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. 2025 Nov 10;20(1):nsaf118. doi: 10.1093/scan/nsaf118

An investigation of the interaction of trait repetitive negative thinking and neuroticism on brain activity during negative self-referential processing: a cross-sectional fMRI study

Marvin S Meiering 1,2,✉, David Weigner 3,4, Rebecca Gruzman 5,6, Sören Enge 7,2, Simone Grimm 8,9,10,2
PMCID: PMC12640242  PMID: 41212534

Abstract

Repetitive negative thinking (RNT) and neuroticism are two of the most prominent transdiagnostic risk factors for internalizing disorders. Previous research highlighted a complex relationship between neuroticism and RNT. However, no study has yet examined their interaction at the neural level. Trait RNT and neuroticism were assessed in a sample of 126 healthy participants, followed by a fMRI task designed to elicit RNT. Linear models were employed to examine the interaction between trait RNT and neuroticism and main effects of RNT on brain changes. No significant interaction between trait RNT and neuroticism in relation to DMN and AI recruitment was found. A positive association was observed between trait RNT and recruitment of the precuneus during negative self-referential processing compared to distraction; however, this result did not survive FDR correction for multiple comparisons. Exploratory analyses revealed that responses of the anterior insula during RNT compared to distraction were inversely related to self-reported trait RNT on the trend-level. Our results confirm the relevance of DMN recruitment for RNT and underscore a putative role of the anterior insula. The absence of a significant interaction between RNT and neuroticism may suggest that an alternative conceptualization of their relationship could better capture their underlying dynamics.

Keywords: fMRI, neuroticism, rumination, worry, RNT

Introduction

Repetitive negative thinking (RNT) is a transdiagnostic risk factor for a wide range of internalizing psychopathologies, characterized by persistent, intrusive thoughts about actual past or possible future negative experiences (Ehring and Watkins 2008, Ehring et al. 2011). While numerous studies support the idea that higher expression of RNT is profoundly entangled with internalizing psychopathology, it has further been characterized as a dimensional trait that is present in the general population, albeit to a lesser extent (Ehring and Watkins 2008).

According to (attentional) control theories of RNT, internal or external stressors can trigger self-referential contemplation to cope with discrepancies or conflicts between perceived reality and one’s goals (Martin and Tesser 1989, 1996, Watkins 2008, Koster et al. 2011). Following a normative trajectory, the goal-conflict is processed in rich contextual detail and at a low level of abstraction in order to guide specific behavioural responses for goal attainment. However, if cognitive resources to effectively disengage from self-referential thinking are lacking, e.g. due to high levels of stress or low levels of cognitive control and conflict signalling, negative affect is prolonged, ultimately increasing the risk for psychopathology (Koster et al. 2011, Marchetti et al. 2016). Since its publication, the cognitive control theory and the impaired disengagement hypothesis of RNT have been supported by a wealth of evidence from healthy and patient samples, showing negative associations between RNT and measures of cognitive control (Watkins 2008, Zetsche et al. 2018, Mennies et al. 2021).

Previous research investigated RNT primarily in its two disorder-specific forms: rumination and worry (Ehring and Watkins 2008). High correlations between self-report measures, shared relationships with depression and anxiety, and factor-analytic findings, provide compelling evidence that both might represent distinct forms of RNT that exert their detrimental effects primarily through shared characteristics (Ehring and Watkins 2008, Arditte et al. 2016, Spinhoven et al. 2018, Taylor and Snyder 2021, Samtani et al. 2022).

The default mode network (DMN) has been consistently implicated in RNT, both in induction paradigms and in the resting state (Makovac et al. 2020). Numerous studies have supported the concept of the DMN as a task-negative network, active at rest and inversely correlated with regions that are associated with goal-directed and cognitively demanding mental processes (Greicius et al. 2003). The core of the DMN is anatomically composed of the medial prefrontal cortex (mPFC), pregenual anterior cingulate cortex (pgACC) and posterior cingulate cortex (PCC) as well as the precuneus (Andrews-Hanna et al. 2010, Pizzagalli 2011). A wealth of evidence suggests that the DMN primarily integrates self-referential, memory, social, and semantic processes to create and maintain a coherent narrative of the self, facilitating the psychological phenomenon of subjective continuity (Menon 2023).

