Neural Correlates of Complex Multi-Choice Decisions

大脑可以被看作是一个极其复杂的高维动力系统。尽管 由于其复杂性，通常只有非常有限的大脑活动测量可用于记录-例如， 脑电图（EEG）。非线性动力学提供了从非线性动力学中提取信息的工具。 一个有限的测量，以确定不变的非线性特性的基础动态 系统在延迟微分分析（DDA）中， 从数据的动态结构中构建;这是数据可以被 映射。通过将模型限制为低维，我们确保DDA不受 过拟合，对噪声不敏感，并且很好地推广到新数据。DDA已应用于 人类颅内睡眠记录以检测睡眠纺锤波并表征其时空特征 发展在拟议的项目中，这种方法也将应用于来自大型研究的EEG数据 精神分裂症在这两个数据集中，观察到的不同现象可以与不同的 潜在的皮层状态通过发现检测睡眠纺锤波的DDA模型，可以获得见解 这些信息可以用来改进复杂的电路模型， 一代同样，通过找到可靠区分精神分裂症患者和对照组的模型， 主题，我们可以更好地了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问题，包括 其他神经和精神疾病。

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.