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Dissecting the impact of depression on decision-making

Published online by Cambridge University Press:  08 July 2019

Victoria M. Lawlor ,
Christian A. Webb ,
Thomas V. Wiecki ,
Michael J. Frank ,
Madhukar Trivedi ,
Diego A. Pizzagalli  and
Daniel G. Dillon Open the ORCID record for Daniel G. Dillon [Opens in a new window]
Victoria M. Lawlor
Affiliation:
Center for Depression, Anxiety and Stress Research, McLean Hospital/Harvard Medical School, Belmont, Massachusetts, USA Emory University, Atlanta, Georgia, USA
Christian A. Webb
Affiliation:
Center for Depression, Anxiety and Stress Research, McLean Hospital/Harvard Medical School, Belmont, Massachusetts, USA
Thomas V. Wiecki
Affiliation:
Quantopian, Inc, Boston, Massachusetts, USA
Michael J. Frank
Affiliation:
Brown University, Providence, Rhode Island, USA
Madhukar Trivedi
Affiliation:
UT Southwestern Medical Center, Dallas, Texas, USA
Diego A. Pizzagalli
Affiliation:
Center for Depression, Anxiety and Stress Research, McLean Hospital/Harvard Medical School, Belmont, Massachusetts, USA
Daniel G. Dillon*
Affiliation:
Center for Depression, Anxiety and Stress Research, McLean Hospital/Harvard Medical School, Belmont, Massachusetts, USA
*
Author for correspondence: Daniel G. Dillon, E-mail: ddillon@mclean.harvard.edu
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Abstract

Background

Cognitive deficits in depressed adults may reflect impaired decision-making. To investigate this possibility, we analyzed data from unmedicated adults with Major Depressive Disorder (MDD) and healthy controls as they performed a probabilistic reward task. The Hierarchical Drift Diffusion Model (HDDM) was used to quantify decision-making mechanisms recruited by the task, to determine if any such mechanism was disrupted by depression.

Methods

Data came from two samples (Study 1: 258 MDD, 36 controls; Study 2: 23 MDD, 25 controls). On each trial, participants indicated which of two similar stimuli was presented; correct identifications were rewarded. Quantile-probability plots and the HDDM quantified the impact of MDD on response times (RT), speed of evidence accumulation (drift rate), and the width of decision thresholds, among other parameters.

Results

RTs were more positively skewed in depressed v. healthy adults, and the HDDM revealed that drift rates were reduced—and decision thresholds were wider—in the MDD groups. This pattern suggests that depressed adults accumulated the evidence needed to make decisions more slowly than controls did.

Conclusions

Depressed adults responded slower than controls in both studies, and poorer performance led the MDD group to receive fewer rewards than controls in Study 1. These results did not reflect a sensorimotor deficit but were instead due to sluggish evidence accumulation. Thus, slowed decision-making—not slowed perception or response execution—caused the performance deficit in MDD. If these results generalize to other tasks, they may help explain the broad cognitive deficits seen in depression.

Keywords

Computational modelingdecision-makingdepressiondrift diffusion modelreward
Type
Original Articles
Information
Psychological Medicine , Volume 50 , Issue 10 , July 2020 , pp. 1613 - 1622
Copyright
Copyright © Cambridge University Press 2019