Moreover, the DMN itself is dynamically modulated by interactions with two additional large-scale networks: the fronto-parietal (FPN) and salience network (SAL; Menon 2011). In contrast to the DMN, the FPN is profoundly implicated in cognitively demanding processes as well as attentional control and primarily comprises the dorsolateral prefrontal and posterior parietal cortices (Seeley et al. 2007, Menon 2011, Menon and D’Esposito 2022). The SAL encompasses the anterior insula as well as the dorsal anterior cingulate cortex (dACC) and is involved in detection of salient stimuli and allocation of mental resources like attention (Seeley et al. 2007, Sridharan et al. 2008, Menon 2011, Uddin 2015, Seeley 2019). Also, the SAL represents a central hub in the triple network model, orchestrating the recruitment of the DMN and FPN (Menon 2011, 2023). More specifically, the SAL processes external input from sensory regions and subsequently suspends the DMN, which disrupts the internal narrative the DMN is continuously engaged in. Thereupon, the FPN is recruited to attend to and cope with the external demand. Once the external processing is finalized, the DMN resumes activity and integrates the experience into the global narrative of the self and the world (Menon 2023). However, this dynamic process can become rigid resulting in sustained DMN recruitment when external demands would require inter-network flexibility to allocate sufficient attentional resources to the external world. A wealth of evidence suggests that this inability to flexibly switch between FPN and DMN in response to environmental demands may underlie the debilitating repetitiveness that characterizes RNT (Grimm et al. 2009, Hamilton et al. 2011, Koster et al. 2013, Belleau et al. 2015, Kaiser et al. 2015, Lydon-Staley et al. 2019).

Neuroticism, traditionally defined as the propensity to experience negative affect, emotional instability, and elevated responses to (potentially) aversive stimuli, is a personality dimension that has been described as a non-specific marker of vulnerability for psychopathology because of its predictive validity for common mental disorders and yet limited ability to discriminate etiologically distinct subgroups (Eysenck 1967, Watson et al. 1988, Rothbart et al. 2000, Ormel et al. 2004, Rammstedt and John 2005, DeYoung 2015). An abundance of evidence points to a complex relationship between neuroticism, RNT, and psychopathology on the self-report level (Feldner et al. 2006, Merino et al. 2014, Iqbal and Dar 2015, Merino et al. 2016, Zvolensky et al. 2016, Blanke et al. 2022, Liu et al. 2023). Additionally, recent studies have shown that the DMN, which is also crucial to RNT as described above, is associated with neuroticism (Aghajani et al. 2014, Servaas et al. 2014, Gentili et al. 2017, Chou et al. 2023, Zhi et al. 2024). But, to our current knowledge, no study has investigated a potential interaction of trait RNT and neuroticism in relation to the DMN during negative self-referential processing. Conceptually, it has been proposed that RNT is critically dependent on negative mood, either through transient negative mood, as it is present during typical emotional responses to adverse experiences, or through persistent negative mood, as it can be observed in individuals with high neuroticism (Whitmer and Gotlib 2013, Marchetti et al. 2016). Following this line of reasoning, we previously formulated an amplification hypothesis similar to that proposed by Marchetti et al. (2016): the negative effect of dysfunctional coping strategies specific to internalizing psychopathology such as RNT is amplified by high levels of neuroticism, a rather uninformative and distal risk factor (Meiering et al. 2023). This notion is further substantiated by theoretical considerations suggesting that negative self-referential thinking—and its grounding in sustained activation of the DMN—underlie neuroticism (Perkins et al. 2015).

We therefore hypothesize that RNT and neuroticism interactively relate to DMN recruitment during experimentally induced negative self-oriented processing. Due to the central role of the SAL in modulating DMN recruitment, we also aim to exploratorily investigate if RNT and neuroticism interactively explain SAL recruitment during negative self-referential processing.

Materials and Methods

A protocol detailing all paradigms, questionnaires, hypotheses, procedures, and analytical approaches employed during the course of this study has already been published elsewhere by our group to enhance scientific transparency and reproducibility (Meiering et al. 2023). The following sections are in large parts drawn from this publication and the given information was curated and extended to include the most important aspects.

Participants

Based on an a priori power analysis (see Meiering et al. 2023), a sample of 126 healthy adults between the ages of 18 and 45 was enrolled in this study. Exclusion criteria included a diagnosed mental disorder, psychotherapy or psychopharmacological treatment in the past 12 months, or meeting any of the MRI exclusion criteria. Furthermore, subjects were instructed to refrain from alcohol and other psychoactive substances 48 h before the MRI assessment as well as methylxanthines (caffeine) at the day of the MRI assessments. Subjects were recruited by e-mail, personal approach, and by advertising the study in the online management system for psychological studies conducted at MSB Medical School Berlin and Freie Universität Berlin. The study was approved by the Institutional Review Board of the MSB Medical School Berlin, and all subjects gave written informed consent to participate.

Procedure

After online screening to determine subject eligibility, several self-report questionnaires were employed (detailed below) followed by the rumination and worry induction task (RWIT) during fMRI at the Center for Cognitive Neuroscience Berlin. The RWIT (based on Nolen-Hoeksema and Morrow 1993, Cooney et al. 2010, Paulesu et al. 2010, Kowalski et al. 2019, Chen et al. 2020) was employed to investigate brain activation changes elicited by the common denominator of rumination and worry: RNT.

