The Effects of High-definition Transcranial Direct Current Stimulation on Cognitive Bias Among Individuals With Social Anxiety Symptoms
Phase: NA
Conditions: Social Anxiety Disorder
Sponsor: South China Normal University

 
Study Protocol 
 
 
 
 
 
 
 
 
 
 
Title 
The effects of High-definition transcranial direct 
 
current stimulation on cognitive bias among youth 
 
with social anxiety symptoms 
NCT number 
NCT07099521 
Date 
July 23, 2025 

Literature review 
Social anxiety is the third most common mental health issue after depression and 
alcohol abuse (Aune et al., 2022), particularly for youth aged from 15 to 24 (Tang et al., 
2022). From the perspective of the information processing, cognitive bias is the main 
cause of the occurrence and maintenance of social anxiety symptoms in youth, 
encompassing three aspects: attention bias, interpretation bias, and memory bias (Amir 
& Bomyea, 2010; Kuckertz & Amir, 2014). Specifically, youth with attention bias tend 
to focus on the threat-related cues in the social environment (attentional vigilance), and 
then quickly shift their attention away (attentional avoidance). Building on attention 
bias, interpretation bias may lead socially anxious youth to interpret ambiguous social 
information negatively and attributions negative events to internal causes (Amir & 
Bomyea, 2010; Haller et al., 2016; Martinelli et al., 2022). In addition, memory bias 
refers to the tendency to recall or recognize negative stimuli, especially self-related 
negative information (Kuckertz & Amir, 2014; Morgan, 2010). These three cognitive 
biases form a self-reinforcing circle, perpetuating and exacerbating social anxiety 
symptoms (Nikolić, 2020).  
Current treatments for cognitive bias, such as cognitive bias modification, can 
only yield a significant but small and unstable effect. (Enock et al., 2014; Heeren et al., 
2015; Liu et al., 2017). As a result, recent research has shifted toward targeting the 
underlying mechanisms to achieve more robust reductions in cognitive bias, 
particularly at the neural circuit level (Heeren et al., 2016). High-definition transcranial 
direct current stimulation (HD-tDCS) is a non-invasive brain stimulation technique that 
delivers direct current through electrodes placed on the scalp. This current induces 
polarization of neuron cell membranes, thereby enhancing or inhibiting cortical activity 
(Kuo et al., 2013; Stein et al., 2020). For individuals with social anxiety, they often 
exhibit reduced regulatory control of prefrontal cortex (PFC) over the limbic system, 
particularly in the left dorsolateral prefrontal cortex (DLPFC) (Cremers & Roelofs, 
2016). Therefore, previous tDCS studies adopted anodal tDCS to the left DLPFC and 
demonstrated that both attention, interpretation and memory bias in clinical populations 
can be significantly improved (Heeren et al., 2016; Nejati et al., 2022; Nejati et al., 

2021).  
However, most intervention studies just applied online tDCS stimulation to modify 
cognitive bias while overlooking its offline effects. Compared to short-term online 
effects, long-term offline effects are crucial for reducing intervention dependence and 
restoring normal social functioning. Moreover, previous studies have lacked direct 
tDCS interventions targeting socially anxious youth, resulting in insufficient 
consideration of the core characteristics and specific cognitive processing patterns 
associated with social anxiety. Specifically, regarding attention bias, prior studies has 
primarily focused on attentional vigilance while neglecting attentional avoidance 
(Jafari et al., 2021; Nejati & Estaji, 2024), a key characteristic of youth with social 
anxiety (Morales et al., 2015). In terms of interpretation bias, previous studies have 
failed to incorporate social contexts that may trigger negative evaluations into their 
measurement paradigms (Nejati et al., 2021), making it difficult to generalize findings 
from other clinical populations to socially anxious youth. Regarding memory bias, 
individuals with social anxiety tend to focus on their own performance in social 
situations, making them more likely to encode and retrieve self-referential negative 
memories. Thus, it is essential to examine tDCS effects on negative memory bias within 
a self-referential encoding framework. To address these gaps, the present study 
implemented offline tDCS stimulation and adjusted the stimulation protocol to enhance 
sustained intervention effects, and adopted a group-specific assessment paradigm to 
improve the precision of cognitive bias evaluation in socially anxious youth. In addition, 
while many studies have examined the effects of tDCS on attention, interpretation or 
memory bias individually (Jafari et al., 2021; Nejati & Estaji, 2024; Sanchez-Lopez et 
al., 2018; Winker et al., 2019), its influence on psychopathology symptoms remains 
underexplored. Given this, the current study utilized HD-tDCS as an intervention to 
explore changes in social anxiety symptoms. 
In current study, we adopted a comparative intervention study with randomized 
controlled in which we delivered multiple courses of offline anodal HD-tDCS over the 
left DLPFC. The aim of this study was to explore the offline impact of HD-tDCS on 
cognitive bias (including attention, interpretation and memory bias) among youth with 

