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Corticostriatothalamic reward prediction error signals and executive control in late-life depression

Published online by Cambridge University Press:  16 October 2014

A. Y. Dombrovski*
Affiliation:
Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
K. Szanto
Affiliation:
Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
L. Clark
Affiliation:
University of British Columbia, Vancouver, Canada
H. J. Aizenstein
Affiliation:
Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
H. W. Chase
Affiliation:
Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
C. F. Reynolds III
Affiliation:
Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
G. J. Siegle
Affiliation:
Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
*
*Address for correspondence: Dr A. Y. Dombrovski, 100 North Bellefield Ave, Room 742, Pittsburgh, PA 15213, USA. (Email: dombrovskia@upmc.edu)

Abstract

Background

Altered corticostriatothalamic encoding of reinforcement is a core feature of depression. Here we examine reinforcement learning in late-life depression in the theoretical framework of the vascular depression hypothesis. This hypothesis attributes the co-occurrence of late-life depression and poor executive control to prefrontal/cingulate disconnection by vascular lesions.

Method

Our fMRI study compared 31 patients aged ⩾60 years with major depression to 16 controls. Using a computational model, we estimated neural and behavioral responses to reinforcement in an uncertain, changing environment (probabilistic reversal learning).

Results

Poor executive control and depression each explained distinct variance in corticostriatothalamic response to unexpected rewards. Depression, but not poor executive control, predicted disrupted functional connectivity between the striatum and prefrontal cortex. White-matter hyperintensities predicted diminished corticostriatothalamic responses to reinforcement, but did not mediate effects of depression or executive control. In two independent samples, poor executive control predicted a failure to persist with rewarded actions, an effect distinct from depressive oversensitivity to punishment. The findings were unchanged in a subsample of participants with vascular disease. Results were robust to effects of confounders including psychiatric comorbidities, physical illness, depressive severity, and psychotropic exposure.

Conclusions

Contrary to the predictions of the vascular depression hypothesis, altered encoding of rewards in late-life depression is dissociable from impaired contingency learning associated with poor executive control. Functional connectivity and behavioral analyses point to a disruption of ascending mesostriatocortical reward signals in late-life depression and a failure of cortical contingency encoding in elderly with poor executive control.

