Hostname: page-component-8448b6f56d-42gr6 Total loading time: 0 Render date: 2024-04-18T15:20:45.328Z Has data issue: false hasContentIssue false

Brain response to emotional faces in anxiety and depression: neural predictors of cognitive behavioral therapy outcome and predictor-based subgroups following therapy

Published online by Cambridge University Press:  10 November 2020

Heide Klumpp*
Affiliation:
Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
Jagan Jimmy
Affiliation:
Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
Katie L. Burkhouse
Affiliation:
Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
Runa Bhaumik
Affiliation:
Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
Jennifer Francis
Affiliation:
Department of Psychiatry & Behavioral Sciences, Rush University Medical Center, Chicago, IL, USA
Michelle G. Craske
Affiliation:
Department of Psychology and Department of Psychiatry and Biobehavioral Sciences, University of California-Los Angeles, Los Angeles, CA, USA
K. Luan Phan
Affiliation:
Department of Psychiatry and Behavioral Health, The Ohio State University, Columbus, OH, USA
Olusola Ajilore
Affiliation:
Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
*
Author for correspondence: Heide Klumpp, E-mail: hklumpp@uic.edu

Abstract

Background

Neuroimaging studies have shown variance in brain response to emotional faces predicts cognitive behavioral therapy (CBT) outcome. An important next step is to determine if individual differences in neural predictors of CBT response represent distinct patient groups.

Methods

In total, 90 patients with internalizing disorders completed a face-matching task during functional magnetic resonance imaging before and after 12 weeks of CBT and 45 healthy controls completed the task before and after 12 weeks. Patients exhibiting a pre-to-post CBT >50% reduction in symptom severity on two measures were considered treatment responders. Regions of interest (ROIs) for angry, fearful, and happy faces were submitted to receiver operating characteristic (ROC) curve analysis. Significant ROIs were then submitted to decision tree analysis to classify responder/non-responder subgroups. Psychophysiological interactions (PPI) were used to explore functional connectivity in the region(s) that delineated subgroups.

Results

A total of 51 patients were treatment responders and ROC curve results were significant for all face types though specific regions varied. Decision tree results revealed superior occipital response to angry faces identified patient subgroups such that the subgroup with ‘high’ occipital activity had more responders than the ‘low’ occipital subgroup. Following CBT, the high, relative to low, occipital subgroup was less symptomatic. Controls exhibited stable superior occipital activation over time. Whole-brain PPI showed reduced baseline superior occipital-postcentral gyrus functional connectivity in responders compared to non-responders.

Conclusions

Preliminary findings indicate patients characterized by relatively more pre-treatment superior occipital gyrus engagement to angry faces and reduced superior occipital-postcentral gyrus connectivity, relative to non-responders, may represent a phenotype likely to benefit from CBT.

