III: Small: Collaborative Research: Functional Network Discovery for Brain Connectivity

神经科学正处于一个历史时刻，在非侵入性和体内绘制人类大脑的连接才刚刚开始，有许多未解的问题。 虽然大脑中的解剖结构已经众所周知了几十年，但它们如何结合起来形成特定任务的网络仍然没有完全探索。了解这些网络是什么，以及它们如何发展，恶化和在个体之间变化，将为疾病诊断提供一系列好处，了解创造力的神经基础，甚至从长远来看，大脑增强。虽然机器学习和数据挖掘已经在工业和科学领域的真实的实际应用中取得了重大进展，但大多数现有工作都集中在较低级别的任务上，如预测标签，聚类和降维。这就要求从业者将他们更复杂的任务（如网络发现）硬塞进算法的设置中。这项资助的重点是过渡到更复杂的更高级别的发现任务，特别是从表示为张量的时空数据中引出网络。这里的时空数据是一个人的fMRI扫描，表示为一个四维张量，张量中的每个条目都是一个数据点，表示在那个时间和位置的大脑活动。整体问题的焦点是将这些数据简化为一个认知网络，包括识别大脑的活动区域以及它们之间发生的相互作用。这项工作将包括以下三个相互交织的任务：i）监督和半监督网络发现，ii）复杂网络发现和iii）群体中的网络发现。在监督/半监督设置中，发现的网络涉及解剖结构的一些组合之间的协调活动。由于所有或一些结构都是沿着它们的边界沿着的，因此这被称为监督（或半监督）问题。通过复杂网络发现，团队将不再寻找单一的协调活动网络，而是寻找结构/区域之间具有复杂（超越坐标）关系的多个网络。最后，通过群体中的网络发现，以前研究单个扫描的工作将扩展到扫描群体。 群体可以是执行相同任务的个体的集合或随时间收集的单个个体的扫描。研究这些人群可以解决创新的问题，如：“一个人的网络如何在发展，衰老或疾病的过程中发生变化？”以及“一个群体的网络与另一个群体的网络有何不同？”"

Neuroscience is at a moment in history where mapping the connectivity of the human brain non- invasively and in vivo has just begun with many unanswered questions. While the anatomical structures in the brain have been well known for decades, how they are used in combination to form task specific networks has still not been completely explored. Understanding what these networks are, and how they develop, deteriorate, and vary across individuals will provide a range of benefits from disease diagnosis, to understanding the neural basis of creativity, and even in the very long term to brain augmentation. Though machine learning and data mining has made significant inroads into real world practical applications in industry and the sciences, most existing work focuses on lower-level tasks such as predicting labels, clustering and dimension reduction. This requires the practitioner to shoe-horn their more complex tasks, such as network discovery, into the algorithm's settings. The focus of this grant is a transition to more complex higher-level discovery tasks and in particular, eliciting networks from spatio-temporal data represented as a tensor. Here the spatio-temporal data is an fMRI scan of a person represented as a four dimensional tensor with each entry in the tensor being a data point that indicates the brain activity at that time and location. The overall problem focus is to simplify this data into a cognitive network consisting of identifying active regions of the brains and the interactions that occur between them. The work will consist of three intertwined tasks as follows: i) Supervised and Semi-supervised Network Discovery, ii) Complex Network Discovery and iii) Network Discovery in Populations. In the supervised/semi-supervised setting, the networks discovered involves coordinated activity among some combination of anatomical structures Since all or some of the structures are given along with their boundaries, this is termed a supervised (or semi-supervised) problem. With complex network discovery the team will move beyond finding a single network of coordinated activity to finding multiple networks with complex (beyond coordinates) relationships between the structures/regions. Finally with network discovery in populations , the previous work that studies an individual scan will be expanded to a population of scans. A population may be a collection of individuals performing the same task or a single individual's scans collected over time. Studying such populations allows addressing innovative questions such as: "How does one individual's network change over the course of development, aging, or disease?" and "How do the networks differ for one group of individuals to that of another group?"