EarlyScreen: Multi-scale Instance Fusion for Predicting Neural Activation and Psychopathology in Preschool Children

Manasa Kalanadhabhatta, Adrelys Mateo Santana, Zhongyang Zhang, Deepa Ganesan, Adam S. Grabell, Tauhidur Rahman
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引用次数: 3

Abstract

Emotion dysregulation in early childhood is known to be associated with a higher risk of several psychopathological conditions, such as ADHD and mood and anxiety disorders. In developmental neuroscience research, emotion dysregulation is characterized by low neural activation in the prefrontal cortex during frustration. In this work, we report on an exploratory study with 94 participants aged 3.5 to 5 years, investigating whether behavioral measures automatically extracted from facial videos can predict frustration-related neural activation and differentiate between low- and high-risk individuals. We propose a novel multi-scale instance fusion framework to develop EarlyScreen – a set of classifiers trained on behavioral markers during emotion regulation. Our model successfully predicts activation levels in the prefrontal cortex with an area under the receiver operating characteristic (ROC) curve of 0.85, which is on par with widely-used clinical assessment tools. Further, we classify clinical and non-clinical subjects based on their psychopathological risk with an area under the ROC curve of 0.80. Our model’s predictions are consistent with standardized psychometric assessment scales, supporting its applicability as a screening procedure for emotion regulation-related psychopathological disorders. To the best of our knowledge, EarlyScreen is the first work to use automatically extracted behavioral features to characterize both neural activity and the diagnostic status of emotion regulation-related disorders in young children. We present insights from mental health professionals supporting the utility of EarlyScreen and discuss considerations for its subsequent deployment. CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing systems and tools ; • Computing methodologies → Machine learning ; • Applied computing → Psychology Health informatics . Multi-scale Neural Psychopathology
EarlyScreen:多尺度实例融合预测学龄前儿童神经激活和精神病理
众所周知,儿童早期的情绪失调与几种精神病理状况(如多动症、情绪和焦虑障碍)的高风险有关。在发育神经科学研究中,情绪失调的特征是沮丧时前额叶皮层的神经激活低。在这项工作中,我们报告了一项对94名年龄在3.5至5岁之间的参与者进行的探索性研究,调查从面部视频中自动提取的行为测量是否可以预测与挫折相关的神经激活,并区分低风险个体。我们提出了一种新的多尺度实例融合框架来开发EarlyScreen——一套在情绪调节过程中对行为标记进行训练的分类器。我们的模型成功地预测了前额皮质的激活水平,受试者工作特征(ROC)曲线下的面积为0.85,这与广泛使用的临床评估工具相当。此外,我们根据受试者的精神病理风险对临床和非临床受试者进行分类,ROC曲线下面积为0.80。我们的模型预测与标准化的心理测量评估量表一致,支持其作为情绪调节相关精神病理障碍筛选程序的适用性。据我们所知,EarlyScreen是第一个使用自动提取的行为特征来描述幼儿情绪调节相关疾病的神经活动和诊断状态的工作。我们介绍了支持EarlyScreen实用程序的心理健康专业人员的见解,并讨论了后续部署的考虑因素。CCS概念:•以人为中心的计算→无处不在的移动计算系统和工具;•计算方法→机器学习;•应用计算机→心理健康信息学。多尺度神经精神病理学
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