273 research outputs found

    Sincronismo cardiolocomotor : interação entre parâmetros locomotores, neuromusculares e fisiológicos e sua repercussão sobre a bioenergética da corrida de longa distância

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    Introdução: O sincronismo cardiolocomotor (SCL) ocorre quando as frequências desses dois sistemas assumem um ritmo oscilatório comum e tem sido observado em atividades cíclicas. Porém, pouco se sabe sobre sua repercussão no desempenho esportivo. Dentre os possíveis efeitos do SCL destaca-se o aperfeiçoamento da função cardíaca e a consequente otimização da perfusão sanguínea nos músculos ativos, com redução no dispêndio energético do músculo cardíaco. Tais efeitos podem impactar positivamente a economia de corrida. Objetivo: Analisar as interações entre parâmetros locomotores, neuromusculares e fisiológicos, especialmente o SCL, e a repercussão dessas interações na bioenergética da corrida de longa distância. Métodos: Corredores de longa distância realizaram testes de corrida em esteira rolante, com duração entre três e cinco minutos cada, em diferentes velocidades. Sinais de eletrocardiografia (ECG) e eletromiografia de superfície (EMG) dos músculos vasto lateral e gastrocnêmio medial foram registrados para determinar o SCL por meio da Coerência Wavelet que retornou o Coeficiente de Coerência Wavelet (CCW) variando de 0 (ausência de coerência) até 1 (coerência perfeita), bem como as frequências de sincronização (Freq Sincro). Nós consideramos o SCL como manifesto quando o CCW > 0,8. Os parâmetros espaço-temporais da corrida foram obtidos por cinemetria e os parâmetros energéticos (custo de transporte) e hemodinâmicos (pulso de oxigênio) por análise de gases metabólicos Na análise estatística usou-se os Modelos Lineares Mistos Generalizados (GLMMs), com nível de significância de 5%. Resultados: Em todos os testes, observamos poucos eventos considerados como SCL, em média menos de 1% dos dados analisados, embora tenhamos observado de forma sistemática um componente de frequência (Fsincro) nos sinais centrado na frequência de passo, algo entre 160 a 170 passos min-1, que pode ser indicativo do arrastamento da frequência cardíaca pela frequência locomotora. As diferenças estatísticas encontradas nas variáveis hemodinâmicas e metabólicas não parecem ser consequência da SCL. Conclusão: Visto que o SCL não foi identificado nos nossos achados, a repercussão deste nas variáveis mecânicas, hemodinâmicas e metabólicas, incluindo a economia de corrida, são apenas especulativas.Introduction: Cardiolocomotor synchronization (CLS) occurs when the frequencies of these two systems assume a common oscillatory rhythm and has been observed in cyclic activities such, but little is known about its repercussion in sports performance. Among the possible effects of CLS are the improvement of cardiac function and the consequent optimization of blood perfusion in the muscles involved in the activity, with reduction of the energy expenditure of the cardiac muscle. Such effects may positively impact the running economy. Aims: To analyze the interactions between locomotor, neuromuscular and physiological parameters, especially cardiolocomotor synchrony, and the repercussion of these interactions on bioenergetics of long distance running. Methods: Long distance runners performed treadmill running tests, lasting between three and five minutes each, at different speeds. Electrocardiography (ECG) and surface electromyography (SEMG) of the vastus lateralis and medial gastrocnemius muscles signals were recorded to determine the CLS by Wavelet Coherence that returned the Wavelet Coherence Coefficient (WCC) ranging from 0 (no coherence) to 1 (perfect coherence), as well as the synchronization frequencies (Freq Synchro). We considered CLS as manifested when WCC > 0.8. The running spatiotemporal parameters were obtained by cinematic and the energetic (cost of transport) and hemodynamic (oxygen pulse) parameters by metabolic gases analysis. Statistical analysis was performed using Generalized Mixed Linear Models (GLMMs), with a significance level of 5%. Results: In all tests, we observed rarely events considered as CLS, on average less than 1% of the data analyzed, although we systematically observed a component of frequency (Freq Synchro) in the signals centered on the step frequency, something between 160 and 170 steps min-1, which may be indicative of the entrainment of the heart rate by the locomotor frequency. The statistical differences found in hemodynamic variables and metabolic variables do not seem to be a consequence of CLS. Conclusion: Since CLS was not identified in our findings, its repercussion on mechanical, hemodynamic and metabolic variables, including running economy, is just speculation

