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The widespread use of mobile devices and laptops has replaced traditional paper-based learning and the question of how the brain efficiency of digital tablet-based learning differs from that of paper-based learning remains unclear. The purpose of this study was to investigate the difference in brain efficiency for learning between paper-based and digital tablet-based learning by measuring activity in the prefrontal cortex (PFC) using functional near-infrared spectroscopy. Thirty-two subjects were randomly assigned to the paper-based learning or the digital tablet-based learning group. Subjects in each group performed a memory task that required memorizing a three-minute novel (encoding phase) on a paper or digital tablet, followed by a test in which they answered four multiple-choice questions based on the novel's content. To compare both groups, behavioral performance on the test (retrieval phase) and activity in the PFC were measured. As a result, no significant difference in behavioral performance between both groups was observed (p > 0.05). However, the paper-based learning group showed significantly lower activity in the PFC in the encoding phase than the digital tablet-based learning group (p < 0.05) but not in the retrieval phase. The current study demonstrated that brain efficiency in encoding is higher in subjects with paper-based learning than those with digital tablet-based learning. This finding has important implications for education, particularly in terms of the pros and cons of electronic document-based learning. [ABSTRACT FROM AUTHOR]

This document is a summary of the top papers published in the journal Brain Sciences in 2022-2023. The papers cover a range of topics in neuropsychology, including the brain correlates of social and emotional processes, stress-related pathologies, phobias, substance use disorders, and addiction. One paper focuses on the negative effects of excessive use of social networking sites, while another examines the toxic effects of synthetic cannabinoids. Other papers explore the brain correlates of eating disorders, stress measurement in daily life situations, the role of body memories in mental health problems, cognitive impairments in patients with psychotic episodes, the impact of the COVID-19 pandemic on clinical and research activities, and the study of normal and atypical development. Two papers specifically address developmental dyslexia, looking beyond the traditional phonological interpretation of the disorder. Overall, these papers contribute to a better understanding of various neuropsychological issues and offer insights for potential interventions and treatments. [Extracted from the article]

Mental health and wellbeing are increasingly threatened in the current post-pandemic times, with stress, especially in students, reaching preoccupying levels. In addition, while many educational programs are unidimensional (i.e., lacking integration between physical, emotional and cognitive elements), there are ways to promote physical, social and mental health in children and adolescents. In this opinion paper, we will discuss the importance of an integrative approach for health development and examine relevant factors, such as awareness and emotional intelligence. We will highlight evidence ranging from behavioral to electrophysiological, structural and molecular, and report several recent studies supporting the effectiveness of a holistic approach in supporting wellbeing and creativity in children and adults, and detailing a specific paradigm named the Quadrato Motor Training (QMT). QMT is a specifically structured movement meditation, involving cognitive, motor and affective components. Finally, we will support a holistic view on education, integrating motion, emotion and cognition to develop a person-centered, or in this case student-centered, approach to wellbeing and health. [ABSTRACT FROM AUTHOR]

The technology for transcranial magnetic stimulation (TMS) has significantly changed over the years, with important improvements in the signal generators, the coils, the positioning systems, and the software for modeling, optimization, and therapy planning. In this systematic literature review (SLR), the evolution of each component of TMS technology is presented and analyzed to assess the limitations to overcome. This SLR was carried out following the PRISMA 2020 statement. Published articles of TMS were searched for in four databases (Web of Science, PubMed, Scopus, IEEE). Conference papers and other reviews were excluded. Records were filtered using terms about TMS technology with a semi-automatic software; articles that did not present new technology developments were excluded manually. After this screening, 101 records were included, with 19 articles proposing new stimulator designs (18.8%), 46 presenting or adapting coils (45.5%), 18 proposing systems for coil placement (17.8%), and 43 implementing algorithms for coil optimization (42.6%). The articles were blindly classified by the authors to reduce the risk of bias. However, our results could have been influenced by our research interests, which would affect conclusions for applications in psychiatric and neurological diseases. Our analysis indicates that more emphasis should be placed on optimizing the current technology with a special focus on the experimental validation of models. With this review, we expect to establish the base for future TMS technological developments. [ABSTRACT FROM AUTHOR]

Phase synchronization serves as an effective method for analyzing the synchronization of electroencephalogram (EEG) signals among brain regions and the dynamic changes of the brain. The purpose of this paper is to study the construction of the functional brain network (FBN) based on phase synchronization, with a special focus on neural processes related to human balance regulation. This paper designed four balance paradigms of different difficulty by blocking vision or proprioception and collected 19-channel EEG signals. Firstly, the EEG sequences are segmented by sliding windows. The phase-locking value (PLV) of core node pairs serves as the phase-screening index to extract the valid data segments, which are recombined into new EEG sequences. Subsequently, the multichannel weighted phase lag index (wPLI) is calculated based on the new EEG sequences to construct the FBN. The experimental results show that due to the randomness of the time points of body balance adjustment, the degree of phase synchronization of the datasets screened by PLV is more obvious, improving the effective information expression of the subsequent EEG data segments. The FBN topological structures of the wPLI show that the connectivity of various brain regions changes structurally as the difficulty of human balance tasks increases. The frontal lobe area is the core brain region for information integration. When vision or proprioception is obstructed, the EEG synchronization level of the corresponding occipital lobe area or central area decreases. The synchronization level of the frontal lobe area increases, which strengthens the synergistic effect among the brain regions and compensates for the imbalanced response caused by the lack of sensory information. These results show the brain regional characteristics of the process of human balance regulation under different balance paradigms, providing new insights into endogenous neural mechanisms of standing balance and methods of constructing brain networks. [ABSTRACT FROM AUTHOR]

The purpose of this reply is to address the comments given by Kelly et al. on our original paper "Unique Brain Network Identification Number for Parkinson's and Healthy Individuals using Structural MRI". We agree to the inadvertent rounding pitfall in our original paper due to the non-inclusion of symbolic math toolbox (MATLAB). We now provide the actual ranges (with decimal values) of the UBNIN values of healthy individuals and those with Parkinson's disease and further observations. Upon further introspection, we propose another variant, called Modified-UBNIN (UBNIN-MT,MN) which is highly weighted on the node with the highest network degree (i.e., connections). The italicized sentences within inverted commas are statements from Kelly et al.'s comment paper. [ABSTRACT FROM AUTHOR]

This summary provides an overview of a document titled "From Bench to Bedside: Motor–Cognitive Interactions." The document consists of ten papers and six reviews that explore the connection between motor and cognitive abilities and their relevance in neurodegenerative illnesses and clinical groups such as dementia, stroke, Parkinson's disease, and multiple sclerosis. The papers cover topics such as attention, perception, diagnostic tools, intervention strategies, and the relationship between neurocognitive disorders and motor impairment. The document also includes a review on the neuroimaging outcomes of physical therapy in patients with psychosis and the impact of dual-task training on motor and cognitive skills in patients with Parkinson's disease. Overall, these studies contribute to our understanding of motor-cognitive interactions and offer potential applications in various fields. [Extracted from the article]

