Trait repetitive negative thinking in depression is associated with functional connectivity in negative thinking state rather than resting state

Abstract

Resting-state functional connectivity (RSFC) has been proposed as a potential indicator of repetitive negative thinking (RNT) in depression, while inconsistent findings have been reported. This study utilized connectome-based predictive modeling (CPM) to investigate whether RSFC and negative-thinking-state functional connectivity (NTFC) could predict RNT in individuals with Major Depressive Disorder (MDD). Although RSFC distinguished between healthy and depressed individuals, it did not predict trait RNT (as assessed by the Ruminative Responses Scale-Brooding subscale) in depressed individuals. Conversely, NTFC predicted trait RNT in depressed individuals with significant accuracy, but could not differentiate between healthy and depressed individuals. Connectome-wide analysis revealed that negative thinking in depression was associated with higher FC between default mode and executive control regions, which was not observed in RSFC. Our findings suggest that RNT in depression is associated with an active mental process involving multiple brain regions across functional networks that is not represented in the resting state.

Functional brain network dynamics of brooding in depression: insights from real-time fMRI neurofeedback

“…However, our experimental design did not include a brooding condition post-neurofeedback. Consequently, changes in brooding-related dFNC associated with neurofeedback were examined in resting-state, which may not be as sensitive as mood-induction tasks in capturing neuronal indices of brooding and rumination (Berman et al, 2014; Chen (••) & Yan (•超•), 2021; Misaki et al, 2023), contributing to the weak effects observed in the exploratory neurofeedback analysis. These exploratory findings should hence be interpreted with caution, since the observed significance did not survive correction for multiple comparisons, and group by time interaction effects were not considered due to the small subgroup sizes.…”

“…We hypothesized that: (1) During brooding, compared to HCs, MDD subjects would show significant decreases in time spent and increases in temporal variability in distinct dFNC states with strong connections within and between various DMN (e.g., PCC, mPFC), CEN (e.g., DLPFC), SN (e.g., insula, dorsal ACC), and subcortical (e.g., hippocampus, thalamus) regions, building upon prior observations of brooding-related static FC (Berman et al, 2011; X. Li et al, 2022; Ordaz et al, 2017; Philippi et al, 2022; Pisner et al, 2019; Satyshur et al, 2018) and rumination-related dynamic FC (Chen (••) & Yan (•超•), 2021; Kaiser et al, 2016; Kucyi & Davis, 2014) alterations; and(2) increase in brooding severity would be significantly associated with decrease in time spent and increased temporal variability in the dFNC states during brooding rather than resting-state, as experimentally-induced brooding is expected to be more sensitive in capturing the active cognitive aspect of brooding compared to resting-state (Berman et al, 2014; Chen (••) & Yan (•超•), 2021; Misaki et al, 2023). Since our clinical trial did not include a brooding condition after neurofeedback, we performed an exploratory analysis to identify any changes in the dynamics of brooding-related dFNC states from pre-to post-neurofeedback resting-state.…”

“…(2) increase in brooding severity would be significantly associated with decrease in time spent and increased temporal variability in the dFNC states during brooding rather than resting-state, as experimentally-induced brooding is expected to be more sensitive in capturing the active cognitive aspect of brooding compared to resting-state (Berman et al, 2014; ) & Yan ( 超 ), 2021; Misaki et al, 2023). Since our clinical trial did not include a brooding condition after neurofeedback, we performed an exploratory analysis to identify any changes in the dynamics of brooding-related dFNC states from pre-to post-neurofeedback resting-state.…”

Neural substrates of predicting anhedonia symptoms in major depressive disorder via connectome‐based modeling

“…Other studies have also reported heightened FC in the frontal‐limbic circuits of MDD patients with anhedonia 8,9 . However, the research outcomes have been inconsistent, and these abnormal FC are widely distributed across various brain regions 10,11 . Therefore, this study aimed to establish a predictive networks model for anhedonia symptoms using whole‐brain FC patterns.…”

“… 8 , 9 However, the research outcomes have been inconsistent, and these abnormal FC are widely distributed across various brain regions. 10 , 11 Therefore, this study aimed to establish a predictive networks model for anhedonia symptoms using whole‐brain FC patterns. The improvement in diagnosing and predicting anhedonia in MDD patients can be achieved by identifying abnormal FC features and constructing predictive models.…”