Hostname: page-component-586b7cd67f-dsjbd Total loading time: 0 Render date: 2024-11-27T17:53:01.707Z Has data issue: false hasContentIssue false
  • English
  • Français

Neural predictors and effects of cognitive behavioral therapy for depression: the role of emotional reactivity and regulation

Published online by Cambridge University Press:  11 February 2019

Harry Rubin-Falcone ,
Jochen Weber ,
Ronit Kishon ,
Kevin Ochsner ,
Lauren Delaparte ,
Bruce Doré ,
Sudha Raman ,
Bryan T. Denny ,
Maria A. Oquendo  and
J. John Mann
...Show all authors
Harry Rubin-Falcone*
Affiliation:
Department of Psychiatry, Columbia University, New York, NY, USA Division of Molecular Imaging and Neuropathology, New York State Psychiatric Institute and Columbia University, New York, NY, USA
Jochen Weber
Affiliation:
Department of Psychology, Columbia University, New York, NY, USA
Ronit Kishon
Affiliation:
Department of Psychiatry, Columbia University, New York, NY, USA
Kevin Ochsner
Affiliation:
Department of Psychology, Columbia University, New York, NY, USA
Lauren Delaparte
Affiliation:
Department of Psychology, Stony Brook University, Stony Brook, NY, USA
Bruce Doré
Affiliation:
Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA, USA
Sudha Raman
Affiliation:
Department of Psychiatry, Columbia University, New York, NY, USA Division of Molecular Imaging and Neuropathology, New York State Psychiatric Institute and Columbia University, New York, NY, USA
Bryan T. Denny
Affiliation:
Department of Psychology, Rice University, Houston, TX, USA
Maria A. Oquendo
Affiliation:
Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
J. John Mann
Affiliation:
Department of Psychiatry, Columbia University, New York, NY, USA Division of Molecular Imaging and Neuropathology, New York State Psychiatric Institute and Columbia University, New York, NY, USA
Jeffrey M. Miller
Affiliation:
Department of Psychiatry, Columbia University, New York, NY, USA Division of Molecular Imaging and Neuropathology, New York State Psychiatric Institute and Columbia University, New York, NY, USA
*
Author for correspondence: Harry Rubin-Falcone, E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Background

Cognitive behavioral therapy (CBT) is an effective treatment for many patients suffering from major depressive disorder (MDD), but predictors of treatment outcome are lacking, and little is known about its neural mechanisms. We recently identified longitudinal changes in neural correlates of conscious emotion regulation that scaled with clinical responses to CBT for MDD, using a negative autobiographical memory-based task.

Methods

We now examine the neural correlates of emotional reactivity and emotion regulation during viewing of emotionally salient images as predictors of treatment outcome with CBT for MDD, and the relationship between longitudinal change in functional magnetic resonance imaging (fMRI) responses and clinical outcomes. Thirty-two participants with current MDD underwent baseline MRI scanning followed by 14 sessions of CBT. The fMRI task measured emotional reactivity and emotion regulation on separate trials using standardized images from the International Affective Pictures System. Twenty-one participants completed post-treatment scanning. Last observation carried forward was used to estimate clinical outcome for non-completers.

Results

Pre-treatment emotional reactivity Blood Oxygen Level-Dependent (BOLD) signal within hippocampus including CA1 predicted worse treatment outcome. In contrast, better treatment outcome was associated with increased down-regulation of BOLD activity during emotion regulation from time 1 to time 2 in precuneus, occipital cortex, and middle frontal gyrus.

Conclusions

CBT may modulate the neural circuitry of emotion regulation. The neural correlates of emotional reactivity may be more strongly predictive of CBT outcome. The finding that treatment outcome was predicted by BOLD signal in CA1 may suggest overgeneralized memory as a negative prognostic factor in CBT outcome.

Keywords

CBTcognitive behavioral therapyemotion regulationemotional reactivityfMRImajor depressive disorderMDDtreatment mechanismstreatment prediction
Type
Original Articles
Information
Psychological Medicine , Volume 50 , Issue 1 , January 2020 , pp. 146 - 160
Copyright
Copyright © Cambridge University Press 2019 