First, the definition of brain connectivity is not univocal, and diverse terms (structural, functional, and effective connectivity) are used in the literature, which are often confusing or ambiguous, depending on the approach used and on the aspects that should be emphasized. All papers in this Special Issue are focused on connectivity, which is the influence that the activity in one brain region can have on that in another brain region. Thanks to the development of advanced techniques for functional brain imaging and the tuning of sophisticated signal-processing methods, the interest in brain connectivity estimation has experienced an enormous expansion. As is widely understood, brain functioning depends on the interaction among several neural populations, which are linked via complex connectivity circuits and work together (in antagonistic or synergistic ways) to exchange information, synchronize their activity, adapt plastically to external stimuli or internal requirements, and more generally to participate in solving multifaceted cognitive tasks. [Extracted from the article]

Background: Over the past decade, many studies in the field of transcranial direct current stimulation (tDCS) in stroke have been published in scholarly journals. However, a scientometric analysis focusing on tDCS after stroke is still missing. The purpose of this study is to deliver a bibliometric analysis to investigate the global hotspots and frontiers in the domain of tDCS in stroke from 2012 to 2021. Methods: Articles and reviews related to tDCS in stroke were retrieved and obtained from the Web of Science core collection database from 2012 to 2021. Data visualization and analysis were conducted by using CiteSpace, VOSviewer, and Microsoft Excel 2019. Results: Finally, 371 publications were included in the scientometric analysis, including 288 articles and 83 reviews. The results showed that the number of publications per year increased from 15 to 68 in the last 10 years. Neurosciences was the main research hotspot category (n = 201). Frontiers in Human Neuroscience was the most published journal with 14 papers. The most productive author, institution, and country were Fregni F (n = 13), the League of European Research Universities (n = 37), and the United States of America (n = 98), respectively. A burstness analysis of keywords and the literature indicated that current studies in the field of tDCS in stroke focused on poststroke aphasia, tDCS combined with robotic therapy, and anatomical parameters. Conclusion: The research of tDCS in stroke is predicted to remain a research hotspot in the future. We recommend investigating the curative effect of other different tDCS closed-loop rehabilitation methods for different stroke dysfunctions. In conclusion, this bibliometric study presented the hotspots and trends of tDCS in stroke over the last decade, which may help researchers manage their further studies. [ABSTRACT FROM AUTHOR]

The aim of this study was to verify whether the level of mental resilience would differentiate reading comprehension performance when using different information carriers. More than 150 people filled out a test regarding the level of resilience. They then participated in a survey using fNIRS. Their task was to read a one-page informational text and answer several questions. The results showed no differences in correct answers between groups of people with different levels of resilience. In the groups of people with high and low levels of resilience, the number of correct answers was not differentiated by the type of carrier. Among those with moderate levels of resilience, better results were obtained by those who read text printed on paper. Analyses of neuronal mechanisms showed that the type of carrier differentiated brain activity in each group. Obtaining the same number of correct answers in the test was the result of different neuronal mechanisms activated in those who used a computer and those who read a printed text. [ABSTRACT FROM AUTHOR]

Auditory steady-state response (ASSR) is the result of the brain's ability to follow and entrain its oscillatory activity to the phase and frequency of periodic auditory stimulation. Gamma-band ASSR has been increasingly investigated with intentions to apply it in neuropsychiatric disorders diagnosis as well as in brain–computer interface technologies. However, it is still debatable whether attention can influence ASSR, as the results of the attention effects of ASSR are equivocal. In our study, we aimed to systemically review all known articles related to the attentional modulation of gamma-band ASSRs. The initial literature search resulted in 1283 papers. After the removal of duplicates and ineligible articles, 49 original studies were included in the final analysis. Most analyzed studies demonstrated ASSR modulation with differing attention levels; however, studies providing mixed or non-significant results were also identified. The high versatility of methodological approaches including the utilized stimulus type and ASSR recording modality, as well as tasks employed to modulate attention, were detected and emphasized as the main causality of result inconsistencies across studies. Also, the impact of training, inter-individual variability, and time of focus was addressed. [ABSTRACT FROM AUTHOR]

Tracheostomy ventilation (TV) can increase survival time for people living with motor neurone disease (MND); however, the use of TV varies between countries. Concerns regarding anticipated quality of life (QoL) are among the reasons given by healthcare professionals for not recommending this intervention, yet little is known about QoL in this context. This scoping review was conducted to examine the evidence on QoL for those with MND who use TV and family members involved in their care. Using the methodological guidance of the Joanna Briggs Institute, 23 papers were identified for inclusion, and findings were inductively analysed to identify key themes. We found that people living with MND tend to rate QoL post TV more positively than anticipated by healthcare professionals or family members. QoL was found to be related to positive relationships and activities the person could maintain. Feeling able to make a choice and an adequate level of financial resources were also important factors. Family members tended to experience lower QoL, associated with the uncertainty surrounding an emergency procedure and the complexity of subsequently required care. More evidence on QoL from the perspectives of people with MND who use TV is needed to support decision making and inform guidance. [ABSTRACT FROM AUTHOR]

Paper-based or IT tools can be used in telerehabilitation mode to improve the skills of children with developmental dyslexia (DD), seeking to increase reading speed and reduce errors. Telerehabilitation is the provision of remote treatments in which the patient works autonomously in a familiar environment under the remote monitoring, through telecommunication means, of an expert operator. Through telerehabilitation, children with DD can receive treatments outside the specialist clinic, at home or school, via internet connections, and through advanced technological platforms. These procedures allow adequate communication with the family, intensity of treatment, self-adaptivity of exercises, and child engagement; these factors are crucial for a high intervention efficacy. Recent studies have supported the effectiveness of the telerehabilitation of reading in children with DD, with some studies reporting no differences in efficacy between remote and in-person methods. Nevertheless, many points remain to be clarified about the procedures and methods required by telerehabilitation, the variables linked to its effectiveness (e.g., the impact of the intensity of the training and the neuropsychological profile of the child), and the comparative validity of different tele-treatment paths. These aspects are discussed in the present paper. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue of the journal Brain Sciences on recent advances in the pathophysiology and therapeutic approaches in epilepsy. The document highlights the global impact of epilepsy, with over 65 million people affected worldwide. It emphasizes the need for a deeper understanding of the biochemical, structural, physiological, and molecular changes in epileptic brains to develop novel therapeutic targets and treatment options. The special issue includes twelve papers, including research articles, review articles, and a systematic review, covering topics such as drug-resistant epilepsy, neuroinflammation, post-traumatic epilepsy, surgical treatments, status epilepticus, self-limited focal epilepsy, transition readiness, epilepsy networks, and interictal spikes. The articles aim to expand our understanding of epilepsy and contribute to the development of new treatment approaches. [Extracted from the article]