social anxiety symptoms. In addition, the researchers sought to further investigate the 
impact of tDCS stimulation on social anxiety. We hypothesized that anodal tDCS would 
activates the left DLPFC to facilitate cognitive control of threat-related information 
among youth with social anxiety, thereby reducing threat attention, interpretation and 
memory bias, and even improving social anxiety symptoms. 
 
Method 
Participants 
This study recruited youth with social anxiety symptoms from the South China Normal 
University in Guangdong Province, China. Participants who were: 1) aged 15 to 24 
years; 2) scored above 55 and below 95 on the Liebowitz Social Anxiety Scale  (the 
cut-off score for social anxiety in the Chinese version (Liebowitz, 1987); 3) scored 
below 5 on the Patient Health Questionnaire (Kroenke et al., 2001) were eligible. Those 
with 1) any psychiatric disorders; 2) current or past diseases or injuries related to the 
brain; 3) medical pumps, pacemakers and cochlear implants in the body; 4) current 
pharmacological or mental treatments; 5) tDCS or TMS experiences over past year 
were also excluded.  
 
Randomized and blinding  
Participants were randomly assigned to either tDCS or sham groups using a computer-
generated randomization table, with a 1:1 allocation ratio. Participants remained 
blinded to treatment conditions throughout the intervention period. Researchers only 
received allocation information immediately prior to procedure implementation. 
 
Interventions and study procedures 
The whole HD-tDCS intervention process was 10 sessions for 5 consecutive days (20 
minutes each session and 2 sessions a day). All participants received active or sham 
HD-tDCS stimulation via a device from Soterix Medical, Inc (Woodbridge, NJ, USA). 
According to the 10/20 International EEG System, a “4 x 1 ring set-up” was placed in 
F3, FP1, FT7, C3 and FZ. F3 is the central anode electrode, which refers to the left 

DLPFC, surrounded by the other 4 return cathode electrodes (Heeren et al., 2016; Jafari 
et al., 2021; Liu et al., 2023; Müller et al., 2022; Nejati et al., 2021; Parlikar et al., 2021). 
Both the tDCS group and the sham group were given electric current at the same 
electrode position. Normally, the current gradually rose to 2mA for the first 30 seconds, 
then continued for 20 minutes and then dropping to 0mA at the end of the stimulation. 
In the sham group, the current ramped up to 2mA for 30 seconds at the start and end of 
the stimulus and then ramped down without the participants’ being aware of it, 
generating the same sensation as the active condition. During the intervention, 
participants were asked to maintain a relaxed and immobile posture. 
Prior to the study, participants completed an enrollment screening questionnaire to 
determine their suitability for tDCS intervention. Participants who met the inclusion 
criteria were randomly divided into the tDCS and the sham group. Both two groups 
received 10 sessions of stimulation with 3 hours intervals between daily sessions (2 
sessions daily, 5 days in total). Psychological symptoms (including social anxiety 
symptoms, cognitive bias and trait anxiety) and cognitive bias task performance 
(including dot probe task, word-sentence association paradigm, free recall and 
recognition task) were assessed before the first tDCS stimulation (pre-test) and after the 
last tDCS stimulation (post-test). According to the Latin square, the order of the three 
cognitive bias tasks was randomly counterbalanced among the participants. Finally, in 
order to ensure the safety of the tDCS stimulation and to compare the perception of this 
stimulation between different groups, a side-effect checklist was filled out by the 
participants after each session of stimulation. In addition, psychological symptoms 
were assessed again at 4 weeks follow-up. Participants in the entire experiment were 
rewarded with 280 RMB. 
 
