Dynamic Neuroimaging with High-Resolution SSVEPs

使用高分辨率 SSVEP 进行动态神经成像

• 批准号: 7924172
• 负责人: RAMESH SRINIVASAN
• 金额: $ 58.69万
• 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/7924172
• 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 Depression; 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; 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、模拟和理论EEG/MEG研究将开发现代工程工具，供使用稳态视觉诱发电位（SSVEP）进行生理研究的认知和医学科学家将来使用。 SSVEP提供了新皮层动态和认知功能的强大措施，在很大程度上是无伪影的。 这些工具可能适用于各种疾病状态，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、阿尔茨海默病等）。 所提出的工具结合联合收割机脑电图与脑磁图和新的范例和分析方法来量化动态（时空）属性的SSVEP。 该框架将用于空间和特征注意任务中的局部和分布式脑网络的实验研究。 实验SSVEP数据将被解释的背景下，细胞组装形成嵌入的“突触作用领域”的本地化和分布式的大脑网络的理论模型的基础上真正的生理和解剖的背景。 这种理论结构提供了生理学和EEG/SSVEP数据之间的必要联系。 这种方法的本质力量在于，局部和分布式大脑网络在不同的频率范围内运行，因此可以很容易地用SSVEP进行研究。 以这种方式，SSVEP动力学，认知过程和理论模型之间的三重对应关系将得到。 在这些研究中开发的EEG和SSVEP工具应该为后来应用于广泛的特定认知或医疗条件的研究的生理解释提供坚实的基础。 这些工具将作为高分辨率EEG、MEG和SSVEP分析、建模和仿真软件免费分发，并附有支持手册和示例。 公共卫生相关性：EEG是一种广泛使用的研究和诊断工具，用于各种疾病状态，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，结合相应的脑动力学和脑电磁场物理学理论模型，显着推进基于EEG的神经科学。