Psychiatric Annals

CME Article 

Pathology-Congruent Biases as Biomarkers for Psychopathology

Abram Davidov, MD; Tracy L. Greer, PhD, MSCS

Abstract

Examination of emotional dysregulation and associated biases in psychiatric disorders may yield promising biomarkers of psychopathology. Psychiatric disorders are associated with altered attentional bias (tendency to orient attention to preferred stimuli) and interpretational bias (tendency to assign specific meaning to ambiguous stimuli). Biases seen in these disorders tend to be “pathology-congruent,” with preferential attention to stimuli that align with symptoms and concerns of a specific disorder. These biases have been predictive of clinical outcomes and may, therefore, support their utility as biomarkers of treatment response. This review discusses the clinical relevance of pathology-congruent biases of depression (propensity for negative information), anxiety (over-awareness of threat), and addictive disorders (heightened awareness and preference of addiction cues). Further, this review briefly highlights attention bias modification, a therapy designed specifically to address these biases. The potential for use of biases as biomarkers of treatment response, treatment targets, and differential disease indicators is also discussed. [Psychiatr Ann. 2020;50(6):250–254.]

Abstract

Examination of emotional dysregulation and associated biases in psychiatric disorders may yield promising biomarkers of psychopathology. Psychiatric disorders are associated with altered attentional bias (tendency to orient attention to preferred stimuli) and interpretational bias (tendency to assign specific meaning to ambiguous stimuli). Biases seen in these disorders tend to be “pathology-congruent,” with preferential attention to stimuli that align with symptoms and concerns of a specific disorder. These biases have been predictive of clinical outcomes and may, therefore, support their utility as biomarkers of treatment response. This review discusses the clinical relevance of pathology-congruent biases of depression (propensity for negative information), anxiety (over-awareness of threat), and addictive disorders (heightened awareness and preference of addiction cues). Further, this review briefly highlights attention bias modification, a therapy designed specifically to address these biases. The potential for use of biases as biomarkers of treatment response, treatment targets, and differential disease indicators is also discussed. [Psychiatr Ann. 2020;50(6):250–254.]

Psychiatric disorders are heterogeneous in biology and presentation. The classification schema adopted in clinical practice has proven to be insufficient to fully capture psychopathologic processes within psychiatric disorders.1 The search for biomarkers (objectively measured indicators of a biologic process) may support an alternative framework for classifying psychiatric disorders by objective signs as opposed to subjective symptoms.2 The Research Domain Criteria (RDoC) was established to help elucidate the underlying biology of psychiatric illness for this reason.3 Cognition is a domain of particular interest in RDoC, as cognitive deficits are evident across the wide breadth of psychiatric disorders.4 This is also evident in the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5);5 as an example, both criterion 8 of major depressive disorder (diminished ability to think or concentrate, or indecisiveness) and criterion 3 of generalized anxiety disorder (difficulty concentrating or mind going blank) have overlapping cognitive difficulties.6 The wide distribution among disorders and variable severity of impact make cognition a prime candidate for biomarker-driven subtypes. Ideally, biomarkers will be identified that can aid in differentiating between a variety of disorders and provide trackable outcomes of treatment response and/or disease course.

Attentional and interpretational biases are specific aspects of affective cognition that can serve as a bridge between our current nomenclatures of psychiatric disorders and neurophysiologically focused dysfunction. Attentional bias is a process whereby a person orients their attention toward preferential stimuli, whereas interpretational bias is the tendency to assign specific meaning to ambiguous stimuli. Psychiatric disorders seem to have “pathology-congruent” biases, in which the stimulus content aligns with symptoms and concerns associated with the disorder.7 For example, patients with depression have heightened attention toward negatively valenced stimuli compared to neutral or positive stimuli.8 Experimental tasks have been developed to assess bias outcomes such as reaction time, memory recall, or neural activity after stimulus introduction. These outcomes provide representation of the intersection between emotionally laden and non-emotionally laden cognitive processes. Bias outcomes have been associated with useful clinical correlates with potential to inform risk susceptibility, symptom monitoring, and treatment considerations. This review focuses on exemplars of pathology-congruent biases within psychiatric disorders and their clinical relevance, including their potential use as meaningful biomarkers of psychopathology and associated treatment response.

