Immersive VR for upper-extremity rehabilitation in patients with neurological disorders: a scoping review.

BACKGROUND: Neurological disorders, such as stroke and chronic pain syndromes, profoundly impact independence and quality of life, especially when affecting upper extremity (UE) function. While conventional physical therapy has shown effectiveness in providing some neural recovery in affected individuals, there remains a need for improved interventions. Virtual reality (VR) has emerged as a promising technology-based approach for neurorehabilitation to make the patient's experience more enjoyable. Among VR-based rehabilitation paradigms, those based on fully immersive systems with headsets have gained significant attention due to their potential to enhance patient's engagement. METHODS: This scoping review aims to investigate the current state of research on the use of immersive VR for UE rehabilitation in individuals with neurological diseases, highlighting benefits and limitations. We identified thirteen relevant studies through comprehensive searches in Scopus, PubMed, and IEEE Xplore databases. Eligible studies incorporated immersive VR for UE rehabilitation in patients with neurological disorders and evaluated participants' neurological and motor functions before and after the intervention using clinical assessments. RESULTS: Most of the included studies reported improvements in the participants rehabilitation outcomes, suggesting that immersive VR represents a valuable tool for UE rehabilitation in individuals with neurological disorders. In addition, immersive VR-based interventions hold the potential for personalized and intensive training within a telerehabilitation framework. However, further studies with better design are needed for true comparison with traditional therapy. Also, the potential side effects associated with VR head-mounted displays, such as dizziness and nausea, warrant careful consideration in the development and implementation of VR-based rehabilitation programs. CONCLUSION: This review provides valuable insights into the application of immersive VR in UE rehabilitation, offering the foundation for future research and clinical practice. By leveraging immersive VR's potential, researchers and rehabilitation specialists can design more tailored and patient-centric rehabilitation strategies, ultimately improving the functional outcome and enhancing the quality of life of individuals with neurological diseases.

Feasibility interventional study investigating PAIN in neurorehabilitation through wearabLE SensorS (PAINLESS): a study protocol.

INTRODUCTION: Millions of people survive injuries to the central or peripheral nervous system for which neurorehabilitation is required. In addition to the physical and cognitive impairments, many neurorehabilitation patients experience pain, often not widely recognised and inadequately treated. This is particularly true for multiple sclerosis (MS) patients, for whom pain is one of the most common symptoms. In clinical practice, pain assessment is usually conducted based on a subjective estimate. This approach can lead to inaccurate evaluations due to the influence of numerous factors, including emotional or cognitive aspects. To date, no objective and simple to use clinical methods allow objective quantification of pain and the diagnostic differentiation between the two main types of pain (nociceptive vs neuropathic). Wearable technologies and artificial intelligence (AI) have the potential to bridge this gap by continuously monitoring patients' health parameters and extracting meaningful information from them. Therefore, we propose to develop a new automatic AI-powered tool to assess pain and its characteristics during neurorehabilitation treatments using physiological signals collected by wearable sensors. METHODS AND ANALYSIS: We aim to recruit 15 participants suffering from MS undergoing physiotherapy treatment. During the study, participants will wear a wristband for three consecutive days and be monitored before and after their physiotherapy sessions. Measurement of traditionally used pain assessment questionnaires and scales (ie, painDETECT, Doleur Neuropathique 4 Questions, EuroQoL-5-dimension-3-level) and physiological signals (photoplethysmography, electrodermal activity, skin temperature, accelerometer data) will be collected. Relevant parameters from physiological signals will be identified, and AI algorithms will be used to develop automatic classification methods. ETHICS AND DISSEMINATION: The study has been approved by the local Ethical Committee (285-2022-SPER-AUSLBO). Participants are required to provide written informed consent. The results will be disseminated through contributions to international conferences and scientific journals, and they will also be included in a doctoral dissertation. TRIAL REGISTRATION NUMBER: NCT05747040.

Multi-modal analysis of infant cry types characterization: Acoustics, body language and brain signals.

