Electrophysiological Correlates of Cognition in Depression

NCT ID: NCT03998748

Last Updated: 2023-01-19

Basic Information

• Recruitment Status: TERMINATED
• Clinical Phase: NA
• Total Enrollment: 80 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2019-10-08
• Study Completion Date: 2022-08-01

Brief Summary

Biogenetic messages about the etiology of mental illness (e.g., the "chemical imbalance theory" of depression) are increasing but the impact that these have on decision-making and motivation is not yet clear. This study will evaluate the impact of biogenetic feedback on cognitive control and default-mode network functioning, as well as motivation for different psychiatric treatment modalities. Participants with major depressive disorder (MDD) will be instructed that they are being tested for genetic susceptibility to depression and will be randomized to receive feedback that they either do or do not have a genetic predisposition to depression. Before and after receiving this feedback, brain activity will be assessed using high-density electroencephalogram (EEG). The investigators hypothesize that those exposed to the genetic feedback condition will evidence heightened ruminative default mode network activity and perceive medications to be more effective than psychotherapy.

Detailed Description

A. Background and Significance Depictions of psychiatric illnesses as stemming largely from biological and genetic vulnerabilities have increased substantially in recent years (Deacon, 2013; Lebowitz & Appelbaum, 2019; Schomerus et al., 2012). These messages are disseminated by physicians, pharmaceutical companies, anti-stigma campaigns, researchers, and the popular media alike. Although at first blush messages emphasizing genetic susceptibility may seem helpful in reducing stigma, growing research points to serious unintended consequences (Haslam & Kvaale, 2015). Specifically, when participants believe their depression is due to biogenetic abnormalities, they expect to suffer for longer periods of time (Kemp, Lickel, & Deacon, 2014), endorse more depressive symptoms (Lebowitz & Ahn, 2017), and feel they have less control over their mood (Lebowitz & Ahn, 2018). Moreover, biogenetic messaging has no impact on stigma (Haslam & Kvaale, 2015). Despite the accumulating self-reported evidence that biogenetic messaging may be harmful, nothing is known about how such messages impact neural correlates of self-reflection and cognitive control - two key processes thought to subserve adaptive self-regulation that may be disrupted among individuals with major depressive disorder (MDD, Pizzagalli, 2011). This study will fill this knowledge gap by comparing resting and task-related electroencephalography (EEG) between adults with MDD randomly assigned to receive either positive or negative information about their genetic susceptibility to depression. Completion of this project will characterize the neural impacts of widespread messages about the etiology of depression. This study may inform clinical decision making, public policy, and guidelines regarding how mental health is discussed.

B. Specific Aims:

Aim1: To examine the impact of biogenetic messaging on default-mode network (DMN) Hypothesis 1: The DMN refers to a network of functionally connected brain regions that are most active at rest and during retrospection (Buckner, Andrews-Hanna, & Schacter, 2008; Raichle, 2015). The DMN has been consistently found to be overactive in the context of depressive disorders (Pizzagalli, 2011), especially in the context of elevated rumination. Capitalizing on approaches to probe DMN functionality using source-localized EEG activity implemented in the mentor's lab (Whitton et al., 2018) the investigators expect that the DMN will be increased following the positive (vulnerable) genetic feedback manipulation. This would indicate that biogenetic messaging increases potentially maladaptive rumination.

