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Abstract Background Mass spectrometry protein profiling is a promising tool for biomarker discovery in clinical proteomics. However, the development of a reliable approach for the separation of protein signals from noise is required. In this paper, LIMPIC, a computational method for the detection of protein peaks from linear-mode MALDI-TOF data is proposed. LIMPIC is based on novel techniques for background noise reduction and baseline removal. Peak detection is performed considering the presence of a non-homogeneous noise level in the mass spectrum. A comparison of the peaks collected from multiple spectra is used to classify them on the basis of a detection rate parameter, and hence to separate the protein signals from other disturbances. Results LIMPIC preprocessing proves to be superior than other classical preprocessing techniques, allowing for a reliable decomposition of the background noise and the baseline drift from the MALDI-TOF mass spectra. It provides lower coefficient of variation associated with the peak intensity, improving the reliability of the information that can be extracted from single spectra. Our results show that LIMPIC peak-picking is effective even in low protein concentration regimes. The analytical comparison with commercial and freeware peak-picking algorithms demonstrates its superior performances in terms of sensitivity and specificity, both on in-vitro purified protein samples and human plasma samples. Conclusion The quantitative information on the peak intensity extracted with LIMPIC could be used for the recognition of significant protein profiles by means of advanced statistic tools: LIMPIC might be valuable in the perspective of biomarker discovery.

Investigating the neural mechanisms underlying both cooperative and competitive joint actions may have a wide impact in many social contexts of human daily life. An effective pipeline of analysis for hyperscanning data recorded in a naturalistic context with a cooperative and competitive motor task has been missing. We propose an analytical pipeline for this type of joint action data, which was validated on electroencephalographic (EEG) signals recorded in a proof-of-concept study on two dyads playing cooperative and competitive table tennis. Functional connectivity maps were reconstructed using the corrected imaginary part of the phase locking value (ciPLV), an algorithm suitable in case of EEG signals recorded during turn-based competitive joint actions. Hyperbrain, within-, and between-brain functional connectivity maps were calculated in three frequency bands (i.e., theta, alpha, and beta) relevant during complex motor task execution and were characterized with graph theoretical measures and a clustering approach. The results of the proof-of-concept study are in line with recent findings on the main features of the functional networks sustaining cooperation and competition, hence demonstrating that the proposed pipeline is promising tool for the analysis of joint action EEG data recorded during cooperation and competition using a turn-based motor task. [ABSTRACT FROM AUTHOR]

A novel multimodal experimental setup and dyadic study protocol were designed to investigate the neurophysiological underpinnings of joint action through the synchronous acquisition of EEG, ECG, EMG, respiration and kinematic data from two individuals engaged in ecologic and naturalistic cooperative and competitive joint actions involving face-to-face real-time and real-space coordinated full body movements. Such studies are still missing because of difficulties encountered in recording reliable neurophysiological signals during gross bodymovements, in synchronizingmultiple devices, and in defining suitable study protocols. The multimodal experimental setup includes the synchronous recording of EEG, ECG, EMG, respiration and kinematic signals of both individuals via two EEG amplifiers and a motion capture system that are synchronized via a single-board microcomputer and custom Python scripts. EEG is recorded using new dry sports electrode caps. The novel study protocol is designed to best exploit the multimodal data acquisitions. Table tennis is the dyadic motor task: it allows naturalistic and face-to-face interpersonal interactions, free in-time and in-space full body movement coordination, cooperative and competitive joint actions, and two task difficulty levels to mimic changing external conditions. Recording conditions--including minimum table tennis rally duration, sampling rate of kinematic data, total duration of neurophysiological recordings--were defined according to the requirements of a multilevel analytical approach including a neural level (hyperbrain functional connectivity, Graph Theoretical measures and Microstate analysis), a cognitive-behavioral level (integrated analysis of neural and kinematic data), and a social level (extending Network Physiology to neurophysiological data recorded from two interacting individuals). Four practical tests for table tennis skills were defined to select the study population, permitting to skill-match the dyad members and to form two groups of higher and lower skilled dyads to explore the influence of skill level on joint action performance. Psychometric instruments are included to assess personality traits and support interpretation of results. Studying joint action with our proposed protocol can advance the understanding of the neurophysiological mechanisms sustaining daily life joint actions and could help defining systems to predict cooperative or competitive behaviors before being overtly expressed, particularly useful in real-life contexts where social behavior is a main feature. [ABSTRACT FROM AUTHOR]

