CAREER: Efficient Statistical Inference using Neuroimaging data for Sample Enrichment and Optimizing Power

职业：使用神经影像数据进行有效的统计推断以富集样本并优化功效

• 批准号: 1252725
• 负责人: Vikas Singh
• 金额: $ 47.8万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-04-15 至 2021-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1252725&HistoricalAwards=false
• 关键词: CAREER; Efficient; Statistical; Inference; using

Hypothesis testing on neuroimaging data traditionally has made use of classical statistical tests (on uni-variate response variables). This makes sub-optimal use of the structure of images, particularly problematic if the two groups being tested have weak differences to begin with. Failure to detect statistically significant differences may imply failure of the experiment itself. Acquiring more images is expensive but also occasionally infeasible. This project develops technologies to address these problems (particularly those dealing with differential analysis of brain images) via the lens of computer vision and machine learning. The algorithmic component of this project is (1) a suite of convex optimization based multi-modal learning schemes to seamlessly leverage a spectrum of brain imaging data, (2) new multi-resolution representations for inference with surface/network based signals (data derived from structural/functional brain images), and (3) using these mechanisms for boosting statistical power even in experiments with small sample sizes.The project has broad scientific impact. Extending the operating range of statistical image analysis methods for neuroimaging will foster a new inter-disciplinary area at the interface of computer vision, biostatistics, and machine learning, which is highly intellectually stimulating. The research team brings real neuroimaging research data for undergraduate/graduate students to explore and study. The project goals also include training and mentoring of students, increased involvement of under-represented groups, seminars, and an extensive set of outreach activities. In addition, the resultant software tools drive the analysis of neuroscience studies, which has clear broad societal impact.

对神经成像数据的假设检验传统上使用经典的统计检验(对单变量反应变量)。这使得图像结构的使用处于次优状态，如果被测试的两组人一开始就有微弱的差异，那么这就特别成问题了。未能检测到统计上的显著差异可能意味着实验本身的失败。获取更多的图像是昂贵的，但有时也不可行。这个项目开发了通过计算机视觉和机器学习的镜头来解决这些问题的技术(特别是那些处理大脑图像的差异分析的技术)。这个项目的算法部分是(1)一套基于凸优化的多模式学习方案，以无缝地利用脑成像数据的频谱，(2)用于与基于表面/网络的信号(来自结构/功能脑图像的数据)进行推理的新的多分辨率表示，以及(3)使用这些机制来提高统计能力，即使在小样本量的实验中也是如此。该项目具有广泛的科学影响。扩展神经成像统计图像分析方法的操作范围将在计算机视觉、生物统计学和机器学习的界面上培育一个新的交叉学科领域，这是高度智力刺激的。研究团队带来了真实的神经影像研究数据，供本科生/研究生探索研究。该项目的目标还包括对学生进行培训和指导、增加代表不足群体的参与、举办研讨会和一系列广泛的外联活动。此外，由此产生的软件工具推动了神经科学研究的分析，这显然具有广泛的社会影响。