Characterization of Brain Noise using Multimodal Mutual Information

描述（由申请人提供）：本项目的目的是利用功能性磁共振成像（fMRI）、近红外光学（NIRS）和全身生理学测量的并行多模态记录，以分离每种类型噪声的贡献。 该项目的新奇之处在于使用这些并行测量之间共享的互信息，以识别脑成像信号中的噪声源。 这将使我们能够更好地理解这种噪音的性质，更好地设计过滤器和分析以消除它，并改善对大脑活动中微小变化的检测。 我们假设，i）并发的光学和fMRI信号之间的互信息将产生更现实的模型的噪声结构在这些模态和ii），更好地定量了解这种噪声将导致识别的局限性，在目前的分析程序和优化改进的统计技术。 目标1. 使用并发的多模态光学和fMRI记录，识别仪器和系统噪声源对fMRI BOLD（血氧水平依赖）信号的贡献。 目标2. 描述基线生理学在受试者间和受试者内使用正中神经刺激与NIRS和fMRI同步进行的重测实验中系统偏倚的作用。 目标3. 研究受试者注意力在介导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，并确定实际噪声对标准分析模型假设的影响。 公共卫生相关性：虽然功能性MRI可以产生高质量的大脑活动图像，但这些图像需要对多次重复试验的数据进行平均，因为通常与功能性MRI信号相关的噪声水平很高。 初步数据表明，这种噪音大部分起源于大脑内部，可能与时间相关的因素有关，如任务中的注意力或警惕性。 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性质，并通过优化数据分析方法来提高检测大脑中小的和不常见的事件的能力，以提高功能事件的检测。

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.