Aim 2: To examine the impact of biogenetic messaging on cognitive control Hypothesis 2: Cognitive control refers to a suite of functions that allow humans to monitor, detect, and respond to conflicting information and mistakes, and to mobilize internal resources to resolve such occurrences from happening in the future (Braver, 2012; Miller & Cohen, 2001). One commonly studied facet of cognitive control is error monitoring, which refers to the ability to detect and respond to mistakes. The error-related negativity (ERN) is elicited 0-100ms following an error and the error positivity (Pe) is elicited 200-400ms post-error (Gehring, Liu, Orr, & Carp, 2012). Post-error behavioral adjustments include post-error slowing and post-error improvement in accuracy. Previous research suggests that depressive symptoms correlate with ERN and Pe amplitudes (Compton et al., 2008; Holmes & Pizzagalli, 2008; Olvet, Klein, & Hajcak, 2010; Schroder, Moran, Infantolino, & Moser, 2013). Induction of genetic messaging about intelligence increased the Pe amplitude but also reduced the correlation between Pe and post-error performance (Schroder, Moran, Donnellan, & Moser, 2014). Accordingly, in the current study, the investigators expect the Pe to be increased and a reduced relationship between Pe and post-error behavior in the vulnerable genetic condition.

Aim 3: To evaluate self-reported motivation for treatment, expectancies, and preferences Hypothesis 3: Previous research has documented a cost in self-reported motivation and future expectancies following receiving biogenetic information about depression (Kemp et al., 2014; Lebowitz & Ahn, 2017). The investigators expect to replicate these effects in a sample of individuals with MDD. The investigators expect that participants receiving vulnerable genetic feedback will 1) endorse poorer perceived control over their emotions, 2) expect to have depression for a longer period of time, 3) endorse a preference for pharmacotherapy versus psychotherapy and 4) view pharmacotherapy as more effective than psychotherapy.

C. Description of the Research Design Participants The sample will consist of 80 male and female unmedicated adults with MDD, aged 18-45. Participants will be recruited primarily through Cragslist ads, flyering, and contacting participants who were previously enrolled in studies at the Center for Depression, Anxiety and Stress Research. After passing an initial phone screen, participants will complete the Mini International Neuropsychiatric Interview (MINI; Sheehan et al., 1988). Exclusion criteria for all participants will include failure to meet EEG safety requirements, current drug use, history of alcohol and drug dependence, lifetime history of psychosis and bipolar disorder, and imminent suicidal ideation. After the interview, participants will be asked to complete the Beck Depression Inventory (BDI-II, Beck, Steer, & Brown, 1996), the Quick Inventory of Depressive Symptoms (QIDS, Rush et al., 2003), the Ruminative Response Style Questionnaire (RRS, Treynor, Gonzalez, & Nolen-Hoeksema, 2003), the Penn State Worry Questionnaire (PSWQ, Meyer, Miller, Metzger, & Borkovec, 1990), the Positive And Negative Affective Schedule (PANAS, Clark & Watson, 1991) and Visual Analogue Mood Scale (VAMS, Aitken, 1969).

Baseline EEG Assessment After participants are deemed eligible, they will complete the baseline EEG assessment. Participants will be fitted with a 96-channel EEG cap. The baseline EEG assessment consists of two tasks. First, resting EEG data will be collected (8 min) in which participants will sit calmly with their eyes open or closed (randomly alternated in one-minute intervals). The resting EEG allows for collection of DMN. Second, participants will perform a flanker task (20 min). The flanker task is a well-validated task in which participants view five horizontal arrows on the computer screen and respond as quickly and as accurately to the central (target) stimulus using a response pad. Participants will complete 30 practice trials to titrate task difficulty in the main blocks, and 350 test trials. The ERN, Pe, behavioral adjustments and VAMS will be recorded from this task.

Saliva Sample and Genetic "Testing" Following completion of the flanker task, participants will be informed they will be taking a saliva sample to determine their genetic susceptibility to depression. Using a previously validated procedure (Lebowitz & Ahn, 2017, 2018), participants will be provided with a "saliva testing kit", which consists of a plastic box containing a glucose test strip (which participants are led to believe gauges salivary levels of 5-Hydroxyindoleacetic acid (5-HIAA) as part of a genetic susceptibility test) and a small amount of mouthwash (containing glucose) in a plastic container. Participants will be provided with instructions on the computer screen for how to complete the saliva testing themselves. Participants will rinse their mouths with mouthwash for seven seconds, spit the mouthwash into the box, and insert the test strip under their tongues for 10 seconds, and then wait for 30 seconds. The test strip will turn brown as the strip is sensitive to glucose. Participants will be given a computer prompt to indicate which color their test strip turned (brown or pink) and will be randomly assigned to receive computer feedback indicating that a brown test strip means they 1) have a genetic vulnerability to depression or 2) do not have such a vulnerability. The feedback consists of one paragraph describing 5-HIAA and its implications for depression based on past research. The research assistant (RA) will be blind to condition assignment.

