Characterization of Brain Noise using Multimodal Mutual Information

使用多模态互信息表征脑噪声

• 批准号: 8082320
• 负责人: Theodore James Huppert
• 金额: $ 31.56万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-04-01 至 2014-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8082320
• 关键词: Affect; Attention; Blood; Blood flow; Brain; Brain imaging; Characteristics; Color; Data; Data Analyses; Detection; Dorsal; Estimation Techniques; Event; Excision; Eye; Frequencies; Functional Magnetic Resonance Imaging; Goals; Image; Knowledge; Lateral; Lead; Linear Models; Magnetic Resonance Imaging; Measurement; Measures; Mediating; Mediator of activation protein; Methods; Modality; Modeling; Nature; Noise; Optics; Oxygen; Performance; Physiological; Physiology; Prefrontal Cortex; Procedures; Process; Role; Signal Transduction; Source; Spin Labels; Statistical Methods; Structure; Systematic Bias; Techniques; Testing; Time; Variant; Work; base; blood oxygen level dependent; cofactor; computerized data processing; design; executive function; improved; insight; instrument; interest; median nerve; optical imaging; research study; response; vigilance

DESCRIPTION (provided by applicant): The objective of this project is to utilize concurrent multimodal recordings using functional magnetic resonance imaging (fMRI), near-infrared optical (NIRS) and measures of systemic physiology in order to separate the contributions of each of these types of noise. The novelty of this project will be the use of mutual information shared between these concurrent measurements in order to identify the sources of noise in the brain-imaging signal. This will allow us to better understand the nature of this noise, to better design filters and analysis to remove it, and to improve the detection of small changes in activity in the brain. We hypothesize that i) mutual information between concurrent optical and fMRI signals will generate more realistic models of the noise structure in these modalities and ii) that a better quantitative understanding of this noise will lead to the identification of limitations in current analysis procedures and to the optimization of improved statistical techniques. Aim 1. Identify the contributions of instrumental and systemic sources of noise to the fMRI BOLD (blood oxygen level dependent) signal using concurrent multimodal optical and fMRI recordings. Aim 2. Characterize the role of baseline physiology in systematic biases during inter- and intra-subject test-retest experiments using median-nerve stimulation with concurrent NIRS and fMRI. Aim 3. Investigate the role of subject attention in mediating single-trial variability in the fMRI BOLD signal. Aim 4. Compare approaches for reducing physiological noise in fMRI and NIRS and determine the effect of realistic noise on the assumptions of the standard analysis model. PUBLIC HEALTH RELEVANCE: Although functional MRI can produce high quality images of brain activity, these images require the averaging of data over many trial repetitions because of the high level of noise typically associated with fMRI signals. Preliminary data has suggested that much of this noise originates inside the brain and may be associated with time-dependent factors such as subject attention or vigilance in the task. The goal of this work is to improve the ability to detect small and infrequent events in the brain by better understanding the nature of this noise through concurrent multimodal methods and by optimizing data analysis methods to improve detection of functional events.

描述（由申请人提供）：该项目的目的是使用功能性磁共振成像（fMRI），近红外光学（NIR）和全身生理学的测量来利用并发的多模式记录，以分离每种类型的噪声的贡献。 该项目的新颖性将是使用这些并发测量之间共享的共享信息，以识别大脑成像信号中噪声的来源。 这将使我们能够更好地理解这种噪声的性质，更好地设计过滤器和分析以将其删除，并改善大脑活动活动的微小变化的检测。 我们假设i）i）同时的光学信号和fMRI信号之间的相互信息将在这些方式中产生更现实的噪声结构模型，而ii）ii）对这种噪声的更好定量理解将导致当前分析程序中的限制以及改进的统计技术的优化。 目标1。使用并发的多模式光学和fMRI记录，确定噪声源和全身性噪声源对fMRI BOLD（血氧水平依赖性）信号的贡献。 AIM 2。表征基线生理在使用并发NIRS和fMRI的中间刺激刺激和fMRI的中位数刺激的内部和受试者内测试测试实验中的系统偏差中的作用。 AIM 3。研究主体注意力在介导fMRI BOLD信号中介导单审论变异性中的作用。 AIM 4。比较降低fMRI和NIR中生理噪声的方法，并确定逼真的噪声对标准分析模型假设的影响。 公共卫生相关性：尽管功能性MRI可以产生大脑活动的高质量图像，但由于通常与fMRI信号相关的高噪声，这些图像需要在许多试验重复中取平均值。 初步数据表明，大部分噪声都起源于大脑内部，可能与时间依赖的因素有关，例如主题注意力​​或任务的警惕。 这项工作的目的是通过更好地理解这种噪声的性质，通过并发的多模式方法来提高大脑中小小的和不常见的事件的能力，并通过优化数据分析方法来改善功能事件的检测。