Reward-Processing Behavior in Depressed Participants Relative to Healthy Volunteers: A Systematic Review and Meta-analysis | Depressive Disorders | JAMA Psychiatry | JAMA Network
[Skip to Navigation]
Sign In
Figure 1.  Flow Diagram of Study Selection and Inclusion
Flow Diagram of Study Selection and Inclusion
Figure 2.  Forest Plot of Option Valuation, Reward Response Vigor, and Grip Force
Forest Plot of Option Valuation, Reward Response Vigor, and Grip Force

Rectangles and horizontal lines represent, respectively, standardized mean difference (SMD) scores and 95% confidence intervals of individual studies (A, Option valuation studies. B, Reward response vigor studies. C, Grip force task). Diamonds represent the summary effects and 95% confidence intervals for the respective reward processing subcomponent category. HC indicates healthy control; MDD, major depressive disorder.

Figure 3.  Forest Plot of Reward Bias and Reinforcement Learning
Forest Plot of Reward Bias and Reinforcement Learning

Rectangles and horizontal lines represent, respectively, standardized mean difference (SMD) scores and 95% confidence intervals of individual studies (A, Reward bias studies. B, Reinforcement learning studies). Diamonds represent the summary effects and 95% confidence intervals for the respective reward processing subcomponent category. HC indicates healthy control; MDD, major depressive disorder.

Figure 4.  Contour-Enhanced Funnel Plot
Contour-Enhanced Funnel Plot

The solid black vertical line represents the observed overall summary effect. The unfilled funnel represents 95% confidence intervals for individual effect estimates, assuming no bias. The dashed black vertical line represents the summary effect when including imputed studies (using the trim-and-fill method). The dashed orange line represents the Egger regression line. The light blue funnel represents the 95% confidence intervals and the dark blue funnel represents the 99% confidence intervals for individual effect estimates, including imputed studies. The colored shapes represent individual included effect estimates: red circles for option valuation, blue squares for reward response vigor, orange inverted triangle for the single grip force task, green diamonds for reward bias, and purple triangles for reinforcement learning. All dark gray shapes represent effect estimates imputed by trim-and-fill.

