3 research outputs found

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

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    IntroductionEven 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.MethodsMultimodal 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.ResultsWe 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.ConclusionOur 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

    Mapping the structural connectivity fingerprints of corticostriatal circuits in Huntington’s disease

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    Treball de fi de grau en Bioinformàtica. Curs 2019-2020Tutora: Estela Càmara ManchaLa malaltia de Huntington és una malaltia genètica i neurodegenerativa causada per una mutació al gen HTT que provoca una serie de símpotmes motrius, cognitius i psiquiàtrics. Tot i així, hi ha un alt grau de heterogenoïtat en l’origen i evolució de cada símptome. Els tres circuits corticoestriatals principals (motor, cognitiu i motivacional) estan afectats per la neurodegeneració, la qual podria ser la font de les diferències interindividuals entre els pacients. L’objectiu d’aquest estudi és caracteritzar la connectivitat estructural dels tres circuits principals amb la finalitat de delinear patrons de neurodegeneració específics que podrien ser la base de diferents perfils simptomàtics.La enfermedad de Huntington es una enfermedad genética y neurodegenerativa causada por una mutación en el gen HTT, e provoca una mezcla de síntomas motores, cognitivos y psiquiátricos. No obstante, existe un alto grado de heterogeneidad en el orígen y evolución de cada síntoma. Los tres circuitos corticoestriatales principales (motor, cognitivo y motivacional) están afectados por la neurodegeneración, la cual podría ser una fuente de las diferencias interindividuales entre los pacientes. El objetivo de este estudio es caracterizar la conectividad estructural de los tres circuitos principales con el fin de delinear patrones de neurodegeneración específicos que podrían ser la base de diferentes perfiles sintomáticos.Huntington’s disease (HD) is a genetic neurodegenerative disease caused by a mutation in the HTT gene which involves a mixture of symptoms, including motor, cognitive and psychiatric deficits. However, there is a high degree of heterogeneity in the prominence and evolution of each type of symptom. The three main cortico-striatal circuits (motor, cognitive control and motivational) result affected by neurodegeneration, which could be one possible source of such interindividual differences among HD patients. The aim of this research is to characterize the structural connectivity of the three main cortico-striatal circuits in order to delineate specific neurodegeneration patterns that might underlay the different symptomatic profiles in HD

    Data_Sheet_1_How can cry acoustics associate newborns’ distress levels with neurophysiological and behavioral signals?.PDF

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    IntroductionEven 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.MethodsMultimodal 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.ResultsWe 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.ConclusionOur 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.</p
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