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

摘要/摘要： 据报道，注意力缺陷(AD)是一种跨心理健康和神经障碍的问题。它是作为一种极端存在的 连续分布的性状在普通人群中的状态。AD作为ADHD的一个关键组成部分，往往与 有多个神经认知领域的损害，特别是在注意力/警觉性、工作记忆、加工 速度和响应的可变性。到目前为止，大多数关于AD的研究都集中在额顶环路上，而对此知之甚少 关于额-丘脑-小脑回路(FCC)与AD有关。为了扩大我们对阿尔茨海默病神经机制的认识， 这项研究旨在描述FCC改变与神经认知和AD症状的关系，利用纵向脑 ABCD队列中的影像数据、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 FCC中多模式脑图像数据(灰质、白质、REST)之间关系建模的学习算法 状态fMRI功能连接和情绪N-back任务fMRI激活)和神经认知测量 基线时的领域(注意力/警觉性、工作记忆、处理速度和反应变异性)。然后我们就会 将迁移学习技术应用于神经认知潜在的神经成像特征来评估AD。然后, 在目标2中，我们将重点研究FCC神经影像特征的纵向变化与 神经认知和阿尔茨海默病两年。我们将应用与目标1相同的高级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 基因变化也受到基因图谱和环境因素的影响。在目标3中，我们将应用多变量数据挖掘 算法提取与FCC神经影像特征相关的遗传因素，并建立AD和 除FCC外，使用提取的遗传因素、社会人口和环境因素对AD的变化 多模式神经影像特征。最后，在目标4中，我们将使用Year验证FCC-Genetic-Environmental-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-Genetic-AD模型。调查结果 这项研究将具体说明FCC关键区域在每个神经认知领域和贡献下的变化 各神经认知域对FCC神经元功能所介导的AD症状的影响。大脑、基因和环境模型 将有助于识别由于普通人群中的FCC改变而具有AD风险的亚群，并帮助指定 跨越精神障碍界限的患者，他们有因FCC异常而恶化AD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.