Dynamic Resting-State Functional Connectivity in Major Depression

Abstract

Major depressive disorder (MDD) is characterized by abnormal resting-state functional connectivity (RSFC), especially in medial prefrontal cortical (MPFC) regions of the default network. However, prior research in MDD has not examined dynamic changes in functional connectivity as networks form, interact, and dissolve over time. We compared unmedicated individuals with MDD (n=100) to control participants (n=109) on dynamic RSFC (operationalized as SD in RSFC over a series of sliding windows) of an MPFC seed region during a resting-state functional magnetic resonance imaging scan. Among participants with MDD, we also investigated the relationship between symptom severity and RSFC. Secondary analyses probed the association between dynamic RSFC and rumination. Results showed that individuals with MDD were characterized by decreased dynamic (less variable) RSFC between MPFC and regions of parahippocampal gyrus within the default network, a pattern related to sustained positive connectivity between these regions across sliding windows. In contrast, the MDD group exhibited increased dynamic (more variable) RSFC between MPFC and regions of insula, and higher severity of depression was related to increased dynamic RSFC between MPFC and dorsolateral prefrontal cortex. These patterns of highly variable RSFC were related to greater frequency of strong positive and negative correlations in activity across sliding windows. Secondary analyses indicated that increased dynamic RSFC between MPFC and insula was related to higher levels of recent rumination. These findings provide initial evidence that depression, and ruminative thinking in depression, are related to abnormal patterns of fluctuating communication among brain systems involved in regulating attention and self-referential thinking.

Divergent topological architecture of the default mode network as a pretreatment predictor of early antidepressant response in major depressive disorder

“…The SFGdor is a core structure of the DMN subserving introspective emotions and cognitive functions40. Neuroimaging studies have demonstrated that depressed patients show higher activation in the SFG41 and increased functional connectivity between the dorsolateral and medial superior frontal cortex42. This study uncovered distinct patterns of neural substrates underlying emotional regulation, which were correlated with the different responses to antidepressant treatment between RD and NRD.…”

On Stabilizing the Variance of Dynamic Functional Brain Connectivity Time Series

“…To date, sFC has been applied to many areas of the brain research, from a general understanding of the network topology of the brain (De Luca et al, 2006; Fransson, 2005; Greicius et al, 2003), task modulation (Fransson, 2006), neurodevelopment (Power et al, 2010), and clinical applications (Fox and Greicius, 2010). In the case of dFC, quantitative studies of the fluctuations of signal covariance over time offers a possibility to explore the dynamics of the brain and it has already found applications; from understanding basic brain processes such as levels of consciousness (Barttfeld et al, 2015), mind wandering (Schaefer et al, 2014), and development (Hutchison and Morton, 2015), to clinical applications such as depression (Kaiser et al, 2016) and schizophrenia (Damaraju et al, 2014; Ma et al, 2014). …”

“…In the neuroimaging dFC literature, the variance is often stabilized by applying the Fisher transformation to the connectivity time series (a nonexhaustive list includes: Allen et al, 2014; Barttfeld et al, 2015; Damaraju et al, 2014; Elton and Gao, 2015; Hutchison and Morton, 2015; Kaiser et al, 2016; Kucyi and Davis, 2014; Leonardi et al, 2014; Schaefer et al, 2014). Generally, there are good reasons for doing so, since a reasonably stable signal variance is required to be able to accurately quantify changes in dynamic brain functional connectivity, which often is the primary goal of the analysis.…”

A Hitchhiker's Guide to Functional Magnetic Resonance Imaging

“…Dynamic FC analysis has the potential to clarify the constant changes in patterns of neural activity and may be a more appropriate choice for the analysis of rs-fMRI studies (Bassett et al, 2011; Cabral et al, 2011; Madhyastha et al, 2015; Kaiser et al, 2016). The technique can be implemented using the sliding window correlations approach (most common) (Hindriks et al, 2016), time-frequency analysis (Chang and Glover, 2010), single-volume co-activation patterns (Liu et al, 2013), repeating sequences of BOLD activity (Pan et al, 2013), or through phase synchronization (Glerean et al, 2012).…”