PDF(Pubmed)
Abstract：
BACKGROUND: Children\'s cognitive performance fluctuates across multiple timescales. However, fluctuations have often been neglected in favour of research into average cognitive performance, limiting the unique insights into cognitive abilities and development that cognitive variability may afford. Preliminary evidence suggests that greater variability is associated with increased symptoms of neurodevelopmental disorders, and differences in behavioural and neural functioning. The relative dearth of empirical work on variability, historically limited due to a lack of suitable data and quantitative methodology, has left crucial questions unanswered, which the CODEC (COgnitive Dynamics in Early Childhood) study aims to address.
METHODS: The CODEC cohort is an accelerated 3-year longitudinal study which encompasses 600 7-to-10-year-old children. Each year includes a \'burst\' week (3 times per day, 5 days per week) of cognitive measurements on five cognitive domains (reasoning, working memory, processing speed, vocabulary, exploration), conducted both in classrooms and at home through experience sampling assessments. We also measure academic outcomes and external factors hypothesised to predict cognitive variability, including sleep, mood, motivation and background noise. A subset of 200 children (CODEC-MRI) are invited for two deep phenotyping sessions (in year 1 and year 3 of the study), including structural and functional magnetic resonance imaging, eye-tracking, parental measurements and questionnaire-based demographic and psychosocial measures. We will quantify developmental differences and changes in variability using Dynamic Structural Equation Modelling, allowing us to simultaneously capture variability and the multilevel structure of trials nested in sessions, days, children and classrooms.
CONCLUSIONS: CODEC\'s unique design allows us to measure variability across a range of different cognitive domains, ages, and temporal resolutions. The deep-phenotyping arm allows us to test hypotheses concerning variability, including the role of mind wandering, strategy exploration, mood, sleep, and brain structure. Due to CODEC\'s longitudinal nature, we are able to quantify which measures of variability at baseline predict long-term outcomes. In summary, the CODEC study is a unique longitudinal study combining experience sampling, an accelerated longitudinal \'burst\' design, deep phenotyping, and cutting-edge statistical methodologies to better understand the nature, causes, and consequences of cognitive variability in children.
BACKGROUND: ClinicalTrials.gov - NCT06330090.
METHODS: The CODEC cohort is an accelerated 3-year longitudinal study which encompasses 600 7-to-10-year-old children. Each year includes a \'burst\' week (3 times per day, 5 days per week) of cognitive measurements on five cognitive domains (reasoning, working memory, processing speed, vocabulary, exploration), conducted both in classrooms and at home through experience sampling assessments. We also measure academic outcomes and external factors hypothesised to predict cognitive variability, including sleep, mood, motivation and background noise. A subset of 200 children (CODEC-MRI) are invited for two deep phenotyping sessions (in year 1 and year 3 of the study), including structural and functional magnetic resonance imaging, eye-tracking, parental measurements and questionnaire-based demographic and psychosocial measures. We will quantify developmental differences and changes in variability using Dynamic Structural Equation Modelling, allowing us to simultaneously capture variability and the multilevel structure of trials nested in sessions, days, children and classrooms.
CONCLUSIONS: CODEC\'s unique design allows us to measure variability across a range of different cognitive domains, ages, and temporal resolutions. The deep-phenotyping arm allows us to test hypotheses concerning variability, including the role of mind wandering, strategy exploration, mood, sleep, and brain structure. Due to CODEC\'s longitudinal nature, we are able to quantify which measures of variability at baseline predict long-term outcomes. In summary, the CODEC study is a unique longitudinal study combining experience sampling, an accelerated longitudinal \'burst\' design, deep phenotyping, and cutting-edge statistical methodologies to better understand the nature, causes, and consequences of cognitive variability in children.
BACKGROUND: ClinicalTrials.gov - NCT06330090.
摘要：
背景：儿童的认知表现在多个时间尺度上波动。然而,波动往往被忽略，而不是对平均认知表现的研究，限制对认知变异性可能提供的认知能力和发展的独特见解。初步证据表明，更大的变异性与神经发育障碍的症状增加有关，以及行为和神经功能的差异。关于变异性的实证工作相对缺乏，由于缺乏合适的数据和定量方法，历史上受到限制，留下了关键问题没有答案，CODEC(幼儿认知动力学)研究旨在解决这一问题。
方法：CODEC队列是一项为期3年的加速纵向研究，涵盖600名7至10岁儿童。每年包括一个“爆发”周(每天3次，每周5天)对五个认知领域进行认知测量(推理，工作记忆,处理速度,词汇,探索)，通过经验抽样评估在教室和家里进行。我们还测量了学术成果和假设的外部因素，以预测认知变异性，包括睡眠,心情,动机和背景噪音。邀请200名儿童（CODEC-MRI）参加两次深度表型研究（在研究的第1年和第3年），包括结构和功能磁共振成像，眼动追踪,父母的测量和基于问卷的人口统计学和社会心理测量。我们将使用动态结构方程模型量化发展差异和变异性的变化，使我们能够同时捕获变异性和嵌套在会话中的试验的多层次结构，days,孩子和教室。
结论：CODEC的独特设计使我们能够测量一系列不同认知领域的变异性，年龄,和时间决议。深层表型臂使我们能够测试有关变异性的假设，包括走神的角色,战略探索,心情,睡眠,和大脑结构。由于编解码器的纵向性质，我们能够量化基线时哪些变异性指标可预测长期结局.总之,CODEC研究是一项独特的纵向研究，结合了经验抽样，加速的纵向“爆裂”设计，深层表型，以及尖端的统计方法来更好地理解自然，原因,以及儿童认知变异的后果。
背景：ClinicalTrials.gov-NCT06330090。
方法：CODEC队列是一项为期3年的加速纵向研究，涵盖600名7至10岁儿童。每年包括一个“爆发”周(每天3次，每周5天)对五个认知领域进行认知测量(推理，工作记忆,处理速度,词汇,探索)，通过经验抽样评估在教室和家里进行。我们还测量了学术成果和假设的外部因素，以预测认知变异性，包括睡眠,心情,动机和背景噪音。邀请200名儿童（CODEC-MRI）参加两次深度表型研究（在研究的第1年和第3年），包括结构和功能磁共振成像，眼动追踪,父母的测量和基于问卷的人口统计学和社会心理测量。我们将使用动态结构方程模型量化发展差异和变异性的变化，使我们能够同时捕获变异性和嵌套在会话中的试验的多层次结构，days,孩子和教室。
结论：CODEC的独特设计使我们能够测量一系列不同认知领域的变异性，年龄,和时间决议。深层表型臂使我们能够测试有关变异性的假设，包括走神的角色,战略探索,心情,睡眠,和大脑结构。由于编解码器的纵向性质，我们能够量化基线时哪些变异性指标可预测长期结局.总之,CODEC研究是一项独特的纵向研究，结合了经验抽样，加速的纵向“爆裂”设计，深层表型，以及尖端的统计方法来更好地理解自然，原因,以及儿童认知变异的后果。
背景：ClinicalTrials.gov-NCT06330090。