Emotion recognition using the electroencephalogram (EEG) has garnered significant attention within the realm of human–computer interaction due to the wealth of genuine emotional data stored in EEG signals. However, traditional emotion recognition methods are deficient in mining the connection between multi-domain features and fitting their advantages. In this paper, we propose a novel capsule Transformer network based on a multi-domain feature for EEG-based emotion recognition, referred to as MES-CTNet. The model's core consists of a multichannel capsule neural network(CapsNet) embedded with ECA (Efficient Channel Attention) and SE (Squeeze and Excitation) blocks and a Transformer-based temporal coding layer. Firstly, a multi-domain feature map is constructed by combining the space–frequency–time characteristics of the multi-domain features as inputs to the model. Then, the local emotion features are extracted from the multi-domain feature maps by the improved CapsNet. Finally, the Transformer-based temporal coding layer is utilized to globally perceive the emotion feature information of the continuous time slices to obtain a final emotion state. The paper fully experimented on two standard datasets with different emotion labels, the DEAP and SEED datasets. On the DEAP dataset, MES-CTNet achieved an average accuracy of 98.31% in the valence dimension and 98.28% in the arousal dimension; it achieved 94.91% for the cross-session task on the SEED dataset, demonstrating superior performance compared to traditional EEG emotion recognition methods. The MES-CTNet method, utilizing a multi-domain feature map as proposed herein, offers a broader observation perspective for EEG-based emotion recognition. It significantly enhances the classification recognition rate, thereby holding considerable theoretical and practical value in the EEG emotion recognition domain. [ABSTRACT FROM AUTHOR]

This document is an editorial that summarizes several research papers on the topic of human recognition and the interactions between different channels of recognition, such as voice and face processing. The papers explore various aspects of this topic, including voice processing in challenging listening conditions, the role of familiarity in voice and face recognition, and the functional reorganization of neural systems in blind individuals. The papers highlight the importance of familiarity factors in recognition, the role of brain asymmetries, and the early integration of voice and face processing. The findings suggest that voice and face recognition are closely linked and influenced by familiarity, and that the right temporal lobe may play a significant role in vocal processing abilities in blind individuals. [Extracted from the article]

This document discusses the emerging trends in biomedical signal processing for intelligent emotion recognition. It highlights the potential impact of these advancements on various fields, including mental health, neuroscience, robotics, advertising, and marketing. The document also presents six research papers that delve into the world of emotion recognition using EEG signals and advanced artificial intelligence techniques. These papers explore novel approaches to subject-independent emotion recognition, spatial-temporal feature learning, electrode combination, ensemble learning techniques, and the application of neuroscience in the tourism industry. The document emphasizes the importance of interdisciplinary approaches and advanced technologies in enhancing our understanding of emotions and their neural substrates. [Extracted from the article]

Over the past three decades, substantial advancements have occurred in non-invasive brain stimulation (NIBS). These developments encompass various non-invasive techniques aimed at modulating brain function. Among the most widely utilized methods today are transcranial magnetic stimulation (TMS) and transcranial electrical stimulation (TES), which include direct- or alternating-current transcranial stimulation (tDCS/tACS). In addition to these established techniques, newer modalities have emerged, broadening the scope of non-invasive neuromodulation approaches available for research and clinical applications in movement disorders, particularly for Parkinson's disease (PD) and, to a lesser extent, atypical Parkinsonism (AP). All NIBS techniques offer the opportunity to explore a wide range of neurophysiological mechanisms and exert influence over distinct brain regions implicated in the pathophysiology of Parkinsonism. This paper's first aim is to provide a brief overview of the historical background and underlying physiological principles of primary NIBS techniques, focusing on their translational relevance. It aims to shed light on the potential identification of biomarkers for diagnostic and therapeutic purposes, by summarising available experimental data on individuals with Parkinsonism. To date, despite promising findings indicating the potential utility of NIBS techniques in Parkinsonism, their integration into clinical routine for diagnostic or therapeutic protocols remains a subject of ongoing investigation and scientific debate. In this context, this paper addresses current unsolved issues and methodological challenges concerning the use of NIBS, focusing on the importance of future research endeavours for maximizing the efficacy and relevance of NIBS strategies for individuals with Parkinsonism. [ABSTRACT FROM AUTHOR]

The global issue of substance abuse demands ongoing initiatives aligned with the United Nations Sustainable Development Goals. With drug use remaining prevalent worldwide, interventions are critical to addressing the associated health challenges and societal implications. Exercise and physical activities have emerged as integral components of substance use disorder (SUD) treatment, offering promising avenues for prevention, intervention, and recovery. Recent research underscores the efficacy of exercise in reducing substance cravings, promoting abstinence, and improving overall well-being. However, integrating exercise into SUD recovery programs presents challenges such as dropout rates and cultural considerations. This paper synthesizes existing literature on exercise integration into SUD recovery, highlighting strategies for enhancing treatment outcomes and addressing barriers to exercise adherence. Drawing on cognitive–behavioral therapy, experiential learning, motivational interviewing, and goal-setting techniques, the holistic approach outlined in this paper aims to empower individuals both mentally and physically, fostering resilience and supporting long-term recovery. In conclusion, new initiatives need to be taken by advocating for inclusive policies, promoting community engagement, and fostering collaborations across sectors. By doing so, stakeholders can optimize the effectiveness of exercise programs and contribute to sustainable rehabilitation efforts for individuals with SUD. [ABSTRACT FROM AUTHOR]

The article introduces a new method called the Unique Brain Network Identification Number (UBNIN) for storing binary neuroimaging connectivity data. It compares the UBNIN method with three other methods for storing brain-state data and discusses the pros and cons of each approach. The UBNIN method has three variations with different levels of compactness and information loss. The article concludes that while the UBNIN method has advantages, it requires more storage space compared to other methods and does not provide detailed information about connections between brain areas. The article also includes two tables comparing different properties of brain-state representations. The authors of the paper were involved in various aspects of the research and declare no conflicts of interest. [Extracted from the article]