    Using Wearable Sensors to Measure Interpersonal Synchrony in Actors and Audience Members During a Live Theatre Performance

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    Studying social interaction in real-world settings is of increasing importance to social cognitive researchers. Theatre provides an ideal opportunity to study rich face-to-face interactions in a controlled, yet natural setting. Here we collaborated with Flute Theatre to investigate interpersonal synchrony between actors-actors, actors-audience and audience-audience within a live theatrical setting. Our 28 participants consisted of 6 actors and 22 audience members, with 5 of these audience members being audience participants in the show. The performance was a compilation of acting, popular science talks and demonstrations, and an audience participation period. Interpersonal synchrony was measured using inertial measurement unit (IMU) wearable accelerometers worn on the heads of participants, whilst audio-visual data recorded everything that occurred on the stage. Participants also completed post-show self-report questionnaires on their engagement with the overall scientists and actors performance. Cross Wavelet Transform (XWT) and Wavelet Coherence Transform (WCT) analysis were conducted to extract synchrony at different frequencies, pairing with audio-visual data. Findings revealed that XWT and WCT analysis are useful methods in extracting the multiple types of synchronous activity that occurs when people perform or watch a live performance together. We also found that audience members with higher ratings on questionnaire items such as the strength of their emotional response to the performance, or how empowered they felt by the performance, showed a high degree of interpersonal synchrony with actors during the acting segments of performance. We further found that audience members rated the scientists performance higher than the actors performance on questions related to their emotional response to the performance as well as, how uplifted, empowered, and connected to social issues they felt. This shows the types of potent connections audience members can have with live performances. Additionally, our findings highlight the importance of the performance context for audience engagement, in our case a theatre performance as part of public engagement with science rather than a stand-alone theatre performance. In sum we conclude that interdisciplinary real-world paradigms are an important and understudied route to understanding in-person social interactions

    Biomedical Signal Analysis of the Brain and Systemic Physiology

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    Near-infrared spectroscopy (NIRS) is a non-invasive and easy-to-use diagnostic technique that enables real-time tissue oxygenation measurements applied in various contexts and for different purposes. Continuous monitoring with NIRS of brain oxygenation, for example, in neonatal intensive care units (NICUs), is essential to prevent lifelong disabilities in newborns. Moreover, NIRS can be applied to observe brain activity associated with hemodynamic changes in blood flow due to neurovascular coupling. In the latter case, NIRS contributes to studying cognitive processes allowing to conduct experiments in natural and socially interactive contexts of everyday life. However, it is essential to measure systemic physiology and NIRS signals concurrently. The combination of brain and body signals enables to build sophisticated systems that, for example, reduce the false alarms that occur in NICUs. Furthermore, since fNIRS signals are influenced by systemic physiology, it is essential to understand how the latter impacts brain signals in functional studies. There is an interesting brain body coupling that has rarely been investigated yet. To take full advantage of these brain and body data, the aim of this thesis was to develop novel approaches to analyze these biosignals to extract the information and identify new patterns, to solve different research or clinical questions. For this the development of new methodological approaches and sophisticated data analysis is necessary, because often the identification of these patterns is challenging or not possible with traditional methods. In such cases, automatic machine learning (ML) techniques are beneficial. The first contribution of this work was to assess the known systemic physiology augmented (f)NIRS approach for clinical use and in everyday life. Based on physiological and NIRS signals of preterm infants, an ML-based classification system has been realized, able to reduce the false alarms in NICUs by providing a high sensitivity rate. In addition, the SPA-fNIRS approach was further applied in adults during a breathing task. The second contribution of this work was the advancement of the classical fNIRS hyperscanning method by adding systemic physiology measures. For this, new biosignal analyses in the time-frequency domain have been developed and tested in a simple nonverbal synchrony task between pairs of subjects. Furthermore, based on SPA-fNIRS hyperscanning data, another ML-based system was created, which is able distinguish familiar and unfamiliar pairs with high accuracy. This approach enables to determine the strength of social bonds in a wide range of social interaction contexts. In conclusion, we were the first group to perform a SPA-fNIRS hyperscanning study capturing changes in cerebral oxygenation and hemodynamics as well as systemic physiology in two subjects simultaneously. We applied new biosignals analysis methods enabling new insights into the study of social interactions. This work opens the door to many future inter-subjects fNIRS studies with the benefit of assessing the brain-to-brain, the brain-to-body, and body-to-body coupling between pairs of subjects

