Extraction of Functional Subnetworks in Autism Using Multimodal MRI

使用多模态 MRI 提取自闭症功能子网

• 批准号: 8654362
• 负责人: JAMES S DUNCAN
• 金额: $ 35.63万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-06-01 至 2016-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8654362
• 关键词: Address; Affect; Amygdaloid structure; Anatomy; Autistic Disorder; Behavior; Behavioral; Biological; Biological Markers; Brain; Characteristics; Child; Data; Derivation procedure; Detection; Development; Diffusion; Diffusion Magnetic Resonance Imaging; Disease; Ear; Effectiveness; Evaluation; Exhibits; Face; Facial Expression; Family; Functional Magnetic Resonance Imaging; Functional disorder; Fusiform gyrus; Genetic Risk; Goals; Group Identifications; Heterogeneity; Human; Image; Individual; Inferior frontal gyrus; Lateral; Lead; Location; Magnetic Resonance Imaging; Measures; Medial; Memory; Methods; Modality; Modeling; Motion; Motion Perception; Neurodevelopmental Disorder; Noise; Pathogenesis; Pathway interactions; Patients; Pattern; Positioning Attribute; Prefrontal Cortex; Prevalence; Prevention approach; Publishing; Reporting; Research; Research Personnel; Rest; Risk; Risk Factors; Running; Series; Severities; Shapes; Siblings; Signal Transduction; Societies; Structure; Structure of middle temporal gyrus; Structure of superior temporal sulcus; Techniques; Testing; Time; United States; Weight; Work; autism spectrum disorder; base; brain volume; comparison group; cost; endophenotype; mathematical methods; multimodality; neuroimaging; prevent; response; social

DESCRIPTION (provided by applicant): The Autism spectrum disorders (ASD) are devastating neurodevelopmental disorders with a rising prevalence in the United States that is currently estimated at 1 in 110. The cost of ASD to affected people, families, and society is enormous. Research aimed at uncovering the pathogenesis of this condition, and potentially leading to rational approaches to prevention or treatment, is of the greatest importance. We propose here to use multi- modal neuroimaging to identify biomarkers of risk for autism. The discovery of reliable biomarkers will aid in the identification of individuals with ASD as well as those who will subsequently develop or are already developing subtle signs of ASD. In addition, biomarkers could serve to identify early biological risk factors for ASD, ultimately allowing us to achieve the goal of preventing the development of the disorder in people at risk or reducing the degree of severity in those affected. Biomarkers related to increased brain volume derived from structural magnetic resonance imaging (MRI) have revealed differences between individuals with ASD and typically-developing controls. Functional MRI (fMRI) differences have also been found (for instance, using tasks related to face identity and facial expression as shown by our group). Recent evidence suggests altered brain connectivity from both structural (based on diffusion tensor imaging, DTI) and functional measures in ASD. However, all of these alterations are quite subtle and the findings have been inconsistent. It is our hypothesis in our proposed work that the use of anatomic and diffusion information to guide and constrain the extraction of ASD-related subnetworks whose definition is based on both functional signals and connectivity information will provide more sensitive and robust image-derived biomarkers for ASD. Thus, we focus our efforts on the development of a unique mathematical approach that will estimate three functionally-connected subnetworks in the brain related to ASD and a motion perception task. We will use a multi-view integration strategy to jointly consider fMRI time course strength/coherence and DTI-based structural paths. This approach will: i.) estimate voxels where activation is most likely in response to our motion perception task and compute model-enhanced activation regression parameters, ii.) use these voxels to estimate structurally-informed, functionally-connected subnetworks and iii.) derive important ASD-related parameters of activation signal strength and connectivity that can be used as quantitative biomarkers. We will first apply the strategy to typically developing children and confirm the utility of our measures by illustrating that our approach can produce reliable biomarker information in comparison to a large number of fMRI runs and show that this information is reproducible over multiple acquisitions. Then, we will demonstrate the effectiveness of our new biomarkers by examining the signal change and connectivity parameters derived from three ASD-related functional subnetworks that respond to our motion perception task. We will evaluate how well these measures can stratify three subject groups: children with ASD, unaffected siblings of children with ASD and typically developing children. We will then compare the results to three alternative strategies.

描述（由申请人提供）：自闭症谱系障碍（ASD）是一种破坏性的神经发育障碍，在美国的患病率正在上升，目前估计为110分之一。自闭症谱系障碍给患者、家庭和社会造成的损失是巨大的。研究旨在揭示这种情况的发病机制，并可能导致合理的预防或治疗方法，是最重要的。我们在此建议使用多模态神经成像来识别自闭症风险的生物标志物。可靠的生物标记物的发现将有助于识别患有自闭症谱系障碍的个体，以及那些随后将发展或已经发展出自闭症谱系障碍细微症状的个体。此外，生物标志物可以用于识别自闭症谱系障碍的早期生物学风险因素，最终使我们能够实现在高危人群中预防疾病发展或降低患者严重程度的目标。来自结构磁共振成像（MRI）的与脑容量增加相关的生物标志物揭示了ASD患者与正常发展对照组之间的差异。功能性核磁共振成像（fMRI）也发现了差异（例如，使用与面部身份和面部表情相关的任务，如我们小组所示）。最近的证据表明，从结构（基于弥散张量成像，DTI）和功能测量来看，ASD患者的大脑连通性发生了改变。然而，所有这些变化都非常微妙，研究结果也不一致。在我们提出的工作中，我们假设使用解剖学和扩散信息来指导和约束ASD相关子网络的提取，这些子网络的定义基于功能信号和连接信息，将为ASD提供更敏感和健壮的图像衍生生物标志物。因此，我们将重点放在开发一种独特的数学方法上，该方法将估计大脑中与ASD和运动感知任务相关的三个功能连接的子网络。我们将使用多视图集成策略来共同考虑fMRI时间过程强度/相干性和基于dti的结构路径。这种方法将：i.)估计响应我们的运动感知任务最有可能激活的体素，并计算模型增强的激活回归参数，ii.)使用这些体素来估计结构信息，功能连接的子网，iii.)导出激活信号强度和连通性的重要asd相关参数，这些参数可以用作定量生物标志物。我们将首先将该策略应用于正常发育的儿童，并通过说明我们的方法可以产生可靠的生物标志物信息，与大量的fMRI运行相比，并表明该信息在多次获取中是可重复的，从而确认我们措施的实用性。然后，我们将通过检查响应我们的运动感知任务的三个asd相关功能子网络的信号变化和连接参数来证明我们的新生物标志物的有效性。我们将评估这些措施如何很好地对三组受试者进行分类：自闭症儿童、自闭症儿童的未受影响的兄弟姐妹和正常发育的儿童。然后，我们将结果与三种替代策略进行比较。