Intrinsic Network Brain Dysfunction Correlates With Temporal Complexity in Generalized Anxiety Disorder and Panic Disorder

Background: Generalized anxiety disorder (GAD) and panic disorder (PD) are the two severe subtypes of anxiety disorders (ADs), which are similar in clinical manifestation, pathogenesis, and treatment. Earlier studies have taken a whole-brain perspective on GAD and PD in the assumption that intrinsic...

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Bibliographic Details
Main Authors: Ding, H. (Author), Li, J. (Author), Wang, C. (Author), Xu, H. (Author), Xu, L. (Author)
Format: Article
Language:English
Published: Frontiers Media S.A. 2021
Subjects:
Online Access:View Fulltext in Publisher
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001 10.3389-fnhum.2021.647518
008 220427s2021 CNT 000 0 und d
020 |a 16625161 (ISSN) 
245 1 0 |a Intrinsic Network Brain Dysfunction Correlates With Temporal Complexity in Generalized Anxiety Disorder and Panic Disorder 
260 0 |b Frontiers Media S.A.  |c 2021 
856 |z View Fulltext in Publisher  |u https://doi.org/10.3389/fnhum.2021.647518 
520 3 |a Background: Generalized anxiety disorder (GAD) and panic disorder (PD) are the two severe subtypes of anxiety disorders (ADs), which are similar in clinical manifestation, pathogenesis, and treatment. Earlier studies have taken a whole-brain perspective on GAD and PD in the assumption that intrinsic fluctuations are static throughout the entire scan. However, it has recently been suggested that the dynamic alternations in functional connectivity (FC) may reflect the changes in macroscopic neural activity patterns underlying the critical aspects of cognition and behavior, and thus may act as biomarkers of disease. Methods: In this study, the resting-state functional MRI (fMRI) data were collected from 26 patients with GAD, 22 patients with PD, and 26 healthy controls (HCs). We investigated dynamic functional connectivity (DFC) by using the group spatial independent component analysis, a sliding window approach, and the k-means clustering methods. For group comparisons, the temporal properties of DFC states were analyzed statistically. Results: The dynamic analysis demonstrated two discrete connectivity “States” across the entire group, namely, a more segregated State I and a strongly integrated State II. Compared with HCs, patients with both GAD and PD spent more time in the weakly within-network State I, while performing fewer transitions and dwelling shorter in the integrated State II. Additionally, the analysis of DFC strength showed that connections associated with ADs were identified including the regions that belonged to default mode (DM), executive control (EC), and salience (SA) networks, especially the connections between SA and DM networks. However, no significant difference was found between the GAD and PD groups in temporal features and connection strength. Conclusions: More common but less specific alterations were detected in the GAD and PD groups, which implied that they might have similar state-dependent neurophysiological mechanisms and, in addition, could hopefully help us better understand their abnormal affective and cognitive performances in the clinic. © Copyright © 2021 Xu, Xu, Ding, Li and Wang. 
650 0 4 |a dynamic functional connectivity 
650 0 4 |a functional MRI 
650 0 4 |a generalized anxiety disorder 
650 0 4 |a neural networks 
650 0 4 |a panic disorder 
700 1 |a Ding, H.  |e author 
700 1 |a Li, J.  |e author 
700 1 |a Wang, C.  |e author 
700 1 |a Xu, H.  |e author 
700 1 |a Xu, L.  |e author 
773 |t Frontiers in Human Neuroscience