Table.  Search Results for Reward-Processing Categories
Search Results for Reward-Processing Categories
1.
Ferrari  AJ, Charlson  FJ, Norman  RE,  et al.  Burden of depressive disorders by country, sex, age, and year: findings from the global burden of disease study 2010.   PLoS Med. 2013;10(11). doi:10.1371/journal.pmed.1001547Google Scholar
2.
Casacalenda  N, Perry  JC, Looper  K.  Remission in major depressive disorder: a comparison of pharmacotherapy, psychotherapy, and control conditions.   Am J Psychiatry. 2002;159(8):1354-1360. doi:10.1176/appi.ajp.159.8.1354PubMedGoogle ScholarCrossref
3.
Roiser  JP, Elliott  R, Sahakian  BJ.  Cognitive mechanisms of treatment in depression.   Neuropsychopharmacology. 2012;37(1):117-136. doi:10.1038/npp.2011.183PubMedGoogle ScholarCrossref
4.
Rock  PL, Roiser  JP, Riedel  WJ, Blackwell  AD.  Cognitive impairment in depression: a systematic review and meta-analysis.   Psychol Med. 2014;44(10):2029-2040. doi:10.1017/S0033291713002535PubMedGoogle ScholarCrossref
5.
Chen  C, Takahashi  T, Nakagawa  S, Inoue  T, Kusumi  I.  Reinforcement learning in depression: a review of computational research.   Neurosci Biobehav Rev. 2015;55:247-267. doi:10.1016/j.neubiorev.2015.05.005PubMedGoogle ScholarCrossref
6.
Husain  M, Roiser  JP.  Neuroscience of apathy and anhedonia: a transdiagnostic approach.   Nat Rev Neurosci. 2018;19(8):470-484. doi:10.1038/s41583-018-0029-9PubMedGoogle ScholarCrossref
7.
Vinckier  F, Gourion  D, Mouchabac  S.  Anhedonia predicts poor psychosocial functioning: results from a large cohort of patients treated for major depressive disorder by general practitioners.   Eur Psychiatry. 2017;44:1-8. doi:10.1016/j.eurpsy.2017.02.485PubMedGoogle ScholarCrossref
8.
Hall  RCW, Platt  DE, Hall  RCW.  Suicide risk assessment: a review of risk factors for suicide in 100 patients who made severe suicide attempts: evaluation of suicide risk in a time of managed care.   Psychosomatics. 1999;40(1):18-27. doi:10.1016/S0033-3182(99)71267-3PubMedGoogle ScholarCrossref
9.
Uher  R, Perlis  RH, Henigsberg  N,  et al.  Depression symptom dimensions as predictors of antidepressant treatment outcome: replicable evidence for interest-activity symptoms.   Psychol Med. 2012;42(5):967-980. doi:10.1017/S0033291711001905PubMedGoogle ScholarCrossref
10.
Admon  R, Pizzagalli  DA.  Dysfunctional reward processing in depression.   Curr Opin Psychol. 2015;4:114-118. doi:10.1016/j.copsyc.2014.12.011PubMedGoogle ScholarCrossref
11.
Clark  L, Dombrovski  AY, Siegle  GJ,  et al.  Impairment in risk-sensitive decision-making in older suicide attempters with depression.   Psychol Aging. 2011;26(2):321-330. doi:10.1037/a0021646PubMedGoogle ScholarCrossref
12.
Pizzagalli  DA, Iosifescu  D, Hallett  LA, Ratner  KG, Fava  M.  Reduced hedonic capacity in major depressive disorder: evidence from a probabilistic reward task.   J Psychiatr Res. 2008;43(1):76-87. doi:10.1016/j.jpsychires.2008.03.001PubMedGoogle ScholarCrossref
13.
Knutson  B, Westdorp  A, Kaiser  E, Hommer  D.  FMRI visualization of brain activity during a monetary incentive delay task.   Neuroimage. 2000;12(1):20-27. doi:10.1006/nimg.2000.0593PubMedGoogle ScholarCrossref
14.
Chase  HW, Michael  A, Bullmore  ET, Sahakian  BJ, Robbins  TW.  Paradoxical enhancement of choice reaction time performance in patients with major depression.   J Psychopharmacol. 2010;24(4):471-479. doi:10.1177/0269881109104883PubMedGoogle ScholarCrossref
15.
Thoma  P, Norra  C, Juckel  G, Suchan  B, Bellebaum  C.  Performance monitoring and empathy during active and observational learning in patients with major depression.   Biol Psychol. 2015;109:222-231. doi:10.1016/j.biopsycho.2015.06.002PubMedGoogle ScholarCrossref
16.
Keren  H, O’Callaghan  G, Vidal-Ribas  P,  et al.  Reward Processing in depression: a conceptual and meta-analytic review across fMRI and EEG studies.   Am J Psychiatry. 2018;175. doi:10.1176/appi.ajp.2018.17101124Google Scholar
17.
Ng  TH, Alloy  LB, Smith  DV.  Meta-analysis of reward processing in major depressive disorder reveals distinct abnormalities within the reward circuit.   Transl Psychiatry. 2019;9(1):293. doi:10.1038/s41398-019-0644-xPubMedGoogle ScholarCrossref
18.
Zhang  W-N, Chang  S-H, Guo  L-Y, Zhang  K-L, Wang  J.  The neural correlates of reward-related processing in major depressive disorder: a meta-analysis of functional magnetic resonance imaging studies.   J Affect Disord. 2013;151(2):531-539. doi:10.1016/j.jad.2013.06.039PubMedGoogle ScholarCrossref
19.
Kable  JW, Glimcher  PW.  The neural correlates of subjective value during intertemporal choice.   Nat Neurosci. 2007;10(12):1625-1633. doi:10.1038/nn2007PubMedGoogle ScholarCrossref
20.
Glimcher  PW.  Understanding dopamine and reinforcement learning: the dopamine reward prediction error hypothesis.   Proc Natl Acad Sci U S A. 2011;108(suppl 3):15647-15654. doi:10.1073/pnas.1014269108PubMedGoogle ScholarCrossref
21.
Eshel  N, Roiser  JP.  Reward and punishment processing in depression.   Biol Psychiatry. 2010;68(2):118-124. doi:10.1016/j.biopsych.2010.01.027PubMedGoogle ScholarCrossref
22.
Argyropoulos  SV, Nutt  DJ.  Anhedonia revisited: is there a role for dopamine-targeting drugs for depression?   J Psychopharmacol. 2013;27(10):869-877. doi:10.1177/0269881113494104PubMedGoogle ScholarCrossref
23.
Bogdan  R, Nikolova  YS, Pizzagalli  DA.  Neurogenetics of depression: a focus on reward processing and stress sensitivity.   Neurobiol Dis. 2013;52:12-23. doi:10.1016/j.nbd.2012.05.007PubMedGoogle ScholarCrossref
24.
Treadway  MT, Zald  DH.  Reconsidering anhedonia in depression: lessons from translational neuroscience.   Neurosci Biobehav Rev. 2011;35(3):537-555. doi:10.1016/j.neubiorev.2010.06.006PubMedGoogle ScholarCrossref
25.
Treadway  MT, Zald  DH.  Parsing anhedonia: translational models of reward-processing deficits in psychopathology.   Curr Dir Psychol Sci. 2013;22(3):244-249. doi:10.1177/0963721412474460PubMedGoogle ScholarCrossref
26.
Pizzagalli  DA.  Depression, stress, and anhedonia: toward a synthesis and integrated model.   Annu Rev Clin Psychol. 2014;10(1):393-423. doi:10.1146/annurev-clinpsy-050212-185606PubMedGoogle ScholarCrossref
27.
Whitton  AE, Treadway  MT, Pizzagalli  DA.  Reward processing dysfunction in major depression, bipolar disorder and schizophrenia.   Curr Opin Psychiatry. 2015;28(1):7-12. doi:10.1097/YCO.0000000000000122PubMedGoogle ScholarCrossref
28.
Galvan  A.  Adolescent development of the reward system.   Front Hum Neurosci. 2010;4:6. doi:10.3389/neuro.09.006.2010PubMedGoogle Scholar
29.
Download citation of The Newcastle–Ottawa Scale (NOS) for Assessing the Quality of Non-Randomized Studies in Meta-Analysis. Accessed November 21, 2019. https://www.researchgate.net/publication/261773681_The_Newcastle-Ottawa_Scale_NOS_for_Assessing_the_Quality_of_Non-Randomized_Studies_in_Meta-Analysis/citation/download.
30.
Cléry-Melin  M-L, Schmidt  L, Lafargue  G, Baup  N, Fossati  P, Pessiglione  M.  Why don’t you try harder? an investigation of effort production in major depression.   PLoS One. 2011;6(8):e23178. doi:10.1371/journal.pone.0023178Google Scholar
31.
Viechtbauer  W.  Conducting meta-analyses in R with the metafor.   J Stat Softw. 2010;36(3):1-48. doi:10.18637/jss.v036.i03Google ScholarCrossref
32.
Wagner  J, Chelaru  F, Kancherla  J,  et al.  Metaviz: interactive statistical and visual analysis of metagenomic data.   Nucleic Acids Res. 2018;46(6):2777-2787. doi:10.1093/nar/gky136PubMedGoogle ScholarCrossref
33.
Thompson  SG, Sharp  SJ.  Explaining heterogeneity in meta-analysis: a comparison of methods.   Stat Med. 1999;18(20):2693-2708. doi:10.1002/(SICI)1097-0258(19991030)18:20<2693::AID-SIM235>3.0.CO;2-VPubMedGoogle ScholarCrossref
34.
Higgins  JPT, Thompson  SG, Deeks  JJ, Altman  DG.  Measuring inconsistency in meta-analyses.   BMJ. 2003;327(7414):557-560. doi:10.1136/bmj.327.7414.557PubMedGoogle ScholarCrossref
35.
Peters  JL, Sutton  AJ, Jones  DR, Abrams  KR, Rushton  L.  Contour-enhanced meta-analysis funnel plots help distinguish publication bias from other causes of asymmetry.   J Clin Epidemiol. 2008;61(10):991-996. doi:10.1016/j.jclinepi.2007.11.010PubMedGoogle ScholarCrossref
36.
Egger  M, Davey Smith  G, Schneider  M, Minder  C.  Bias in meta-analysis detected by a simple, graphical test.   BMJ. 1997;315(7109):629-634. doi:10.1136/bmj.315.7109.629PubMedGoogle ScholarCrossref
37.
Schimmack  U, Francis  G, McNeil  J,  et al R-Index 1 Running Head: R-Index Quantifying Statistical Research Integrity: The Replicabilty-Index. Published online 2014. Accessed June 16, 2020. https://pdfs.semanticscholar.org/5c9f/a44d1e2ffe0c48b8062fe83233d44daf7059.pdf
38.
Baek  K, Kwon  J, Chae  J-H,  et al.  Heightened aversion to risk and loss in depressed patients with a suicide attempt history.   Sci Rep. 2017;7(1):11228. doi:10.1038/s41598-017-10541-5PubMedGoogle ScholarCrossref
39.
Charpentier  CJ, Aylward  J, Roiser  JP, Robinson  OJ.  Enhanced risk aversion, but not loss aversion, in unmedicated pathological anxiety.   Biol Psychiatry. 2017;81(12):1014-1022. doi:10.1016/j.biopsych.2016.12.010PubMedGoogle ScholarCrossref
40.
Chung  D, Kadlec  K, Aimone  JA, McCurry  K, King-Casas  B, Chiu  PH.  Valuation in major depression is intact and stable in a non-learning environment.   Sci Rep. 2017;7:44374. doi:10.1038/srep44374PubMedGoogle ScholarCrossref
41.
Dombrovski  AY, Siegle  GJ, Szanto  K, Clark  L, Reynolds  CF, Aizenstein  H.  The temptation of suicide: striatal gray matter, discounting of delayed rewards, and suicide attempts in late-life depression.   Psychol Med. 2012;42(6):1203-1215. doi:10.1017/S0033291711002133PubMedGoogle ScholarCrossref
42.
Murphy  FC, Rubinsztein  JS, Michael  A,  et al.  Decision-making cognition in mania and depression.   Psychol Med. 2001;31(4):679-693. Accessed July 31, 2018. https://www.ncbi.nlm.nih.gov/pubmed/11352370. doi:10.1017/S0033291701003804PubMedGoogle ScholarCrossref
43.
Subramaniapillai  M, Mansur  RB, Zuckerman  H,  et al.  Association between cognitive function and performance on effort based decision making in patients with major depressive disorder treated with Vortioxetine.   Compr Psychiatry. 2019;94:152113. doi:10.1016/j.comppsych.2019.07.006PubMedGoogle Scholar
44.
Treadway  MT, Bossaller  NA, Shelton  RC, Zald  DH.  Effort-based decision-making in major depressive disorder: a translational model of motivational anhedonia.   J Abnorm Psychol. 2012;121(3):553-558. doi:10.1037/a0028813PubMedGoogle ScholarCrossref
45.
Yang  XH, Huang  J, Zhu  CY,  et al.  Motivational deficits in effort-based decision making in individuals with subsyndromal depression, first-episode and remitted depression patients.   Psychiatry Res. 2014;220(3):874-882. doi:10.1016/j.psychres.2014.08.056PubMedGoogle ScholarCrossref
46.
Admon  R, Kaiser  RH, Dillon  DG,  et al.  Dopaminergic enhancement of striatal response to reward in major depression.   Am J Psychiatry. 2017;174(4):378-386. doi:10.1176/appi.ajp.2016.16010111PubMedGoogle ScholarCrossref
47.
Arrondo  G, Segarra  N, Metastasio  A,  et al.  Reduction in ventral striatal activity when anticipating a reward in depression and schizophrenia: a replicated cross-diagnostic finding.   Front Psychol. 2015;6:1280. doi:10.3389/fpsyg.2015.01280PubMedGoogle ScholarCrossref
48.
