International Journal of Psychotherapy Practice and Research

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Research Article Open Access
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  • Resting-State Functional Connectivity Predicts Emotional Conflict Control

    Song Xue 1   Wei Xu 1  

    1School of psychology, Nanjing normal University, Nanjing, 210097, China


    Emotional conflict control refers to the ability to select task-relevant emotional information and ignore task-irrelevant emotional distractors. Previous fMRI studies provide some evidence about brain structure and function related to emotional conflict control. Yet, the underlying resting-state functional connectivity was largely unknown. Here, this is the first study to explore the resting-state functional connectivity related to emotional conflict. According to the literature which used the whole-brain analysis to investigate the key brain area associated with emotional conflict, we select the amygdala (AMY) as the seed region. We then investigated the association between emotional conflict and functional connectivity between amygdala (AMY) and another brain region in a large sample. We found the emotional conflict effect was positively correlated with functional connectivity strength between AMY (the seed ROI) and right supplementary motor area (SMA). This finding implied that the functional connectivity between AMY and SMA was linked to emotional conflict and that AMY was the key region which plays a crucial role in emotional conflict.

    Author Contributions
    Received 23 Sep 2019; Accepted 30 Sep 2019; Published 17 Oct 2019;

    Academic Editor: Shuai Li, Department of Engineering University of Cambridge UK.

    Checked for plagiarism: Yes

    Review by: Single-blind

    Copyright ©  2019 Song Xue, et al.

    Creative Commons License     This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

    Competing interests

    The authors have declared that no competing interests exist.


    Song Xue, Wei Xu (2019) Resting-State Functional Connectivity Predicts Emotional Conflict Control. International Journal of Psychotherapy Practice and Research - 1(4):1-8.

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    DOI 10.14302/issn.2574-612X.ijpr-19-3045


    Conflict control refers to the ability to select task-relevant information and ignore task-irrelevant distractors 1 In the daily life, many emotionally salient stimuli around us will interfere with our goal behavior. Individuals must inhibit the emotional interference and resolve the “emotional conflict” that stem from cognitive control 2, 3. Emotion conflict control was the important executive function for both healthy people and some clinical patients such as mood and anxiety disorders 4, 5, 6.

    Previous studies often used the face-word Stroop task to measure the emotional conflict effect in both healthy people and clinical study 5, 6, 7, 8. During this task, participants need to judge facial expression of target face while ignoring the meaning of superimposed words. Many previous fMRI studies provide some evidence about the important brain regions and neural activity during this paradigm 2, 7, 9, 10, 11. For instance, Etkin et al.found that task activation in amygdala (AMY) reflected the amount of emotional conflict 2. Egner et al. found AMY and rostral anterior cingulate (rACC) were sensitive to emotional conflict 7. These two regions were two dissociable neural circuits for resolving emotional conflict or cognitive conflict. Chechko et al. suggest that inferior frontal gyrus (IFG) and supplementary motor area (SMA) also played an important role in emotional conflict resolution besides AMY 11. These findings focused on functional task activation during specific experimental paradigm and provided some neural evidence of emotional conflict. Besides, Deng et al. found that the regional gray matter volume (rGMV) of orbitofrontal cortex was associated with emotional conflict effect 12. Xue et al. found that the amplitude of low frequency fluctuations (ALFF) of AMY was also related to emotional conflict control 13. However, the resting-state functional connectivity related to emotional conflict was largely unknown. The spontaneous fluctuations in the BOLD signal during resting-state reflected the intrinsic functional activity of the brain and relate to extrinsic task performance 14, 15. Previous study suggests that this intrinsic resting-state activity also could predict brain activation and behavioral performance 16. Thus, the aim of the present study was to explore the underlying resting-state functional connectivity related to emotional conflict. In the present study, we used seed-based functional connectivity to investigate the resting-state functional connectivity of emotional conflict control. Previous studies often used the amplitude of low frequency fluctuations (ALFF) as an index of resting-state brain activity to study the association about human cognition 17, emotion 18, and personality 19. Also, the ALFF provide a potential biomarker for a variety of mental disorders, such as depression 20; schizophrenia 21; and mild cognitive impairment 22. This index was the more stable measure index for resting-state fMRI to reflect regional properties of intrinsic brain dynamics 23. Specifically, Xue et al. found that was the emotional conflict associated with the ALFF of AMY 13.

