期刊论文详细信息
BMC Psychiatry
Bias and discriminability during emotional signal detection in melancholic depression
Michael Breakspear2  Gordon Parker1  Matthew Hyett3 
[1]Black Dog Institute, Prince of Wales Hospital, Hospital Road, Randwick, NSW 2031, Australia
[2]The Royal Brisbane and Women’s Hospital, Herston, QLD 4029, Australia
[3]School of Psychiatry, University of New South Wales, Prince of Wales Hospital, Hospital Road, Randwick, NSW 2031, Australia
关键词: Signal detection;    Melancholia;    Depression;    Decision-making;    Bayesian analysis;   
Others  :  1123621
DOI  :  10.1186/1471-244X-14-122
 received in 2013-09-05, accepted in 2014-04-08,  发布年份 2014
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【 摘 要 】

Background

Cognitive disturbances in depression are pernicious and so contribute strongly to the burden of the disorder. Cognitive function has been traditionally studied by challenging subjects with modality-specific psychometric tasks and analysing performance using standard analysis of variance. Whilst informative, such an approach may miss deeper perceptual and inferential mechanisms that potentially unify apparently divergent emotional and cognitive deficits. Here, we sought to elucidate basic psychophysical processes underlying the detection of emotionally salient signals across individuals with melancholic and non-melancholic depression.

Methods

Sixty participants completed an Affective Go/No-Go (AGN) task across negative, positive and neutral target stimuli blocks. We employed hierarchical Bayesian signal detection theory (SDT) to model psychometric performance across three equal groups of those with melancholic depression, those with a non-melancholic depression and healthy controls. This approach estimated likely response profiles (bias) and perceptual sensitivity (discriminability). Differences in the means of these measures speak to differences in the emotional signal detection between individuals across the groups, while differences in the variance reflect the heterogeneity of the groups themselves.

Results

Melancholic participants showed significantly decreased sensitivity to positive emotional stimuli compared to those in the non-melancholic group, and also had a significantly lower discriminability than healthy controls during the detection of neutral signals. The melancholic group also showed significantly higher variability in bias to both positive and negative emotionally salient material.

Conclusions

Disturbances of emotional signal detection in melancholic depression appear dependent on emotional context, being biased during the detection of positive stimuli, consistent with a noisier representation of neutral stimuli. The greater heterogeneity of the bias across the melancholic group is consistent with a more labile disorder (i.e. variable across the day). Future work will aim to understand how these findings reflect specific individual differences (e.g. prior cognitive biases) and clarify whether such biases change dynamically during cognitive tasks as internal models of the sensorium are refined and updated in response to experience.