Depressive Disorders

A symptom-driven analysis of DSM-5 diagnostic criteria indicates 227 different possible combinations that result in a diagnosis of major depressive disorder.9 Further, there are other symptoms (eg, loss of insight) that are not regularly assessed within diagnostic criteria but that may play a role in identifying discrete subtypes.10 Behavioral tasks and stimuli-induced neuroimaging can provide objective data to help identify distinct groups or subtypes of depressed people under psychiatric burden.

The emotion recognition task is a paradigm that allows for testing of interpretational bias. In the task, participants are shown human faces bearing expressions at varying levels of intensity—a smiling face may be designated as “happy,” a frowning face may be designated as “sad,” and a face without expression in any valence may be designated as “neutral.”11 Compared to healthy non-depressed controls, patients with depression are more likely to interpret ambiguous information as negative (eg, conflate neutral faces as sad faces),12 and sustain attention to negative stimuli (eg, focus more on sad faces when compared to neutral faces).13 Persons who are depressed are also more likely to remember negative information (eg, have greater recall for negative words),14 with one meta-analysis finding that participants with depression had a greater attentional bias toward negative information with a moderate effect size of 0.37.15 These studies indicate objective evidence of negative biases that exist in depression, which aligns with Beck's cognitive model.16

The evidence that these observable negative biases are related to the depression syndrome is significant. Negative biases are seen in people who are at high risk of developing depression,17 and they can predict onset of a depressive episode.18 These negative biases and associated altered neural activity resolve with successful treatment of depression, including pharmacotherapy,19 neurostimulation,20 and cognitive behavioral therapy.21

Cognitive and emotional task performance, including speed and accuracy of emotion recognition, has been shown to predict both remission status as well as differential response to specific medications.22 Furthermore, biased emotional processing has been observed in first-degree relatives of people with depression compared to healthy controls, suggesting that negative emotional bias may serve as a biomarker of familial vulnerability for the development of depression.23 Thus, emotional biases may aid in predicting both treatment response as well as disease risk.

Brain regions underlying differences between healthy controls and people with depression during emotional bias tasks include the amygdala, hippocampus, and prefrontal cortex.21 Differential activity in these brain regions can also serve to predict responders versus nonresponders24 and identify depressive subtype.25 Recent evidence suggests that people with stronger resting state electroencephalography signatures are more capable of emotion regulation.26 These data highlight the benefit of combining behavioral data with neuroimaging and neurophysiological data to better understand the relationship between brain function and behavior.

Anxiety Disorders

Anxiety disorders are pervasive and often difficult to disentangle, with comorbidity rates between anxiety disorders reaching 60%.27 This overlap betrays a likely significant underlying neural mechanism between anxiety disorders. A unifying pathophysiology would be helpful in diagnosis and treatment development.

Processing of perceived threat has been studied with respect to the etiology and maintenance of anxiety disorders, and it has been shown to be a neurocognitive process that reliably distinguishes people with state or trait anxiety features from healthy controls.28 One meta-analysis has shown similar effect sizes of attention bias to threating stimuli (eg, angry faces) between generalized anxiety disorder (GAD), social phobia, obsessive-compulsive disorder, panic disorder, and posttraumatic stress disorder,28 signifying a shared mechanism between anxiety disorders. This bias toward perceived threat is sensitive to increasing intensity of stimulus29 and is associated with severity of clinical symptoms.30

Neuroimaging studies have identified hyperactivity in the amygdala to be associated with threat bias behavior in people with anxiety compared to healthy controls.31 This seems to be true regardless of the underlying specific anxiety diagnosis.31 Future studies of amygdala response during pathology-congruent stimuli tasks could further establish pathophysiologic mechanisms. Additionally, examination of resting state functional connectivity in limbic and executive networks in people with major depressive disorder or GAD, compared to healthy controls showed hyperactivity in the limbic network, consisting of amygdala, hippocampus, ventromedial prefrontal cortex, and subgenual anterior cingulate cortex in both patient groups. This hyperactivity predicted threat bias.32 Interestingly, the two patient groups performed similarly, suggesting that additional work is needed to better understand the relationship between neural activity, behavioral performance, and the ability for these biomarkers to differentiate between disorders rather than indicate a dysregulation that is shared across disorders. However, it should be noted that similar emotional dysregulation and associated neural alterations are also seen in other disorders such as attention-deficit/hyperactivity disorder,33,34 again suggesting the need for further investigation of shared versus distinct pathophysiology across disorders.

