Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037;
Institute for Neural Computation, University of California, San Diego, La Jolla, CA 92093.
Proc Natl Acad Sci U S A. 2019 Feb 26;116(9):3847-3852. doi: 10.1073/pnas.1810572116. Epub 2019 Feb 11.
Natural systems, including the brain, often seem chaotic, since they are typically driven by complex nonlinear dynamical processes. Disruption in the fluid coordination of multiple brain regions contributes to impairments in information processing and the constellation of symptoms observed in neuropsychiatric disorders. Schizophrenia (SZ), one of the most debilitating mental illnesses, is thought to arise, in part, from such a network dysfunction, leading to impaired auditory information processing as well as cognitive and psychosocial deficits. Current approaches to neurophysiologic biomarker analyses predominantly rely on linear methods and may, therefore, fail to capture the wealth of information contained in whole EEG signals, including nonlinear dynamics. In this study, delay differential analysis (DDA), a nonlinear method based on embedding theory from theoretical physics, was applied to EEG recordings from 877 SZ patients and 753 nonpsychiatric comparison subjects (NCSs) who underwent mismatch negativity (MMN) testing via their participation in the Consortium on the Genetics of Schizophrenia (COGS-2) study. DDA revealed significant nonlinear dynamical architecture related to auditory information processing in both groups. Importantly, significant DDA changes preceded those observed with traditional linear methods. Marked abnormalities in both linear and nonlinear features were detected in SZ patients. These results illustrate the benefits of nonlinear analysis of brain signals and underscore the need for future studies to investigate the relationship between DDA features and pathophysiology of information processing.
自然系统，包括大脑，通常看起来是混沌的，因为它们通常是由复杂的非线性动力过程驱动的。多个脑区的流体协调中断导致信息处理受损，并出现神经精神障碍的一系列症状。精神分裂症（SZ）是最具致残性的精神疾病之一，部分原因被认为是这种网络功能障碍，导致听觉信息处理受损以及认知和社会心理缺陷。目前的神经生理生物标志物分析方法主要依赖于线性方法，因此可能无法捕捉到整个 EEG 信号中包含的大量信息，包括非线性动力学。在这项研究中，延迟差分分析（DDA）是一种基于理论物理中嵌入理论的非线性方法，应用于通过参与精神分裂症遗传学联合会（COGS-2）研究进行错配负波（MMN）测试的 877 名 SZ 患者和 753 名非精神病对照受试者（NCS）的 EEG 记录。DDA 揭示了两组与听觉信息处理相关的显著非线性动力学结构。重要的是，与传统线性方法相比，DDA 变化先于观察到的变化。在 SZ 患者中检测到线性和非线性特征的明显异常。这些结果说明了脑信号非线性分析的好处，并强调需要进一步研究来探讨 DDA 特征与信息处理的病理生理学之间的关系。