As recent studies have suggested a relationship between rumination and negative mood (Whitmer and Gotlib 2013, Chen et al. 2020), participants completed a dysphoric mood induction procedure prior to the start of the RWIT, by recalling an autobiographical situation that evoked negative emotions such as guilt, shame, sadness, or fear, and vividly remembering this situation for 2 min. Next, participants were instructed to think about the negative event by means of self-oriented questions proposed to elicit rumination and worry, respectively. For instance, an item proposed to elicit rumination was “Why can’t I handle events like this better?” and for worry “How could the event have a negative impact on my future?”. As a contrast condition, subjects were shown 4 brief sentences describing a neutrally valenced situation and instructed to vividly imagine it, e.g. “A typical classroom from school” (distraction; DIS). Each condition included four questions or sentences shown for 90 s, forming a continuous block of 6 minutes for each condition. Condition blocks were separated by an instruction slide, presented for 30 s. The rumination and worry conditions were presented in a randomized order, whereas the distraction condition was always presented last to ensure that the experimental conditions were experienced continuously. In total, completion of the RWIT took approximately 22 min (see Fig. 1). The rumination and worry conditions are collapsed statistically at the analytical stage to obtain the RNT condition (RNT).

Figure 1.

Figure 1.

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Exemplary procedure of the RWIT paradigm.

The RWIT paradigm probed negative self-referential processing, starting with the induction of a dysphoric mood by vividly imagining a negative autobiographical. The dysphoric mood induction was followed by two blocks of questions relating to the negative event to evoke rumination and worry, respectively. Finally, distraction was evoked by prompting statements that describe neutral scenes as the control condition. The order of the rumination and worry conditions were randomized and a total of four sentences were presented during the conditions. fMRI, functional magnetic resonance imaging.

Psychometric assessments

In accordance with recent suggestions to increase the reliability of self-reports and therefore improve brain-behaviour effect sizes for fMRI research, a confirmatory factor analysis was used to obtain factor scores resembling RNT and neuroticism (DeYoung et al. 2025). Aggregating the information of several questionnaires that probe similar constructs into factor scores improves reliability by removing variance that is not attributable to their common latent factor (DiStefano et al. 2009). Consequently, these estimates provide a more reliable measure of the latent factor than either questionnaire alone, enhancing the validity and sensitivity of subsequent statistical analyses.

In this vein, the rumination subscale of the Cognitive Emotion Regulation Questionnaire (CERQ; Loch et al. 2011), the Perseverative Thinking Questionnaire total score (PTQ; Ehring et al. 2011), the RSQ brooding subscale (RSQ; Treynor et al. 2003, Huffziger and Kühner 2012) and the Penn State Worry Questionnaire (PSWQ; Meyer et al. 1990, Stöber 1995) were used to obtain factor scores of RNT for each of the participants. Correlations between the questionnaires ranged between .34 and .56.

A similar approach was used to measure neuroticism. The Negative Affect scale of the trait Positive and Negative Affect Schedule (PANAS; Watson et al. 1988, Breyer and Bluemke 2016), the Negative Affectivity scale of the Adult Temperament Questionnaire (ATQ; Rothbart et al. 2000, Wiltink et al. 2006), the Negative Emotionality scale from the Big Five Inventory 2 (BFI-2; Rammstedt and John 2005) and Neuroticism from the short version of the Big Five Inventory (BFI-K; Danner et al. 2016, Soto and John 2017, Danner et al. 2019) as well as the trait Anxiety subscale from the State Trait Anxiety Inventory (STAI; Spielberger et al. 1983, Bertrams and Englert 2013) were aggregated into a factor score reflecting neuroticism using confirmatory factor analysis. Correlations between the questionnaires ranged between .33 and .82.

Finally, a translated version of the Brief State Rumination Inventory (BSRI; Marchetti et al. 2018) was administered before the MRI acquisition and directly after the RWIT paradigm to investigate changes in state rumination induced by the paradigm. The BSRI consists of eight statements assessing the present expression of ruminative ideation on a visual analogue scale ranging from 0 to 100 and has been shown to be an efficient and psychometrically valid self-report questionnaire (Marchetti et al. 2018).

To ensure data quality of the self-report measurements, three items were included to assess the participants’ attention (e.g. “To show that you have read this question, please select “rarely applicable”.”). Only individuals who answered at least two of these items correctly were included in the analyses (DeSimone et al. 2015).

MRI acquisition and analysis

Detailed information regarding the MRI sequences be found in the supplemental material.

