Cognitive Domains Classification Using fNIRS-EEG

抽象的。功能性近红外光谱(FNIRS)是一种神经成像技术，它使用低水平的 光(650-900 nm)，用于非侵入性测量脑血容量和氧合的变化。同时， 脑电(EEG)测量神经的振荡活动，它可以分为各种类型 节奏频段。我们提出了一种范式转换分析方法，其中不是使用EEG或 为了估计和测试特定任务的从头开始的大脑激活模式，我们利用我们现有的 从功能磁共振成像中完成的类似任务中了解大脑活动的常见模式，以创建一组可测试[空] 假设；即“fNIRS/EEG的测量模式是否与(例如)预期的一致。 工作记忆任务的一般类别是什么？ 我们的创新方案是将大脑信号映射到复杂大脑下面的“认知域” 而不是自己完成任务。具体地说，我们的目的是使用 使用由DSM-V定义的六个关键认知域定义的空间地图的大脑信号；语言， 知觉-运动、执行功能、复杂注意力、社会认知以及学习和记忆。这些 地图是从对数百项现有功能磁共振研究的荟萃分析中生成的，这些研究汇集了与以下各项相关的所有任务 认知域作为一个关键词，允许我们从各种不同的大型存储库中开发模型训练 任务。这种大脑信号的重新参数化和压缩到这个低维空间中提供了 与任务无关、可统计测试且可解释的信号，可实时检查。主 这项研究的目的是： 目的1.开发和验证认知领域的fNIRS-EEG特征。 ·目的2.fNIRS-EEG特征的重测信度。 目的3.预测模型的定量灵敏度-特异度报告。 目的4.实现认知领域分类的实时fNIRS-EEG。

Abstract. Functional near-infrared spectroscopy (fNIRS) is a neuroimaging technique that uses low-levels of light (650-900 nm) to noninvasively measure changes in cerebral blood volume and oxygenation. Meanwhile, electroencephalography (EEG) measures the neural oscillatory activity which can be divided into various rhythms frequency bands. We propose a paradigm shifting analysis approach where instead of using EEG or fNIRS to estimate and test the de novo brain activation pattern for a specific task, we utilize our existing knowledge of common patterns of brain activity from similar tasks done in fMRI to create a set of testable [null] hypotheses; namely, “is the pattern of fNIRS/EEG measured [not] consistent with what is expected from (e.g.) the general category of working memory tasks?” Our innovative proposal is to map brain signals onto the “cognitive domains” underlying complex brain functions rather than task themselves. Specifically, we purpose to use a projection (reparameterization) of brain signals using spatial maps defined by the six key cognitive domains defined by the DSM-V; language, perceptual-motor, executive function, complex attention, social cognition, and learning and memory. These maps are generated from meta-analysis of hundreds of existing fMRI studies pooled across all tasks related to the cognitive domain as a keyword allowing us to develop model training from a large repository of varied tasks. This reparameterization and compression of the brain signals into this low dimensional space provides a task-agnostic, statistically testable, and interpretable signal, which can be examined in real-time. The main aims of this study are: · Aim 1. Develop and validate fNIRS-EEG features in cognitive domains. · Aim 2. Test-retest reliability of fNIRS-EEG features. · Aim 3. Quantitative sensitivity-specificity report of the prediction model. · Aim 4. Implementation real-time fNIRS-EEG for cognitive domain classification.