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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]

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 study of the human psyche has elucidated a bipartite structure of logic reflecting the quantum–classical nature of the world. Accordingly, we posited an approach toward studying the brain by means of the quantum–classical dynamics of a mixed Weyl symbol. The mixed Weyl symbol can be used to describe brain processes at the microscopic level and, when averaged over an appropriate ensemble, can provide a link to the results of measurements made at the meso and macro scale. Within this approach, quantum variables (such as, for example, nuclear and electron spins, dipole momenta of particles or molecules, tunneling degrees of freedom, and so on) can be represented by spinors, whereas the electromagnetic fields and phonon modes can be treated either classically or semi-classically in phase space by also considering quantum zero-point fluctuations. Quantum zero-point effects can be incorporated into numerical simulations by controlling the temperature of each field mode via coupling to a dedicated Nosé–Hoover chain thermostat. The temperature of each thermostat was chosen in order to reproduce quantum statistics in the canonical ensemble. In this first paper, we introduce a general quantum–classical Hamiltonian model that can be tailored to study physical processes at the interface between the quantum and the classical world in the brain. While the approach is discussed in detail, numerical calculations are not reported in the present paper, but they are planned for future work. Our theory of brain dynamics subsumes some compatible aspects of three well-known quantum approaches to brain dynamics, namely the electromagnetic field theory approach, the orchestrated objective reduction theory, and the dissipative quantum model of the brain. All three models are reviewed. [ABSTRACT FROM AUTHOR]

The paper concentrates on the solitary waves that are retrievable from the generalized Boussinesq equation. The numerical simulations are displayed in the paper that gives a visual perspective to the model studied in neurosciences. The Laplace–Adomian decomposition scheme makes this visualization of the solitons possible. The numerical simulations are being reported for the first time using an elegant approach. The results would be helpful for neuroscientists and clinical studies in Medicine. The novelty lies in the modeling that is successfully conducted with an impressively small error measure. In the past, the model was integrated analytically only to recover soliton solutions and its conserved quantities. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is a field of computer science that deals with the simulation of human intelligence using machines so that such machines gain problem-solving and decision-making capabilities similar to that of the human brain. Neuroscience is the scientific study of the struczture and cognitive functions of the brain. Neuroscience and AI are mutually interrelated. These two fields help each other in their advancements. The theory of neuroscience has brought many distinct improvisations into the AI field. The biological neural network has led to the realization of complex deep neural network architectures that are used to develop versatile applications, such as text processing, speech recognition, object detection, etc. Additionally, neuroscience helps to validate the existing AI-based models. Reinforcement learning in humans and animals has inspired computer scientists to develop algorithms for reinforcement learning in artificial systems, which enables those systems to learn complex strategies without explicit instruction. Such learning helps in building complex applications, like robot-based surgery, autonomous vehicles, gaming applications, etc. In turn, with its ability to intelligently analyze complex data and extract hidden patterns, AI fits as a perfect choice for analyzing neuroscience data that are very complex. Large-scale AI-based simulations help neuroscientists test their hypotheses. Through an interface with the brain, an AI-based system can extract the brain signals and commands that are generated according to the signals. These commands are fed into devices, such as a robotic arm, which helps in the movement of paralyzed muscles or other human parts. AI has several use cases in analyzing neuroimaging data and reducing the workload of radiologists. The study of neuroscience helps in the early detection and diagnosis of neurological disorders. In the same way, AI can effectively be applied to the prediction and detection of neurological disorders. Thus, in this paper, a scoping review has been carried out on the mutual relationship between AI and neuroscience, emphasizing the convergence between AI and neuroscience in order to detect and predict various neurological disorders. [ABSTRACT FROM AUTHOR]