【 授权许可】

   
2014 Hyett et al.; licensee BioMed Central Ltd.

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【 参考文献 】
  • [1]Parker G, Hadzi-Pavlovic D: Melancholia: A Disorder of Movement and Mood. A Phenomenological and Neurobiological Review. Cambridge, UK: Cambridge University Press; 1996.
  • [2]Taylor MA, Fink M: Melancholia: The Diagnosis, Pathophysiology and Treatment of Depressive Illness. New York, NY: Cambridge University Press; 2006.
  • [3]Austin M-P, Mitchell P, Goodwin GM: Cognitive deficits in depression: possible implications for functional neuropathology. Br J Psychiatry 2001, 178:200-206.
  • [4]Bench CJ, Friston KJ, Brown RG, Scott LC, Frackowiak RS, Dolan RJ: The anatomy of melancholia: focal abnormalities of cerebral blood flow in major depression. Psychol Med 1992, 22(3):607-615.
  • [5]Paulus MP, Yu AJ: Emotion and decision-making: affect-driven belief systems in anxiety and depression. Trends Cog Sci 2012, 16(9):476-483.
  • [6]Franzen MD: Reliability and Validity in Neuropsychological Assessment. 2nd edition. New York, NY: Plenum Publishers; 2000.
  • [7]Dayan P, Hinton GE, Neal RM, Zemel RS: The Helmholtz machine. Neural Comput 1995, 7(5):889-904.
  • [8]Friston K: A theory of cortical responses. Philos Trans R Soc Lond B Biol Sci 2005, 360(1456):815-836.
  • [9]Knill DC, Pouget A: The Bayesian brain: the role of uncertainty in neural coding and computation. Trends Neurosci 2004, 27(12):712-719.
  • [10]Bayes T: An essay towards solving a problem in the doctrine of chances. Phil Trans Roy Soc Lond 1763, 53:370-418.
  • [11]Lee MD: BayesSDT: software for Bayesian inference with signal detection theory. Behav Res Methods 2008, 40(2):450-456.
  • [12]Lee MD, Wagenmakers E-J: Bayesian Cognitive Modeling: A Practical Course. Cambridge, UK: Cambridge University Press; 2014.
  • [13]Green DM, Swets JA: Signal Detection Theory and Psychophysics. New York, NY: Wiley; 1966.
  • [14]Wickens TD: Elementary Signal Detection Theory. Oxford, UK: Oxford University Press; 2002.
  • [15]Rouder JN, Lu J: An introduction to Bayesian hierarchical models with an application in the theory of signal detection. Psychon Bull Rev 2005, 12(4):573-604.
  • [16]Lee MD, Wagenmakers E-J: Bayesian statistical inference in psychology: comment on Trafimow (2003). Psychol Rev 2005, 112(3):662-668.
  • [17]Behrens TE, Woolrich MW, Walton ME, Rushworth MF: Learning the value of information in an uncertain world. Nat Neurosci 2007, 10(9):1214-1221.
  • [18]Karim M, Harris JA, Langdon A, Breakspear M: The influence of prior experience and expected timing on vibrotactile discrimination. Front Neurosci 2013, 7:255.
  • [19]Griffiths TL, Tenenbaum JB: Optimal predictions in everyday cognition. Psychol Sci 2006, 17(9):767-773.
  • [20]Beck AT, Alford BA: Depression: Causes and Treatment. 2nd edition. Philadelphia, PA: University of Pennsylvania Press; 2009.
  • [21]Huys QJ, Dayan P: A Bayesian formulation of behavioral control. Cognition 2009, 113(3):314-328.
  • [22]Mathews A, MacLeod C: Cognitive vulnerability to emotional disorders. Annu Rev Clin Psychol 2005, 1:167-195.
  • [23]Beck AT: Cognitive Therapy of Depression. New York, NY: Guildford Press; 1979.
  • [24]Mathews A, Ridgeway V, Williamson DA: Evidence for attention to threatening stimuli in depression. Behav Res Ther 1996, 34(9):695-705.
  • [25]Gilboa-Schechtman E, Erhard-Weiss D, Jeczemien P: Interpersonal deficits meet cognitive biases: memory for facial expressions in depressed and anxious men and women. Psychiatry Res 2002, 113(3):279-293.
  • [26]Elliott R, Rubinsztein JS, Sahakian BJ, Dolan RJ: The neural basis of mood-congruent processing biases in depression. Arch Gen Psychiatry 2002, 59(7):597-604.
  • [27]Sheehan DV, Lecrubier Y, Sheehan KH, Amorim P, Janavs J, Weiller E, Hergueta T, Baker R, Dunbar GC: The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J Clin Psychiatry 1998, 59:22-33.
  • [28]Rush AJ, Trivedi MH, Ibrahim HM, Carmody TJ, Arnow B, Klein DN, Markowitz JC, Ninan PT, Kornstein S, Manber R, Thase ME, Kocsis JH, Keller MB: The 16-Item Quick Inventory of Depressive Symptomatology (QIDS), clinician rating (QIDS-C), and self-report (QIDS-SR): a psychometric evaluation in patients with chronic major depression. Biol Psychiatry 2003, 54(5):573-583.
  • [29]Wechsler Adult Intelligence Scale®: Third Edition (WAIS®–III). San Antonio, TX: Pearson Assessments; 2001.
  • [30]Wechsler Test of Adult Reading™ (WTAR™). San Antonio, TX: Pearson Assessments; 2001.
  • [31]American Psychiatric Association: Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR). Arlington, VA: American Psychiatric Publishing; 2000.
  • [32]Spielberger CD: Manual for the State-Trait Anxiety Inventory (Form Y). Menlo Park, CA: Mind Garden; 1983.
  • [33]Parker G, Hadzi-Pavlovic D, Wilhelm K, Hickie I, Brodaty H, Boyce P, Mitchell P, Eyers K: Defining melancholia: properties of a refined sign-based measure. Br J Psychiatry 1994, 164(3):316-326.