Background: As the field of sensor-based rehabilitation continues to expand, it is important to gain a comprehensive understanding of its current research landscape. This study aimed to conduct a bibliometric analysis to identify the most influential authors, institutions, journals, and research areas in this field. Methods: A search of the Web of Science Core Collection was performed using keywords related to sensor-based rehabilitation in neurological diseases. The search results were analyzed with CiteSpace software using bibliometric techniques, including co-authorship analysis, citation analysis, and keyword co-occurrence analysis. Results: Between 2002 and 2022, 1103 papers were published on the topic, with slow growth from 2002 to 2017, followed by a rapid increase from 2018 to 2022. The United States was the most active country, while the Swiss Federal Institute of Technology had the highest number of publications among institutions. Sensors published the most papers. The top keywords included rehabilitation, stroke, and recovery. The clusters of keywords comprised machine learning, specific neurological conditions, and sensor-based rehabilitation technologies. Conclusions: This study provides a comprehensive overview of the current state of sensor-based rehabilitation research in neurological diseases, highlighting the most influential authors, journals, and research themes. The findings can help researchers and practitioners to identify emerging trends and opportunities for collaboration and can inform the development of future research directions in this field. [ABSTRACT FROM AUTHOR]

(1) Background: ADHD is recognized as one of the most common neurodevelopmental disorders. The worldwide prevalence of ADHD is estimated at 5.3%; however, estimates vary as a function of a number of factors, including diagnostic methods, age, sex and geographical location. A review of studies is needed to clarify the epidemiology of ADHD in Canada. (2) Methods: A search strategy was created in PubMed and adapted for MEDLINE and PsycINFO. Papers were included if they examined diagnosed ADHD prevalence and/or incidence rates in any region of Canada, age group and gender. A snowball technique was used to identify additional papers from reference lists, and experts in the field were consulted. (3) Results: Ten papers included in this review reported on prevalence, and one reported on incidence. One study provided an overall prevalence estimate across provinces for adults of 2.9%, and one study provided an overall estimate across five provinces for children and youth of 8.6%. Across age groups (1 to 24 years), incidence estimates ranged from 0.4% to 1.2%, depending on province. Estimates varied by age, gender, province, region and time. (4) Conclusions: The overall Canadian ADHD prevalence estimate is similar to worldwide estimates for adults. Most studies reported on prevalence rather than incidence. Differences in estimates across provinces may reflect the varying number of practitioners available to diagnose and prescribe medication for ADHD across provinces. To achieve a more comprehensive understanding of the epidemiology of ADHD in Canada, a study is needed that includes all provinces and territories, and that considers estimates in relation to age, gender, ethnicity, geographical region, socioeconomic status and access to mental healthcare coverage. Incidence rates need further examination to be determined. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue of the journal Brain Sciences titled "Neurological and Psychiatric Disorders in the COVID-19 Era." The issue aims to expand knowledge about the impact of COVID-19 on neurological and psychiatric disorders and how the outbreak has affected their management. The issue includes six original scientific papers. One study found an association between pulmonary embolism in COVID-19 patients and neurological complications such as stroke and seizures. Another study reviewed the possible mechanisms of SARS-CoV-2 damage in the body, including neurological disorders. A systematic review identified risk factors for depressive symptoms during COVID-19-related restrictions. Another study evaluated long-term neuropsychiatric sequelae in COVID-19 survivors and found that a significant portion still experienced symptoms. A review focused on the impact of COVID-19 on neurology training, and the final paper examined emotional distress in COVID-19 patients admitted to a rehabilitation unit. The special issue aims to provide new insights for future research initiatives. [Extracted from the article]

Hypnosis is a commonly used therapy option in dentistry and medicine for fear and pain reduction. Nevertheless, it is viewed very critically, as there is still insufficient evidence for a treatment effect. Specific phobia of dental treatment and dental anxiety are prevalent conditions that can cause an oral health impairment. This paper critically reviews 19 clinical trials aimed at reducing dental anxiety and fear avoidance in adults, published in peer-reviewed journals between 1979 and 2021. The search identified 257 papers; 223 were selected after removing duplicates. A total of 188 articles were excluded after title and abstract evaluation; 35 full text articles were assessed for eligibility. Another 10 papers were discharged after full text evaluation, as these were case reports and questionnaires. Six papers were discharged due to the lack of a comparable scale to measure dental anxiety. The following treatment techniques were reviewed: various forms of cognitive-behavioral therapy (CBT), relaxation training, benzodiazepine premedication, self-hypnosis by audio therapy, hypnotherapy, hypnosis, and nitrous oxide sedation. CBT delivered in a variety of formats, including one-session treatment, showed the most evidence for the efficacy of reducing anxiety. A wide heterogeneity of methods allowed only the inclusion of five studies to the performed meta-analysis, showing contrasting results for the application of hypnosis. The main reason for this issue is the great variety in methods used, making a distinct assessment of hypnotic interventions difficult. However, the results of the systematic review are promising in that hypnosis can also be regarded as powerful and successful method for anxiety reduction, while there are also studies with a small or even slightly negative effect. Therefore, further research is needed. Within the limitations of the current study, a more consistent use of methods to examine anxiety for hypnosis research is recommended. [ABSTRACT FROM AUTHOR]

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by inattention, hyperactivity, and impulsivity that affects a large number of young people in the world. The current treatments for children living with ADHD combine different approaches, such as pharmacological, behavioral, cognitive, and psychological treatment. However, the computer science research community has been working on developing non-pharmacological treatments based on novel technologies for dealing with ADHD. For instance, social robots are physically embodied agents with some autonomy and social interaction capabilities. Nowadays, these social robots are used in therapy sessions as a mediator between therapists and children living with ADHD. Another novel technology for dealing with ADHD is serious video games based on a brain–computer interface (BCI). These BCI video games can offer cognitive and neurofeedback training to children living with ADHD. This paper presents a systematic review of the current state of the art of these two technologies. As a result of this review, we identified the maturation level of systems based on these technologies and how they have been evaluated. Additionally, we have highlighted ethical and technological challenges that must be faced to improve these recently introduced technologies in healthcare. [ABSTRACT FROM AUTHOR]