BACKGROUND: Infant crying is the first attempt babies use to communicate during their initial months of life. A misunderstanding of the cry message can compromise infant care and future neurodevelopmental process. METHODS: An exploratory study collecting multimodal data (i.e., crying, electroencephalography (EEG), near-infrared spectroscopy (NIRS), facial expressions, and body movements) from 38 healthy full-term newborns was conducted. Cry types were defined based on different conditions (i.e., hunger, sleepiness, fussiness, need to burp, and distress). Statistical analysis, Machine Learning (ML), and Deep Learning (DL) techniques were used to identify relevant features for cry type classification and to evaluate a robust DL algorithm named Acoustic MultiStage Interpreter (AMSI). RESULTS: Significant differences were found across cry types based on acoustics, EEG, NIRS, facial expressions, and body movements. Acoustics and body language were identified as the most relevant ML features to support the cause of crying. The DL AMSI algorithm achieved an accuracy rate of 92%. CONCLUSIONS: This study set a precedent for cry analysis research by highlighting the complexity of newborn cry expression and strengthening the potential use of infant cry analysis as an objective, reliable, accessible, and non-invasive tool for cry interpretation, improving the infant-parent relationship and ensuring family well-being.

How can cry acoustics associate newborns' distress levels with neurophysiological and behavioral signals?

Introduction: Even though infant crying is a common phenomenon in humans' early life, it is still a challenge for researchers to properly understand it as a reflection of complex neurophysiological functions. Our study aims to determine the association between neonatal cry acoustics with neurophysiological signals and behavioral features according to different cry distress levels of newborns. Methods: Multimodal data from 25 healthy term newborns were collected simultaneously recording infant cry vocalizations, electroencephalography (EEG), near-infrared spectroscopy (NIRS) and videos of facial expressions and body movements. Statistical analysis was conducted on this dataset to identify correlations among variables during three different infant conditions (i.e., resting, cry, and distress). A Deep Learning (DL) algorithm was used to objectively and automatically evaluate the level of cry distress in infants. Results: We found correlations between most of the features extracted from the signals depending on the infant's arousal state, among them: fundamental frequency (F0), brain activity (delta, theta, and alpha frequency bands), cerebral and body oxygenation, heart rate, facial tension, and body rigidity. Additionally, these associations reinforce that what is occurring at an acoustic level can be characterized by behavioral and neurophysiological patterns. Finally, the DL audio model developed was able to classify the different levels of distress achieving 93% accuracy. Conclusion: Our findings strengthen the potential of crying as a biomarker evidencing the physical, emotional and health status of the infant becoming a crucial tool for caregivers and clinicians.

Evaluating Dual Task Neurological Costs with Functional Near-Infrared Spectroscopy: A Preliminary Report in Healthy Athletes.

BACKGROUND: Dual task assessments, which simultaneously challenge and assess cognitive and motor performance, have been used to improve the assessment of athletes with sports-related concussions (SRC). Our lab created a Dual Task Screen (DTS) to evaluate athletes with SRCs, and we have established that it is a valid behavioral measure, as it consistently elicits poorer behavioral performance under dual, compared to single, task conditions. Here, we used a Neuroimaging-Compatible (NC) version of the DTS, named the NC-DTS, which uses portable functional near-infrared spectroscopy (fNIRS) to assess behavioral performance and neural recruitment during single and dual tasks. Our study objective was to evaluate healthy athletes and establish whether the NC-DTS is a valid dual task neurological assessment that can elicit different patterns of neural recruitment during dual versus single task conditions. METHODS: Twenty-five healthy collegiate athletes completed the NC-DTS in a single laboratory visit. The NC-DTS includes a lower and upper extremity subtask; both include single motor, single cognitive, and dual task conditions. The NC-DTS was administered in a block design, where conditions (i.e., single motor, single cognitive, and dual task) were repeated five times to generate average behavioral performance and task-dependent neural recruitment in superficial cortical regions including: prefrontal cortex, bilateral primary motor and sensory cortices, and posterior parietal cortex. Neural recruitment was measured with fNIRS and quantified using oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR) metrics. A single-tailed, within subject t-test was used to compare average dual task behavioral performance to average single task behavioral performance. Pairwise comparisons, that were family-wise-error (FWE) corrected, were used to compare localized neural recruitment during dual versus single task conditions. RESULTS: As observed in previous studies, the NC-DTS elicited significantly poorer behavioral performance under dual, compared to single, task conditions. Additionally, dual task conditions of the NC-DTS elicited significantly greater neural recruitment in regions of the brain associated with attention allocation and task-specific demands in three of four comparisons. CONCLUSIONS: These preliminary results suggest that the NC-DTS is a valid dual task neurological assessment which warrants future work using the NC-DTS to evaluate athletes with SRCs.

