Extraction of Functional Subnetworks in Autism Using Multimodal MRI

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，目前估计为每110人中有1人。自闭症给受影响的人、家庭和社会带来的代价是巨大的。旨在揭示这种疾病的发病机制，并可能导致合理的预防或治疗方法的研究是最重要的。我们建议在这里使用多模式神经成像来识别自闭症风险的生物标记物。可靠生物标志物的发现将有助于识别ASD患者以及那些随后将发展或已经发展为ASD细微迹象的人。此外，生物标记物可以帮助识别ASD的早期生物风险因素，最终使我们能够实现预防高危人群的疾病发展或减轻受影响人群的严重程度的目标。来自结构磁共振成像(MRI)的与脑体积增加相关的生物标记物已经揭示了ASD患者和典型发育中的对照组之间的差异。功能磁共振成像(FMRI)的差异也被发现(例如，使用与面孔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，ASD患者的大脑连接从结构(基于弥散张量成像，DTI)和功能测量两方面都发生了改变。然而，所有这些变化都是相当微妙的，而且发现并不一致。我们的假设是，使用解剖和扩散信息来指导和约束ASD相关子网络的提取，其定义基于功能信号和连接信息，将为ASD提供更敏感和更稳健的图像衍生生物标志物。因此，我们集中精力开发一种独特的数学方法，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子网络。我们将使用多视图集成策略来联合考虑fMRI时间进程强度/一致性和基于DTI的结构路径。这一方法将：i.)估计体素，其中激活最有可能响应我们的运动感知任务，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上知情的、功能上连接的子网络和III。)得出与ASD相关的重要激活参数、信号强度和连接性，这些参数可用作定量生物标记物。我们将首先将该策略应用于典型的发育儿童，并通过说明我们的方法可以产生可靠的生物标记物信息来确认我们的措施的有效性，与大量的fMRI运行相比，并表明这些信息在多次采集中是可重现的。然后，我们将通过检查来自三个与ASD相关的功能子网络对我们的运动感知任务做出反应的信号变化和连接参数来证明我们新的生物标记物的有效性。我们将评估这些措施在多大程度上能够很好地区分三个受试者群体：自闭症儿童、自闭症儿童的未受影响的兄弟姐妹和典型的发育中儿童。然后，我们将把结果与三种替代策略进行比较。

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.