Effects of Transcranial Direct Current Stimulation on Reward Learning in Subclinical Depression.

Dysphoria Clinical Trial

Official title:

Effects of Transcranial Direct Current Stimulation on Reward Learning in Subclinical Depression.

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT03393312
• Other study ID #: R67041/RE002
• Status: Recruiting
• Phase: N/A
• Start date: February 2, 2018
• Est. completion date: December 1, 2021

Study information

• Verified date: April 2021
• Source: University of Oxford
• Contact: Verena Sarrazin, MSc
• Phone: 07759903366
• Email: verena.sarrazin[@]psych.ox.ac.uk
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

This project will test whether transcranial direct current stimulation (tDCS) over the dorsolateral prefrontal cortex (DLPFC) can alter reward learning behaviour in subclinical depression. tDCS is a neuromodulation technique that uses weak electrical current to increase (anodal stimulation) or decrease (cathodal stimulation) the excitability of the stimulated brain region. A growing body of evidence indicates that repeated administration of prefrontal tDCS can ameliorate symptoms of depression. A main characteristic of depression is that patients show a bias towards processing negative relative to positive information. Previously, we have found that a single session of prefrontal tDCS applied during task performance increased learning rate for reward outcomes in healthy adults. Here, we will test whether stimulation induces a similar behavioural effect in individuals with subclinical depression. We will test the prediction that tDCS will increase learning rates for reward outcomes in a reinforcement learning task. The findings will contribute to understanding the cognitive effects of prefrontal tDCS in subclinical depression. The ultimate aim, to be explored through further studies, is to understand and improve how tDCS might be used in the treatment of depressive disorders.

Description:

The development of non-invasive brain stimulation techniques offers new approaches for the treatment of depression. TMS is an approved treatment for treatment-resistant depression in the UK and USA. tDCS is currently under investigation as a potentially cheaper, safer and more accessible alternative. A recent meta-analysis suggests that it has a moderate antidepressant effect (Razza et al., 2020).

One potential way of improving the antidepressant efficacy of tDCS might be to combine it with a reward learning task. There is evidence from rodent and human studies that tDCS enhances activity-dependent synaptic plasticity and behavioural learning and retention (Fritsch et al., 2010; O'Shea et al., 2017; Reis et al., 2009).

In depression, people prioritize the processing of negative information at the cost of positive information and this negative bias is theorized to play a major role in the maintenance of depressive symptoms (Kube, Schwarting, Rozenkrantz, Glombiewski, & Rief, 2020). Therefore, by having depressed participants learn from reward, and increase their learning/retention via tDCS, this could potentially counteract negative bias and thus increase the antidepressant potential of tDCS.

In an earlier proof-of-concept study in young healthy volunteers (without low mood), we showed that tDCS applied during, but not before, an information-bias learning task increased reward learning rates. Here, we will test for the same effect in young healthy volunteers with subclinical depression. The study will involve online screening, a face-to-face screening session and two tDCS + learning sessions. Task performance will be fitted with a computational reinforcement learning model. Analyses will include both computational and non-computational measures of task behaviour.

Primary hypotheses:

1. Bifrontal compared to sham tDCS applied during the information-bias learning task will increase the learning rate from positive outcomes.

2. Bifrontal compared to sham tDCS applied during the task will increase the percentage of win-driven choices.

3. The effects of bifrontal tDCS will be cognitive state dependent, i.e. the predicted effects will be specific to stimulation applied during but not prior to learning.

Secondary hypothesis:

A growing body of evidence suggests that depression and anxiety are characterised by a reduced ability to adjust learning rates to volatility of action-outcome contingencies (Browning, Behrens, Jocham, O'Reilly, & Bishop, 2015; Gagne, Zika, Dayan, & Bishop, 2020; Pulcu & Browning, 2017). Therefore, we will also investigate whether bifrontal tDCS might increase participants' ability to adjust their learning rates to the volatility context. This is exploratory, since there is no current evidence that tDCS can improve this ability.

