Mapping directional interactions in executive control networks during emotional and non-emotional conflict processing

Effective goal-directed behavior depends on resolving conflict between relevant and irrelevant information. This process is supported by brain network interactions. Distractor type, such as emotional versus non-emotional stimuli, and individual differences can modulate brain network efficiency and lead to behavioural alterations [1]. This modulation is clinically relevant because difficulties in adapting to conflict are associated with heightened sensitivity to emotional stimuli in different psychiatric disorders, including individuals with subclinical depression [2]. Despite evidence that altered emotional conflict processing contributes to attentional difficulties,the underlying connectivity across core executive brain regions supporting emotional and non-emotional conflict processing remains uncharacterised.

Hence, this research project aimedto identify core brain regions underlying both emotional and general conflict processing from an fMRI dataset and to quantify the directed connections between these regions. The further aim of the study was to analyse whether subclinical depression scores modulate connectivity among executive-control network regions.

Neuroimaging analyses were performed in MATLAB with Statistical Parametric Mapping (SPM25) toolbox [3]. Existing task-based fMRI dataset from 21 non-clinical participants (9 females; mean age = 22.29) is used. Participants completed an emotional Flanker task and filled out the Depression and Anxiety Stress Scale (DASS). Participants had no history of any psychiatric disorders and showed normal to moderate depression scores. Whole-brain univariate GLM analyses were used to identify the associated region of interests in emotion- and incongruence-related effects. Contrast-derived BOLD signals from each region for each subject were entered into 10-region deterministic bilinearDynamic Causal Modelling (DCM)[4] , with emotion and incongruence modelled as modulatory inputs. Group-level DCM analysis tested task-dependent connectivity effects and depression-related modulation using DASS depression scores.

Preliminary Results: Bayesian model reduction and model averaging revealed reliable task-dependent changes in effective connectivity for both emotion and incongruence. At the group level, out of 54 included in the model, emotion significantly modulated 11 directed connections whereas incongruence showed broader modulation across 21. Depression scores were only subclinical but reliably explained individual differences in task-dependent connectivity, with modest effects across 15 emotion-related connections and 19 incongruence-related connections.

Conclusions

Positive or negative emotional distractors modulated causal connectivity in more targeted pathways, while incongruence involved broader reconfiguration across perceptual, attentional, and control regions. For instance, the DCM found that emotion increased connectivity from the right anterior insula (salience region) to the right superior parietal lobule (attention region). These results show the enhanced salience-to-attention signaling during emotion processing. In contrast, during incongruence, the DCM revealed reduced top-down influence from the dorsal anterior cingulate cortex (cognitive control region) to the lateral occipital region (visual-perceptual area). This suggests that top-down control in these regions is reduced when there is more conflict. Finally, depression scores were associated with stronger incongruence-related coupling, for instance from the right inferior frontal gyrus (cognitive control region) to the other control related regions like dorsal Anterior Cingulate Cortex. This demonstrates that higher score in subclinical depression was associated with higher control coupling in brain networks. Broadly, the findings show that even subclinical depressive tendencies may alter affective–executive network coordination, and could affect conflict processing.

References

1. Chen, T., Becker, B., Camilleri, J., Wang, L., Yu, S., Eickhoff, S. B., & Feng, C. (2018). A domain-general brain network underlying emotional and cognitive interference processing: evidence from coordinate-based and functional connectivity meta-analyses. Brain structure & function, 223(8), 3813–3840. https://doi.org/10.1007/s00429-018-1727-9
2. Wang, W., Xie, Q., Zhang, L. et al. Dynamic Cognitive Characteristics of Emotional Conflict in Individuals with Subthreshold Depression: an Event-Related Potential Study. Int J Ment Health Addiction 24, 1234–1251 (2026). https://doi.org/10.1007/s11469-025-01448-8
3. Tierney et al., (2025). SPM 25: open source neuroimaging analysis software. Journal of Open Source Software, 10(110), 8103, https://doi.org/10.21105/joss.08103.
4. Friston, K. J., Preller, K. H., Mathys, C., Cagnan, H., Heinzle, J., Razi, A., & Zeidman, P. (2019). Dynamic causal modelling revisited. NeuroImage, 199, 730–744. https://doi.org/10.1016/j.neuroimage.2017.02.045