Hostname: page-component-8448b6f56d-gtxcr Total loading time: 0 Render date: 2024-04-19T20:17:21.674Z Has data issue: false hasContentIssue false

Brain mechanisms of anxiety's effects on cognitive control in major depressive disorder

Published online by Cambridge University Press:  13 June 2016

N. P. Jones*
Affiliation:
Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
H. W. Chase
Affiliation:
Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
J. C. Fournier
Affiliation:
Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
*
*Address for correspondence: N. P. Jones, Western Psychiatric Institute and Clinic, 3811 O'Hara Street, Pittsburgh, PA 15216, USA. (Email: jonesnp@upmc.edu)

Abstract

Background

Adults with major depressive disorder (MDD) demonstrate increased susceptibility to interfering effects of anxiety on cognitive control; although under certain conditions adults with MDD are able to compensate for these effects. The brain mechanisms that may facilitate the ability to compensate for anxiety either via the recruitment of additional cognitive resources or via the regulation of interference from anxiety remain largely unknown. To clarify these mechanisms, we examined the effects of anxiety on brain activity and amygdala–prefrontal functional connectivity in adults diagnosed with MDD.

Method

A total of 22 unmedicated adults with MDD and 18 healthy controls (HCs) performed the Tower of London task under conditions designed to induce anxiety, while undergoing a functional magnetic resonance imaging assessment.

Results

During the easy condition, the MDD group demonstrated equivalent planning accuracy, longer planning times, elevated amygdala activity and left rostrolateral prefrontal cortex (RLPFC) hyperactivity relative to HCs. Anxiety mediated observed group differences in planning times, as well as differences in amygdala activation, which subsequently mediated observed differences in RLPFC activation. During the easy condition, the MDD group also demonstrated increased negative amygdala–dorsolateral prefrontal cortex (DLPFC) connectivity which correlated with improved planning accuracy. During the hard condition, HCs demonstrated greater DLPFC activation and stronger negative amygdala–DLPFC connectivity, which was unrelated to planning accuracy.

Conclusions

Our results suggest that persons with MDD compensate for anxiety-related limbic activation during low-load cognitive tasks by recruiting additional RLPFC activation and through increased inhibitory amygdala–DLPFC communication. Targeting these neural mechanisms directly may improve cognitive functioning in MDD.

