Altered brain entropy and functional connectivity patterns in generalized anxiety disorder patients

Generalized anxiety disorder (GAD) is a highly prevalent disease characterized by chronic, pervasive, and intrusive worry. Previous resting-state functional MRI (fMRI) studies on GAD have mainly focused on conventional static linear features. Entropy analysis of resting-state functional magnetic resonance imaging (rs-fMRI) has recently been adopted to characterize brain temporal dynamics in some neuropsychological or psychiatric diseases. However, the nonlinear dynamic complexity of brain signals has been rarely explored in GAD. We measured the approximate entropy (ApEn) and sample entropy (SampEn) of the resting-state fMRI data from 38 GAD patients and 37 matched healthy controls (HCs). The brain regions with significantly different ApEn and SampEn values between the two groups were extracted. Using these brain regions as seed points, we also investigated whether there are differences in whole brain resting-state function connectivity (RSFC) pattern between GADs and HCs. Correlation analysis was subsequently conducted to investigate the association between brain entropy, RSFC and the severity of anxiety symptoms. A linear support vector machine (SVM) was used to assess the discriminative power of BEN and RSFC features among GAD patients and HCs. Compared to the HCs, patients with GAD showed increased levels of ApEn in the right angular cortex (AG) and increased levels of SampEn in the right middle occipital gyrus (MOG) as well as the right inferior occipital gyrus (IOG). Contrarily, compared to the HCs, patients with GAD showed decreased RSFC between the right AG and the right inferior parietal gyrus (IPG). The SVM-based classification model achieved 85.33 % accuracy (sensitivity: 89.19 %; specificity: 81.58 %; and area under the receiver operating characteristic curve: 0.9018). The ApEn of the right AG and the SVM-based decision value was positively correlated with the Hamilton Anxiety Scale (HAMA). This study used cross-sectional data and sample size was small. Patients with GAD showed increased level of nonlinear dynamical complexity of ApEn in the right AG and decreased linear features of RSFC in the right IPG. Combining the linear and nonlinear features of brain signals may be used to effectively diagnose psychiatric disorders.