    On the other hand, functional connectivity was another widely used approach in the resting-state fMRI study 14, 24, 18. This method examined inter-regional correlations among spontaneous low-frequency fluctuations in the BOLD signal during rest 25. We investigated the association of emotional conflict with resting-state functional connectivity among different brain regions.

    To our knowledge, no study has yet explored the relationship between the functional connectivity and emotional conflict. Here, in the present study, we investigated the functional connectivity of resting-state fMRI signals to elucidate the intrinsic neural basis of emotional conflict. Based to prior studies 2, 7, 13, we selected the AMY as the seed region. We hypothesized that emotional conflict shall be associated with the strength of functional connectivity between AMY and frontal brain region (e.g., SMA).

    Materials and Methods


    Two hundred and thirty-four healthy university students (144 females; average age = 19.87 years, standard deviation SD = 1.08) with no history of psychiatric or neurological disorders. The present study was a part of an ongoing research project (GBB project) which explored the associations among gene, brain, and behavior 26, 27. This data set was analysis and some results were reported at our previous study 13. All participants had normal eyesight or corrected eyesight, no color blindness, and were right-handed (as indicated on the Edinburgh Handedness Inventory; 28 ). Written informed consent was obtained from all the participants prior to the study. Both behavioral and MRI protocols were approved by the Institutional Review Board of Southwest University.

    Emotional Conflict Task and Behavior Analysis

    The face-word Stroop task was adopted as the experimental paradigm to measure emotional conflict for this study 12, 13. In this task, participants need to see a face picture which was superimposed an emotion word, and they were instructed to identify the facial expression of the target face while ignoring the meaning of the words by pressing a button.

    The target stimuli consisted of 5 male and 5 female face pictures, with either happy or sad expression, selected from the Chinese affective picture system 29. Each face picture was superimposed two Chinese characters, “愉快” (which means “happy”) or “悲伤” (which means “sad”). The combinations of facial expressions and superimposed words yielded two conditions: a congruent condition (e.g., character meaning happy superimposed onto a happy face picture) and an incongruent condition (e.g., character meaning happy superimposed onto a sad face picture). The stimuli were programmed by E-Prime 2.0 software.

    A total 120 trials consisting of an equal amount of congruent and incongruent trials were included in the formal experiment and another 24 trials was for practice. Stimuli were presented in pseudo-random order for avoiding repetition priming effect 30. The timing and order of each trial was as follows: a fixation dot was presented for a specific duration (500 ms) followed by a blank screen of variable duration (300–500 ms). Then, the target face appeared for 1000 ms at the center of the screen. Participants had to respond within 1500 ms. The inter-trial interval (ITI) varied randomly between 800 ms and 1200 ms, with a mean of 1000 ms.

    All behavior data analysis was implemented in SPSS 18. For each participant, we calculated mean accuracy and reaction time (RT) for each condition. The paired t test was performed on the accuracy and RT data, respectively. The difference between mean RT of incongruent trials and mean RT of congruent trials was defined as “emotional conflict effect” or “emotional interference effect” 12, 13.

    Assessment of General Intelligence

    To adequate control for individual differences in emotional conflict control, all participants completed the Combined Raven's Test (CRT, Chinese version). The CRT was widely used for intelligence testing in China, and it has been proved to have good reliability and validity 31. The CRT comprises 72 nonverbal items and each item consists of a matrix with a missing piece that is to be filled in by selecting the best answer from 6 or 8 alternatives. The number of correct answers while completing the CRT test within 40 minutes was used as an index of general intelligence, and it was served as a covariate in our statistical analyses.