Carl  H, Walsh  E, Eisenlohr-Moul  T,  et al.  Sustained anterior cingulate cortex activation during reward processing predicts response to psychotherapy in major depressive disorder.   J Affect Disord. 2016;203:204-212. doi:10.1016/j.jad.2016.06.005PubMedGoogle ScholarCrossref
49.
DelDonno  SR, Weldon  AL, Crane  NA,  et al.  Affective personality predictors of disrupted reward learning and pursuit in major depressive disorder.   Psychiatry Res. 2015;230(1):56-64. doi:10.1016/j.psychres.2015.08.011PubMedGoogle ScholarCrossref
50.
DelDonno  SR, Mickey  BJ, Pruitt  PJ,  et al.  Influence of childhood adversity, approach motivation traits, and depression on individual differences in brain activation during reward anticipation.   Biol Psychol. 2019;146:107709. doi:10.1016/j.biopsycho.2019.05.009PubMedGoogle Scholar
51.
DelDonno  SR, Karstens  AJ, Cerny  B,  et al.  The titrated monetary incentive delay task: sensitivity, convergent and divergent validity, and neural correlates in an RDoC sample.   J Clin Exp Neuropsychol. 2019;41(5):512-529. doi:10.1080/13803395.2019.1585519PubMedGoogle ScholarCrossref
52.
Pizzagalli  DA, Holmes  AJ, Dillon  DG,  et al.  Reduced caudate and nucleus accumbens response to rewards in unmedicated individuals with major depressive disorder.   Am J Psychiatry. 2009;166(6):702-710. doi:10.1176/appi.ajp.2008.08081201PubMedGoogle ScholarCrossref
53.
Sankar  A, Yttredahl  AA, Fourcade  EW,  et al.  Dissociable neural responses to monetary and social gain and loss in women with major depressive disorder.   Front Behav Neurosci. 2019;13:149. doi:10.3389/fnbeh.2019.00149PubMedGoogle ScholarCrossref
54.
Smoski  MJ, Rittenberg  A, Dichter  GS.  Major depressive disorder is characterized by greater reward network activation to monetary than pleasant image rewards.   Psychiatry Res. 2011;194(3):263-270. doi:10.1016/j.pscychresns.2011.06.012PubMedGoogle ScholarCrossref
55.
Takamura  M, Okamoto  Y, Okada  G,  et al.  Patients with major depressive disorder exhibit reduced reward size coding in the striatum.   Prog Neuropsychopharmacol Biol Psychiatry. 2017;79:317-323. doi:10.1016/j.pnpbp.2017.07.006Google ScholarCrossref
56.
Xie  W, Li  H, Luo  X,  et al.  Anhedonia and pain avoidance in the suicidal mind: behavioral evidence for motivational manifestations of suicidal ideation in patients with major depressive disorder.   J Clin Psychol. 2014;70(7):681-692. doi:10.1002/jclp.22055PubMedGoogle ScholarCrossref
57.
Aylward  J, Hales  C, Robinson  E, Robinson  OJ.  Translating a rodent measure of negative bias into humans: the impact of induced anxiety and unmedicated mood and anxiety disorders.   Psychol Med. 2020;50(2):237-246. doi:10.1017/S0033291718004117PubMedGoogle ScholarCrossref
58.
Henriques  JB, Davidson  RJ.  Decreased responsiveness to reward in depression.   Cogn Emot. 2000;14(5):711-724. doi:10.1080/02699930050117684Google ScholarCrossref
59.
Lawlor  VM, Webb  CA, Wiecki  TV,  et al.  Dissecting the impact of depression on decision-making.   Psychol Med. 2019;1-10. doi:10.1017/S0033291719001570PubMedGoogle Scholar
60.
Liu  W-H, Valton  V, Wang  L-Z, Zhu  Y-H, Roiser  JP.  Association between habenula dysfunction and motivational symptoms in unmedicated major depressive disorder.   Soc Cogn Affect Neurosci. 2017;12(9):1520-1533. doi:10.1093/scan/nsx074PubMedGoogle ScholarCrossref
61.
Vrieze  E, Pizzagalli  DA, Demyttenaere  K,  et al.  Reduced reward learning predicts outcome in major depressive disorder.   Biol Psychiatry. 2013;73(7):639-645. doi:10.1016/j.biopsych.2012.10.014PubMedGoogle ScholarCrossref
62.
Alexopoulos  GS, Manning  K, Kanellopoulos  D,  et al.  Cognitive control, reward-related decision making and outcomes of late-life depression treated with an antidepressant.   Psychol Med. 2015;45(14):3111-3120. doi:10.1017/S0033291715001075PubMedGoogle ScholarCrossref
63.
Cella  M, Dymond  S, Cooper  A.  Impaired flexible decision-making in major depressive disorder.   J Affect Disord. 2010;124(1-2):207-210. doi:10.1016/j.jad.2009.11.013PubMedGoogle ScholarCrossref
64.
Deisenhammer  EA, Schmid  SK, Kemmler  G, Moser  B, Delazer  M.  Decision making under risk and under ambiguity in depressed suicide attempters, depressed non-attempters and healthy controls.   J Affect Disord. 2018;226:261-266. doi:10.1016/j.jad.2017.10.012PubMedGoogle ScholarCrossref
65.
Dezfouli  A, Griffiths  K, Ramos  F, Dayan  P, Balleine  BW.  Models that learn how humans learn: the case of decision-making and its disorders.   PLoS Comput Biol. 2019;15(6):e1006903. doi:10.1371/journal.pcbi.1006903PubMedGoogle Scholar
66.
Gradin  VB, Kumar  P, Waiter  G,  et al.  Expected value and prediction error abnormalities in depression and schizophrenia.   Brain. 2011;134(Pt 6):1751-1764. doi:10.1093/brain/awr059PubMedGoogle ScholarCrossref
67.
Hall  GBC, Milne  AMB, Macqueen  GM.  An fMRI study of reward circuitry in patients with minimal or extensive history of major depression.   Eur Arch Psychiatry Clin Neurosci. 2014;264(3):187-198. doi:10.1007/s00406-013-0437-9PubMedGoogle ScholarCrossref
68.
Hegedűs  KM, Szkaliczki  A, Gál  BI, Andó  B, Janka  Z, Álmos  PZ.  Decision-making performance of depressed patients within 72 h following a suicide attempt.   J Affect Disord. 2018;235:583-588. doi:10.1016/j.jad.2018.04.082PubMedGoogle ScholarCrossref
69.
Herzallah  MM, Moustafa  AA, Natsheh  JY,  et al.  Learning from negative feedback in patients with major depressive disorder is attenuated by SSRI antidepressants.   Front Integr Neurosci. 2013;7:67. doi:10.3389/fnint.2013.00067PubMedGoogle ScholarCrossref
70.
Jollant  F, Bellivier  F, Leboyer  M,  et al.  Impaired decision making in suicide attempters.   Am J Psychiatry. 2005;162(2):304-310. doi:10.1176/appi.ajp.162.2.304PubMedGoogle ScholarCrossref
71.
Jollant  F, Richard-Devantoy  S, Ding  Y, Turecki  G, Bechara  A, Near  J.  Prefrontal inositol levels and implicit decision-making in healthy individuals and depressed patients.   Eur Neuropsychopharmacol. 2016;26(8):1255-1263. doi:10.1016/j.euroneuro.2016.06.005PubMedGoogle ScholarCrossref
72.
Kumar  P, Goer  F, Murray  L,  et al.  Impaired reward prediction error encoding and striatal-midbrain connectivity in depression.   Neuropsychopharmacology. 2018;43(7):1581-1588. doi:10.1038/s41386-018-0032-xPubMedGoogle ScholarCrossref
73.
Liu  WH, Chan  RCK, Wang  LZ,  et al.  Deficits in sustaining reward responses in subsyndromal and syndromal major depression.   Prog Neuropsychopharmacol Biol Psychiatry. 2011;35(4):1045-1052. doi:10.1016/j.pnpbp.2011.02.018PubMedGoogle ScholarCrossref
74.
McGovern  AR, Alexopoulos  GS, Yuen  GS, Morimoto  SS, Gunning-Dixon  FM.  Reward-related decision making in older adults: relationship to clinical presentation of depression.   Int J Geriatr Psychiatry. 2014;29(11):1125-1131. doi:10.1002/gps.4200PubMedGoogle ScholarCrossref
75.
Moutoussis  M, Rutledge  RB, Prabhu  G,  et al.  Neural activity and fundamental learning, motivated by monetary loss and reward, are intact in mild to moderate major depressive disorder.   PLoS One. 2018;13(8):e0201451. doi:10.1371/journal.pone.0201451PubMedGoogle Scholar
76.
Must  A, Szabó  Z, Bódi  N, Szász  A, Janka  Z, Kéri  S.  Sensitivity to reward and punishment and the prefrontal cortex in major depression.   J Affect Disord. 2006;90(2-3):209-215. doi:10.1016/j.jad.2005.12.005PubMedGoogle ScholarCrossref
77.
Nord  CL, Lawson  RP, Huys  QJM, Pilling  S, Roiser  JP.  Depression is associated with enhanced aversive Pavlovian control over instrumental behaviour.   Sci Rep. 2018;8(1):12582. doi:10.1038/s41598-018-30828-5PubMedGoogle ScholarCrossref
78.
Rothkirch  M, Tonn  J, Köhler  S, Sterzer  P.  Neural mechanisms of reinforcement learning in unmedicated patients with major depressive disorder.   Brain. 2017;140(4):1147-1157. doi:10.1093/brain/awx025PubMedGoogle ScholarCrossref
79.
Saperia  S, Da Silva  S, Siddiqui  I,  et al.  Reward-driven decision-making impairments in schizophrenia.   Schizophr Res. 2019;206:277-283. doi:10.1016/j.schres.2018.11.004PubMedGoogle ScholarCrossref
80.
Walsh  AEL, Browning  M, Drevets  WC, Furey  M, Harmer  CJ.  Dissociable temporal effects of bupropion on behavioural measures of emotional and reward processing in depression.   Philos Trans R Soc B Biol Sci. 2018;373(1742):20170030. doi:10.1098/rstb.2017.0030Google Scholar
81.
Terrin  N, Schmid  CH, Lau  J, Olkin  I.  Adjusting for publication bias in the presence of heterogeneity.   Stat Med. 2003;22(13):2113-2126. doi:10.1002/sim.1461PubMedGoogle ScholarCrossref
82.
Huys  QJ, Pizzagalli  DA, Bogdan  R, Dayan  P.  Mapping anhedonia onto reinforcement learning: a behavioural meta-analysis.   Biol Mood Anxiety Disord. 2013;3(1):12. doi:10.1186/2045-5380-3-12PubMedGoogle ScholarCrossref
83.
Collins  AGE, Frank  MJ.  How much of reinforcement learning is working memory, not reinforcement learning? a behavioral, computational, and neurogenetic analysis.   Eur J Neurosci. 2012;35(7):1024-1035. doi:10.1111/j.1460-9568.2011.07980.xPubMedGoogle ScholarCrossref
84.
Romans  SE, Tyas  J, Cohen  MM, Silverstone  T.  Gender differences in the symptoms of major depressive disorder.   J Nerv Ment Dis. 2007;195(11):905-911. doi:10.1097/NMD.0b013e3181594cb7PubMedGoogle ScholarCrossref
85.
Marschner  A, Mell  T, Wartenburger  I, Villringer  A, Reischies  FM, Heekeren  HR. Reward-based decision-making and aging. In:  Brain Research Bulletin. Vol 67. Elsevier; 2005:382-390. doi:10.1016/j.brainresbull.2005.06.010
86.
Sareen  J, Afifi  TO, McMillan  KA, Asmundson  GJG.  Relationship between household income and mental disorders: findings from a population-based longitudinal study.   Arch Gen Psychiatry. 2011;68(4):419-427. doi:10.1001/archgenpsychiatry.2011.15PubMedGoogle ScholarCrossref
87.
Layard  R, Nickell  S, Mayraz  G.  The marginal utility of income.   J Public Econ. 2008;92(8-9):1846-1857. doi:10.1016/j.jpubeco.2008.01.007Google ScholarCrossref
88.
Elliott  R, Sahakian  BJ, Herrod  JJ, Robbins  TW, Paykel  ES.  Abnormal response to negative feedback in unipolar depression: evidence for a diagnosis specific impairment.   J Neurol Neurosurg Psychiatry. 1997;63(1):74-82. doi:10.1136/jnnp.63.1.74PubMedGoogle ScholarCrossref
89.
Amsterdam  JD, Settle  RG, Doty  RL, Abelman  E, Winokur  A.  Taste and smell perception in depression.   Biol Psychiatry. 1987;22(12):1481-1485. doi:10.1016/0006-3223(87)90108-9PubMedGoogle ScholarCrossref
90.
Swiecicki  L, Zatorski  P, Bzinkowska  D, Sienkiewicz-Jarosz  H, Szyndler  J, Scinska  A.  Gustatory and olfactory function in patients with unipolar and bipolar depression.   Prog Neuropsychopharmacol Biol Psychiatry. 2009;33(5):827-834. doi:10.1016/j.pnpbp.2009.03.030PubMedGoogle ScholarCrossref
91.
Dichter  GS, Smoski  MJ, Kampov-Polevoy  AB, Gallop  R, Garbutt  JC.  Unipolar depression does not moderate responses to the Sweet Taste Test.   Depress Anxiety. 2010;27(9):859-863. doi:10.1002/da.20690PubMedGoogle ScholarCrossref
92.
Arrondo  G, Murray  GK, Hill  E,  et al.  Hedonic and disgust taste perception in borderline personality disorder and depression.   Br J Psychiatry. 2015;207(1):79-80. doi:10.1192/bjp.bp.114.150433PubMedGoogle ScholarCrossref
93.
Berlin  I, Givry-Steiner  L, Lecrubier  Y, Puech  AJ.  Measures of anhedonia and hedonic responses to sucrose in depressive and schizophrenic patients in comparison with healthy subjects.   Eur Psychiatry. 1998;13(6):303-309. doi:10.1016/S0924-9338(98)80048-5PubMedGoogle ScholarCrossref
Limit 200 characters
Limit 25 characters
Conflicts of Interest Disclosure