Type
Original Article
Copyright
Copyright © The Author(s) 2020. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Adolphs, R. (2002). Neural systems for recognizing emotion. Current Opinion in Neurobiology, 12(2), 169177. http://dx.doi.org/10.1016/S0959-4388(02)00301-X.CrossRefGoogle ScholarPubMed
Adolphs, R., Damasio, H., Tranel, D., & Damasio, A. R. (1996). Cortical systems for the recognition of emotion in facial expressions. The Journal of Neuroscience, 16, 76787687.CrossRefGoogle ScholarPubMed
Bayliss, P., & Holttum, S. (2015). Experiences of antidepressant medication and cognitive-behavioural therapy for depression: A grounded theory study. Psychology and Psychotherapy: Theory, Research and Practice, 88, 317334.CrossRefGoogle ScholarPubMed
Beck, A. T., Rush, A. J., Shaw, B. F., & Gary, E. (1979). Cognitive therapy for depression. New York: Guilford.Google Scholar
Brett, M., Anton, J-L., Valabregue, R., & Poline, J-B. (2002). Region of interest analysis using an SPM toolbox [abstract].Google Scholar
Bryant, R. A., Felmingham, K., Kemp, A., Das, P., Hughes, G., Peduto, A., & Williams, L. (2008). Amygdala and ventral anterior cingulate activation predicts treatment response to cognitive behaviour therapy for post-traumatic stress disorder. Psychological Medicine, 38, 555561.CrossRefGoogle ScholarPubMed
Carpenter, J. K., Andrews, L. A., Witcraft, S. M., Powers, M. B., Smits, J. A. J., & Hofmann, S. G. (2018). Cognitive behavioral therapy for anxiety and related disorders: a meta‐analysis of randomized placebo‐controlled trials. Depression and Anxiety, 35(6), 502514. http://dx.doi.org/10.1002/da.2018.35.issue-6.CrossRefGoogle ScholarPubMed
Craske, M. G., David, D. H., & Tracy, O. (1992). Mastery of your anxiety and worry. Albany: Graywind Publications.Google Scholar
Cuijpers, P., Berking, M., Andersson, G., Quigley, L., Kleiboer, A., & Dobson, K. S. (2013). A meta-analysis of cognitive-behavioural therapy for adult depression, alone and in comparison with other treatments. The Canadian Journal of Psychiatry, 58(7), 376385. http://dx.doi.org/10.1177/070674371305800702.CrossRefGoogle ScholarPubMed
Cuijpers, P., Hollon, S. D., van Straten, A., Bockting, C., Berking, M., & Andersson, G. (2013). Does cognitive behaviour therapy have an enduring effect that is superior to keeping patients on continuation pharmacotherapy? A meta-analysis. BMJ Open, 3(4), e002542. 10.1136/bmjopen-2012-002542.CrossRefGoogle ScholarPubMed
Davis, M., & Whalen, P. J. (2001). The amygdala: Vigilance and emotion. Molecular Psychiatry, 6(1), 1334. http://dx.doi.org/10.1038/sj.mp.4000812.CrossRefGoogle ScholarPubMed
Doehrmann, O., Ghosh, S. S., Polli, F. E., Reynolds, G. O., Horn, F., Keshavan, A., … Gabrieli, J. D. (2013). Predicting treatment response in social anxiety disorder from functional magnetic resonance imaging. JAMA Psychiatry, 70, 87.CrossRefGoogle ScholarPubMed
First, M., Williams, J., Karg, R., & Spitzer, R. (2015). Structured Clinicial Interview for DSM-5-Research Version (SCID-5 for DSM-5, Research Version; SCID-5-RV). Arlington, VA: American Psychiatric Association.Google Scholar
Fonzo, G. A., Ramsawh, H. J., Flagan, T. M., Sullivan, S. G., Letamendi, A., Simmons, A. N., …Stein, M. B. (2015). Common and disorder-specific neural responses to emotional faces in generalised anxiety, social anxiety and panic disorders. British Journal of Psychiatry, 206(3), 206215. http://dx.doi.org/10.1192/bjp.bp.114.149880.CrossRefGoogle ScholarPubMed
Friston, K. J., Buechel, C., Fink, G. R., Morris, J., Rolls, E., & Dolan, R. J. (1997). Psychophysiological and modulatory interactions in neuroimaging. NeuroImage, 6, 218229.CrossRefGoogle ScholarPubMed
Fu, C. H. Y., Williams, S. C. R., Cleare, A. J., Scott, J., Mitterschiffthaler, M. T., Walsh, N. D., … Murray, R. M. (2008). Neural responses to sad facial expressions in major depression following cognitive behavioral therapy. Biological Psychiatry, 64, 505512.CrossRefGoogle ScholarPubMed
Fusar-Poli, P., Placentino, A., Carletti, F., Landi, P., Allen, P., Surguladze, S., … Politi, P. (2009). Functional atlas of emotional faces processing: A voxel-based meta-analysis of 105 functional magnetic resonance imaging studies. Journal of Psychiatry & Neuroscience : JPN, 34(6), 418432.Google ScholarPubMed
Gorka, S. M., Young, C. B., Klumpp, H., Kennedy, A. E., Francis, J., Ajilore, O., … Phan, K. L. (2019). Emotion-based brain mechanisms and predictors for SSRI and CBT treatment of anxiety and depression: A randomized trial. Neuropsychopharmacology, 44, 16391648.CrossRefGoogle ScholarPubMed