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

Beck, AT (1979) Cognitive Therapy of Depression. New York: Guilford Press.Google Scholar
Beck, AT, Ward, CH, Mendelson, M, Moc, J and Erbauh, J (1961) An inventory for measuring depression. Archives of General Psychiatry 4, 53–63.Google Scholar
Beck, AT, Kovacs, M and Weissman, A (1979) Assessment of suicidal intention: the Scale for Suicide Ideation. Journal of Consulting and Clinical Psychology 47, 343–352.Google Scholar
Beck, JS (1995) Cognitive Therapy: Basics and Beyond. New York: Guilford Press.Google Scholar
Buhle, JT, Silvers, JA, Wager, TD, Lopez, R, Onyemekwu, C and Kober, H (2014) Cognitive reappraisal of emotion: a meta-analysis of human neuroimaging studies. Cerebral Cortex 24, 2981–2990.Google Scholar
Burgess, PW, Gilbert, SJ and Dumontheil, I (2007) Function and localization within rostral prefrontal cortex (area 10). Philosophical Transactions of the Royal Society B: Biological Sciences 362, 887–899.Google Scholar
Costafreda, SG, Khanna, A, Mourao-Miranda, J and Fu, CH (2009) Neural correlates of sad faces predict clinical remission to cognitive behavioural therapy in depression. NeuroReport 20, 637–641.Google Scholar
Delaveau, P, Jabourian, M, Lemogne, C, Guionnet, S, Bergouignan, L and Fossati, P (2011) Brain effects of antidepressants in major depression: a meta-analysis of emotional processing studies. Journal of Affective Disorders 130, 66–74.Google Scholar
Denny, BT and Ochsner, KN (2014) Behavioral effects of longitudinal training in cognitive reappraisal. Emotion 14, 425–433.Google Scholar
Denny, BT, Fan, J, Liu, X, Ochsner, KN, Guerreri, S, Mayson, S, Rimsky, L, McMaster, A, New, AS, Goodman, M, Siever, LJ and Koenigsberg, HW (2015 a). Elevated amygdala activity during reappraisal anticipation predicts anxiety in avoidant personality disorder. Journal of Affective Disorders 172, 1–7.Google Scholar
Denny, BT, Inhoff, MC, Zerubavel, N, Davachi, L and Ochsner, KN (2015 b). Getting over it: long-lasting effects of emotion regulation on amygdala response. Psychological Science 26, 1377–1388.Google Scholar
DeRubeis, RJ, Siegle, GJ and Hollon, SD (2008). Cognitive therapy versus medication for depression: treatment outcomes and neural mechanisms. Nature Reviews Neuroscience 9, 788–796.Google Scholar
Dorfel, D, Lamke, JP, Hummel, F, Wagner, U, Erk, S and Walter, H (2014) Common and differential neural networks of emotion regulation by detachment, reinterpretation, distraction, and expressive suppression: a comparative fMRI investigation. NeuroImage 101, 298–309.Google Scholar
Drevets, WC (2003) Neuroimaging abnormalities in the amygdala in mood disorders. Annals of the New York Academy of Sciences 985, 420–444.Google Scholar
Eklund, A, Nichols, TE and Knutsson, H (2016) Cluster failure: why fMRI inferences for spatial extent have inflated false-positive rates. Proceedings of the National Academy of Sciences of the United States of America 113, 7900–7905.Google Scholar
Fales, CL, Barch, DM, Rundle, MM, Mintun, MA, Mathews, J, Snyder, AZ and Sheline, YI (2009) Antidepressant treatment normalizes hypoactivity in dorsolateral prefrontal cortex during emotional interference processing in major depression. Journal of Affective Disorders 112, 206–211.Google Scholar
First, M, Spitzer, R, Gibbon, M and Williams, J (1995) Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-I/P, Version 2.0). New York: Biometrics Research Department, New York State Psychiatric Institute.Google Scholar
Fonzo, GA, Goodkind, MS, Oathes, DJ, Zaiko, YV, Harvey, M, Peng, KK, Weiss, ME, Thompson, AL, Zack, SE, Lindley, SE, Arnow, BA, Jo, B, Gross, JJ, Rothbaum, BO and Etkin, A (2017 a). PTSD psychotherapy outcome predicted by brain activation during emotional reactivity and regulation. The American Journal of Psychiatry 12, 1163–1174.Google Scholar
Fonzo, GA, Goodkind, MS, Oathes, DJ, Zaiko, YV, Harvey, M, Peng, KK, Weiss, ME, Thompson, AL, Zack, SE, Mills-Finnerty, CE, Rosenberg, BM, Edelstein, R, Wright, RN, Kole, CA, Lindley, SE, Arnow, BA, Jo, B, Gross, JJ, Rothbaum, BO and Etkin, A (2017 b). Selective effects of psychotherapy on frontopolar cortical function in PTSD. The American Journal of Psychiatry 12, 1175–1184.Google Scholar
