About this trial
The purpose of this study is to assess the mechanisms of the effect of transcranial direct current stimulation (tDCS) in alleviating subthreshold depressive symptoms in young individuals, and individualized outcome prediction is carried out.
Eligibility criteria
This trial accepts healthy volunteersQualifiers
1) Be between the ages of 12-25;
2) is right-handed on the Edinburgh Handedness Inventory;
3) 2-4 of the 9 depressive symptoms defined by the American Diagnostic and Statistical Manual of Diseases, Fifth Edition (DSM-5), lasting more than 1 week; or more than 5 depressive symptoms lasting more than 1 week but less than 2 weeks (i.e., not meeting criteria for a major depressive episode) (by DSM-5 Clinical Semi-Definitive Interview (K-SADS-PL) (Gilbody et al., 2017, JAMA);
4) Hamilton Depression Scale (HAMD-24) 24 points≥ 8 points, < 24 points;
Disqualifiers
1) Individuals with the following diagnoses according to DSM-5: psychiatric disorders due to organic diseases, substance-related and addictive disorders, neurodevelopmental disorders, schizophrenia spectrum and other psychotic disorders, bipolar disorder (assessed by K-SADS-PL);
2) Have two or more DSM-V-defined manic symptoms for 4 days but do not meet the criteria for hypomania or meet the DSM-V defined criteria for hypomanic symptoms for only 2-3 days;
3) Severe or unstable physical illness;
4) have a moderate or higher risk of suicide (as assessed by the Columbia Suicide Severity Scale);
Trial design
Parallel
Treatments tested in this trial
tdcs lDLPFC
DeviceThe current is 2mA, duration is 1200s, fade in 30s, fade out 30s, anode is on the F3, cathode is on the FP2.
tdcs rVLPFC
DeviceThe current is 2mA, duration is 1200s, fade in 30s, fade out 30s, anode is on the F6, cathode is on the FP1.
Sham
DeviceThe current only persist in the fisrt 30s, duration is 1200s, fade in 30s, fade out 30s, anode is on the F3/F6, cathode is on the FP2/1.
Treatment groups
Trial outcomes
Primary outcomes
The effect of tDCS in alleviating subthreshold depressive symptoms
In young individuals with subthreshold depressive symptoms, we used a latent growth model to test whether 10 trials of tDCS for stimulation of lDLPFC or rVLPFC had immediate and sustained (1 \& 3 months follow-up) effects on altered depressive symptoms, LPFC-DMN function regulation, positive and negative emotional processing. The latent growth curve model can fit the data well at multiple time points and analyze the correlation of the rate of change of multiple variables. We will measure participants' emotions using the original word score that has been pre-experimentally confirmed, and use the Dynamic Causal Modelling to calculate the resting-state functional regulation of LPFC to DMN (rsFC intensity from LPFC to DMN). We randomized young individuals 1:1:1 to lDLPFC, rVLPFC, and pseudo-stimulation (sham) to test the tDCS effect.
Secondary outcomes
Neuroaffective mechanisms of tDCS in alleviating subthreshold depressive symptoms
We will examine the cross-sectional association between LPFC-DMN functional regulation, positive and negative emotional processing, and depressive symptoms at each time point, and test whether there is a longitudinal association between the immediate and persistent effects of tDCS on the above variables. We will use the cross-lagged structure equation model to test whether the effects of tDCS on LPFC-DMN functional regulation (neural mechanism) and positive and negative emotional processing (affective mechanism) significantly mediate the effect of tDCS on depressive symptoms (outcome variables). We will clarify the temporal sequence of mechanism-outcome effects by testing whether mechanism effects at earlier time points can prospectively predict outcome effects at subsequent time points to infer causal relationships.
To predict the individual effect of tDCS in alleviating subthreshold depressive symptoms
Based on the above analysis results, we will use the Connectome-Based Predictive Model (CPM) method to construct emotional neural data models that predict individual effects. With the advantage of data fitting in machine learning methods, we will screen emotional neurovariables that are significantly related to the level and change of depressive symptoms based on the results of the above mechanism, and build a prediction model around them. We will focus on testing a machine learning model that prospectively predicts the effect of individual interventions: this model will use baseline or earlier time point data to predict the tDCS effect longitudinally at subsequent time points of individual participants. We will also integrate all variables that are cross-sectionally and vertically associated with the level and change of solitary autonomy to build a comprehensive predictive model.
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