Corticostriatothalamic reward prediction error signals and executive control in late-life depression
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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.
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Depressed patients exhibit both hyposensitivity to rewards and oversensitivity to punishments, attributed to alterations in mesolimbic prediction error signaling, and may exhibit a constrained option space ('tunnel vision') during decision-making.
“For instance, depressed patients exhibit both hyposensitivity to rewards and oversensitivity to punishments 96,97 , attributed to alterations in mesolimbic prediction error signaling 97-99 , and may exhibit a constrained option space ('tunnel vision') during decision-making 100 .”
From Neuroplasticity in cognitive and psychological mechanisms of depression: an integrative model (2020) · cites this paper as [96]
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Experimental — accuracy not yet measuredIn late-life depression, MDD patients showed significantly reduced increase of functional connectivity between the striatum and left operculoinsular cortex, right ventrolateral PFC, temporal gyrus, thalamus, and PCC in response to unpredicted reward vs unpredicted punishment compared with age-matched nondepressed controls.
“In late-life depression, MDD patients showed significantly reduced increase of functional connectivity between the striatum and operculoinsular cortex (left), the ventrolateral PFC (right), temporal gyrus, thalamus, and posterior cingulate cortex (PCC) in response to unpredicted reward vs unpredicted punishment compared with age-matched nondepressed controls (Dombrovski et al., 2015).”
From Circuit Mechanisms of Reward, Anhedonia, and Depression (2019) · cites this paper as Dombrovski et al., 2015
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Cites
Diagnostic and Statistical Manual of Mental Disorders
Williams, J. B. W., & First, M. B. (2013) · Oxford University Press eBooks · doi:10.1093/acrefore/9780199975839.013.104 · Cited by 109,841 · see citing works
Hamilton, M. (1960) · Journal of Neurology, Neurosurgery & Psychiatry · doi:10.1136/jnnp.23.1.56 · Cited by 32,817 · see citing works
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225 · see citing works
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,263 · see citing works
AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages
Cox, R. W. (1996) · Computers and Biomedical Research · doi:10.1006/cbmr.1996.0014 · Cited by 10,913 · see citing works
A neural substrate of prediction and reward
Schultz, W., Dayan, P., & Montague, P. R. (1997) · Science · doi:10.1126/science.275.5306.1593 · Cited by 9,388 · see citing works
Rescorla, R. A. (1972) · Medical Entomology and Zoology · Cited by 7,268 · see citing works
The Behavior of Organisms: An Experimental Analysis
Jones, F. N., & Skinner, B. F. (1939) · The American Journal of Psychology · doi:10.2307/1416495 · Cited by 4,388 · see citing works
Forman, S. D., Cohen, J. D., Fitzgerald, M., Eddy, W. F., Mintun, M. A., & Noll, D. C. (1995) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910330508 · Cited by 3,202 · see citing works
Skinner, B. F. (1938) · Cited by 3,184 · see citing works
Psychological Assessment Resources
Abidin, R. R. (1995) · Medical Entomology and Zoology · Cited by 3,140 · see citing works
Misunderstanding analysis of covariance.
Miller, G. A., & Chapman, J. P. (2001) · Journal of Abnormal Psychology · doi:10.1037/0021-843x.110.1.40 · Cited by 2,136 · see citing works
Dissociable Roles of Ventral and Dorsal Striatum in Instrumental Conditioning
O'Doherty, J., Dayan, P., Schultz, J., Deichmann, R., Friston, K., & Dolan, R. J. (2004) · Science · doi:10.1126/science.1094285 · Cited by 2,088 · see citing works
A framework for mesencephalic dopamine systems based on predictive Hebbian learning
Montague, P., Dayan, P., & Sejnowski, T. (1996) · The Journal of Neuroscience · doi:10.1523/jneurosci.16-05-01936.1996 · Cited by 2,066 · see citing works
Miller, M. D., Paradis, C. F., Houck, P. R., Mazumdar, S., Stack, J. A., Rifai, A. H., Mulsant, B., & Reynolds, C. F. (1992) · Psychiatry Research · doi:10.1016/0165-1781(92)90005-n · Cited by 1,990 · see citing works
Self-citationFailure to escape traumatic shock.
