Neural Correlates of Complex Multi-Choice Decisions

大脑可以被视为一个极其复杂和高维的动力系统。尽管 它的复杂性，只有非常有限的大脑活动测量通常可以记录--例如。 脑电(EEG)。非线性动力学提供了提取信息的工具 确定基本动力学不变非线性性质的有限测量 系统。在延迟差分分析(DDA)中，提出了一种低维非线性泛函嵌入方法 建立在数据的动态结构上；这是数据可以基于的基础 已映射。通过将模型约束到低维，我们确保了DDA对 过度拟合，对噪声不敏感，对新数据很好地泛化。DDA已应用于 人类睡眠的颅内记录以检测睡眠纺锤波并描述其时空特征 发展。在拟议的项目中，这种方法也将应用于一项大型研究的脑电数据 精神分裂症的症状。在这两个数据集中，不同的观测现象可以链接到不同的 潜在的皮质状态。通过寻找检测睡眠纺锤波的DDA模型，可以获得洞察力 这些信息可以用来改进复杂的电路模型 一代。同样，通过找到可靠地区分精神分裂症患者和对照组的模型 研究对象，我们可以更好地理解可能导致 感觉加工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 更多的神经和精神障碍。

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.