The Efficacy of Neural Stimulation in Individuals With Schizophrenia
Phase: NA
Conditions: Schizophrenia, Schizo Affective Disorder
Sponsor: The University of Texas at Dallas

 
 
 
 
 
Title: The Efficacy of Neural Stimulation in Individuals With 
Schizophrenia 
 
NCT number: NCT05746494  
 
Document date: Nov. 19, 2024 

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Trial Protocol 
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1. Study Overview 
2 
Paranoia is the unfounded and pervasive belief that people intend to hurt oneself (Freeman et al., 
3 
2007) and is a prominent symptom in schizophrenia spectrum disorders (SSD) (Green & Phillips, 
4 
2004), with over 90% of individuals with first-episode SSD showing heightened levels of 
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paranoia (Moutoussis et al., 2007). Elevated levels of paranoia can predict the onset of psychosis 
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in youth with clinical high risk (Cannon et al., 2008), and has been associated with impaired 
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social functioning (Hajdúk et al., 2019; Pinkham, Harvey, et al., 2016), more suicidal ideation 
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and behaviors (Freeman et al., 2011, 2019), and increased violence (Coid et al., 2013; Keers et 
9 
al., 2014) in individuals with SSD. Given these negative consequences, efficient treatments for 
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paranoia are desperately needed in SSD. Therefore, there has been growing interest in 
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elucidating the neuropathological processes underpinning paranoia, which may pave the way for 
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developing biologically based interventions, such as neurostimulation, among patients with 
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significant levels of paranoia. A technique used in this project, transcranial direct current 
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stimulation (tDCS), may hold significant promise for these purposes due to its few adverse 
15 
effects, tolerable experience, relatively low cost, and portability. Additionally, this study’s use of 
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ecological momentary assessments (EMA) will facilitate a better understanding of how treatment 
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effects can be observed in, and impacted by, real-world social situations, thereby laying the 
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groundwork for elucidating the duration of real-world improvements and developing potential 
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long-term treatment gains for individuals with SSD. 
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21 
2. Background 
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2.1 Occurrence of Paranoia 
23 
Paranoid ideation is the unfounded and pervasive belief that people intend to harm 
24 
oneself (Freeman, 2007) and is a prominent symptom in schizophrenia spectrum disorders (SSD) 
25 
(Freeman et al., 2002; Green & Phillips, 2004), with over 90% of individuals with first episode 
26 
SSD showing heightened levels of paranoia (Moutoussis et al., 2007). In collaboration with the 
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World Health Organization, an early prospective study followed 1379 individuals with psychotic 
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risks in 10 countries and found that when symptomatic individuals made their initial contact with 
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services, nearly 50% of all cases of psychosis were characterized by the presence of persecutory 
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delusions, the second most common symptom being delusions of reference (Sartorius et al., 
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1986). Social-environmental risk factors for paranoid ideation include adverse childhood 
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experiences (e.g., trauma, parental neglect) (Bentall et al., 2012; Shevlin et al., 2015; Sitko et al., 
33 
2014), deprived neighborhoods (Wickham et al., 2014), social stress (Kesting et al., 2013), lower 
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social rank (Berry et al., 2018; Freeman et al., 2014; Saalfeld et al., 2018), and discrimination 
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(Janssen et al., 2003). At the individual level, cognitive and psychological antecedents of 
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paranoia include anxiety and depression (Ben-Zeev et al., 2009; Freeman et al., 2012), sleep 
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disturbance (Freeman et al., 2009; Kasanova et al., 2020), anomalous internal experience 
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(Freeman, 2007; Freeman & Garety, 2014), worrying thinking style (Freeman & Garety, 2014; 
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Martinelli et al., 2013), negative cognitions about the self and others (e.g., low self-esteem, being 
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critical of the self, expecting negative interpersonal interactions) (Bentall & Fernyhough, 2008; 
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Freeman & Garety, 2014), as well as reasoning biases (e.g., “jumping to conclusions”, belief 
42 
updating) (Dudley et al., 2016; Krkovic et al., 2021; Pytlik et al., 2020). Although paranoia has 
43 
historically been linked to SSD, it is now conceptualized as existing on a transdiagnostic 
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2 
 