Identify all potential conflicts of interest that might be relevant to your comment.

Conflicts of interest comprise financial interests, activities, and relationships within the past 3 years including but not limited to employment, affiliation, grants or funding, consultancies, honoraria or payment, speaker's bureaus, stock ownership or options, expert testimony, royalties, donation of medical equipment, or patents planned, pending, or issued.

Err on the side of full disclosure.

If you have no conflicts of interest, check "No potential conflicts of interest" in the box below. The information will be posted with your response.

Not all submitted comments are published. Please see our commenting policy for details.

Limit 140 characters
Limit 3600 characters or approximately 600 words
    Original Investigation
    July 29, 2020

    Reward-Processing Behavior in Depressed Participants Relative to Healthy Volunteers: A Systematic Review and Meta-analysis

    Author Affiliations
    • 1Institute of Cognitive Neuroscience, University College London, London, England
    • 2Department of Psychiatry, University of Oxford, Oxford, England
    • 3Division of Psychiatry, University College London, London, England
    • 4Max Planck Centre for Computational Psychiatry and Aging Research, University College London, England
    • 5Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, England
    JAMA Psychiatry. 2020;77(12):1286-1295. doi:10.1001/jamapsychiatry.2020.2139
    Key Points

    Question  Are patients with depression associated with impairment on behavioral tests of reward processing compared with healthy control individuals?

    Findings  In this systematic review and meta-analysis of data from 48 case-control studies of reward-processing tasks, patients with depression showed a small to medium impairment in reward processing across all tasks. They showed medium to large impairments in reward bias, small to medium impairments in option valuation and reinforcement learning, and small (nonsignificant) impairments in reward response vigor.

    Meaning  In this systematic review and meta-analysis, depression is associated with behavioral reward-processing impairments, although this could vary depending on the precise subcomponent measured.

    Abstract

    Importance  Dysfunctional reward processing is a leading candidate mechanism for the development of certain depressive symptoms, such as anhedonia. However, to our knowledge, there has not yet been a systematic assessment of whether and to what extent depression is associated with impairments on behavioral reward-processing tasks.

    Objective  To determine whether depression is associated with impairments in reward-processing behavior.

    Data Sources  The MEDLINE/PubMed, Embase, and PsycInfo databases were searched for studies that investigated reward processing using performance on behavioral tasks by individuals with depression and nondepressed control groups, published between January 1, 1946, and August 16, 2019.

    Study Selection  Studies that contained data regarding performance by depressed and healthy control groups on reward-processing tasks were included in the systematic review and meta-analysis.

    Data Extraction and Synthesis  Summary statistics comparing performance between depressed and healthy groups on reward-processing tasks were converted to standardized mean difference (SMD) scores, from which summary effect sizes for overall impairment in reward processing and 4 subcomponent categories were calculated. Study quality, heterogeneity, replicability-index, and publication bias were also assessed.

    Main Outcome and Measures  Performance on reward-processing tasks.

    Results  The final data set comprised 48 case-control studies (1387 healthy control individuals and 1767 individuals with major depressive disorder). The mean age was 37.85 years and 58% of the participants were women. These studies used tasks assessing option valuation (n = 9), reward bias (n = 6), reward response vigor (n = 12), reinforcement learning (n = 20), and grip force (n = 1). Across all tasks, depression was associated with small to medium impairments in reward-processing behavior (SMD = 0.345; 95% CI, 0.209-0.480). When examining reward-processing subcomponent categories, impairment was associated with tasks assessing option valuation (SMD = 0.309; 95% CI, 0.147-0.471), reward bias (SMD = 0.644; 95% CI, 0.270-1.017), and reinforcement learning (SMD = 0.352; 95% CI, 0.115-0.588) but not reward response vigor (SMD = 0.083; 95% CI, −0.144 to 0.309). The medication status of the major depressive disorder sample did not explain any of the variance in the overall effect size. There was significant between-study heterogeneity overall and in all subcomponent categories other than option valuation. Significant publication bias was identified overall and in the reinforcement learning category.

    Conclusions and Relevance  Relative to healthy control individuals, individuals with depression exhibit reward-processing impairments, particularly for tests of reward bias, option valuation, and reinforcement learning. Understanding the neural mechanisms driving these associations may assist in designing novel interventions.

    Introduction

    Depression is the leading cause of disability worldwide,1 and the effectiveness of therapeutic agents for depression is limited.2 A lack of detailed understanding of the mechanisms underlying depressive symptoms, such as low mood, fatigue, and anhedonia, is a major barrier to the development of more effective treatment strategies.

    It is now well established that depression is associated with disrupted cognitive processing,3 for both nonaffective (cold) and affective (hot) information. This includes reward processing,4,5 which describes how organisms use reinforcement-related perceptions to guide goal-directed behaviors. A reward-processing framework is especially useful for understanding symptoms associated with motivation, such as reduced interest and activity,6 which warrant better understanding because they are associated with poorer outcomes7,8 and treatment response.9

    Reward processing can be divided into a number of subcomponents. According to 1 conceptualization,6 reward processing proceeds according to the following sequence of cognitive operations: (1) option generation, the generation of potentially rewarding behavioral options; (2) decision-making, where options are subjected to a cost-benefit evaluation, which balances the utility of potential rewards against associated costs (eg, the potential effort of obtaining those rewards), resulting in the selection of one of the options; (3) anticipation, an anticipatory or preparatory phase associated with physiological arousal before the reward is obtained; (4) action and effort, engagement in action to obtain the reward goal; (5) consummation, the hedonic effect arising from interacting with the reward goal (or alternatively, the frustration of an omitted outcome); and (6) reinforcement learning, learning how to modify behavior in future interactions with similar stimuli using an update signal.

    Not all of these subcomponents are straightforward to assess using objective behavioral tasks; anticipation and hedonic impact are typically assessed using physiological responses and self-report, respectively. Over the past 2 decades, reward-processing dysfunction in depression has been the focus of numerous studies, typically using tasks falling into the following 4 categories.10

    Option Valuation

    Part of subcomponent 2 in the previous section, option valuation describes the process by which individuals evaluate reward-related options when given explicit information about possible options (eg, reward, cost, and probability). An individual’s choice is assumed to reflect the weights that they place on potential rewards and costs (costs may include a potential loss of points/money or the effort needed to obtain the reward).6 Studies investigating this domain of reward processing, eg, using the Cambridge Gambling Task, have reported that individuals with depression were less willing than control individuals to place high bets when reward probabilities were high.11

    Reward Bias

    Also thought to reflect subcomponent 2, reward bias is measured while individuals make difficult decisions (often perceptual) that are rewarded asymmetrically, distinguishing this process from option valuation. Information relating to potential rewards/losses/probabilities is typically not provided explicitly. The reward bias measure, derived from signal detection theory, reflects an individual’s tendency to choose more frequently rewarded stimuli, regardless of perceptual accuracy.12 Individuals with depression have been reported to exhibit weaker reward biases than control individuals.12

    Reward Response Vigor

    Part of subcomponent (4), reward response vigor reflects the speed with which an individual executes an action to obtain a reward. The difference between this and the former 2 types of measure is that here, the measure relates to the actual action taken, not simply the choice to take it. This category includes tasks such as the Monetary Incentive Delay Task13 and the Cued Reinforcement Reaction Time Task.14

    Reinforcement Learning

    Part of subcomponent 6, reinforcement learning describes the process by which an individual uses feedback to change their behavior in the future. Changes in behavior over time are assumed to reflect the updating of value expectations assigned to available behaviors.6 Studies using probabilistic learning tasks report that individuals with depression use feedback less effectively than control individuals to accumulate reward.15

    Three meta-analyses have examined abnormal reward-related neural processing in depression,16-18 all of which identified lower striatal responses. The striatum, part of the basal ganglia, connects reciprocally with prefrontal areas, (ventral parts of which code stimulus value19 and were found to be blunted in 2 of the meta-analyses) as well as the midbrain, which signals the discrepancy between expected and received reward.20 Together these areas form part of the brain’s reward circuit, which modulates reward-related behavior and learning.20 However, to our knowledge, there has been no meta-analysis of the behavioral reward-processing literature, although several narrative reviews exist.5,6,10,21-27 While narrative reviews can provide a useful overview of the field and an opportunity to develop theoretical accounts, they cannot directly address whether disruptions in reward processing are consistent across samples or quantify the magnitude of identified effects.

    Therefore, we aimed to produce a quantitative summary of this literature by conducting a systematic review and meta-analysis of reward-processing behavior in depression. This is an important step in determining whether reward-processing dysfunction is useful for understanding depression. The aims of our meta-analysis were to clarify (1) the nature and extent of differences between depressed and healthy groups on behavioral measures of reward processing and (2) the relative strength and consistency of differences within different reward-processing subdomains.

    Method
    Systematic Review

    The Ovid MEDLINE/PubMed, Embase, and PsycInfo databases were searched for articles published between January 1, 1946, and August 16, 2019, inclusive, with titles or abstracts containing the terms (deci* or reward* or motivat* or incentiv* or effort*) and (depress*) and (task* or paradigm* or battery*). The inclusion criteria were as follows: (1) case-control design; (2) included a healthy control group; (3) included a group with major depressive disorder (MDD), assessed according to DSM-IV/DSM-5 or International Statistical Classification of Diseases and Related Health Problems, Tenth Revision criteria; (4) participants were 18 years or older (because there are important differences between developing and adult reward systems)28; (5) participants performed a reward-processing task; and (6) task rewards were explicit, ie, money, points, water, or food (we did not include studies that used outcomes that could be considered purely informational, eg, happy/sad faces or variants of correct/incorrect, to ensure specificity). Although it could be argued that these are social rewards, the distinction between purely informational feedback and socially rewarding feedback is unclear in such contexts. A focused search for social reward tasks in depression yielded no eligible studies. The final 2 criteria were (7) samples did not overlap with other included data sets and (8) studies reported data on a behavioral measure of reward processing that could be converted to a case-control standardized mean difference (SMD) score. If articles were otherwise suitable but did not contain such data, the data were requested from the authors.

    Where possible, selected behavioral measures related only to reward (ie, not also punishment). For some tasks, this was not possible (because all nonreward outcomes were punishments). Articles were independently assessed by D.C.H. and A.N. Conflicts were highlighted using the Covidence software package (Covidence) and resolved through in-person discussion.