Substance Use Disorders

The field of addictive disorders has recognized the issue of phenotypic heterogeneity in patients who meet criteria for addiction to identical agents. To combat this issue, the Addictions Neuroclinical Assessment (ANA) was conceptualized as a neuroscience-based framework for addictive disorders.35 Like RDoC, the ANA has created a domain-based system that is core to the neuroscience of the cycle of addiction—executive function, incentive salience, and negative emotionality. Of these three domains, incentive salience (the attractiveness of perceived stimuli) can be tested with attentional bias tasks.

In the addiction field, attention bias tasks present the participant with a variety of stimuli, some of which are related to their specific addiction (eg, a picture of cocaine for a patient with cocaine use disorder). People with substance use disorders have altered biases toward stimuli related to their addiction, often shown with altered cortico-striatal dopaminergic activity.36 One functional magnetic resonance study found that when people with cocaine use disorder were even subliminally presented with images of cocaine there was an increase of activity in the amygdala and ventral striatum (relative to subliminal neutral images), indicating an unconscious component.37 This dysregulated activity occurs in patients with addictive tendencies toward a variety of substances (eg, cocaine, and alcohol) and behaviors (eg, gambling).38 In other studies, activity in the above-mentioned circuitry and associated regions (ventromedial prefrontal cortex, anterior cingulate cortex, ventral striatum, and precuneus) during stimulus presentation were predictors of relapse in people who had recovered from alcoholism, and also predicted the severity of symptoms post relapse.39

As is the case with other psychiatric disorders, the relapse rates of substance use disorders are significant, with 60% of patients returning to substance use within 1 year from treatment.40 The identification of biomarkers offers the possibility of identifying subgroups of patients at high risk for relapse and may allow for early intervention or closer monitoring.

Attention Bias Modification

Attention bias modification therapy uses the same tasks that identify pathology-congruent biases and trains people to gain control over their responses.41 Therapies have been designed for anxiety, depression, and substance use disorders, with randomized controlled trials showing moderate effect in treating anxiety disorders and less evidence of efficacy for depressive and substance use disorders.42 Currently, the field of attention bias modification is undergoing a technological upheaval, with many internet- and mobile-based interventions being developed.43 However, these interventions have only shown limited efficacy.44 Moreover the connection between bias modification and symptom resolution has been under increased scrutiny. A recent meta-analysis of reaction time tasks that measured threat bias failed to show a correlation between symptom severity at baseline and altered reaction time to threatening stimulus.45 Although there is great potential for the mobile health application of therapies to modify attention bias, there needs to be more reliable indicators for capturing discrepant biases.41 Perhaps combining neuro-feedback technology with pathology-congruent bias theories may prove to be a way to get more consistent and credible results.46

Conclusion

Pathology-congruent biases are seen in a variety of psychiatric disorders, including depressive, anxious, and substance use disorders. This specific niche of cognitive psychology has generated a body of evidence that is leading to emerging therapies, such as attention bias modification. Integrating cognitive neuroscience approaches such as neuroimaging and electroencephalography techniques during task performance has helped to elucidate brain regions involved in and/or disrupted in association with varying psychiatric illnesses. Emotional and attentional biases have been shown to serve as biomarkers of treatment response (including differential response to medications) as well as potential risk of psychiatric disease.

Importantly, there is a wide variety of both emotionally laden and non-emotionally laden cognitive processes that have shown variable evidence as putative biomarkers of psychiatric disease pathology or treatment response. Combining attentional and interpretational bias tasks with tasks representing other cognitive domains (eg, executive function, reward learning) may also substantively increase the potential clinical implications and applicability of attentional bias. Although a bias-focused examination specific for psychiatry is yet to exist, evidence from the wide berth of cognitive psychology may one day be amalgamated into a precise clinical tool.

It is important to note that although biases are an informative area in which to focus on biomarkers to inform disease pathology and treatment, there are a wide variety of putative cognitive biomarkers that show similar promise, including alterations in reward, motivation, impulsivity, and social cognition, as well as many aspects of executive function, learning, and memory. Ultimately, the integration of behavioral and neuroimaging data will facilitate multimodal specification of cognitive biomarkers, which may enhance precision medicine approaches for treating psychiatric disorders.

