RI: Small: Collaborative Research: A Topological Analysis of Uncertainly Representation in the Brain

RI：小：协作研究：大脑中不确定表征的拓扑分析

• 批准号: 1718853
• 负责人: Junzhou Huang
• 金额: $ 21万
• 依托单位: University of Texas at Arlington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-15 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1718853&HistoricalAwards=false
• 关键词: RI; Small; Collaborative; Research; Topological

Characterizing how brain regions activate, collaborate, and interact in cognition empowers us with advanced approaches to help humans make the right decisions on high stress jobs, prevent drug abuse, and treat neurological disorders. This project will study cognitive control in terms of the uncertainty representation, namely, how brains execute the same cognitive task with different levels of uncertainty. Based on theory and algorithms in topology data analysis, the project will analyze brain functional MRI images using novel topological descriptors, which directly model global interactions between brain regions in a principled manner. These descriptors will be used in novel learning models to discover brain activity patterns that are crucial for uncertainty representation. The outcome of the project will include (1) new knowledge in uncertainty representation, e.g., fine-scale activity patterns and interactions between brain regions correlated to the uncertainty level; (2) new topological analysis tools for brain imaging study. This project will bring research and educational opportunities to graduate and undergraduate students from both computer science and neuroscience. The PIs will also mentor students from underrepresented groups and high school students through the CUNY College Now program.This project will create new computational topology algorithms to extract rich information from the intrinsic structure of data. Novel machine learning methods will be created in order to leverage the topological structures for not only prediction, but also knowledge discovery. A novel interactive data exploration platform based on topological features will be developed for brain imaging study. These techniques and software will be validated on task-evoked fMRI data to produce quantitative assessments of accuracy and to characterize advantages and limitations of these approaches. Domain experts will validate the quality of the approach in validating scientific hypotheses and data exploration.

表征大脑区域如何在认知中激活、协作和交互，使我们能够使用先进的方法来帮助人类在高压力工作中做出正确的决定，防止药物滥用，并治疗神经疾病。这个项目将从不确定性表征的角度来研究认知控制，即大脑如何在不同程度的不确定性下执行相同的认知任务。基于拓扑数据分析的理论和算法，该项目将使用新的拓扑描述符来分析脑功能MRI图像，该描述符以原则性的方式直接对大脑区域之间的全局交互进行建模。这些描述符将用于新的学习模型，以发现对不确定性表征至关重要的大脑活动模式。该项目的成果将包括(1)不确定性表示方面的新知识，例如，与不确定性水平相关的精细活动模式和大脑区域之间的相互作用；(2)用于脑成像研究的新拓扑分析工具。该项目将为计算机科学和神经科学的毕业生和本科生带来研究和教育机会。PI还将通过CUNY College Now计划指导代表人数不足的群体的学生和高中生。该项目将创建新的计算拓扑算法，从数据的内在结构中提取丰富的信息。新的机器学习方法将被创造出来，以便利用拓扑结构不仅用于预测，而且还用于知识发现。将开发一种基于拓扑特征的新型交互式数据探索平台，用于脑成像研究。这些技术和软件将在任务诱发的功能磁共振数据上得到验证，以产生准确性的定量评估，并表征这些方法的优势和局限性。领域专家将在验证科学假设和数据探索方面验证该方法的质量。

项目成果

Adversarial Learning with Local Coordinate Coding

• 发表时间: 2018-06
• 期刊: ArXiv
• 作者: Jiezhang Cao;Yong Guo;Qingyao Wu;Chunhua Shen;Junzhou Huang;Mingkui Tan

Context-Aware Domain Adaptation in Semantic Segmentation

• DOI: 10.1109/wacv48630.2021.00056
• 发表时间: 2020-03
• 期刊: 2021 IEEE Winter Conference on Applications of Computer Vision (WACV)
• 作者: Jinyu Yang;Weizhi An;Chao-chao Yan;P. Zhao;Junzhou Huang

Bagging MSA Learning: Enhancing Low-Quality PSSM with Deep Learning for Accurate Protein Structure Property Prediction

• DOI: 10.1007/978-3-030-45257-5_6
• 发表时间: 2020-05
• 作者: Yuzhi Guo;Jiaxiang Wu;Hehuan Ma;Sheng Wang;Junzhou Huang

Graph Convolutional Nets for Tool Presence Detection in Surgical Videos

• DOI: 10.1007/978-3-030-20351-1_36
• 发表时间: 2019-06
• 作者: Sheng Wang;Zheng Xu;Chaochao Yan;Junzhou Huang

Comprehensive Study on Enhancing Low-Quality Position-Specific Scoring Matrix with Deep Learning for Accurate Protein Structure Property Prediction: Using Bagging Multiple Sequence Alignment Learning

• DOI: 10.1089/cmb.2020.0416
• 发表时间: 2021-02-22
• 期刊: JOURNAL OF COMPUTATIONAL BIOLOGY
• 影响因子: 1.7
• 作者: Guo，Yuzhi;Wu，Jiaxiang;Huang，Junzhou
• 通讯作者: Huang，Junzhou