Seizures are the most prevalent clinical indication of neurological disorders in neonates. In this study, a class-imbalance aware and explainable deep learning approach based on Convolutional Neural Networks (CNNs) and Graph Attention Networks (GATs) is proposed for the accurate automated detection of neonatal seizures. The proposed model integrates the temporal information of EEG signals with the spatial information on the EEG channels through the graph representation of the multi-channel EEG segments. One-dimensional CNNs are used to automatically develop a feature set that accurately represents the differences between seizure and nonseizure epochs in the time domain. By employing GAT, the attention mechanism is utilized to emphasize the critical channel pairs and information flow among brain regions. GAT coefficients were then used to empirically visualize the important regions during the seizure and nonseizure epochs, which can provide valuable insight into the location of seizures in the neonatal brain. Additionally, to tackle the severe class imbalance in the neonatal seizure dataset using under-sampling and focal loss techniques are used. Overall, the final Spatio-Temporal Graph Attention Network (ST-GAT) outperformed previous benchmarked methods with a mean AUC of 96.6% and Kappa of 0.88, demonstrating its high accuracy and potential for clinical applications. [ABSTRACT FROM AUTHOR]

The ability to establish a connection between the direction of the other's gaze and the object that is observed has important implications in the development of social cognition and learning. In this study, we analyzed alpha and theta band oscillations in one group of 9‐month‐old infants by implementing a face‐to‐face live paradigm, which presented the infants with a triadic social interaction with a real human being. We compared neural activations in two experimental conditions: Congruent and Incongruent gaze shift following the appearance of an object. In the Incongruent object‐gaze shift condition, we observed an increase of the theta power in comparison with the Congruent condition. We also found an enhancement of the alpha activity during the Congruent versus the Incongruent object‐gaze condition. These findings confirm the involvement of the theta and alpha band activity in the detection of the gaze of others when it shifts toward a referential target. We consider that the theta band modulation could be associated with the processing of unexpected events. Furthermore, the increase of the alpha band activity during the Congruent object‐gaze condition seems to be in agreement with prior findings on the mechanisms of internally controlled attention that emerge before the first year of life. The implementation of a live paradigm elicited a partially different oscillatory pattern in comparison with non‐live standard paradigms, supporting the importance of an ecological set‐up reproducing real‐life conditions to study the development of social cognition. Our article addresses a key question in the development of social cognition and learning in infants: understanding the neural mechanisms involved in object‐gaze processing. The novel aspect of our work regards the use of a live face‐to‐face paradigm, which could greatly contribute to obtain more reliable information on how infants process referential gaze in a real‐life context. [ABSTRACT FROM AUTHOR]

Dry electrodes for electroencephalography (EEG) allow new fields of application, including telemedicine, mobile EEG, emergency EEG, and long-term repetitive measurements for research, neurofeedback, or brain–computer interfaces. Different dry electrode technologies have been proposed and validated in comparison to conventional gel-based electrodes. Most previous studies have been performed at a single center and by single operators. We conducted a multi-center and multi-operator study validating multipin dry electrodes to study the reproducibility and generalizability of their performance in different environments and for different operators. Moreover, we aimed to study the interrelation of operator experience, preparation time, and wearing comfort on the EEG signal quality. EEG acquisitions using dry and gel-based EEG caps were carried out in 6 different countries with 115 volunteers, recording electrode-skin impedances, resting state EEG and evoked activity. The dry cap showed average channel reliability of 81% but higher average impedances than the gel-based cap. However, the dry EEG caps required 62% less preparation time. No statistical differences were observed between the gel-based and dry EEG signal characteristics in all signal metrics. We conclude that the performance of the dry multipin electrodes is highly reproducible, whereas the primary influences on channel reliability and signal quality are operator skill and experience. [ABSTRACT FROM AUTHOR]