Seligman, M. E., & Maier, S. F. (1967) · Journal of Experimental Psychology · doi:10.1037/h0024514 · Cited by 1,981 · cited 1× · see citing works
“This inability to learn reward contingencies is depressogenic: a potentially controllable environment becomes uncontrollable, akin to that found in learned helplessness experiments (Seligman & Maier, 1967).”
Basal ganglia and cerebellar loops: motor and cognitive circuits
Middleton, F. (2000) · Brain Research Reviews · doi:10.1016/s0165-0173(99)00040-5 · Cited by 1,940 · see citing works
'Vascular Depression' Hypothesis
Alexopoulos, G. S. (1997) · Archives of General Psychiatry · doi:10.1001/archpsyc.1997.01830220033006 · Cited by 1,843 · see citing works
Snyder, H. R. (2013) · Psychological Bulletin · doi:10.1037/a0028727 · Cited by 1,667 · see citing works
Temporal difference models and reward-related learning in the human brain
O'Doherty, J. P., Dayan, P., Friston, K., Critchley, H., & Dolan, R. J. (2003) · Neuron · doi:10.1016/s0896-6273(03)00169-7 · Cited by 1,522 · see citing works
McLaren, D. G., Ries, M. L., Xu, G., & Johnson, S. C. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.03.068 · Cited by 1,445 · see citing works
Understanding dopamine and reinforcement learning: the dopamine reward prediction error hypothesis
Glimcher, P. W. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1014269108 · Cited by 955 · see citing works
The vascular depression hypothesis: mechanisms linking vascular disease with depression
Taylor, W. D., Aizenstein, H. J., & Alexopoulos, G. S. (2013) · Molecular Psychiatry · doi:10.1038/mp.2013.20 · Cited by 923 · see citing works
Self-citation(2001) · PubMed · Cited by 780 · see citing works
Different time courses of learning-related activity in the prefrontal cortex and striatum.
Pasupathy, A., & Miller, E. K. (2005) · Nature · doi:10.1038/nature03287 · Cited by 712 · see citing works
Global Connectivity of Prefrontal Cortex Predicts Cognitive Control and Intelligence
Cole, M. W., Yarkoni, T., Repovs, G., Anticevic, A., & Braver, T. S. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.0536-12.2012 · Cited by 613 · see citing works
Hornak, J., O'Doherty, J., Bramham, J., Rolls, E. T., Morris, R. G., Bullock, P. R., & Polkey, C. E. (2004) · Journal of Cognitive Neuroscience · doi:10.1162/089892904322926791 · Cited by 603 · see citing works
Bedside Assessment of Executive Cognitive Impairment: The Executive Interview
Royall, D. R., Mahurin, R. K., & Gray, K. F. (1992) · Journal of the American Geriatrics Society · doi:10.1111/j.1532-5415.1992.tb03646.x · Cited by 595 · see citing works
A mathematical model for simple learning.
Bush, R. R., & Mosteller, F. C. (1951) · Psychological Review · doi:10.1037/h0054388 · Cited by 590 · see citing works
Reward and Punishment Processing in Depression
Eshel, N., & Roiser, J. P. (2010) · Biological Psychiatry · doi:10.1016/j.biopsych.2010.01.027 · Cited by 575 · see citing works
Expected value and prediction error abnormalities in depression and schizophrenia
Gradin, V. B., Kumar, P., Waiter, G., Ahearn, T., Stickle, C., Milders, M., Reid, I., Hall, J., & Steele, J. D. (2011) · Brain · doi:10.1093/brain/awr059 · Cited by 485 · see citing works
Alexopoulos, G. S., Hoptman, M. J., Kanellopoulos, D., Murphy, C. F., Lim, K. O., & Gunning, F. M. (2012) · Journal of Affective Disorders · doi:10.1016/j.jad.2011.12.002 · Cited by 437 · see citing works
Robbins, T. W. (2007) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2007.2097 · Cited by 413 · see citing works
Prefrontal mechanisms of behavioral flexibility, emotion regulation and value updating
Rudebeck, P. H., Saunders, R. C., Prescott, A. T., Chau, L. S., & Murray, E. A. (2013) · Nature Neuroscience · doi:10.1038/nn.3440 · Cited by 383 · see citing works
Abnormal temporal difference reward-learning signals in major depression
