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）：该项目的目标是利用功能磁共振成像（fMRI）、近红外光学（NIRS）和系统生理学测量的并发多模式记录，以便区分每种类型的贡献的噪音。 该项目的新颖之处在于利用这些并发测量之间共享的相互信息来识别大脑成像信号中的噪声源。 这将使我们能够更好地了解这种噪声的本质，更好地设计滤波器和分析来消除它，并改进对大脑活动微小变化的检测。 我们假设 i) 并发光学信号和功能磁共振成像信号之间的互信息将在这些模式中生成更真实的噪声结构模型，并且 ii) 对这种噪声的更好的定量理解将导致识别当前分析程序中的局限性，并导致优化改进的统计技术。 目标 1. 使用并发多模态光学和 fMRI 记录，确定仪器和系统噪声源对 fMRI BOLD（血氧水平相关）信号的贡献。 目标 2. 使用并发 NIRS 和 fMRI 进行正中神经刺激，表征受试者间和受试者内重复测试实验中基线生理学在系统偏差中的作用。 目标 3. 研究受试者注意力在调节 fMRI BOLD 信号单次试验变异性中的作用。 目标 4. 比较减少 fMRI 和 NIRS 中生理噪声的方法，并确定现实噪声对标准分析模型假设的影响。 公共健康相关性：虽然功能性 MRI 可以生成大脑活动的高质量图像，但由于通常与功能 MRI 信号相关的高水平噪声，这些图像需要对多次试验重复的数据进行平均。 初步数据表明，大部分噪音源自大脑内部，可能与时间相关因素有关，例如任务中的受试者注意力或警惕性。 这项工作的目标是通过并发多模态方法更好地理解这种噪声的本质，并通过优化数据分析方法来改进功能事件的检测，从而提高检测大脑中小且不频繁事件的能力。