Dynamic Neuroimaging with High-Resolution SSVEPs

使用高分辨率 SSVEP 进行动态神经成像

• 批准号: 7735495
• 负责人: RAMESH SRINIVASAN
• 金额: $ 60.43万
• 依托单位: UNIVERSITY OF CALIFORNIA-IRVINE
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-01-01 至 2013-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7735495
• 关键词: Alzheimer' s Disease; Anatomy; Area; Attention; Attention deficit hyperactivity disorder; Brain; Clinical Research; Cognitive; Coma; Computer software; Craniocerebral Trauma; Data; Diagnostics Research; Disease; Electroencephalography; Electromagnetic Fields; Engineering; Epilepsy; Foundations; Frequencies; Future; Generations; Goals; Head; Licensing; Magnetic Resonance Imaging; Manuals; Measures; Medical; Mental disorders; Methods; Modeling; Morphologic artifacts; Network-based; Neurologic; Neurosciences; Nonlinear Dynamics; Physics; Physiological; Physiology; Process; Property; Resolution; Role; Schizophrenia; Scientist; Sensory; Sleep Disorders; Solutions; Source; Stimulus; Stroke; Synapses; Theoretical model; Visual Cortex; Visual evoked cortical potential; base; cell assembly; cognitive function; depression; directed attention; models and simulation; neocortical; network models; neuroimaging; public health relevance; research study; simulation; tool

DESCRIPTION (provided by applicant): The proposed experimental, simulation, and theoretical EEG/MEG studies will develop modern engineering tools for future use by cognitive and medical scientists who carry out physiological studies using steady-state visual evoked potentials (SSVEPs). SSVEP provides robust measures of neocortical dynamic and cognitive function that are largely artifact-free. These tools are potentially applicable to a wide variety of disease states, including mental disorders (ADHD, depression, schizophrenia, depression, sleep disorders, etc.) and neurological conditions (epilepsies, head trauma, strokes, coma, Alzheimer's disease, etc.). The proposed tools combine EEG with MEG and new paradigms and analytic methods to quantify dynamic (spatial-temporal) properties of SSVEPs. This framework will be used in experimental studies of localized and distributed brain networks in spatial and feature attention tasks. The experimental SSVEP data will be interpreted in the context of cell assembly formation embedded within a background of "synaptic action fields" using theoretical models of localized and distributed brain networks based on genuine physiology and anatomy. This theoretical construct provides the necessary connection between physiology and EEG/SSVEP data. The essential power of this method is that localized and distributed brain networks operate over different frequency ranges and thus can be easily investigated with SSVEPs. In this manner, a triple correspondence between SSVEP dynamics, cognitive processes, and theoretical models will be obtained. The EEG and SSVEP tools developed in these studies should provide firm foundations for the physiological interpretation of later studies applied to a wide range of specific cognitive or medical conditions. These tools will be freely distributed as software for high-resolution EEG, MEG, and SSVEP analysis, modeling and simulation with a supporting manual and examples. PUBLIC HEALTH RELEVANCE: EEG is a widely used research and diagnostic tool in a wide variety of disease states including mental disorders and neurological conditions. This project significantly advances EEG based neuroscience by developing new experimental methods for cognitive and clinical studies in conjunction with corresponding theoretical models of brain dynamics and physics of electromagnetic fields of the brain.

描述（由申请人提供）：拟议的实验，仿真和理论EEG/MEG研究将开发现代工程工具，用于认知和医学科学家将来使用，他们使用稳态的视觉诱发潜力（SSVEPS）进行生理研究。 SSVEP提供了新皮质动态和认知功能的强大度量，这些措施在很大程度上不含伪影。 这些工具可能适用于多种疾病状态，包括精神障碍（ADHD，抑郁症，精神分裂症，抑郁症，睡眠障碍等）和神经系统疾病（癫痫，头部创伤，中风，昏迷，阿尔茨海默氏病等）。 所提出的工具将脑电图与MEG和新的范式和分析方法结合在一起，以量化SSVEP的动态（时空）特性。 该框架将用于空间和特征注意任务的局部和分布式大脑网络的实验研究。 实验性SSVEP数据将在使用基于真正的生理学和解剖学的局部和分布式大脑网络的理论模型中解释在嵌入“突触作用场”背景中的细胞组装形成的背景下进行解释。 该理论构造提供了生理学与EEG/SSVEP数据之间的必要联系。 该方法的基本力量是，局部和分布的大脑网络在不同的频率范围内运行，因此可以轻松地使用SSVEP进行研究。 以这种方式，将获得SSVEP动力学，认知过程和理论模型之间的三重对应关系。 这些研究中开发的脑电图和SSVEP工具应为后来的研究提供了牢固的基础，以适用于广泛的特定认知或医疗状况。 这些工具将作为高分辨率脑电图，MEG和SSVEP分析，建模和仿真的软件自由分配，并使用支持手册和示例。 公共卫生相关性：EEG是一种广泛使用的研究和诊断工具，在各种疾病状态，包括精神障碍和神经系统疾病。 该项目通过开发用于认知和临床研究的新实验方法与大脑电磁场的脑动力学和物理学的相应理论模型，从而显着提高基于EEG的神经科学。