Taiwan International Graduate Program in Interdisciplinary Neuroscience, National Yang Ming Chiao Tung University and Academia Sinica, Taipei, Taiwan; Laboratory of Precision Psychiatry, Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan; Institute of Statistical Science, Academia Sinica, Taipei, Taiwan.
Institute of Statistical Science, Academia Sinica, Taipei, Taiwan.
Schizophr Res. 2021 Dec;238:10-19. doi: 10.1016/j.schres.2021.08.026. Epub 2021 Sep 22.
Nonlinear dynamical analysis has been used to quantify the complexity of brain signal at temporal scales. Power law scaling is a well-validated method in physics that has been used to describe the dynamics of a system in the frequency domain, ranging from noisy oscillation to complex fluctuations. In this research, we investigated the power-law characteristics in a large-scale resting-state fMRI data of schizophrenia and healthy participants derived from Taiwan Aging and Mental Illness cohort. We extracted the power spectral density (PSD) of resting signal by Fourier transform. Power law scaling of PSD was estimated by determining the slope of the regression line fitting to the logarithm of PSD. t-Test was used to assess the statistical difference in power law scaling between schizophrenia and healthy participants. The significant differences in power law scaling were found in six brain regions. Schizophrenia patients have significantly more positive power law scaling (i.e., more homogenous frequency components) at four brain regions: left precuneus, left medial dorsal nucleus, right inferior frontal gyrus, and right middle temporal gyrus and less positive power law scaling (i.e., more dominant at lower frequency range) in bilateral putamen compared with healthy participants. Moreover, significant correlations of power law scaling with the severity of psychosis were found. These findings suggest that schizophrenia has abnormal brain signal complexity linked to psychotic symptoms. The power law scaling represents the dynamical properties of resting-state fMRI signal may serve as a novel functional brain imaging marker for evaluating patients with mental illness.
非线性动力学分析已被用于量化脑信号在时间尺度上的复杂性。幂律标度是物理学中一种经过充分验证的方法，用于描述系统在频域中的动力学，范围从嘈杂的振荡到复杂的波动。在这项研究中，我们研究了来自台湾老龄化和精神疾病队列的精神分裂症和健康参与者的大尺度静息态 fMRI 数据中的幂律特征。我们通过傅里叶变换提取静息信号的功率谱密度（PSD）。通过确定拟合 PSD 对数的回归线的斜率来估计 PSD 的幂律标度。t 检验用于评估精神分裂症和健康参与者之间幂律标度的统计学差异。在六个脑区发现了幂律标度的显著差异。与健康参与者相比，精神分裂症患者在四个脑区（左楔前叶、左内侧背核、右额下回和右颞中回）的 PSD 中具有显著更多的正幂律标度（即更多同质的频率成分），而在双侧壳核中具有显著更少的正幂律标度（即更低频率范围内的主导性更强）。此外，幂律标度与精神病严重程度之间存在显著相关性。这些发现表明，精神分裂症与精神病症状相关的大脑信号复杂性异常。幂律标度代表静息态 fMRI 信号的动力学特性，可能成为评估精神疾病患者的新型功能脑成像标志物。
