Hostname: page-component-78c5997874-94fs2 Total loading time: 0 Render date: 2024-11-10T10:52:00.322Z Has data issue: false hasContentIssue false
  • English
  • Français

Conflict monitoring and adaptation as reflected by N2 amplitude in obsessive–compulsive disorder

Published online by Cambridge University Press:  18 January 2017

A. Riesel ,
J. Klawohn ,
N. Kathmann  and
T. Endrass
A. Riesel*
Affiliation:
Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
J. Klawohn
Affiliation:
Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
N. Kathmann
Affiliation:
Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
T. Endrass
Affiliation:
Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany Department of Psychology, Institute of Clinical Psychology and Psychotherapy, Technische Universität Dresden, Dresden, Germany Institute of Psychology, Otto-von-Guericke-Universität Magdeburg, Magdeburg, Germany
*
*Address for correspondence: A. Riesel, Department of Psychology, Humboldt-Universität zu Berlin, Rudower Chaussee 18, 12489 Berlin, Germany. (Email: anja.riesel@hu-berlin.de)
Get access Rights & Permissions [Opens in a new window]

Abstract

Background

Feelings of doubt and perseverative behaviours are key symptoms of obsessive–compulsive disorder (OCD) and have been linked to hyperactive error and conflict signals in the brain. While enhanced neural correlates of error monitoring have been robustly shown, far less is known about conflict processing and adaptation in OCD.

Method

We examined event-related potentials during conflict processing in 70 patients with OCD and 70 matched healthy comparison participants, focusing on the stimulus-locked N2 elicited in a flanker task. Conflict adaptation was evaluated by analysing sequential adjustments in N2 and behaviour, i.e. current conflict effects as a function of preceding conflict.

Results

Patients with OCD showed enhanced N2 amplitudes compared with healthy controls. Further, patients showed stronger conflict adaptation effects on reaction times and N2 amplitude. Thus, the effect of previous compatibility was larger in patients than in healthy participants as indicated by greater N2 adjustments in change trials (i.e. iC, cI). As a result of stronger conflict adaptation in patients, N2 amplitudes were comparable between groups in incompatible trials following incompatible trials.

Conclusions

Larger N2 amplitudes and greater conflict adaptation in OCD point to enhanced conflict monitoring leading to increased recruitment of cognitive control in patients. This was most pronounced in change trials and was associated with stronger conflict adjustment in N2 and behaviour. Thus, hyperactive conflict monitoring in OCD may be beneficial in situations that require a high amount of control to resolve conflict, but may also reflect an effortful process that is linked to distress and symptoms of OCD.

Keywords

Conflict adaptationconflict monitoringerror-related negativityN2obsessive–compulsive disorder
Type
Original Articles
Information
Psychological Medicine , Volume 47 , Issue 8 , June 2017 , pp. 1379 - 1388
Copyright
Copyright © Cambridge University Press 2017 