continuum (Combs et al., 2006). Emerging research has shown that paranoid thinking is not 
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restricted to a single diagnosis; instead, it is present in a variety of clinical conditions, including 
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major depressive disorder and bipolar disorder (Bentall et al., 2009), post-traumatic stress 
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disorder (PTSD) (Alsawy et al., 2015; Freeman et al., 2013), and Alzheimer’s disease (AD) 
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(Kloszewska, 1998; Ropacki & Jeste, 2005). For example, in 2003, Cook and colleagues (Cook 
49 
et al., 2003) proposed a distinction between paranoid and misidentification subtypes of AD, with 
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a median prevalence of delusions in AD estimated to be 36% (Ropacki & Jeste, 2005). In AD, 
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delusions typically involve paranoid elements, such as the belief that people are trying to hurt or 
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steal (Kloszewska, 1998). This continuum also extends into the general population where the 
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prevalence of paranoid ideation was estimated to be strikingly high (i.e., at least 10-15%) 
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(Freeman, 2007). Similarly, in a large community sample in England (N = 7281), 18.6% adults 
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endorsed paranoid thoughts, and 1.8% reported being the target of possible plans to inflict them 
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with serious harm (Freeman et al., 2011). This pattern extends into adolescence with a recent 
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study based on 801 adolescents in the United Kingdom finding that 7-32% of teenagers reported 
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having paranoid thoughts on a weekly basis and that girls demonstrate considerably higher levels 
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of paranoia than boys (Bird et al., 2019). 
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2.2 Paranoia and Social Functioning  
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Elevated levels of paranoid ideation can predict the onset of psychosis in youth with 
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clinical high risk (Cannon et al., 2008) and have been linked to a number of negative outcomes 
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including more suicidal ideation and behaviors (Freeman et al., 2011, 2019), increased violence 
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(Coid et al., 2013; Keers et al., 2014), and impaired social functioning (Hajdúk et al., 2019; 
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Pinkham, Harvey, et al., 2016) in individuals with SSD. Social functioning appears to be 
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particularly impacted. In comparison to non-paranoid patients with schizophrenia, patients with 
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active paranoid ideation demonstrated larger deficits maintaining social interactions and social 
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acceptability in the real world (Pinkham, Harvey, et al., 2016). A recent study on children and 
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adolescents (age = 11-17 years; N = 301) seeking mental health services revealed that paranoia 
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exhibited the strongest unique association with peer problems and was linked to increased 
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psychological problems over time (Bird et al., 2021). Further, Fett and colleagues (Fett et al., 
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2022) examined the daily situations associated with paranoid thoughts in clinical patients with 
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psychotic disorders, first-degree relatives, and healthy controls based on the experience sampling 
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method (ESM). They found that patients were frequently alone (i.e., 68.5% of the study time) 
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and had higher levels of paranoia when alone compared to being with other people. In contrast, 
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their first-degree relatives experienced more paranoia when spending time with others than when 
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alone, and there was no significant relation between time alone vs. with others and paranoia in 
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the healthy controls (Fett et al., 2022). Previous findings also indicate that difficulties in 
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interpersonal relationships were strongly related to “the perception of being discussed by others 
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behind their back” in both patients with SSD and a community sample as revealed in a study 
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based on network analyses (Hajdúk et al., 2019). In the patient group, the “perceived prior 
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experience of being treated unfairly” aspect of paranoia was uniquely associated with worse 
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social functioning, while the “negative opinions about people” aspect of paranoia was uniquely 
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linked to poorer interpersonal functioning in the general community (Hajdúk et al., 2019). 
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Finally, in both clinical and general populations, paranoia appears to mediate the relationship 
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between childhood adversity and social functioning (Palmier-Claus et al., 2016). 
87 
Research on subclinical paranoia has also provided similar evidence regarding the 
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3 
 
