Neural Correlates of Complex Multi-Choice Decisions

复杂多项选择决策的神经关联

• 批准号: 10001101
• 负责人: Aaron L Sampson
• 金额: $ 6.87万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-30 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10001101
• 关键词: Address; Agreement; Award; Biological Markers; Brain; Clinical; Collaborations; Complex; Data; Data Collection; Data Set; Detection; Development; Dimensions; Electroencephalogram; Ensure; Environment; Etiology; Experimental Designs; Future; General Hospitals; Generations; Genetic; Goals; Human; Immune; Investigation; Link; Maps; Massachusetts; Measurement; Measures; Medical; Memory; Mental disorders; Methods; Modeling; Neurosciences; Noise; Nonlinear Dynamics; Patients; Positioning Attribute; Property; Research; Research Project Grants; Role; Schizophrenia; Sensory; Sleep; Spatial Distribution; Stimulus; Structure; Symptoms; Techniques; Work; dynamic system; high dimensionality; insight; nervous system disorder; neural correlate; neuropsychiatric disorder; novel; response; sleep spindle; spatiotemporal; tool

The brain can be viewed as an extremely complex and high-dimensional dynamical system. Despite its complexity, only very limited measures of brain activity are generally accessible to recording–e.g. the electroencephalogram (EEG). Nonlinear dynamics provides the tools to extract information from a limited measurement to determine the invariant nonlinear properties of the underlying dynamical system. In Delay Differential Analysis (DDA), a low-dimensional nonlinear functional embedding is built from the dynamical structure of the data; this serves as a basis onto which the data can be mapped. By constraining the models used to low dimensionality, we ensure that DDA is immune to overfitting, insensitive to noise, and generalizes well to new data. DDA has already been applied to human intracranial recordings of sleep to detect sleep spindles and characterize their spatiotemporal development. In the proposed project, this method will also be applied to EEG data from a large study of schizophrenia. In both of these datasets, distinct observed phenomena can be linked to different underlying cortical states. By finding DDA models which detect sleep spindles, insights can be gained into their dynamics, and this information can be used to refine sophisticated circuit models for their generation. Likewise, by finding models which reliably distinguish schizophrenia patients from control subjects, we can develop a better understanding of the dynamical differences that might give rise to sensory processing deficits and other symptoms of schizophrenia. Further extensions of this work could help to address aditional questions related to functionally distinct states of the brain including in additional neurological and psychiatric disorders.

大脑可以被视为一个极其复杂和高维的动力系统。尽管 由于其复杂性，通常只能记录非常有限的大脑活动测量数据，例如 脑电图（EEG）。非线性动力学提供了从中提取信息的工具 确定潜在动态的不变非线性特性的有限测量 系统。在延迟微分分析（DDA）中，低维非线性函数嵌入是 根据数据的动态结构构建；这是数据可以作为基础 映射。通过将模型限制为低维，我们确保 DDA 不受 过度拟合，对噪声不敏感，并且可以很好地推广到新数据。 DDA已应用于 人类颅内睡眠记录，用于检测睡眠纺锤波并表征其时空特征 发展。在拟议的项目中，该方法还将应用于大型研究的脑电图数据 精神分裂症。在这两个数据集中，不同的观察到的现象可以链接到不同的 底层皮质状态。通过寻找检测睡眠纺锤波的 DDA 模型，可以获得见解 进入它们的动态，并且该信息可用于完善其复杂的电路模型 一代。同样，通过寻找能够可靠地区分精神分裂症患者与对照组的模型 主题，我们可以更好地理解可能引起的动态差异 感觉处理缺陷和精神分裂症的其他症状。这项工作的进一步扩展 可以帮助解决与大脑功能不同状态相关的其他问题，包括 其他神经和精神疾病。