Frith, CD, Friston, K, Liddle, PF and Frackowiak, RS (1991) Willed action and the prefrontal cortex in man: a study with PET. Proceedings of the Royal Society B: Biological Sciences 244, 241–246.Google Scholar
Fu, CH, Williams, SC, Cleare, AJ, Scott, J, Mitterschiffthaler, MT and Walsh, ND (2008) Neural responses to sad facial expressions in major depression following cognitive behavioral therapy. Biological Psychiatry 64, 505–512.Google Scholar
Gloaguen, V, Cottraux, J, Cucherat, M and Blackburn, IM (1998) A meta-analysis of the effects of cognitive therapy in depressed patients. Journal of Affective Disorders 49, 59–72.Google Scholar
Golkar, A, Lonsdorf, TB, Olsson, A, Lindstrom, KM, Berrebi, J, Fransson, P, Schalling, M, Ingvar, M and Ohman, A (2012) Distinct contributions of the dorsolateral prefrontal and orbitofrontal cortex during emotion regulation. PLoS ONE 7, e48107.Google Scholar
Gotlib, IH and Hammen, CL (2009) Handbook of Depression. New York, N.Y.: The Guilford Press.Google Scholar
Gotlib, IH and Joormann, J (2010) Cognition and depression: current status and future directions. Annual Review of Clinical Psychology 6, 285–312.Google Scholar
Hamilton, M (1960) A rating scale for depression. Journal of Neurology, Neurosurgery, and Psychiatry 23, 56–62.Google Scholar
Heller, AS, Johnstone, T, Shackman, AJ, Light, SN, Peterson, MJ and Kolden, GG (2009) Reduced capacity to sustain positive emotion in major depression reflects diminished maintenance of fronto-striatal brain activation. Proceedings of the National Academy of Sciences of the United States of America 106, 22445–22450.Google Scholar
Herold, D, Spengler, S, Sajonz, B, Usnich, T and Bermpohl, F (2016) Common and distinct networks for self-referential and social stimulus processing in the human brain. Brain Structure and Function 221, 3475–3485.Google Scholar
Hollon, SD, Thase, ME and Markowitz, JC (2002) Treatment and prevention of depression. Psychological Science in the Public Interest 3, 39–77.Google Scholar
Johnstone, T, van Reekum, CM, Urry, HL, Kalin, NH and Davidson, RJ (2007) Failure to regulate: counterproductive recruitment of top-down prefrontal-subcortical circuitry in major depression. The Journal of Neuroscience 27, 8877–8884.Google Scholar
Kendler, KS, Karkowski, LM and Prescott, CA (1999) Causal relationship between stressful life events and the onset of major depression. The American Journal of Psychiatry 156, 837–841.Google Scholar
Kircanski, K, Joormann, J and Gotlib, IH (2012) Cognitive aspects of depression. Wiley Interdisciplinary Reviews: Cognitive Science 3, 301–313.Google Scholar
Lang, PJ, Greenwald, MK, Bradley, MM and Hamm, AO (1993) Looking at pictures: affective, facial, visceral, and behavioral reactions. Psychophysiology 30, 261–273.Google Scholar
Leppanen, JM (2006) Emotional information processing in mood disorders: a review of behavioral and neuroimaging findings. Current Opinion in Psychiatry 19, 34–39.Google Scholar
Linnman, C, Moulton, EA, Barmettler, G, Becerra, L and Borsook, D (2012) Neuroimaging of the periaqueductal gray: state of the field. NeuroImage 60, 505–522.Google Scholar
Maldjian, JA, Laurienti, PJ, Kraft, RA and Burdette, JH (2003) An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets. NeuroImage 19, 1233–1239.Google Scholar
Messina, I, Sambin, M, Palmieri, A and Viviani, R (2013) Neural correlates of psychotherapy in anxiety and depression: a meta-analysis. PLoS ONE 8, e74657.Google Scholar
Miller, JM, Schneck, N, Siegle, GJ, Chen, Y, Ogden, RT, Kikuchi, T, Oquendo, MA, Mann, JJ and Parsey, RV (2013) fMRI response to negative words and SSRI treatment outcome in major depressive disorder: a preliminary study. Psychiatry Research 214, 296–305.Google Scholar
Ochsner, KN, Bunge, SA, Gross, JJ and Gabrieli, JD (2002) Rethinking feelings: an FMRI study of the cognitive regulation of emotion. Journal of Cognitive Neuroscience 14, 1215–1229.Google Scholar