Brain image preprocessing and analyses were carried out using FEAT (FMRI Expert Analysis Tool; Woolrich et al. 2001, 2004a, 2004b) version 6, as part of FSL (FMRIB’s Software Library; Smith et al. 2004; Woolrich et al. 2009; Jenkinson et al. 2012). Detailed information on the methodological regimen employed for the MRI data can be found in the supplement. Briefly, T1 anatomical data was biasfield corrected and registered to MNI space. Then, the fMRI data was smoothed (5 mm kernel, FWHM), registered to MNI space including a fieldmap correction, denoised using ICA-AROMA (Pruim et al. 2015) and high-pass filtered. On the first-level, a General Linear Model was employed to estimate BOLD signal changes during RNT compared to distraction as implemented in the paradigm. Finally, FSL featquery was used to extract the 90th percentile of BOLD percent signal changes (PSC) in predefined ROIs.

Regions of interest

The rationale for choosing relevant regions of interest (ROIs) was decided hierarchically. Whenever an anatomical mask was available, the ROI was defined anatomically to preserve spatial specificity. However, if a ROI cannot be defined based on anatomical landmarks, as, e.g. the pgACC or the mPFC, we decided to use functionally derived ROIs.

The following ROIs were defined as primary for the analysis of the DMN: PCC, precuneus, and mPFC. Both, PCC and precuneus were defined anatomically using probabilistic maps thresholded at 50% from the Harvard-Oxford Anatomical Atlas. Since the mPFC represents a region that is functionally defined rather than anatomically, the mPFC ROI included in the CONN toolbox was used here (Whitfield-Gabrieli and Nieto-Castanon 2012). The ROI is based on a spatial independent component analysis derived from resting-state scans of 497 healthy subjects from the Human Connectome Project, resulting in 32 functional networks (Whitfield-Gabrieli and Nieto-Castanon 2012). Exploratorily, the anterior insula (defined anatomically based on the Harvard-Oxford Anatomical Atlas and split into an anterior and posterior part based on the central insular sulcus (Uddin et al. 2017)) and dACC (10 mm sphere based on an automated neurosynth meta-analysis with the term “dacc”) were included in the analysis as nodes of the SAL. The pgACC (10 mm sphere based on an automated neurosynth meta-analysis with the term “rostral anterior”) was included additionally as an extension of the anterior DMN. The ROIs of the pgACC and mPFC did not overlap to preserve spatial specificity. All ROIs are depicted in Figure S1 (see online supplementary material for a colour version of this figure) and can be accessed here: https://doi.org/10.17605/OSF.IO/P8DWX.

Statistical analysis

Statistical tests were conducted in R version 4.3.1 using the nonparametric bootstrap method with 10 000 iterations (Posit Software 2023, R Core Team 2023).

Latent variable modelling

In line with recent suggestions to improve effect sizes for brain–behaviour relationships by increasing the reliability of self-report questionnaires (DeYoung et al. 2025), a confirmatory factor analysis was used to estimate the expression of RNT as a latent factor indicated by the RSQ brooding subscale, CERQ rumination subscale, PSWQ and PTQ. First, indicators were z-standardized to ensure similar scaling of the observed variables. To test for multivariate normality, the energy and Mardia tests were used as implemented in the MVN package (Korkmaz et al. 2014). Subsequently, the R package lavaan was employed to fit the model and extract model fit indices, factor loadings and their associated 95% bootstrap confidence intervals as well as RNT factor scores on the single subject level (Rosseel 2012). A maximum likelihood estimator was used to fit the model, assuming CFI ≥ .95, TLI ≥ .9 and SRMR ≤ .08 to indicate good fit (Byrne 1994, Hu and Bentler 1999). RMSEA will be reported for descriptive reasons but not interpreted due to its known unreliability in face of small degree of freedoms and small sample sizes (Kenny et al. 2015). The same analytical regimen was performed to extract neuroticism factor scores based on the Negative Affectivity dimension of the ATQ, the Negative Emotionality dimension of the BFI-2, the Neuroticism scale of the BFI-K, the Negative Affect scale of the PANAS (trait) as well as the trait anxiety scale of the STAI.

Regression

Associations between neuronal measures and self-reports were estimated using Tukey’s bi-weight regression models employing an MM-estimator and bootstrapping to hedge statistical results against the excessive influence of outliers and violations of the normality assumption. For statistical testing, bootstrap confidence intervals and bootstrap-based P-values are reported. To test the moderation effects, the RNT*neuroticism interaction term was tested for statistical significance. In case of no significant moderation, the interaction term was omitted from the model. Since neuroticism is considered an unspecific risk factor for psychopathology and therefore worthy of consideration when investigating RNT, we kept neuroticism factor scores as a covariate of no interest in the model. Finally, all analyses were adjusted for the effects of age and sex as covariates of no interest.