Sample Size Calculation 
The required sample size was calculated using G*Power software. Based on 
repeated measures analysis of variance, the sample size for each group was at least 27 
(effect size = 0.25, = 0.05, power = 0.95). To avoid the impact caused by loss, an 
additional 10 people were included in each group, that is, 37 people. A total of 74 people 

were recruited in two groups. 
 
Assessments and outcomes 
Questionnaires 
The Liebowitz Social Anxiety Scale (LSAS) was used to distinguish participants 
with social anxiety (defined as an LSAS score ≥ 55) and assess the changes of anxiety 
symptoms between pre- and post-tDCS intervention. This scale involves 24 Likert-
items with higher score indicating severe social anxiety (Gong et al., 2023; Liebowitz, 
1987). The Patient Health Questionnaire (PHQ-9) was only used to screen those without 
depression. The total score of the nine items was calculated, ranging from 0 to 27, with 
a cut-off score of 5 (Kroenke et al., 2001; Wang et al., 2014). In addition, participants 
also completed the Negative cognitive processing bias questionnaire (NCPBQ) and the 
Spielberger Trait Anxiety Inventory - Trait (STAI-T). The NCPBQ consists of 17 items 
divided into three subscales with three lie detection items: a 5-item attention bias, a 5-
item interpretation bias and a 4-item memory bias. It was measured using a 5-point 
Likert scale from 1 (“disagree completely”) to 5 (“agree completely”). A higher score 
represented a higher level of cognitive bias (Miao et al., 2022; Zhang, 2015). The STAI-
T is an inventory concerning and evaluating trait anxiety. There are 20 items with 4-
point Likert style from 1 (“not at all”) to 4 (“very much”) (Spielberger et al., 1971). All 
of these questionnaires in this study have high reliability (Cronbach’s alpha  0.80). 
Some sociodemographic information was also collected, with sex, ethnicity, 
residence and only-child status as binary variables, and socio-economic level, 
relationship with parents and family harmony as continuous variables. 
Dot probe task 
Dot probe task is a common experimental paradigm for measuring attention bias 
(MacLeod et al., 1986). After appearance of a fixed cross in the center of the computer 
screen, a pair of faces appear side by side. Then, a probe appears on the location 
previously occupied by one of the two faces. Participants were asked to response the 
location of the probe as quickly as possible and press the appropriate keys to answer. If 
the probe was presented at a location that matched the attention, participants would 

response more quickly than the mismatched location. In current study, the experimental 
task included 40 facial stimuli (20 male and 20 female), forming 30 face pairs (20 
neutral-angry and 10 neutral-neutral combinations). Based on valence scores from 
preliminary testing, angry faces were categorized as either high-intensity and low-
intensity. All these faces were selected from the Chinese facial affective picture system 
(Gong et al., 2011). The number of trials for each type of stimuli location (left or right) 
and probe location (left or right) were equal. And a total of 120 trials were delivered in 
one block (120 trials = 30 face-pairs * 2 face locations * 2 probe locations). The fixed 
cross appeared for 500ms and then a pair of facial stimuli (neutral-angry or neutral-
neutral) appeared for 500ms. The probe (“*”) then appeared and remained until a 
response (“F” for left and “J” for right) was made. The inter-trial interval was 1500ms.  
This study used the trail-level bias score (TL-BS) as a measure of the main 
outcomes of this task (Zvielli et al., 2015). TL-BS is an indicator of reaction time (RT), 
calculated by subtracting contiguous pairs of congruent trials (probe and emotional face 
appeared on the same location of the screen) from incongruent trials (probe and 
emotional face appeared on the opposite location of the screen). Positive scores 
represent a tendency to be alert and attentive to emotional faces, while negative scores 
represent avoidance of these stimuli. A score close to zero indicated a balance of 
attention to emotional and neutral faces. Specifically, there are 4 TL-BS parameters: (1) 
Mean TL-BSpositive indicates the average value of TL-BSs  0ms; (2) Mean TL-BSnegative 
indicates the average value of TL-BSs  0ms; (3) Peak TL-BSpositive indicates the 
maximum TL-BS value; (4) Peak TL-BSnegative indicates the minimum TL-BS value. 
Word sentence association paradigm 
Word sentence association paradigm (WSAP) was used to assess the interpretation 
bias in youth with social anxiety (Beard & Amir, 2009). Following the appearance of a 
fixed cross (500ms), an ambiguous sentence describing the social situation would be 
presented in the center of the screen (2000ms). Then, a word would appear at random 
for 500ms: the word might imply a threatening interpretation (e.g., “boring” or 
“rejection”), or imply a benign one (e.g., “captivating” or “accepted”). Participants 
were asked to decide whether the word was related to the ambiguous sentence (“F” for 