In the past few years, significant advancements in microscopic imaging technology have led to the production of numerous high-resolution images capturing brain neurons at the micrometer scale. The reconstructed structure of neurons from neuronal images can serve as a valuable reference for research in brain diseases and neuroscience. Currently, there lacks an accurate and efficient method for neuron reconstruction. Manual reconstruction remains the primary approach, offering high accuracy but requiring significant time investment. While some automatic reconstruction methods are faster, they often sacrifice accuracy and cannot be directly relied upon. Therefore, the primary goal of this paper is to develop a neuron reconstruction tool that is both efficient and accurate. The tool aids users in reconstructing complete neurons by calculating the confidence of branches during the reconstruction process. The method models the neuron reconstruction as multiple Markov chains, and calculates the confidence of the connections between branches by simulating the reconstruction artifacts in the results. Users iteratively modify low-confidence branches to ensure precise and efficient neuron reconstruction. Experiments on both the publicly accessible BigNeuron dataset and a self-created Whole-Brain dataset demonstrate that the tool achieves high accuracy similar to manual reconstruction, while significantly reducing reconstruction time. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to improving the detection of mild cognitive impairment (MCI) through the use of super-resolved structural magnetic resonance imaging (MRI) and optimized deep learning models. The study introduces enhancements to the perceptual quality of super-resolved 2D structural MRI images using advanced loss functions, modifications to the upscaler part of the generator, and experiments with various discriminators within a generative adversarial training setting. It empirically demonstrates the effectiveness of super-resolution in the MCI detection task, showcasing performance improvements across different state-of-the-art classification models. The paper also addresses the challenge of accurately capturing perceptual image quality, particularly when images contain checkerboard artifacts, and proposes a methodology that incorporates hyperparameter optimization through a Pareto optimal Markov blanket (POMB). This approach systematically explores the hyperparameter space, focusing on reducing overfitting and enhancing model generalizability. The research findings contribute to the field by demonstrating that super-resolution can significantly improve the quality of MRI images for MCI detection, highlighting the importance of choosing an adequate discriminator and the potential of super-resolution as a preprocessing step to boost classification model performance. [ABSTRACT FROM AUTHOR]

Non-invasive neuromodulation techniques are widely utilized to study and improve cognitive function, with the aim of modulating different cognitive processes. For workers performing high-intensity mental and physical tasks, extreme fatigue may not only affect their working efficiency but may also lead to cognitive decline or cognitive impairment, which, in turn, poses a serious threat to their physical health. The use of non-invasive neuromodulation techniques has important research value for improving and enhancing cognitive function. In this paper, we review the research status, existing problems, and future prospects of transcranial direct current stimulation (tDCS), transcranial alternating current stimulation (tACS), transcranial magnetic stimulation (TMS), and transcutaneous acupoint stimulation (TAS), which are the most studied physical methods in non-invasive neuromodulation techniques to improve and enhance cognition. The findings presented in this paper will be of great reference value for the in-depth study of non-invasive neuromodulation techniques in the field of cognition. [ABSTRACT FROM AUTHOR]

Supervised classification algorithms for processing epileptic EEG signals rely heavily on the label information of the data, and existing supervised methods cannot effectively solve the problem of analyzing unlabeled epileptic EEG signals. In the traditional unsupervised clustering algorithm, the number of clusters and the global parameters must be predetermined, and the algorithm's analytical results are combined with a huge number of subjective errors, which affects the detection accuracy. For this reason, this paper proposes an unsupervised multivariate feature adaptive clustering analysis algorithm based on epileptic EEG signals. First, CEEMDAN and CWT are introduced into the epileptic EEG signal after preprocessing for joint denoising to further improve the signal quality. Then, the multivariate feature set of the signal is extracted and constructed, which includes nonlinear, time, frequency, and time-frequency characteristics. To reveal the hidden structures and correlations in the high-dimensional feature data, t-SNE dimensionality reduction is introduced. Finally, the DBSCAN clustering algorithm is optimized using the SSA algorithm to achieve adaptive selection of cluster number and global parameters.It not only enhances the clustering performance and reliability of the clustering results, but also avoids subjective errors in the analysis results. It provides a pre-theoretical foundation for the successful development of future seizure prediction devices and has good application prospects in clinical diagnosis and daily monitoring of patients. [ABSTRACT FROM AUTHOR]

The study of complex process models often encounters challenges in terms of comprehensibility. This paper explores using modularization as a strategy to mitigate such challenges, notably the reduction in complexity. Previous research has delved into the comprehensibility of modularized process models, yet an unresolved question about the cognitive factors at play during their comprehension still needs to be answered. Addressing the latter, the paper presents findings from an innovative study combining eye-tracking and concurrent think-aloud techniques involving 25 participants. The study aimed to comprehend how individuals comprehend process models when presented in three different modular formats: flattened process models, models with grouped elements, and models with subprocesses. The results shed light on varying comprehension strategies employed by participants when navigating through these modularized process models. The paper concludes by suggesting avenues for future research guided by these insights. [ABSTRACT FROM AUTHOR]

In this paper, we discuss how the clustering analysis technique can be applied to analyze functional magnetic resonance imaging (fMRI) time-series data in the context of glioblastoma (GBM), a highly heterogeneous brain tumor. The precise characterization of GBM is challenging and requires advanced analytical approaches. We have synthesized the existing literature to provide an overview of how clustering algorithms can help identify unique patterns within the dynamics of GBM. Our review shows that the clustering of fMRI time series has great potential for improving the differentiation between various subtypes of GBM, which is pivotal for developing personalized therapeutic strategies. Moreover, this method proves to be effective in capturing temporal changes occurring in GBM, enhancing the monitoring of disease progression and response to treatment. By thoroughly examining and consolidating the current research, this paper contributes to the understanding of how clustering techniques applied to fMRI data can refine the characterization of GBM. This article emphasizes the importance of incorporating cutting-edge data analysis techniques into neuroimaging and neuro-oncology research. By providing a detailed perspective, this approach may guide future investigations and boost the development of tailored therapeutic strategies for GBM. [ABSTRACT FROM AUTHOR]

Terahertz radiation falls within the spectrum of hydrogen bonding, molecular rotation, and vibration, as well as van der Waals forces, indicating that many biological macromolecules exhibit a strong absorption and resonance in this frequency band. Research has shown that the terahertz radiation of specific frequencies and energies can mediate changes in cellular morphology and function by exciting nonlinear resonance effects in proteins. However, current studies have mainly focused on the cellular level and lack systematic studies on multiple levels. Moreover, the mechanism and law of interaction between terahertz radiation and neurons are still unclear. Therefore, this paper analyzes the mechanisms by which terahertz radiation modulates the nervous system, and it analyzes and discusses the methods by which terahertz radiation modulates neurons. In addition, this paper reviews the laws of terahertz radiation's influence on neuronal morphology and kinetic properties and discusses them in detail in terms of terahertz radiation frequency, energy, and time. In the future, the safety of the terahertz radiation system should be considered first to construct the safety criterion of terahertz modulation, and the spatial resolution of the terahertz radiation system should be improved. In addition, the systematic improvement of the laws and mechanisms of terahertz modulation of the nervous system on multiple levels is the key to applying terahertz waves to neuroscience. This paper can provide a platform for researchers to understand the mechanism of the terahertz–nervous system interaction, its current status, and future research directions. [ABSTRACT FROM AUTHOR]