Results

A total of 122 subjects completed the fMRI assessments, 10 subjects were excluded from further analyses due to poor fMRI data quality (> 4 mm head motion or substantial artefacts assessed by visual inspection of the data). Additionally, two participants did not pass the quality check (failing ≥ 2 out of 3 attention items evenly distributed across questionnaires). Accordingly, for brain-behaviour relationships, a final sample of 110 subjects remained. The final sample consisted of 31 males (28.18%) and 79 females (71.82%) with a mean age of 23.42 (SD = 4.03) ranging from 18 to 40 years.

Latent variable modelling

Repetitive negative thinking

A latent factor model was fitted using confirmatory factor analysis including questionnaire data from 122 subjects. Energy and Mardia tests suggested violation of the multivariate normality assumption (Energy: E-statistic = 1.342, P = .016; Mardia: skewnessstatistic = 60.070, skewnessp-value = .005, kurtosisstatistic = −0.420, kurtosisP-value = .675). The measurement model included four indicators (PTQ, RSQ brooding subscale, CERQ rumination subscale, PSWQ) and a single higher-order factor (RNT). Model fit indices provided consistent evidence for excellent model fit (χ2(1) = 1.246, pboot = .531, CFI = .998, TLI = .991, SRMR = .017, RMSEA = 0.045) and high factor loadings were observed (see Table S1, see online supplementary material for a colour version of this table).

Neuroticism

A latent factor model for neuroticism was fitted. The Energy and Mardia tests suggested violation of the multivariate normality assumption (Energy: E-statistic = 1.750, P < .001; Mardia: skewnessstatistic = 155.606, skewnessP-value < .001, kurtosisstatistic = 5.377, kurtosisP-value < .001). The measurement model included five indicators (ATQ Negative Affectivity, BFI-2 Negative Emotionality, BFI-K Neuroticism, PANAS Negative Affect, STAI trait Anxiety) and a single higher-order factor (Neuroticism). Model fit indices provided evidence for excellent model fit (χ2(4) = 9.659, pboot = .180, CFI = .985, TLI = .963, SRMR = .032, RMSEA = 0.108) and sufficiently high factor loadings were observed (see Table S1, see online supplementary material for a colour version of this table).

Effect of task

Reporting and discussion of the effects of RNT induced by the paradigm on BOLD signal changes is beyond the scope of this article and therefore included in the supplemental material. However, it should be noted that the paradigm significantly induced ruminative ideation (MD = 97.85, SD = 187.7, 95%-CI = [63.06, 132.85], Cohen’s d = 0.556), based on repeated measure comparisons of the BSRI before and after the RWIT. Moreover, significantly increased activity during rumination, worry and RNT compared to distraction was found in all regions investigated.

Regression

No significant trait RNT*neuroticism interactions were observed in the predefined DMN nodes during RNT compared to distraction. Although, a significant positive association emerged between RNT factor scores and precuneus recruitment (see Table 1 and Fig. 2), neither the PCC nor the mPFC emerged to be significantly associated with RNT factor scores. Notably, the association between RNT factor scores and precuneus activity did not survive FDR correction. Results of all statistical analyses can be obtained from Tables S4 and S5 in the supplement (see online supplementary material for a colour version of these tables).

Table 1.

Regression results.

Region B 95%-BCI β p-value p-FDR
A) Contrast: RNT>DIS | Self-report: RNT factor scores * neuroticism factor scores
PCC −0.006 [−0.150, 0.046] −0.013 .670 .972
Precuneus −0.006 [−0.162, 0.058] −0.011 .807 .972
mPFC 0.024 [−0.142, 0.079] 0.040 .982 .982
B) Contrast: RNT>DIS | Self-report: RNT factor scores
PCC 0.029 [−0.105, 0.168] 0.083 .624 .972
Precuneus 0.129 [0.004, 0.250] 0.351 .042 .438
mPFC 0.058 [−0.055, 0.164] 0.141 .298 .670
AI right −0.066 [−0.146, 0.007] −0.299 .073 .438
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A) interaction effects; B) simple regression without interaction. B, unstandardized coefficient; CI, bootstrap confidence interval; β, standardized coefficient; p-FDR, FDR-corrected p-value; DIS, distraction; RNT, repetitive negative thinking; PCC, posterior cingulate cortex; mPFC, medial prefrontal cortex; AI, anterior insula.

Figure 2.

Figure 2.

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Associations between brain activity and trait RNT.

A significant association (uncorrected) between precuneus activity (RNT>DIS) and self-reported trait RNT emerged. Exploratory analysis uncovered a negative trend-level relationship (uncorrected) between activity of the AI (RNT>DIS) and trait RNT. No associations emerged between trait RNT and activity in the mPFC or PCC (RNT>DIS). mPFC, medial prefrontal cortex; PCC, posterior cingulate cortex; AI, anterior insula; PSC, percent signal change. Data points are residualized for age, sex and neuroticism. Grey shaded areas represent 95% confidence bands.

