CRCNS: Multimodal Dynamic Causal Learning for Neuroimaging

CRCNS：神经影像多模式动态因果学习

• 批准号: 10462768
• 负责人: Sergey Plis
• 金额: $ 30.95万
• 依托单位: GEORGIA STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-05 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10462768
• 关键词: Ache; Address; Adoption; Algorithms; Base of the Brain; Biological Markers; Brain; Brain Diseases; Brain imaging; Brain region; Cognitive; Communication; Communities; Computer software; Data; Data Collection; Data Set; Diffusion Magnetic Resonance Imaging; Disease; Disease model; Electroencephalography; Environment; Functional Magnetic Resonance Imaging; Functional disorder; Goals; Individual; Knowledge; Lead; Learning; Link; Magnetic Resonance Imaging; Maps; Measurement; Measures; Mental disorders; Methods; Modality; Modeling; Names; Neurosciences; Output; Psyche structure; Research; Research Proposals; Sample Size; Sampling; Schizophrenia; Scientific Advances and Accomplishments; Scientist; Signal Transduction; Specific qualifier value; Speed; Structure; System; Techniques; Testing; Uncertainty; algorithmic methodologies; base; causal model; diagnostic tool; diverse data; dynamic system; experimental study; imaging modality; improved; insight; learning algorithm; learning strategy; machine learning method; member; multimodal data; multimodal neuroimaging; multimodality; neural circuit; neuroimaging; novel; predictive tools; psychologic; relating to nervous system; success; theories; virtual

CRCNS Research Proposal: Collaborative Research: Multimodal Dynamic Causal Learning for Neuroimaging A Project Description A.1 Introduction Many analyses of fMRI and other neuroimaging data aim to discover the underlying causal or commu- nication structures that generated that activity.1,2 An accurate characterization of these brain structures is important for understanding neural circuits, systems-level neuroscience, and the neural bases of var- ious cognitive psychological phenomena or mental diseases. Brain structures learned from neuroimag- ing data also provide a powerful diagnostic tool for predicting everything from the concepts currently in one’s mind3–5 to whether one suffers from different mental diseases.6–10 Given the importance of such brain connectivity networks, it is unsurprising that a wide variety of learning algorithms have been developed for different neuroimaging modalities. In particular, many of these methods aim to infer the underlying causal or connectivity networks from data (in contrast with model comparison methods such as dynamic causal modeling (DCM)11). These network discovery methods have achieved some notable successes,2,6,12–21 but have also largely failed to address two issues that can impede our ability to achieve improved understanding of the full, working brain. Our project will develop, validate, and apply methods that solve both of these challenges. First, existing brain connectivity inference methods can be roughly divided into two groups: static me- thods that do not actually treat the brain as a dynamic system (e.g., IMaGES22 and most of the ap- proaches tested by Smith et al.23); and dynamic methods that explicitly measure and model the dy- namics of the brain. Static methods obviously fail to use all of the available information. In contrast, dynamic methods use the full structure of the measurements, but essentially all such methods24–28 infer causal and connectivity structures at the timescale of the measurement modality, rather than at the brain’s causal timescale. However, the networks learned from data at the measurement and brain timescales can be quite different, even given solutions for all of the other statistical and measurement problems facing neuroimaging analysis.29 Moreover, the important facts about causal or connectivity structure are frequently about which brain regions communicate directly with which other brain re- gions, which requires a focus on the brain timescale, not the measurement modality timescale. It is thus scientifically critical that we have methods that can determine the causal connections that exist at the timescale of the underlying neural systems, not just those that are found at the timescale of our particular neuroimaging methods. Second, there are multiple neuroimaging measurement modalities, each with their own strengths and weaknesses. There are obvious and widely recognized advantages of multimodal information fusion: 1) access to multiple, richer datasets, larger sample sizes, and improved estimation quality; 2) improved spatial coverage of the brain compared to fast dynamic modalities alone; 3) improved dynamic coverage of the range of signals that are informative about interactions of brain networks; and 4) enhanced estimation quality and reductions in modality-specific deficiencies due to complementary aspects of different modalities. These advantages are heavily exploited for feature and representation learning; our group has been highly active in this field.10,30–37 However, to our knowledge, no methods have been developed and validated that can learn causal information (effective connectivity) using data from multiple modalities. There exist machine learning methods (developed by members of our group) that can combine causal information from disparate datasets,38–41 but these methods have never been applied to multimodal neuroimaging data. Moreover, use of these methods to combine spatially precise (e.g. fMRI) and dynamically precise (e.g. EEG, MEG) modalities requires a theory of the differences in 1