This umbrella review aimed to systematically identify the peri-operative risk factors associated with post-operative cognitive dysfunction (POCD) using meta-analyses of observational studies. To date, no review has synthesised nor assessed the strength of the available evidence examining risk factors for POCD. Database searches from journal inception to December 2022 consisted of systematic reviews with meta-analyses that included observational studies examining pre-, intra- and post-operative risk factors for POCD. A total of 330 papers were initially screened. Eleven meta-analyses were included in this umbrella review, which consisted of 73 risk factors in a total population of 67,622 participants. Most pertained to pre-operative risk factors (74%) that were predominantly examined using prospective designs and in cardiac-related surgeries (71%). Overall, 31 of the 73 factors (42%) were associated with a higher risk of POCD. However, there was no convincing (class I) or highly suggestive (class II) evidence for associations between risk factors and POCD, and suggestive evidence (class III) was limited to two risk factors (pre-operative age and pre-operative diabetes). Given that the overall strength of the evidence is limited, further large-scale studies that examine risk factors across various surgery types are recommended. [ABSTRACT FROM AUTHOR]

"Emergence", the phenomenon where a complex system displays properties, behaviours, or dynamics not trivially reducible to its constituent elements, is one of the defining properties of complex systems. Recently, there has been a concerted effort to formally define emergence using the mathematical framework of information theory, which proposes that emergence can be understood in terms of how the states of wholes and parts collectively disclose information about the system's collective future. In this paper, we show how a common, foundational component of information-theoretic approaches to emergence implies an inherent instability to emergent properties, which we call flickering emergence. A system may, on average, display a meaningful emergent property (be it an informative coarse-graining, or higher-order synergy), but for particular configurations, that emergent property falls apart and becomes misinformative. We show existence proofs that flickering emergence occurs in two different frameworks (one based on coarse-graining and another based on multivariate information decomposition) and argue that any approach based on temporal mutual information will display it. Finally, we argue that flickering emergence should not be a disqualifying property of any model of emergence, but that it should be accounted for when attempting to theorize about how emergence relates to practical models of the natural world. [ABSTRACT FROM AUTHOR]

After decades of being ignored, the concept of beauty, as understood by the non-architect, has recently been making a comeback in architecture, not so much in the practice itself, as in appeals for design solutions that are more human-centered and not dictated by abstract principles. Architectural beauty needs to be evaluated from its effects on human health. This study discusses two diagnostic tools for measuring the degree of architectural "beauty" and presents the results of the pilot application of one of them. The goal is to use diagnostic imaging for evaluations. Analytical elements are introduced from disciplines with which practitioners are normally not familiar, such as artificial intelligence, medicine, neuroscience, visual attention and image-processing software, etc. In addition to the diagnostic tools, this paper ties related ideas on objective beauty into a novel synthesis. These results support the idea of a feasible, "objective" way to evaluate what the users will consider as beautiful, and set the stage for an upcoming larger study that will quantitatively correlate the two methods. [ABSTRACT FROM AUTHOR]

There is a noticeable gap between results of cognitive neuroscientific research into basic mathematical abilities and philosophical and empirical investigations of mathematics as a distinct intellectual activity. The paper explores the relevance of a Wittgensteinian framework for dealing with this discrepancy. [ABSTRACT FROM AUTHOR]

Our actions can be triggered by intentions, incentives or intrinsic values. Recent neuroscientific research has yielded some results about the growth mindset and intrinsic motivation. With the advances in neuroscience and motivational studies, there is a global need to utilize this information to inform educational practice and research. Yet, little is known about the neuroscientific interplay between growth mindset and intrinsic motivation. This paper attempts to draw on the theories of growth mindset and intrinsic motivation, together with contemporary ideas in neuroscience, outline the potential for neuroscientific research in education. It aims to shed light on the relationship between growth mindset and intrinsic motivation in terms of supporting a growth mindset to facilitate intrinsic motivation through neural responses. Recent empirical research from the educational neuroscience perspective that provides insights into the interplay between growth mindset and intrinsic motivation will also be discussed. [ABSTRACT FROM AUTHOR]