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

American Psychiatric Association (2013). Diagnostic and Statistical Manual of Mental Disorders, fifth edn. American Psychiatric Association: Washington, DC.Google Scholar
Berg, P, Scherg, M (1994). A multiple source approach to the correction of eye artifacts. Electroencephalography and Clinical Neurophysiology 90, 229–241.Google Scholar
Botvinick, MM, Braver, TS, Barch, DM, Carter, CS, Cohen, JD (2001). Conflict monitoring and cognitive control. Psychological Review 108, 624–652.Google Scholar
Bush, G, Shin, LM (2006). The Multi-Source Interference Task: an fMRI task that reliably activates the cingulo-frontal-parietal cognitive/attention network. Nature Protocols 1, 308–313.Google Scholar
Carter, CS, van Veen, V (2007). Anterior cingulate cortex and conflict detection: an update of theory and data. Cognitive, Affective, and Behavioral Neuroscience 7, 367–379.Google Scholar
Chamberlain, SR, Blackwell, AD, Fineberg, NA, Robbins, TW, Sahakian, BJ (2005). The neuropsychology of obsessive compulsive disorder: the importance of failures in cognitive and behavioural inhibition as candidate endophenotypic markers. Neuroscience and Biobehavioral Reviews 29, 399–419.Google Scholar
Chamberlain, SR, Fineberg, NA, Menzies, LA, Blackwell, AD, Bullmore, ET, Robbins, TW, Sahakian, BJ (2007). Impaired cognitive flexibility and motor inhibition in unaffected first-degree relatives of patients with obsessive–compulsive disorder. American Journal of Psychiatry 164, 335–338.Google Scholar
Ciesielski, KT, Rowland, LM, Harris, RJ, Kerwin, AA, Reeve, A, Knight, JE (2011). Increased anterior brain activation to correct responses on high-conflict Stroop task in obsessive–compulsive disorder. Clinical Neurophysiology 122, 107–113.Google Scholar
Clayson, PE, Larson, MJ (2011). Effects of repetition priming on electrophysiological and behavioral indices of conflict adaptation and cognitive control. Psychophysiology 48, 1621–1630.Google Scholar
Clayson, PE, Larson, MJ (2012). Cognitive performance and electrophysiological indices of cognitive control: a validation study of conflict adaptation. Psychophysiology 49, 627–637.Google Scholar
Crye, J, Laskey, B, Cartwright-Hatton, S (2010). Non-clinical obsessions in a young adolescent population: frequency and association with metacognitive variables. Psychology and Psychotherapy – Theory Research and Practice 83, 15–26.Google Scholar
Dennis, TA, Chen, CC (2009). Trait anxiety and conflict monitoring following threat: an ERP study. Psychophysiology 46, 122–131.Google Scholar
Endrass, T, Klawohn, J, Schuster, F, Kathmann, N (2008). Overactive performance monitoring in obsessive–compulsive disorder: ERP evidence from correct and erroneous reactions. Neuropsychologia 46, 1877–1887.CrossRefGoogle ScholarPubMed
Endrass, T, Schuermann, B, Kaufmann, C, Spielberg, R, Kniesche, R, Kathmann, N (2010). Performance monitoring and error significance in patients with obsessive–compulsive disorder. Biological Psychology 84, 257–263.Google Scholar
Endrass, T, Ullsperger, M (2014). Specificity of performance monitoring changes in obsessive–compulsive disorder. Neuroscience and Biobehavioral Reviews 46, 124–138.Google Scholar
Falkenstein, M, Hohnsbein, J, Hoormann, J, Blanke, L (1991). Effects of crossmodal divided attention on late ERP components. II. Error processing in choice reaction tasks. Electroencephalography and Clinical Neurophysiology 78, 447–455.Google Scholar
First, MB, Gibbon, M, Spitzer, RL, Williams, JB (1996). Structured Clinical Interview for DSM-IV Axis I Disorders: Non-Patient Edition (SCID-I, version 2.0) . Biometrics Research, New York State Psychiatric Institute: New York.Google Scholar
