Anxiety disorders (ADs) are a group of mental disorders characterized by excessive fear, anxiety, and related behavioral abnormalities, which have a great impact on the social function and quality of life of the patients, and thus impose a great burden on the family and society (Grupp et al., 2014). Among them, generalized anxiety disorder (GAD) and panic disorder (PD), with anxiety as the core emotional experience, are very similar in clinical features, forms of the disease, and treatment, and often coexist. Specifically, GAD shows a persistent omnidirectional difficulty with autonomic nervous system symptoms, and PD is characterized by a paroxysmal, prominent autonomic symptom, with different degrees of avoidance behavior in both groups. Currently, in order to explore a new diagnostic system in line with the biological findings rather than the clinical symptoms, it is necessary to detect their common and specific neural physiopathology.

Based on the resting-state functional MRI (RS-fMRI) data, the static functional connectivity (FC) analysis has been widely used to indicate the neural physiopathology of various diseases (Bell and Sejnowski, 1995; Lawrie et al., 2002; Greicius et al., 2007; Hahn et al., 2011; Zhong et al., 2018). Nevertheless, different degrees of attention, mind-wandering, and even mood swings may occur during the scanning process, which leads to the observed blood-oxygen-level-dependent (BOLD) signal nonstationary and deviates from the hypothesis. Recently, the temporal features of brain activities, acquired from fMRI, can be characterized by applying a dynamic functional connectivity (DFC) approach (Allen et al., 2014; Calhoun et al., 2014). The full repertoire of the resting-state functional networks has been regarded as continuously and dynamically active (Smith et al., 2009). Current studies have revealed that DFC metrics may index the changes in macroscopic neural activity patterns underlying the critical aspects of cognition and behavior (Hutchison et al., 2013), and thus be of great significance for the early diagnosis and prediction of the severity of mental illness (Damaraju et al., 2014; Li et al., 2014; Ou et al., 2015).

For the brain mechanism of AD, the “limbic-PFC circuitry” was the mainstream view in the past decade. Based on this, Calhoon and Tye (2015) proposed a hypothesis that reconciled the region-specific studies of anxiety into a broader network (Calhoon and Tye, 2015). At present, many studies have progressed in understanding the neural basis of ADs via the FC analysis and detected that multiple key functional networks play an important part in generating different symptoms of ADs. For GAD, studies have identified that the amygdala and the prefrontal cortex (PFC) play important roles in “emotion dysregulation” (Hilbert et al., 2014), and that the altered FC was found between these regions of interest (i.e., the amygdala and the PFC) and the default mode network (DMN) (Makovac et al., 2018). Besides, the integrity of functional brain networks was globally disrupted in GAD, showing impairments consistent with the neurobiological models of GAD (involving amygdala, PFC, and cingulate cortex) (Li et al., 2016), appearing that the DMN, cerebellar (CB) network, executive control network (ECN), and salience network (SAN) may be altered (Etkin et al., 2009; Yao et al., 2017). For PD, it is reported that right superior temporal gyrus (STG), left dorsomedial prefrontal cortex (dmPFC), and right orbital frontal cortex (OFC) have a co-atrophy relationship with each other, and these regions are related to the behavioral domains of audition, music, emotion, and execution. The left dorsolateral prefrontal cortex (dlPFC) co-activates with bilateral dmPFC, and these regions are related to the behavioral domains of social cognition and emotion of sadness (Wu et al., 2018). The greater FC between somatosensory cortex and thalamus in PD was more likely linked to interoceptive processing (Cui et al., 2016). Furthermore, the DMN, SAN, and SMN may be altered in PD (Pannekoek et al., 2013; Shin et al., 2013; Kim and Yoon, 2018). In brief, to elucidate diagnostic nosology and promote disorder-specific therapies, it appears to be distinctly necessary for the resting-state studies that directly compare DFC between subjects with different ADs.

In this study, by using the independent component analysis (ICA) and DFC analysis on the RS-fMRI data, we aimed to reveal the characteristics of dynamic connectivity in patients with GAD and with PD. We made the following hypotheses: (1) the patients with AD and healthy controls (HCs) have DFC alterations within the resting-state networks and (2) there are more common and less specific alterations between patients with GAD and PD.

In the past few years, many studies have turned to focus on the temporal features in the fluctuations of spontaneous brain activities and come to a preliminary conclusion that DFC may serve as an efficient diagnostic biomarker for neuropsychiatric disease (Allen et al., 2014; Nieuwhof and Helmich, 2017). To our knowledge, this study is the first whole-brain resting-state FC (RSFC) analysis to investigate common and specific time-varying FC alterations between patients with GAD and PD, focusing on the temporal features and the strength of dynamic states.

As stated in this study, two distinct connectivity states were identified across the entire group, one of which was characterized by weakly connected but more frequent, and the other was integrated and strongly connected but less frequent. Comparing patients with AD to HCs, we found that the expression of segregated State I increased by 15.01% in the AD group, while the occurrence of integrated State II turned lower. As concluded in an earlier DFC study on major depression disorder (MDD), the increased occurrence in the weakly connected state indicated that patients with MDD would have more severe depressive symptoms (Yao et al., 2019). By means of the Spearman's correlation analysis, there is significantly positive correlation between fractional windows and HAMA scores, implying that the DFC index may contribute to the diagnosis and the severity evaluation of ADs.