References

  1. Wolfers T, Doan NT, Kaufmann T, et al. Mapping the heterogeneous phenotype of schizophrenia and bipolar disorder using normative models. JAMA Psychiatry. 2018;75(11):1146–1155. doi:10.1001/jamapsychiatry.2018.2467 [CrossRef] PMID:30304337
  2. Tamminga CA, Ivleva EI. Using brain-based phenotyping to improve discovery in psychiatry. JAMA Psychiatry. 2018;75(11):1103–1104. doi:10.1001/jamapsychiatry.2018.2451 [CrossRef] PMID:30304331
  3. Insel T, Cuthbert B, Garvey M, et al. Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. Am J Psychiatry. 2010;167(7):748–751. doi:10.1176/appi.ajp.2010.09091379 [CrossRef] PMID:20595427
  4. Etkin A, Gyurak A, O'Hara R. A neurobiological approach to the cognitive deficits of psychiatric disorders. Dialogues Clin Neurosci. 2013;15(4):419–429. PMID:24459409
  5. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders. 5th ed. Arlington, VA; American Psychiatric Publishing; 2013.
  6. Hallion LS, Steinman SA, Kusmierski SN. Difficulty concentrating in generalized anxiety disorder: an evaluation of incremental utility and relationship to worry. J Anxiety Disord. 2018;53:39–45. doi:10.1016/j.janxdis.2017.10.007 [CrossRef] PMID:29175616
  7. Savulich G, Shergill S, Yiend J. Biased cognition in psychosis. J Exp Psychopathol. 2012;3(4):514–536. doi:10.5127/jep.016711 [CrossRef]
  8. De Raedt R, Koster EH. Understanding vulnerability for depression from a cognitive neuroscience perspective: a reappraisal of attentional factors and a new conceptual framework. Cogn Affect Behav Neurosci. 2010;10(1):50–70. doi:10.3758/CABN.10.1.50 [CrossRef] PMID:20233955
  9. Zimmerman M, Ellison W, Young D, Chelminski I, Dalrymple K. How many different ways do patients meet the diagnostic criteria for major depressive disorder?Compr Psychiatry. 2015;56:29–34. doi:10.1016/j.comppsych.2014.09.007 [CrossRef] PMID:25266848
  10. Fried EI. The 52 symptoms of major depression: lack of content overlap among seven common depression scales. J Affect Disord. 2017;208:191–197. doi:10.1016/j.jad.2016.10.019 [CrossRef] PMID:27792962
  11. Montagne B, Kessels RP, De Haan EH, Perrett DI. The emotion recognition task: a paradigm to measure the perception of facial emotional expressions at different intensities. Percept Mot Skills. 2007;104(2):589–598. doi:10.2466/pms.104.2.589-598 [CrossRef] PMID:17566449
  12. Münkler P, Rothkirch M, Dalati Y, Schmack K, Sterzer P. Biased recognition of facial affect in patients with major depressive disorder reflects clinical state. PLoS One. 2015;10(6):e0129863–e0129863. doi:10.1371/journal.pone.0129863 [CrossRef] PMID:26039710
  13. Joormann J, Gotlib IH. Selective attention to emotional faces following recovery from depression. J Abnorm Psychol. 2007;116(1):80–85. doi:10.1037/0021-843X.116.1.80 [CrossRef] PMID:17324018
  14. Fritzsche A, Dahme B, Gotlib IH, et al. Specificity of cognitive biases in patients with current depression and remitted depression and in patients with asthma. Psychol Med. 2010;40(5):815–826. doi:10.1017/S0033291709990948 [CrossRef] PMID:19719897
  15. Peckham AD, McHugh RK, Otto MW. A meta-analysis of the magnitude of biased attention in depression. Depress Anxiety. 2010;27(12):1135–1142. doi:10.1002/da.20755 [CrossRef] PMID:21049527
  16. Beck AT. The evolution of the cognitive model of depression and its neurobiological correlates. Am J Psychiatry. 2008;165(8):969–977. doi:10.1176/appi.ajp.2008.08050721 [CrossRef] PMID:18628348
  17. Chan SWY, Goodwin GM, Harmer CJ. Highly neurotic never-depressed students have negative biases in information processing. Psychol Med. 2007;37(9):1281–1291. doi:10.1017/S0033291707000669 [CrossRef] PMID:17493298
  18. Smith EM, Reynolds S, Orchard F, Whalley HC, Chan SWY. Cognitive biases predict symptoms of depression, anxiety and wellbeing above and beyond neuroticism in adolescence. J Affect Disord. 2018;241:446–453. doi:10.1016/j.jad.2018.08.051 [CrossRef] PMID:30145516