    D.C.H. and K.K. rated the included studies on factors that may bias results, using a rating tool based on the Newcastle-Ottawa scale for assessing the quality of nonrandomized studies in meta-analyses (eMethods 1 in the Supplement).29 Studies were rated on whether cases and controls were sampled from the same population, how precisely they were defined (eTable 1 in the Supplement), whether they were matched on age, sex, IQ, and personal/household-income or occupation, and whether cases were restricted to only a specific subpopulation (eg, individuals who attempted suicide) (eTable 2 in the Supplement). Where reported, measures of anhedonia or cold cognition were used as continuous moderators, as were the mean age and proportion of women in each study sample. Studies were coded as containing either exclusively unmedicated or at least some medicated cases (eMethods 2 and eTable 3 in the Supplement).

    Meta-analysis

    Relevant behavioral measures (eTable 4 in the Supplement) from each study were categorized as measuring option valuation, reward bias, reward response vigor, or reinforcement learning (eTable 5 in the Supplement), then converted to an SMD score and standard error (eMethods 3 and eTable 6 in the Supplement). Within option valuation, reward bias, and reward response vigor, a positive SMD represents a greater response to reward by the control than depressed group (eg, higher weighting of reward in a gambling task, greater reward bias in a signal detection task, or faster response in the rewarded [vs unrewarded] condition of a reaction time task, respectively). Within reinforcement learning, a positive SMD represents faster use of feedback to maximize reward accumulation by the control group than the depressed group (eg, a greater proportion of high-probability reward choices in a 2-arm bandit task). One study assessed the effect of reward on grip force production30 and so is excluded from these categories, although included in calculations relating to the overall sample.

    The meta-analysis was performed in the statistical packages metafor31 and metaviz32 (R Statistical Programming) using a restricted maximum likelihood estimator model.33 Heterogeneity was assessed using a point estimate of the among-study variance of true effect sizes (τ2) and the approximate proportion of total variability (I2).34 Sensitivity analysis involved stepwise removal of studies to assess the effect of their removal on effect size and heterogeneity.

    Funnel plot asymmetry was assessed using visual inspection of a contour enhanced funnel plot35 and the Egger test36 (using a 2-sided significance threshold of P = .10 because the Egger test has low power when the number of studies is low). Studies missing owing to publication bias were estimated and imputed using the trim-and-fill method. Moderator analyses used random-effects categorical or metaregression models, overall, and in subcomponent categories. Replicability indexes were calculated by reducing the observed median power of studies by the discrepancy between it and the percentage of significant results (within a given category).37

    Results

    Data from 48 studies11,12,15,30,38-80 containing 3154 participants (1387 healthy control individuals and 1767 individuals with MDD) were analyzed. Although we searched primarily for case-control studies, we also attempted to identify relevant longitudinal, population-based studies; none were identified. Figure 1 outlines the search results and the Table outlines the numbers of studies and participants in each category.

    Meta-analysis Results

    Across all studies, there was a small to medium reward processing impairment in depressed compared to healthy groups (SMD, 0.345; 95% CI, 0.209-0.480). Analysis of the 4 subcomponent categories (Table; Figure 2 and Figure 3) revealed a small to medium impairment in option valuation (SMD, 0.309; 95% CI, 0.147-0.471), a medium to large impairment in reward bias (SMD, 0.644; 95% CI, 0.270-1.017), a small to negligible (nonsignificant) impairment in reward response vigor (SMD, 0.083; 95% CI, −0.144 to 0.309) and a small to medium impairment in reinforcement learning (SMD, 0.352; 95% CI, 0.115-0.588).

    Overall interstudy heterogeneity was substantial (τ2 = 0.15; I2 = 68%). There was no interstudy heterogeneity in the option valuation category (τ2 = 0.0; I2 = 0%). There was low to moderate heterogeneity in the reward response vigor category (τ2 = 0.05; I2 = 34%). There was substantial to considerable heterogeneity in the reward bias (τ2 = 0.16; I2 = 75%) and reinforcement learning (τ2 = 0.21; I2 = 76%) categories. Excluding 1 study59 reduced heterogeneity in the reward bias category to moderate to substantial (τ2 = 0.08; I2 = 55%) and increased the effect size to large (SMD, 0.784; 95% CI, 0.453-1.116). Excluding 1 study14 eliminated heterogeneity in the reward response vigor category (τ2 = 0.0; I2 = 0%); the effect size remained small to negligible and nonsignificant (SMD, 0.157; 95% CI, −0.033 to 0.347). Removing individual studies did not reduce interstudy heterogeneity either in the other categories or in the overall sample.

    Moderator Analysis

    Potential sources of bias, including the reward processing categories, were assessed as moderators. Of the total variation in effect sizes, 68% was owing to between-study differences. The medication status of the MDD sample (unmedicated vs at least partially medicated) explained 0% of the variance in the global effect size, with no effect of medication status in any sub-component category.

    In those studies that reported summary statistics of anhedonia scores (n = 18) or cold-cognitive task performance (n = 8), SMDs of respective measures used as continuous moderators revealed no significant effects of either on reward processing (too few studies were available to perform meaningful analyses in subcomponent categories).

    Studies including an exclusively elderly sample (n = 4) yielded a smaller (P < .001) and nonsignificant effect (SMD, −0.127; 95% CI, −0.555 to 0.300) than those including nonelderly samples (n = 44; SMD, 0.390; 95% CI, 0.254-0.525). Studies that clearly matched groups for sex yielded a larger effect than those that did not (eResults in the Supplement); however, there was no moderation effect of the proportion of female participants in study samples. All other moderator analyses of the overall sample yielded nonsignificant results (eResults in the Supplement).

    Publication Bias

    Overall publication bias was significant (Egger test: z = 2.082; P = .04; Figure 4); however, genuine between-study heterogeneity may be mistaken for publication bias.81 Overall median power and R index were low (median power, 22%; R index, 7%). It was not possible to assess publication bias, median power, or R index scores in the option valuation or reward bias categories owing to the low number of studies in both. Publication bias was significant in the reinforcement learning category (z = 3.092; P = .002) and nonsignificant in the reward response vigor category. Median power and R index scores were low in both reward response vigor (median power, 6%; R index, 0%) and reinforcement learning (median power, 25%; R index, 16%) categories.

    Discussion

    To our knowledge, this is the first systematic review and meta-analysis to quantify behavioral reward-processing differences between depressed and healthy groups. Across 48 studies, we found that depressed groups had small to medium (SMD, 0.345) reward-processing impairments relative to healthy groups. However, there were potentially important differences between reward-processing subcomponents.

    The largest impairment was observed in the reward bias category (SMD, 0.644), which is unlikely to be owing to impairment in non–reward-based processing (eg, perceptual) because the signal detection theory discrimination measure did not differ significantly between the groups in most included studies. From this meta-analysis, the reward bias impairment in depression appears most robust. Prior computational analysis suggests that this may be driven by lower reward valuation.82

    The reinforcement learning category yielded a small to medium effect (SMD, 0.352). Many tasks in this category could not allow reward response to be disentangled from confounds such as working memory impairment.83 Future studies that investigate reinforcement learning impairments in depression may benefit from using tasks that allow the contributions of hot and cold cognitive impairment to be dissociated.83

    Individuals with depression exhibited a small to medium impairment in option valuation (SMD, 0.309), which is associated with “decisional anhedonia” resulting from impaired cost-benefit decision-making.24 The reward response vigor category exclusively contained tasks measuring reaction times, which may be vulnerable to attentional confounds. While these cannot be fully controlled for, we go some way toward doing so by including only tasks that compared responding in more vs less rewarded conditions. While reward response vigor yielded a nonsignificant summary effect (SMD, 0.083), the single study30 that assessed grip force production yielded a large effect (SMD, 1.17). Speculatively, this might indicate that reward-processing impairments in depression are associated more with fatigability than the speed of action, consistent with the higher prevalence of anergia than psychomotor retardation in depression.84 Further research on the effect of reward on grip force in depression is warranted.

    R index values suggest that significant results may be difficult to replicate. However, the R index is conservative and less precise when true power is low.37 Assuming that future studies can expect to yield effect sizes comparable with the overall summary effect in this meta-analysis (SMD, 0.345), they will require sample sizes of 133 per group to achieve a power of 0.8 at a significance of .05 (2-tailed). This is considerably larger than the mean sample size in the included studies (33 per group).

    Moderator analysis revealed no significant association for between-study variation and either anhedonia or cold cognitive performance on reward-processing impairment. These results should be interpreted cautiously owing to the low number of studies that could be included and heterogeneity in measuring these constructs. The assessment of within-study variation in such factors would have been more informative, but the necessary correlation coefficients were rarely reported; future studies should report correlations between reward processing and anhedonia and/or cold cognitive impairment. Those studies that recruited exclusively elderly participants yielded a nonsignificant summary effect (SMD, −0.127). Speculatively, this may be owing to the effect of healthy aging on reward processing.85 None of the included studies controlled for personal/household income, a potentially important oversight in studies that use monetary rewards because depression is associated with lower household income86 (and, by extension, a higher utility of money)87 than the general population. Future studies that use monetary rewards should assess income levels.

    Limitations

    Several limitations of our analyses merit comment. First, reward processing comprises a heterogeneous set of processes, which we categorized according to 4 subcomponents. However, there are several potential ways to measure function in each category. Therefore, this meta-analysis sometimes combines dissimilar measures in its summary statistics. For example, option valuation contains studies that probe the effect of reward on the willingness to exert effort (in 3 cases) and to take risks (in 6 cases). Second, the medicated samples were often not entirely medicated, used a variety of medications (even within-study), and at different doses. Therefore, the nonsignificant moderation result for medication status is difficult to interpret. The effect of medication on reward processing in depression is best investigated in the context of randomized clinical trials. Third, we did not investigate response to punishment (because very few of the studies investigated responses to punishment separately from reward). Given the importance of sensitivity to punishment in some cognitive models of depression,88 this is an important omission. Fourth, there was significant heterogeneity, overall and in all categories except option valuation, making the interpretation of the summary effects less clear. Fifth, there was significant publication bias overall and in reinforcement learning particularly, resulting in a potential overestimation of the summary effects. Sixth, we were unable to examine 2 important reward-processing components, the anticipation and hedonic effect of rewards. A literature search for studies in these categories yielded 5 studies,89-93 which investigated the hedonic effect of oral sucrose solutions in healthy vs depressed groups. However, of these studies, too few contained suitable data for the meta-analysis to include a hedonic effect category. Seventh, this systematic review and meta-analysis summarizes the findings of case-control studies, which do not inform us about the causal relationship between reward-processing impairment and depression or its treatment. Longitudinal studies examining reward processing in depression are needed to answer these important questions. Eighth, the effect size estimates were unadjusted for covariates and so may be affected by confounds.