This study investigated the role played by Blocked (low contextual interference) and Random (high contextual interference) schedules in the acquisition of rhythmic footstep sequences by adolescent females (N =40, M age = 15.8 yr., SD = 1.3). After a baseline assessment ([T.sub.0]), motor learning was assessed after six sessions ([T.sub.1]) in terms of the participants' ability to reproduce the step sequences using visual and auditory guidance. A retention test ([T.sub.2]) also occurred 21 days after [T.sub.1]. Analysis of variance yielded significant main effects of group, test, and an interaction. Both groups improved performance from [T.sub.0] to [T.sub.1], but the Blocked schedule group outperformed the Random schedule group at [T.sub.1]. Significant group differences were not observed at [T.sub.2]. The Random schedule group's performance did not significantly change from [T.sub.1] to [T.sub.2], and the Blocked schedule group's performance significantly decreased. Apparently, the learning of rhythmic footstep dance sequences was not influenced by practice organization. DOI 10.2466/PMS.110.1.77-84

Background: Artefact removal in neonatal electroencephalography (EEG) by visual inspection generally depends on the expertise of the operator, is time consuming and is not a consistent pre-processing step to the pipeline for the automated EEG analysis. Therefore, there is the need for the automated detection and removal of artefacts in neonatal EEG, especially of distinct and predominant artefacts such as flat line segments (mainly caused by instrumental error where contact between electrodes and head box is lost) and large amplitude fluctuations (related to neonatal movements). Method: A threshold-based algorithm for the automated detection and removal of flat line segments and large amplitude fluctuations in neonatal EEG of infants at term-equivalent age is developed. The algorithm applies thresholds to the absolute second difference, absolute amplitude, absolute first difference and the ratio between the frequency content above 50 Hz and the frequency content across all frequencies. Results: The algorithm reaches a median accuracy of 0.91, a median hit rate of 0.91 and a median false discovery rate of 0.37. Also, a significant improvement (≈10%) in the performance of a four-stage sleep classifier is observed after artefact removal with the proposed algorithm as compared to before its application. Significance: An automated artefact removal method contributes to the pipeline of automated EEG analysis. The proposed algorithm has shown to have good performance and to be effective in neonatal EEG applications. [ABSTRACT FROM AUTHOR]

Multi-channel electroencephalography (EEG) is commonly used to monitor brain development as it captures changes in the neonatal brain function. Early parenteral nutrients support cerebellar metabolic maturation using 1H-MRS in preterm ne... B Lundberg R b B SP a sp b , Basu S SP a sp , Andescavage N SP a sp , Pradhan S SP a sp , Ottolini K, Kapse K SP a sp , Murnick J SP a sp , Limperopoulos C SP a sp I SP a sp Children's National Medical Center, Washington, United States i B BACKGROUND/PURPOSE: b Nutrition in premature infants influences brain development and neurodevelopmental outcomes. Brain metabolite composition during early human brain development as measured by quantitative in vivo 1 H magnetic resonance spectroscopy: Brain Composition in Early Human Development. Breast milk-associated oxysterol reverses neonatal white matter injury through Gli2-dependent... B Chao A b B SP a sp b , Matak P SP a sp , Pegram K SP a sp , Dubois L SP b sp , Thompson J SP b sp , Jain V SP c sp , Younge N SP a sp , Gregory S SP c sp , Goldberg R SP a sp , Benner E SP a sp I SP a sp Division of Neonatology, Department of Pediatrics, Duke University Medical Center, Durham, United States i I SP b sp Duke Proteomics and Metabolomics Shared Resource, Durham, United States i I SP c sp Duke Molecular Physiology Institute, Durham, United States i B BACKGROUND AND PURPOSE: b White matter injury (WMI) is the most common brain injury leading to poor neurologic outcomes in premature infants. [Extracted from the article]