  19. Fu CH, Costafreda SG, Sankar A, et al. Multimodal functional and structural neuroimaging investigation of major depressive disorder following treatment with duloxetine. BMC Psychiatry. 2015;15(1):82. doi:10.1186/s12888-015-0457-2 [CrossRef] PMID:25880400
  20. Redlich R, Bürger C, Dohm K, et al. Effects of electroconvulsive therapy on amygdala function in major depression - a longitudinal functional magnetic resonance imaging study. Psychol Med. 2017;47(12):2166–2176. doi:10.1017/S0033291717000605 [CrossRef]PMID:28397635
  21. Ritchey M, Dolcos F, Eddington KM, Strauman TJ, Cabeza R. Neural correlates of emotional processing in depression: changes with cognitive behavioral therapy and predictors of treatment response. J Psychiatr Res. 2011;45(5):577–587. doi:10.1016/j.jpsychires.2010.09.007 [CrossRef]PMID:20934190
  22. Etkin A, Patenaude B, Song YJC, et al. A cognitive-emotional biomarker for predicting remission with antidepressant medications: a report from the iSPOT-D trial. Neuropsychopharmacology. 2015;40(6):1332–1342. doi:10.1038/npp.2014.333 [CrossRef] PMID:25547711
  23. Le Masurier M, Cowen PJ, Harmer CJ. Emotional bias and waking salivary cortisol in relatives of patients with major depression. Psychol Med. 2007;37(3):403–410. doi:10.1017/S0033291706009184 [CrossRef] PMID:17109777
  24. Ruhé HG, Booij J, Veltman DJ, Michel MC, Schene AH. Successful pharmacologic treatment of major depressive disorder attenuates amygdala activation to negative facial expressions: a functional magnetic resonance imaging study. J Clin Psychiatry. 2012;73(4):451–459. doi:10.4088/JCP.10m06584 [CrossRef] PMID:21903032
  25. Day CV, Gatt JM, Etkin A, DeBattista C, Schatzberg AF, Williams LM. Cognitive and emotional biomarkers of melancholic depression: an iSPOT-D report. J Affect Disord. 2015;176:141–150. doi:10.1016/j.jad.2015.01.061 [CrossRef] PMID:25710095
  26. Wu W, Zhang Y, Jiang J, et al. An electroencephalographic signature predicts antidepressant response in major depression. Nat Biotechnol. 2020;38(4):439–447. doi:10.1038/s41587-019-0397-3 [CrossRef] PMID:32042166
  27. Goldstein-Piekarski AN, Williams LM, Humphreys K. A trans-diagnostic review of anxiety disorder comorbidity and the impact of multiple exclusion criteria on studying clinical outcomes in anxiety disorders. Transl Psychiatry. 2016;6(6):e847–e847. doi:10.1038/tp.2016.108 [CrossRef] PMID:27351601
  28. Bar-Haim Y, Lamy D, Pergamin L, Bakermans-Kranenburg MJ, van IJzendoorn MH. Threat-related attentional bias in anxious and nonanxious individuals: a meta-analytic study. Psychol Bull. 2007;133(1):1–24. doi:10.1037/0033-2909.133.1.1 [CrossRef] PMID:17201568
  29. Mogg K, Bradley BP. Anxiety and attention to threat: cognitive mechanisms and treatment with attention bias modification. Behav Res Ther. 2016;87:76–108. doi:10.1016/j.brat.2016.08.001 [CrossRef] PMID:27616718
  30. Abend R, de Voogd L, Salemink E, et al. Association between attention bias to threat and anxiety symptoms in children and adolescents. Depress Anxiety. 2018;35(3):229–238. doi:10.1002/da.22706 [CrossRef] PMID:29212134
  31. Kret ME, Ploeger A. Emotion processing deficits: a liability spectrum providing insight into comorbidity of mental disorders. Neurosci Biobehav Rev. 2015;52:153–171. doi:10.1016/j.neubiorev.2015.02.011 [CrossRef] PMID:25725415
  32. Bijsterbosch JD, Ansari TL, Smith S, et al. Stratification of MDD and GAD patients by resting state brain connectivity predicts cognitive bias. Neuroimage Clin. 2018;19:425–433. doi:10.1016/j.nicl.2018.04.033 [CrossRef] PMID:30035026
