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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]

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]

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]

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]

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]

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]

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]

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]

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]