Quantifiable markers of ASD via multivariate MEG-DTI combination

描述(由申请人提供)：ASD研究的目标是使用不同的成像方式识别与ASD不同亚型相关的脑水平内表型或单变量生物标记物。基于DTI的研究为ASD的白质(WM)结构和结构连通性的病理诱导和神经发育变化提供了洞察力，以及与ASD相关的神经元缺陷的神经生物学基础。基于语音感知、处理和认知过程中诱发的神经磁性活动，脑磁图衍生的时间特征也在研究特定的症状，如LI。DTI和MEG研究已经能够单独表征ASD的不同方面及其一些相关症状(如LI)，这一事实鼓舞着我们，在本建议中，我们的目标是将基于MEG的时间特征和基于DTI的结构连通性特征相结合，以创建多参数空间 使用模式分类的ASD的时间特征。由于疾病和发育带来的高度异质性，为ASD人群创建联合多变量特征变得具有挑战性，表现为可能与不同的神经元缺陷相关的各种微妙亚型，以及由于受试者无法完成扩散和电生理方案而导致的缺失模式的数据集的存在。在这项建议中，我们的目标是通过创建可量化的自闭症多参数时空标记来应对这些挑战，方法是将基于MEG的时间特征与基于DTI的结构连通性特征结合起来，使用在具有LI的ASD人群上创建的模式分类器来学习自闭症的潜在病理模式。我们识别时空兼容的DTI-MEG特征(目标1)，并创建各种多参数模式分类器，通过学习尽管部分缺失数据的群体异质性来量化ASD病理(目标2)。通过在AIM 3中对具有LI作为异质性来源之一的ASD人群的应用，我们将创建分类器，该分类器将阐明组合扩散和脑磁图信息的重要性，识别最能表征ASD的区域和连通性组合，并提供与每个受试者相关的异常分数，其有助于量化损害的可能性， 从而丰富了诊断决策。包含人口异质性和缺失数据的分类器在适用于任何人口方面将是通用和灵活的，因为这些分类器可以很容易地用新的脑磁图和DTI特征进行再训练，并将有助于希望纳入时空信息的未来研究。

DESCRIPTION (provided by applicant): Research in ASD has aimed at identifying brain level endophenotypes or univariate biomarkers associated with the different subtypes of ASD using different imaging modalities. DTI-based studies have provided insights into pathology-induced and neuro-developmental changes in white matter (WM) architecture and structural connectivity for ASD, and the neurobiological basis for neuronal deficits associated with ASD. Specific symptoms such as LI are also being investigated via MEG derived temporal signatures based on evoked neuromagnetic activity during speech perception, processing and cognition. Encouraged by the fact that DTI and MEG studies have individually been able to characterize different aspects of ASD and some of its related symptoms like LI, in this proposal we aim at combining the MEG-based temporal signatures with the DTI-based structural connectivity signatures to create a multi-parametric spatio temporal signature of ASD using pattern classification. Creating joint multivariate signatures for an ASD population is rendered challenging by high heterogeneity imposed by disease and development~ manifest as various subtle subtypes possibly associated with differential neuronal deficits and the presence of datasets with missing modalities due to the inability of the subject in completing both diffusion and electrophysiology protocols. In this proposal, we aim to address these challenges, by creating quantifiable multi-parametric spatio-temporal markers of autism learnt from the underlying pathology patterns of the population, by combining the MEG-based temporal signatures with the DTI-based structural connectivity signatures using pattern classifiers created on an ASD population with LI. We identify spatio-temporally compatible DTI- MEG features (aim 1) and create various multi- parametric pattern classifiers that will quantify ASD pathology by learning the population heterogeneity despite partially missing data (aim 2). Via the application in aim 3, to an ASD population with LI as one of the sources of heterogeneity, we will have created classifiers that will elucidate he importance of combination diffusion and MEG information, identify the regional and connectivity combinations that best characterize ASD, and provide abnormality scores associated with each subject that can aid in quantifying the likelihood of impairment, thereby enriching diagnosis decisions. The classifiers that embrace population heterogeneity and missing data will be generic and flexible in applicability to any population, as these can be easily retrained with new MEG and DTI features, and will aid future studies that wish to incorporate spatio-temporal information.