The total seizure burden (p = 0.003, Kruskal-Wallis test) and maximum hourly seizure burden (p = 0.007, Kruskal-Wallis test) were both significantly associated with outcome so that the deceased neonates and those who developed epilepsy had a higher total seizure burden and maximum hourly seizure burden than those with a favorable outcome. B RESULTS: b The statistical tests indicated that graph metrics derived from different FCMs for pre-seizure and seizure segments significantly differed for both EEG montages (p < 0.001), with global efficiency having the largest variations between pre-seizure and seizure segments for both montages and all functional networks, and mean clustering coefficient having the smallest differences (Fig. These data support the use of screening cvEEG to detect seizures in preterm infants with ICH, as about one-third of high-risk preterm neonates with encephalopathy/abnormal imaging had EEG-seizures despite a lack of clinically-suspected seizures. Feeding during therapeutic hypothermia is safe and may improve outcomes in newborns with peri... B Alburaki W b SP a,b sp , Scringer-Wilkes M SP c sp , Dawoud F SP c sp , Oliver N SP c sp , Lind J SP c sp , Zein H SP c,d sp , Leijser L SP c,d sp , Esser M SP c,d sp , Mohammad K SP c,d sp I SP a sp Horizon Health Network, Saint John, Canada i I SP b sp Dalhousie and Memorial Universities, Saint John, Canada i I SP c sp Alberta Health Services, Calgary, Canada i I SP d sp University of Calgary, Calgary, Canada i B OBJECTIVE: b We assessed the impact of early enteral feeding introduction during therapeutic hypothermia on time to reach full enteral feeding (FEF) and other feeding related outcomes in infants born at =35 weeks gestational age and diagnosed with moderate to severe Hypoxic-Ischemic Encephalopathy. [Extracted from the article]

We conducted a counterbalanced repeated measure trial to investigate the effect of different internal and external associative strategies on endurance performance. Seventeen college-aged students were randomly assigned to three experimental conditions to test the notion that different attention-performance types (optimal Type 1, functional Type 2, and dysfunctional Type 3) would influence endurance time on a cycling task. Specifically, Type 1 represented an effortless and automatic, "low-feeling" attentional mode. Type 2 referred to an associative focus directed at core components of the task. Type 3 represented an attentional focus directed at irrelevant components of the task. Participants completed three time-to-exhaustion-tests while reporting their perceived exertion and affective states (arousal and hedonic tone). Results revealed that Type 1 and Type 2 attentional strategies, compared with Type 3 strategy, exerted functional effects on performance, whereas a Type 3 strategy was linked to lower performance, and lower levels of arousal and pleasantness. Applied implications are discussed. [ABSTRACT FROM AUTHOR]

The assessment of a method for removing artifacts from electroencephalography (EEG) datasets often disregard verifying that global brain dynamics is preserved. In this study, we verified that the recently introduced optimized fingerprint method and the automatic removal of cardiac interference (ARCI) approach not only remove physiological artifacts from EEG recordings but also preserve global brain dynamics, as assessed with a new approach based on microstate analysis. We recorded EEG activity with a high-resolution EEG system during two resting-state conditions (eyes open, 25 volunteers, and eyes closed, 26 volunteers) known to exhibit different brain dynamics. After signal decomposition by independent component analysis (ICA), the independent components (ICs) related to eyeblinks, eye movements, myogenic interference, and cardiac electromechanical activity were identified with the optimized fingerprint method and ARCI approach and statistically compared with the outcome of the expert classification of the ICs by visual inspection. Brain dynamics in two different groups of denoised EEG signals, reconstructed after having removed the artifactual ICs identified by either visual inspection or the automated methods, was assessed by calculating microstate topographies, microstate metrics (duration, occurrence, and coverage), and directional predominance (based on transition probabilities). No statistically significant differences between the expert and the automated classification of the artifactual ICs were found (p > 0.05). Cronbach's α values assessed the high test–retest reliability of microstate parameters for EEG datasets denoised by the automated procedure. The total EEG signal variance explained by the sets of global microstate templates was about 80% for all denoised EEG datasets, with no significant differences between groups. For the differently denoised EEG datasets in the two recording conditions, we found that the global microstate templates and the sequences of global microstates were very similar (p < 0.01). Descriptive statistics and Cronbach's α of microstate metrics highlighted no significant differences and excellent consistency between groups (p > 0.5). These results confirm the ability of the optimized fingerprint method and the ARCI approach to effectively remove physiological artifacts from EEG recordings while preserving global brain dynamics. They also suggest that microstate analysis could represent a novel approach for assessing the ability of an EEG denoising method to remove artifacts without altering brain dynamics. [ABSTRACT FROM AUTHOR]

