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Despite a recent surge in research examining parent–child neural similarity using fMRI, there remains a need for further investigation into how such similarity may play a role in children's emotional adjustment. Moreover, no prior studies explored the potential contextual factors that may moderate the link between parent–child neural similarity and children's developmental outcomes. In this study, 32 parent–youth dyads (parents:Mage= 43.53 years, 72% female; children:Mage= 11.69 years, 41% female) watched an emotion-evoking animated film while being scanned using fMRI. We first quantified how similarly emotion network interacts with other brain regions in responding to the emotion-evoking film between parents and their children. We then examined how such parent–child neural similarity is associated with children's emotional adjustment, with attention to the moderating role of family cohesion. Results revealed that higher parent–child similarity in functional connectivity pattern during movie viewing was associated with better emotional adjustment, including less negative affect, lower anxiety, and greater ego resilience in youth. Moreover, such associations were significant only among families with higher cohesion, but not among families with lower cohesion. The findings advance our understanding of the neural mechanisms underlying how children thrive by being in sync and attuned with their parents, and provide novel empirical evidence that the effects of parent–child concordance at the neural level on children's development are contextually dependent. SIGNIFICANCE STATEMENTWhat neural processes underlie the attunement between children and their parents that helps children thrive? Using a naturalistic movie-watching fMRI paradigm, we find that greater parent–child similarity in how emotion network interacts with other brain regions during movie viewing is associated with youth's better emotional adjustment including less negative affect, lower anxiety, and greater ego resilience. Interestingly, these associations are only significant among families with higher cohesion, but not among those with lower cohesion. Our findings provide novel evidence that parent–child shared neural processes to emotional situations can confer benefits to children, and underscore the importance of considering specific family contexts in which parent–child neural similarity may be beneficial or detrimental to children's development, highlighting a crucial direction for future research.
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In-the-Wild Affect Analysis of Children with ASD Using Heart Rate
Recognizing the affective state of children with autism spectrum disorder (ASD) in real-world settings poses challenges due to the varying head poses, illumination levels, occlusion and a lack of datasets annotated with emotions in in-the-wild scenarios. Understanding the emotional state of children with ASD is crucial for providing personalized interventions and support. Existing methods often rely on controlled lab environments, limiting their applicability to real-world scenarios. Hence, a framework that enables the recognition of affective states in children with ASD in uncontrolled settings is needed. This paper presents a framework for recognizing the affective state of children with ASD in an in-the-wild setting using heart rate (HR) information. More specifically, an algorithm is developed that can classify a participant’s emotion as positive, negative, or neutral by analyzing the heart rate signal acquired from a smartwatch. The heart rate data are obtained in real time using a smartwatch application while the child learns to code a robot and interacts with an avatar. The avatar assists the child in developing communication skills and programming the robot. In this paper, we also present a semi-automated annotation technique based on facial expression recognition for the heart rate data. The HR signal is analyzed to extract features that capture the emotional state of the child. Additionally, in this paper, the performance of a raw HR-signal-based emotion classification algorithm is compared with a classification approach based on features extracted from HR signals using discrete wavelet transform (DWT). The experimental results demonstrate that the proposed method achieves comparable performance to state-of-the-art HR-based emotion recognition techniques, despite being conducted in an uncontrolled setting rather than a controlled lab environment. The framework presented in this paper contributes to the real-world affect analysis of children with ASD using HR information. By enabling emotion recognition in uncontrolled settings, this approach has the potential to improve the monitoring and understanding of the emotional well-being of children with ASD in their daily lives.
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- Award ID(s):
- 2114808
- PAR ID:
- 10501758
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Sensors
- Volume:
- 23
- Issue:
- 14
- ISSN:
- 1424-8220
- Page Range / eLocation ID:
- 6572
- Subject(s) / Keyword(s):
- Transformers Biosensors Multi-modal representation learning
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/s23146572