    Image Acquisition

    MRI scanning was conducted on a Siemens 3T scanner (MAGENTOM Trio, a Tim system) with an eight-channel phased array coil. For each participant, 242 functional images were acquired with a gradient echo type Echo Planar Imaging (EPI) sequence (echo time (TE) = 30 ms; repetition time (TR) = 2000 ms; flip angle = 90 degrees; slices = 32; slice thickness = 3.0 mm; slice gap = 1 mm; field of view (FOV) = 220 × 220 mm2; resolution matrix = 64 × 64; in-plane resolution = 3.4 × 3.4 mm2 ; interslice skip = 0.99 mm). In addition, a high-resolution T1 weighted magnetization prepared gradient echo sequence (MPRAGE: TR/TE/TI = 1900/2.52/900ms, flip angle = 9 degrees, matrix = 256 × 256) anatomical scan also was acquired for registration purposes and anatomically localizing the functional regions. One hundred and seventy-six contiguous sagittal slices were obtained with 1 × 1 mm in-plane resolution and 1 mm slice thickness. Before the resting-state fMRI scanning, participants were instructed to keep their eyes closed without falling asleep, and to keep their head as still as possible during the scanning.

    Image Preprocessing

    Image preprocessing was performed using statistical parametric mapping software (SPM8, and using the data processing assistant for resting state (DPARSF, For each subject, the first 10 images were discarded due to instability of the initial MRI signal and adaptation of participants to the circumstance. The remaining 232 images were preprocessed, which included slice timing, head motion correction, spatial normalization, and smooth. Briefly, the 232 images were slice acquisition corrected, aligned to the middle images for head-motion correction. The various covariates including white matter, cerebrospinal fluid, and Friston 24-parameter were regressed out in order to reduce potential impact of physiological artifacts 32. Prior study has proved that regressing out Friston 24-parameter is more effective than other movement correction methods 33. None of the subjects had more than 2.5 mm maximum displacement in x, y, or z translation and 2.5° of angular motion during the whole fMRI scan. Then, the corrected images were spatially normalized to the Montreal Neurological Institute (MNI) EPI template in SPM8 and resampled to 3×3×3 mm3voxels. The images then were spatially smoothed with a Gaussian kernel (full-width at half-maximum FWHM = 6 mm). In order to be consistent with previous, we selected 6 mm isotropic Gaussian kernel 13. Finally, the smoothed data was linearly detrended, and was filtered using a typical bandpass (0.01–0.08 Hz) to reduce the influences of high-frequency noise and low-frequency drift.

    Functional Connectivity Analysis and Connectivity-Behavior Analysis

    To explore whether the key region we identified in the ALFF-behavior analysis interacted with other brain regions to predict the emotional conflict, a seed-based whole-brain functional connectivity approach was conducted. We selected the AMY (MNI: 2, 14, 50) as the seed region according to previous study 13. The region which was associated with emotional conflict in ALFF was as seed region of interest. We created spherical ROI (radius = 8 mm) according to the peak of seed region identified in the ALFF-behavior analysis. For each participant, the correlation coefficients, between mean time series of all voxels with the seed region and the time series of each voxel of other brain regions, were calculated. The correlation coefficients were converted into Z scores using Fisher’s r-to-z transformation, which formed a z-functional connectivity map for each participant.

    Then, we performed a partial correlation analysis to explore the functional connectivity that was related to emotional conflict. The gender, age, and IQ were treated as the confounding covariates, and the interference effect of emotional conflict was treaded as the covariate of the interest. The corrected cluster threshold was set at p < 0.05 (AlphaSim corrected for multiple comparisons, with a combined individual voxel p value < 0.01 with cluster size > 75 voxels).


    Behavior Analysis

    The demographic information of the sample (age, gender, and Raven’s score) and detailed behavior data were reported in our previous study 13. The paired t test was performed on the accuracy and RT data to detect the emotional conflict effect, respectively. The emotional conflict effect was 21 + 2 ms (RT of congruent trials minus RT of incongruent trials). The emotional conflict effect was not influenced by gender and IQ.