Various methods have been employed to investigate different aspects of brain activity modulation related to the performance of a cycling task. In our study, we examined how functional connectivity and brain network efficiency varied during an endurance cycling task. For this purpose, we reconstructed EEG signals at source level: we computed current densities in 28 anatomical regions of interest (ROIs) through the eLORETA algorithm, and then we calculated the lagged coherence of the 28 current density signals to define the adjacency matrix. To quantify changes of functional network efficiency during an exhaustive cycling task, we computed three graph theoretical indices: local efficiency (LE), global efficiency (GE), and density (D) in two different frequency bands, Alpha and Beta bands, that indicate alertness processes and motor binding/fatigue, respectively. LE is a measure of functional segregation that quantifies the ability of a network to exchange information locally. GE is a measure of functional integration that quantifies the ability of a network to exchange information globally. D is a global measure of connectivity that describes the extent of connectivity in a network. This analysis was conducted for six different task intervals: pre-cycling; initial, intermediate, and final stages of cycling; and active recovery and passive recovery. Fourteen participants performed an incremental cycling task with simultaneous EEG recording and rated perceived exertion monitoring to detect the participants' exhaustion. LE remained constant during the endurance cycling task in both bands. Therefore, we speculate that fatigue processes did not affect the segregated neural processing. We observed an increase of GE in the Alpha band only during cycling, which could be due to greater alertness processes and preparedness to stimuli during exercise. Conversely, although D did not change significantly over time in the Alpha band, its general reduction in the Beta bands during cycling could be interpreted within the framework of the neural efficiency hypothesis, which posits a reduced neural activity for expert/automated performances. We argue that the use of graph theoretical indices represents a clear methodological advancement in studying endurance performance. [ABSTRACT FROM AUTHOR]

Hyperscanning studies, wherein brain activity is recorded from multiple participants simultaneously, offer an opportunity to investigate interpersonal dynamics during interactive tasks at the neurophysiological level. In this study, we employed a dyadic juggling paradigm and electroencephalography (EEG) hyperscanning to evaluate functional connectivity between EEG sources within and between jugglers' brains during individual and interactive juggling. We applied graph theoretical measures to identify significant differences in functional connectivity between the individual and interactive juggling conditions. Connectivity was measured in multiple juggler pairs with various skill levels where dyads were either skill-level matched or skill-level unmatched. We observed that global efficiency was reduced during paired juggling for less skilled jugglers and increased for more skilled jugglers. When jugglers were skill-level matched, additional reductions were found in the mean clustering coefficient and small-world topology during interactive juggling. A significant difference in hemispheric brain lateralization was detected between skill-level matched and skill-level unmatched jugglers during interactive juggling: matched jugglers had an increased right hemisphere lateralization while unmatched jugglers had an increased left hemisphere lateralization. These results reveal multiple differences in functional brain networks during individual and interactive juggling and suggest that similarities and disparities in individual skills can impact inter-brain dynamics in the performance and learning of motor tasks. [ABSTRACT FROM AUTHOR]

EEG recordings are generally affected by interference from physiological and non-physiological sources which may obscure underlying brain activity and hinder effective EEG analysis. In particular, cardiac interference can be caused by the electrical activity of the heart and/or cardiovascular activity related to blood flow. Successful EEG application in sports science settings requires a method for artifact removal that is automatic and flexible enough to be applied in a variety of acquisition conditions without requiring simultaneous ECG recordings that could restrict movement. We developed an automatic method for classifying and removing both electrical cardiac and cardiovascular artifacts (ARCI) that does not require additional ECG recording. Our method employs independent component analysis (ICA) to isolate data independent components (ICs) and identifies the artifactual ICs by evaluating specific IC features in the time and frequency domains. We applied ARCI to EEG datasets with cued artifacts and acquired during an eyes-closed condition. Data were recorded using a standard EEG wet cap with either 128 or 64 electrodes and using a novel dry electrode cap with either 97 or 64 dry electrodes. All data were decomposed into different numbers of components to evaluate the effect of ICA decomposition level on effective cardiac artifact detection. ARCI performance was evaluated by comparing automatic ICs classifications with classifications performed by experienced investigators. Automatic and investigator classifications were highly consistent resulting in an overall accuracy greater than 99% in all datasets and decomposition levels, and an average sensitivity greater than 90%. Best results were attained when data were decomposed into a fewer number of components where the method achieved perfect sensitivity (100%). Performance was also evaluated by comparing automatic component classification with externally recorded ECG. Results showed that ICs automatically classified as artifactual were significantly correlated with ECG activity whereas the other ICs were not. We also assessed that the interference affecting EEG signals was reduced by more than 82% after automatic artifact removal. Overall, ARCI represents a significant step in the detection and removal of cardiac-related EEG artifacts and can be applied in a variety of acquisition settings making it ideal for sports science applications. [ABSTRACT FROM AUTHOR]

