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的研究旨在鉴定使用不同成像模式与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ASD的白质（WM）结构（WM）结构和结构连接性的病理诱导的和神经开发变化提供了见解，以及与ASD相关的神经元缺陷的神经生物学基础。在语音感知，加工和认知期间，基于诱发的神经磁性活性，还通过MEG得出的时间标志研究了特定的症状，例如LI症状。鼓励DTI和MEG研究单独地表征ASD及其某些相关症状（例如LI）的不同方面，在此提案中，我们旨在将基于MEG的时间标志与基于DTI的基于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的时间签名。为ASD人群创建共同的多元特征，这是由于疾病和发育施加的高异质性而变得挑战。在该提案中，我们旨在通过创建可量化的自闭症的多参数时空标记来解决这些挑战，这是通过将基于MEG的基于MEG基于MEG的时间签名与基于DTI的结构连通性签名相结合的，使用与LI的ASD人群创建的模式分类器，通过将基于MEG的时间签名与基于DTI的结构连通性签名相结合。我们确定时空兼容的DTI-MEG特征（AIM 1）并创建各种多参数模式分类器，尽管部分缺少数据，但可以通过学习总体异质性来量化ASD病理（AIM 2）。通过AIM 3中的应用程序，将其作为异质性的ASD人群之一，我们将创建分类器，以阐明他对组合扩散和MEG信息的重要性，确定最能表征ASD的区域和连接组合，并提供与每个主题相关的异常得分，并有助于量化具有量学范围的范围，以量化量化的异常。从而丰富诊断决策。包含人口异质性和缺少数据的分类器将对任何人群的适用性具有通用和灵活性，因为这些分类器可以通过新的MEG和DTI特征轻松地重新训练，并将有助于未来希望结合时空信息的研究。