scholarly journals Abnormal Functional Connectivity of the Amygdala-Based Network in Resting-State fMRI in Adolescents with Generalized Anxiety Disorder

2015 ◽  
Vol 21 ◽  
pp. 459-467 ◽  
Author(s):  
Fang Zhang
SLEEP ◽  
2017 ◽  
Vol 40 (suppl_1) ◽  
pp. A418-A418
Author(s):  
EF Pace-Schott ◽  
JP Zimmerman ◽  
RM Bottary ◽  
EG Lee ◽  
MR Milad ◽  
...  

2022 ◽  
Author(s):  
Jonas L Steinhäuser ◽  
Adam R Teed ◽  
Obada Al-Zoubi ◽  
René Hurlemann ◽  
Gang Chen ◽  
...  

Differences in the correlated activity of networked brain regions have been reported in individuals with generalized anxiety disorder (GAD) but an overreliance on the null-hypothesis significance testing (NHST) framework limits the identification and characterization of disorder-relevant relationships. In this preregistered study, we applied a Bayesian statistical framework as well as NHST to the analysis of resting-state fMRI scans from females with GAD and demographically matched healthy comparison females. Eleven a-priori hypotheses about functional correlativity (FC) were evaluated using Bayesian (multilevel model) and frequentist (t-test) inference. Reduced FC between the ventromedial prefrontal cortex (vmPFC) and the posterior-mid insula (PMI) was confirmed by both statistical approaches. FC between the vmPFC-anterior insula, the amygdala-PMI, and the amygdala-dorsolateral prefrontal cortex (dlPFC) region pairs did not survive multiple comparison correction using the frequentist approach. However, the Bayesian model provided evidence for these region pairs having decreased FC in the GAD group. Leveraging Bayesian modeling, we demonstrate decreased FC of the vmPFC, insula, amygdala, and dlPFC in females with GAD. Exploiting the Bayesian framework revealed FC abnormalities between region pairs excluded by the frequentist analysis, as well as other previously undescribed regions, demonstrating the benefits of applying this statistical approach to resting state FC data.


2017 ◽  
Vol 265 ◽  
pp. 26-34 ◽  
Author(s):  
Edward F. Pace-Schott ◽  
Jared P. Zimmerman ◽  
Ryan M. Bottary ◽  
Erik G. Lee ◽  
Mohammed R. Milad ◽  
...  

2021 ◽  
Vol 15 ◽  
Author(s):  
Yang Du ◽  
Hailong Li ◽  
Hongqi Xiao ◽  
Mei Wang ◽  
Wei Zhang ◽  
...  

Trait anxiety is considered a vulnerability factor for the development of generalized anxiety disorder (GAD). The amygdala is related to both trait anxiety and GAD. Thus, we investigated amygdala-based functional connectivity (FC) in drug-naive non-comorbid GAD patients and explored its associations with personality, symptoms, and illness severity. FC analyses using the bilateral amygdala as seeds were performed with resting-state functional MRI data from 38 GAD patients and 20 matched healthy controls (HCs). Clinical characteristics were correlated with FC Z-scores from regions showing significant group differences. Furthermore, moderation analyses were used to explore the conditional effect of illness severity measured by the Clinical Global Impression–Severity (CGI-S) scale on the relationship between FC and trait anxiety. Relative to HCs, GAD patients showed hypoconnectivity between the amygdala and the rostral anterior cingulate cortex (rACC), inferior frontal gyrus (IFG), parahippocampal gyrus, and cerebellum and hyperconnectivity between the amygdala and the superior temporal gyrus (STG), insula, and postcentral gyrus. In GAD patients, amygdala–rACC connectivity was negatively associated with symptom severity and trait anxiety, and amygdala–IFG connectivity was positively associated with symptom severity. Moreover, CGI-S scores moderated the negative correlation between trait anxiety and amygdala–rACC FC. We demonstrate that there is extensive amygdala-based network dysfunction in patients with GAD. More importantly, amygdala–rACC connectivity plays a key role in the neural pathology of trait anxiety. Finally, the more severe the illness, the stronger the negative association between trait anxiety and amygdala–rACC FC. Our results emphasize the importance of personalized intervention in GAD.


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