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

神经科学正处于一个以非侵入性和活体绘制人脑连接性图的历史时刻，许多悬而未决的问题才刚刚开始。虽然大脑中的解剖结构几十年来已经广为人知，但如何将它们结合起来形成任务特定网络仍未完全被探索。了解这些网络是什么，以及它们是如何发展、退化和在不同个体之间变化的，将提供一系列好处，从疾病诊断到理解创造力的神经基础，甚至从很长一段时间来看，对大脑增强都有帮助。虽然机器学习和数据挖掘已经在工业和科学的实际应用中取得了重大进展，但大多数现有的工作都集中在较低级别的任务上，如预测标签、聚类和降维。这需要实践者将他们更复杂的任务，如网络发现，硬塞进算法的设置中。这项拨款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得出网络。这里，时空数据是用四维张量表示的人的fMRI扫描，张量中的每个条目都是指示那个时间和位置的大脑活动的数据点。整个问题的焦点是将这些数据简化为一个认知网络，该网络由识别大脑的活跃区域和它们之间发生的相互作用组成。这项工作将包括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)种群中的网络发现。在监督/半监督环境中，所发现的网络涉及解剖结构的某种组合之间的协调活动，因为所有或部分结构都与它们的边界一起给出，这被称为监督(或半监督)问题。有了复杂的网络发现，团队将超越寻找协调活动的单个网络，而是找到结构/区域之间具有复杂(超越坐标)关系的多个网络。最后，随着群体中的网络发现，以前研究个人扫描的工作将扩展到群体扫描。群体可以是执行相同任务的个人的集合，也可以是随着时间的推移收集的单个个人的扫描。研究这类人群可以解决一些创新问题，例如：“一个人的关系网在发育、衰老或疾病过程中是如何变化的？”以及“一组人的网络与另一组人的网络有什么不同？”

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?"