Exploratory analyses

In addition to the regions investigated above, we conducted the same analytical regimen as used for the predefined DMN nodes for the AI, dACC and pgACC. Exploratory moderation analysis did not reveal significant interaction effects (Table S4, see online supplementary material for a colour version of this table). However, regression analyses targeting simple main effects revealed a negative trend-level association between activity of the right AI and RNT factor scores for the RNT>DIS contrast (see Table 1 and Fig. 2).

Discussion

This cross-sectional fMRI study aimed to explore the interaction of trait RNT and neuroticism on DMN activity during negative self-referential processing in healthy adults. To the best of our knowledge, this is the first study of its kind to examine the relationship between these two transdiagnostic phenomena on the circuit-level. Our results do not support an interaction between trait RNT and neuroticism to explain DMN recruitment during negative self-referential processing. Nevertheless, individuals scoring higher on self-report measures of RNT exhibited greater recruitment of the precuneus during negative self-referential processing. Moreover, a trend-level finding emerged demonstrating that subjects with lower levels of trait RNT showed higher AI activity during RNT compared to distraction.

Consistent with previous findings and our a priori hypotheses, a significant positive relationship of moderate magnitude was found between trait RNT factor scores and precuneus recruitment during negative self-referential processing compared to distraction (Burkhouse et al. 2017, Makovac et al. 2020). The precuneus is traditionally thought to be a central node of the posterior DMN involved in mental imagery, episodic memory, consciousness and mind wandering (Cavanna and Trimble 2006, Andrews-Hanna et al. 2010, Utevsky et al. 2014, Fox et al. 2015). Consequently, individuals prone to engage in dysfunctional repetitive negative thinking in response to negative events might recruit the posterior DMN to a greater extent.

However, several lines of evidence challenge the traditional conceptualization of the precuneus as a mere branch of the DMN, highlighting its involvement in cognitively demanding tasks and driving adaptive network changes in response to environmental demands (Utevsky et al. 2014, Lyu et al. 2021, Dadario and Sughrue 2023, Dosenbach et al. 2025). Results from graph theoretical and structural studies further support the idea of the precuneus as one of the most important hubs in the brain, due to its pivotal role in higher order cognition, integration and modulation of networks implicated in various domains of psychological functioning (Tomasi and Volkow 2011, Van Den Heuvel and Sporns 2013, Tanglay et al. 2022). However, while hubs increase the efficiency of information processing and integration, they come at a cost. If the integrity of a hub is compromised, the functions it generates are likely to derail, potentially causing downstream behavioural impairments (Van Den Heuvel and Sporns 2013). Although speculative at this point, the specific association between precuneus activity and RNT may indicate that the posterior DMN hub is compromised in individuals prone to RNT. Nevertheless, this finding should be interpreted with caution, since it was only significant before rigorous p-FDR correction. We decided to report it anyway to make this result available for potential future meta-analytical investigations as well as recent suggestions to focus on effect sizes rather than arbitrarily chosen thresholds of statistical significance (Cumming and Finch 2005, Cumming 2014).

Although DMN nodes are most consistently associated with RNT, a variety of additional regions contribute to complex higher-order cognition such as self-referential processing (Fox et al. 2015). Exploratory analyses revealed a negative trend-level association of small to moderate magnitude between activity of the right AI and RNT factor scores. This finding is consistent with previous investigations of RNT and AI recruitment (Piguet et al. 2014). The AI is a central hub of the salience network, yet it is reliably associated with self-referential processing and mind-wandering in healthy individuals (Menon 2011, Uddin 2015). More specifically, the AI is primarily responsible for detecting salient stimuli and subsequently orchestrating the flexible recruitment of the DMN and FPN in response to environmental demands (Menon 2011, 2023). Thus, the SAL disrupts DMN recruitment and facilitates FPN function. Speculatively, a negative association between AI recruitment and trait RNT may suggest that individuals are less prone to RNT when sufficient AI capacity is available to maintain adaptive network switching during negative self-referential processing. However, it should be noted that the finding was based on an exploratory analysis and is not statistically significant even before p-FDR correction, necessitating further investigation in larger samples or clinical populations. The present results should thereby be interpreted with caution and considered exploratory. According to recent suggestions to focus on effect sizes rather than arbitrary measures of statistical significance like p-values, we reported these results anyway due to the moderate effect size of the association (Cumming and Finch 2005, Cumming 2014).