relevant and “J” for irrelevant). There were 30 ambiguous sentences in a block, 
corresponding to 30 threatening words and 30 benign words, for a total of 60 word-
sentence pairs. The word-sentence pairs would be presented in random order, and no 
pairs were repeated. This study calculated the percentage of threaten and benign 
interpretations that participants endorsed as being related to the sentence, which was 
prescribed as the primary result of interpretation bias. We also recorded the response 
time and calculated the bias index (BI): threat BI = response times (reject threat – 
endorse threat) and benign BI = response times (endorse benign – reject benign). 
Free recall and recognition task  
This study used free recall and recognition task to assess memory bias by the 
number of positive/negative words remembered. First, in the “encoding phase”, after a 
short time for a fixed cross (500ms), 30 words (15 positive words and 15 negative words) 
were presented randomly in turn (each word occurred for 2000ms). Participants were 
asked to read these words aloud and answer coding questions. The questions were 
divided into three categories, each corresponding to different coding conditions, 
including other-reference encoding (“To what extent does this word describe Jiong 
He?”. Jiong He is a very popular Chinese host), encoding with reference to the 
perception of self by others (“To what extent does this word describe what people think 
of you?”) and the self-reference encoding (“To what extent does this word describe 
you?”). Participants were asked to answer these questions using a four-point Likert 
scale, ranging from 1 (“Not at all”) to 4 (“Completely”). Each encoding conditions 
involved 10 words (5 positive words and 5 negative words), which were counter-
balanced across participants. After this phase, there was a 3-minute distraction task, 
during which participants were guided through 40 calculation problems. They were 
then given 2 minutes to freely recall the words that had appeared before, regardless of 
the order of the words. Next, they completed a recognition task that contained 30 
previously shown words and another 30 disturbing words. All of these words were 
presented for 4000ms in random sequence and selected from a glossary related to social 
anxiety, covering five aspects: communication skills, interpersonal perception, social 
performance, academic achievement, and social competence (Kalenzaga & Jouhaud, 

2018). We assessed memory bias using accuracy rates for both positive and negative 
words in free recall and recognition tasks. Accuracy was calculated as: (number of 
correctly recalled/recognized words) ÷ (total number of words presented). 
 
Data preparation and statistical analysis 
Before formal analysis, this study addressed errors and outliers in the three tasks, 
respectively. Trials with incorrect reactions and with RTs greater than 3000ms, lower 
than 200ms, or greater than ±3 standard deviation were excluded when calculating 
each participants’ task performances. We also treated attention, interpretation and 
memory bias index of ±3 standard deviation as extreme values. 
Firstly, group differences across these two groups were measured using t-tests for 
scale and behavioral variables, and Chi-square tests for sociodemographic variables. To 
explore the intervention effect of tDCS on task performance, repeated-measures 
ANOVA were conducted with “group” (tDCS vs. sham group) as the between-subject 
and “time” (pre-test vs. post-test) as the within-subject factors. The outcomes of dot 
probe task, word sentence association paradigm and free recall and recognition task 
served as dependent variables. Before the ANOVA analysis, we used the Levene test to 
evaluate the homogeneity of variance of the data and the results showed that the data 
basically satisfied the homogeneity of variance hypothesis. The Bartlett and Mauchly’s 
test were also performed to assess the sphericity of the data, and the degrees of freedom 
were modified by the Greenhouse-Geisser approach if needed. For subjective scale data, 
we employed 2-level linear mixed-effects regression, which included three fixed effects 
(group, time and the interaction between group and time) and one random effect 
(participants). All of these analyses were conducted using SPSS 25.0 and R software. 
The significance level was set at p < 0.05, and the marginal significance level was set 
at p < 0.1. Effect sizes were reported by the partial eta-squared (2) for ANOVA, and 
the Cohen’s d for pairwise comparisons. 
 