Data contamination due to physiological artifacts such as those generated by eyeblinks, eye movements, and muscle activity continues to be a central concern in the acquisition and analysis of electroencephalographic (EEG) data. This issue is further compounded in EEG sports science applications where the presence of artifacts is notoriously difficult to control because behaviors that generate these interferences are often the behaviors under investigation. Therefore, there is a need to develop effective and efficient methods to identify physiological artifacts in EEG recordings during sports applications so that they can be isolated from cerebral activity related to the activities of interest. We have developed an EEG artifact detection model, the Fingerprint Method, which identifies different spatial, temporal, spectral, and statistical features indicative of physiological artifacts and uses these features to automatically classify artifactual independent components in EEG based on a machine leaning approach. Here, we optimized our method using artifact-rich training data and a procedure to determine which features were best suited to identify eyeblinks, eye movements, and muscle artifacts. We then applied our model to an experimental dataset collected during endurance cycling. Results reveal that unique sets of features are suitable for the detection of distinct types of artifacts and that the Optimized Fingerprint Method was able to correctly identify over 90% of the artifactual components with physiological origin present in the experimental data. These results represent a significant advancement in the search for effective means to address artifact contamination in EEG sports science applications. [ABSTRACT FROM AUTHOR]

Background. EEG may be affected by artefacts hindering the analysis of brain signals. Data-driven methods like independent component analysis (ICA) are successful approaches to remove artefacts from the EEG. However, the ICA-based methods developed so far are often affected by limitations, such as: the need for visual inspection of the separated independent components (subjectivity problem) and, in some cases, for the independent and simultaneous recording of the inspected artefacts to identify the artefactual independent components; a potentially heavy manipulation of the EEG signals; the use of linear classification methods; the use of simulated artefacts to validate the methods; no testing in dry electrode or high-density EEG datasets; applications limited to specific conditions and electrode layouts. Methods. Our fingerprint method automatically identifies EEG ICs containing eyeblinks, eye movements, myogenic artefacts and cardiac interference by evaluating 14 temporal, spatial, spectral, and statistical features composing the IC fingerprint. Sixtytwo real EEG datasets containing cued artefacts are recorded with wet and dry electrodes (128 wet and 97 dry channels). For each artefact, 10 nonlinearSVMclassifiers are trained on fingerprints of expert-classified ICs. Training groups include randomly chosen wet and dry datasets decomposed in 80 ICs. The classifiers are tested on the IC-fingerprints of different datasets decomposed into 20, 50, or 80 ICs. The SVM performance is assessed in terms of accuracy, False Omission Rate (FOR), Hit Rate (HR), False Alarm Rate (FAR), and sensitivity (p). For each artefact, the quality of the artefact-free EEG reconstructed using the classification of the best SVM is assessed by visual inspection and SNR. Results. The best SVM classifier for each artefact type achieved average accuracy of 1 (eyeblink), 0.98 (cardiac interference), and 0.97 (eye movement and myogenic artefact). Average classification sensitivity (p) was 1 (eyeblink), 0.997 (myogenic artefact), 0.98 (eye movement), and 0.48 (cardiac interference). Average artefact reduction ranged from a maximum of 82% for eyeblinks to a minimum of 33% for cardiac interference, depending on the effectiveness of the proposed method and the amplitude of the removed artefact. The performance of the SVM classifiers did not depend on the electrode type, whereas it was better for lower decomposition levels (50 and 20 ICs). Discussion. Apart from cardiac interference, SVM performance and average artefact reduction indicate that the fingerprint method has an excellent overall performance in the automatic detection of eyeblinks, eye movements and myogenic artefacts, which is comparable to that of existing methods. Being also independent from simultaneous artefact recording, electrode number, type and layout, and decomposition level, the proposed fingerprint method can have useful applications in clinical and experimental EEG settings. [ABSTRACT FROM AUTHOR]