Characterizing Counterion-Dependent Aggregation of Rhodamine B by Classical Molecular Dynamics Simulations.

The aggregation in a solution of charged dyes such as Rhodamine B (RB) is significantly affected by the type of counterion, which can determine the self-assembled structure that in turn modulates the optical properties. RB aggregation can be boosted by hydrophobic and bulky fluorinated tetraphenylborate counterions, such as F5TPB, with the formation of nanoparticles whose fluorescence quantum yield (FQY) is affected by the degree of fluorination. Here, we developed a classical force field (FF) based on the standard generalized Amber parameters that allows modeling the self-assembling process of RB/F5TPB systems in water, consistent with experimental evidence. Namely, the classical MD simulations employing the re-parametrized FF reproduce the formation of nanoparticles in the RB/F5TPB system, while in the presence of iodide counterions, only RB dimeric species can be formed. Within the large, self-assembled RB/F5TPB aggregates, the occurrence of an H-type RB-RB dimer can be observed, a species that is expected to quench RB fluorescence, in agreement with the experimental data of FQY. The outcome provides atomistic details on the role of the bulky F5TPB counterion as a spacer, with the developed classical FF representing a step towards reliable modeling of dye aggregation in RB-based materials.

Electroencephalographic interbrain synchronization in children with disabilities, their parents, and neurologic music therapists.

As with typically developing children, children with cerebral palsy and autism spectrum disorder develop important socio-emotional rapport with their parents and healthcare providers. However, the neural mechanisms underlying these relationships have been less studied. By simultaneously measuring the brain activity of multiple individuals, interbrain synchronization could serve as a neurophysiological marker of social-emotional responses. Music evokes emotional and physiological responses and enhances social cohesion. These characteristics of music have fostered its deployment as a therapeutic medium in clinical settings. Therefore, this study investigated two aspects of interbrain synchronization, namely, its phase and directionality, in child-parent (CP) and child-therapist (CT) dyads during music and storytelling sessions (as a comparison). A total of 17 participants (seven cerebral palsy or autism spectrum disorder children [aged 12-18 years], their parents, and three neurologic music therapists) completed this study, comprising seven CP and seven CT dyads. Each music therapist worked with two or three children. We found that session type, dyadic relationship, frequency band, and brain region were significantly related to the degree of interbrain synchronization and its directionality. Particularly, music sessions and CP dyads were associated with higher interbrain synchronization and stronger directionality. Delta (.5-4 Hz) range showed the highest phase locking value in both CP and CT dyads in frontal brain regions. It appears that synchronization is directed predominantly from parent to child, that is, parents and music therapists' brain activity tended to influence a child's. Our findings encourage further research into neural synchrony in children with disabilities, especially in musical contexts, and its implications for social and emotional development.

Vigilância para Febre do Nilo Ocidental (FNO) em aves de subsistência localizadas em sítio de aves migratórias do Complexo estuarino-lagunar de Ilha Comprida e Iguape/SP / Surveillance for West Nile Fever in backyard chickens located in a migratory bird site of the estuarine-lagoon complex of Ilha Comprida and Iguape/SP