Reference 
Amir, N., & Bomyea, J. (2010). Chapter 14 - Cognitive Biases in Social Anxiety Disorder. In S. G. 
Hofmann & P. M. DiBartolo (Eds.), Social Anxiety (Second Edition) (pp. 373-393). 
Academic Press. https://doi.org/10.1016/B978-0-12-375096-9.00014-6  
 
Aune, T., Nordahl, H. M., & Beidel, D. C. (2022). Social anxiety disorder in adolescents: Prevalence 
and subtypes in the Young-HUNT3 study. Journal of Anxiety Disorders, 87, 
102546.https://doi.org/10.1016/j.janxdis.2022.102546  
 
Beard, C., & Amir, N. (2009). Interpretation in Social Anxiety: When Meaning Precedes Ambiguity. 
Cognitive Therapy and Research, 33(4), 406-415.https://doi.org/10.1007/s10608-009-
9235-0  
 
Cremers, H. R., & Roelofs, K. (2016). Social anxiety disorder: a critical overview of neurocognitive 
research. WIREs Cognitive Science, 7(4), 218-232.https://doi.org/10.1002/wcs.1390  
 
Enock, P. M., Hofmann, S. G., & McNally, R. J. (2014). Attention Bias Modification Training Via 
Smartphone to Reduce Social Anxiety: A Randomized, Controlled Multi-Session 
Experiment. 
Cognitive 
Therapy 
and 
Research, 
38(2), 
200-
216.https://doi.org/10.1007/s10608-014-9606-z  
 
Gong, H., Cao, Q., & Li, M. (2023). Social memory characteristics of non-clinical college students 
with 
social 
anxiety. 
Journal 
of 
Affective 
Disorders, 
326, 
147-
154.https://doi.org/10.1016/j.jad.2023.01.075  
 
Gong, X., Huang, Y.-X., Wang, Y., & Luo, Y.-J. (2011). Revision of the Chinese Facial Affective Picture 
System. Chinese Mental Health Journal, 25(1), 40-46. 
 
Haller, S. P. W., Raeder, S. M., Scerif, G., Cohen Kadosh, K., & Lau, J. Y. F. (2016). Measuring online 
interpretations and attributions of social situations: Links with adolescent social anxiety. 
Journal 
of 
Behavior 
Therapy 
and 
Experimental 
Psychiatry, 
50, 
250-
256.https://doi.org/10.1016/j.jbtep.2015.09.009  
 
Heeren, A., Billieux, J., Philippot, P., De Raedt, R., Baeken, C., de Timary, P., Maurage, P., & 
Vanderhasselt, M.-A. (2016). Impact of transcranial direct current stimulation on 
attentional bias for threat: a proof-of-concept study among individuals with social anxiety 
disorder. 
Social 
Cognitive 
and 
Affective 
Neuroscience, 
12(2), 
251-
260.https://doi.org/10.1093/scan/nsw119  
 
Heeren, A., Mogoașe, C., Philippot, P., & McNally, R. J. (2015). Attention bias modification for social 
anxiety: A systematic review and meta-analysis. Clinical Psychology Review, 40, 76-
90.https://doi.org/10.1016/j.cpr.2015.06.001  
 
Jafari, E., Alizadehgoradel, J., Pourmohseni Koluri, F., Nikoozadehkordmirza, E., Refahi, M., 

Taherifard, M., Nejati, V., Hallajian, A.-H., Ghanavati, E., Vicario, C. M., Nitsche, M. A., & 
Salehinejad, M. A. (2021). Intensified electrical stimulation targeting lateral and medial 
prefrontal cortices for the treatment of social anxiety disorder: A randomized, double-
blind, parallel-group, dose-comparison study. Brain Stimulation, 14(4), 974-
986.https://doi.org/10.1016/j.brs.2021.06.005  
 