    Conclusions

    Conducting a meta-analysis of 48 studies, we found that depression was reliably associated with small to medium reward-processing impairments overall and of varying magnitudes across several reward-processing subdomains. This is important because the cognitive and neural mechanisms underlying reward processing and its subdomains are relatively well understood.24 Research on reward processing may therefore be a credible route to better characterizing mechanistic heterogeneity within depression, as well as potentially highlighting novel targets for treatment.

    Back to top
    Article Information

    Accepted for Publication: May 26, 2020.

    Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2020 Halahakoon DC et al. JAMA Psychiatry.

    Published Online: July 29, 2020. doi:10.1001/jamapsychiatry.2020.2139

    Correction: This article was corrected on October 21, 2020, to change the Open Access status. It was further corrected on March 10, 2021, to fix an error in the Supplement.

    Corresponding Author: Jonathan P. Roiser, MA, PhD, UCL Institute of Cognitive Neuroscience, 17 Queen Sq, London WC1N 3AZ, England (j.roiser@ucl.ac.uk).

    Author Contributions: Dr Roiser had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: Halahakoon, Nair, Roiser.

    Acquisition, analysis, or interpretation of data: All authors.

    Drafting of the manuscript: Halahakoon, Kieslich, Roiser.

    Critical revision of the manuscript for important intellectual content: All authors.

    Statistical analysis: Halahakoon, Kieslich, O'Driscoll, Nair, Roiser.

    Obtained funding: Roiser.

    Administrative, technical, or material support: Halahakoon.

    Supervision: Lewis, Roiser.

    Conflict of Interest Disclosures: Dr Lewis reported grants from University College London during the conduct of the study. Dr Roiser reported personal fees from Cambridge Cognition Ltd and GE Healthcare outside the submitted work. No other disclosures were reported.

    Funding/Support: This work was funded by the Wellcome Trust.