negative impacts of paranoia on social life. A large national study (N = 7281) found that 
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paranoia was linked to impaired social functioning, reduced perceived social support and social 
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cohesion, and more workplace stress (Freeman et al., 2011). Behaviorally, individuals with 
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subclinical paranoia maintained further physical distance from the researcher and spent more 
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time reading the consent form (Combs & Penn, 2004) as compared to non-paranoid peers. 
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Individuals with heightened sub-clinical paranoia also reported less social involvement, fewer 
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social interactions, and more social competence difficulties (Combs et al., 2013) than their peers. 
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Literature regarding the associations between paranoia and specific types of interpersonal 
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relationships is relatively limited, with only 2 studies considered relevant here. One early study 
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investigated the relation between personality disorder symptoms and marital functioning in 99 
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newly-married couples in the community using a daily-dairy method. Results indicated that 
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increased paranoid personality disorder was negatively correlated with relationship sentiment 
100 
(South, 2014). More recently, a study examining how paranoia impacts roommate relationships 
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in 103 same-sex college roommate dyads revealed that students with higher paranoia reported 
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lower roommate relationship satisfaction and poorer college adjustment, and that students who 
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had roommates with higher levels of paranoia also reported lower satisfaction with their 
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roommates (Springfield, Ackerman, et al., 2021).  
105 
2.3 Paranoia and Social Cognition 
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The observed social difficulties associated with paranoia may arise from a variety of 
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deficits in social cognition: the mental processes of perceiving, interpreting, and reacting to the 
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social environment (Green et al., 2015). Social cognitive deficits have been identified as a core 
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feature of mental disorders with marked interpersonal dysfunction, including SSD (Green et al., 
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2015, 2019; Penn et al., 2008). Among all social cognitive domains, hostility/hostile 
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interpretation biases and the propensity to mistrust unfamiliar faces appear to be at the center of 
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paranoid ideation. When encoding ambiguous social information, individuals with elevated 
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levels of paranoia displayed more negative interpretation bias as measured by the scrambled-
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sentences task (SST), such that they created more paranoid sentences compared to the non-
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paranoid individuals (Savulich et al., 2015, 2017; Trotta et al., 2021). Paranoid individuals with 
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schizophrenia, in particular, demonstrated hostile attributional biases [measured by the 
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Ambiguous Intentions Hostility Questionnaire (AIHQ)] and the tendency to view unfamiliar 
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others as untrustworthy (measured by the Trustworthiness Task) (Pinkham, Harvey, et al., 2016). 
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Comparatively, in the healthy population, individuals with subclinical paranoia showed greater 
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hostile and blaming attributional biases in ambiguous situations (measured by AIHQ) compared 
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to those with low paranoia (Combs et al., 2013), and higher levels of paranoia were negatively 
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associated with trustworthiness ratings (measured by the Trustworthiness Task) (Klein et al., 
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2018). As noted above, perceived hostility is one core feature of paranoid ideation, thus the 
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Hostility Scale of the Personality Inventory for DSM-5 (PID-5-HS) has also been used to as a 
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proxy measure of paranoia, which revealed strong associations with AIHQ and paranoia (Buck et 
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al., 2016).  
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2.4 Neural Correlates of Paranoia: The Amygdala-Prefrontal Circuits 
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Given the negative consequences of heightened paranoia, there has been increasing 
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interest in identifying the neuropathological mechanisms underlying paranoid ideation. 
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Identification of such a mechanism would offer promise for developing biologically based 
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interventions, such as neurostimulation, among patients with significant levels of paranoia. 
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2.4.1 The critical role of the Amygdala  
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The etiology of paranoid ideation may have a neural basis rooted in networks involving 