Kumar, P., Waiter, G., Ahearn, T., Milders, M., Reid, I., & Steele, J. D. (2008) · Brain · doi:10.1093/brain/awn136 · Cited by 374 · see citing works
Alexopoulos, G. S., Kiosses, D. N., Choi, S. J., Murphy, C. F., & Lim, K. O. (2002) · American Journal of Psychiatry · doi:10.1176/appi.ajp.159.11.1929 · Cited by 307 · see citing works
MURPHY, F. C., MICHAEL, A., ROBBINS, T. W., & SAHAKIAN, B. J. (2003) · Psychological Medicine · doi:10.1017/s0033291702007018 · Cited by 298 · see citing works
Reward Signals, Attempted Suicide, and Impulsivity in Late-Life Depression
Dombrovski, A. Y., Szanto, K., Clark, L., Reynolds, C. F., & Siegle, G. J. (2013) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2013.75 · Cited by 289 · see citing works
Self-citationEarly cognitive impairment predicts long-term depressive symptoms and quality of life after stroke
Nys, G. M. S., van Zandvoort, M. J. E., van der Worp, H. B., de Haan, E. H. F., de Kort, P. L. M., Jansen, B. P. W., & Kappelle, L. J. (2006) · Journal of the Neurological Sciences · doi:10.1016/j.jns.2006.04.005 · Cited by 288 · see citing works
Chase, H. W., Kumar, P., Eickhoff, S. B., & Dombrovski, A. Y. (2015) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-015-0338-7 · Cited by 225 · see citing works
Self-citationA fully automated method for quantifying and localizing white matter hyperintensities on MR images
Wu, M., Rosano, C., Butters, M. A., Whyte, E. M., Nable, M., Crooks, R., MELTZER, C. C., Reynolds, C. F., & Aizenstein, H. J. (2006) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2006.09.003 · Cited by 194 · see citing works
Self-citationReward/punishment reversal learning in older suicide attempters
Dombrovski, A. Y., Clark, L., Siegle, G. J., Butters, M. A., Ichikawa, N., Sahakian, B. J., & Szanto, K. (2010) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2009.09030407 · Cited by 193 · see citing works
Self-citationHisted, M. H., Pasupathy, A., & Miller, E. K. (2009) · Neuron · doi:10.1016/j.neuron.2009.06.019 · Cited by 188 · see citing works
Dopamine DRD2 Polymorphism Alters Reversal Learning and Associated Neural Activity
Jocham, G., Klein, T. A., Neumann, J., von Cramon, D. Y., Reuter, M., & Ullsperger, M. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.5195-08.2009 · Cited by 178 · see citing works
Response Inhibition Predicts Poor Antidepressant Treatment Response in Very Old Depressed Patients
Sneed, J. R., Roose, S. P., Keilp, J. G., Krishnan, K. R. R., Alexopoulos, G. S., & Sackeim, H. A. (2007) · The American Journal of Geriatric Psychiatry · doi:10.1097/jgp.0b013e3180302513 · Cited by 146 · see citing works
Baldwin, R., Jeffries, S., Jackson, A., Sutcliffe, C., Thacker, N. A., Scott, M. L. J., & Burns, A. S. (2004) · Psychological Medicine · doi:10.1017/s0033291703008870 · Cited by 105 · see citing works
Schlagenhauf, F., Rapp, M. A., Huys, Q. J. M., Beck, A., Wüstenberg, T., Deserno, L., Buchholz, H., Kalbitzer, J., Buchert, R., Bauer, M., et al. (2012) · Human Brain Mapping · doi:10.1002/hbm.22000 · Cited by 103 · see citing works
fMRI Correlates of White Matter Hyperintensities in Late-Life Depression
Aizenstein, H. J., Andreescu, C., Edelman, K. L., Cochran, J. L., Price, J. C., Butters, M. A., Karp, J. F., Patel, M. J., & Reynolds, C. F. (2011) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2011.10060853 · Cited by 103 · see citing works
Self-citationBarch, D. M., D'Angelo, G., Pieper, C., Wilkins, C. H., Welsh-Bohmer, K., Taylor, W., Garcia, K. S., Gersing, K., Doraiswamy, P. M., & Sheline, Y. I. (2012) · The American Journal of Geriatric Psychiatry · doi:10.1097/jgp.0b013e318246b6cb · Cited by 64 · see citing works
Brainstem Aminergic Nuclei and Late-Life Depressive Symptoms
Wilson, R. S., Nag, S., Boyle, P. A., Hizel, L. P., Yu, L., Buchman, A. S., Shah, R. C., Schneider, J. A., Arnold, S. E., & Bennett, D. A. (2013) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2013.2224 · Cited by 58 · see citing works
