML algorithms during decoding directional movements, using a dataset of microelectrode recordings from a macaque during a directional joystick task, and also an open-source dataset. We thus quantified how swSNN-SLSTM performed compared to existing ML models: an unscented Kalman filter, LSTM-based ANN, and membrane-based SNN techniques. Result. The proposed swSNN-SLSTM outperforms both the unscented Kalman filter, the LSTM-based ANN, and the membrane based SNN technique. This shows that incorporating SLSTM can better capture long-term dependencies within neural data. Also, our proposed swSNN-SLSTM algorithm shows promise in reducing power consumption and lowering heat dissipation in implanted BMIs. [ABSTRACT FROM AUTHOR]

The present perspective introduces a novel psychometric tool designed to enhance the evaluation of alexithymia. Alexithymia, a condition marked by difficulties in recognizing and expressing emotions, along with a propensity to direct attention outside rather than toward one's own interior experiences, is commonly investigated through self-report questionnaires. These instruments assume that individuals have sufficient self-awareness and abstraction capabilities, which restricts the understanding of the underlying mechanisms of emotional recognition in individuals who do not possess these capacities. To address this lack, emerging technologies like virtual reality (VR) and 360° videos facilitate the recreation of immersive contexts, enabling subjects to engage with scenarios even remotely. Our innovative tool employs spherical video technology to recreate social and non-social scenarios that elicit emotions. Psychophysiological measures are collected during video observation; then, questions are asked to investigate how the subject consciously processes the emotions they experienced. This multimodal approach aims to capture both implicit and explicit emotion processing, providing a comprehensive assessment. Overall, the proposed psychometric tool offers the potential for a more nuanced understanding of alexithymic traits and their real-life impact, empowering clinicians to tailor treatment processes to individual needs based on a richer set of information. [ABSTRACT FROM AUTHOR]

Light Sheet Fluorescence Microscopy (LSFM) has emerged as a valuable tool for neurobiologists, enabling the rapid and high-quality volumetric imaging of mice brains. However, inherent artifacts and distortions introduced during the imaging process necessitate careful enhancement of LSFM images for optimal 3D reconstructions. This work aims to correct images slice by slice before reconstructing 3D volumes. Our approach involves a three-step process: firstly, the implementation of a deblurring algorithm using the work of K. Becker; secondly, an automatic contrast enhancement; and thirdly, the development of a convolutional denoising auto-encoder featuring skip connections to effectively address noise introduced by contrast enhancement, particularly excelling in handling mixed Poisson–Gaussian noise. Additionally, we tackle the challenge of axial distortion in LSFM by introducing an approach based on an auto-encoder trained on bead calibration images. The proposed pipeline demonstrates a complete solution, presenting promising results that surpass existing methods in denoising LSFM images. These advancements hold potential to significantly improve the interpretation of biological data. [ABSTRACT FROM AUTHOR]