Characterization of Brain Noise using Multimodal Mutual Information

描述（由申请人提供）：本项目的目标是利用功能磁共振成像（fMRI），近红外光学（NIRS）和系统生理学测量来利用并发多模态记录，以分离每种类型的噪声的贡献。这个项目的新颖之处在于利用这些同步测量之间共享的相互信息来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本质，更好地设计过滤器和分析以消除它，并改进对大脑活动中微小变化的检测。我们假设i)并发光学和fMRI信号之间的相互信息将产生这些模态中更真实的噪声结构模型；ii)对这种噪声更好的定量理解将导致识别当前分析程序中的局限性，并优化改进的统计技术。目的1。使用并发多模态光学和功能磁共振成像记录，确定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之间和受试者内部测试-重测实验中系统偏差的作用，使用中神经刺激同时使用近红外光谱和功能磁共振成像。目标3。研究受试者注意在fMRI BOLD信号中介导单次试验变异性的作用。目标4。比较功能磁共振成像和近红外光谱中减少生理噪声的方法，并确定实际噪声对标准分析模型假设的影响。

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.