    Interpersonal synchrony and network dynamics in social interaction [Special issue]

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    Investigation of heart rate variability during sleep apnea

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    Sleep apnea is a disorder, where there are repetitive pauses in respiratory flow of at least 10 seconds or longer duration, and which occur more than five times per hour. Apnea has strong modulating effects on the autonomic nervous system, with prominent heart rate variation. It can be assumed that during sleep, internal influences (sympathetic and parasympathetic nervous system activities) dominate the autonomic nervous system; in addition repetitive apneas are accompanied by a pronounced increase in average heart rate. The aim of this study was to investigate the heart rate variability using spectral analysis and time-frequency analysis during sleep apnea. A total of 22 subjects (18 males and 4 females, 49 ± 20 years) were studied who were experiencing both obstructive sleep apnea and central sleep apnea in whom sleep-disordered breathing was diagnosed. In addition 6 control subjects were studied where sleep apnea was not expected. Spectral and wavelet analysis were used to investigate the heart rate variability from the sleep apnea subjects and control subjects. The results of the wavelet analysis gave information about the parasympathetic (HF) and sympatho-vagal balance (LF: HF) changes as a function of time and frequency. The spectral parameters LF, HF and LF/HF confirmed reduced parasympathetic activity in patients with sleep apnea compared to normal subjects. In addition the repetitive apneas are accompanied by a pronounced increased cyclic variation of heart rate