    Role of the Funder/Sponsor: The Wellcome Trust had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    References
    1.
    Ferrari  AJ, Charlson  FJ, Norman  RE,  et al.  Burden of depressive disorders by country, sex, age, and year: findings from the global burden of disease study 2010.   PLoS Med. 2013;10(11). doi:10.1371/journal.pmed.1001547Google Scholar
    2.
    Casacalenda  N, Perry  JC, Looper  K.  Remission in major depressive disorder: a comparison of pharmacotherapy, psychotherapy, and control conditions.   Am J Psychiatry. 2002;159(8):1354-1360. doi:10.1176/appi.ajp.159.8.1354PubMedGoogle ScholarCrossref
    3.
    Roiser  JP, Elliott  R, Sahakian  BJ.  Cognitive mechanisms of treatment in depression.   Neuropsychopharmacology. 2012;37(1):117-136. doi:10.1038/npp.2011.183PubMedGoogle ScholarCrossref
    4.
    Rock  PL, Roiser  JP, Riedel  WJ, Blackwell  AD.  Cognitive impairment in depression: a systematic review and meta-analysis.   Psychol Med. 2014;44(10):2029-2040. doi:10.1017/S0033291713002535PubMedGoogle ScholarCrossref
    5.
    Chen  C, Takahashi  T, Nakagawa  S, Inoue  T, Kusumi  I.  Reinforcement learning in depression: a review of computational research.   Neurosci Biobehav Rev. 2015;55:247-267. doi:10.1016/j.neubiorev.2015.05.005PubMedGoogle ScholarCrossref
    6.
    Husain  M, Roiser  JP.  Neuroscience of apathy and anhedonia: a transdiagnostic approach.   Nat Rev Neurosci. 2018;19(8):470-484. doi:10.1038/s41583-018-0029-9PubMedGoogle ScholarCrossref
    7.
    Vinckier  F, Gourion  D, Mouchabac  S.  Anhedonia predicts poor psychosocial functioning: results from a large cohort of patients treated for major depressive disorder by general practitioners.   Eur Psychiatry. 2017;44:1-8. doi:10.1016/j.eurpsy.2017.02.485PubMedGoogle ScholarCrossref
    8.
    Hall  RCW, Platt  DE, Hall  RCW.  Suicide risk assessment: a review of risk factors for suicide in 100 patients who made severe suicide attempts: evaluation of suicide risk in a time of managed care.   Psychosomatics. 1999;40(1):18-27. doi:10.1016/S0033-3182(99)71267-3PubMedGoogle ScholarCrossref
    9.
    Uher  R, Perlis  RH, Henigsberg  N,  et al.  Depression symptom dimensions as predictors of antidepressant treatment outcome: replicable evidence for interest-activity symptoms.   Psychol Med. 2012;42(5):967-980. doi:10.1017/S0033291711001905PubMedGoogle ScholarCrossref
    10.
    Admon  R, Pizzagalli  DA.  Dysfunctional reward processing in depression.   Curr Opin Psychol. 2015;4:114-118. doi:10.1016/j.copsyc.2014.12.011PubMedGoogle ScholarCrossref
    11.
    Clark  L, Dombrovski  AY, Siegle  GJ,  et al.  Impairment in risk-sensitive decision-making in older suicide attempters with depression.   Psychol Aging. 2011;26(2):321-330. doi:10.1037/a0021646PubMedGoogle ScholarCrossref
    12.
    Pizzagalli  DA, Iosifescu  D, Hallett  LA, Ratner  KG, Fava  M.  Reduced hedonic capacity in major depressive disorder: evidence from a probabilistic reward task.   J Psychiatr Res. 2008;43(1):76-87. doi:10.1016/j.jpsychires.2008.03.001PubMedGoogle ScholarCrossref
    13.
    Knutson  B, Westdorp  A, Kaiser  E, Hommer  D.  FMRI visualization of brain activity during a monetary incentive delay task.   Neuroimage. 2000;12(1):20-27. doi:10.1006/nimg.2000.0593PubMedGoogle ScholarCrossref
    14.
    Chase  HW, Michael  A, Bullmore  ET, Sahakian  BJ, Robbins  TW.  Paradoxical enhancement of choice reaction time performance in patients with major depression.   J Psychopharmacol. 2010;24(4):471-479. doi:10.1177/0269881109104883PubMedGoogle ScholarCrossref
    15.
    Thoma  P, Norra  C, Juckel  G, Suchan  B, Bellebaum  C.  Performance monitoring and empathy during active and observational learning in patients with major depression.   Biol Psychol. 2015;109:222-231. doi:10.1016/j.biopsycho.2015.06.002PubMedGoogle ScholarCrossref
    16.
    Keren  H, O’Callaghan  G, Vidal-Ribas  P,  et al.  Reward Processing in depression: a conceptual and meta-analytic review across fMRI and EEG studies.   Am J Psychiatry. 2018;175. doi:10.1176/appi.ajp.2018.17101124Google Scholar
    17.
    Ng  TH, Alloy  LB, Smith  DV.  Meta-analysis of reward processing in major depressive disorder reveals distinct abnormalities within the reward circuit.   Transl Psychiatry. 2019;9(1):293. doi:10.1038/s41398-019-0644-xPubMedGoogle ScholarCrossref
    18.
    Zhang  W-N, Chang  S-H, Guo  L-Y, Zhang  K-L, Wang  J.  The neural correlates of reward-related processing in major depressive disorder: a meta-analysis of functional magnetic resonance imaging studies.   J Affect Disord. 2013;151(2):531-539. doi:10.1016/j.jad.2013.06.039PubMedGoogle ScholarCrossref
    19.
    Kable  JW, Glimcher  PW.  The neural correlates of subjective value during intertemporal choice.   Nat Neurosci. 2007;10(12):1625-1633. doi:10.1038/nn2007PubMedGoogle ScholarCrossref
    20.
    Glimcher  PW.  Understanding dopamine and reinforcement learning: the dopamine reward prediction error hypothesis.   Proc Natl Acad Sci U S A. 2011;108(suppl 3):15647-15654. doi:10.1073/pnas.1014269108PubMedGoogle ScholarCrossref
    21.
    Eshel  N, Roiser  JP.  Reward and punishment processing in depression.   Biol Psychiatry. 2010;68(2):118-124. doi:10.1016/j.biopsych.2010.01.027PubMedGoogle ScholarCrossref
    22.
    Argyropoulos  SV, Nutt  DJ.  Anhedonia revisited: is there a role for dopamine-targeting drugs for depression?   J Psychopharmacol. 2013;27(10):869-877. doi:10.1177/0269881113494104PubMedGoogle ScholarCrossref
    23.
    Bogdan  R, Nikolova  YS, Pizzagalli  DA.  Neurogenetics of depression: a focus on reward processing and stress sensitivity.   Neurobiol Dis. 2013;52:12-23. doi:10.1016/j.nbd.2012.05.007PubMedGoogle ScholarCrossref
    24.
    Treadway  MT, Zald  DH.  Reconsidering anhedonia in depression: lessons from translational neuroscience.   Neurosci Biobehav Rev. 2011;35(3):537-555. doi:10.1016/j.neubiorev.2010.06.006PubMedGoogle ScholarCrossref
    25.
    Treadway  MT, Zald  DH.  Parsing anhedonia: translational models of reward-processing deficits in psychopathology.   Curr Dir Psychol Sci. 2013;22(3):244-249. doi:10.1177/0963721412474460PubMedGoogle ScholarCrossref
    26.
    Pizzagalli  DA.  Depression, stress, and anhedonia: toward a synthesis and integrated model.   Annu Rev Clin Psychol. 2014;10(1):393-423. doi:10.1146/annurev-clinpsy-050212-185606PubMedGoogle ScholarCrossref
    27.
    Whitton  AE, Treadway  MT, Pizzagalli  DA.  Reward processing dysfunction in major depression, bipolar disorder and schizophrenia.   Curr Opin Psychiatry. 2015;28(1):7-12. doi:10.1097/YCO.0000000000000122PubMedGoogle ScholarCrossref
    28.
    Galvan  A.  Adolescent development of the reward system.   Front Hum Neurosci. 2010;4:6. doi:10.3389/neuro.09.006.2010PubMedGoogle Scholar
    29.
    Download citation of The Newcastle–Ottawa Scale (NOS) for Assessing the Quality of Non-Randomized Studies in Meta-Analysis. Accessed November 21, 2019. https://www.researchgate.net/publication/261773681_The_Newcastle-Ottawa_Scale_NOS_for_Assessing_the_Quality_of_Non-Randomized_Studies_in_Meta-Analysis/citation/download.
    30.
    Cléry-Melin  M-L, Schmidt  L, Lafargue  G, Baup  N, Fossati  P, Pessiglione  M.  Why don’t you try harder? an investigation of effort production in major depression.   PLoS One. 2011;6(8):e23178. doi:10.1371/journal.pone.0023178Google Scholar
    31.
    Viechtbauer  W.  Conducting meta-analyses in R with the metafor.   J Stat Softw. 2010;36(3):1-48. doi:10.18637/jss.v036.i03Google ScholarCrossref
    32.
    Wagner  J, Chelaru  F, Kancherla  J,  et al.  Metaviz: interactive statistical and visual analysis of metagenomic data.   Nucleic Acids Res. 2018;46(6):2777-2787. doi:10.1093/nar/gky136PubMedGoogle ScholarCrossref
    33.
    Thompson  SG, Sharp  SJ.  Explaining heterogeneity in meta-analysis: a comparison of methods.   Stat Med. 1999;18(20):2693-2708. doi:10.1002/(SICI)1097-0258(19991030)18:20<2693::AID-SIM235>3.0.CO;2-VPubMedGoogle ScholarCrossref
    34.
    Higgins  JPT, Thompson  SG, Deeks  JJ, Altman  DG.  Measuring inconsistency in meta-analyses.   BMJ. 2003;327(7414):557-560. doi:10.1136/bmj.327.7414.557PubMedGoogle ScholarCrossref
    35.
    Peters  JL, Sutton  AJ, Jones  DR, Abrams  KR, Rushton  L.  Contour-enhanced meta-analysis funnel plots help distinguish publication bias from other causes of asymmetry.   J Clin Epidemiol. 2008;61(10):991-996. doi:10.1016/j.jclinepi.2007.11.010PubMedGoogle ScholarCrossref
    36.
    Egger  M, Davey Smith  G, Schneider  M, Minder  C.  Bias in meta-analysis detected by a simple, graphical test.   BMJ. 1997;315(7109):629-634. doi:10.1136/bmj.315.7109.629PubMedGoogle ScholarCrossref
    37.
    Schimmack  U, Francis  G, McNeil  J,  et al R-Index 1 Running Head: R-Index Quantifying Statistical Research Integrity: The Replicabilty-Index. Published online 2014. Accessed June 16, 2020. https://pdfs.semanticscholar.org/5c9f/a44d1e2ffe0c48b8062fe83233d44daf7059.pdf
    38.
    Baek  K, Kwon  J, Chae  J-H,  et al.  Heightened aversion to risk and loss in depressed patients with a suicide attempt history.   Sci Rep. 2017;7(1):11228. doi:10.1038/s41598-017-10541-5PubMedGoogle ScholarCrossref
    39.
    Charpentier  CJ, Aylward  J, Roiser  JP, Robinson  OJ.  Enhanced risk aversion, but not loss aversion, in unmedicated pathological anxiety.   Biol Psychiatry. 2017;81(12):1014-1022. doi:10.1016/j.biopsych.2016.12.010PubMedGoogle ScholarCrossref
    40.
    Chung  D, Kadlec  K, Aimone  JA, McCurry  K, King-Casas  B, Chiu  PH.  Valuation in major depression is intact and stable in a non-learning environment.   Sci Rep. 2017;7:44374. doi:10.1038/srep44374PubMedGoogle ScholarCrossref
    41.
    Dombrovski  AY, Siegle  GJ, Szanto  K, Clark  L, Reynolds  CF, Aizenstein  H.  The temptation of suicide: striatal gray matter, discounting of delayed rewards, and suicide attempts in late-life depression.   Psychol Med. 2012;42(6):1203-1215. doi:10.1017/S0033291711002133PubMedGoogle ScholarCrossref
    42.
    Murphy  FC, Rubinsztein  JS, Michael  A,  et al.  Decision-making cognition in mania and depression.   Psychol Med. 2001;31(4):679-693. Accessed July 31, 2018. https://www.ncbi.nlm.nih.gov/pubmed/11352370. doi:10.1017/S0033291701003804PubMedGoogle ScholarCrossref
    43.
    Subramaniapillai  M, Mansur  RB, Zuckerman  H,  et al.  Association between cognitive function and performance on effort based decision making in patients with major depressive disorder treated with Vortioxetine.   Compr Psychiatry. 2019;94:152113. doi:10.1016/j.comppsych.2019.07.006PubMedGoogle Scholar
    44.
    Treadway  MT, Bossaller  NA, Shelton  RC, Zald  DH.  Effort-based decision-making in major depressive disorder: a translational model of motivational anhedonia.   J Abnorm Psychol. 2012;121(3):553-558. doi:10.1037/a0028813PubMedGoogle ScholarCrossref
    45.
    Yang  XH, Huang  J, Zhu  CY,  et al.  Motivational deficits in effort-based decision making in individuals with subsyndromal depression, first-episode and remitted depression patients.   Psychiatry Res. 2014;220(3):874-882. doi:10.1016/j.psychres.2014.08.056PubMedGoogle ScholarCrossref
    46.
    Admon  R, Kaiser  RH, Dillon  DG,  et al.  Dopaminergic enhancement of striatal response to reward in major depression.   Am J Psychiatry. 2017;174(4):378-386. doi:10.1176/appi.ajp.2016.16010111PubMedGoogle ScholarCrossref
    47.
    Arrondo  G, Segarra  N, Metastasio  A,  et al.  Reduction in ventral striatal activity when anticipating a reward in depression and schizophrenia: a replicated cross-diagnostic finding.   Front Psychol. 2015;6:1280. doi:10.3389/fpsyg.2015.01280PubMedGoogle ScholarCrossref
    48.
