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; 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.

抽象的。功能性近红外光谱（FNIRS）是一种使用低水平的神经影像学技术 光（650-900 nm）无创测量脑血容量和氧合的变化。同时， 脑电图（EEG）测量可以分为各种的神经振荡活性 节奏频带。我们提出了一种范式转移分析方法，而不是使用脑电图或 fnirs估算和测试从头大脑激活模式的特定任务，我们利用现有的 从fMRI中完成的类似任务的大脑活动模式的知识以创建一组可测试的[null] 假设；也就是说：“是fnirs/eeg的模式[不]与（例如）所期望的相一致的。 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基础复杂大脑的“认知领域” 功能而不是任务本身。具体而言，我们旨在使用投影（重新聚集） 使用由DSM-V定义的六个关键认知域定义的空间图；语言， 感知运动，执行功能，复杂的关注，社会认知以及学习和记忆。这些 地图是通过对数百种现有的fMRI研究的荟萃分析产生的，这些研究在与 认知领域作为关键字，使我们能够从大型存储库中开发模型培训 任务。大脑信号到这个低维空间的这种重新聚集和压缩提供了 任务不合时宜的，统计的可测试和可解释的信号，可以实时检查。主 这项研究的目的是： ·AIM 1。在认知域中开发和验证FNIRS-EEG特征。 ·AIM 2。fnirs-EEG功能的重新测试可靠性。 ·目标3。预测模型的定量灵敏度特异性报告。 ·AIM 4。实施实时FNIRS-EEG用于认知领域分类。