Other temporal properties were also found significantly different among GAD, PD, and HC groups in the segregated state, suggesting that the AD group tends to display a more stable pattern. We observed that the AD group dwelled longer in segregated State I and spent less time in the integrated State II. Compatible with the results obtained in other neurological and psychiatric conditions, the disease group stayed longer in the state with sparse connections (Yao et al., 2016; Liu et al., 2017) and may indicate the decreased information communication (Wang et al., 2019). Furthermore, consistent with evidence suggesting that within-network communication is essential for motor execution, the mean dwell time of the AD group in State I had significant positive correlations with the HAMA-somatic anxiety score. Notably, compared with the PD group, the GAD group occurred more frequently and occupied more time in the segregated State I. Referring to previous findings, it was suggested that the increased FC between hippocampus/parahippocampus and fusiform gyrus in GAD were mainly related to a fear generalization-related neural circuit, which was regarded as evaluation processing, whereas the greater FC between somatosensory cortex and thalamus in PD were more likely correlated to detection processing (Cui et al., 2016). In this respect, our result is in line with what has been reported earlier (Fiorenzato et al., 2019), suggesting that increased brain network functional segregation was closely associated with cognitive performance.

However, contrary to these reports, the reduction in transitioning between two FC patterns fails to distinguish patients with AD from HCs. Meanwhile, among the GAD, PD, and HC groups, the number of transitioning between states in GAD statistically differed from that in HCs, indicating that GAD exhibited more steady dynamic connectivity pattern during the whole scanning. Considering the fact that the rate of transitions may serve as a measure of reduced cognitive flexibility (Guitart-Masip et al., 2016; Yao et al., 2019), our findings suggested that patients with GAD may suffer more severe cognitive dysfunction.

Taken together, our findings show that the DFC properties may efficiently differentiate patients with AD from HCs and thus have the potential of a clinical biomarker. Moreover, further studies with larger sample sizes are needed to confirm whether patients with GAD are more sensitive to the DFC properties than patients with PD.

Further, regarding the strength of dynamic states, we observed that almost half of the within- and between-network connections in two states (26/61) were associated with the triple networks. This model is known as a unifying pattern to explain the neural physiopathology of psychiatric and neurological disorders, involving SAN, DMN, and ECN (Menon, 2011).

Specifically, in the weakly connected State I, the decreased functional connections in ADs were mostly found in SAN–DMN and DMN–ECN. For SAN, it is implicated that the left and right dorsal Anterior Cingulate Cortex (dACC) were involved in the processing of somatosensory information, attentional control, and self-awareness (Bisley and Goldberg, 2010; Koechlin, 2011). For DMN, it is known to be in correlation with self-reference (Kelley et al., 2002; Northoff et al., 2006) and plays an important role in monitoring the internal mental landscape (Greicius et al., 2003; Qin and Northoff, 2011). In particular, many studies have provided clues to FC alterations that both GAD and PD have abnormal RSFC in DMN and SAN (Pannekoek et al., 2013; Andrew et al., 2014; Yao et al., 2017; Kim and Yoon, 2018). Interestingly, we found that the connections between SAN and DMN in ADs were remarkably decreased compared with HCs, suggesting that patients with AD may be weak in identifying the most homeostatically relevant among several internal and external stimuli.

Meanwhile, in accordance with an earlier meta-analysis, decreased connectivity in the DMN and ECN may be related to poor emotion regulation (indicated by hyperactivity of the amygdala), which has been regarded as a central feature in the neuropathophysiology of ADs (Xu et al., 2019). Additionally, the findings in the MDD reported similar results, revealing that the reduced communication in DMN–ECN may be due to difficulty in switching from a “default-state” to an “executive-state” (Hamilton et al., 2013; Mulders et al., 2015; Yao et al., 2019). Notably, for State I, we also observed eight stronger connections between DMN and AUD (HCs > AD), adding to the view that anxiety may lead to poor auditory information processing. Comparing GAD to PD, however, we did not find any significant differences, implying that these two ADs may have common resting-state network activation.

This study should be interpreted cautiously due to several limitations as followed. First, patients with PD were out of drug-free state when the fMRI scanning was performed, so that we cannot exclude the medical effects on FC. Second, taking sample size into consideration, this study may fail to detect some group differences. Despite the reason that the PD group is less sensitive in the DFC analyses, the non-significant values of correlations between DFC features and HAMA scores in the PD group may result from the sample size. Therefore, more participants need to be performed for further verification. Also, according to earlier evidence, the AD is bound up with cognitive and affective dysfunctions (Liberzon et al., 2015; Brinkmann et al., 2017; Neufang et al., 2018), but our brain findings did not correlate with the specific cognitive performance on the attention, executive, and memory domains. Finally, we performed the RS-fMRI data acquisitions in a duration of 8 min for only once, resulting in a lack of two scanning runs for checking the consistency of the analyses. Moreover, comply with an earlier study, the length of resting-state acquisitions needed to be longer than 10 min in the DFC analyses, so as to precisely detect the temporal properties.

This study analyzed an in-depth assessment regarding DFC features in the GAD and PD groups. Most notably, our findings may suggest that patients with AD can be distinguished from HCs according to the DFC alterations in the resting state. Besides, the GAD group might have the potential to be more sensitive to these time-varying properties than the PD group, which requires further analyses in future studies. Additionally, our study shows that connections associated with ADs were identified including the regions that belonged to DM, EC, and SA networks, especially the connections between SAN and DMN, which may help to explain the abnormal affective and cognitive functions. These findings implied that GAD and PD may have similar state-dependent neurophysiological mechanisms and, in addition, the DFC characteristics could hopefully help us better understand their abnormal affective and cognitive performances in the clinic.

The original contributions generated for the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author/s.

The studies involving human participants were reviewed and approved by Medical ethics committee of Brain Hospital Affiliated to Nanjing Medical University. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.