III: Small: Modeling Multi-Level Connectivity of Brain Dynamics

III：小：模拟大脑动力学的多级连接

• 批准号: 1908299
• 负责人: Ruogu Fang
• 金额: $ 50万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1908299&HistoricalAwards=false
• 关键词: III; Small; Modeling; Multi; Level

The temporal dynamics of blood flows through the network of cerebral arteries and veins provides a window into the health of the human brain. Since the brain is vulnerable to disrupted blood supply, brain dynamics serves as a crucial indicator for many kinds of neurological diseases such as stroke, brain cancer, and Alzheimer's disease. Existing efforts at characterizing brain dynamics have predominantly centered on 'isolated' models in which data from single-voxel, single-modality, and single-subject are characterized. However, the brain is a vast network, naturally connected on structural and functional levels, and multimodal imaging provides complementary information on this natural connectivity. Thus, the current isolated models are deemed not capable of offering the platform necessary to enable many of the potential advancements in understanding, diagnosing, and treating neurological and cognitive diseases, leaving a critical gap between the current computational modeling capabilities and the needs in brain dynamics analysis. This project aims to bridge this gap by exploiting multi-scale structural (voxel, vasculature, tissue) connectivity and multi-modal (anatomical, angiography, perfusion) connectivity to develop an integrated connective computational paradigm for characterizing and understanding brain dynamics.The approach consists of three thrusts: (1) multi-scale structural connectivity modeling to quantify brain dynamics beyond a single voxel; (2) multimodal dynamic dictionary learning for mining hidden complementary information; and (3) multicenter evaluation to assess the efficacy of the proposed models at three nationally renowned healthcare systems. Successful project completion would potentially transform the rapidly evolving field of brain dynamics modeling, facilitate basic neuroscience discovery and enable comprehensive identification of neurovascular diseases. Aiming to broaden its impact this project will also implement educational initiatives to expose students, middle school teachers, and medical professionals to 'CS for All,' to foster interests in STEM and cross-disciplinary careers, and to promote research on the convergence of computer science and computational thinking for brain health and neuromedicine.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

通过脑动脉和静脉网络的血液流动的时间动力学提供了一个了解人类大脑健康的窗口。由于大脑容易受到血液供应中断的影响，因此大脑动力学是许多神经系统疾病（如中风，脑癌和阿尔茨海默病）的关键指标。现有的努力，在表征脑动力学主要集中在“孤立”的模型，其中数据从单体素，单模态，和单一主题的特点。然而，大脑是一个巨大的网络，在结构和功能层面上自然相连，多模式成像提供了有关这种自然连接性的补充信息。因此，目前的孤立模型被认为不能提供必要的平台，使许多潜在的进步，在理解，诊断和治疗神经和认知疾病，留下了一个关键的差距，目前的计算建模能力和脑动力学分析的需求。该项目旨在通过利用多尺度结构（体素、脉管系统、组织）连通性和多模态（解剖学、血管造影术、灌注）连通性来开发用于表征和理解脑动力学的集成连接计算范例。该方法包括三个方面：（1）多尺度结构连通性建模以量化单个体素之外的脑动力学;（2）多模态动态字典学习，用于挖掘隐藏的互补信息;以及（3）多中心评估，以评估所提出的模型在三个全国知名的医疗保健系统中的有效性。项目的成功完成可能会改变快速发展的脑动力学建模领域，促进基础神经科学发现，并实现神经血管疾病的全面识别。为了扩大其影响力，该项目还将实施教育举措，让学生、中学教师和医疗专业人员接触“全民CS”，培养对STEM和跨学科职业的兴趣，以及促进计算机科学和计算思维在大脑健康和神经医学方面的融合研究。该奖项反映了NSF的法定使命，并通过评估被认为值得支持使用基金会的知识价值和更广泛的影响审查标准。

项目成果

Machine learning and individual variability in electric field characteristics predict tDCS treatment response.

• DOI: 10.1016/j.brs.2020.10.001
• 发表时间: 2020-11
• 期刊: Brain stimulation
• 影响因子: 7.7
• 作者: Albizu A;Fang R;Indahlastari A;O'Shea A;Stolte SE;See KB;Boutzoukas EM;Kraft JN;Nissim NR;Woods AJ
• 通讯作者: Woods AJ

Multimodal CT Image Super-Resolution via Transfer-GAN

通过 Transfer-GAN 实现多模态 CT 图像超分辨率

• 发表时间: 2020
• 期刊: Biomedical Engineering Society Annual Meeting
• 作者: Xiao, Yao;Fang, Ruogu
• 通讯作者: Fang, Ruogu

Transfer generative adversarial network for multimodal CT image super-resolution (Conference Presentation)

用于多模态 CT 图像超分辨率的转移生成对抗网络（会议演示）

• DOI: 10.1117/12.2549533
• 发表时间: 2020
• 期刊: Medical Imaging 2020: Image Processing
• 作者: Xiao, Yao;Fang, Ruogu
• 通讯作者: Fang, Ruogu

Rotational and reflectional equivariant convolutional neural network for data-limited applications: Multiphase flow demonstration

• DOI: 10.1063/5.0066049
• 发表时间: 2021-08
• 期刊: Physics of Fluids
• 影响因子: 4.6
• 作者: B. Siddani;S. Balachandar;R. Fang

DOMINO: Domain-Aware Model Calibration in Medical Image Segmentation

• DOI: 10.1007/978-3-031-16443-9_44
• 发表时间: 2022-09
• 期刊: ArXiv
• 作者: Skylar E. Stolte;Kyle Volle;A. Indahlastari;Alejandro Albizu;A. Woods;Kevin Brink;Matthew Hale;R. Fang