TDCS Interventions for Subthreshold Depressive Symptoms in Young Individuals.

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.

Time frame: 2 weeks, 1 month and 3 months

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.