A Febre do Nilo Ocidental (FNO), é uma arbovirose emergente, zoonótica, causada pelo Vírus do Nilo Ocidental (VNO), arbovírus da família Flaviviridae, do gênero Flavivírus, com ampla distribuição mundial. Vírus foi isolado pela primeira vez em 1937, em uma paciente febril, na província de West Nile, em Uganda, e documentado em 1951 em Israel, havendo registros de ocorrência em aves, equinos e no homem. Nas Américas, o primeiro surto ocorreu em 1999 na cidade de Nova York (EUA), com mortalidade de milhares de aves silvestres e 121 pessoas, um marco histórico para a doença e na construção das bases de vigilância do vírus. A doença se espalhou pelas Américas, com a primeira evidência na América do Sul, em equinos na Colômbia, em 2005. No Brasil, o primeiro registro ocorreu no Mato Grosso do Sul em equinos, no ano de 2010, e o primeiro caso humano em 2014, no Piauí, sendo comprovada a circulação viral em aves silvestres, galinhas e equinos nos últimos dez anos. A transmissão ocorre pela picada do mosquito infectado do Gênero Culex, mantendo um ciclo enzoótico de transmissão mosquito-ave silvestre-mosquito, sendo que as aves silvestres são hospedeiros primários, e destacam - se na disseminação viral entre as Américas, através de deslocamentos migratórios, sem haver elucidação dos mecanismos de dispersão. A infecção de galinhas, equinos e do homem é acidental, por apresentarem uma viremia rápida e curta, tornando os elos finais da cadeia de transmissão ou sentinelas, para a doença. A FNO é aguda, subclínica ou com quadro febril, que pode ocasionar síndrome neurológica grave em até 10% dos casos, com recuperação ou óbito em 1% dos casos, não havendo tratamento ou vacina disponível no Brasil. A FNO é de Notificação Obrigatória - IN n° 50/2013 - MAPA. Características inerentes à galinhas, de suscetibilidade à infecção, resistência à doença e alta sensibilidade à testes de detecção viral, tornam esta espécie um modelo de sentinela para o VNO, em sítios migratórios, aonde coexistem aves silvestres, culicídeos e criações de fundo de quintal que ficam vulneráveis à infecção, demonstrando a importância da vigilância para a FNO no Brasil. O presente estudo teve como objetivos a revisão de literatura sobre a FNO e o estudo de circulação viral em aves de fundo de quintal de 17 propriedades do entorno do sítio de aves migratórias do complexo estuarino-lagunar de Ilha Comprida e Iguape/SP, com colheita de amostras de sangue em 113 galinhas, para a detecção do VNO e outros flavivírus pela técnica RT-PCR. Não houve a detecção de atividade viral nas amostras analisadas. Contudo, a ações de vigilância para o VNO e o monitoramento contínuo dos sítios migratórios de aves devem ser rotineiros, para evitar a introdução e disseminação da doença, destacando - se as notificações de sinais clínicos neurológicos ou mortalidade em aves.

West Nile Fever (WNF) is an emerging zoonotic arbovirus caused by the West Nile Virus (WNV), a flavivirus of the Flaviviridae family with worldwide distribution. The virus was first isolated in 1937 from a febrile patient in the West Nile province of Uganda and documented in 1951 in Israel. WNV has been reported to occur in birds, horses, and humans. The first outbreak in the Americas occurred in 1999 in New York City, with thousands of wild birds dying and 121 human deaths. The disease has since spread throughout the Americas, with the first evidence in South America occurring in horses in Colombia in 2005. In Brazil, the first WNV record occurred in horses in Mato Grosso do Sul in 2010, and the first human case was reported in 2014 in Piauí. WNV circulation has been confirmed in wild birds, chickens, and horses in the past ten years. The virus is transmitted by infected Culex mosquitoes, maintaining an enzootic cycle of mosquito-wild bird-mosquito transmission. Wild birds are the primary hosts and play a significant role in viral dissemination across the Americas through migratory movements, with no elucidation of the dispersal mechanisms. Chickens, horses, and humans are accidental hosts due to their rapid and short viremia, making them the final links in the transmission chain or sentinels for the disease. WNF is acute or subclinical, with fever that can lead to severe neurological syndromes in up to 10% of cases, with recovery or death in 1% of cases. There is no treatment or vaccine available for WNF in Brazil. WNF is a mandatory reportable disease according to IN n° 50/2013 - MAPA. The susceptibility of chickens to infection, resistance to disease, and high sensitivity to viral detection tests make this species a sentinel model for WNV in migratory sites where wild birds, culicids, and backyard farming coexist and are vulnerable to infection. Therefore, continuous surveillance for WNF in Brazil is crucial. This study aimed to review the literature on WNF and to investigate viral circulation in backyard chickens in 17 properties around the migratory bird site of the estuarine-lagoon complex of Ilha Comprida and Iguape/SP, with blood samples collected from 113 chickens for WNV and other flavivirus detection by RT-PCR. No viral activity was detected in the analyzed samples. However, surveillance for WNV and continuous monitoring of migratory bird sites should be routine to prevent the introduction and spread of the disease, with notifications of clinical neurological signs or mortality in birds.

