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

CRCNS 研究提案：协作研究：发现群体级功能差异空间中的网络结构

• 批准号: 1822575
• 负责人: Archana Venkataraman
• 金额: $ 87.4万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1822575&HistoricalAwards=false
• 关键词: CRCNS; Research; Proposal; Collaborative; Discovering

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.

大规模研究大脑活动的相关模式（功能连接）可以提供一个独特的一瞥神经精神功能和疾病的内部运作。然而，目前的方法遵循一个不太理想的临床分析程序：他们首先适合每个人的模型，然后分别确定组的差异。在实践中，这种方法往往涉及整个大脑的分布式功能变化，这些变化难以解释，忽略了有关患者队列的关键信息，并且无法在研究中复制。这个项目通过假设每一种神经精神障碍都反映了大脑中一系列协调的中断，对这个问题进行了全新的审视。因此，患者和神经典型对照之间的诱导功能差异应该是相互依赖的，并形成自己的子网络。这一策略反映了该领域越来越多的观点，即复杂的神经精神障碍是系统水平的功能障碍，而不是孤立效应的集合。更进一步，在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。最后，该项目将为在广泛的神经精神疾病中发现稳健和有针对性的生物标志物铺平道路。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层次。任务1是通过两个互补的拓扑结构开发基于网络的功能差异的核心模型。也就是说，一个社区架构表明，给定的赤字来自一个子集的异常沟通的大脑区域，而扩散模型假设，赤字是链接到一个稀疏的区域枢纽集，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MRI的结构信息和通过估计随时间变化的网络差异来扩展核心框架。最后，任务3将采用纯粹的数据驱动方法来进行基于半监督表示学习的网络估计。在这些技术创新的同时，任务4将解决与三种最常见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。主要研究者将根据该项目的结果发布一个灵活的功能连接组学计算平台。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

A Biologically Interpretable Graph Convolutional Network to Link Genetic Risk Pathways and Imaging Phenotypes of Disease

连接遗传风险途径和疾病成像表型的生物学可解释图卷积网络

• 发表时间: 2022
• 期刊: International Conference on Learning Representations
• 作者: Ghosal, Sayan;Chen, Qiang;Pergola, Giulio;Goldman, Aaron L;Ulrich, William;Weinberger, Daniel R;Venkataraman, Archana
• 通讯作者: Venkataraman, Archana

A Generative-Predictive Framework to Capture Altered Brain Activity in fMRI and its Association with Genetic Risk: Application to Schizophrenia

捕获 fMRI 中大脑活动变化的生成预测框架及其与遗传风险的关联：在精神分裂症中的应用

• DOI: 10.1117/12.2511220
• 发表时间: 2019
• 期刊: SPIE Medical Imaging
• 作者: Ghosal, Sayan;Chen, Qiang;Goldman, Aaron L.;Ulrich, William;Berman, Karen F.;Weinberger, Daniel R.;Mattay, Venkata S.;Venkataraman, Archana
• 通讯作者: Venkataraman, Archana

M-GCN: A Multimodal Graph Convolutional Network to Integrate Functional and Structural Connectomics Data to Predict Multidimensional Phenotypic Characterizations

• 发表时间: 2021
• 作者: N. S. D'Souza;M. B. Nebel;D. Crocetti;Joshua Robinson;S. Mostofsky;A. Venkataraman

A generative-discriminative framework that integrates imaging, genetic, and diagnosis into coupled low dimensional space

• DOI: 10.1016/j.neuroimage.2021.118200
• 发表时间: 2021-06
• 期刊: NeuroImage
• 影响因子: 5.7
• 作者: Sayan Ghosal;Qiang Chen;G. Pergola;A. Goldman;William Ulrich;K. Berman;G. Blasi;L. Fazio;A. Rampino;A. Bertolino;D. Weinberger;V. Mattay;A. Venkataraman

Bridging Imaging, Genetics, and Diagnosis in a Coupled Low-Dimensional Framework

• DOI: 10.1007/978-3-030-32251-9_71
• 发表时间: 2019-10
• 作者: Sayan Ghosal;Qiang Chen;A. Goldman;William Ulrich;K. Berman;D. Weinberger;V. Mattay;A. Venkataraman