Quantifiable markers of ASD via multivariate MEG-DTI combination

描述（由申请人提供）： ASD的研究旨在使用不同的成像方式来识别与ASD的不同亚型相关的脑水平内表型或单变量生物标志物。 基于DTI的研究为ASD的白色物质（WM）结构和结构连接的病理诱导和神经发育变化以及与ASD相关的神经元缺陷的神经生物学基础提供了见解。 特定的症状，如LI也正在调查通过MEG衍生的时间签名的基础上诱发的神经磁活动在语音感知，处理和认知。由于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），因此在此建议中，我们旨在将基于MEG的时间特征与基于DTI的结构连接特征相结合，以创建多参数空间特征。 使用模式分类的ASD的时间签名。 由于疾病和发育造成的高度异质性，为ASD人群创建联合多变量特征变得具有挑战性-表现为可能与差异神经元缺陷相关的各种细微亚型以及由于受试者无法完成扩散和电生理方案而存在缺失模态的数据集。在这项提案中，我们的目标是解决这些挑战，通过创建可量化的多参数的时空标记的自闭症学习从人口的基本病理模式，通过结合MEG为基础的时间签名与DTI为基础的结构连接签名使用模式分类器创建的ASD人口与LI。我们识别时空兼容的DTI-MEG特征（目的1），并创建各种多参数模式分类器，其将通过学习群体异质性来量化ASD病理，尽管部分缺失数据（目的2）。通过目标3中的应用，对于具有LI作为异质性来源之一的ASD人群，我们将创建分类器，其将阐明组合扩散和MEG信息的重要性，识别最佳表征ASD的区域和连通性组合，并提供与每个受试者相关的异常评分，其可以帮助量化损伤的可能性， 从而丰富诊断决策。 包含人群异质性和缺失数据的分类器将是通用的，并且适用于任何人群，因为这些可以很容易地用新的MEG和DTI功能重新训练，并且将有助于未来希望纳入时空信息的研究。

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.