Kalenzaga, S., & Jouhaud, V. (2018). The self-reference effect in memory: an implicit way to assess 
affective 
self-representations 
in 
social 
anxiety. 
Memory, 
26(7), 
894-
903.https://doi.org/10.1080/09658211.2018.1430833  
 
Kroenke, K., Spitzer, R. L., & Williams, J. B. (2001). The PHQ-9: validity of a brief depression severity 
measure. 
Journal 
of 
General 
Internal 
Medicine, 
16(9), 
606-
613.https://doi.org/10.1046/j.1525-1497.2001.016009606.x  
 
Kuckertz, J. M., & Amir, N. (2014). Chapter 16 - Cognitive Biases in Social Anxiety Disorder. In S. G. 
Hofmann & P. M. DiBartolo (Eds.), Social Anxiety (Third Edition) (pp. 483-510). Academic 
Press. https://doi.org/10.1016/B978-0-12-394427-6.00016-9  
 
Kuo, H.-I., Bikson, M., Datta, A., Minhas, P., Paulus, W., Kuo, M.-F., & Nitsche, M. A. (2013). 
Comparing Cortical Plasticity Induced by Conventional and High-Definition 4 × 1 Ring 
tDCS: 
A 
Neurophysiological 
Study. 
Brain 
Stimulation, 
6(4), 
644-
648.https://doi.org/10.1016/j.brs.2012.09.010  
 
Liebowitz, M. R. (1987). Social phobia. Modern Problems of Pharmacopsychiatry, 22, 141-
173.https://doi.org/10.1159/000414022  
 
Liu, C., Xie, Y., Hao, Y., Zhang, W., Yang, L., Bu, J., Wei, Z., Wu, H., Pescetelli, N., & Zhang, X. (2023). 
Using multisession tDCS stimulation as an early intervention on memory bias processing 
in 
subthreshold 
depression. 
Psychophysiology, 
60(1), 
e14148.https://doi.org/10.1111/psyp.14148  
 
Liu, H., Li, X., Han, B., & Liu, X. (2017). Effects of cognitive bias modification on social anxiety: A 
meta-analysis. PloS One, 12(4), e0175107.https://doi.org/10.1371/journal.pone.0175107  
 
MacLeod, C., Mathews, A., & Tata, P. (1986). Attentional bias in emotional disorders. Journal of 
Abnormal Psychology, 95(1), 15-20.https://doi.org/10.1037//0021-843x.95.1.15  
 
Martinelli, A., Grüll, J., & Baum, C. (2022). Attention and interpretation cognitive bias change: A 
systematic review and meta-analysis of bias modification paradigms. Behaviour Research 
and Therapy, 157, 104180.https://doi.org/10.1016/j.brat.2022.104180  
 
Miao, K., Liu, X., Zhang, X., Li, Y., Liao, X., Zhang, R., Feng, Z., & Chen, Z. (2022). Revision and 
psychometric properties of the negative cognitive processing bias scale [Original 
Research]. Frontiers in Psychiatry, 13.https://doi.org/10.3389/fpsyt.2022.1013108  

 
Morales, S., Pérez-Edgar, K. E., & Buss, K. A. (2015). Attention Biases Towards and Away from 
Threat Mark the Relation between Early Dysregulated Fear and the Later Emergence of 
Social 
Withdrawal. 
Journal 
of 
Abnormal 
Child 
Psychology, 
43(6), 
1067-
1078.https://doi.org/10.1007/s10802-014-9963-9  
 
Morgan, J. (2010). Autobiographical memory biases in social anxiety. Clinical Psychology Review, 
30(3), 288-297.https://doi.org/10.1016/j.cpr.2009.12.003  
 
Müller, D., Habel, U., Brodkin, E. S., & Weidler, C. (2022). High-definition transcranial direct current 
stimulation (HD-tDCS) for the enhancement of working memory – A systematic review 
and 
meta-analysis 
of 
healthy 
adults. 
Brain 
Stimulation, 
15(6), 
1475-
1485.https://doi.org/10.1016/j.brs.2022.11.001  
 
Nejati, V., & Estaji, R. (2024). The impact of transcranial direct current stimulation on attention bias 
modification in children with ADHD. Journal of Neural Transmission, 131(7), 823-
832.https://doi.org/10.1007/s00702-024-02775-1  
 