There is still controversy surrounding the definition and mechanisms of consciousness. The constrained disorder principle (CDP) defines complex systems by their dynamic borders, limiting their inherent disorder. In line with the CDP, the brain exhibits a disorder bounded by dynamic borders essential for proper function, efficient energy use, and life support under continuous perturbations. The brain's inherent variability contributes to its adaptability and flexibility. Neuronal signal variability challenges the association of brain structures with consciousness and methods for assessing consciousness. The present paper discusses some theories about consciousness, emphasizing their failure to explain the brain's variability. This paper describes how the CDP accounts for consciousness's variability, complexity, entropy, and uncertainty. Using newly developed second-generation artificial intelligence systems, we describe how CDP-based platforms may improve disorders of consciousness (DoC) by accounting for consciousness variability, complexity, entropy, and uncertainty. This platform could be used to improve response to current interventions and develop new therapeutic regimens for patients with DoC in future studies. [ABSTRACT FROM AUTHOR]

While both Attention deficit hyperactivity disorder (ADHD) and schizophrenia are considered to have neurodevelopmental origins with associated impairments in executive functioning, there is a paucity of clinical guidelines pertaining specifically to this comorbidity. We sought to summarize the existing literature on ADHD in early psychosis patients, focusing on issues that would be most relevant to clinical practice. For this narrative review, we completed a search on PubMed and PsycINFO with 22 papers meeting criteria for review. Overall, it appears that a diagnosis of ADHD in childhood increases the risk of the subsequent development of a primary psychotic disorder. This risk may be modified by higher rates of substance use and could be related to shared premorbid risk factors for both conditions, such as obstetrical complications. Stimulant use has been associated with the onset of psychotic symptoms in some individuals, but it is unclear whether certain subgroups are more susceptible. Despite the fact that these two conditions co-occur relatively frequently, there is currently a lack of objective diagnostic tests for ADHD specific to populations with primary psychotic disorders, and a paucity of evidence on whether stimulants are effective for ADHD symptoms in this sub-group. Future research is warranted to investigate whether stimulant treatment confers any additional risks for symptom exacerbation in individuals with primary psychotic disorders taking antipsychotic maintenance treatment. [ABSTRACT FROM AUTHOR]

The electroencephalogram (EEG) is a cornerstone tool for the diagnosis, management, and prognosis of selected patient populations. EEGs offer significant advantages such as high temporal resolution, real-time cortical function assessment, and bedside usability. The quantitative EEG (qEEG) added the possibility of long recordings being processed in a compressive manner, making EEG revision more efficient for experienced users, and more friendly for new ones. Recent advancements in commercially available software, such as Persyst, have significantly expanded and facilitated the use of qEEGs, marking the beginning of a new era in its application. As a result, there has been a notable increase in the practical, real-world utilization of qEEGs in recent years. This paper aims to provide an overview of the current applications of qEEGs in daily neurological emergencies and ICU practice, and some elementary principles of qEEGs using Persyst software in clinical settings. This article illustrates basic qEEG patterns encountered in critical care and adopts the new terminology proposed for spectrogram reporting. [ABSTRACT FROM AUTHOR]

Spatial orientation and navigation are complex cognitive functions that integrate sensory information, attention, and memory, enabling individuals to locate themselves in their environment. These abilities decline with age, signaling cognitive impairment in neurological patients, and significantly limit the autonomy of the elderly. Current neuropsychological assessments fall short in accurately measuring everyday wayfinding abilities, particularly in borderline cases of cognitive decline. This paper reviews various neuropsychological assessments, including Benton's Judgment of Line Orientation Test, the Almeria Spatial Memory Recognition Test, the Spatial Span subtest from the Wechsler Memory Scale, and the Spatial Orientation in Immersive Virtual Environment Maze Test, evaluating their effectiveness in delineating spatial orientation and navigation skills. The review identifies significant gaps in the validity and reliability of these tests, particularly in their shortened versions, and highlights the potential of virtual reality environments as promising tools for improving diagnostic precision. The findings underscore the need for further research to refine these tools, ensuring they accurately capture cognitive decline and improve the differential diagnosis of neurodegenerative conditions like Alzheimer's disease. Such advancements hold promise for enhancing the quality of care and autonomy for the elderly. [ABSTRACT FROM AUTHOR]

In recent research, dendritic neuron-based models have shown promise in effectively learning and recognizing object motion direction within binary images. Leveraging the dendritic neuron structure and On–Off Response mechanism within the primary cortex, this approach has notably reduced learning time and costs compared to traditional neural networks. This paper advances the existing model by integrating bio-inspired components into a learnable dendritic neuron-based artificial visual system (AVS), specifically incorporating mechanisms from horizontal and bipolar cells. This enhancement enables the model to proficiently identify object motion directions in grayscale images, aligning its threshold with human-like perception. The enhanced model demonstrates superior efficiency in motion direction recognition, requiring less data (90% less than other deep models) and less time for training. Experimental findings highlight the model's remarkable robustness, indicating significant potential for real-world applications. The integration of bio-inspired features not only enhances performance but also opens avenues for further exploration in neural network research. Notably, the application of this model to realistic object recognition yields convincing accuracy at nearly 100%, underscoring its practical utility. [ABSTRACT FROM AUTHOR]

Multiple sclerosis (MS) is a chronic and progressive neurological disease that affects the central nervous system, resulting in a wide spectrum of cognitive, emotional, and physical deficits. The progressive course of MS poses significant challenges to patients and has a profound impact on health-related quality of life (HRQoL). The style of coping adopted plays a critical role in determining how individuals with MS adapt to and face the challenges of the disease and their overall well-being. This paper aims to examine the impact of coping strategies on HRQoL in young adults un-/minimally impaired (<5 years, EDSS ≤ 2.5) by MS (age 18–35 years). This retrospective cross-sectional cohort study included 98 young adults (33 males and 65 females) with relapsing–remitting MS who underwent neurological assessment using the Expanded Disability Status Scale. Participants completed the Italian version of the Multiple Sclerosis QoL-54 (MSQoL-54), which provides a physical and mental health score, and the Coping Orientation to Problems Experienced Inventory (Brief-COPE). The results showed a significant relationship between COPE scores and physical and mental health. Subjects affected by MS who tend to use more frequent coping strategies such as active planning, personal growth, and acceptance showed a better overall well-being and quality of life. These findings are relevant to clinical practice given the need to understand the coping variable to improve HRQoL. Understanding these relationships is crucial for developing effective interventions to enhance the well-being of MS subjects. [ABSTRACT FROM AUTHOR]