  33. Biederman J, DiSalvo M, Woodworth KY, et al. Toward operationalizing deficient emotional self-regulation in newly referred adults with ADHD: a receiver operator characteristic curve analysis. Eur Psychiatry. 2020;63(1):e21. doi:10.1192/j.eurpsy.2019.11 [CrossRef]. PMID:32093797
  34. Shaw P, Stringaris A, Nigg J, Leibenluft E. Emotion dysregulation in attention deficit hyperactivity disorder. Am J Psychiatry.2014;171(3):276–293. doi:10.1176/appi.ajp.2013.13070966 [CrossRef] PMID:24480998
  35. Kwako LE, Momenan R, Litten RZ, Koob GF, Goldman D. Addictions neuroclinical assessment: a neuroscience-based framework for addictive disorders. Biol Psychiatry. 2016;80(3):179–189. doi:10.1016/j.biopsych.2015.10.024 [CrossRef] PMID:26772405
  36. Nutt DJ, Lingford-Hughes A, Erritzoe D, Stokes PRA. The dopamine theory of addiction: 40 years of highs and lows. Nat Rev Neurosci. 2015;16(5):305–312. doi:10.1038/nrn3939 [CrossRef] PMID:25873042
  37. Childress AR, Ehrman RN, Wang Z, et al. Prelude to passion: limbic activation by “unseen” drug and sexual cues. PLoS One. 2008;3(1):e1506. doi:10.1371/journal.pone.0001506 [CrossRef] PMID:18231593
  38. Chase HW, Eickhoff SB, Laird AR, Hogarth L. The neural basis of drug stimulus processing and craving: an activation likelihood estimation meta-analysis. Biol Psychiatry. 2011;70(8):785–793. doi:10.1016/j.biopsych.2011.05.025 [CrossRef] PMID:21757184
  39. Seo D, Lacadie CM, Tuit K, Hong KI, Constable RT, Sinha R. Disrupted ventromedial prefrontal function, alcohol craving, and subsequent relapse risk. JAMA Psychiatry. 2013;70(7):727–739. doi:10.1001/jamapsychiatry.2013.762 [CrossRef] PMID:23636842
  40. McLellan AT, Lewis DC, O'Brien CP, Kleber HD. Drug dependence, a chronic medical illness: implications for treatment, insurance, and outcomes evaluation. JAMA. 2000;284(13):1689–1695. doi:10.1001/jama.284.13.1689 [CrossRef] PMID:11015800
  41. Zhang MW, Ying JB, Song G, Fung DSS, Smith HE. Recent advances in attention bias modification for substance addictions. Int J Environ Res Public Health. 2018;15(4):676. doi:10.3390/ijerph15040676 [CrossRef] PMID:29617325
  42. Jones EB, Sharpe L. Cognitive bias modification: a review of meta-analyses. J Affect Disord. 2017;223:175–183. doi:10.1016/j.jad.2017.07.034 [CrossRef] PMID:28759865
  43. Zhang M, Ying J, Song G, Fung DSS, Smith H. Web-based cognitive bias modification interventions for psychiatric disorders: scoping review. JMIR Ment Health. 2019;6(10):e11841–e11841. doi:10.2196/11841 [CrossRef] PMID:31651410
  44. McNally RJ. Attentional bias for threat: crisis or opportunity?Clin Psychol Rev. 2019;69:4–13. doi:10.1016/j.cpr.2018.05.005 [CrossRef] PMID:29853421
  45. Kruijt A-W, Parsons S, Fox E. A meta-analysis of bias at baseline in RCTs of attention bias modification: no evidence for dot-probe bias towards threat in clinical anxiety and PTSD. J Abnorm Psychol. 2019;128(6):563–573. doi:10.1037/abn0000406 [CrossRef] PMID:31368735
  46. Linhartová P, Látalová A, Kóša B, Kašpárek T, Schmahl C, Paret C. fMRI neurofeedback in emotion regulation: A literature review. Neuroimage. 2019;193:75–92. doi:10.1016/j.neuroimage.2019.03.011 [CrossRef] PMID:30862532
Authors

Abram Davidov, MD, is a Research Associate. Tracy L. Greer, PhD, MSCS, is an Associate Professor. Both authors are affiliated with the Center for Depression Research and Clinical Care, Department of Psychiatry, University of Texas Southwestern Medical Center.

Address correspondence to Tracy L. Greer, PhD, MSCS, Department of Psychiatry, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX 75390-9119; email: tracy.greer@utsouthwestern.edu.

Disclosure: Tracy L. Greer has been a consultant for H. Lundbeck A/S and has performed contracted research for Janssen Research and Development, LLC. The remaining author has no relevant financial relationships to disclose.

10.3928/00485713-20200504-01

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