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the amygdala, an important hub of the limbic system that has been implicated in multiple 
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processes related to paranoia (e.g., trust, fear conditioning) (Davis & Whalen, 2001; Koscik & 
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Tranel, 2011). Numerous studies have  supported the role of the amygdala in unconscious fear 
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(Öhman et al., 2000, 2007), conditioned fear (Rogan et al., 1997; Wilensky et al., 2006), 
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perceiving fearful or threatening facial expressions (Adams et al., 2003; Adolphs et al., 1995; 
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Graham et al., 2007) and linguistic threats (Isenberg et al., 1999; Schmidt et al., 2010), as well as 
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detecting saliency related to reward (Anderson & Phelps, 2001; Cunningham & Brosch, 2012; 
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Mahler & Berridge, 2012; Zheng et al., 2017). In addition, the amygdala is important in 
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differentiating between ingroup and outgroup members (Hart et al., 2000; McCutcheon et al., 
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2018), which some propose as the fundamental evolutionary function of paranoid ideation 
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(Raihani & Bell, 2019). For example, a recent study recruited Black and White British 
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participants and asked them to view Black and White race faces while undergoing MRI scanning 
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(McCutcheon et al., 2018). They found that both groups exhibited increased activity in right 
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amygdala in response to outgroup faces; however, greater right amygdala activation was 
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observed in the Black group when viewing outgroup faces than in the White group, and lower 
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neighborhood ingroup density was associated with greater right amygdala activation to white 
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faces in the Black group (McCutcheon et al., 2018). The interpretations of these findings are 
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consistent with prior literature suggesting that increased amygdala activation/responses may 
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reflect perceived outgroup threats instead of racial bias or prejudice (Chekroud et al., 2014). 
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Indeed, although paranoia is characterized by the conviction that others intend to cause harm, 
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these assumptions often apply to a specific social group (e.g., neighbors, police) and involve 
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misunderstanding of group boundaries and activities (Raihani & Bell, 2019). 
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In patients with SSD, neuroscientific findings are somewhat counterintuitive, in that 
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reduced, rather than increased, amygdala activity has been found in paranoid individuals when 
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completing social threat perception tasks (Goghari et al., 2010; Pinkham, Hopfinger, Pelphrey, et 
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al., 2008) and when viewing pictures of negative emotions (Williams et al., 2004, 2007). To 
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reconcile these findings with increased amygdala activity in non-clinical populations under 
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fearful and threatening situations (LaBar et al., 1998; McCutcheon et al., 2018; Öhman, 2005; 
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Phelps et al., 2001), research revealed that such task-related amygdala hypoactivity was actually 
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associated with heightened resting cerebral blood flow (CBF) in schizophrenia patients with 
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prominent paranoid ideation, compared with non-paranoid schizophrenia patients and healthy 
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controls (Pinkham et al., 2015). Task-driven neural activity and resting-state/baseline neural 
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functioning capture distinct features of brain function. In traditional functional magnetic 
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resonance imaging (fMRI), signals are collected based on oxygen levels in the blood (i.e., BOLD 
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signal), with the rationale that increased neuronal activity will lead to more oxygen use and 
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subsequent blood flow from the surrounding regions to support metabolism (Heeger & Ress, 
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2002). Therefore, stimulus-based signals can reveal the regions associated with certain brain 
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functions corresponding to the tasks, whereas resting state signals reflect spontaneous brain 
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activity without performing any tasks (Smitha et al., 2017). As such, these findings imply that 
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schizophrenia patients with active paranoid ideation may have elevated levels of amygdala 
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activity at rest and without being exposed to any threatening stimuli, which may in turn produce 
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a ceiling effect and limit the potential for increased task-evoked activation in amygdala imaging 
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5 
 
