To study the neurophysiological basis of attention deficit hyperactivity disorder (ADHD), clinicians use electroencephalography (EEG) which record neuronal electrical activity on the cortex. The most commonly-used metric in ADHD is the theta-to-beta spectral power ratio (TBR) that is based on a single-channel analysis. However, initial findings for this measure have not been replicated in other studies. Thus, instead of focusing on single-channel spectral power, a novel model for investigating interactions (dependence) between channels in the entire network is proposed. Although dependence measures such as coherence and partial directed coherence (PDC) are well explored in studying brain connectivity, these measures only capture linear dependence. Moreover, in designed clinical experiments, these dependence measures are observed to vary across subjects even within a homogeneous group. To address these limitations, we propose the mixed-effects functional-coefficient autoregressive (MX-FAR) model which captures between-subject variation by incorporating subject-specific random effects. The advantages of MX-FAR are the following: (1.) it captures potential non-linear dependence between channels; (2.) it is nonparametric and hence flexible and robust to model mis-specification; (3.) it can capture differences between groups when they exist; (4.) it accounts for variation across subjects; (5.) the framework easily incorporates well-known inference methods from mixed-effects models; (6.) it can be generalized to accommodate various covariates and factors. Finally, we apply the proposed MX-FAR model to analyze multichannel EEG signals and report novel findings on altered brain functional networks in ADHD.
翻译:为研究注意力缺乏超动性障碍(ADHD)的神经生理基础研究,临床医生使用电子脑法(EEEG)来记录大脑皮层的神经神经电动活动。在ADHD中最常用的衡量标准是基于单一通道分析的光谱比(TBR),然而,其他研究没有复制这一措施的初步结果。因此,建议了一种调查整个网络各频道之间相互作用(依赖性)的新模式,而不是侧重于单通道光谱动力。虽然在研究大脑连接性时,很好地探讨了一致性和部分定向一致性(DDC)等依赖性措施,但这些措施仅反映线性依赖性。此外,在设计临床实验时,观察到这些依赖性措施在不同学科之间有所不同,甚至在一个同质组内也是如此。为了解决这些局限性,我们建议采用混合效应的功能共振动性自动递增压模型(MX-FAR)模型,通过纳入具体主题的随机效应来捕捉到各种源间的变化。MX-FAR的优点如下:(一)它从潜在的非线性因素中捕捉到机型的模型(DDD)在多式网络中,在变变变变变;最后,(ER)它不动的轨道上(O)。