Fast Network Inference Methods for Connectome Analysis

描述（由申请人提供）：该R21项目的目标是在目前不可能的数据规模下，从神经成像数据中实现生物病理学现实的、全脑的、"宏观"网络分析。绘制人类大脑的连接图是神经科学的一个重大开放问题。理解“连接体”可能是解开大脑功能及其与正常行为和精神疾病之间联系的关键一步。目前用于从神经成像数据识别大脑活动网络的计算方法跨越从简单但计算高效到详细且生物学动机但计算昂贵的范围。虽然后者很流行，但目前计算成本太高，无法将其用于全脑、数据驱动的网络推理--这是重建人类连接体所必需的建模。在这项工作中，我们将开发和验证新一代网络推理算法，该算法将实现大型神经成像数据集的全脑、数据驱动、生物药理学详细网络建模。我们的方法侧重于评估或“评分”单个网络模型的计算费用-这是用于网络推理的组合优化算法核心的关键步骤。我们用从数据中学习到的快速近似值替换了精确的评分函数，从而在整体优化中产生了数量级的加速。在这项工作中，我们将为两类广泛使用的神经成像数据网络模型开发，验证，表征和分发该技术的版本：动态贝叶斯网络（DBN）和动态因果模型（DCM）。我们通过三个具体目标来实现这一目标：具体目标1：将我们先前在一般数据分析问题中的快速网络识别工作扩展到神经成像分析的非线性可测量活动网络（传感器空间DBN）。具体目标2：为基于生物药理学的潜在变量网络模型（如动态因果模型（DCM））开发适当的代理函数。具体目标3：测试以下假设：与精确评分搜索相比，基于代理的搜索更快地识别DBN和DCM，并且规模更大，模型质量没有显著损失。这项工作为未来研究大脑功能和功能障碍的网络基础铺平了道路，包括探索精神分裂症，精神病或慢性药物成瘾等精神疾病的有效连接基质。 公共卫生相关性：这项工作将能够从目前不切实际的神经成像数据中识别出生物医学上详细的大脑活动网络模型。这将使我们更好地了解行为和精神疾病（如精神分裂症、精神病、阿尔茨海默病或慢性药物成瘾）背后的大脑活动网络。反过来，这些精神疾病中大脑活动的改进模型可能为更好地预测发病，诊断，监测和治疗铺平道路。

DESCRIPTION (provided by applicant): The goal of this R21 project is to enable biophysically realistic, whole-brain, "macro-scale" network anal- ysis from neuroimaging data, at data scales that are not currently possible. Mapping the connectivity of the human brain is one of the great open problems of neuroscience. Understanding the "connectome" is likely to be a key step in unraveling the function of the brain and its connection to both normal behavior and mental illness. Current computational methods for identifying the brain activity networks from neuroimag- ing data span a spectrum from descriptively simple, but computationally efficient, to descriptively detailed and biophysically motivated, but computationally expensive. While popular, the latter models are currently so computationally expensive that it is infeasible to use them for whole-brain, data-driven network inference - the kind of modeling that is necessary to reconstructing the human connectome. In this work, we will develop and validate a new generation of network inference algorithms that will enable whole-brain, data- driven, biophysically-detailed network modeling of large neuroimaging data sets. Our approach focuses on the computational expense of evaluating, or "scoring," individual network models - a key step in the heart of the combinatorial optimization algorithms used for network inference. We replace the exact scoring function with a fast approximation learned from data, yielding orders of magnitude speedup in the optimization as a whole. In this work, we will develop, validate, characterize, and distribute versions of this technique for two classes of widely-used network models for neuroimaging data: dynamic Bayesian networks (DBNs) and dynamic causal models (DCMs). We accomplish this through three specific aims: Specific Aim 1: Extend our prior work on fast network identification in general data analysis problems to nonlinear measurable activity networks (sensor space DBNs) for neuroimaging analysis. Specific Aim 2: Develop appropriate proxy functions for biophysically-grounded, latent variable network models, such as dynamic causal models (DCMs). Specific Aim 3: Test the hypothesis that proxy-based search identifies DBNs and DCMs faster, and at larger scale, with no significant loss of model quality, compared to exact scoring search. This work paves the way for future studies of the network underpinnings of brain function and dysfunction, including probing the effective connectivity substrates of mental illnesses such as schizophrenia, psychopa- thy, or chronic drug addiction. PUBLIC HEALTH RELEVANCE: This work will enable the identification of biophysically-detailed network models of brain activity from neuroimaging data at scales that are currently impractical. That will allow us to better understand the brain activity networks underlying behavior and mental illnesses such as schizophrenia, psychopathy, Alzheimer's disease, or chronic drug addiction. In turn, improved models of the brain's activity in these mental illnesses may pave the way to better prediction of onset, diagnosis, monitoring, and treatment.