Sample entropy (SaEn), calculated for center of pressure (COP) trajectories, is often distinct for compromised postural control, e.g., in Parkinson, stroke, or concussion patients, but the interpretation of COP-SaEn remains subject to debate. The purpose of this paper is to test the hypotheses that COP-SaEn is related (Hypothesis 1; H1) to the complexity of the postural movement structures, i.e., to the utilization and coordination of the mechanical degrees of freedom; or (Hypothesis 2; H2) to the irregularity of the individual postural movement strategies, i.e., to the neuromuscular control of these movements. Twenty-one healthy volunteers (age 26.4 ± 2.4; 10 females), equipped with 27 reflective markers, stood on a force plate and performed 2-min quiet stances. Principal movement strategies (PMs) were obtained from a principal component analysis (PCA) of the kinematic data. Then SaEn was calculated for the COP and PM time-series. H1 was tested by correlating COP-SaEn to the relative contribution of the PMs to the subject specific overall movement and H2 by correlating COP-SaEn and PM-SaEn. Both hypotheses were supported. This suggests that in a healthy population the COP-SaEn is linked to the complexity of the coordinative structure of postural movements, as well as to the irregularity of the neuromuscular control of specific movement components. [ABSTRACT FROM AUTHOR]

It is plausible to hypothesize that the spiking responses of certain neurons represent functions of the spiking signals of other neurons. A natural ensuing question concerns how to use experimental data to infer what kind of a function is being computed. Model-based approaches typically require assumptions on how information is represented. By contrast, information measures are sensitive only to relative behavior: information is unchanged by applying arbitrary invertible transformations to the involved random variables. This paper develops an approach based on the information bottleneck method that attempts to find such functional relationships in a neuron population. Specifically, the information bottleneck method is used to provide appropriate compact representations which can then be parsed to infer functional relationships. In the present paper, the parsing step is specialized to the case of remapped-linear functions. The approach is validated on artificial data and then applied to recordings from the motor cortex of a macaque monkey performing an arm-reaching task. Functional relationships are identified and shown to exhibit some degree of persistence across multiple trials of the same experiment. [ABSTRACT FROM AUTHOR]

It is plausible to hypothesize that the spiking responses of certain neurons represent functions of the spiking signals of other neurons. A natural ensuing question concerns how to use experimental data to infer what kind of a function is being computed. Model-based approaches typically require assumptions on how information is represented. By contrast, information measures are sensitive only to relative behavior: information is unchanged by applying arbitrary invertible transformations to the involved random variables. This paper develops an approach based on the information bottleneck method that attempts to find such functional relationships in a neuron population. Specifically, the information bottleneck method is used to provide appropriate compact representations which can then be parsed to infer functional relationships. In the present paper, the parsing step is specialized to the case of remapped-linear functions. The approach is validated on artificial data and then applied to recordings from the motor cortex of a macaque monkey performing an arm-reaching task. Functional relationships are identified and shown to exhibit some degree of persistence across multiple trials of the same experiment. [ABSTRACT FROM AUTHOR]

In recent decades, historians of religions have turned to, and developed, entirely new methodologies for the study of religion and human consciousness. Foremost among these are a collection of approaches often termed the "cognitive science of religion" (CSR), typically drawing on cognitive science, neuroscience, linguistics, and contemporary metaphor theory. Although we are still "early" in this enterprise, I hope to show how a meaningful dialogue between religious studies and contemporary neuroscience and cognitive science can help us to better understand some intriguing mystical texts and practices from a tradition of medieval South Asian Hinduism. Known collectively as the ... Sahajiyās, these followers of transgressive and antinomian Tantric Yoga provide excellent examples for exploring the nature of religion, ritual, consciousness, embodiment, identity, gender, emotions and sexuality. This paper will show how the study of these rich materials from 17th through 18th century Bengal in northeastern South Asia can be enhanced using insights from the philosopher, Shaun Gallagher, and the neurologist, Patrick McNamara. [ABSTRACT FROM AUTHOR]

Personality is the characteristic set of an individual's behavioral and emotional patterns that evolve from biological and environmental factors. The recognition of personality profiles is crucial in making human–computer interaction (HCI) applications realistic, more focused, and user friendly. The ability to recognize personality using neuroscientific data underpins the neurobiological basis of personality. This paper aims to automatically recognize personality, combining scalp electroencephalogram (EEG) and machine learning techniques. As the resting state EEG has not so far been proven efficient for predicting personality, we used EEG recordings elicited during emotion processing. This study was based on data from the AMIGOS dataset reflecting the response of 37 healthy participants. Brain networks and graph theoretical parameters were extracted from cleaned EEG signals, while each trait score was dichotomized into low- and high-level using the k-means algorithm. A feature selection algorithm was used afterwards to reduce the feature-set size to the best 10 features to describe each trait separately. Support vector machines (SVM) were finally employed to classify each instance. Our method achieved a classification accuracy of 83.8% for extraversion, 86.5% for agreeableness, 83.8% for conscientiousness, 83.8% for neuroticism, and 73% for openness. [ABSTRACT FROM AUTHOR]

