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Zeynep Başgöze1, Kathryn Cullen2, Didem Gökçay1
1 Middle East Technical University, Informatics Institute, Ankara, Turkey
2 University of Minnesota, Psychiatry, Twin Cities, MN, USA
zeynep.basgoze@gmail.com rega0026@umn.edu dgokcay@metu.edu.tr
Cognitive and Emotional
Components of Emotional Conflict
Resolution: an fMRI study
Introduction
We developed an emotional Word-Face Stroop Task: emotional interference is generated between emotional words in the foreground
and affective faces in the background. Our task is tested behaviorally both in Turkish (1) and English (2) populations and is
demonstrated to successfully measure the emotional interference effect (1). In this study, we investigated the brain activations resulting
from this task in an American population, and observed its validity with respect to other literature in emotional conflict resolution.
Method
Participants: 4F, 4M healthy native English speakers
(age=33.3±5.2)
Neuroimaging Procedures: Data acquisition: 3T Siemens MR
scanner at CMRR at the University of Minnesota. T1 weighted
MPRAGE hi-resolution anatomical scan: TR=2500ms, TE= 3.03ms,
1mm slice thickness, FOV 256, 1x1 voxel size, 224 slices. Four
runs of 3.5 min EPI scans: 100 echo planar imaging volumes, FOV
192, TR=2s, TE=30ms, 34 contiguous AC-PC aligned axial slices, 4
mm slices thickness and 3x3mm voxel size.
Stimuli: Event-related Word-Face Stroop task: 32 positive & 32
negative words with neutral arousal levels chosen from ANEW [3].
Positive words: valence=7.27±0.43, arousal=4.84±0.39;
Negative words: valence=2.59±0.52; arousal=4.74±0.33
Words appeared on happy or sad faces picked from Minear and
Park face database [4].
Results
Acknowledgments
This study is supported by 2214a scholarship from TÜBITAK, Turkish National Science Foundation.
The author is grateful to CMRR, University of Minnesota, for data acquisition; and Prof. Metehan Çiçek for
guidance in the Turkish version of this task.
[1] Basgoze, Z. (2008), ‘Emotional conflict resolution in healthy and depressed populations’ (Master’s Thesis), Informatics Institute, Middle East Technical University, Ankara, Turkey.
(recipient of ‘2008 Best MS Thesis ‘ award issued byt Middle East Technical University)
[2] Basgoze, Z. (2013), ‘Functional Localization of Emotional Conflict Resolution on Healthy and Depressed Populations’, unpublished report, TUBITAK 2214a International Doctoral Research Fellowship
Programme.
[3] Bradley, M. M., & Lang, P. J. (1999), ‘Affective norms for English words (ANEW): Stimuli, instruction manual and affective ratings. Technical report C-1’, Gainesville, FL.: The Center fo Research in
Psychophysiology, University of Florida.
[4] Minear, M., & Park, D. C. (2004). A lifespan database of adult facial stimuli. Behavior Research Methods, Instruments, & Computers, 36(4), 630-633.
[5] Bush, G., Luu, P., & Posner, M. I. (2000), ‘Cognitive and emotional influences in anterior cingulate cortex’, Trends Cogn Sci, vol. 4, no. 6, pp. 215-222.
[6] Mayberg, H. S., Liotti, M., Brannan, S. K., McGinnis, S., Mahurin, R. K., Jerabek, P. A., et al. (1999), ‘Reciprocal limbic-cortical function and negative mood: converging PET findings in depression and
normal sadness’, Am J Psychiatry, vol. 156, no.5, pp. 675-682.
[7] Egner, T., & Hirsch, J. (2005), ‘Cognitive control mechanisms resolve conflict through cortical amplification of task-relevant information’, Nat Neurosci, vol. 8, no. 12, pp. 1784-1790.
[8] Ballmaier, M., Toga, A. W., Blanton, R. E., Sowell, E. R., Lavretsky, H., Peterson, J., et al. (2004), ‘Anterior cingulate, gyrus rectus, and orbitofrontal abnormalities in elderly depressed patients: an MRI-
based parcellation of the prefrontal cortex’, Am J Psychiatry, vol. 161, no.1, pp. 99-108.
[9] Frodl, T., Scheuerecker, J., Albrecht, J., Kleemann, A. M., Muller-Schunk, S., Koutsouleris, N., et al. (2007), ‘Neuronal correlates of emotional processing in patients with major depression’, World J Biol
Psychiatry, pp. 1-7.
[10] Diener, C., Kuehner, C., Brusniak, W., Ubl, B., Wessa, M., Flor, H. (2012), ‘A meta-analysis of neurofunctional imaging studies of emotion and cognition in major depression’, NeuroImage , vol. 61, pp.
677–685.
METU
Experimental Procedure: Participants were asked to judge the
emotional valence of the words (positive or negative) instead of
the valence of the face pictures on the background. They saw
each word twice, overlaid in either congruent or incongruent
settings with respect to the affective pictures.
There were 4 runs of the experiment, each consisting of 16
congruent and 16 incongruent trials. In an event related design,
each stimulus was presented for 2000ms with 2000ms,
4000ms, 6000ms ISI.
Statistical analysis: 2X2 repeated measures ANOVA:
congruence (congruent-incongruent) and valence (positive-
negative).
Congruency contrast: Figure 1
Participants’ left medial frontal gyrus got activated significantly
more for the congruent cases than the incongruent ones as seen
in figure 1. Other significantly active regions for congruency: Right
dorsolateral prefrontal gyrus, DLPFC (BA 9), Left Insula, Right
Cerebellum (Pyramis).
Valence contrast: Figure 2
Right inferior parietal lobule (BA40) got significantly more active for
the positive words than the negative ones as seen in figure 2. For
valence, Left postcentral gyrus (BA 4), right and left cerebellum,
left middle temporal gyrus (BA 21), right precentral gyrus (BA 44)
are also activated.
Results implicate lateralization effects, which need to be
investigated further.
Our Word-Face Stroop Task returned functional activity patterns
compatible with the literature. Especially the significant activity in
insula and DLPFC (BA9) point to important implications in error
and conflict detection, emotional response modulation as reported
in [5],[6],[7]; as well as conflict adaptation, emotion regulation and
resolving conflicts as reported in [8],[9]. Postcentral gyrus and
inferior parietal activity we observed for valence condition are
known to have important roles in processing emotionally loaded
stimuli [10].
Figure 1: Congruency
MNI [-20, 34, 18]: BA9
L Medial Frontal Gyrus
(p<0.001,
clustersize>10 voxels)
Figure 2: valence
MNI [62, -42, 40]: BA40
R Inferior Parietal Gyrus
(p<0.001,
cluster size>10 voxels)
Conclusion
References
Figures
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