    Oscillatory architecture of memory circuits

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    The coordinated activity between remote brain regions underlies cognition and memory function. Although neuronal oscillations have been proposed as a mechanistic substrate for the coordination of information transfer and memory consolidation during sleep, little is known about the mechanisms that support the widespread synchronization of brain regions and the relationship of neuronal dynamics with other bodily rhythms, such as breathing. During exploratory behavior, the hippocampus and the prefrontal cortex are organized by theta oscillations, known to support memory encoding and retrieval, while during sleep the same structures are dominated by slow oscillations that are believed to underlie the consolidation of recent experiences. The expression of conditioned fear and extinction memories relies on the coordinated activity between the mPFC and the basolateral amygdala (BLA), a neuronal structure encoding associative fear memories. However, to date, the mechanisms allowing this long-range network synchronization of neuronal activity between the mPFC and BLA during fear behavior remain virtually unknown. Using a combination of extracellular recordings and open- and closed-loop optogenetic manipulations, we investigated the oscillatory and coding mechanisms mediating the organization and coupling of the limbic circuit in the awake and asleep brain, as well as during memory encoding and retrieval. We found that freezing, a behavioral expression of fear, is tightly associated with an internally generated brain state that manifests in sustained 4Hz oscillatory dynamics in prefrontal-amygdala circuits. 4Hz oscillations accurately predict the onset and termination of the freezing state. These oscillations synchronize prefrontal-amygdala circuits and entrain neuronal activity to dynamically regulate the development of neuronal ensembles. This enables the precise timing of information transfer between the two structures and the expression of fear responses. Optogenetic induction of prefrontal 4Hz oscillations promotes freezing behavior and the formation of long-lasting fear memory, while closed-loop phase specific manipulations bidirectionally modulate fear expression. Our results unravel a physiological signature of fear memory and identify a novel internally generated brain state, characterized by 4Hz oscillations. This oscillation enables the temporal coordination and information transfer in the prefrontal-amygdala circuit via a phase-specific coding mechanism, facilitating the encoding and expression of fear memory. In the search for the origin of this oscillation, we focused our attention on breathing, the most fundamental and ubiquitous rhythmic activity in life. Using large-scale extracellular recordings from a number of structures, including the medial prefrontal cortex, hippocampus, thalamus, amygdala and nucleus accumbens in mice we identified and characterized the entrainment by breathing of a host of network dynamics across the limbic circuit. We established that fear-related 4Hz oscillations are a state-specific manifestation of this cortical entrainment by the respiratory rhythm. We characterized the translaminar and transregional profile of this entrainment and demonstrated a causal role of breathing in synchronizing neuronal activity and network dynamics between these structures in a variety of behavioral scenarios in the awake and sleep state. We further revealed a dual mechanism of respiratory entrainment, in the form of an intracerebral corollary discharge that acts jointly with an olfactory reafference to coordinate limbic network dynamics, such as hippocampal ripples and cortical UP and DOWN states, involved in memory consolidation. Respiration provides a perennial stream of rhythmic input to the brain. In addition to its role as the condicio sine qua non for life, here we provide evidence that breathing rhythm acts as a global pacemaker for the brain, providing a reference signal that enables the integration of exteroceptive and interoceptive inputs with the internally generated dynamics of the hippocampus and the neocortex. Our results highlight breathing, a perennial rhythmic input to the brain, as an oscillatory scaffold for the functional coordination of the limbic circuit, enabling the segregation and integration of information flow across neuronal networks

    Time-evaluation Model for Live Musical Interaction with Multiple Performers

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    In questo studio vengono analizzate possibili misure oggettive per la definizione di qualità di una performance musicale. Viene adottotato, per la prma volta in questo ambito un approccio basato sull'inferenza bayesiana. Inoltre l'analisi e i risultati ottenti hanno permesso di realizzare una prima applicazione che ha lo scopo di aiutare i musicisti a mantenere una costante riproduzione degli intervalli durante una performance musicale e infine fornisce una valutazione della qualità

    Tackling the Inverse Problem for Non-Autonomous Systems: Application to the Life Sciences.