Neuroscience has traditionally relied on manually observing laboratory animals in controlled environments. Researchers usually record animals behaving freely or in a restrained manner and then annotate the data manually. The manual annotation is not desirable for three reasons; (i) it is time-consuming, (ii) it is prone to human errors, and (iii) no two human annotators will 100% agree on annotation, therefore, it is not reproducible. Consequently, automated annotation for such data has gained traction because it is efficient and replicable. Usually, the automatic annotation of neuroscience data relies on computer vision and machine learning techniques. In this article, we have covered most of the approaches taken by researchers for locomotion and gesture tracking of specific laboratory animals, i.e. rodents. We have divided these papers into categories based upon the hardware they use and the software approach they take. We have also summarized their strengths and weaknesses. [ABSTRACT FROM AUTHOR]

Sensory integration, now trademarked as Ayres Sensory Integration® or ASI, is based on principles of neuroscience and provides a framework for understanding the contributions of the sensory and motor foundations of human behavior. The theory and practice of ASI continues to evolve as greater understanding of the neurobiology of human behavior emerges. In this paper we examine core constructs of ASI identified in the seminal work of Dr. Jean Ayres, and present current neuroscience research that underlies the main patterns of sensory integration function and dysfunction. We consider how current research verifies and clarifies Ayres' propositions by describing functions of the vestibular, proprioceptive, and tactile sensory systems, and exploring their relationships to ocular, postural, bilateral integration, praxis, and sensory modulation. We close by proposing neuroplasticity as the mechanisms underlying change as a result of ASI intervention. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the connectivity alterations of the subcortical brain network due to Alzheimer's disease (AD). Mostly, the literature investigated AD connectivity abnormalities at the whole brain level or at the cortex level, while very few studies focused on the sub-network composed only by the subcortical regions, especially using diffusion-weighted imaging (DWI) data. In this work, we examine a mixed cohort including 46 healthy controls (HC) and 40 AD patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) data set. We reconstruct the brain connectome through the use of state of the art tractography algorithms and we propose a method based on graph communicability to enhance the information content of subcortical brain regions in discriminating AD. We develop a classification framework, achieving 77% of area under the receiver operating characteristic (ROC) curve in the binary discrimination AD vs. HC only using a 12 × 12 subcortical features matrix. We find some interesting AD-related connectivity patterns highlighting that subcortical regions tend to increase their communicability through cortical regions to compensate the physical connectivity reduction between them due to AD. This study also suggests that AD connectivity alterations mostly regard the inter-connectivity between subcortical and cortical regions rather than the intra-subcortical connectivity. [ABSTRACT FROM AUTHOR]

Neuroimaging is a highly important field of neuroscience, with direct implications for the early diagnosis and progression monitoring of brain-associated diseases. Neuroimaging techniques are categorized into structural, functional and molecular neuroimaging, each possessing advantages and disadvantages in terms of resolution, invasiveness, toxicity of contrast agents and costs. Nanotechnology-based approaches for neuroimaging mostly involve the development of nanocarriers for incorporating contrast agents or the use of nanomaterials as imaging agents. Inorganic and organic nanoparticles, liposomes, micelles, nanobodies and quantum dots are some of the most studied candidates for the delivery of contrast agents for neuroimaging. This paper focuses on describing the conventional modalities used for imaging and the applications of nanotechnology for developing novel strategies for neuroimaging. The aim is to highlight the roles of nanocarriers for enhancing and/or overcome the limitations associated with the most commonly utilized neuroimaging modalities. For future directions, several techniques that could benefit from the increased contrast induced by using imaging probes are presented. [ABSTRACT FROM AUTHOR]