    Carl  H, Walsh  E, Eisenlohr-Moul  T,  et al.  Sustained anterior cingulate cortex activation during reward processing predicts response to psychotherapy in major depressive disorder.   J Affect Disord. 2016;203:204-212. doi:10.1016/j.jad.2016.06.005PubMedGoogle ScholarCrossref
    49.
    DelDonno  SR, Weldon  AL, Crane  NA,  et al.  Affective personality predictors of disrupted reward learning and pursuit in major depressive disorder.   Psychiatry Res. 2015;230(1):56-64. doi:10.1016/j.psychres.2015.08.011PubMedGoogle ScholarCrossref
    50.
    DelDonno  SR, Mickey  BJ, Pruitt  PJ,  et al.  Influence of childhood adversity, approach motivation traits, and depression on individual differences in brain activation during reward anticipation.   Biol Psychol. 2019;146:107709. doi:10.1016/j.biopsycho.2019.05.009PubMedGoogle Scholar
    51.
    DelDonno  SR, Karstens  AJ, Cerny  B,  et al.  The titrated monetary incentive delay task: sensitivity, convergent and divergent validity, and neural correlates in an RDoC sample.   J Clin Exp Neuropsychol. 2019;41(5):512-529. doi:10.1080/13803395.2019.1585519PubMedGoogle ScholarCrossref
    52.
    Pizzagalli  DA, Holmes  AJ, Dillon  DG,  et al.  Reduced caudate and nucleus accumbens response to rewards in unmedicated individuals with major depressive disorder.   Am J Psychiatry. 2009;166(6):702-710. doi:10.1176/appi.ajp.2008.08081201PubMedGoogle ScholarCrossref
    53.
    Sankar  A, Yttredahl  AA, Fourcade  EW,  et al.  Dissociable neural responses to monetary and social gain and loss in women with major depressive disorder.   Front Behav Neurosci. 2019;13:149. doi:10.3389/fnbeh.2019.00149PubMedGoogle ScholarCrossref
    54.
    Smoski  MJ, Rittenberg  A, Dichter  GS.  Major depressive disorder is characterized by greater reward network activation to monetary than pleasant image rewards.   Psychiatry Res. 2011;194(3):263-270. doi:10.1016/j.pscychresns.2011.06.012PubMedGoogle ScholarCrossref
    55.
    Takamura  M, Okamoto  Y, Okada  G,  et al.  Patients with major depressive disorder exhibit reduced reward size coding in the striatum.   Prog Neuropsychopharmacol Biol Psychiatry. 2017;79:317-323. doi:10.1016/j.pnpbp.2017.07.006Google ScholarCrossref
    56.
    Xie  W, Li  H, Luo  X,  et al.  Anhedonia and pain avoidance in the suicidal mind: behavioral evidence for motivational manifestations of suicidal ideation in patients with major depressive disorder.   J Clin Psychol. 2014;70(7):681-692. doi:10.1002/jclp.22055PubMedGoogle ScholarCrossref
    57.
    Aylward  J, Hales  C, Robinson  E, Robinson  OJ.  Translating a rodent measure of negative bias into humans: the impact of induced anxiety and unmedicated mood and anxiety disorders.   Psychol Med. 2020;50(2):237-246. doi:10.1017/S0033291718004117PubMedGoogle ScholarCrossref
    58.
    Henriques  JB, Davidson  RJ.  Decreased responsiveness to reward in depression.   Cogn Emot. 2000;14(5):711-724. doi:10.1080/02699930050117684Google ScholarCrossref
    59.
    Lawlor  VM, Webb  CA, Wiecki  TV,  et al.  Dissecting the impact of depression on decision-making.   Psychol Med. 2019;1-10. doi:10.1017/S0033291719001570PubMedGoogle Scholar
    60.
    Liu  W-H, Valton  V, Wang  L-Z, Zhu  Y-H, Roiser  JP.  Association between habenula dysfunction and motivational symptoms in unmedicated major depressive disorder.   Soc Cogn Affect Neurosci. 2017;12(9):1520-1533. doi:10.1093/scan/nsx074PubMedGoogle ScholarCrossref
    61.
    Vrieze  E, Pizzagalli  DA, Demyttenaere  K,  et al.  Reduced reward learning predicts outcome in major depressive disorder.   Biol Psychiatry. 2013;73(7):639-645. doi:10.1016/j.biopsych.2012.10.014PubMedGoogle ScholarCrossref
    62.
    Alexopoulos  GS, Manning  K, Kanellopoulos  D,  et al.  Cognitive control, reward-related decision making and outcomes of late-life depression treated with an antidepressant.   Psychol Med. 2015;45(14):3111-3120. doi:10.1017/S0033291715001075PubMedGoogle ScholarCrossref
    63.
    Cella  M, Dymond  S, Cooper  A.  Impaired flexible decision-making in major depressive disorder.   J Affect Disord. 2010;124(1-2):207-210. doi:10.1016/j.jad.2009.11.013PubMedGoogle ScholarCrossref
    64.
    Deisenhammer  EA, Schmid  SK, Kemmler  G, Moser  B, Delazer  M.  Decision making under risk and under ambiguity in depressed suicide attempters, depressed non-attempters and healthy controls.   J Affect Disord. 2018;226:261-266. doi:10.1016/j.jad.2017.10.012PubMedGoogle ScholarCrossref
    65.
    Dezfouli  A, Griffiths  K, Ramos  F, Dayan  P, Balleine  BW.  Models that learn how humans learn: the case of decision-making and its disorders.   PLoS Comput Biol. 2019;15(6):e1006903. doi:10.1371/journal.pcbi.1006903PubMedGoogle Scholar
    66.
    Gradin  VB, Kumar  P, Waiter  G,  et al.  Expected value and prediction error abnormalities in depression and schizophrenia.   Brain. 2011;134(Pt 6):1751-1764. doi:10.1093/brain/awr059PubMedGoogle ScholarCrossref
    67.
    Hall  GBC, Milne  AMB, Macqueen  GM.  An fMRI study of reward circuitry in patients with minimal or extensive history of major depression.   Eur Arch Psychiatry Clin Neurosci. 2014;264(3):187-198. doi:10.1007/s00406-013-0437-9PubMedGoogle ScholarCrossref
    68.
    Hegedűs  KM, Szkaliczki  A, Gál  BI, Andó  B, Janka  Z, Álmos  PZ.  Decision-making performance of depressed patients within 72 h following a suicide attempt.   J Affect Disord. 2018;235:583-588. doi:10.1016/j.jad.2018.04.082PubMedGoogle ScholarCrossref
    69.
    Herzallah  MM, Moustafa  AA, Natsheh  JY,  et al.  Learning from negative feedback in patients with major depressive disorder is attenuated by SSRI antidepressants.   Front Integr Neurosci. 2013;7:67. doi:10.3389/fnint.2013.00067PubMedGoogle ScholarCrossref
    70.
    Jollant  F, Bellivier  F, Leboyer  M,  et al.  Impaired decision making in suicide attempters.   Am J Psychiatry. 2005;162(2):304-310. doi:10.1176/appi.ajp.162.2.304PubMedGoogle ScholarCrossref
    71.
    Jollant  F, Richard-Devantoy  S, Ding  Y, Turecki  G, Bechara  A, Near  J.  Prefrontal inositol levels and implicit decision-making in healthy individuals and depressed patients.   Eur Neuropsychopharmacol. 2016;26(8):1255-1263. doi:10.1016/j.euroneuro.2016.06.005PubMedGoogle ScholarCrossref
    72.
    Kumar  P, Goer  F, Murray  L,  et al.  Impaired reward prediction error encoding and striatal-midbrain connectivity in depression.   Neuropsychopharmacology. 2018;43(7):1581-1588. doi:10.1038/s41386-018-0032-xPubMedGoogle ScholarCrossref
    73.
    Liu  WH, Chan  RCK, Wang  LZ,  et al.  Deficits in sustaining reward responses in subsyndromal and syndromal major depression.   Prog Neuropsychopharmacol Biol Psychiatry. 2011;35(4):1045-1052. doi:10.1016/j.pnpbp.2011.02.018PubMedGoogle ScholarCrossref
    74.
    McGovern  AR, Alexopoulos  GS, Yuen  GS, Morimoto  SS, Gunning-Dixon  FM.  Reward-related decision making in older adults: relationship to clinical presentation of depression.   Int J Geriatr Psychiatry. 2014;29(11):1125-1131. doi:10.1002/gps.4200PubMedGoogle ScholarCrossref
    75.
    Moutoussis  M, Rutledge  RB, Prabhu  G,  et al.  Neural activity and fundamental learning, motivated by monetary loss and reward, are intact in mild to moderate major depressive disorder.   PLoS One. 2018;13(8):e0201451. doi:10.1371/journal.pone.0201451PubMedGoogle Scholar
    76.
    Must  A, Szabó  Z, Bódi  N, Szász  A, Janka  Z, Kéri  S.  Sensitivity to reward and punishment and the prefrontal cortex in major depression.   J Affect Disord. 2006;90(2-3):209-215. doi:10.1016/j.jad.2005.12.005PubMedGoogle ScholarCrossref
    77.
    Nord  CL, Lawson  RP, Huys  QJM, Pilling  S, Roiser  JP.  Depression is associated with enhanced aversive Pavlovian control over instrumental behaviour.   Sci Rep. 2018;8(1):12582. doi:10.1038/s41598-018-30828-5PubMedGoogle ScholarCrossref
    78.
    Rothkirch  M, Tonn  J, Köhler  S, Sterzer  P.  Neural mechanisms of reinforcement learning in unmedicated patients with major depressive disorder.   Brain. 2017;140(4):1147-1157. doi:10.1093/brain/awx025PubMedGoogle ScholarCrossref
    79.
    Saperia  S, Da Silva  S, Siddiqui  I,  et al.  Reward-driven decision-making impairments in schizophrenia.   Schizophr Res. 2019;206:277-283. doi:10.1016/j.schres.2018.11.004PubMedGoogle ScholarCrossref
    80.
    Walsh  AEL, Browning  M, Drevets  WC, Furey  M, Harmer  CJ.  Dissociable temporal effects of bupropion on behavioural measures of emotional and reward processing in depression.   Philos Trans R Soc B Biol Sci. 2018;373(1742):20170030. doi:10.1098/rstb.2017.0030Google Scholar
    81.
    Terrin  N, Schmid  CH, Lau  J, Olkin  I.  Adjusting for publication bias in the presence of heterogeneity.   Stat Med. 2003;22(13):2113-2126. doi:10.1002/sim.1461PubMedGoogle ScholarCrossref
    82.
    Huys  QJ, Pizzagalli  DA, Bogdan  R, Dayan  P.  Mapping anhedonia onto reinforcement learning: a behavioural meta-analysis.   Biol Mood Anxiety Disord. 2013;3(1):12. doi:10.1186/2045-5380-3-12PubMedGoogle ScholarCrossref
    83.
    Collins  AGE, Frank  MJ.  How much of reinforcement learning is working memory, not reinforcement learning? a behavioral, computational, and neurogenetic analysis.   Eur J Neurosci. 2012;35(7):1024-1035. doi:10.1111/j.1460-9568.2011.07980.xPubMedGoogle ScholarCrossref
    84.
    Romans  SE, Tyas  J, Cohen  MM, Silverstone  T.  Gender differences in the symptoms of major depressive disorder.   J Nerv Ment Dis. 2007;195(11):905-911. doi:10.1097/NMD.0b013e3181594cb7PubMedGoogle ScholarCrossref
    85.
    Marschner  A, Mell  T, Wartenburger  I, Villringer  A, Reischies  FM, Heekeren  HR. Reward-based decision-making and aging. In:  Brain Research Bulletin. Vol 67. Elsevier; 2005:382-390. doi:10.1016/j.brainresbull.2005.06.010
    86.
    Sareen  J, Afifi  TO, McMillan  KA, Asmundson  GJG.  Relationship between household income and mental disorders: findings from a population-based longitudinal study.   Arch Gen Psychiatry. 2011;68(4):419-427. doi:10.1001/archgenpsychiatry.2011.15PubMedGoogle ScholarCrossref
    87.
    Layard  R, Nickell  S, Mayraz  G.  The marginal utility of income.   J Public Econ. 2008;92(8-9):1846-1857. doi:10.1016/j.jpubeco.2008.01.007Google ScholarCrossref
    88.
    Elliott  R, Sahakian  BJ, Herrod  JJ, Robbins  TW, Paykel  ES.  Abnormal response to negative feedback in unipolar depression: evidence for a diagnosis specific impairment.   J Neurol Neurosurg Psychiatry. 1997;63(1):74-82. doi:10.1136/jnnp.63.1.74PubMedGoogle ScholarCrossref
    89.
    Amsterdam  JD, Settle  RG, Doty  RL, Abelman  E, Winokur  A.  Taste and smell perception in depression.   Biol Psychiatry. 1987;22(12):1481-1485. doi:10.1016/0006-3223(87)90108-9PubMedGoogle ScholarCrossref
    90.
    Swiecicki  L, Zatorski  P, Bzinkowska  D, Sienkiewicz-Jarosz  H, Szyndler  J, Scinska  A.  Gustatory and olfactory function in patients with unipolar and bipolar depression.   Prog Neuropsychopharmacol Biol Psychiatry. 2009;33(5):827-834. doi:10.1016/j.pnpbp.2009.03.030PubMedGoogle ScholarCrossref
    91.
    Dichter  GS, Smoski  MJ, Kampov-Polevoy  AB, Gallop  R, Garbutt  JC.  Unipolar depression does not moderate responses to the Sweet Taste Test.   Depress Anxiety. 2010;27(9):859-863. doi:10.1002/da.20690PubMedGoogle ScholarCrossref
    92.
    Arrondo  G, Murray  GK, Hill  E,  et al.  Hedonic and disgust taste perception in borderline personality disorder and depression.   Br J Psychiatry. 2015;207(1):79-80. doi:10.1192/bjp.bp.114.150433PubMedGoogle ScholarCrossref
    93.
    Berlin  I, Givry-Steiner  L, Lecrubier  Y, Puech  AJ.  Measures of anhedonia and hedonic responses to sucrose in depressive and schizophrenic patients in comparison with healthy subjects.   Eur Psychiatry. 1998;13(6):303-309. doi:10.1016/S0924-9338(98)80048-5PubMedGoogle ScholarCrossref
    ×