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References

Barch, DM, Carter, CS, Gold, JM, Johnson, SL, Kring, AM, MacDonald, AW III, Pizzagalli, DA, Ragland, JD, Silverstein, SM and Strauss, ME (2017) Explicit and implicit reinforcement learning across the psychosis spectrum. Journal of Abnormal Psychology 126, 694–711.CrossRefGoogle ScholarPubMed
Bates, D, Mächler, M, Bolker, BM and Walker, SC (2014) Fitting linear mixed-effects models using lme4. Journal of Statistical Software 67, 1–48.Google Scholar
Beste, C, Adelhöfer, N, Gohil, K, Passow, S, Roessner, V and Li, SC (2018) Dopamine modulates the efficiency of sensory evidence accumulation during perceptual decision making. International Journal of Neuropsychopharmacology 21, 649–655.CrossRefGoogle ScholarPubMed
Biringer, E, Mykletun, A, Sundet, K, Kroken, R, Stordal, KI and Lund, A (2007) A longitudinal analysis of neurocognitive function in unipolar depression. Journal of Clinical and Experimental Neuropsychology 29, 879–891.CrossRefGoogle ScholarPubMed
Burt, DB, Zembar, MJ and Niederehe, G (1995) Depression and memory impairment: a meta-analysis of the association, its pattern, and specificity. Psychological Bulletin 117, 285–305.CrossRefGoogle Scholar
Der-Avakian, A, D'souza, MS, Pizzagalli, DA and Markou, A (2013) Assessment of reward responsiveness in the response bias probabilistic reward task in rats: implications for cross-species translational research. Translational Psychiatry 3, e297.CrossRefGoogle ScholarPubMed
Dillon, DG, Dobbins, IG and Pizzagalli, DA (2013) Weak reward source memory in depression reflects blunted activation of VTA/SN and parahippocampus. Social Cognitive and Affective Neuroscience 9, 1576–1583.CrossRefGoogle ScholarPubMed
Douglas, KM and Porter, RJ (2009) Longitudinal assessment of neuropsychological function in major depression. Australian and New Zealand Journal of Psychiatry 43, 1105–1117.CrossRefGoogle ScholarPubMed
First, MB, Spitzer, RL, Gibbon, M and Williams, JBW (2002) Structured Clinical Interview for DSM-IV-TR Axis I Disorders, Research Version, Patient Edition. New York: (SCID-I/P) Biometrics Research, New York State Psychiatric Institute.Google Scholar
Frank, MJ, Gagne, C, Nyhus, E, Masters, S, Wiecki, TV, Cavanagh, JF and Badre, D (2015) fMRI and EEG predictors of dynamic decision parameters during human reinforcement learning. Journal of Neuroscience 35, 485–494.CrossRefGoogle ScholarPubMed
Gold, JI and Shadlen, MN (2007) The neural basis of decision making. Annual Review of Neuroscience 30, 535–574.CrossRefGoogle ScholarPubMed
Hamilton, M (1960) A rating scale for depression. Journal of Neurology, Neurosurgery, and Psychiatry 23, 56–62.CrossRefGoogle ScholarPubMed
Jiang, J, Zhao, YJ, Hu, XY, Du, MY, Chen, ZQ, Wu, M, Li, KM, Zhu, HY, Kumar, P and Gong, QY (2017) Microstructural brain abnormalities in medication-free patients with major depressive disorder: a systematic review and meta-analysis of diffusion tensor imaging. Journal of Psychiatry & Neuroscience 42, 150–163.CrossRefGoogle ScholarPubMed
Kluyver, T, Ragan-Kelley, B, Pérez, F, Granger, BE, Bussonnier, M, Frederic, J, Kelley, K, Hamrick, JB, Grout, J, Corlay, S and Ivanov, P (2016) Jupyter notebooks-a publishing format for reproducible computational workflows. In Loizides, F and Schmidt, B (eds) Positioning and Power in Academic Publishing: Players, Agents and Agendas. IOS Press: Amsterdam, pp. 87–90.Google Scholar
Krajbich, I, Lu, D, Camerer, C and Rangel, A (2012) The attentional drift-diffusion model extends to simple purchasing decisions. Frontiers in Psychology 3, 193.CrossRefGoogle ScholarPubMed
Kruschke, J (2014) Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan. Oxford: Elsevier Science.Google Scholar
Levinson, AR, Speed, BC, Infantolino, ZP and Hajcak, G (2017) Reliability of the electrocortical response to gains and losses in the doors task. Psychophysiology 54, 601–607.CrossRefGoogle ScholarPubMed
Liu, WH, Roiser, JP, Wang, LZ, Zhu, YH, Huang, J, Neumann, DL, Shum, DH, Cheung, EF and Chan, RC (2016) Anhedonia is associated with blunted reward sensitivity in first-degree relatives of patients with major depression. Journal of Affective Disorders 190, 640–648.CrossRefGoogle ScholarPubMed
Luking, KR, Nelson, BD, Infantolino, ZP, Sauder, CL and Hajcak, G (2017) Internal consistency of functional magnetic resonance imaging and electroencephalography measures of reward in late childhood and early adolescence. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 2, 289–297.Google ScholarPubMed
MacQueen, GM, Campbell, S, McEwen, BS, Macdonald, K, Amano, S, Joffe, RT, Nahmias, C, Young, LT (2003) Course of illness, hippocampal function, and hippocampal volume in major depression. Proceedings of the National Academy of Sciences, USA 100, 1387–1392.CrossRefGoogle ScholarPubMed