In recent years, EEG-based emotion recognition technology has made progress, but there are still problems of low model efficiency and loss of emotional information, and there is still room for improvement in recognition accuracy. To fully utilize EEG's emotional information and improve recognition accuracy while reducing computational costs, this paper proposes a Convolutional-Recurrent Hybrid Network with a dual-stream adaptive approach and an attention mechanism (CSA-SA-CRTNN). Firstly, the model utilizes a CSAM module to assign corresponding weights to EEG channels. Then, an adaptive dual-stream convolutional-recurrent network (SA-CRNN and MHSA-CRNN) is applied to extract local spatial-temporal features. After that, the extracted local features are concatenated and fed into a temporal convolutional network with a multi-head self-attention mechanism (MHSA-TCN) to capture global information. Finally, the extracted EEG information is used for emotion classification. We conducted binary and ternary classification experiments on the DEAP dataset, achieving 99.26% and 99.15% accuracy for arousal and valence in binary classification and 97.69% and 98.05% in ternary classification, and on the SEED dataset, we achieved an accuracy of 98.63%, surpassing relevant algorithms. Additionally, the model's efficiency is significantly higher than other models, achieving better accuracy with lower resource consumption. [ABSTRACT FROM AUTHOR]

(1) Background: Most intracranial aneurysms (IAs) can be treated either with microsurgical clipping or endovascular techniques. In a few cases, simultaneous treatment utilizing both modalities in a hybrid operation room may be favorable. This study analyzes the indication and benefits of a true hybrid approach (tHA) that combines simultaneous endovascular and microsurgical procedures for treatment of IAs in one session. (2) Methods: All patients receiving a true hybrid procedure between 2010 and 2022 in our institution were included. Demographic characteristics, neurological symptoms, pre-interventional treatments, angiographic findings, and postoperative clinical and radiological outcomes were analyzed. Results are discussed in the light of a systematic literature review on reported true hybrid procedures for IA treatment. (3) Results: In total, 10 tHAs were performed. Of these, coiling and concomitant decompressive craniectomy or hematoma evacuation was performed on six occasions. In two patients, multiple IAs were treated with different modalities during the same procedure. In two patients, intraoperative conditions did not allow for complete IA clipping, and the remnant was coiled in the same session. The review of the literature revealed nine papers comprising 58 IAs treated with a tHA. (4) Conclusions: The need for a tHA for IA treatment is rare and limited to highly selective cases. In our experience, tHAs have been most valuable in an emergency setting concerning ruptured IAs. Furthermore, tHAs may also be considered in patients with multiple aneurysms in different vascular territories. [ABSTRACT FROM AUTHOR]

The presence of artificial light at night has emerged as an anthropogenic stressor in recent years. Various sources of light pollution have been shown to affect circadian physiology with serious consequences for metabolic pathways, possibly disrupting pineal melatonin production with multiple adverse health effects. The suppression of melatonin at night may also affect human mental health and contribute to the development or exacerbation of psychiatric disorders in vulnerable individuals. Due to the high burden of circadian disruption in affective disorders, it has been hypothesized that light pollution impacts mental health, mainly affecting mood regulation. Hence, the aim of this review was to critically summarize the evidence on the effects of light pollution on mood symptoms, with a particular focus on the role of circadian rhythms in mediating this relationship. We conducted a narrative review of the literature in the PubMed, Scopus, and Web of Science datasets. After the screening process, eighteen papers were eligible for inclusion. The results clearly indicate a link between light pollution and the development of affective symptoms, with a central role of sleep disturbances in the emergence of mood alterations. Risk perception also represents a crucial topic, possibly modulating the development of affective symptoms in response to light pollution. The results of this review should encourage a multidisciplinary approach to the design of healthier environments, including lighting conditions among the key determinants of human mental health. [ABSTRACT FROM AUTHOR]

The ability to screen for agents that can promote the development and/or maintenance of neuronal networks creates opportunities for the discovery of novel agents for the treatment of central nervous system (CNS) disorders. Over the past 10 years, advances in robotics, artificial intelligence, and machine learning have paved the way for the improved implementation of live-cell imaging systems for drug discovery. These instruments have revolutionized our ability to quickly and accurately acquire large standardized datasets when studying complex cellular phenomena in real-time. This is particularly useful in the field of neuroscience because real-time analysis can allow efficient monitoring of the development, maturation, and conservation of neuronal networks by measuring neurite length. Unfortunately, due to the relative infancy of this type of analysis, standard practices for data acquisition and processing are lacking, and there is no standardized format for reporting the vast quantities of data generated by live-cell imaging systems. This paper reviews the current state of live-cell imaging instruments, with a focus on the most commonly used equipment (IncuCyte systems). We provide an in-depth analysis of the experimental conditions reported in publications utilizing these systems, particularly with regard to studying neurite outgrowth. This analysis sheds light on trends and patterns that will enhance the use of live-cell imaging instruments in CNS drug discovery. [ABSTRACT FROM AUTHOR]

To investigate the reliability of L2 listening tests and explore potential factors affecting the reliability, a reliability generalization (RG) meta-analysis was conducted in the present study. A total number of 122 alpha coefficients of L2 listening tests from 92 published articles were collected and submitted to a linear mixed effects RG analysis. The papers were coded based on a coding scheme consisting of 16 variables classified into three categories: study features, test features, and statistical results. The results showed an average reliability of 0.818 (95% CI: 0.803 to 0.833), with 40% of reliability estimates falling below the lower bound of CI. The presence of publication bias and heterogeneity was found in the reliability of L2 listening tests, indicating that low reliability coefficients were likely omitted from some published studies. In addition, two factors predicting the reliability of L2 listening tests were the number of items and test type (standardized and researcher- or teacher-designed tests). The study also found that reliability is not a moderator of the relationship between L2 listening scores and theoretically relevant constructs. Reliability induction was identified in reporting the reliability of L2 listening tests, too. Implications for researchers and teachers are discussed. [ABSTRACT FROM AUTHOR]

Sleep bruxism (SB) is a sleep-related behavior characterized as rhythmic (phasic) or non-rhythmic (tonic) masticatory muscle activity. SB is a common sleep behavior with a predominantly central origin. The aim of this systematic review was to evaluate the relationship between inflammatory status and SB according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 (PRISMA 2020). The research was registered at PROSPERO (CRD42023395985). We performed a systematic literature analysis using five different databases. Furthermore, the backward snowballing technique was applied to identify additional papers. Initially, 28 papers were screened from the database search, and 162 papers were revealed in the backward snowballing process. Eventually, five articles were included. Data concerning the inflammatory status of patients experiencing SB were investigated and summarized. Due to the heterogeneity of the compared studies, only a qualitative comparison and narrative summary were performed. The results suggest that SB could be associated with systemic inflammation. In fact, this systematic review revealed that there are no papers conclusively showing that the inflammatory status in bruxers is comparable to non-bruxers. However, each of the examined studies utilized different methods of assessing systemic inflammation, which makes the results dubious. [ABSTRACT FROM AUTHOR]