Post-Manipulation EEG and Self-reported Assessment Immediately following the genetic test manipulation, participants will complete the PANAS to assess state affects and then repeat the resting EEG recording and flanker task. They will then complete a battery of self-report measures to gauge their hypothetical mental health treatment preferences and expectancies, and perceived willingness to engage in treatment in the future. They will also complete the VAMS, the Implicit Theories Questionnaire (Schroder, Dawood, Yalch, Donnellan, & Moser, 2015), the Negative Mood Regulation Scale (Catanzaro & Mearns, 1990), the Perceptions of Depression Scale(Deacon & Baird, 2009), and the Prognostic Pessimism Scale (Lebowitz, Ahn, & Nolen-Hoeksema, 2013). Participants will also complete a manipulation check to assess perceived credibility of the genetic testing.

Debriefing Procedure At the end of the session, all participants will be thoroughly debriefed. Following previously published procedures (Lebowitz & Ahn, 2017), debriefing will entail the Co-I - who has a PhD in clinical psychology - explaining that no genetic testing actually took place. The Co-I will explain that the mouthwash consisted of glucose and that when exposed to glucose, the test strip turns brown. Participants will be shown both feedback screens (susceptible and non-susceptible feedback). The concept of randomized assignment will be discussed. Participants will be encouraged to ask questions during this period. Finally, participants will complete a short quiz consisting of items that ask whether or not genetic testing took place. Participants will be required to respond accurately; if they do not respond accurately after debriefing, the Co-I will again emphasize that no genetic testing took place until full comprehension is achieved.

Conditions

Major Depressive Disorder

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: FACTORIAL
• Primary Study Purpose: BASIC_SCIENCE
• Blinding Strategy: DOUBLE

Study Groups

Experimental

This group of participants will receive the feedback that they have a genetic vulnerability to depression.

Group Type: EXPERIMENTAL

Sham Genetic Feedback

Intervention Type: OTHER

Participants will be told either that they have or do not have a genetic predisposition to developing depression.

Control

This group of participants will receive the feedback that they do not have a genetic vulnerability to depression.

Group Type: ACTIVE_COMPARATOR

Sham Genetic Feedback

Intervention Type: OTHER

Participants will be told either that they have or do not have a genetic predisposition to developing depression.

Interventions

Sham Genetic Feedback

Participants will be told either that they have or do not have a genetic predisposition to developing depression.

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

• Age 18-45
• Written informed consent
• BDI-II score greater than or equal to 14 (Beck et al.,1996)
• Right-handed (Chapman & Chapman,1987)
• Normal or corrected-to-normal vision and hearing
• Fluency in written and spoken English
• Absence of any psychotropic medications for at least 2 weeks
• Absence of any psychotherapy for at least 2 weeks