A pediatric near-infrared spectroscopy brain-computer interface based on the detection of emotional valence.

Brain-computer interfaces (BCIs) are being investigated as an access pathway to communication for individuals with physical disabilities, as the technology obviates the need for voluntary motor control. However, to date, minimal research has investigated the use of BCIs for children. Traditional BCI communication paradigms may be suboptimal given that children with physical disabilities may face delays in cognitive development and acquisition of literacy skills. Instead, in this study we explored emotional state as an alternative access pathway to communication. We developed a pediatric BCI to identify positive and negative emotional states from changes in hemodynamic activity of the prefrontal cortex (PFC). To train and test the BCI, 10 neurotypical children aged 8-14 underwent a series of emotion-induction trials over four experimental sessions (one offline, three online) while their brain activity was measured with functional near-infrared spectroscopy (fNIRS). Visual neurofeedback was used to assist participants in regulating their emotional states and modulating their hemodynamic activity in response to the affective stimuli. Child-specific linear discriminant classifiers were trained on cumulatively available data from previous sessions and adaptively updated throughout each session. Average online valence classification exceeded chance across participants by the last two online sessions (with 7 and 8 of the 10 participants performing better than chance, respectively, in Sessions 3 and 4). There was a small significant positive correlation with online BCI performance and age, suggesting older participants were more successful at regulating their emotional state and/or brain activity. Variability was seen across participants in regards to BCI performance, hemodynamic response, and discriminatory features and channels. Retrospective offline analyses yielded accuracies comparable to those reported in adult affective BCI studies using fNIRS. Affective fNIRS-BCIs appear to be feasible for school-aged children, but to further gauge the practical potential of this type of BCI, replication with more training sessions, larger sample sizes, and end-users with disabilities is necessary.

Automatic pain assessment on cancer patients using physiological signals recorded in real-world contexts.

Pain assessment represents the first fundamental stage for proper pain management, but currently, methods applied in clinical practice often lack in providing a satisfying characterization of the pain experience. Automatic methods based on the analysis of physiological signals (e.g., photoplethysmography, electrodermal activity) promise to overcome these limitations, also providing the possibility to record these signals through wearable devices, thus capturing the physiological response in everyday life. After applying preprocessing, feature extraction and feature selection methods, we tested several machine learning algorithms to develop an automatic classifier fed with physiological signals recorded in real-world contexts and pain ratings from 21 cancer patients. The best algorithm achieved up to 72% accuracy. Although performance can be improved by enlarging the dataset, preliminary results proved the feasibility of assessing pain by using physiological signals recorded in real-world contexts.

Accuracy of EEG Biomarkers in the Detection of Clinical Outcome in Disorders of Consciousness after Severe Acquired Brain Injury: Preliminary Results of a Pilot Study Using a Machine Learning Approach.

