Altered Local and Large-Scale Dynamic Functional Connectivity Variability in Posttraumatic Stress Disorder: A Resting-State fMRI Study

Abstract

Posttraumatic stress disorder (PTSD) is a psychiatric condition that can emerge after exposure to an exceedingly traumatic event. Previous neuroimaging studies have indicated that PTSD is characterized by aberrant resting-state functional connectivity (FC). However, few existing studies on PTSD have examined dynamic changes in resting-state FC related to network formation, interaction, and dissolution over time. In this study, we compared the dynamic resting-state local and large-scale FC between PTSD patients ( n = 22) and healthy controls (HC; n = 22; conducted as standard deviation in resting-state local and large-scale FC over a series of sliding windows). Local dynamic FC was examined by calculating the dynamic regional homogeneity (dReHo), and large-scale dynamic FC (dFC) was investigated between regions with significant dReHo group differences. For the PTSD patients, we also investigated the relationship between symptom severity and dFC/dReHo. Our results showed that PTSD patients were characterized by I) increased dynamic (more variable) dReHo in left precuneus (PCu); II) increased dynamic (more variable) dFC between the left PCu and left insula; and III) decreased dFC between left PCu and left inferior parietal lobe (IPL), and decreased dFC between left PCu and right PCu. However, there is no significant correlation between the clinical indicators and dReHo/dFC after the family-wise-error (FWE) correction. These findings provided the initial evidence that PTSD is characterized by aberrant patterns of fluctuating communication within brain system such as the default mode network (DMN) and among different brain systems such as the salience network and the DMN.

Back to the Basics: Resting State Functional Connectivity of the Reticular Activation System in PTSD and its Dissociative Subtype

“…The spontaneous neuronal activation observed during resting state functional magnetic resonance imaging (fMRI) is frequently utilized to illustrate psychopathological alterations in cortical and in subcortical brain networks in psychiatric disorders, serving as a key biomarker in post-traumatic stress disorder (PTSD). [1][2][3][4][5][6][7][8][9][10][11][12][13][14] Importantly, emerging evidence has underscored the importance of incorporating deep-layer midbrain/brainstem neural circuits into the neurobiological framework of PTSD. 1,2,[15][16][17][18][19] The reticular activation system (RAS) serves a fundamental role toward the gating of salient, environmental information to higher order, cortical brain structures to facilitate the generation and the maintenance of an arousal state.…”

Aberrant regional homogeneity in post-traumatic stress disorder after traffic accident: A resting-state functional MRI study

“…In this study, the decreased ReHo value in the right angular was negatively correlated with the symptom severity of PTSD (CAPS score). In our previous study of dynamic functional connectivity of PTSD, we found a decreased dynamic functional connectivity in the angular gyrus (Fu et al, 2019). Previous diffusion tensor imaging also found a diminished subcortical integrity in angular gyrus (Schuff et al, 2011).…”

Dynamic resting-state functional connectivity and pitch identification ability in nonmusicians.

“…Dynamic seed-based RSFC analysis for each participant was performed using the Temporal Dynamic Analysis toolkits that were based on DPABI V4.5 (also see Chen et al, 2019; Fu et al, 2019; Yan et al, 2017; Zhang et al, 2018 with DPARSF or DPABI). Sliding window-based analysis, which is sensitive to time-dependent variations (Chen et al, 2019; Hindriks et al, 2016; Liu et al, 2017; Yip et al, 2017), was applied to examine the seed-based RSFC over the whole brain.…”

“…Dynamic RS-fMRI has been used to analyze and identify the time-varying and recurring RS-fMRI subpatterns of coupling among brain regions to create the brain “chronnectome” (Calhoun et al, 2014; Filippi et al, 2019). Researchers have recently improved the analytical methods for dynamic RS-fMRI by using sliding window analysis (i.e., correlations among discrete large-scale brain regions with a short and sliding temporal window; Fong et al, 2019; Fu et al, 2019; Handwerker et al, 2012; Liu et al, 2017; Yan et al, 2017).…”