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.

表征大脑区域如何激活，合作和互动在认知中如何使我们能够采用先进的方法来帮助人类在高压力工作，预防药物滥用和治疗神经系统疾病上做出正确的决定。该项目将根据不确定性表示的认知控制，即，大脑如何以不同级别的不确定性执行相同的认知任务。基于拓扑数据分析中的理论和算法，该项目将使用新型拓扑描述符分析大脑功能MRI图像，这些拓扑描述符将大脑区域之间的全局相互作用直接以原则性的方式模拟。这些描述符将用于新的学习模型中，以发现对不确定性表示至关重要的大脑活动模式。该项目的结果将包括（1）不确定性表示的新知识，例如，精细的活动模式以及与不确定性水平相关的大脑区域之间的相互作用； （2）用于大脑成像研究的新拓扑分析工具。该项目将为来自计算机科学和神经科学的研究生带来研究和教育机会。 PI还将通过CUNY College Now计划指导来自代表性不足的团体和高中生的学生。此项目将创建新的计算拓扑算法，以从数据的内在结构中提取丰富的信息。将创建新颖的机器学习方法，以利用拓扑结构不仅预测，而且还可以发现知识。基于拓扑特征的新型交互式数据探索平台将用于大脑成像研究。这些技术和软件将在任务引起的fMRI数据上进行验证，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。领域专家将验证验证科学假设和数据探索的方法的质量。

项目成果

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

Adaptive graph convolutional neural networks

• DOI: 10.1609/aaai.v32i1.11691
• 发表时间: 2018-01-01
• 期刊: AAAI
• 作者: Li, R.;Wang, S.;Huang, J.
• 通讯作者: Huang, J.

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

SMILES-BERT: Large Scale Unsupervised Pre-Training for Molecular Property Prediction

• DOI: 10.1145/3307339.3342186
• 发表时间: 2019-01-01
• 期刊: ACM-BCB'19: PROCEEDINGS OF THE 10TH ACM INTERNATIONAL CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY AND HEALTH INFORMATICS
• 作者: Wang, Sheng;Guo, Yuzhi;Huang, Junzhou
• 通讯作者: Huang, Junzhou

Weakly Supervised Deep Learning for Thoracic Disease Classification and Localization on Chest X-rays

• DOI: 10.1145/3233547.3233573
• 发表时间: 2018-01-01
• 期刊: ACM-BCB'18: PROCEEDINGS OF THE 2018 ACM INTERNATIONAL CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY, AND HEALTH INFORMATICS
• 作者: Yan, Chaochao;Yao, Jiawen;Huang, Junzhou
• 通讯作者: Huang, Junzhou