Accurate outcome detection in neuro-rehabilitative settings is crucial for appropriate long-term rehabilitative decisions in patients with disorders of consciousness (DoC). EEG measures derived from high-density EEG can provide helpful information regarding diagnosis and recovery in DoC patients. However, the accuracy rate of EEG biomarkers to predict the clinical outcome in DoC patients is largely unknown. This study investigated the accuracy of psychophysiological biomarkers based on clinical EEG in predicting clinical outcomes in DoC patients. To this aim, we extracted a set of EEG biomarkers in 33 DoC patients with traumatic and nontraumatic etiologies and estimated their accuracy to discriminate patients' etiologies and predict clinical outcomes 6 months after the injury. Machine learning reached an accuracy of 83.3% (sensitivity = 92.3%, specificity = 60%) with EEG-based functional connectivity predicting clinical outcome in nontraumatic patients. Furthermore, the combination of functional connectivity and dominant frequency in EEG activity best predicted clinical outcomes in traumatic patients with an accuracy of 80% (sensitivity = 85.7%, specificity = 71.4%). These results highlight the importance of functional connectivity in predicting recovery in DoC patients. Moreover, this study shows the high translational value of EEG biomarkers both in terms of feasibility and accuracy for the assessment of DoC.

Automated Movement Analysis to Predict Cerebral Palsy in Very Preterm Infants: An Ambispective Cohort Study.

The General Movements Assessment requires extensive training. As an alternative, a novel automated movement analysis was developed and validated in preterm infants. Infants < 31 weeks' gestational age or birthweight ≤ 1500 g evaluated at 3−5 months using the general movements assessment were included in this ambispective cohort study. The C-statistic, sensitivity, specificity, positive predictive value, and negative predictive value were calculated for a predictive model. A total of 252 participants were included. The median gestational age and birthweight were 274/7 weeks (range 256/7−292/7 weeks) and 960 g (range 769−1215 g), respectively. There were 29 cases of cerebral palsy (11.5%) at 18−24 months, the majority of which (n = 22) were from the retrospective cohort. Mean velocity in the vertical direction, median, standard deviation, and minimum quantity of motion constituted the multivariable model used to predict cerebral palsy. Sensitivity, specificity, positive, and negative predictive values were 55%, 80%, 26%, and 93%, respectively. C-statistic indicated good fit (C = 0.74). A cluster of four variables describing quantity of motion and variability of motion was able to predict cerebral palsy with high specificity and negative predictive value. This technology may be useful for screening purposes in very preterm infants; although, the technology likely requires further validation in preterm and high-risk term populations.

Does music induce interbrain synchronization between a non-speaking youth with cerebral palsy (CP), a parent, and a neurologic music therapist? A brief report.

Shared emotional experiences during musical activities among musicians can be coupled with brainwave synchronization. For non-speaking individuals with CP, verbal communication may be limited in expressing mutual empathy. Therefore, this case study explored interbrain synchronization among a non-speaking CP (female, 18 yrs), her parent, and a music therapist by measuring their brainwaves simultaneously during four music and four storytelling sessions. In only the youth-parent dyad, we observed a significantly higher level of interbrain synchronization during music rather than story-telling condition. However, in both the youth-parent and youth-therapist dyad, regardless of condition type, significant interbrain synchronization emerged in frontal and temporal lobes in the low-frequency bands, which are associated with socio-emotional responses. Although interbrain synchronization may have been induced by multiple factors (e.g., external stimuli, shared empathetic experiences, and internal physiological rhythms), the music activity setting deserves further study as a potential facilitator of neurophysiological synchrony between youth with CP and caregivers/healthcare providers.

A comparison and classification of oscillatory characteristics in speech perception and covert speech.

Covert speech, the mental imagery of speaking, has been studied increasingly to understand and decode thoughts in the context of brain-computer interfaces. In studies of speech comprehension, neural oscillations are thought to play a key role in the temporal encoding of speech. However, little is known about the role of oscillations in covert speech. In this study, we investigated the oscillatory involvements in covert speech and speech perception. Data were collected from 10 participants with 64 channel EEG. Participants heard the words, 'blue' and 'orange', and subsequently mentally rehearsed them. First, continuous wavelet transform was performed on epoched signals and subsequently two-tailed t-tests between two classes (tasks) were conducted to determine statistical differences in frequency and time (t-CWT). In the current experiment, a task comprised speech perception or covert rehearsal of a word while a condition was the discrimination between tasks. Features were extracted using t-CWT and subsequently classified using a support vector machine. θ and γ phase amplitude coupling (PAC) was also assessed within tasks and across conditions between perception and covert activities (i.e. cross-task). All binary classifications accuracies (80-90%) significantly exceeded chance level, supporting the use of t-CWT in determining relative oscillatory involvements. While the perception condition dynamically invoked all frequencies with more prominent θ and α activity, the covert condition favoured higher frequencies with significantly higher γ activity than perception. Moreover, the perception condition produced significant θ-γ PAC, possibly corroborating a reported linkage between syllabic and phonemic sampling. Although this coupling was found to be suppressed in the covert condition, we found significant cross-task coupling between perception θ and covert speech γ. Covert speech processing appears to be largely associated with higher frequencies of EEG. Importantly, the significant cross-task coupling between speech perception and covert speech, in the absence of within-task covert speech PAC, seems to support the notion that the γ- and θ-bands reflect, respectively, shared and unique encoding processes across tasks.