Background: Extensive evidence supports the notion that physical activity (PA) promotes healthy aging and contributes to the prevention of brain damage and dementia. However, there are relevant differences in individual response to exercise that could mediate the extent of the PA‐induced benefits. Here, we propose the application of the novel technology that is currently being developed by the EMBRACE project researchers to the characterization of the individual physiological response to exercise in older adults. Method: EMBRACE is an intersectoral and international consortium that brings together experts in biomedical engineering, material science, signal processing, neuroscience and social psychology from 3 academic and 3 industrial partners across 3 EU countries to develop: 1) a new mobile and wireless dry electrode EEG system suitable for monitoring brain activity during full body movements; 2) novel body network sensors and a multimodal alignment system for simultaneously recording of neural, physiological and kinematic signals from two interacting subjects; 3) novel analytical solutions for motion artefact removal and multi‐level analysis of multimodal data; and 4) a new research dyadic paradigm to exploit the technological advances. Result: The novel technological solutions that are being currently developed by the EMBRACE consortium will enable the study of joint action at the neural, cognitive, behavioral and social levels while the participant engages in PA (individually or in social interaction), wearing a comfortable and long‐lasting acquisition system. Conclusion: A detailed characterization of the individual response to exercise will help in the optimization of exercise routines with the aim to elicit the desired physiological response to enhance brain health and prevent dementia. [ABSTRACT FROM AUTHOR]

A novel system for the neuro-motor rehabilitation of upper limbs was validated in three sub-acute post-stroke patients. The system permits synchronized cortical and kinematic measures by integrating high-resolution EEG, passive robotic device and Virtual Reality. The brain functional re-organization was monitored in association with motor patterns replicating activities of daily living (ADL). Patients underwent 13 rehabilitation sessions. At sessions 1, 7 and 13, clinical tests were administered to assess the level of motor impairment, and EEG was recorded during rehabilitation task execution. For each session and rehabilitation task, four kinematic indices of motor performance were calculated and compared with the outcome of clinical tests. Functional source maps were obtained from EEG data and projected on the real patients' anatomy (MRI data). Laterality indices were calculated for hemispheric dominance assessment. All patients showed increased participation in the rehabilitation process. Cortical activation changes during recovery were detected in relation to different motor patterns, hence verifying the system's suitability to add quantitative measures of motor performance and neural recovery to classical tests. We conclude that this system seems a promising tool for novel robot-based rehabilitation paradigms tailored to individual needs and neuro-motor responses of the patients. [ABSTRACT FROM AUTHOR]

The purpose of this study was to evaluate the effect of a continuous and a discretized Visual Biofeedback (VBF) on balance performance in upright stance. The coordinates of the Centre of Pressure (CoP), extracted from a force plate, were processed in real-time to implement the two VBFs, administered to two groups of 12 healthy participants. In the first group, a representation of the CoP was continuously shown, while in the second group, the discretized VBF was provided at an irregular frequency (that depended on the subject's performance) by displaying one out of a set of five different emoticons, each corresponding to a specific area covered by the current position of the CoP. In the first case, participants were asked to maintain a white spot within a given square area, whereas in the second case they were asked to keep the smiling emoticon on. Trials with no VBF were administered as control. The effect of the two VBFs on balance was studied through classical postural parameters and a subset of stabilogram diffusion coefficients. To quantify the amount of time spent in stable conditions, the percentage of time during which the CoP was inside the stability area was calculated. Both VBFs improved balance maintainance as compared to the absence of any VBF. As compared to the continuous VBF, in the discretized VBF a significant decrease of sway path, diffusion and Hurst coefficients was found. These results seem to indicate that a discretized VBF favours a more natural postural behaviour by promoting a natural intermittent postural control strategy. [ABSTRACT FROM AUTHOR]

