CRII: SCH: Characterizing, Modeling and Evaluating Brain Dynamics

脑动力学反映了大脑的健康或病理状态，具有可量化的、可重复的和指示性的动力学值，尽管它对大脑具有内在的和至关重要的意义，但仍然是脑科学中了解和研究最少的领域。不像其他的大脑信息，如结构和顺序的维度，都被广泛研究的模型和方法成功开发，第五维度，动态，直到最近才开始接受系统的分析，从研究界。最先进的模型受到几个基本限制，这些限制严重抑制了动态参数计算的准确性和可靠性。首先，动态参数是在空间上独立地从大脑的每个体素中获得的，由于大脑是“连通的”，因此遗漏了基本的空间信息。其次，目前的模型仅依靠单个患者的数据来估计动态参数，而没有利用由数十亿患有类似疾病的患者组成的大医疗数据。该项目旨在开发一个数据驱动的脑动力学表征、建模和评估框架，其中包括第5维的新概念-脑动力学-以补充结构4-D大脑的完整图像。该项目研究了如何将大脑的动态计算作为一个不同于传统模型的图像重建和去噪问题，并分析了不同模型对动态分析的影响。将开发一个数据驱动的、可扩展的框架来描述大脑的功能和动态。该框架能够充分利用4-D大脑时空数据和大医学数据，从而准确估计大脑动态，而这在体素独立模型和单个患者模型中无法反映出来。该模型和框架将在模拟和真实双剂量计算机断层扫描灌注图像数据上进行评估，然后通过与佛罗里达国际大学赫伯特韦特海姆医学院、纽约长老会医院/威尔康奈尔医学院（WCMC）和霍夫斯特拉大学诺斯韦尔医学院的合作，与最先进的脑动力学计算方法进行比较。该研究将极大地推动量化和分析大脑结构和动力学以及两者之间相互作用的最新技术，以诊断大脑疾病，包括急性和慢性疾病。这种统一的方法汇集了计算机科学、生物工程、认知神经科学和神经放射学等领域，创建了一个框架，用于精确测量和分析第5维度——大脑动力学——与来自空间数据的三维和来自时间数据的一维的四维大脑相结合。该项目的成果将被纳入研究生水平的机器学习、计算神经科学和医学图像分析等多学科课程。该项目将在大脑分析领域开辟几个新的研究方向，并将教育和培养年轻的研究人员，促进未被充分代表的少数民族参与计算机科学研究，并为他们提供新的见解，模型和工具，以便在少数民族服务的大学发展未来的大脑动力学研究。

Brain dynamics, which reflects the healthy or pathological states of the brain with quantifiable, reproducible, and indicative dynamics values, remains the least understood and studied area of brain science despite its intrinsic and critical importance to the brain. Unlike other brain information such as the structural and sequential dimensions that have all been extensively studied with models and methods successfully developed, the 5th dimension, dynamics, has only very recently started receiving systematic analysis from the research community. The state-of-the-art models suffer from several fundamental limitations that have critically inhibited the accuracy and reliability of the dynamic parameters' computation. First, dynamic parameters are derived from each voxel of the brain spatially independently, and thus miss the fundamental spatial information since the brain is ?connected?. Second, current models rely solely on single-patient data to estimate the dynamic parameters without exploiting the big medical data consisting of billions of patients with similar diseases. This project aims to develop a framework for data-driven brain dynamics characterization, modeling and evaluation that includes the new concept of a 5th dimension - brain dynamics - to complement the structural 4-D brain for a complete picture. The project studies how dynamic computing of the brain as a distinct problem from the image reconstruction and de-noising of convention models, and analyzes the impact of different models for the dynamics analysis. A data-driven, scalable framework will be developed to depict the functionality and dynamics of the brain. This framework enables full utilization of 4-D brain spatio-temporal data and big medical data, resulting in accurate estimations of the dynamics of the brain that are not reflected in the voxel-independent models and the single patient models. The model and framework will be evaluated on both simulated and real dual-dose computed tomography perfusion image data and then compared with the state-of-the-art methods for brain dynamics computation by leveraging collaborations with Florida International University Herbert Wertheim College of Medicine, NewYork-Presbyterian Hospital / Weill Cornell Medical College (WCMC) and Northwell School of Medicine at Hofstra University. The proposed research will significantly advance the state-of-the-art in quantifying and analyzing brain structure and dynamics, and the interplay between the two for brain disease diagnosis, including both the acute and chronic diseases. This unified approach brings together fields of Computer Science, Bioengineering, Cognitive Neuroscience and Neuroradiology to create a framework for precisely measuring and analyzing the 5th dimension - brain dynamics - integrated with the 4-D brain with three dimensions from spatial data and one dimension from temporal data. Results from the project will be incorporated into graduate-level multi-disciplinary courses in machine learning, computational neuroscience and medical image analysis. This project will open up several new research directions in the domain of brain analysis, and will educate and nurture young researchers, advance the involvement of underrepresented minorities in computer science research, and equip them with new insights, models and tools for developing future research in brain dynamics in a minority serving university.