Ochsner, KN, Ray, RD, Cooper, JC, Robertson, ER, Chopra, S, Gabrieli, JD and Gross, JJ (2004). For better or for worse: neural systems supporting the cognitive down- and up-regulation of negative emotion. NeuroImage 23, 483–499.Google Scholar
Ritchey, M, Dolcos, F, Eddington, KM, Strauman, TJ and Cabeza, R (2011) Neural correlates of emotional processing in depression: changes with cognitive behavioral therapy and predictors of treatment response. Journal of Psychiatric Research 45, 577–587.Google Scholar
Rosenblau, G, Sterzer, P, Stoy, M, Park, S, Friedel, E, Heinz, A, Pilhatsch, M, Bauer, M and Strohle, A (2012) Functional neuroanatomy of emotion processing in major depressive disorder is altered after successful antidepressant therapy. Journal of Psychopharmacology 26, 1424–1433.Google Scholar
Rubin-Falcone, H, Weber, J, Kishon, R, Ochsner, K, Delaparte, L, Dore, B, Zanderigo, F, Oquendo, MA, Mann, JJ and Miller, JM (2017) Longitudinal effects of cognitive behavioral therapy for depression on the neural correlates of emotion regulation. Psychiatry Research: Neuroimaging 271, 82–90.Google Scholar
Schlichting, M, Zeithamova, D and Preston, AR (2014) CA1 subfield contributions to memory integration and inference. Hippocampus 24, 1248–1260.Google Scholar
Siegle, GJ, Steinhauer, SR, Thase, ME, Stenger, VA and Carter, CS (2002) Can't shake that feeling: event-related fMRI assessment of sustained amygdala activity in response to emotional information in depressed individuals. Biological Psychiatry 51, 693–707.Google Scholar
Siegle, GJ, Carter, CS and Thase, ME (2006) Use of FMRI to predict recovery from unipolar depression with cognitive behavior therapy. The American Journal of Psychiatry 163, 735–738.Google Scholar
Silvers, JA, Hubbard, AD, Biggs, E, Shu, J, Fertuck, E, Chaudhury, S, Grunebaum, MF, Weber, J, Kober, H, Chesin, M, Brodsky, BS, Koenigsberg, H, Ochsner, KN and Stanley, B (2016) Affective lability and difficulties with regulation are differentially associated with amygdala and prefrontal response in women with Borderline Personality Disorder. Psychiatry Research: Neuroimaging 254, 74–82.Google Scholar
Simons, LE, Moulton, EA, Linnman, C, Carpino, E, Becerra, L and Borsook, D (2014) The human amygdala and pain: evidence from neuroimaging. Human Brain Mapping 35, 527–538.Google Scholar
Stephanou, K, Davey, CG, Kerestes, R, Whittle, S and Harrison, BJ (2017) Hard to look on the bright side: neural correlates of impaired emotion regulation in depressed youth. Social Cognitive and Affective Neuroscience 12, 1138–1148.Google Scholar
Van Daele, T, Griffith, JW, Van den Bergh, O and Hermans, D (2014) Overgeneral autobiographical memory predicts changes in depression in a community sample. Cognition and Emotion 28, 1303–1312.Google Scholar
Williams, LM, Korgaonkar, MS, Song, YC, Paton, R, Eagles, S, Goldstein-Piekarski, A, Grieve, SM, Harris, AW, Usherwood, T and Etkin, A (2015) Amygdala reactivity to emotional faces in the prediction of general and medication-specific responses to antidepressant treatment in the randomized iSPOT-D trial. Neuropsychopharmacology 40, 2398–2408.Google Scholar
Woo, CW, Krishnan, A and Wager, TD (2014) Cluster-extent based thresholding in fMRI analyses: pitfalls and recommendations. NeuroImage 91, 412–419.Google Scholar
Woolrich, M (2008) Robust group analysis using outlier inference. NeuroImage 41, 286–301.Google Scholar
Worsley, KJ (2001) Functional MRI: an introduction to methods. Ch 14, In Statistical Analysis of Activation Images. (ed. Jezzard, P., Matthews, PM, Smith, SM), pp. 251–270. Oxford University Press: New York.Google Scholar
Yesudas, EH and Lee, TM (2015) The role of cingulate cortex in vicarious pain. BioMed Research International 2015, 719615.Google Scholar
Zilverstand, A, Parvaz, MA and Goldstein, RZ (2017) Neuroimaging cognitive reappraisal in clinical populations to define neural targets for enhancing emotion regulation. A systematic review. NeuroImage 151, 105–116.Google Scholar