    Functional Connectivity-Behavior Analysis

    To explore whether the AMY identified in prior ALFF-behavior analysis interacted with other brain regions to predict the emotional conflict, we performed a seed-based whole-brain functional connectivity analysis. The AMY (MNI: 2, 14, 50) was defined as the seed region of interest (ROI). We found that after controlling age, gender, and Raven’s scores, emotional conflict was positively correlated with functional connectivity strength between the AMY (the seed ROI) and right supplementary motor area (SMA) (MNI: 21, -33, 51; t = 3.70; cluster size = 130 voxels; p < 0.05 corrected) (Figure 1, Table 1).

    Figure 1. Correlation between seed-based functional connectivity (FC) and emotional conflict. The FC between the AMY and the right supplementary motor area (SMA) was positively correlated with emotional conflict. Scatter plot between the AMY-SMA FC and emotional conflict is shown for illustration purposes.
    Figure 1.

    Table 1. Brain regions showing significant correlations between the strength of resting-state functional connectivity with Amygdala and emotion conflict effect.
    Anatomical region Side Cluster size(#voxels) Peak voxel
          T x y z
    Supplementary motor area R 130 3.70 21 -33 51

    All the clusters survived p < 0.05, Alphasim corrected (individual voxel threshold p < 0.01 and a minimum cluster size of 75).


    To our knowledge, the present study was the first to explore the resting-state functional connectivity related to emotional conflict. We used face-word task to measure the emotional conflict 12, 13. At the behavior level, the RT results showed significant emotional conflict effect. At the neural level, we firstly investigated the association between emotional conflict and functional connectivity. Specifically, we did a seed-based (AMY as the seed region) functional connectivity analysis, and we found that functional connectivity between AMY and right supplementary motor area (SMA) was associated with emotional conflict.

    Consistent with prior hypotheses, we found that emotional conflict was positively correlated with the strength of functional connectivity between AMY and SMA, and that increased AMY-SMA connectivity was associated with worse face-word Stroop performance. The SMA played a key role in cognitive control, motor control, and movement 34. During the emotional conflict task, Chechko et al. found that SMA was activated in the incongruent condition compared to congruent condition, which might be linked to premotor planning 10. Moreover, previous studies demonstrated that the SMA and AMY were coactivated during the perception of emotional expressions 35, 36, 37, and that the SMA and AMY functionally connected during both the positive and negative emotional stimuli processing 38. In addition, a recent study provided evidence of a structural connection between AMY and motor-related areas by using diffusion-weighted magnetic resonance imaging method 39. Taken together, we thought that, during the present face-word Stroop task, AMY-SMA connectivity might be associated with the perception of emotional expressions and motor control, which might contribute to the emotional conflict resolution. In some recent studies, researchers use machine learning method to investigate networks 40, 41. We might study the association between brain network and emotional conflict in the future.

    To examine the robustness of the ALFF-behavior correlation results and functional connectivity-behavior correlation results, we performed the prediction analysis in both results. The prediction analysis results showed that the correlation between AMY-SMA connectivity and behavior were steady. Therefore, we verified the results that AMY-SMA functional connectivity were associated with emotional conflict resolution. These findings provided a better understanding of the neural mechanism underlying emotional conflict control.


    This research was supported by the National Natural Science Foundation of China (31800915), and the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province, China (18KJD190002).