Sample size: Our primary goal is to test hypothesis 1 that bifrontal compared to sham tDCS applied during the information-bias learning task will increase the learning rate from positive outcomes. In our previous study in healthy volunteers we observed this effect, which was most pronounced in the losses-volatile condition (paired t-test contrasting bifrontal and sham tDCS in the losses volatile blocks: t(19) = 2.88, p = .009), with an effect size of Cohen's d = 0.522. To detect an effect of this size with a power of 80% and an alpha level of .05 requires 31 participants. To account for a potential overestimation of the effect size we will recruit 40 participants, which should yield a power level of .89 for the effect size observed in our previous study. To keep sample sizes equal across conditions, we will recruit another 40 participants for the offline condition.

Screening: Participants will be asked to fill out a pre-screening online depression questionnaire (Beck Depression Inventory - II (BDI)) and a safety screening form to identify potential contraindications to tDCS. Participants with a BDI score of ≥10 will be invited to a 1-hour screening session. The structured clinical interview (SCID) will be administered and the researcher will interview the participant about their tDCS safety screening questionnaire. If there are no contraindications to tDCS and no indications of current or history of bipolar disorder, the researcher will schedule two tDCS testing sessions.

Experimental task: Participants will perform the Information Bias Learning Task developed by (Pulcu & Browning, 2017). The same task protocol as in our previous study will be used (Overman, Sarrazin, Browning, & O'Shea, 2021). In short, participants will be asked to press a button to choose between two shapes on each trial. After they choose, the word "win" and "loss" will appear on the screen, each associated with one of the two shapes. The wins and losses are independent of each other, i.e. both can also appear with the same shape. If the chosen shape is associated with a win this leads to a 10 pence financial gain; a loss is minus 10 pence. The cumulative total is shown continuously in the bottom centre of the screen throughout the task. Participants start with a total of £1.50. This task is designed to assess the extent to which participants choices are relatively influenced by win vs. loss outcomes. Participants will perform 6 task blocks of 80 trials. In the first and sixth block ("both volatile blocks"), the wins and losses each have a 75% probability of being associated with one of the shapes. The shape the wins or losses are associated with changes over time. In the second to fifth task blocks, one outcome is associated with one shape in 75% of the trials. The shape the wins or losses are associated with changes over time. The other outcome is associated with both shapes in 50% of the trials. Each participant performs two "wins-volatile" and two "losses-volatile" blocks in alternating order. Half of the participants perform a "win-volatile" block first, and the other half a "loss-volatile" block first.

Testing sessions: Participants will be asked to fill out mood and anxiety questionnaires. At the beginning of the first session the Trait subscale of the State Trait Anxiety Inventory (STAI)(Spielberger, 1983) will be completed. In both sessions, before and after performing the task participants will complete the State subscale of the STAI and the Positive and Negative Affect Scales (PANAS)(Watson, Clark, & Tellegen, 1988)). The researcher will explain the computerised task, and the participant will get the opportunity to practise the task. The researcher will then set up the tDCS equipment with the anode over the left dorsolateral prefrontal cortex (DLPFC) and the cathode over the right DLPFC, in the F3 and F4 EEG electrode positions. The researcher will briefly test the tDCS to ensure that the participant is comfortable with the stimulation. The participant will perform one ("both volatile") baseline block of the task without tDCS. In the 'online' condition, tDCS will be applied during the task - for a duration of 20 minutes at 2 mA during the second and third task blocks. After the stimulation ends, participants will perform the remaining task blocks 4 and 5 (to test for persistence of any effects post-tDCS), followed by a repeat of the "both volatile" block performed at baseline. In the 'offline' condition, all procedures will be the same except that stimulation will be applied after the baseline ("both volatile") block and before the remaining blocks, while participants sit at rest.

Statistical analysis:

To test hypothesis 1, that bifrontal compared to sham tDCS applied during the task will increase the learning rate from win outcomes, we will run a repeated measures analysis of variance (ANOVA) on the learning rates in the "wins-volatile" and "losses-volatile" blocks, including the within-subject factors tDCS Condition (bifrontal vs. sham), Valence (win vs. loss outcomes), Volatility (wins-volatile vs. losses-volatile blocks) and Time (first vs. second half of the four task blocks). Baseline learning rates for wins and losses in the first task block of the first session will be included as covariates.