Foa, E, Huppert, J, Leiberg, S, Langner, R, Kichic, R, Hajcak, G, Salkovskis, P (2002). The Obsessive–Compulsive Inventory: development and validation of a short version. Psychological Assessment 14, 485–496.Google Scholar
Folstein, JR, Van Petten, C, (2008). Influence of cognitive control and mismatch on the N2 component of the ERP: a review. Psychophysiology 45, 152–170.Google Scholar
Ford, JM (1999). Schizophrenia: the broken P300 and beyond. Psychophysiology 36, 667–682.Google Scholar
Gehring, WJ, Himle, J, Nisenson, LG (2000). Action-monitoring dysfunction in obsessive–compulsive disorder. Psychological Science 11, 1–6.Google Scholar
Gehring, WJ, Coles, MG, Meyer, DE, Donchin, E (1995). A brain potential manifestation of error-related processing. Electroencephalography and Clinical Neurophysiology 44, 261–272.Google Scholar
Gonner, S, Leonhart, R, Ecker, W (2008). The Obsessive–Compulsive Inventory-Revised (OCI-R): validation of the German version in a sample of patients with OCD, anxiety disorders, and depressive disorders. Journal of Anxiety Disorders 22, 734–749.CrossRefGoogle Scholar
Goodman, WK, Price, LH, Rasmussen, SA, Mazure, C, Fleischmann, RL, Hill, CL, Heninger, GR, Charney, DS (1989). The Yale–Brown Obsessive Compulsive Scale. I. Development, use, and reliability. Archives of General Psychiatry 46, 1006–1011.Google Scholar
Gratton, G, Coles, MG, Donchin, E (1992). Optimizing the use of information: strategic control of activation of responses. Journal of Experimental Psychology: General 121, 480–506.Google Scholar
Gründler, TO, Cavanagh, JF, Figueroa, CM, Frank, MJ, Allen, JJ (2009). Task-related dissociation in ERN amplitude as a function of obsessive–compulsive symptoms. Neuropsychologia 47, 1978–1987.Google Scholar
Grützmann, R, Riesel, A, Klawohn, J, Kathmann, N, Endrass, T (2014). Complementary modulation of N2 and CRN by conflict frequency. Psychophysiology 51, 761–772.Google Scholar
Hajcak, G, Franklin, ME, Foa, EB, Simons, RF (2008). Increased error-related brain activity in pediatric obsessive–compulsive disorder before and after treatment. American Journal of Psychiatry 165, 116–123.Google Scholar
Hand, I, Büttner-Westphal, H (1991). Die Yale–Brown Obsessive Compulsive Scale (Y-BOCS): ein halbstrukturiertes Interview zur Beurteilung des Schweregrades von Denk- und Handlungszwängen [The Yale–Brown Obsessive Compulsive Scale (Y-BOCS): a semi-structured interview for assessing severity of compulsive cognitions and behaviour. Verhaltenstherapie 1, 223–225.Google Scholar
Hautzinger, M, Keller, F, Kühner, C (2006). Beck Depressions-Inventar (BDI-II) [Beck Depression Inventory (BDI-II)] . Harcourt Test Services: Frankfurt.Google Scholar
Herrmann, MJ, Jacob, C, Unterecker, S, Fallgatter, AJ (2003). Reduced response-inhibition in obsessive–compulsive disorder measured with topographic evoked potential mapping. Psychiatry Research 120, 265–271.Google Scholar
Iannaccone, R, Hauser, TU, Staempfli, P, Walitza, S, Brandeis, D, Brem, S (2015). Conflict monitoring and error processing: new insights from simultaneous EEG-fMRI. NeuroImage 105, 395–407.Google Scholar
Kerns, JG (2006). Anterior cingulate and prefrontal cortex activity in an fMRI study of trial-to-trial adjustments on the Simon task. NeuroImage 33, 399–405.CrossRefGoogle Scholar
Kerns, JG, Cohen, JD, MacDonald, AW III, Cho, RY, Stenger, VA, Carter, CS (2004). Anterior cingulate conflict monitoring and adjustments in control. Science 303, 1023.Google Scholar
Keskin-Ergen, Y, Tükel, R, Aslantaş-Ertekin, B, Ertekin, E, Oflaz, S, Devrim-Üçok, M (2014). N2 and P3 potentials in early-onset and late-onset patients with obsessive–compulsive disorder. Depression and Anxiety 31, 997–1006.Google Scholar