Exclusion Criteria

• Participants with suicidal ideation where study participation is deemed unsafe by the study clinician
• Serious or unstable medical illness (cardiovascular, hepatic, renal, respiratory, endocrine, neurologic, or hematologic, autoimmune disease, etc.)
• History of seizures or seizure disorder
• Patients with psychotic features
• Current use of other psychotropic drugs
• Current use of psychotherapy
• Clinical or laboratory evidence of hypothyroidism, hyperthyroidism, or other thyroid disorder that is not controlled by medication
• Patients with a lifetime history of electroconvulsive therapy (ECT)
• Evidence of sickle cell anemia, Raynaud's disease, ulcerative skin diseases, and hemophilia
• Evidence of significant inconsistencies in self-report measures
• History or current diagnosis of dementia
• Illness receiving acute treatment at time of EEG session (e.g., taking antibiotics)
• Infections illness (either transient or chronic, such as Lyme disease) at time of EEG session
• Hairstyles that prevent application of the EEG cap (e.g., braids, dread locks, corn rows, recently dyed hair)
• History of any psychiatric genotyping
• History of regular marijuana use (5-7x) per week before age 15
• History of significant head injury of concussion with loss of consciousness of two minutes or more, or head injury with lingering functional/psychological impact
• Any alcohol-induced blackouts within the past year
• Any current drug use as assessed by a urine drug test (covering cocaine, cannabinoids, opiates, amphetamines, methamphetamines, phencyclidine, MDMA, benzodiazepines, methadone, oxycodone, tricyclic antidepressants, and barbiturates)

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 45 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Mclean Hospital

OTHER

Sponsor Role: lead

Responsible Party

Diego Pizzagalli

Director, Center for Depression, Anxiety and Stress Research

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Diego A Pizzagalli, PhD

Role: PRINCIPAL_INVESTIGATOR

Mclean Hospital

Locations

McLean Hospital

Belmont, Massachusetts, United States

Countries

United States

References

Deacon BJ. The biomedical model of mental disorder: a critical analysis of its validity, utility, and effects on psychotherapy research. Clin Psychol Rev. 2013 Nov;33(7):846-61. doi: 10.1016/j.cpr.2012.09.007. Epub 2013 Apr 8.

Reference Type: BACKGROUND

PMID: 23664634 (View on PubMed)

Haslam, N., & Kvaale, E. P. (2015). Biogenetic Explanations of Mental Disorder: The Mixed-Blessings Model. Current Directions in Psychological Science, 24(5), 399-404.

Reference Type: BACKGROUND

Lebowitz MS, Appelbaum PS. Biomedical Explanations of Psychopathology and Their Implications for Attitudes and Beliefs About Mental Disorders. Annu Rev Clin Psychol. 2019 May 7;15:555-577. doi: 10.1146/annurev-clinpsy-050718-095416. Epub 2018 Nov 16.

Reference Type: BACKGROUND

PMID: 30444641 (View on PubMed)

Schomerus G, Schwahn C, Holzinger A, Corrigan PW, Grabe HJ, Carta MG, Angermeyer MC. Evolution of public attitudes about mental illness: a systematic review and meta-analysis. Acta Psychiatr Scand. 2012 Jun;125(6):440-52. doi: 10.1111/j.1600-0447.2012.01826.x. Epub 2012 Jan 13.

Reference Type: BACKGROUND

PMID: 22242976 (View on PubMed)

Beck, A., Steer, R., & Brown, G. (1996). Beck Depression Inventory-II. San Antonio.

Reference Type: BACKGROUND

Aitken RC. Measurement of feelings using visual analogue scales. Proc R Soc Med. 1969 Oct;62(10):989-93. doi: 10.1177/003591576906201005. No abstract available.

Reference Type: BACKGROUND

PMID: 4899510 (View on PubMed)

Braver TS. The variable nature of cognitive control: a dual mechanisms framework. Trends Cogn Sci. 2012 Feb;16(2):106-13. doi: 10.1016/j.tics.2011.12.010. Epub 2012 Jan 12.

Reference Type: BACKGROUND

PMID: 22245618 (View on PubMed)

Buckner RL, Andrews-Hanna JR, Schacter DL. The brain's default network: anatomy, function, and relevance to disease. Ann N Y Acad Sci. 2008 Mar;1124:1-38. doi: 10.1196/annals.1440.011.

Reference Type: BACKGROUND

PMID: 18400922 (View on PubMed)

Catanzaro SJ, Mearns J. Measuring generalized expectancies for negative mood regulation: initial scale development and implications. J Pers Assess. 1990 Summer;54(3-4):546-63. doi: 10.1080/00223891.1990.9674019.