studies when truly threatening stimuli are presented (Pinkham et al., 2015). Collectively, the 
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literature indicates that heightened amygdala function at baseline may be posited as a crucial 
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neural mechanism underlying paranoid ideation and that neuromodulation techniques aiming to 
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inhibit/suppress amygdala function at rest may have important therapeutic implications in 
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reducing paranoia.  
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2.4.2 The Amygdala-prefrontal Circuits 
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Intriguingly, the amygdala collaborates with the prefrontal cortex (PFC) in processing 
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and modulating responses to potential threats (Gold et al., 2015; Lee et al., 2012), and 
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importantly, PFC is hypothesized to receive and down-regulate threat signals detected by the 
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amygdala (Gold et al., 2015; Lee et al., 2012; Wager et al., 2008), suggesting that effective 
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modulation of amygdala by PFC may limit paranoid thinking. Supporting this mechanistic model, 
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previous studies have found that PFC-amygdala coupling may undergird successful emotion 
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regulation (Lee et al., 2012), and decreased activations in PFC have been reported in paranoid 
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individuals with schizophrenia (Williams et al., 2004, 2007). Further, increased functional 
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connectivity within limbic networks (i.e., between hippocampus and amygdala) and PFC has 
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been associated with elevated levels of paranoia in patients with SSD (Walther et al., 2021), and 
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hyperconnectivity between amygdala and PFC was also found in a paranoid patient group 
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compared with a non-paranoid patient group in our previous work specifically examining the 
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neural circuits implicated in paranoid ideation (Fan et al., 2021). Collectively, these findings 
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indicate that the observed hyperconnectivity may be due to a hypervigilant amygdala that is 
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constantly sending false alarms to PFC and that ineffective regulation of these signals by PFC 
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may result in paranoid thinking. As such, these findings also point to the possibility of 
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attenuating paranoid thinking by stimulating PFC, which may further regulate and inhibit 
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amygdala function. 
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The VLPFC in particular represents an important region in modulating amygdala 
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functioning (Monk et al., 2008) and inhibiting paranoid ideation, as suggested by previous work 
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linking reduced activity in VLPFC and increased activation in amygdala to elevated levels of 
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paranoia (Pinkham, Hopfinger, Ruparel, et al., 2008). When comparing brain activations in 
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rating untrustworthy vs. trustworthy faces in paranoid individuals with schizophrenia, there was 
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no significant difference in activations of the social cognitive network [including amygdala, 
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fusiform gyrus, superior temporal sulcus, ventrolateral prefrontal cortex (VLPFC), medial PFC 
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(MPFC)]; however, a greater activation of these brain regions for untrustworthy faces was 
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observed in nonparanoid individuals with schizophrenia and healthy controls, and such an 
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increased activation was correlated with better social functioning (Pinkham, Hopfinger, Ruparel, 
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et al., 2008). Although the literature convergently highlights the major role of the VLPFC-
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amygdala circuit in developing and maintaining paranoid ideation, these studies are correlational 
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and do not involve any experimental manipulation of VLPFC and/or amygdala function to reveal 
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the hypothesized causal effects of these processes on paranoia. As detailed below, transcranial 
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direct current stimulation (tDCS) offers a way to directly alter neural activation that can be used 
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to test such mechanistic models. 
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2.5 Neural Stimulation in Schizophrenia 
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2.5.1 Transcranial Direct Current Stimulation (tDCS) 
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In recent years, there has been a growing interest in employing tDCS to modulate brain 
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function noninvasively (Berryhill, 2014; Filmer et al., 2014). tDCS delivers low amplitude direct 
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6 
 