Nejati, V., Heyrani, R., & Nitsche, M. (2022). Attention bias modification through transcranial direct 
current stimulation (tDCS): A review. Neurophysiologie Clinique, 52(5), 341-
353.https://doi.org/10.1016/j.neucli.2022.09.002  
 
Nejati, V., Khalaji, S., Goodarzi, H., & Nitsche, M. (2021). The role of ventromedial and dorsolateral 
prefrontal cortex in attention and interpretation biases in individuals with general anxiety 
disorder (GAD): A tDCS study. Journal of Psychiatric Research, 144, 269-
277.https://doi.org/10.1016/j.jpsychires.2021.10.034  
 
Nikolić , M. (2020). Disturbed Social Information Processing as a Mechanism in the Development 
of 
Social 
Anxiety 
Disorder. 
Child 
Development 
Perspectives, 
14(4), 
258-
264.https://doi.org/10.1111/cdep.12390  
 
Parlikar, R., Vanteemar S, S., Shivakumar, V., Narayanaswamy C, J., Rao P, N., & Ganesan, V. (2021). 
High definition transcranial direct current stimulation (HD-tDCS): A systematic review on 
the treatment of neuropsychiatric disorders. Asian Journal of Psychiatry, 56, 
102542.https://doi.org/10.1016/j.ajp.2020.102542  
 
Sanchez-Lopez, A., Vanderhasselt, M.-A., Allaert, J., Baeken, C., & De Raedt, R. (2018). 
Neurocognitive mechanisms behind emotional attention: Inverse effects of anodal tDCS 
over the left and right DLPFC on gaze disengagement from emotional faces. Cognitive, 
Affective, & Behavioral Neuroscience, 18(3), 485-494.https://doi.org/10.3758/s13415-
018-0582-8  
 
Spielberger, C. D., Gonzalez-Reigosa, F., Martinez-Urrutia, A., Natalicio, L. F., & Natalicio, D. S. 
(1971). 
The 
state-trait 
anxiety 
inventory. 
Revista 
Interamericana 
de 

Psicologia/Interamerican journal of psychology, 5(3 & 4). 
 
Stein, D. J., Fernandes Medeiros, L., Caumo, W., & Torres, I. L. (2020). Transcranial direct current 
stimulation in patients with anxiety: current perspectives. Neuropsychiatric Disease and 
Treatment, 161-169. 
 
Tang, X., Liu, Q., Cai, F., Tian, H., Shi, X., & Tang, S. (2022). Prevalence of social anxiety disorder 
and symptoms among Chinese children, adolescents and young adults: A systematic 
review 
and 
meta-analysis 
[Systematic 
Review]. 
Frontiers 
in 
Psychology, 
13.https://doi.org/10.3389/fpsyg.2022.792356  
 
Wang, W., Bian, Q., Zhao, Y., Li, X., Wang, W., Du, J., Zhang, G., Zhou, Q., & Zhao, M. (2014). 
Reliability and validity of the Chinese version of the Patient Health Questionnaire (PHQ-
9) 
in 
the 
general 
population. 
General 
Hospital 
Psychiatry, 
36(5), 
539-
544.https://doi.org/10.1016/j.genhosppsych.2014.05.021  
 
Winker, C., Rehbein, M. A., Sabatinelli, D., Dohn, M., Maitzen, J., Roesmann, K., Wolters, C. H., Arolt, 
V., & Junghoefer, M. (2019). Noninvasive Stimulation of the Ventromedial Prefrontal 
Cortex Indicates Valence Ambiguity in Sad Compared to Happy and Fearful Face 
Processing 
[Original 
Research]. 
Frontiers 
in 
Behavioral 
Neuroscience, 
13.https://doi.org/10.3389/fnbeh.2019.00083  
 
Zhang, R. (2015). Preparation and application of negative cognitive processing Bias questionnaire 
The Third Military Medical University].  
 
Zvielli, A., Bernstein, A., & Koster, E. H. W. (2015). Temporal Dynamics of Attentional Bias. Clinical 
Psychological Science, 3(5), 772-788.https://doi.org/10.1177/2167702614551572