Gur, R. C., Sara, R., Hagendoorn, M., Marom, O., Hughett, P., Macy, L., … Gur, R. E. (2002). A method for obtaining 3-dimensional facial expressions and its standardization for use in neurocognitive studies. Journal of Neuroscience Methods, 115(2), 137143. http://dx.doi.org/10.1016/s0165-0270(02)00006-7.CrossRefGoogle ScholarPubMed
Hamilton, M. (1959). The assessment of anxiety states by rating. British Journal of Medical Psychology, 32, 5055.CrossRefGoogle ScholarPubMed
Hamilton, M. (1960). A rating scale for depression. Journal of Neurology, Neurosurgery, and Psychiatry, 23, 5662.CrossRefGoogle ScholarPubMed
Hester, N. (2019). Perceived negative emotion in neutral faces: Gender-dependent effects on attractiveness and threat. Emotion, 19(8), 14901494. http://dx.doi.org/10.1037/emo0000525.CrossRefGoogle ScholarPubMed
Hope, D. A., Heimberg, R. G., & Turk, C. L. (2006). Managing social anxiety: A cognitive-behavioral therapy approach. New York: Oxford University Press.Google Scholar
Hunnius, S., de Wit, T. C. J., Vrins, S., & von Hofsten, C. (2011). Facing threat: infants' and adults' visual scanning of faces with neutral, happy, sad, angry, and fearful emotional expressions. Cognition & Emotion, 25(2), 193205. http://dx.doi.org/10.1080/15298861003771189.CrossRefGoogle ScholarPubMed
Infantolino, Z. P., Luking, K. R., Sauder, C. L., Curtin, J. J., & Hajcak, G. (2018). Robust is not necessarily reliable: from within-subjects fMRI contrasts to between-subjects comparisons. NeuroImage, 173, 146152.CrossRefGoogle Scholar
Iwamura, Y. (1998). Hierarchical somatosensory processing. Current Opinion in Neurobiology, 8(4), 522528. http://dx.doi.org/10.1016/S0959-4388(98)80041-X.CrossRefGoogle ScholarPubMed
Kass, G. V. (1980). An exploratory technique for investigating large quantities of categorical data. Applied Statistics, 29(2), 119127.CrossRefGoogle Scholar
Klumpp, H., Fitzgerald, D. A., & Phan, K. L. (2013). Neural predictors and mechanisms of cognitive behavioral therapy on threat processing in social anxiety disorder. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 45, 8391.CrossRefGoogle ScholarPubMed
Knutson, B. (1996). Facial expressions of emotion influence interpersonal trait inferences. Journal of Nonverbal Behavior, 20, 165182.CrossRefGoogle Scholar
Kragel, P, & Labar, K. S. (2016). Somatosensory representations link the perception of emotional expressions and sensory experience. eNeuro, 3, ENEURO.009015.CrossRefGoogle ScholarPubMed
Labuschagne, I., Phan, K. L., Wood, A., Angstadt, M., Chua, P., Heinrichs, M., … Nathan, P. J. (2010). Oxytocin Attenuates Amygdala Reactivity to Fear in Generalized Social Anxiety Disorder. Neuropsychopharmacology, 35(12), 24032413. http://dx.doi.org/10.1038/npp.2010.123.CrossRefGoogle ScholarPubMed
Li, W., Qin, W., Liu, H., Fan, L., Wang, J., Jiang, T., … Yu, C. (2013). Subregions of the human superior frontal gyrus and their connections. NeuroImage, 78(), 4658. http://dx.doi.org/10.1016/j.neuroimage.2013.04.011.CrossRefGoogle ScholarPubMed
Liang, Y., Liu, B., Li, X., & Wang, P. (2018). Multivariate pattern classification of facial expressions based on large-scale functional connectivity. Frontiers in Human Neuroscience, 12, 94.CrossRefGoogle ScholarPubMed
Liebowitz, M. R. (1987). Social phobia. Modern Problems of Pharmacopsychiatry, 22, 141173.CrossRefGoogle ScholarPubMed
Loerinc, A. G., Meuret, A. E., Twohig, M. P., Rosenfield, D., Bluett, E. J., & Craske, M. G. (2015). Response rates for CBT for anxiety disorders: Need for standardized criteria. Clinical Psychology Review, 42, 7282.CrossRefGoogle ScholarPubMed
Lovibond, P. F., & Lovibond, S. H. (1995). The structure of negative emotional states: Comparison of the Depression Anxiety Stress Scales (DASS) with the beck depression and anxiety inventories. Behaviour Research and Therapy, 33, 335343.CrossRefGoogle ScholarPubMed
Martell, C. R., Dimidjian, S., & Herman-Dunn, R. (2010). Behavioral activation for depression. New York, NY: The Guildford Press.Google Scholar
Meyer, T. J., Miller, M. L., Metzger, R. L., & Borkovec, T. D. (1990). Development and validation of the Penn State Worry Questionnaire. Behaviour Research and Therapy, 28, 487495.CrossRefGoogle ScholarPubMed
