Psychological resilience negatively correlates with resting-state brain network flexibility in young healthy adults: a dynamic functional magnetic resonance imaging study

Abstract

Background: Psychological resilience is an important personality trait whose decrease is associated with many common psychiatric disorders, but the neural mechanisms underlying it remain largely unclear. In this study, we aimed to explore the neural correlates of psychological resilience in healthy adults by investigating its relationship with functional brain network flexibility, a fundamental dynamic feature of brain network defined by switching frequency of its modular community structures.Methods: Resting-state functional magnetic resonance imaging (fMRI) scans were acquired from 41 healthy adults, whose psychological resilience was quantified by the Connor-Davidson Resilience Scale (CD-RISC). Dynamic functional brain network was constructed for each subject, whose flexibility was calculated at all the global, subnetwork and region-of-interest (ROI) levels. After that, the associations between CD-RISC score and brain network flexibility were assessed at all levels by partial correlations controlling for age, sex, education and head motion. Correlation was also tested between the CD-RISC score and modularity of conventional static brain network for comparative purposes.Results: The CD-RISC score was significant negatively correlated with the brain network flexibility at global level (r=−0.533, P=0.001), and with flexibility of the visual subnetwork at subnetwork level (r=−0.576, corrected P=0.002). Moreover, significant (corrected P<0.05) or trends for (corrected P<0.10) negative correlations were found between the CD-RISC score and flexibilities of a number of visual and default-mode areas at ROI level. Meanwhile, the modularity of static brain network did not reveal significant correlation with CD-RISC score (P>0.05).Conclusions: Our results suggest that excessive fluctuations of the functional brain community structures during rest may be indicative of a lower psychological resilience, and the visual and default-mode systems may play crucial roles in such relationship. These findings may provide important implications for improving our understanding of the psychological resilience.

Altered Temporal Variability of Local and Large-Scale Resting-State Brain Functional Connectivity Patterns in Schizophrenia and Bipolar Disorder

“…The details about brain imaging data acquisition and preprocessing can be found in one of our recently published studies (14). Briefly, rs-fMRI and T1-weighted structural images were scanned for each participant on a 3.0 T Philips MRI scanner (repetition time = 2,000 ms, echo time = 30 ms, slice number = 36, field of view = 240 × 240 mm 2 , acquisition matrix = 144 × 144, flip angle = 90°, and number of time points = 250 for rs-fMRI images; repetition time = 7.5 ms, echo time = 3.7 ms, slice number = 180, field of view = 240 × 240 mm 2 , acquisition matrix = 256 × 200, and flip angle = 8°for T1-weighted images).…”

“…Therefore, the "dynamic FC" has become a hot-spot in rs-fMRI studies to capture the temporal fluctuations of brain FC patterns during the scan (11). Notably, the dynamic features of FC have been associated with a wide range of cognitive and affective processes such as learning (12), executive cognition (13), psychological resilience (14), and emotion (15), as well as multiple common psychiatric and neurological disorders such as autism (16), Alzheimer's disease (17), and major depressive disorder (18,19). These findings thus highlight the importance of studying dynamic FC for further improving our understanding of both brain functions and dysfunctions.…”

Disrupted dynamic functional connectivity in distinguishing subjective cognitive decline and amnestic mild cognitive impairment based on the triple-network model

“…The dynamic FC (DFC) analysis has become a hotspot in rsfMRI researches to capture the temporal uctuation of brain FC during the scan [27]. Previous researches have demonstrated that quanti cation of dynamic FC disruption might be a sensitive biomarker and/or prognostic indicator of disease progression and cognitive function [28,29]. Moreover, some studies have highlighted the potential role of DFC analysis in improving the accuracy of disease diagnosis, which made it necessary to apply DFC analysis to AD spectrum diagnosis [30].…”

Intracranial brain stimulation modulates fMRI-based network switching

“…By extending the concept of modularity to several dimensions, es-fMRI connectivity can be represented in terms of a spatiotemporal network model of the brain (Finc et al, 2020; Lydon-Staley et al, 2018; Shine et al, 2016). Multilayer network flexibility, or switching, is associated with cognitive functions including working memory (Braun et al, 2015), reasoning (Pedersen et al, 2018a), reward (Gerraty et al, 2018) and fatigue (Betzel et al, 2017) as well as alterations in multiple psychological and neurological disorders (Gifford et al, 2020; Harlalka et al, 2019; Long et al, 2019; Paban et al, 2019; Shao et al, 2019; Tian et al, 2020a). There is also evidence that brain network switching changes in response to behavioural training.…”