Kim, MS, Kim, YY, Yoo, SY, Kwon, JS (2007). Electrophysiological correlates of behavioral response inhibition in patients with obsessive–compulsive disorder. Depression and Anxiety 24, 22–31.Google Scholar
Klawohn, J, Riesel, A, Grutzmann, R, Kathmann, N, Endrass, T (2014). Performance monitoring in obsessive–compulsive disorder: a temporo-spatial principal component analysis. Cognitive, Affective, and Behavioral Neuroscience 14, 983–995.Google Scholar
Kopp, B, Rist, F, Mattler, U (1996). N200 in the flanker task as a neurobehavioral tool for investigating executive control. Psychophysiology 33, 282–294.Google Scholar
Larson, MJ, Clawson, A, Clayson, PE, Baldwin, SA (2013). Cognitive conflict adaptation in generalized anxiety disorder. Biological Psychology 94, 408–418.Google Scholar
Larson, MJ, Clayson, PE, Clawson, A (2014). Making sense of all the conflict: a theoretical review and critique of conflict-related ERPs. International Journal of Psychophysiology 93, 283–297.Google Scholar
Leue, A, Lange, S, Beauducel, A (2012). Modulation of the conflict monitoring intensity: the role of aversive reinforcement, cognitive demand, and trait-BIS. Cognitive, Affective, and Behavioral Neuroscience 12, 287–307.Google Scholar
Leue, A, Weber, B, Beauducel, A (2014). How do working-memory-related demand, reasoning ability and aversive reinforcement modulate conflict monitoring? Frontiers in Human Neuroscience 8, 210.Google Scholar
Liu, Y, Gehring, WJ, Weissman, DH, Taylor, SF, Fitzgerald, KD (2012). Trial-by-trial adjustments of cognitive control following errors and response conflict are altered in pediatric obsessive compulsive disorder. Frontiers in Psychiatry 3, 41.Google Scholar
Mataix-Cols, D, Rosario-Campos, MC, Leckman, JF (2005). A multidimensional model of obsessive–compulsive disorder. American Journal of Psychiatry 162, 228–238.Google Scholar
Mathews, CA, Perez, VB, Delucchi, KL, Mathalon, DH (2012). Error-related negativity in individuals with obsessive–compulsive symptoms: toward an understanding of hoarding behaviors. Biological Psychology 89, 487–494.Google Scholar
Meyer, A, Weinberg, A, Klein, D, Hajcak, G (2011). The development of the error-related negativity (ERN) and its relationship with anxiety: evidence from 8 to 13 year-olds. Developmental Cognitive Neuroscience 2, 152–161.CrossRefGoogle Scholar
Mayr, U, Awh, E, Laurey, P (2003). Conflict adaptation effects in the absence of executive control. Nature Neuroscience 6, 450–452.Google Scholar
Montgomery, SA, Åsberg, M (1979). A new depression scale designed to be sensitive to change. British Journal of Psychiatry 134, 382–389.Google Scholar
Morault, PM, Bourgeois, M, Laville, J, Bensch, C, Paty, J (1997). Psychophysiological and clinical value of event-related potentials in obsessive–compulsive disorder. Biological Psychiatry 42, 46–56.CrossRefGoogle ScholarPubMed
Neumann, N, Schulte, R (1989). MADR-Skala zur psychometrischen Beurteilung depressiver Symptome (MADRS) [MADR Scale for Psychometric Assessment of Depressive Symptoms (MADRS)]. Perimed: Erlangen.Google Scholar
Nieuwenhuis, S, Stins, JF, Posthuma, D, Polderman, TJ, Boomsma, DI, de Geus, EJ (2006). Accounting for sequential trial effects in the flanker task: conflict adaptation or associative priming? Memory and Cognition 34, 1260–1272.Google Scholar
Penades, R, Catalan, R, Rubia, K, Andres, S, Salamero, M, Gasto, C (2007). Impaired response inhibition in obsessive compulsive disorder. European Psychiatry 22, 404–410.Google Scholar
Perrin, F, Pernier, J, Bertrand, O, Echallier, JF (1989). Spherical splines for scalp potential and current-density mapping. Electroencephalography and Clinical Neurophysiology 72, 184–187.Google Scholar