The investigation of the functional changes in the sensorimotor cortex has important clinical implications as deviations from normal development can anticipate developmental disorders. The functional properties of the sensorimotor cortex can be characterized through the rolandic mu rhythm, already present during infancy. However, how the sensorimotor network develops from early infancy to adulthood, and how sensorimotor processing contributes to the generation of perceptual-motor coupling remains largely unknown. Here, we analyzed magnetoencephalographic (MEG) data recorded in two groups of infants (11-24 and 26-47 weeks), two groups of children (24-34 and 36-60 months), and a control group of adults (20-39 years), during intermixed conditions of rest and prehension. The MEG sensor array was positioned over the sensorimotor cortex of the contralateral hemisphere. We characterized functional connectivity and topological properties of the sensorimotor network across ages and conditions through synchronization likelihood and segregation/integration measures in an individual mu rhythm frequency range. All functional measures remained almost unchanged during the first year of life, whereas they varied afterwards through childhood to reach adult values, demonstrating an increase of both segregation and integration properties. With age, the sensorimotor network evolved from a more random (infants) to a "small-world" organization (children and adults), more efficient both locally and globally. These findings are in line with prior studies on structural and functional brain development in infants, children and adults. We could not demonstrate any significant change in the functional properties of the sensorimotor cortex in the prehension condition with respect to rest. Our results support the view that, since early infancy, the functional properties of the developing sensorimotor cortex are modulated by maturation. [ABSTRACT FROM AUTHOR]

We propose a system for the neuro-motor rehabilitation of upper limbs in stroke survivors. The system is composed of a passive robotic device (Trackhold) for kinematic tracking and gravity compensation, five dedicated virtual reality (VR) applications for training of distinct movement patterns, and high-resolution EEG for synchronous monitoring of cortical activity. In contrast to active devices, the Trackhold omits actuators for increased patient safety and acceptance levels, and for reduced complexity and costs. VR applications present all relevant information for task execution as easy-to-understand graphics that do not need any written or verbal instructions. High-resolution electroencephalography (HR-EEG) is synchronized with kinematic data acquisition, allowing for the epoching of EEG signals on the basis of movement-related temporal events. Two healthy volunteers participated in a feasibility study and performed a protocol suggested for the rehabilitation of post-stroke patients. Kinematic data were analyzed by means of in-house code. Open source packages (EEGLAB, SPM, and GMAC) and in-house code were used to process the neurological data. Results from kinematic and EEG data analysis are in line with knowledge from currently available literature and theoretical predictions, and demonstrate the feasibility and potential usefulness of the proposed rehabilitation system to monitor neuro-motor recovery. [ABSTRACT FROM PUBLISHER]

We analyzed the effectiveness of linear short- and long-term variability time domain parameters, an index of sympatho-vagal balance (SDNN/RMSSD) and entropy in differentiating fetal heart rate patterns (fHRPs) on the fetal heart rate (fHR) series of 5, 3 and 2 min duration reconstructed from 46 fetal magnetocardiograms. Gestational age (GA) varied from 21 to 38 weeks. FHRPs were classified based on the fHRstandard deviation. In sleep states, we observed that vagal influence increased with GA, and entropy significantly increased (decreased) with GA (SDNN/RMSSD), demonstrating that a prevalence of vagal activity with autonomous nervous system maturation may be associated with increased sleep state complexity. In active wakefulness, we observed a significant negative (positive) correlation of short-term (long-term) variability parameters with SDNN/RMSSD. ANOVA statistics demonstrated that longterm irregularity and standard deviation of normal-to-normal beat intervals (SDNN) best differentiated among fHRPs. Our results confirm that short- and long-term variability parameters are useful to differentiate between quiet and active states, and that entropy improves the characterization of sleep states. All measures differentiated fHRPs more effectively on very short HR series, as a result of the fMCG high temporal resolution and of the intrinsic timescales of the events that originate the different fHRPs. [ABSTRACT FROM AUTHOR]