Kraus Tzipi Horowitz, Bebar Marwan, Jacobson Adi, Hutton John
Educational Neuroimaging Group, Faculty of Education in Science and Technology, Technion- Israel Institute of Technology, Haifa, Israel.
Faculty of Biomedical Engineering, Technion- Israel Institute of Technology, Haifa, Israel.
Brain Imaging Behav. 2025 Jul 2. doi: 10.1007/s11682-025-01037-2.
The quality of parent-child interaction during shared reading ("shared reading quality") is strongly linked to cognitive and relational benefits. However, the relationship between shared reading quality and activation and synchronization of reading-related brain networks has not yet been characterized. The current study involved 22 4-year-old girls who completed functional MRI including a validated stories listening task, and a primary parent. Prior to MRI, video observation of the parent and child reading together was conducted and later coded using a standardized scoring form quantifying parent-child verbal and nonverbal interaction. Behavioral measures included demographics and a maternal depression scale. To achieve this goal, fMRI stories-listening data was utilized to create a diffusion maps algorithm and then to classify the level of parent-child interaction during the shared reading observation. The algorithm clustered children with higher parent-child engagement scores with fMRI diffusion patterns in regions of the brain known to support reading. This study establishes proof-of-concept that applying this diffusion maps algorithm to brain functional connectivity data can reliably predict parent-child interaction during shared book reading. It also suggests that an algorithmic approach may be a novel, data-driven means to quantify parent-child interaction in different contexts (e.g., reading, play) and populations.
在亲子共读期间的亲子互动质量(“共读质量”)与认知和关系方面的益处紧密相关。然而,共读质量与阅读相关脑网络的激活和同步之间的关系尚未得到明确描述。当前的研究涉及22名4岁女童,她们完成了功能磁共振成像,包括一项经过验证的故事聆听任务,还有一位主要家长参与。在进行磁共振成像之前,对亲子共同阅读进行了视频观察,随后使用标准化评分表进行编码,以量化亲子之间的言语和非言语互动。行为测量包括人口统计学数据和一项母亲抑郁量表。为实现这一目标,利用功能磁共振成像故事聆听数据创建了扩散映射算法,然后对共读观察期间的亲子互动水平进行分类。该算法将亲子互动得分较高的儿童与大脑中已知支持阅读区域的功能磁共振成像扩散模式聚类在一起。这项研究证实了将这种扩散映射算法应用于脑功能连接数据能够可靠地预测亲子共读期间的亲子互动这一概念验证。它还表明,算法方法可能是一种新颖的、数据驱动的手段,用于量化不同情境(如阅读、玩耍)和人群中的亲子互动。