Brain-Computer Interfaces for Children With Complex Communication Needs and Limited Mobility: A Systematic Review.

Brain-computer interfaces (BCIs) represent a new frontier in the effort to maximize the ability of individuals with profound motor impairments to interact and communicate. While much literature points to BCIs' promise as an alternative access pathway, there have historically been few applications involving children and young adults with severe physical disabilities. As research is emerging in this sphere, this article aims to evaluate the current state of translating BCIs to the pediatric population. A systematic review was conducted using the Scopus, PubMed, and Ovid Medline databases. Studies of children and adolescents that reported BCI performance published in English in peer-reviewed journals between 2008 and May 2020 were included. Twelve publications were identified, providing strong evidence for continued research in pediatric BCIs. Research evidence was generally at multiple case study or exploratory study level, with modest sample sizes. Seven studies focused on BCIs for communication and five on mobility. Articles were categorized and grouped based on type of measurement (i.e., non-invasive and invasive), and the type of brain signal (i.e., sensory evoked potentials or movement-related potentials). Strengths and limitations of studies were identified and used to provide requirements for clinical translation of pediatric BCIs. This systematic review presents the state-of-the-art of pediatric BCIs focused on developing advanced technology to support children and youth with communication disabilities or limited manual ability. Despite a few research studies addressing the application of BCIs for communication and mobility in children, results are encouraging and future works should focus on customizable pediatric access technologies based on brain activity.

Automated movement recognition to predict motor impairment in high-risk infants: a systematic review of diagnostic test accuracy and meta-analysis.

AIM: To assess the sensitivity and specificity of automated movement recognition in predicting motor impairment in high-risk infants. METHOD: We searched MEDLINE, Embase, PsycINFO, CINAHL, Web of Science, and Scopus databases and identified additional studies from the references of relevant studies. We included studies that evaluated automated movement recognition in high-risk infants to predict motor impairment, including cerebral palsy (CP) and non-CP motor impairments. Two authors independently assessed studies for inclusion, extracted data, and assessed methodological quality using the Quality Assessment of Diagnostic Accuracy Studies-2. Meta-analyses were performed using hierarchical summary receiver operating characteristic models. RESULTS: Of 6536 articles, 13 articles assessing 59 movement variables in 1248 infants under 5 months corrected age were included. Of these, 143 infants had CP. The overall sensitivity and specificity for motor impairment were 0.73 (95% confidence interval [CI] 0.68-0.77) and 0.70 (95% CI 0.65-0.75) respectively. Comparatively, clinical General Movements Assessment (GMA) was found to have sensitivity and specificity of 98% (95% CI 74-100) and 91% (95% CI 83-93) respectively. Sensor-based technologies had higher specificity (0.88, 95% CI 0.80-0.93). INTERPRETATION: Automated movement recognition technology remains inferior to clinical GMA. The strength of this study is its meta-analysis to summarize performance, although generalizability of these results is limited by study heterogeneity.

Stress Assessment by Combining Neurophysiological Signals and Radio Communications of Air Traffic Controllers.