Type
Original Articles
Copyright
Copyright © Cambridge University Press 2016 

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

Abend, R, Dan, O, Maoz, K, Raz, S, Bar-Haim, Y (2014). Reliability, validity and sensitivity of a computerized visual analog scale measuring state anxiety. Journal of Behavior Therapy and Experimental Psychiatry 45, 447453.Google Scholar
Adhikari, A, Lerner, TN, Finkelstein, J, Pak, S, Jennings, JH, Davidson, TJ, Ferenczi, E, Gunaydin, LA, Mirzabekov, JJ, Ye, L, Kim, S-Y, Lei, A, Deisseroth, K (2015). Basomedial amygdala mediates top-down control of anxiety and fear. Nature 527, 179185.Google Scholar
Anticevic, A, Barch, DM, Repovs, G (2010). Resisting emotional interference: brain regions facilitating working memory performance during negative distraction. Cognitive, Affective, and Behavioral Neuroscience 10, 159173.Google Scholar
Baron, RM, Kenny, DA (1986). The moderator–mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology 51, 11731182.Google Scholar
Bishop, SJ (2009). Trait anxiety and impoverished prefrontal control of attention. Nature Neuroscience 12, 9298.Google Scholar
Clarke, R, Johnstone, T (2013). Prefrontal inhibition of threat processing reduces working memory interference. Frontiers in Human Neuroscience 7, 228.Google Scholar
Cox, R (1996). AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Computational Biomedical Research 29, 162173.Google Scholar
Davey, HM, Barratt, AL, Butow, PN, Deeks, JJ (2007). A one-item question with a Likert or visual analog scale adequately measured current anxiety. Journal of Clinical Epidemiology 60, 356360.Google Scholar
Degras, D, Lindquist, MA (2014). A hierarchical model for simultaneous detection and estimation in multi-subject fMRI studies. NeuroImage 98, 6172.Google Scholar
Desrochers, TM, Chatham, CH, Badre, D (2015). The necessity of rostrolateral prefrontal cortex for higher-level sequential behavior. Neuron 87, 13571368.Google Scholar
Dolcos, F, Kragel, P, Wang, L, McCarthy, G (2006). Role of the inferior frontal cortex in coping with distracting emotions. Neuroreport 17, 15911594.Google Scholar
Dolcos, F, McCarthy, G (2006). Brain systems mediating cognitive interference by emotional distraction. Journal of Neuroscience 26, 20722079.Google Scholar
Erk, S, Mikschl, A, Stier, S, Ciaramidaro, A, Gapp, V, Weber, B, Walter, H (2010). Acute and sustained effects of cognitive emotion regulation in major depression. Journal of Neuroscience 30, 1572615734.Google Scholar
Etkin, A, Schatzberg, AF (2011). Common abnormalities and disorder-specific compensation during implicit regulation of emotional processing in generalized anxiety and major depressive disorders. American Journal of Psychiatry 168, 968978.Google Scholar
Etkin, A, Wager, TD (2007). Functional neuroimaging of anxiety: a meta-analysis of emotional processing in PTSD, social anxiety disorder, and specific phobia. American Journal of Psychiatry 164, 14761488.Google Scholar
Fales, C, Barch, D, Burgess, G, Schaefer, A, Mennin, D, Gray, J, Braver, T (2008). Anxiety and cognitive efficiency: differential modulation of transient and sustained neural activity during a working memory task. Cognitive, Affective, and Behavioral Neuroscience 8, 239253.Google Scholar
First, MB, Spitzer, RL, Gibbon, M, Williams, JB (1996). Structured Clinical Interview for DSM-IV Axis I Disorders Patient Edition. Biometrics Research Department, New York State Psychiatric Institute: New York.Google Scholar
Fissell, K, Tseytlin, E, Cunningham, D, Carter, CS, Schneider, W, Cohen, JD (2003). Fiswidgets: a graphical computing environment for neuroimaging analysis. Neuroinformatics 1, 111125.Google Scholar
Forster, S, Nunez Elizalde, AO, Castle, E, Bishop, SJ (2015). Unraveling the anxious mind: anxiety, worry, and frontal engagement in sustained attention versus off-task processing. Cerebral Cortex 25, 609618.Google Scholar
Godard, J, Grondin, S, Baruch, P, Lafleur, MF (2011). Psychosocial and neurocognitive profiles in depressed patients with major depressive disorder and bipolar disorder. Psychiatry Research 190, 244252.Google Scholar
Gold, AL, Morey, RA, McCarthy, G (2015). Amygdala–prefrontal cortex functional connectivity during threat-induced anxiety and goal distraction. Biological Psychiatry 77, 394403.Google Scholar
Guthrie, D, Buchwald, JS (1991). Significance testing of difference potentials. Psychophysiology 28, 240244.Google Scholar
Harvey, P-O, Fossati, P, Pochon, J-B, Levy, R, LeBastard, G, Lehéricy, S, Allilaire, J-F, Dubois, B (2005). Cognitive control and brain resources in major depression: an fMRI study using the n-back task. NeuroImage 26, 860869.Google Scholar
Hayes, AF, Preacher, KJ (2010). Quantifying and testing indirect effects in simple mediation models when the constituent paths are nonlinear. Multivariate Behavioral Research 45, 627660.Google Scholar
Jones, NP, Siegle, G, Mandell, D (2015). Motivational and emotional influences on cognitive control in depression: a pupillometry study. Cognitive, Affective, and Behavioral Neuroscience 15, 263275.Google Scholar
Kalu, U, Sexton, C, Loo, C, Ebmeier, K (2012). Transcranial direct current stimulation in the treatment of major depression: a meta-analysis. Psychological Medicine 42, 17911800.Google Scholar
