Frontal-thalamo-cerebellar circuitry of attention deficit via imaging-genetic-environmental analyses

摘要/总结： 据报道，注意力缺陷（AD）是心理健康和神经系统疾病中一个令人担忧的问题。它作为一种极端的存在而存在 一般人群中连续分布的特征的状况。 AD 作为 ADHD 的一个关键组成部分通常与 多个神经认知领域受损，特别是注意力/警觉性、工作记忆、处理能力 速度和响应变化。迄今为止，大多数关于 AD 的研究都集中在额叶-顶叶环路，而人们知之甚少 额叶-丘脑-小脑回路 (FCC) 与 AD 相关。为了扩展我们对 AD 神经机制的了解， 本研究旨在利用纵向大脑来描绘 FCC 改变与神经认知和 AD 症状之间的关系 ABCD 队列中的成像数据、基因组学、神经认知、环境数据。首先，在目标 1 中，我们将应用高级深度 学习算法对 FCC 中多模态大脑图像数据（灰质、白质、休息 状态 fMRI 功能连接、情绪 N-back 任务 fMRI 激活）和四个神经认知测量 基线的领域（注意力/警觉性、工作记忆、处理速度和反应变异性）。然后我们会 将迁移学习技术应用于神经认知的潜在神经影像特征来估计 AD。然后， 在目标 2 中，我们将重点关注 FCC 神经影像特征的纵向变化与 两年内研究神经认知和AD。我们将像目标 1 一样应用先进的机器学习方法来识别 FCC 神经认知纵向变化的动态特征，然后转移到AD。 AD症状和症状 变化还受到遗传特征和环境因素的影响。在目标 3 中，我们将应用多元数据挖掘 算法提取与 FCC 神经影像特征相关的遗传因素，并构建 AD 和 AD 的预测模型 除了 FCC 之外，还使用提取的遗传因素、社会人口和环境因素来分析 AD 的变化 多模态神经影像学特征。最后，在目标 4 中，我们将使用年份验证 FCC-遗传-环境-AD 模型 4 跟踪 ABCD 队列中的数据并使用独立的 PNC 队列验证 FCC-遗传-AD 模型。研究结果 这项研究将详细说明每个神经认知领域的 FCC 关键区域的变化和贡献 每个神经认知域与 FCC 神经元特征介导的 AD 症状的关系。大脑、基因和环境模型 AD 的评估将有助于识别由于一般人群中 FCC 改变而具有 AD 风险的亚人群，并帮助指定 跨越精神障碍界限的患者，由于 FCC 异常而有导致 AD 恶化的风险。

Abstract/Summary: Attention deficit (AD) is a reported concern across mental health and neurological disorders. It exists as an extreme condition of a continuously distributed trait in the general population. AD as a key component of ADHD is often associated with impairments in multiple neurocognitive domains, particularly in attention/vigilance, working memory, processing speed, and response variability. To date, most investigations on AD focus on frontal-parietal circuity, and less is known about the frontal-thalamo-cerebellar circuitry (FCC) relates to AD. To extend our knowledge on neural mechanisms of AD, this study aims to delineate FCC alteration in relation to neurocognition and AD symptoms, leveraging longitudinal brain imaging data, genomics, neurocognition, environmental data in ABCD cohort. First, in Aim 1 we will apply advanced deep learning algorithms to model the relationship between multimodal brain image data in FCC (gray matter, white matter, rest state fMRI functional connectivity, and emotional N-back task fMRI activation) and neurocognitive measures in the four domains (attention/vigilance, working memory, processing speed, and response variability) at baseline. And then we will apply the transfer learning techniques to the latent neuroimaging features underlying neurocognition to estimate AD. Then, in Aim 2 we will focus on the relation between longitudinal changes of FCC neuroimaging features and the changes in neurocognition and AD in two years. We will apply advanced machine learning methods just as in Aim 1 to identify FCC dynamic features underlying longitudinal changes in neurocognition, and then transfer to AD. AD symptoms and symptom changes are also affected by genetic profiles and environmental factors. In Aim 3 we will apply multivariate data mining algorithms to extract genetic factors associated with FCC neuroimaging features, and build a prediction model for AD and changes of AD using genetic factors extracted, social demographic and environmental factors, in addition to FCC multimodal neuroimaging features. Lastly, in Aim 4 we will validate the FCC-genetic-environmental-AD model using Year 4 follow up data in ABCD cohort and validate the FCC-genetic-AD model using an independent PNC cohort. The findings from this study will specify alterations in crucial regions of FCC underlying each neurocognition domain and contribution of each neurocognition domain to AD symptoms mediated by FCC neuronal features. Brain, gene, and environmental model of AD will help identify a subpopulation with risk for AD due to FCC alterations in the general population, and help specify patients across the boundaries of mental disorders who are risk for worsening AD due to FCC abnormalities.