Air Traffic Control (ATC) has been classified as the fourth most stressful job. In this regard, sixteen controllers were asked to perform ecological ATC simulation during which behavioral (Radio Communications with pilots - RCs), subjective (stress perception) and neurophysiological signals (brain activity and skin conductance - SC) were collected. All the considered parameters reported significant changes under high stress conditions. In particular, the theta, alpha, and beta brain rhythms increased significantly (all p<0.05) all over the brain areas, and both the SC components exhibited higher values (p<0.01). Additionally, the number of speech under high stress decreased significantly (p<10-4) while both the mean and median value of the F0 component of the RC increased (p<0.01). The results can be employed to objectively measure and track the controller's stress level while dealing with ATC activities to better tailoring the workshift and maintaining high safety levels.

Customized Access Technology for Children using Head Movement Recognition.

Children with cerebral palsy and complex communication needs face limitations in their access technology (AT) usage. Speech recognition software and conventional ATs (e.g., mechanical switches) can be insufficient for those with speech impairment and limited control of voluntary motion. Automatic recognition of head movements represents a promising pathway. Previous studies have shown the robustness of head pose estimation algorithms on adult participants, but further research is needed to use these methods with children. An algorithm for head movement recognition was implemented and evaluated on videos recorded in a naturalistic environment when children were playing a videogame. A face-tracking algorithm was used to detect the main facial landmarks. Head poses were then estimated using the Pose from Orthography and Scaling with Iterations (POSIT) algorithm and three head movements were classified through Hidden Markov Models (HMMs). Preliminary classification results obtained from the analysis of videos of five typically developing children showed an accuracy of up to 95.6% in predicting head movements.

Surface-Confined Macrocyclization via Dynamic Covalent Chemistry.

Surface-confined synthesis is a promising approach to build complex molecular nanostructures including macrocycles. However, despite the recent advances in on-surface macrocyclization under ultrahigh vacuum, selective synthesis of monodisperse and multicomponent macrocycles remains a challenge. Here, we report on an on-surface formation of [6 + 6] Schiff-base macrocycles via dynamic covalent chemistry. The macrocycles form two-dimensional crystalline domains on the micrometer scale, enabled by dynamic conversion of open-chain oligomers into well-defined ∼3.0 nm hexagonal macrocycles. We further show that by tailoring the length of the alkyl substituents, it is possible to control which of three possible products-oligomers, macrocycles, or polymers-will form at the surface. In situ scanning tunneling microscopy imaging combined with density functional theory calculations and molecular dynamics simulations unravel the synergistic effect of surface confinement and solvent in leading to preferential on-surface macrocyclization.

Exploring the capabilities of optical pump X-ray probe NEXAFS spectroscopy to track photo-induced dynamics mediated by conical intersections.

X-ray spectroscopy is gaining a growing interest in the scientific community, as it represents a versatile and powerful experimental toolbox for probing the dynamics of both core and valence electronic excitations, nuclear motions and material structure, with element and site specificity. Among the various X-ray based techniques, near-edge X-ray absorption fine structure (NEXAFS) spectroscopy, which investigates the energy and probability of resonant core-to-valence transitions, has started to be applied to organic molecules: a recent UV-pump X-ray probe time-resolved NEXAFS experiment [Wolf et al., Nat. Commun., 2017, 8, 1] has shown the capability of the technique to provide information about the ultrafast internal conversion between the bright ππ* and the dark nπ* electronic states of the nucleobase thymine. In the present contribution we introduce an accurate theoretical approach for the simulation of NEXAFS spectra of organic molecules, employing azobenzene as a test case. The electronic structure calculations, which provide both energy levels and transition probabilities of core-to-valence excitations, were here performed with a high level multiconfigurational method, the restricted active space self consistent field (RASSCF/RASPT2). GS- and nπ*-NEXAFS spectra were obtained on the top of key molecular geometries (as the optimized cis, trans and conical intersection(s) structures) as well as along the fundamental isomerization coordinates (namely, symmetric and asymmetric bendings of the phenyl rings, and torsion around the central dihedral). We eventually characterize and explain the origin of the simulated signals, highlighting the specific signatures that make it possible to follow the excited state evolution from the nπ* Franck-Condon point, towards the conical intersection(s).