current to the brain through electrodes placed on the scalp, which theoretically hyperpolarizes or 
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depolarizes the membrane potentials and then alters the excitability of the cortex (i.e., to increase 
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or decrease the likelihood of neuronal firing) (Brunoni et al., 2012; Priori et al., 2009). Further, 
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the effects of tDCS are hypothesized to extend beyond the stimulation time through synaptic 
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plasticity (Kronberg et al., 2017; Liebetanz et al., 2002), suggesting that long-term brain, and the 
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corresponding behavioral, alterations might be expected as a result of repetitive tDCS. In general, 
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tDCS has been found effective in improving the symptoms of patients with depression (mostly 
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by targeting brainstem and the frontal cortex) (Fregni et al., 2006; Kalu et al., 2012), AD (mostly 
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by targeting the temporal cortex) (Boggio et al., 2011, 2012; Cai et al., 2019), and stroke (Elsner 
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et al., 2013; Hamilton et al., 2011). In healthy populations, tDCS has also demonstrated efficacy 
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in improving cognitive function (e.g., working memory) (Berryhill & Jones, 2012; Hoy et al., 
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2013) and motor processing ability (Antal et al., 2011; Hunter et al., 2009). Additional benefits 
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of tDCS include few adverse effects, high tolerability, relatively low cost, and high portability 
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(Bikson et al., 2016, 2018). 
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Despite the promise of tDCS and the early successes noted above, tDCS has many 
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unknowns regarding its mechanism and best practices for application, which can make the 
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process of experimental design difficult. There is no universally accepted methodology for the 
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optimal stimulation protocol, and the current understanding of the short- and long-term processes 
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of tDCS is still somewhat limited (Nitsche et al., 2008). General considerations include 
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positioning of electrodes, the intensity and duration of stimulation, “online” vs. “offline” 
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stimulation (detailed below), single vs. multiple/repeated sessions, and individual variability. In 
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stimulating human brains, low-intensity currents (i.e., a few mA) are often employed; when 
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applying tDCS to individuals with SSD, a range of 1-3 mA has been used in previous research, 
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with 2 mA being the most frequently used intensity parameter (Brunoni et al., 2014; Gupta et al., 
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2018). Each stimulation session typically lasts between 15 and 30 minutes, with 20 minutes 
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being the most usual duration (Brunoni et al., 2014; Gupta et al., 2018). Other more complicated 
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methodological considerations involve choosing between an “online” or “offline” study design. 
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“Online” stimulation means that the main outcomes are assessed during the stimulation, while 
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“offline” stimulation means that participants are asked to sit quietly during stimulation rather 
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than engage in a specific task, assuming that aftereffects of tDCS will be observed in subsequent 
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task performance (Brunoni et al., 2012). The “online” design is posited to train new cognitive or 
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motor abilities, and thus may be most beneficial to rehabilitation (Priori et al., 2009). For 
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example, the majority of studies treating post-stoke aphasia used the “online” approach as 
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demonstrated in a review (de Aguiar et al., 2015), probably because online tDCS may maximize 
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the benefits of speech therapy sessions by facilitating treatment effects, while offline tDCS is 
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hypothesized to only activate the language system and prepare it for the tasks that will be used in 
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subsequent interventions (de Aguiar et al., 2015). Finally, it is not surprising that repeated 
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sessions may produce more prominent treatment effects compared to just one single session. A 
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recent study that applied single-session tDCS to patients with schizophrenia found null results in 
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reducing auditory hallucinations (Selvaraj et al., 2021); on the contrary, a meta-analysis revealed 
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that patients with schizophrenia who received tDCS twice daily or more than 10 sessions in total 
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had reduced auditory hallucinations, and that those who received at least 10 tDCS sessions also 
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reported reduced negative symptoms (Kim et al., 2019). Similar suggestions come from another 
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meta-analysis indicating that higher frequencies (e.g., 2 sessions each day) and total number of 
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7 
 