The primary hypothesis of our study was a positive interaction between trait RNT and neuroticism to explain DMN recruitment during negative self-oriented processing, based on the assumption that the association of RNT and DMN recruitment is amplified by high levels of neuroticism (Meiering et al. 2023). In this context, neuroticism is considered a global and rather distal vulnerability factor (Ormel et al. 2004, 2013). RNT, here conceptualized as a dysfunctional coping mechanism and therefore proximal risk factor, exerts a direct effect on the DMN, independent from neuroticism. If both—RNT and neuroticism—coincide, their effects were hypothesized to potentiate and introduce DMN changes, over and above RNT individually. Our hypothesis was based on prior research showing that the DMN is involved in RNT as well as neuroticism (Aghajani et al. 2014, Servaas et al. 2014, Gentili et al. 2017, Gaviria et al. 2021a, 2021b, Chou et al. 2023, Zhi et al. 2024), together with considerations that RNT and neuroticism are related albeit distinct constructs that exert their effects on psychopathology independently from each other (Wang et al. 2020, Whisman et al. 2020). Additionally, molecular approaches support this notion by showing that RNT and neuroticism share the same genetic origin but separate over the course of development in response to environmental influences (Du Pont et al. 2019, Forthman et al. 2023). Finally, research suggesting direct as well as conditional effects of neuroticism and RNT on internalizing psychopathology provided additional support for our hypothesis (Feldner et al. 2006, Merino et al. 2014, Zvolensky et al. 2016, Vidal-Arenas et al. 2022). Although rigorously derived from prior research and ensuring transparency by publishing the hypotheses and methods before data analysis, none of the predefined regions of interest provided evidence in favour of this amplification hypothesis. Consequently, the relationship between RNT, neuroticism and DMN recruitment during self-referential processing may be better described using a different theoretical model. Some studies have suggested that the interrelationship of neuroticism and RNT may be conceptualized as a mediation (Verstraeten et al. 2009, Tortella-Feliu et al. 2010, Iqbal and Dar 2015, Merino et al. 2016, Chen et al. 2020, Mohammadkhani et al. 2021). However, due to increasing concerns that mediation analyses may misrepresent the directionality and causality of relationships in cross-sectional data, we consider such an investigation to be beyond the scope of the present study (O’Laughlin et al. 2018). Nevertheless, the null finding presented here still holds scientific value for the field, by providing valuable data for potential meta-analytical investigations and informing theoretical consideration of the interrelationship of RNT and neuroticism.

Several additional reasons could have contributed to this null finding. First, our sample consisted of currently healthy individuals. Subjects were only excluded if they had been diagnosed with, or treated for, a mental disorder in the 12 months prior to the fMRI scan. This may have had a negative impact on both neural and self-report measures, reducing the power to detect meaningful dependencies and statistically taxing interaction effects. Moreover, results can only be generalized to relatively healthy, young individuals. Future studies investigating the association between brain activity and trait RNT during repetitive negative thinking should include clinical populations to enhance the translational value and relevance for mental disorders. Another important aspect is the cross-sectional design, limiting the ability to derive causal relationships. In addition, BOLD contrast estimates are highly dependent on whether the conditions of interest are sampled often enough or whether the experimental manipulations elicit large effects. The RNT induction paradigm used here is limited in both aspects, since each condition is represented by a single continuous block of stimuli only and the distraction condition might have been too similar to the rumination and worry conditions. Although participants were instructed to clearly visualize a scene, the prompts may have elicited autobiographical memory retrieval instead. Another aspect to consider is that order effects could have contaminated the control condition. Although the rumination and worry conditions were counterbalanced across subjects, the distraction condition was always presented last, which may have caused fatigue, spillover, or simple order effects to contaminate the control condition with increased mind-wandering and consequently interfered with task engagement. Future studies should take these considerations into account by decomposing the continuous blocks into their constituent components and fully randomizing task conditions. Additionally, rather than employing a passive distraction condition, future research could incorporate a cognitively demanding control condition to better maximize network-level differences between task conditions. Furthermore, interaction effects are notoriously underpowered (McClelland and Judd 1993), raising the question of whether the sample size used in this study was sufficient to detect a continuous-by-continuous interaction. However, as shown by the marginal effect sizes and the scatter plots provided in the supplement, it is highly unlikely that the lack of a significant interaction between trait RNT and neuroticism is due to insufficient statistical power in nearly all ROIs. If a true moderation effect of trait RNT and neuroticism on functional brain activity does exist, detecting it would likely require a sample size in the thousands, questioning its informative value to advance neurobiologically grounded theories of RNT and neuroticism. Additionally, as evidenced by the histograms included in the supplement which show adequate variability in self-reported trait RNT and neuroticism, the possibility of a null result being driven by limited variance in these measures can be reasonably ruled out.