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    The common assumption that a dynamical system found in nature can be considered as isolated and autonomous is frequently a poor approximation. In reality, there are always external influences, and these are often too strong to ignore. In the case of an interacting oscillatory systems, they may e.g. modify their natural frequencies or coupling amplitudes. The main objective of this thesis is to study, detect and understand in greater detail the effect of external dynamical influences on interacting self-sustained oscillators. Theoretical framework for the analysis of synchronization between non-autonomous oscillating systems is discussed. Multiple-scale analysis is applied on a phase oscillators model with slowly varying frequency. This analysis revealed the analytic form of the synchronization state with respect to slow and fast time-variations. Limit-cycle oscillators are used to study amplitude dynamics and to investigate synchronization transitions, which occur in the bifurcation points where the equilibrium solution for the phase difference and amplitudes changes their stability. Bifurcation diagrams as functions of coupling parameters are also constructed. In a case of non-autonomous interacting oscillators, the phase difference varies dynamically, the external influences can be the cause for synchronization transitions between different synchronization orders, and lag synchronization is hardly achievable. It is also demonstrated that the time-variations of the form of the coupling function alone can be the cause for synchronization transitions. A method is introduced for analysis of interactions between time-dependent coupled oscillators, based on the signals they generate. It distinguishes unsynchronized dynamics from noise-induced phase slips, and enables the evolution of the coupling functions and other parameters to be followed. The technique is based on Bayesian inference of the time-evolving parameters, achieved by shaping the prior densities to incorporate knowledge of previous samples. The dynamics can be inferred from phase variables, in which case a finite number of Fourier base functions are used, or from state variables exploiting the model state base functions. The latter is used for detection of generalized synchronization. The method is tested numerically and applied to reveal and quantify the time-varying nature of synchronization, directionality and coupling functions from cardiorespiratory and analogue signals. It is found that, in contrast to many systems with time-invariant coupling functions, the functional relations for the interactions of an open (biological) system can in itself be a time-varying process. The cardiorespiratory analysis demonstrated that not only the parameters, but also the functional relationships, can be time-varying, and the new technique can effectively follow their evolution. The proposed theory and methods are applied for the analysis of biological oscillatory systems affected by external dynamical influences. The main investigation is performed on physiological measurements under conditions where the breathing frequency is varied linearly in a deterministic way, which introduces non-autonomous time-variability into the oscillating system. Methods able to track time-varying characteristics are applied to signals from the cardiovascular, and the sympathetic neural systems. The time-varying breathing process significantly affected the functioning and regulation of several physiological mechanisms, demonstrating a clear imprint of the particular form of externally induced time-variation. Specifically, the low breathing frequencies provoked more information flow, interfering the coordination and increasing the coupling strength between the oscillatory processes. Statistical analyses are performed to identify significant relationships. The proposed inferential method is applied to cardiorespiratory signals of this kind. The technique successfully identified that the cardiorespiratory coordination depends on, and is regulated to a great extent by, the respiration dynamics. The time-varying respiration acted as a cause for synchronization transitions between different orders. Additional complexity is encountered by the coupling functions which are also identified as time-varying processes. A technique based on wavelet synchrosqueezed transform shows how the instantaneous phase can be extracted from complex mixed-mode signals with time-varying characteristics. The latter is demonstrated on several physiological signals of this kind. The dynamical characterization for the reproducibility of blood flow is shown to be more appropriate than the time-averaged analysis. This also implies that care must be taken when external perturbations are made consecutively. Finally, the study focuses on analysis of analogue simulation of two non-autonomous van der Pol oscillators. The oscillators are unidirectionally coupled, and the frequency of the first oscillator is externally and periodically perturbed. The analogue simulation presents another model which encounters real experimental noise. The intermittent synchronization and the corresponding transitions are detected both through phase, and generalized synchronization, based on a common inferential basis

    How hand movements and speech tip the balance in cognitive development:A story about children, complexity, coordination, and affordances

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    When someone asks us to explain something, such as how a lever or balance scale works, we spontaneously move our hands and gesture. This is also true for children. Furthermore, children use their hands to discover things and to find out how something works. Previous research has shown that children’s hand movements hereby are ahead of speech, and play a leading role in cognitive development. Explanations for this assumed that cognitive understanding takes place in one’s head, and that hand movements and speech (only) reflect this. However, cognitive understanding arises and consists of the constant interplay between (hand) movements and speech, and someone’s physical and social environment. The physical environment includes task properties, for example, and the social environment includes other people. Therefore, I focused on this constant interplay between hand movements, speech, and the environment, to better understand hand movements’ role in cognitive development. Using science and technology tasks, we found that children’s speech affects hand movements more than the other way around. During difficult tasks the coupling between hand movements and speech becomes even stronger than in easy tasks. Interim changes in task properties differently affect hand movements and speech. Collaborating children coordinate their hand movements and speech, and even their head movements together. The coupling between hand movements and speech is related to age and (school) performance. It is important that teachers attend to children’s hand movements and speech, and arrange their lessons and classrooms such that there is room for both

    Interpersonal synchrony when singing in a choir

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