Pitman, RK (1987). A cybernetic model of obsessive–compulsive psychopathology. Comprehensive Psychiatry 28, 334–343.Google Scholar
Rachman, S, de Silva, P (1978). Abnormal and normal obsessions. Behaviour Research and Therapy 16, 233–248.Google Scholar
Ridderinkhof, KR, Ullsperger, M, Crone, EA, Nieuwenhuis, S (2004). The role of the medial frontal cortex in cognitive control. Science 306, 443.Google Scholar
Riesel, A, Endrass, T, Auerbach, LA, Kathmann, N (2015 a). Overactive performance monitoring as an endophenotype for obsessive–compulsive disorder: evidence from a treatment study. American Journal of Psychiatry 172, 665–673.Google Scholar
Riesel, A, Endrass, T, Kaufmann, C, Kathmann, N (2011). Overactive error-related brain activity as a candidate endophenotype for obsessive–compulsive disorder: evidence from unaffected first-degree relatives. American Journal of Psychiatry 168, 317–324.CrossRefGoogle ScholarPubMed
Riesel, A, Kathmann, N, Endrass, T (2014). Overactive performance monitoring in obsessive–compulsive disorder is independent of symptom expression. European Archives of Psychiatry and Clinical Neuroscience 264, 707–717.Google Scholar
Riesel, A, Richter, A, Kaufmann, C, Kathmann, N, Endrass, T (2015 b). Performance monitoring in obsessive–compulsive undergraduates: effects of task difficulty. Brain and Cognition 98, 35–42.CrossRefGoogle ScholarPubMed
Ruchsow, M, Reuter, K, Hermle, L, Ebert, D, Kiefer, M, Falkenstein, M (2007). Executive control in obsessive–compulsive disorder: event-related potentials in a Go/Nogo task. Journal of Neural Transmission 114, 1595–1601.CrossRefGoogle Scholar
Schmidt, JR, De Houwer, J (2011). Now you see it, now you don't: controlling for contingencies and stimulus repetitions eliminates the Gratton effect. Acta Psychologica 138, 176–186.Google Scholar
Schmidt, KH, Metzler, P (1992). WST. Wortschatztest. Beltz Test GmbH: Göttingen.Google Scholar
Steer, RA, Ball, R, Ranieri, WF, Beck, AT (1997). Further evidence for the construct validity of the Beck Depression Inventory-II with psychiatric outpatients. Psychological Reports 80, 443–446.Google Scholar
Stern, ER, Liu, Y, Gehring, WJ, Lister, JJ, Yin, G, Zhang, J, Fitzgerald, KD, Himle, JA, Abelson, JL, Taylor, SF (2010). Chronic medication does not affect hyperactive error responses in obsessive–compulsive disorder. Psychophysiology 47, 913–920.Google Scholar
Stern, ER, Taylor, SF (2014). Cognitive neuroscience of obsessive–compulsive disorder. Psychiatric Clinics of North America 37, 337–352.Google Scholar
Towey, J, Bruder, G, Tenke, C, Leite, P, DeCaria, C, Friedman, D, Hollander, E (1993). Event-related potential and clinical correlates of neurodysfunction in obsessive–compulsive disorder. Psychiatry Research 49, 167–181.Google Scholar
Ullsperger, M, Fischer, A, Nigbur, R, Endrass, T (2014). Neural mechanisms and temporal dynamics of performance monitoring. Trends in Cognitive Sciences 18, 259–267.Google Scholar
van Veen, V, Carter, CS (2002). The anterior cingulate as a conflict monitor: fMRI and ERP studies. Physiology and Behavior 77, 477–482.Google Scholar
Weinberg, A, Klein, DN, Hajcak, G (2012). Increased error-related brain activity distinguishes generalized anxiety disorder with and without comorbid major depressive disorder. Journal of Abnormal Psychology 121, 885–896.Google Scholar
Woolley, J, Heyman, I, Brammer, M, Frampton, I, McGuire, PK, Rubia, K (2008). Brain activation in paediatric obsessive compulsive disorder during tasks of inhibitory control. British Journal of Psychiatry 192, 25–31.Google Scholar
Yeung, N, Botvinick, MM, Cohen, JD (2004). The neural basis of error detection: conflict monitoring and the error-related negativity. Psychological Review 111, 931–959.Google Scholar