    1.M, T S Braver, D M Barch, C S, J D Cohen. (2001) . Conflict monitoring and cognitive control.Psychological Review,108, 624 – 652.
    2.Etkin A, Egner T, D M Peraza, E R Kandel, Hirsch J. (2006) Resolving emotional conflict: a role for the rostral anterior cingulate cortex in modulating activity in the amygdala.Neuron. 51(6), 871-882.
    3.Shen Y, Xue S, Wang K, Qiu J. (2013) Neural time course of emotional conflict control: An ERP study.Neuroscience letters. 541, 34-38.
    4.Etkin A, K E Prater, Hoeft F, Menon V, A F Schatzberg. (2010) Failure of anterior cingulate activation and connectivity with the amygdala during implicit regulation of emotional processing in generalized anxiety disorder.The American journal of psychiatry,167. 545-554.
    5.Etkin A, A F Schatzberg. (2011) Common abnormalities and disorder-specific compensation during implicit regulation of emotional processing in generalized anxiety and major depressive disorders.The American journal of psychiatry,168. 968-978.
    6.Chechko N, Augustin M, Zvyagintsev M, Schneider F, Habel U. (2013) Brain circuitries involved in emotional interference task in major depression disorder.Journal of affective disorders. 149(1), 136-145.
    7.Egner T, Etkin A, Gale S, Hirsch J. (2008) Dissociable neural systems resolve conflict from emotional versus nonemotional distracters.Cerebral. , Cortex 18(6), 1475-1484.
    8.Xue S, Ren G, Kong X, Liu J, Qiu J. (2015) Electrophysiological correlates related to the conflict adaptation effect in an emotional conflict task.Neuroscience letters. 584, 219-223.
    9.Chechko N, Wehrle R, Erhardt A, Holsboer F, Czisch M. (2009) Unstable prefrontal response to emotional conflict and activation of lower limbic structures and brainstem in remitted panic disorder.PloSone. 4(5), 5537.
    10.Chechko N, Kellermann T, Zvyagintsev M, Augustin M, Schneider F. (2012) Brain circuitries involved in semantic interference by demands of emotional and non-emotional distractors.PloSone. 7(5), 38155.
    11.Chechko N, Kellermann T, Schneider F, Habel U. (2014) Conflict adaptation in emotional task underlies the amplification of target.Emotion. 14(2), 321.
    12.Deng Z, Wei D, Xue S, Du X, Hitchman G. (2014) Regional gray matter density associated with emotional conflict resolution: Evidence from voxel-based morphometry.Neuroscience,275. 500-507.
    13.Xue S, Wang X, Chang J, Liu J, Qiu J. (2016) Amplitude of low-frequency oscillations associated with emotional conflict control. , Experimental brain research 234(9), 2561-2566.
    14.M D Fox, M E Raichle. (2007) Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging.Nature Reviews Neuroscience. 8(9), 700-711.
    15.Mennes M, Zuo X-N, Kelly C, A Di Martino, Zang Y-F. (2011) Linking inter-individual differences in neural activation and behavior to intrinsic brain dynamics.Neuroimage. 54(4), 2950-2959.
    16.Zou Q, T J Ross, Gu H, Geng X, X N Zuo. (2013) Intrinsic resting‐state activity predicts working memory brain activation and behavioral performance. Human brain mapping. 34(12), 3204-3215.
    17.Zhang S, C-s R Li. (2010) A neural measure of behavioral engagement: task-residual low-frequency blood oxygenation level-dependent activity in the precuneus.Neuroimage. 49(2), 1911-1918.
    18.Kong F, Hu S, Wang X, Song Y, Liu J. (2015) Neural correlates of the happy life: The amplitude of spontaneous low frequency fluctuations predicts subjective well-being.Neuroimage. 107, 136-145.
    19.Wei L, Duan X, Zheng C, Wang S, Gao Q. (2014) Specific frequency bands of amplitude low‐frequency oscillation encodes personality.Human brain mapping. 35(1), 331-339.
    20.Zhang X, Zhu X, Wang X, Zhu X, Zhong M. (2014) First-episode medication-naive major depressive disorder is associated with altered resting brain function in the affective network.PloSone. 9(1), 85241.