Morris, J. S., Ohman, A., & Dolan, R. J. (1998). Conscious and unconscious emotional learning in the human amygdala. Nature, 393(6684), 467470. http://dx.doi.org/10.1038/30976.CrossRefGoogle ScholarPubMed
Murphy, F. C., Nimmo-Smith, I., & Lawrence, A. D. (2003). Functional neuroanatomy of emotions: A meta-analysis. Cognitive, Affective & Behavioral Neuroscience, 3(3), 207233. http://dx.doi.org/10.3758/cabn.3.3.207.CrossRefGoogle ScholarPubMed
Murray, C. J., & Lopez, A. D. (1997). Global mortality, disability, and the contribution of risk factors: Global burden of disease study. The Lancet, 349, 14361442.CrossRefGoogle ScholarPubMed
Neth, D., & Martinez, A. M. (2009). Emotion perception in emotionless face images suggests a norm-based representation. Journal of Vision, 9(1), 5.1511. http://dx.doi.org/10.1167/9.1.5.CrossRefGoogle ScholarPubMed
Nord, C. L., Valton, V., Wood, J., & Roiser, J. P. (2017). Power-up: A reanalysis of 'Power Failure' in neuroscience using mixture modeling. The Journal of Neuroscience, 37(34), 80518061. http://dx.doi.org/10.1523/JNEUROSCI.3592-16.2017.CrossRefGoogle ScholarPubMed
Öhman, A, & Mineka, S. (2001). Fears, phobias, and preparedness: toward an evolved module of fear and fear learning. Psychological Review, 108, 483522.CrossRefGoogle ScholarPubMed
Phan, K. L., Angstadt, M., Golden, J., Onyewuenyi, I., Popovska, A., & deWit, H. (2008). Cannabinoid modulation of amygdala reactivity to social signals of threat in humans. The Journal of Neuroscience, 28(10), 23132319. http://dx.doi.org/10.1523/JNEUROSCI.5603-07.2008.CrossRefGoogle ScholarPubMed
Phan, K. L., Coccaro, E. F., Angstadt, M., Kreger, K. J., Mayberg, H. S., Liberzon, I., & Stein, M. B. (2013). Corticolimbic brain reactivity to social signals of threat before and after sertraline treatment in generalized social phobia. Biological Psychiatry, 73, 329336.CrossRefGoogle ScholarPubMed
Pratt, L. A., Druss, B. G., Manderscheid, R. W., & Walker, E. R. (2016). Excess mortality due to depression and anxiety in the United States: Results from a nationally representative survey. General Hospital Psychiatry, 39, 3945.CrossRefGoogle ScholarPubMed
Ruscio, A. M., Brown, T. A., Chiu, W. T., Sareen, J., Stein, M. B., & Kessler, R. C. (2008). Social fears and social phobia in the USA: Results from the national comorbidity survey replication. Psychological Medicine, 38, 1528.CrossRefGoogle ScholarPubMed
Sundermann, B., & Pfleiderer, B. (2012). Functional connectivity profile of the human inferior frontal junction: Involvement in a cognitive control network. BMC Neuroscience, 13, 119.CrossRefGoogle Scholar
Sundvall, L., Ingerslev, H. J., Breth, K., & Kirkegaard, K. (2013). Inter- and intra-observer variability of time-lapse annotations. Human Reproduction (Oxford, England), 28(12), 32153221. http://dx.doi.org/10.1093/humrep/det366.CrossRefGoogle ScholarPubMed
Tomasi, D., & Volkow, N. D. (2011). Association between functional connectivity hubs and brain networks. Cerebral Cortex, 21, 20032013.CrossRefGoogle ScholarPubMed
Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., … Joliot, M. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage, 15, 273289.CrossRefGoogle ScholarPubMed
Veenstra, L., Schneider, I. K., Bushman, B. J., & Koole, S. L.. (2017). Drawn to danger: Trait anger predicts automatic approach behaviour to angry faces. Cognition & Emotion, 31(4), 765771. http://dx.doi.org/10.1080/02699931.2016.1150256.CrossRefGoogle ScholarPubMed
Whalen, P. J. (1998). Fear, vigilance, and ambiguity: Initial neuroimaging studies of the human amygdala. Current Directions in Psychological Science, 7, 177188.CrossRefGoogle Scholar
Willis, J., & Todorov, A. (2006). First impressions: Making up your mind after a 100-ms exposure to a face. Psychological Science, 17(7), 592598. http://dx.doi.org/10.1111/j.1467-9280.2006.01750.x.CrossRefGoogle ScholarPubMed
Wu, J. W., Hseu, S. S., Fuh, J. L., Lirng, J. F., Wang, Y. F., Chen, W. T., … Wang, S. J. (2017). Factors predicting response to the first epidural blood patch in spontaneous intracranial hypotension. Brain, 140(2), 344352. http://dx.doi.org/10.1093/brain/aww328.CrossRefGoogle Scholar
Supplementary material: File

Klumpp et al. Supplementary Materials

Klumpp et al. Supplementary Materials 1

Download Klumpp et al. Supplementary Materials(File)
File 2.3 MB
Supplementary material: File

Klumpp et al. Supplementary Materials

Klumpp et al. Supplementary Materials 2

Download Klumpp et al. Supplementary Materials(File)
File 162 Bytes