Reference Type: BACKGROUND

PMID: 2348341 (View on PubMed)

Clark LA, Watson D. Tripartite model of anxiety and depression: psychometric evidence and taxonomic implications. J Abnorm Psychol. 1991 Aug;100(3):316-36. doi: 10.1037//0021-843x.100.3.316.

Reference Type: BACKGROUND

PMID: 1918611 (View on PubMed)

Compton RJ, Lin M, Vargas G, Carp J, Fineman SL, Quandt LC. Error detection and posterror behavior in depressed undergraduates. Emotion. 2008 Feb;8(1):58-67. doi: 10.1037/1528-3542.8.1.58.

Reference Type: BACKGROUND

PMID: 18266516 (View on PubMed)

Deacon, B. J., & Baird, G. L. (2009). The Chemical Imbalance Explanation of Depression: Reducing Blame at What Cost? Journal of Social and Clinical Psychology, 28(4), 415-435.

Reference Type: BACKGROUND

Gehring, W., Liu. Y., Orr. J., & Carp. J. (2012). The Error-related negativity (ERN/Ne). In S. J. Luck & E. Kappenman (Eds.), Oxford handbook of event-related potential components (pp. 231-291). New York: Oxford University Press.

Reference Type: BACKGROUND

Holmes AJ, Pizzagalli DA. Spatiotemporal dynamics of error processing dysfunctions in major depressive disorder. Arch Gen Psychiatry. 2008 Feb;65(2):179-88. doi: 10.1001/archgenpsychiatry.2007.19.

Reference Type: BACKGROUND

PMID: 18250256 (View on PubMed)

Kemp JJ, Lickel JJ, Deacon BJ. Effects of a chemical imbalance causal explanation on individuals' perceptions of their depressive symptoms. Behav Res Ther. 2014 May;56:47-52. doi: 10.1016/j.brat.2014.02.009. Epub 2014 Mar 6.

Reference Type: BACKGROUND

PMID: 24657311 (View on PubMed)

Lebowitz MS, Ahn WK, Nolen-Hoeksema S. Fixable or fate? Perceptions of the biology of depression. J Consult Clin Psychol. 2013 Jun;81(3):518-27. doi: 10.1037/a0031730. Epub 2013 Feb 4.

Reference Type: BACKGROUND

PMID: 23379262 (View on PubMed)

Lebowitz MS, Ahn WK. Testing positive for a genetic predisposition to depression magnifies retrospective memory for depressive symptoms. J Consult Clin Psychol. 2017 Nov;85(11):1052-1063. doi: 10.1037/ccp0000254.

Reference Type: BACKGROUND

PMID: 29083221 (View on PubMed)

Lebowitz MS, Ahn WK. Blue Genes? Understanding and Mitigating Negative Consequences of Personalized Information about Genetic Risk for Depression. J Genet Couns. 2018 Feb;27(1):204-216. doi: 10.1007/s10897-017-0140-5. Epub 2017 Aug 7.

Reference Type: BACKGROUND

PMID: 28785835 (View on PubMed)

Meyer TJ, Miller ML, Metzger RL, Borkovec TD. Development and validation of the Penn State Worry Questionnaire. Behav Res Ther. 1990;28(6):487-95. doi: 10.1016/0005-7967(90)90135-6.

Reference Type: BACKGROUND

PMID: 2076086 (View on PubMed)

Miller EK, Cohen JD. An integrative theory of prefrontal cortex function. Annu Rev Neurosci. 2001;24:167-202. doi: 10.1146/annurev.neuro.24.1.167.

Reference Type: BACKGROUND

PMID: 11283309 (View on PubMed)

Olvet DM, Klein DN, Hajcak G. Depression symptom severity and error-related brain activity. Psychiatry Res. 2010 Aug 30;179(1):30-7. doi: 10.1016/j.psychres.2010.06.008. Epub 2010 Jul 13.