Type
Original Articles
Copyright
Copyright © Cambridge University Press 2014 

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References

Aizenstein, HJ, Andreescu, C, Edelman, KL, Cochran, JL, Price, J, Butters, MA, Karp, J, Patel, M, Reynolds, CF III (2011). fMRI correlates of white matter hyperintensities in late-life depression. American Journal of Psychiatry 168, 10751082.Google Scholar
Alexopoulos, G, Kiosses, DN, Choi, SJ, Murphy, CF, Lim, KO (2002). Frontal white matter microstructure and treatment response in late-life depression: a preliminary study. American Journal of Psychiatry 159, 19291932.Google Scholar
Alexopoulos, GS, Hoptman, MJ, Kanellopoulos, D, Murphy, CF, Lim, KO, Gunning, FM (2012). Functional connectivity in the cognitive control network and the default mode network in late-life depression. Journal of Affective Disorders 139, 5665.Google Scholar
Alexopoulos, GS, Meyers, BS, Young, RC, Campbell, S, Silbersweig, D, Charlson, M (1997). 'Vascular depression' hypothesis. Archives of General Psychiatry 54, 915922.Google Scholar
APA (2000). Diagnostic and Statistical Manual of Mental Disorders, 4th edn (DSM-IV). American Psychiatric Association: Washington, DC.Google Scholar
Baldwin, R, Jeffries, S, Jackson, A, Sutcliffe, C, Thacker, N, Scott, M, Burns, A (2004). Treatment response in late-onset depression: relationship to neuropsychological, neuroradiological and vascular risk factors. Psychological Medicine 34, 125136.Google Scholar
Barch, DM, D'Angelo, G, Pieper, C, Wilkins, CH, Welsh-Bohmer, K, Taylor, W, Garcia, KS, Gersing, K, Doraiswamy, PM, Sheline, YI (2012). Cognitive improvement following treatment in late-life depression: relationship to vascular risk and age of onset. American Journal of Geriatric Psychiatry 20, 682690.Google Scholar
Bush, RR, Mosteller, F (1951). A mathematical model for simple learning. Psychological Review 58, 313323.Google Scholar
Cole, MW, Yarkoni, T, Repovs, G, Anticevic, A, Braver, TS (2012). Global connectivity of prefrontal cortex predicts cognitive control and intelligence. Journal of Neuroscience 32, 89888999.Google Scholar
Cox, RW (1996). AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical Research 29, 162173.Google Scholar
Dombrovski, AY, Clark, L, Siegle, GJ, Butters, MA, Ichikawa, N, Sahakian, BJ, Szanto, K (2010). Reward/punishment reversal learning in older suicide attempters. American Journal of Psychiatry 167, 699707.Google Scholar
Dombrovski, AY, Szanto, K, Clark, L, Reynolds, CF, Siegle, GJ (2013). Reward signals, attempted suicide, and impulsivity in late-life depression. JAMA Psychiatry 70, 10201030.Google Scholar
Eshel, N, Roiser, JP (2010). Reward and punishment processing in depression. Biological Psychiatry 68, 118124.Google Scholar
First, MSR, Gibbon, M, Williams, JBW (1995). Structured clinical interview for DSM-IV Axis I Disorders – Patient edition (SCID-I/P). Version 2.0.Google Scholar
Forman, SD, Cohen, JD, Fitzgerald, M, Eddy, WF, Mintun, MA, Noll, DC (1995). Improved assessment of significant activation in functional magnetic resonance imaging (fMRI): use of a cluster-size threshold. Magnetic Resonance in Medicine 33, 636647.Google Scholar
Glimcher, PW (2011). Understanding dopamine and reinforcement learning: the dopamine reward prediction error hypothesis. Proceedings of the National Academy of Sciences USA 108 (Suppl. 3), 1564715654.Google Scholar
Gradin, VB, Kumar, P, Waiter, G, Ahearn, T, Stickle, C, Milders, M, Reid, I, Hall, J, Steele, JD (2011). Expected value and prediction error abnormalities in depression and schizophrenia. Brain 134, 17511764.Google Scholar
Hamilton, M (1960). A rating scale for depression. Journal of Neurology, Neurosurgery and Psychiatry 23, 5662.Google Scholar
Histed, MH, Pasupathy, A, Miller, EK (2009). Learning substrates in the primate prefrontal cortex and striatum: sustained activity related to successful actions. Neuron 63, 244253.Google Scholar
Hornak, J, O'Doherty, J, Bramham, J, Rolls, ET, Morris, RG, Bullock, PR, Polkey, CE (2004). Reward-related reversal learning after surgical excisions in orbito-frontal or dorsolateral prefrontal cortex in humans. Journal of Cognitive Neuroscience 16, 463478.Google Scholar
Jocham, G, Klein, TA, Neumann, J, von Cramon, DY, Reuter, M, Ullsperger, M (2009). Dopamine DRD2 polymorphism alters reversal learning and associated neural activity. Journal of Neuroscience 29, 36953704.Google Scholar
Kumar, P, Waiter, G, Ahearn, T, Milders, M, Reid, I, Steele, JD (2008). Abnormal temporal difference reward-learning signals in major depression. Brain 131, 20842093.Google Scholar
Mast, BT, Yochim, B, MacNeill, SE, Lichtenberg, PA (2004). Risk factors for geriatric depression: the importance of executive functioning within the vascular depression hypothesis. Journals of Gerontology Series A: Biological Sciences and Medical Sciences 59, 12901294.Google Scholar
Mattis, S (1988). Dementia rating scale (DRS): professional manual. In Psychological Assessment Resources. Odessa, FL.Google Scholar
