CRCNS Research Proposal: Collaborative Research: Discovering Network Structure in the Space of Group-Level Functional Differences

大规模研究大脑活动的相关模式(功能连接)可以提供对神经精神功能和障碍的内部工作方式的独特一瞥。然而，目前的方法遵循着一种不太理想的临床分析程序：它们首先为每个个体拟合一个模型，然后分别确定群体差异。在实践中，这种方法往往牵涉到整个大脑的分布式功能变化，这些变化很难解释，忽略了关于患者队列的关键信息，并且无法在各研究中复制。这个项目对这个问题有了全新的看法，假设每一种神经精神障碍都反映了大脑中一系列协调的干扰。因此，患者和神经典型对照之间诱发的功能差异应该是相互依赖的，并形成各自的子网络。这一策略反映了该领域越来越多的看法，即复杂的神经精神障碍是系统层面的功能障碍，而不是孤立影响的集合。更进一步，这项工作中开发的推理程序将战略性地利用患者的异质性来指导子网络估计。最终，该项目将为在广泛的神经精神障碍中发现强有力的和有针对性的生物标记物铺平道路。该项目的技术探索将分三个阶段展开，每个阶段都包含一个额外的抽象水平。任务1是通过两个互补的拓扑开发基于网络的功能差异的核心模型。也就是说，社区架构认为，给定的缺陷是由通信异常的大脑区域的一个子集产生的，而扩散模型则假设缺陷与一组稀疏的区域中枢有关，这些区域中枢与大脑的其他部分异常地相互作用。任务2将通过结合来自扩散磁共振成像的结构信息和通过估计时变的网络差异来拓宽核心框架。最后，任务3将采用基于半监督表示学习的纯数据驱动的网络估计方法。在这些技术创新的同时，任务4将解决与三种最普遍的神经发育障碍相关的关键临床问题：自闭症、ADHD和精神分裂症。主要研究人员将根据该项目的结果发布一个灵活的功能连接学计算平台。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Large-scale study correlated patterns of activity in the brain (functional connectivity) can provide a unique glimpse into the inner workings of neuropsychiatric functions and disorders. However, current methods follow a less than optimal procedure for clinical analyses: they first fit a model to each individual, and then separately identify group differences. In practice, this approach tends to implicate distributed functional changes across the brain, which are difficult to interpret, ignore crucial information about the patient cohort, and fail to replicate across studies. This project takes an entirely new look at this problem by hypothesizing that each neuropsychiatric disorder reflects a set of coordinated disruptions in the brain. As a result, the induced functional differences between patients and neurotypical controls should be interdependent and form their own subnetwork. This strategy reflects a growing perception in the field that complex neuropsychiatric disorders are system-level dysfunctions, rather than collections of isolated effects. Going one step further, the inference procedures developed in this work will strategically leverage patient heterogeneity to guide the subnetwork estimation. In this end, this project will pave the way for robust and targeted biomarker discovery across a wide range of neuropsychiatric disorders.The technical exploration of this project will unfold in three stages, each of which incorporates an additional level of abstraction. Task 1 is to develop a core model of network-based functional differences via two complementary topologies. Namely, a community architecture suggests that the given deficit arises from a subset of abnormally communicating brain regions, whereas a spreading model assumes that the deficit is linked to a sparse set of region hubs, which abnormally interact with the rest of the brain. Task 2 will broaden the core framework by incorporating structural information from diffusion MRI and by estimating time-varying network differences. Finally, Task 3 will take a purely data-driven approach to the network estimation based on semi-supervised representation learning. In parallel with these technical innovations, Task 4 will address key clinical questions related to three of the most prevalent neurodevelopmental disorders: autism, ADHD, and schizophrenia. The principal investigators will release a flexible computational platform for functional connectomics based on the results of this project.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.