However, a notable strength of our study is the rigorous inclusion of covariates of no interest, ruling out the possibility that associations with RNT are driven by general vulnerability for internalizing psychopathology, age or sex (Ormel et al. 2004, 2013). Finally, even after following suggestions to improve effect sizes and reliability (DeYoung et al. 2025), none of the brain–behaviour relationships presented here survived rigorous p-FDR correction. According to a strict null hypothesis significance testing standpoint, statistics failing to stay significant after correction for multiple testing are at risk for being false positive and thereby not generalizable to the population they are based on. The results presented here should therefore be interpreted with caution and considered exploratory. However, it is important to note that the informative value of P-values has been widely criticized, with growing calls to place greater emphasis on effect sizes and confidence intervals instead (Cumming and Finch 2005, Cumming 2014). Accordingly, we chose to report results that reached uncorrected significance levels emphasizing effect sizes and confidence intervals to inform future research and support meta-analytic efforts aimed at elucidating the neurofunctional underpinnings of RNT.

Conclusion

To the best of our knowledge, this is the first study to investigate the RNT by neuroticism interaction at the neural level in order to elucidate the underlying neural operating principles of transdiagnostic phenomena associated with internalizing disorders. Although confirming previous notions that trait RNT is associated with increased posterior DMN recruitment, there was no evidence for a link between a RNT by neuroticism interaction and DMN alterations during negative self-referential processing. Extending previous findings implicating the SAL, our results suggest an inverse relationship between AI recruitment and RNT. However, further research investigating the interaction of RNT and neuroticism in patient samples and by using other methods is needed to reach definitive conclusions.

Supplementary Material

nsaf118_Supplementary_Data
nsaf118_supplementary_data.zip (10.1MB, zip)

Acknowledgements

Support by Christian Kainz and Till Nierhaus from the Center for Cognitive Neuroscience Berlin, as well as the participation of all volunteers in the current study are gratefully acknowledged. Further, we want to acknowledge Jale Gaybalizada for her support during data acquisition as well as Sophia Hempel and Tobias Friedrich for their support with processing the results.

Contributor Information

Marvin S Meiering, Institute of Neuroscience and Biopsychology for Clinical Application, Medical School Berlin, Berlin, 14197, Germany; Department of Education and Psychology, Freie Universität Berlin, Berlin, 14195, Germany.

David Weigner, Institute of Neuroscience and Biopsychology for Clinical Application, Medical School Berlin, Berlin, 14197, Germany; Department of Education and Psychology, Freie Universität Berlin, Berlin, 14195, Germany.

Rebecca Gruzman, Institute of Neuroscience and Biopsychology for Clinical Application, Medical School Berlin, Berlin, 14197, Germany; Department of Education and Psychology, Freie Universität Berlin, Berlin, 14195, Germany.

Sören Enge, Institute of Neuroscience and Biopsychology for Clinical Application, Medical School Berlin, Berlin, 14197, Germany.

Simone Grimm, Institute of Neuroscience and Biopsychology for Clinical Application, Medical School Berlin, Berlin, 14197, Germany; Departmenf of Psychiatry and Psychotherapy, Charité-Universitätsmedizin Berlin, Berlin, 12203, Germany; Department of Psychiatry, Psychotherapy and Psychosomatics, Universität Zürich, Zurich, 8032, Switzerland.

Author contributions

Marvin Sören Meiering (Conceptualization [equal], Data curation [equal], Formal analysis [equal], Investigation [equal], Methodology [equal], Project administration [equal], Resources [equal], Software [equal], Validation [lead], Visualization [lead], Writing—original draft [lead], Writing—review & editing [lead]), David Weigner (Conceptualization [equal], Data curation [equal], Investigation [equal], Validation [equal], Writing—review & editing [equal]), Rebecca Gruzman (Conceptualization [equal], Resources [equal], Writing—review & editing [equal]), Sören Enge (Conceptualization [equal], Funding acquisition [equal], Methodology [equal], Supervision [equal], Writing—review & editing [equal]), and Simone Grimm (Conceptualization [equal], Funding acquisition [equal], Methodology [equal], Supervision [equal], Writing—review & editing [equal])

Supplementary data

Supplementary data are available at SCAN online.

Conflict of interest: None declared.

Funding

The study was fully funded by the Medical School Berlin.

Data availability

Data cannot be made available due to the German Federal Data Protection Act (Datenschutzgrundverordnung).

Code availability

The code used here can be requested from the corresponding author.

Ethics approval and consent to participate

All methods were performed in accordance with relevant guidelines and regulations. All participants gave written informed consent to participate in the study. The study was approved by the Institutional Review Board of the Medical School Berlin and conducted in accordance with the declaration of Helsinki.

Consent for publication

All authors listed approved the publication of the manuscript in its current form.

Open practices statement

Data cannot be made available due to the German Federal Data Protection Act (Datenschutzgrundverordnung). However, the code and materials are available upon request. The experiment was not preregistered, but a protocol of the study was published by Meiering et al. (2023).

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Associated Data

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Supplementary Materials

nsaf118_Supplementary_Data
nsaf118_supplementary_data.zip (10.1MB, zip)

Data Availability Statement

Data cannot be made available due to the German Federal Data Protection Act (Datenschutzgrundverordnung).

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