  • [34]Parker G, Fink M, Shorter E, Taylor MA, Akiskal H, Berrios G, Bolwig T, Brown WA, Carroll B, Healy D, Klein DF, Koukopoulos A, Michels R, Paris J, Rubin RT, Spitzer R, Swartz C: Issues for DSM-5: whither melancholia? The case for its classification as a distinct mood disorder. Am J Psychiatry 2010, 167(7):745-747.
  • [35]Parker G, Fletcher K, Hyett M, Hadzi-Pavlovic D, Barrett M, Synnott H: Measuring melancholia: the utility of a prototypic symptom approach. Psychol Med 2009, 39(6):989-998.
  • [36]Robbins TW, James M, Owen AM, Sahakian BJ, McInnes L, Rabbitt P: Cambridge Neuropsychological Test Automated Battery (CANTAB): a factor analytic study of a large sample of normal elderly volunteers. Dementia 1994, 5(5):266-281.
  • [37]Lunn DJ, Thomas A, Best N, Spiegelhalter D: WinBUGS - a Bayesian modelling framework: concepts, structure, and extensibility. Stat Comput 2000, 10:325-337.
  • [38]Gelman A, Rubin DB: Inference from iterative simulation using multiple sequences. Stat Sci 1992, 7:457-511.
  • [39]Geweke J: Evaluating the accuracy of sampling-based approaches to calculating posterior moments. In Bayesian Statistics 4: Proceedings of the Fourth Valencia International Meeting. Edited by Bernardo JM, Berger JO, Dawid AP, Smith AFM. Oxford, UK: Clarendon Press; 1992.
  • [40]Plummer M, Best N, Cowles K, Vines K: CODA: Convergence Diagnosis and Output Analysis for MCMC. R News 2006, 6(1):7-11.
  • [41]Lindley DV: Introduction to Probability and Statistics from a Bayesian Viewpoint. Part 2: Inference. Cambridge, UK: Cambridge University Press; 1965.
  • [42]Hintze JL, Nelson RD: Violin plots: A box plot-density trace synergism. Am Statistician 1998, 52(2):181-184.
  • [43]Murphy FC, Sahakian BJ, Rubinsztein JS, Michael A, Rogers RD, Robbins TW, Paykel ES: Emotional bias and inhibitory control processes in mania and depression. Psychol Med 1999, 29(6):1307-1321.
  • [44]Schulz KP, Fan J, Magidina O, Marks DJ, Hahn B, Halperin JM: Does the emotional go/no-go task really measure behavioral inhibition? Convergence with measures on a non-emotional analog. Arch Clin Neuropsychol 2007, 22(2):151-160.
  • [45]Michopoulos I, Zervas IM, Pantelis C, Tsaltas E, Papakosta VM, Boufidou F, Nikolaou C, Papageorgiou C, Soldatos CR, Lykouras L: Neuropsychological and hypothalamic-pituitary-axis function in female patients with melancholic and non-melancholic depression. Eur Arch Psychiatry Clin Neurosci 2008, 258(4):217-225.
  • [46]Rogers MA, Bellgrove MA, Chiu E, Mileshkin C, Bradshaw JL: Response selection deficits in melancholic but not nonmelancholic unipolar major depression. J Clin Exp Neuropsychol 2004, 26(2):169-179.
  • [47]Ridderinkhof KR, van den Wildenberg WP, Segalowitz SJ, Carter CS: Neurocognitive mechanisms of cognitive control: the role of prefrontal cortex in action selection, response inhibition, performance monitoring, and reward-based learning. Brain Cogn 2004, 56(2):129-140.
  • [48]Hauser MD: Perseveration, inhibition and the prefrontal cortex: a new look. Curr Opin Neurobiol 1999, 9(2):214-222.
  • [49]Glimcher PW: Decisions, Uncertainty, and the Brain: The Science of Neuroeconomics. Cambridge, MA: The MIT Press; 2003.
  • [50]Yu AJ, Dayan P, Cohen JD: Dynamics of attentional selection under conflict: toward a rational Bayesian account. J Exp Psychol Hum Percept Perform 2009, 35(3):700-717.
  • [51]Chamberlain SR, Muller U, Blackwell AD, Clark L, Robbins TW, Sahakian BJ: Neurochemical modulation of response inhibition and probabilistic learning in humans. Science 2006, 311(5762):861-863.
  • [52]Schildkraut JJ: The catecholamine hypothesis of affective disorders: a review of supporting evidence. Am J Psychiatry 1965, 122(5):509-522.
  • [53]Mathys C, Daunizeau J, Friston KJ, Stephan KE: A Bayesian foundation for individual learning under uncertainty. Front Hum Neurosci 2011, 5:39.
  • [54]Paykel ES: Classification of depressed patients: a cluster analysis derived grouping. Br J Psychiatry 1971, 118(544):275-288.
  • [55]Moffoot AP, O'Carroll RE, Bennie J, Carroll S, Dick H, Ebmeier KP, Goodwin GM: Diurnal variation of mood and neuropsychological function in major depression with melancholia. J Affect Disord 1994, 32(4):257-269.
  • [56]Clark DM, Teasdale JD: Diurnal variation in clinical depression and accessibility of memories of positive and negative experiences. J Abnorm Psychol 1982, 91(2):87-95.
  • [57]Carroll BJ: The dexamethasone suppression test for melancholia. Br J Psychiatry 1982, 140:292-304.
  • [58]Rubinow DR, Post RM, Savard R, Gold PW: Cortisol hypersecretion and cognitive impairment in depression. Arch Gen Psychiatry 1984, 41(3):279-283.
  • [59]Harmer CJ, Goodwin GM, Cowen PJ: Why do antidepressants take so long to work? A cognitive neuropsychological model of antidepressant drug action. Br J Psychiatry 2009, 195(2):102-108.
  • [60]Montague PR, Dolan RJ, Friston KJ, Dayan P: Computational psychiatry. Trends Cog Sci 2012, 16(1):72-80.
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