First, we will test for an interaction effect between tDCS Condition and Valence. To test our key prediction, we will run a priori planned contrasts of real vs. sham tDCS on win learning rates. Since in our previous study, the tDCS-induced increase in win learning rates was most pronounced in the losses-volatile condition, we will contrast the real-sham win learning rate separately for the losses-volatile and wins-volatile conditions.

To test hypothesis 2 that bifrontal compared to sham tDCS will increase the proportion of win-driven choices, we will run an ANOVA on the proportion of win-driven choices including the factors tDCS Condition, Volatility and Time. We will test for a main effect of tDCS Condition, as well as for an interaction effect between tDCS Condition and Volatility. The interaction effect will be followed up by planned contrasts of bifrontal versus sham tDCS, separately for the wins-volatile condition and the losses-volatile conditions.

To compare the effect of online versus offline bifrontal tDCS on the win learning rate and the proportion of win-driven choices (hypothesis 3), we will run ANOVAs on the combined dataset (online and offline condition), adding the factor "Stimulation Time" (before vs. during task performance). For learning rates, we will test for a main effect of Stimulation Time and/or an interaction between Stimulation Time and tDCS Condition and/or Valence. Follow-up ANOVAs will separate the data by Stimulation Time and analyse each dataset as described for Hypothesis 1 above, to test for an effect of tDCS on win learning rates. The effect in each condition (whether significant or not) will be quantified and contrasted across the two Stimulation Time conditions, to test the hypothesis that online but not offline tDCS will increase win learning rates. The same analysis approach will be used for the proportion of win-driven choices.

Regarding our secondary hypothesis that online bifrontal compared to sham tDCS might improve participants' ability to adjust their learning rate to the volatility context, we will conduct exploratory analyses, using ANOVA to test the effect of tDCS on the difference in win and loss learning rates between volatile and stable blocks (i.e. dependent variables will be: Win delta LR (win learning rate in wins-volatile blocks minus win learning rate in losses-volatile blocks) and Loss delta LR (loss learning rate in losses volatile blocks minus loss learning rate in wins volatile blocks). We will test for an interaction between Stimulation Time and tDCS Condition on Win and Loss delta LRs. This will be followed up by planned contrasts within and between the online and offline tDCS Conditions, as described above for hypothesis 3.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 80
• Est. completion date: December 1, 2021
• Est. primary completion date: December 1, 2021
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years to 45 Years

Eligibility

Inclusion Criteria:

• Participant is willing and able to give informed consent for participation in the study

• Participant has a score of >9 on Beck's Depression Inventory II (BDI-II)

• Fluent English-speaking

• Right-handed

Exclusion Criteria:

• Currently taking psychoactive medications

• Personal or family history of epileptic fits or seizures

• Family history of extreme mood fluctuations or bipolar disorder

• Currently pregnant or current likelihood of becoming pregnant

• Significant suicidal ideation or depression requiring immediate clinical referral

Related Conditions & MeSH terms

• Depression
• Dysphoria

Intervention

Device:
• Transcranial direct current stimulation (tDCS)
Electric current

Locations

Country	Name	City	State
United Kingdom	FMRIB Centre, John Radcliffe Hospital, University of Oxford	Oxford

Sponsors (2)

Lead Sponsor	Collaborator
University of Oxford	Medical Research Council

Country where clinical trial is conducted

United Kingdom, 

References & Publications (8)

Browning M, Behrens TE, Jocham G, O'Reilly JX, Bishop SJ. Anxious individuals have difficulty learning the causal statistics of aversive environments. Nat Neurosci. 2015 Apr;18(4):590-6. doi: 10.1038/nn.3961. Epub 2015 Mar 2. — View Citation