Madden, DJ, Spaniol, J, Costello, MC, Bucur, B, White, LE, Cabeza, R, Davis, SW, Dennis, NA, Provenzale, JM and Huettel, SA (2008) Cerebral white matter integrity mediates adult age differences in cognitive performance. Journal of Cognitive Neuroscience 21, 289–302.CrossRefGoogle Scholar
Pizzagalli, DA, Jahn, AL and O'Shea, JP (2005) Toward an objective characterization of an anhedonic phenotype: a signal-detection approach. Biological Psychiatry 57, 319–327.CrossRefGoogle ScholarPubMed
Pizzagalli, DA, Iosifescu, D, Hallett, LA, Ratner, KG and Fava, M (2008) Reduced hedonic capacity in major depressive disorder: evidence from a probabilistic reward task. Journal of Psychiatric Research 43, 76–87.CrossRefGoogle ScholarPubMed
Ratcliff, R (1978) A theory of memory retrieval. Psychological Review 85, 59–108.CrossRefGoogle Scholar
Ratcliff, R and McKoon, G (2008) The diffusion decision model: theory and data for two-choice decision tasks. Neural Computation 20, 873–922.CrossRefGoogle ScholarPubMed
Ratcliff, R and Rouder, JN (1998) Modeling response times for two-choice decisions. Psychological Science 9, 347–356.CrossRefGoogle Scholar
Ratcliff, R and Smith, PL (2004) A comparison of sequential sampling models for two-choice reaction time. Psychological Review 111, 333–367.CrossRefGoogle ScholarPubMed
R Core Team (2018) R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. (http://www.R-project.org/).Google Scholar
Rock, PL, Roiser, JP, Riedel, WJ and Blackwell, AD (2014) Cognitive impairment in depression: a systematic review and meta-analysis. Psychological Medicine 44, 2029–2040.CrossRefGoogle ScholarPubMed
Shadlen, MN and Kiani, R (2013) Decision making as a window on cognition. Neuron 80, 791–806.CrossRefGoogle ScholarPubMed
Singmann, H, Bolker, B, Westfall, J, Aust, F, Højsgaard, S, Fox, J, Lawrence, MA, Mertens, U and Love, J (2016) afex: analysis of factorial experiments. R package version 0.16-1.Google Scholar
Snyder, HR (2013) Major depressive disorder is associated with broad impairments on neuropsychological measures of executive function: a meta-analysis and review. Psychological Bulletin 139, 81–132.CrossRefGoogle ScholarPubMed
Thapar, A, Ratcliff, R and McKoon, G (2003) A diffusion model analysis of the effects of aging on letter discrimination. Psychology and Aging 18, 415–429.CrossRefGoogle ScholarPubMed
Treadway, MT and Zald, DH (2011) Reconsidering anhedonia in depression: lessons from translational neuroscience. Neuroscience & Biobehavioral Reviews 35, 537–555.CrossRefGoogle ScholarPubMed
Trivedi, MH, McGrath, PJ, Fava, M, Parsey, RV, Kurian, BT, Phillips, ML, Oquendo, MA, Bruder, G, Pizzagalli, D, Toups, M and Cooper, C (2016) Establishing moderators and biosignatures of antidepressant response in clinical care (EMBARC): rationale and design. Journal of Psychiatric Research 78, 11–23.CrossRefGoogle ScholarPubMed
Vrieze, E, Pizzagalli, DA, Demyttenaere, K, Hompes, T, Sienaert, P, de Boer, P, Schmidt, M and Claes, S (2013) Reduced reward learning predicts outcome in major depressive disorder. Biological Psychiatry 73, 639–645.CrossRefGoogle ScholarPubMed
Watson, D, Weber, K, Assenheimer, JS, Clark, LA, Strauss, ME and McCormick, RA (1995) Testing a tripartite model: i. Evaluating the convergent and discriminant validity of anxiety and depression symptom scales. Journal of Abnormal Psychology 104, 3–14.CrossRefGoogle ScholarPubMed
White, CN and Poldrack, RA (2014) Decomposing bias in different types of simple decisions. Journal of Experimental Psychology: Learning, Memory, and Cognition 40, 385–398.Google ScholarPubMed
White, C, Ratcliff, R, Vasey, M and McKoon, G (2009) Dysphoria and memory for emotional material: a diffusion-model analysis. Cognition and Emotion 23, 181–205.CrossRefGoogle ScholarPubMed
White, CN, Ratcliff, R, Vasey, MW and McKoon, G (2010) Using diffusion models to understand clinical disorders. Journal of Mathematical Psychology 54, 39–52.CrossRefGoogle ScholarPubMed
Wiecki, TV, Sofer, I and Frank, MJ (2013) HDDM: hierarchical Bayesian estimation of the drift-diffusion model in python. Frontiers in Neuroinformatics 7, 14.CrossRefGoogle ScholarPubMed
Zakzanis, KK, Leach, L and Kaplan, E (1998) On the nature and pattern of neurocognitive function in major depressive disorder. Neuropsychiatry, Neuropsychology, & Behavioral Neurology 11, 111–119.Google ScholarPubMed
Zimmerman, M, Martinez, JH, Young, D, Chelminski, I and Dalrymple, K (2013) Severity classification on the Hamilton depression rating scale. Journal of Affective Disorders 150, 384–388.CrossRefGoogle ScholarPubMed
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