McLaren, DG, Ries, ML, Xu, G, Johnson, SC (2012). A generalized form of context-dependent psychophysiological interactions (GPPI): a comparison to standard approaches. Neuroimage 61, 12771286.CrossRefGoogle ScholarPubMed
Middleton, FA, Strick, PL (2000). Basal ganglia and cerebellar loops: motor and cognitive circuits. Brain Research: Brain Research Review 31, 236250.Google Scholar
Miller, GA, Chapman, JP (2001). Misunderstanding analysis of covariance. Journal of Abnormal Psychology 110, 40.Google Scholar
Miller, MD, Paradis, CF, Houck, PR, Mazumdar, S, Stack, JA, Rifai, AH, Mulsant, B, Reynolds, CF III (1992). Rating chronic medical illness burden in geropsychiatric practice and research: application of the cumulative illness rating scale. Psychiatry Research 41, 237248.Google Scholar
Montague, PR, Dayan, P, Sejnowski, TJ (1996). A framework for mesencephalic dopamine systems based on predictive hebbian learning. Journal of Neuroscience 16, 19361947.Google Scholar
Murphy, FC, Michael, A, Robbins, TW, Sahakian, BJ (2003). Neuropsychological impairment in patients with major depressive disorder: the effects of feedback on task performance. Psychological Medicine 33, 455467.Google Scholar
Nys, GM, van Zandvoort, MJ, van der Worp, HB, de Haan, EH, de Kort, PL, Jansen, BP, Kappelle, LJ (2006). Early cognitive impairment predicts long-term depressive symptoms and quality of life after stroke. Journal of Neurological Science 247, 149156.Google Scholar
O'Doherty, J, Dayan, P, Schultz, J, Deichmann, R, Friston, K, Dolan, RJ (2004). Dissociable roles of ventral and dorsal striatum in instrumental conditioning. Science 304, 452454.CrossRefGoogle ScholarPubMed
O'Doherty, JP, Dayan, P, Friston, K, Critchley, H, Dolan, RJ (2003). Temporal difference models and reward-related learning in the human brain. Neuron 38, 329337.Google Scholar
Pasupathy, A, Miller, EK (2005). Different time courses of learning-related activity in the prefrontal cortex and striatum. Nature 433, 873876.Google Scholar
Rescorla, RA, Wagner, AR (1972). A theory of pavlovian conditioning: variations in the effectiveness of reinforcement and nonreinforcement. In Classical Conditioning II (ed. Black, A.H. and Prokasy, W.F.), pp. 6499. Appleton-Century-Crofts: New York, London.Google Scholar
Robbins, TW (2007). Shifting and stopping: fronto-striatal substrates, neurochemical modulation and clinical implications. Philosophical Transactions of the Royal Society of London, Series B: Biological Sciences 362, 917932.CrossRefGoogle ScholarPubMed
Royall, DR, Mahurin, RK, Gray, KF (1992). Bedside assessment of executive cognitive impairment: the executive interview. Journal of American Geriatrics Society 40, 12211226.Google Scholar
Rudebeck, PH, Saunders, RC, Prescott, AT, Chau, LS, Murray, EA (2013). Prefrontal mechanisms of behavioral flexibility, emotion regulation and value updating. Nature Neuroscience 16, 11401145.Google Scholar
Sackeim, HA (2001). The definition and meaning of treatment-resistant depression. Journal of Clinical Psychiatry 62 (Suppl 16), 1017.Google Scholar
Schlagenhauf, F, Rapp, MA, Huys, QJ, Beck, A, Wustenberg, T, Deserno, L, Buchholz, HG, Kalbitzer, J, Buchert, R, Bauer, M, Kienast, T, Cumming, P, Plotkin, M, Kumakura, Y, Grace, AA, Dolan, RJ, Heinz, A (2012). Ventral striatal prediction error signaling is associated with dopamine synthesis capacity and fluid intelligence. Human Brain Mapping 34, 14901499.Google Scholar
Schultz, W, Dayan, P, Montague, PR (1997). A neural substrate of prediction and reward. Science 275, 15931599.Google Scholar
Seligman, ME, Maier, SF (1967). Failure to escape traumatic shock. Journal of Experimental Psychology 74, 19.Google Scholar
Skinner, BF (1938). The Behavior of Organisms; an Experimental Analysis. Appleton-Century Company: New York, London.Google Scholar
Sneed, JR, Roose, SP, Keilp, JG, Krishnan, KR, Alexopoulos, GS, Sackeim, HA (2007). Response inhibition predicts poor antidepressant treatment response in very old depressed patients. American Journal of Geriatric Psychiatry 15, 553563.Google Scholar
Snyder, HR (2013). Major depressive disorder is associated with broad impairments on neuropsychological measures of executive function: a meta-analysis and review. Psychological Bulletin 139, 81132.Google Scholar
Sutton, RS, Barto, AG (1998). Reinforcement Learning: An Introduction. MIT Press: Cambridge, MA.Google Scholar
Taylor, WD, Aizenstein, HJ, Alexopoulos, GS (2013). The vascular depression hypothesis: mechanisms linking vascular disease with depression. Molecular Psychiatry 18, 963974.Google Scholar
Wilson, RS, Nag, S, Boyle, PA, Hizel, LP, Yu, L, Buchman, AS, Shah, RC, Schneider, JA, Arnold, SE, Bennett, DA (2013). Brainstem aminergic nuclei and late-life depressive symptoms. JAMA Psychiatry 70, 13201328.Google Scholar
Wu, M, Rosano, C, Butters, M, Whyte, E, Nable, M, Crooks, R, Meltzer, CC, Reynolds, CF III, Aizenstein, HJ (2006). A fully automated method for quantifying and localizing white matter hyperintensities on MR images. Psychiatry Research 148, 133142.Google Scholar