Reference Type: BACKGROUND

PMID: 20630603 (View on PubMed)

Pizzagalli DA. Frontocingulate dysfunction in depression: toward biomarkers of treatment response. Neuropsychopharmacology. 2011 Jan;36(1):183-206. doi: 10.1038/npp.2010.166. Epub 2010 Sep 22.

Reference Type: BACKGROUND

PMID: 20861828 (View on PubMed)

Raichle ME. The brain's default mode network. Annu Rev Neurosci. 2015 Jul 8;38:433-47. doi: 10.1146/annurev-neuro-071013-014030. Epub 2015 May 4.

Reference Type: BACKGROUND

PMID: 25938726 (View on PubMed)

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 Sep 1;54(5):573-83. doi: 10.1016/s0006-3223(02)01866-8.

Reference Type: BACKGROUND

PMID: 12946886 (View on PubMed)

Schroder HS, Dawood S, Yalch MM, Donnellan MB, Moser JS. The role of implicit theories in mental health symptoms, emotion regulation, and hypothetical treatment choices in college students. Cognit Ther Res. 2015 Apr;39(2):120-139. doi: 10.1007/s10608-014-9652-6. Epub 2014 Nov 2.

Reference Type: BACKGROUND

PMID: 35474696 (View on PubMed)

Schroder HS, Moran TP, Donnellan MB, Moser JS. Mindset induction effects on cognitive control: a neurobehavioral investigation. Biol Psychol. 2014 Dec;103:27-37. doi: 10.1016/j.biopsycho.2014.08.004. Epub 2014 Aug 18.

Reference Type: BACKGROUND

PMID: 25149141 (View on PubMed)

Schroder HS, Moran TP, Infantolino ZP, Moser JS. The relationship between depressive symptoms and error monitoring during response switching. Cogn Affect Behav Neurosci. 2013 Dec;13(4):790-802. doi: 10.3758/s13415-013-0184-4.

Reference Type: BACKGROUND

PMID: 23797948 (View on PubMed)

Treynor, W., Gonzalez, R., & Nolen-Hoeksema, S. (2003). Rumination resconsidered: A psychometric analysis. Cognitive Therapy and Research, 27(3), 247-259. https://doi.org/10.1023/A:1023910315561

Reference Type: BACKGROUND

Whitton AE, Deccy S, Ironside ML, Kumar P, Beltzer M, Pizzagalli DA. Electroencephalography Source Functional Connectivity Reveals Abnormal High-Frequency Communication Among Large-Scale Functional Networks in Depression. Biol Psychiatry Cogn Neurosci Neuroimaging. 2018 Jan;3(1):50-58. doi: 10.1016/j.bpsc.2017.07.001. Epub 2017 Jul 13.

Reference Type: BACKGROUND

PMID: 29397079 (View on PubMed)

Whitton AE, Webb CA, Dillon DG, Kayser J, Rutherford A, Goer F, Fava M, McGrath P, Weissman M, Parsey R, Adams P, Trombello JM, Cooper C, Deldin P, Oquendo MA, McInnis MG, Carmody T, Bruder G, Trivedi MH, Pizzagalli DA. Pretreatment Rostral Anterior Cingulate Cortex Connectivity With Salience Network Predicts Depression Recovery: Findings From the EMBARC Randomized Clinical Trial. Biol Psychiatry. 2019 May 15;85(10):872-880. doi: 10.1016/j.biopsych.2018.12.007. Epub 2018 Dec 19.

Reference Type: BACKGROUND

PMID: 30718038 (View on PubMed)

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 Suppl 20:22-33;quiz 34-57.

Reference Type: BACKGROUND

PMID: 9881538 (View on PubMed)

Other Identifiers

2019P001081

Identifier Type: -

Identifier Source: org_study_id