Kim, C, Kroger, JK, Calhoun, VD, Clark, VP (2015). The role of the frontopolar cortex in manipulation of integrated information in working memory. Neuroscience Letters 595, 2529.Google Scholar
Kraemer, HC, Wilson, GT, Fairburn, CG, Agras, WS (2002). Mediators and moderators of treatment effects in randomized clinical trials. Archives of General Psychiatry 59, 877883.Google Scholar
MacKinnon, DP, Lockwood, CM, Hoffman, JM, West, SG, Sheets, V (2002). A comparison of methods to test mediation and other intervening variable effects. Psychological Methods 7, 83104.Google Scholar
McCall, JG, Al-Hasani, R, Siuda, ER, Hong, DY, Norris, AJ, Ford, CP, Bruchas, MR (2015). CRH engagement of the locus coeruleus noradrenergic system mediates stress-induced anxiety. Neuron 87, 605620.Google Scholar
Minzenberg, MJ, Watrous, AJ, Yoon, JH, Ursu, S, Carter, CS (2008). Modafinil shifts human locus coeruleus to low-tonic, high-phasic activity during functional MRI. Science 322, 17001702.Google Scholar
Muthén, LK, Muthén, BO (1998–2006). Mplus User's Guide, 4th edn. Muthén & Muthén: Los Angeles, CA.Google Scholar
Nelson, HE, Willison, JR (1991). National Adult Reading Test (NART): Test Manual. nfer-Nelson Publishing Co.: Slough, UK.Google Scholar
Preacher, KJ, Hayes, AF (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods 40, 879891.Google Scholar
Ramasubbu, R, Konduru, N, Cortese, F, Bray, S, Gaxiola-Valdez, I, Goodyear, B (2014). Reduced intrinsic connectivity of amygdala in adults with major depressive disorder. Frontiers in Psychiatry 5, 17.Google Scholar
Ray, RD, Zald, DH (2012). Anatomical insights into the interaction of emotion and cognition in the prefrontal cortex. Neuroscience and Biobehavioral Reviews 36, 479501.Google Scholar
Shallice, T (1982). Specific impairments of planning. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 298, 199209.Google Scholar
Siegle, GJ, Steinhauer, SR, Stenger, VA, Konecky, R, Carter, CS (2003). Use of concurrent pupil dilation assessment to inform interpretation and analysis of fMRI data. NeuroImage 20, 114124.Google Scholar
Siegle, GJ, Steinhauer, SR, Thase, ME, Stenger, VA, 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, 693707.Google Scholar
Siegle, GJ, Thompson, WS, Carter, CS, Steinhauer, SR, Thase, ME (2007). Increased amygdala and decreased dorsolateral prefrontal BOLD responses in unipolar depression: related and independent features. Biological Psychiatry 61, 198209.Google Scholar
Stewart, WF, Ricci, JA, Chee, E, Hahn, SR, Morganstein, D (2003). Cost of lost productive work time among US workers with depression. JAMA Psychiatry 289, 31353144.Google Scholar
Tang, Y, Kong, L, Wu, F, Womer, F, Jiang, W, Cao, Y, Ren, L, Wang, J, Fan, G, Blumberg, H (2013). Decreased functional connectivity between the amygdala and the left ventral prefrontal cortex in treatment-naive patients with major depressive disorder: a resting-state functional magnetic resonance imaging study. Psychological Medicine 43, 19211927.Google Scholar
Thibodeau, R, Jorgensen, RS, Kim, S (2006). Depression, anxiety, and resting frontal EEG asymmetry: a meta-analytic review. Journal of Abnormal Psychology 115, 715729.Google Scholar
Tovote, P, Fadok, JP, Luthi, A (2015). Neuronal circuits for fear and anxiety. Nature Reviews. Neuroscience 16, 317331.Google Scholar
Trivedi, MH, Greer, TL (2014). Cognitive dysfunction in unipolar depression: implications for treatment. Journal of Affective Disorders 152–154, 1927.Google Scholar
Trujillo, JP, Gerrits, NJ, Vriend, C, Berendse, HW, van den Heuvel, OA, van der Werf, YD (2015). Impaired planning in Parkinson's disease is reflected by reduced brain activation and connectivity. Human Brain Mapping 36, 37033715.Google Scholar
Unterrainer, JM, Rahm, B, Kaller, CP, Ruff, CC, Spreer, J, Krause, BJ, Schwarzwald, R, Hautzel, H, Halsband, U (2004). When planning fails: individual differences and error-related brain activity in problem solving. Cerebral Cortex 14, 13901397.Google Scholar
Vytal, K, Cornwell, B, Arkin, N, Grillon, C (2012). Describing the interplay between anxiety and cognition: from impaired performance under low cognitive load to reduced anxiety under high load. Psychophysiology 49, 842852.Google Scholar
Wagner, G, Koch, K, Reichenbach, JR, Sauer, H, Schlösser, RGM (2006). The special involvement of the rostrolateral prefrontal cortex in planning abilities: an event-related fMRI study with the Tower of London paradigm. Neuropsychologia 44, 23372347.Google Scholar
Woods, RP, Cherry, SR, Mazzoitta, JC (1992). Rapid automated algorithm for aligning and reslicing PET images. Journal of Computer Assisted Tomography 16, 620633.Google Scholar
Yerkes, RM, Dodson, JD (1908). The relation of strength of stimulus to rapidity of habit-formation. Journal of Comparative Neurology and Psychology 18, 459482.Google Scholar
Yohai, VJ (1987). High breakdown-point and high efficiency robust estimates for regression. Annals of Statistics 15, 642656.Google Scholar
Yun, R, Krystal, J, Mathalon, D (2010). Working memory overload: fronto-limbic interactions and effects on subsequent working memory function. Brain Imaging and Behavior 4, 96108.Google Scholar
Supplementary material: File

Jones supplementary material

Jones supplementary material 1

Download Jones supplementary material(File)
File 1.4 MB