Cognitive Domains Classification Using fNIRS-EEG

使用 fNIRS-EEG 进行认知域分类

• 批准号: 10742003
• 负责人: Hendrik Santosa
• 金额: $ 17.68万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10742003
• 关键词: Area Under Curve; Attention; Back; Blood; Brain; Brain Mapping; Brain imaging; Brain region; Categories; Cerebrum; Classification; Cognitive; Communities; Complex; DSM-V; Data; Data Compression; Databases; Dedications; Detection; Dimensions; Electroencephalography; Electrophysiology (science); Environment; Freedom; Frequencies; Functional Magnetic Resonance Imaging; Human; Human Activities; Image; Knowledge; Label; Language; Lead; Learning; Light; Linear Models; Magnetic Resonance Imaging; Maps; Mathematics; Measurement; Measures; Memory; Meta-Analysis; Metadata; Methods; Modality; Modeling; Motor; Near-Infrared Spectroscopy; Neurons; Noise; Pattern; Penetration; Performance; Protocols documentation; Publications; Receiver Operating Characteristics; Relaxation; Reporting; Reproducibility; Resolution; Rest; Scalp structure; Sensitivity and Specificity; Short-Term Memory; Signal Transduction; Surface; Techniques; Technology; Testing; Time; Training; blood oxygen level dependent; brain computer interface; cerebral blood volume; clinical translation; connectome; cost; cost comparison; executive function; functional near infrared spectroscopy; imaging approach; improved; innovation; interest; multimodality; neural; neuroimaging; novel; novel strategies; portability; predictive modeling; repository; sensor; social cognition

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.

摘要：功能性近红外光谱 (fNIRS) 是一种使用低水平红外光谱的神经成像技术。 光（650-900 nm）可无创地测量脑血容量和氧合的变化。 脑电图 (EEG) 测量神经振荡活动，可分为多种类型 我们提出了一种转变范式分析方法，而不是使用脑电图或 fNIRS 来估计和测试特定任务的从头大脑激活模式，我们利用现有的 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，以创建一组可测试的[null] 假设；即“fNIRS/EEG 测量的模式是否与（例如）预期的一致” 工作记忆任务的一般类别？” 我们的创新提议是将大脑信号映射到复杂大脑的“认知域”上 具体来说，我们的目的是使用函数的投影（重新参数化）。 使用由 DSM-V 语言定义的六个关键认知域定义的空间图来发出大脑信号； 知觉运动、执行功能、复杂注意力、社会认知、学习和记忆。 地图是通过对数百项现有功能磁共振成像研究进行荟萃分析而生成的，这些研究汇集了与以下相关的所有任务 认知领域作为关键字，使我们能够从各种不同的大型存储库中开发模型训练 这种将大脑信号重新参数化和压缩到这个低维空间中提供了一种方法。 与任务无关、可通过实验测试且可解释的信号，可以实时检查。 本研究的目的是： · 目标 1. 开发并验证认知领域的 fNIRS-EEG 功能。 · 目标 2. fNIRS-EEG 特征的反复测试可靠性。 · 目标 3. 预测模型的定量敏感性-特异性报告。 · 目标 4. 实现用于认知域分类的实时 fNIRS-EEG。