Mast, B. T., Yochim, B. P., MacNeill, S. E., & Lichtenberg, P. A. (2004) · The Journals of Gerontology Series A · doi:10.1093/gerona/59.12.1290 · Cited by 47 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- First MSR Gibbon M Williams JBW 1995 Structured clinical interview for DSM-IV Axis I Disorders - Patient edition (SCID-I/P) Version 2.0 ed
Cited by
Sorted by citations
Neuroplasticity in cognitive and psychological mechanisms of depression: an integrative model
Price, R. B., & Duman, R. (2020) · Molecular Psychiatry · doi:10.1038/s41380-019-0615-x · Cited by 721 · see citing works
Circuit Mechanisms of Reward, Anhedonia, and Depression
Höflich, A., Michenthaler, P., Kasper, S., & Lanzenberger, R. (2019) · International Journal of Neuropsychopharmacology · doi:10.1093/ijnp/pyy081 · Cited by 253 · see citing works
Impaired reward prediction error encoding and striatal-midbrain connectivity in depression
Kumar, P., Goer, F., Murray, L., Dillon, D. G., Beltzer, M. L., Cohen, A. L., Brooks, N. H., & Pizzagalli, D. A. (2018) · Neuropsychopharmacology · doi:10.1038/s41386-018-0032-x · Cited by 219 · see citing works
Reinforcement learning in depression: a review of computational research
Chen, C., Takahashi, T., Nakagawa, S., Inoue, T., & Kusumi, I. (2015) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2015.05.005 · Cited by 233 · see citing works
Neural mechanisms of reinforcement learning in unmedicated patients with major depressive disorder
Rothkirch, M., Tonn, J., Köhler, S., & Sterzer, P. (2017) · Brain · doi:10.1093/brain/awx025 · Cited by 124 · see citing works
The neural underpinnings of cognitive flexibility and their disruption in psychotic illness
Waltz, J. A. (2017) · Neuroscience · doi:10.1016/j.neuroscience.2016.06.005 · Cited by 122 · see citing works
- Authors
- Dombrovski, A. Y., Szanto, K., Clark, L., Aizenstein, H. J., Chase, H. W., Reynolds, C. F., & Siegle, G. J.
- Affiliations
- University of Pittsburgh (US)
- University of British Columbia (CA)
- Corresponding author
- Dombrovski, A. Y.
- Venue
- Psychological Medicine
- Publisher
- Cambridge University Press (CUP)
- Published
- 2014-10-16
- Volume
- 45
- Issue
- 7
- Pages
- 1413-1424
- ISSN
- 0033-2917 (ISSN-L)
- 1469-8978
- PMC
- PMC4380546
- OpenAlex
- W2327661987
- Cited by
- 41
- References
- 51
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 3 · 2022: 5 · 2021: 7 · 2020: 2 · 2019: 4 · 2018: 3 · 2017: 4 · 2016: 4 · 2015: 3
- FWCI
- 2.04
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Psychological Medicine (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Bristol-Myers Squibb
- Patient-Centered Outcomes Research Institute
- Institute of Clinical and Translational Sciences
- National Institutes of Health (UL1TR000005)
- Centers for Medicare and Medicaid Services
- National Institute on Aging (P30AG024827)
- National Heart, Lung, and Blood Institute
- National Institute of Mental Health (P30MH090333, R01MH100095, R01MH084921, R01MH080240, K23MH086620, K23MH070471, R01MH085651)
- National Center for Research Resources (UL1RR024153)
- National Center for Advancing Translational Sciences
- National Institute on Minority Health and Health Disparities (P60MD000207)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Health Sciences › Medicine › Pharmacology › Treatment of Major Depression
- Keywords
- geriatric depression, executive function, reward prediction error, reinforcement learning, probabilistic reversal learning, vascular depression hypothesis, white matter hyperintensities, contingency learning, older adults
- MeSH terms
- Aged, Aged, 80 and over, Cerebral Cortex, Major Depressive Disorder, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Reinforcement, Psychology, Reward, Thalamus, Neostriatum, Executive Function
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