CAREER: Large-Scale Computational Neuroimaging of Brain Electrical Activity

职业：脑电活动的大规模计算神经成像

• 批准号: 0955260
• 负责人: Lei Ding
• 金额: $ 40万
• 依托单位: University of Oklahoma Norman Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-15 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0955260&HistoricalAwards=false
• 关键词: CAREER; Large; Scale; Computational; Neuroimaging

Current EEG/MEG based neuroimaging technologies receive limited attentions in functional neuroimaging because their reliable performances have not been successfully demonstrated in sophisticated brain tasks. The need for more reliable, efficient, innovative EEG/MEG neuroimaging technologies that have high spatial and temporal resolutions is urgent. The goal of proposed research is to advance functional neuroimaging technologies in studying human brain functions, via integrating novel techniques from different disciplines, i.e. multi-resolution mathematic models, large-scale computation, advanced signal and image processing, and high-end measurement devices. Intellectual Merit: The proposed research consists of applying the well-established L-1 norm regularization technique to a different application domain, namely EEG/MEG image analysis. The problem of image analysis is an undetermined problem, and as such some form of regularization is required. The traditional approach is to apply L-2 norm minimization, which is a valid method when measurement errors are Gaussian random variables. However, when the measurement errors are exponentially distributed, it is more meaningful to use L-1 norm minimization. Moreover, the L-1 optimal solution is supported on a finite (usually small) number of data points, thus naturally taking advantage of the sparse nature of the optimization problem. The novelty of the proposed research is the investigation of sparsity inducing models of neural behavior. In the case of neuro-imaging, it is known from biological factors that during any one millisecond interval, only a tiny fraction of the neurons in the brain "fire". The proposed research therefore has the potential to identify the parameters that describe this behavior. As partial evidence of this, the PI has obtained some preliminary results based on synthetic data, when the correct answer is known, and the results are encouraging.Broader Impact: The project will generate novel problem-solving strategies for scientific problems using advanced engineering principles, and to clinical practice for neurological patients through the potential for reduction in loss of life, job, and income. By incorporating authentic learning education activities, we will broaden the participation of all levels of students, K-12 teachers, and other educators, in biomedical research and research educations.

目前基于EEG/MEG的神经成像技术在功能性神经成像中受到有限的关注，因为它们的可靠性能尚未在复杂的大脑任务中得到成功证明。迫切需要具有高空间和时间分辨率的更可靠、高效、创新的EEG/MEG神经成像技术。拟议研究的目标是通过整合不同学科的新技术，即多分辨率数学模型，大规模计算，先进的信号和图像处理以及高端测量设备，推进功能神经成像技术在研究人脑功能方面的应用。智力优势：拟议的研究包括应用完善的L-1范数正则化技术到不同的应用领域，即EEG/MEG图像分析。图像分析问题是一个未确定的问题，因此需要某种形式的正则化。传统的方法是应用L-2范数最小化，这是一个有效的方法时，测量误差是高斯随机变量。而当测量误差服从指数分布时，采用L-1范数最小化方法更有意义。此外，L-1最优解是在有限（通常很小）数量的数据点上得到支持的，因此自然地利用了优化问题的稀疏性。所提出的研究的新奇是稀疏诱导模型的神经行为的调查。在神经成像的情况下，从生物学因素可知，在任何一毫秒的时间间隔内，大脑中只有一小部分神经元“放电”。因此，拟议的研究有可能确定描述这种行为的参数。作为这方面的部分证据，PI已经基于已知正确答案的合成数据获得了一些初步结果，结果令人鼓舞。更广泛的影响：该项目将使用先进的工程原理为科学问题产生新的问题解决策略，并通过减少生命、工作和收入损失的潜力为神经系统患者提供临床实践。通过整合真实的学习教育活动，我们将扩大各级学生，K-12教师和其他教育工作者在生物医学研究和研究教育中的参与。