Fritsch B, Reis J, Martinowich K, Schambra HM, Ji Y, Cohen LG, Lu B. Direct current stimulation promotes BDNF-dependent synaptic plasticity: potential implications for motor learning. Neuron. 2010 Apr 29;66(2):198-204. doi: 10.1016/j.neuron.2010.03.035. — View Citation

Gagne C, Zika O, Dayan P, Bishop SJ. Impaired adaptation of learning to contingency volatility in internalizing psychopathology. Elife. 2020 Dec 22;9. pii: e61387. doi: 10.7554/eLife.61387. — View Citation

Kube T, Schwarting R, Rozenkrantz L, Glombiewski JA, Rief W. Distorted Cognitive Processes in Major Depression: A Predictive Processing Perspective. Biol Psychiatry. 2020 Mar 1;87(5):388-398. doi: 10.1016/j.biopsych.2019.07.017. Epub 2019 Jul 29. Review. — View Citation

O'Shea J, Revol P, Cousijn H, Near J, Petitet P, Jacquin-Courtois S, Johansen-Berg H, Rode G, Rossetti Y. Induced sensorimotor cortex plasticity remediates chronic treatment-resistant visual neglect. Elife. 2017 Sep 12;6. pii: e26602. doi: 10.7554/eLife.26602. — View Citation

Pulcu E, Browning M. Affective bias as a rational response to the statistics of rewards and punishments. Elife. 2017 Oct 4;6. pii: e27879. doi: 10.7554/eLife.27879. Erratum in: Elife. 2017 Oct 19;6:. — View Citation

Razza LB, Palumbo P, Moffa AH, Carvalho AF, Solmi M, Loo CK, Brunoni AR. A systematic review and meta-analysis on the effects of transcranial direct current stimulation in depressive episodes. Depress Anxiety. 2020 Jul;37(7):594-608. doi: 10.1002/da.23004. Epub 2020 Feb 26. — View Citation

Reis J, Schambra HM, Cohen LG, Buch ER, Fritsch B, Zarahn E, Celnik PA, Krakauer JW. Noninvasive cortical stimulation enhances motor skill acquisition over multiple days through an effect on consolidation. Proc Natl Acad Sci U S A. 2009 Feb 3;106(5):1590-5. doi: 10.1073/pnas.0805413106. Epub 2009 Jan 21. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Change in learning rate	In our previous study (Overman et al., 2021), model comparison showed that participants' behaviour on this task was best fit by a computational model combining: a Rescorla-Wagner learning rule with a Softmax function, including two separate learning rates for wins and losses; an inverse temperature parameter accounting for choice randomness; and a tendency parameter capturing a potential tendency to favour one shape over the other. Since the current study aims to replicate our previous findings, we will use the same model. The key hypothesis-driven variable of interest for analysis is the win learning rate.	Measure derived from task performance (40mins)
Primary	Change in proportion of win-driven choices	In addition to the computational model, we will also use a non-computational measure, the percentage of "win-driven choices". This is calculated from trials in which the win and loss are both associated with the same shape ("neutral" trials). What shape the participant chooses on the next trial will depend on whether s/he is more influenced by the current win or loss outcome. If the win outcome has a greater influence, the participant will choose the same shape again on the next trial. If the loss is more influential, the participant will avoid the current shape and instead choose the other shape. The proportion of "win-driven choices" is the proportion of trials in which participants choose the same shape on trial n+1 that was associated with both a win and a loss outcome on trial n. The key prediction is that this will be increased by online tDCS.	Measure derived from task performance (40mins)
Secondary	Change in ability to adjust learning rate to volatility	As an exploratory analysis, we will test whether bifrontal tDCS might improve participants' ability to adjust their learning rates to the volatility context. The two outcome measures will be the difference in learning rates between stable and volatile blocks, e.g. the difference in win and/or loss learning rates contrasted between the wins-volatile and losses-volatile blocks (i.e. Win delta LR = win learning rate in wins-volatile blocks minus win learning rate in losses-volatile blocks; Loss delta LR = loss learning rate in losses volatile blocks minus loss learning rate in wins volatile blocks).	Measure derived from task performance (40mins)