跳至主要内容
临床试验/NCT03393312
NCT03393312已完成不适用

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

University of Oxford2 个研究点 分布在 1 个国家目标入组 80 人开始时间: 2018年2月2日最近更新:
适应症

试验速览

阶段
不适用
状态
已完成
入组人数
80
试验地点
2
主要终点
Change in learning rate

研究概览

简要总结

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.

详细描述

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:

研究设计

研究类型
Interventional
分配方式
Randomized
干预模型
Crossover
主要目的
Other
盲法
Triple (Participant, Investigator, Outcomes Assessor)

盲法说明

A study mode on the tDCS device is used, which requires the researcher to enter a code determining whether active or sham stimulation is applied. The codes have been assigned to the participants in a randomised manner by a researcher not involved in the stimulation sessions or assessment of the outcomes. All participants receive active tDCS in one session and sham tDCS in the other session in a randomised order.

入排标准

年龄范围
18 Years 至 45 Years(Adult)
性别
All
接受健康志愿者

入选标准

  • 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

排除标准

  • 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

结局指标

主要结局

Change in learning rate

时间窗: Measure derived from task performance (40mins)

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.

Change in proportion of win-driven choices

时间窗: Measure derived from task performance (40mins)

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.

次要结局

  • Change in ability to adjust learning rate to volatility(Measure derived from task performance (40mins))

研究者

申办方类型
Other
责任方
Principal Investigator
主要研究者

DrJacintaO'Shea

Principal Investigator

University of Oxford

研究点 (2)

Loading locations...

相似试验

Effects of Transcranial Direct Current Stimulation... | 临床试验