Human lying is influenced by cognitive neural mechanisms in the brain, and conducting research on lie detection in speech can help to reveal the cognitive mechanisms of the human brain. Inappropriate deception detection features can easily lead to dimension disaster and make the generalization ability of the widely used semi-supervised speech deception detection model worse. Because of this, this paper proposes a semi-supervised speech deception detection algorithm combining acoustic statistical features and time-frequency two-dimensional features. Firstly, a hybrid semi-supervised neural network based on a semi-supervised autoencoder network (AE) and a mean-teacher network is established. Secondly, the static artificial statistical features are input into the semi-supervised AE to extract more robust advanced features, and the three-dimensional (3D) mel-spectrum features are input into the mean-teacher network to obtain features rich in time-frequency two-dimensional information. Finally, a consistency regularization method is introduced after feature fusion, effectively reducing the occurrence of over-fitting and improving the generalization ability of the model. This paper carries out experiments on the self-built corpus for deception detection. The experimental results show that the highest recognition accuracy of the algorithm proposed in this paper is 68.62% which is 1.2% higher than the baseline system and effectively improves the detection accuracy. [ABSTRACT FROM AUTHOR]

The predictive coding theory is currently widely accepted as the theoretical basis of perception and chronic perceptual disorders are explained as the maladaptive compensation of the brain to a prediction error. Although this gives us a general framework to work with, it is still not clear who may be more susceptible and/or vulnerable to aberrations in this system. In this paper, we study changes in predictive coding through the lens of tinnitus and pain. We take a step back to understand how the predictive coding system develops from infancy, what are the different neural and bio markers that characterise this system in the acute, transition and chronic phases and what may be the factors that pose a risk to the aberration of this system. Through this paper, we aim to identify people who may be at a higher risk of developing chronic perceptual disorders as a reflection of aberrant predictive coding, thereby giving future studies more facets to incorporate in their investigation of early markers of tinnitus, pain and other disorders of predictive coding. We therefore view this paper to encourage the thinking behind the development of preclinical biomarkers to maladaptive predictive coding. [ABSTRACT FROM AUTHOR]

Surgical treatment of neurovascular lesions like intracranial aneurysms, arteriovenous malformations and arteriovenous dural fistulas is still associated with high morbidity. Several recent studies are providing increasing insights into reliable tools to improve surgery and reduce complications. Inadvertent vessel compromise and incomplete occlusion of the lesion represent the most possible complications in neurovascular surgery. It is clear that direct visual examination alone does not allow to identify all instances of vessel compromise. Various modalities, including angiography, microvascular Doppler and neurophysiological studies, have been utilized for hemodynamics of flow vessels in proper clipping of the aneurysm or complete obliteration of the lesion. We intended to review the current knowledge about the intraoperative microvascular Doppler (iMDS) employment in the most updated literature, and explore the most recent implications not only in intracranial aneurysms but also in neurovascular lesions like arteriovenous malformations (AVMs) and arteriovenous dural fistulas (AVDFs). According to the PRISMA guidelines, systematic research in the most updated platform was performed in order to provide a complete overview about iMDS employment in neurovascular surgery. Twelve articles were included in the present paper and analyzed according to specific research areas. iMDS employment could represent a crucial tool to improve surgery in neurovascular lesions. The safety and effectiveness of the surgical treatment of neurovascular lesions like intracranial aneurysm and other neurovascular lesions like AVMs and AVDFs requires careful and accurate consideration regarding the assessment of anatomy and blood flow. Prognosis may depend on suboptimal or incomplete exclusion of the lesion. [ABSTRACT FROM AUTHOR]

Clinical prediction models for spine surgery applications are on the rise, with an increasing reliance on machine learning (ML) and deep learning (DL). Many of the predicted outcomes are uncommon; therefore, to ensure the models' effectiveness in clinical practice it is crucial to properly evaluate them. This systematic review aims to identify and evaluate current research-based ML and DL models applied for spine surgery, specifically those predicting binary outcomes with a focus on their evaluation metrics. Overall, 60 papers were included, and the findings were reported according to the PRISMA guidelines. A total of 13 papers focused on lengths of stay (LOS), 12 on readmissions, 12 on non-home discharge, 6 on mortality, and 5 on reoperations. The target outcomes exhibited data imbalances ranging from 0.44% to 42.4%. A total of 59 papers reported the model's area under the receiver operating characteristic (AUROC), 28 mentioned accuracies, 33 provided sensitivity, 29 discussed specificity, 28 addressed positive predictive value (PPV), 24 included the negative predictive value (NPV), 25 indicated the Brier score with 10 providing a null model Brier, and 8 detailed the F1 score. Additionally, data visualization varied among the included papers. This review discusses the use of appropriate evaluation schemes in ML and identifies several common errors and potential bias sources in the literature. Embracing these recommendations as the field advances may facilitate the integration of reliable and effective ML models in clinical settings. [ABSTRACT FROM AUTHOR]

Based on the seminal publications of Paul Broca and Carl Wernicke who established that aphasic syndromes (disorders of the verbal–linguistic aspects of communication) were predominantly the result of focal left-hemisphere lesions, "language" is traditionally viewed as a lateralized function of the left hemisphere. This, in turn, has diminished and delayed the acceptance that the right hemisphere also has a vital role in language, specifically in modulating affective prosody, which is essential for communication competency and psychosocial well-being. Focal lesions of the right hemisphere may result in disorders of affective prosody (aprosodic syndromes) that are functionally and anatomically analogous to the aphasic syndromes that occur following focal left-hemisphere lesions. This paper will review the deductive research published over the last four decades that has elucidated the neurology of affective prosody which, in turn, has led to a more complete and nuanced understanding of the neurology of language, depression, emotions and memory. In addition, the paper will also present the serendipitous clinical observations (inductive research) and fortuitous inter-disciplinary collaborations that were crucial in guiding and developing the deductive research processes that culminated in the concept that primary emotions and related display behaviors are a lateralized function of the right hemisphere and social emotions, and related display behaviors are a lateralized function of the left hemisphere. [ABSTRACT FROM AUTHOR]

BCI devices are increasingly being used to create interactive interfaces between users and their own psychophysiological signals. Over the years, these systems have seen strong development as they can enable people with limited mobility to make certain decisions to alter their environment. Additionally, their portability and ease of use have allowed a field of research to flourish for the study of cognitive and emotional processes in natural settings. The study of creativity, especially little creativity (little-c), is one example, although the results of this cutting-edge research are often poorly systematized. The purpose of the present paper, therefore, was to conduct a scoping review to describe and systematize the various studies that have been conducted on the application potential of BCI to the field of creativity. Twenty-two papers were selected that collect information on different aspects of creativity, including clinical applications; art experience in settings with high ecological validity; BCI for creative content creation, and participants' engagement. Critical issues and potentialities of this promising area of study are also presented. Implications for future developments towards multi-brain creativity settings and C2 are discussed. [ABSTRACT FROM AUTHOR]