    21.M J Hoptman, Zuo X-N, P D Butler, D C Javitt, D'Angelo D. (2010) Amplitude of low-frequency oscillations in schizophrenia: a resting state fMRI study.Schizophrenia research. 117(1), 13-20.
    22.Han Y, Wang J, Zhao Z, Min B, Lu J. (2011) Frequency-dependent changes in the amplitude of low-frequency fluctuations in amnestic mild cognitive impairment: a resting-state fMRI study.Neuroimage. 55(1), 287-295.
    23.Zuo X-N, A Di Martino, Kelly C, Z E Shehzad, D G Gee. (2010) The oscillating brain: complex and reliable.Neuroimage. 49(2), 1432-1445.
    24.Takeuchi H, Taki Y, Nouchi R, Sekiguchi A, Hashizume H. (2013) Resting state functional connectivity associated with trait emotional intelligence.Neuroimage. 83, 318-328.
    25.Biswal B, F Z Yetkin, V M Haughton, J S Hyde. (1995) Functional connectivity in the motor cortex of resting human brain using echo-planar MRI.Magnetic resonance in medicine. 34(4), 537-541.
    26.Wang S, Wei D, Li W, Li H, Wang K. (2014) A voxel-based morphometry study of regional gray and white matter correlate of self-disclosure.Social neuroscience. 9(5), 495-503.
    27.Kong X, Wei D, Li W, Cun L, Xue S. (2015) Neuroticism and extraversion mediate the association between loneliness and the dorsolateral prefrontal cortex.Experimental brain research. 233(1), 157-164.
    28.R C Oldfield. (1971) The assessment and analysis of handedness: the Edinburgh inventory.Neuropsychologia. 9(1), 97-113.
    29.Bai L, Ma H, Y X Huang, Y J Luo. (2005) The development of native Chinese affective picture system – a pretest in 46 college students.Chinese. , Mental Health Journal 19, 719-722.
    30.Mayr U, Awh E, Laurey P. (2003) Conflict adaptation effects in the absence of executive control.Nature neuroscience. 6(5), 450-452.
    31.Wang D. (2007) A report on the third revision of combined raven's test (CRT-C3) for children in. , China.Chinese Journal of Clinical Psychology 15(6), 559.
    32.K J Friston, Williams S, Howard R, R S Frackowiak, Turner R. (1996) Movement‐related effects in fMRI time‐series.Magnetic resonance in medicine. 35(3), 346-355.
    33.Yan C-G, Cheung B, Kelly C, Colcombe S, R C Craddock. (2013) A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics.Neuroimage. 76, 183-201.
    34.Nachev P, Kennard C, Husain M. (2008) Functional role of the supplementary and pre-supplementary motor areas.Nature Reviews Neuroscience. 9(11), 856-869.
    35.B De Gelder, Snyder J, Greve D, Gerard G, Hadjikhani N. (2004) Fear fosters flight: a mechanism for fear contagion when perceiving emotion expressed by a whole body.Proceedings of the. , National Academy of Sciences of the United States of America 101(47), 16701-16706.
    36.B De Gelder, Snyder J, Greve D, Gerard G, Hadjikhani N. (2004) Fear fosters flight: a mechanism for fear contagion when perceiving emotion expressed by a whole body.Proceedings of the. , National Academy of Sciences of the UnitedStates of America 101(47), 16701-16706.
    37.Grosbras M-H, Paus T. (2006) Brain networks involved in viewing angry hands or faces.Cerebral. , Cortex 16(8), 1087-1096.
    38.Voon V, Brezing C, Gallea C, Ameli R, Roelofs K. (2010) Emotional stimuli and motor conversion disorder.Brain. 054.
    39.Grèzes J, Valabrègue R, Gholipour B, Chevallier C. (2014) A direct amygdala‐motor pathway for emotional displays to influence action: A diffusion tensor imaging study.Human brain mapping. 35(12), 5974-5983.
    40.Korda N, Szörényi B, Shuai L. (2016) Distributed clustering of linear bandits in peer to peer networks.In Journal of machine learning research workshop and conference proceedings,International Machine Learning Societ. 1301-1309.
    41.Li S, Karatzoglou A, Gentile C. (2016) Collaborative filtering bandits.In. Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, ACM 539-548.