Inaudible components of the human infant cry influence haemodynamic responses in the breast region of mothers

Coregulation: A Multilevel Approach via Biology and Behavior

In this article, we explore the concept of coregulation, which encompasses the mutual adaptation between partners in response to one another's biology and behavior. Coregulation operates at both biological (hormonal and nervous system) and behavioral (affective and cognitive) levels and plays a crucial role in the development of self-regulation. Coregulation extends beyond the actions of individuals in a dyad and involves interactive contributions of both partners. We use as an example parent-child coregulation, which is pervasive and expected, as it emerges from shared genetic relatedness, cohabitation, continuous interaction, and the influence of common factors like culture, which facilitate interpersonal coregulation. We also highlight the emerging field of neural attunement, which investigates the coordination of brain-based neural activities between individuals, particularly in social interactions. Understanding the mechanisms and significance of neural attunement adds a new dimension to our understanding of coregulation and its implications for parent-child relationships and child development.

Maternal physiological calming responses to infant suckling at the breast

The mother-infant relation is key to infant physical, cognitive and social development. Mutual regulation and cooperation are required to maintain the dyadic system, but the biological foundation of these responses remains to be clarified. In this study, we report the maternal calming responses to infant suckling during breastfeeding. Using behavioral measures and a Holter electrocardiogram as a readout of the maternal autonomic nervous system, the maternal activities during resting, sitting with her infant on her lap, and breastfeeding were assessed. We found that during breastfeeding, mothers talked less and maternal heart rate was lower than during sitting with the infant without breastfeeding. Congruently, maternal heart rate variability measurements indicated a higher parasympathetic activity during breastfeeding. Time-locked analyses suggested that this maternal calming response was initiated by the tactile stimulation at the breast by the infant face or mouth latch, which preceded the perceived milk ejection. These findings suggest that somatosensory stimuli of breastfeeding activate parasympathetic activity in mothers. Just as how the infant Transport Response facilitates the carrying of infants, the maternal calming responses during breastfeeding may promote efficient milk intake by inhibiting spontaneous maternal activities.

Using CCA-Fused Cepstral Features in a Deep Learning-Based Cry Diagnostic System for Detecting an Ensemble of Pathologies in Newborns

Crying is one of the means of communication for a newborn. Newborn cry signals convey precious information about the newborn's health condition and their emotions. In this study, cry signals of healthy and pathologic newborns were analyzed for the purpose of developing an automatic, non-invasive, and comprehensive Newborn Cry Diagnostic System (NCDS) that identifies pathologic newborns from healthy infants. For this purpose, Mel-frequency Cepstral Coefficients (MFCC) and Gammatone Frequency Cepstral Coefficients (GFCC) were extracted as features. These feature sets were also combined and fused through Canonical Correlation Analysis (CCA), which provides a novel manipulation of the features that have not yet been explored in the literature on NCDS designs, to the best of our knowledge. All the mentioned feature sets were fed to the Support Vector Machine (SVM) and Long Short-term Memory (LSTM). Furthermore, two Hyperparameter optimization methods, Bayesian and grid search, were examined to enhance the system's performance. The performance of our proposed NCDS was evaluated with two different datasets of inspiratory and expiratory cries. The CCA fusion feature set using the LSTM classifier accomplished the best F-score in the study, with 99.86% for the inspiratory cry dataset. The best F-score regarding the expiratory cry dataset, 99.44%, belonged to the GFCC feature set employing the LSTM classifier. These experiments suggest the high potential and value of using the newborn cry signals in the detection of pathologies. The framework proposed in this study can be implemented as an early diagnostic tool for clinical studies and help in the identification of pathologic newborns.