CRCNS研究提案：合作研究：多模式 神经影像学的动态因果学习 项目描述 A.1导言 许多对功能磁共振成像和其他神经成像数据的分析旨在发现潜在的因果关系或共同点。 这些大脑结构的准确表征 对于理解神经回路、系统级神经科学和变异的神经基础非常重要。 各种认知心理现象或精神疾病。从神经影像学中了解到的大脑结构- 收集数据还提供了一个强大的诊断工具，可以从当前的概念中预测一切。 一个人是否患有不同的精神疾病。 考虑到这种大脑连接网络的重要性，各种各样的大脑连接网络的出现也就不足为奇了。 已经为不同的神经成像模态开发了学习算法。特别是，许多 这些方法旨在从数据中推断潜在的因果或连通网络（与 模型比较方法，如动态因果建模（DCM）11）。这些网络发现 方法取得了一些显着的成功，2，6，12-21，但也在很大程度上未能解决两个 这些问题可能会阻碍我们更好地了解完整的工作大脑。我们 项目将开发、验证和应用解决这两个挑战的方法。 首先，现有的脑连接推断方法可以大致分为两组：静态模型和静态模型。 实际上不将大脑视为动态系统的方法（例如，images 22和大多数的ap- 史密斯等人测试的方法23）;和动态方法，明确测量和建模的dy- 大脑的动力学静态方法显然无法使用所有可用信息。与此相反， 动态方法使用测量的完整结构，但基本上所有这些方法24 -28 在测量模式的时间尺度上推断因果和连接结构，而不是在 大脑的因果时间尺度然而，网络从测量和大脑的数据中学习 时间尺度可以是完全不同的，即使给定所有其他统计和测量的解决方案， 神经影像分析面临的问题。29此外，关于因果或连接的重要事实 结构通常是关于哪些大脑区域直接与其他大脑区域进行通信， 这需要关注大脑时间尺度，而不是测量模态时间尺度。是 因此，科学上至关重要的是，我们有方法可以确定存在的因果关系， 在潜在神经系统的时间尺度上，而不仅仅是那些在我们的时间尺度上发现的神经系统。 特别是神经成像方法。 第二，有多种神经影像测量方式，每种都有自己的优势 和弱点多模态信息具有明显的、被广泛认可的优势 融合：1）访问多个更丰富的数据集，更大的样本大小，并提高估计质量; 2)与单独的快速动态模式相比，改善了大脑的空间覆盖范围; 3）改善了 动态覆盖关于大脑网络相互作用的信息的信号范围;以及 4)增强的估计质量和由于互补性而减少的模态特定性缺陷 不同模式的不同方面。这些优点被大量地用于特征和表示 学习;我们的小组在这一领域一直非常活跃。10，30 -37然而，据我们所知， 已经开发和验证，可以使用数据学习因果信息（有效连接） 从多种形式。存在机器学习方法（由我们小组的成员开发） 可以将来自不同数据集的因果信息联合收割机组合起来，38-41，但这些方法从未被 应用于多模态神经成像数据。此外，使用这些方法来联合收割机空间精确 (e.g.功能磁共振成像）和动态精确（如脑电图，脑磁图）模态需要一个理论的差异， 1