Quantifiable markers of ASD via multivariate MEG-DTI combination

通过多元 MEG-DTI 组合可量化 ASD 标记

基本信息

批准号：
8679003
负责人：
Ragini Verma
金额：
$ 20.22万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-06-15 至 2016-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8679003
关键词：
Address Age Algorithms Anisotropy Architecture Atlases Auditory Autistic Disorder Beds Behavior Behavioral Biological Markers Brain Classification Clinical Cognition Communication Complex Data Data Set Development Diagnosis Diagnostic Diffusion Diffusion Magnetic Resonance Imaging Disease Electroencephalography Electrophysiology (science)Future Generic Drugs Heterogeneity Image Impairment Individual Joints Language Learning Machine Learning Magnetoencephalography Maps Measures Methods Modality Neurobiology Neurons Nodal Pathology Pattern Pattern Recognition Phenotype Physiology Population Population Heterogeneity Process Protocols documentation Research Rest Sample Size Scanning Severities Signal Transduction Simulate Social Interaction Source Speech Perception Stimulus Symptoms System Techniques Testing Thalamencephalon Time autism spectrum disorder base design developmental disease endophenotype flexibility imaging modality insight morphometry neuropsychiatry neuropsychological novel outcome forecast prognostic public health relevance relating to nervous system sensor vector white matter

项目摘要

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.

描述（由申请人提供）：ASD 研究旨在使用不同的成像方式识别与 ASD 不同亚型相关的大脑水平内表型或单变量生物标志物。基于 DTI 的研究为自闭症谱系障碍 (ASD) 的白质 (WM) 结构和结构连接的病理诱导和神经发育变化以及与自闭症谱系障碍 (ASD) 相关的神经元缺陷的神经生物学基础提供了见解。 LI 等特定症状也正在通过 MEG 衍生的时间特征进行研究，该时间特征基于语音感知、处理和认知过程中诱发的神经磁活动。受 DTI 和 MEG 研究分别能够描述 ASD 的不同方面及其一些相关症状（如 LI）这一事实的鼓舞，在本提案中，我们的目标是将基于 MEG 的时间特征与基于 DTI 的结构连接特征相结合，以创建多参数空间使用模式分类的 ASD 时间特征。由于疾病和发育所带来的高度异质性，为自闭症谱系障碍人群创建联合多变量特征变得具有挑战性~表现为各种微妙的亚型，可能与差异性神经元缺陷相关，并且由于受试者无法完成扩散而存在模式缺失的数据集和电生理学协议。在本提案中，我们的目标是通过创建从人群的潜在病理模式中学习的可量化的多参数自闭症时空标记，通过将基于 MEG 的时间特征与基于 DTI 的结构连接特征相结合，来应对这些挑战。在具有 LI 的 ASD 群体上创建的模式分类器。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，通过学习群体异质性（尽管部分缺失数据）来量化 ASD 病理学（目标 2）。通过目标 3 中的应用，对于以 LI 作为异质性来源之一的 ASD 群体，我们将创建分类器，阐明组合扩散和 MEG 信息的重要性，识别最能表征 ASD 特征的区域和连通性组合，以及提供与每个受试者相关的异常分数，有助于量化损伤的可能性，从而丰富诊断决策。包含群体异质性和缺失数据的分类器将是通用且灵活的，适用于任何群体，因为这些分类器可以使用新的 MEG 和 DTI 特征轻松地进行重新训练，并将有助于希望纳入时空信息的未来研究。