stimulation sessions (≥10) are essential to obtain optimal treatment benefits in schizophrenia 
265 
(Cheng et al., 2020). Notably, tDCS can be administered safely even when used twice daily 
266 
(2mA, each for 20 min) for up to 4 weeks, as demonstrated by a previous study on schizophrenia 
267 
patients (Lindenmayer et al., 2019).   
268 
2.5.2 Effects of tDCS on Psychotic Symptoms in Schizophrenia 
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Previous research demonstrates that tDCS holds promise in ameliorating some symptoms 
270 
of schizophrenia, in that early case reports and recent studies using randomized control trials 
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have found significant improvement in hallucinations and negative symptoms in patients with 
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SSD by stimulating DLPFC (Bose et al., 2014, 2018; Brunelin et al., 2012; Chang et al., 2018, 
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2020; Gomes et al., 2015, 2018; Lindenmayer et al., 2019; Palm et al., 2016; Sreeraj et al., 2018; 
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Valiengo et al., 2020). Furthermore, improvement in social functioning (Chang et al., 2020; 
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Narita et al., 2017; Shiozawa et al., 2013) and clinical insight (Bose et al., 2014; Chang et al., 
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2018, 2019, 2021; Kao et al., 2020) has also been observed in individuals with SSD, suggesting 
277 
that the therapeutic efficacy of tDCS to DLPFC may be multifold and could have generated 
278 
functional improvement in real life. However, null effects (Fitzgerald et al., 2014; Selvaraj et al., 
279 
2021; Smith et al., 2020) and placebo effects (Fröhlich et al., 2016) have also been reported, 
280 
suggesting that further investigation is needed. Additionally, the abovementioned studies mostly 
281 
investigated hallucinations and positive symptoms in general, with no study focusing specifically 
282 
on paranoia or examining whether the beneficial effects of tDCS can be observed for paranoia 
283 
symptoms.  
284 
2.5.3 VLPFC as the Stimulation Target  
285 
As alluded, mounting research has highlighted the role of VLPFC in the pathophysiology 
286 
of schizophrenia as well as paranoid ideation. For example, decreased activation in VLPFC was 
287 
observed in individuals at ultra-high-risk for psychosis (Koike et al., 2011), and reduced VLPFC 
288 
gray matter volume was associated with more negative symptoms in individuals with 
289 
schizophrenia (Zierhut et al., 2013). A previous study observed decreased functional connectivity 
290 
between the left inferior frontal gyrus (IFG) and VLPFC in patients with auditory verbal 
291 
hallucinations compared to healthy controls, indicating that hallucinations may result from a 
292 
disconnection between the speech production system (e.g., IFG) and the verbal monitoring 
293 
system (e.g., VLPFC) (Clos et al., 2014). Altered structure and activation of VLPFC have also 
294 
been linked to impaired cognitive function in psychosis, including impaired working memory 
295 
(Goldstein et al., 2011; Zierhut et al., 2013), attention (Schneider et al., 2007), cognitive (Buchy 
296 
et al., 2016; Orfei et al., 2013) and metacognitive insight (Spalletta et al., 2014), as well as motor 
297 
(Kaladjian et al., 2007) and response inhibition (Kaladjian et al., 2011).  
298 
Furthermore, VLPFC is also involved in processing and responding to social information. 
299 
Not surprisingly, VLPFC collaborates with amygdala to help facilitate successful emotion 
300 
regulation (Green et al., 2015), a process that is compromised in psychosis (Modinos et al., 2010; 
301 
Morris et al., 2012; Van Der Velde et al., 2015). In addition, increased response in VLPFC was 
302 
observed when viewing angry faces compared with neutral faces in individuals with a history of 
303 
suicidal behaviors (Olié. et al., 2015), emphasizing the role of VLPFC in social threat perception. 
304 
VLPFC has also been linked to the “jumping to conclusions” bias (JTC; a cognitive distortion in 
305 
which individuals make judgements before evaluating all pertinent information thoroughly) 
306 
(Langdon et al., 2010) in schizophrenia. As demonstrated by a recent meta-analysis (Vucurovic 
307 
et al., 2020), JTC was linked to increased effective connectivity between a task-positive network 
308 

8 
 
and a task-negative network (with VLPFC as the region-of-interest to form the network) in both 
309 
patients with delusions and healthy individuals (Andreou et al., 2018). Intriguingly, in the 
310 
general population, attenuated VLPFC activity and connectivity were associated with higher 
311 
social anhedonia (Hooker et al., 2014; Yin et al., 2015), poorer emotion management (Yin et al., 
312 
2015), and more paranoid ideation (Hooker et al., 2014), indicating that reduced VLPFC 
313 
functioning may contribute to elevated levels of paranoia, which may in turn lead to more active 
314 
social withdraw behaviors to prevent potential distress and harm. Collectively, these studies 
315 
provide important evidence supporting VLPFC as a target for reducing paranoid ideation in 
316 
schizophrenia, and indicate that empirical studies examining the efficacy of modulating VLPFC 
317 
functioning on paranoia are warranted. 
318 
To begin to address this gap, our preliminary work tested the efficacy of single-session 
319 
tDCS to VLPFC among undergraduate students with heightened subclinical paranoia. While 
320 
preliminary, we found a significant reduction in paranoid ideation after active stimulation but not 
321 
sham stimulation (Springfield, Isa, et al., 2021). Combined with previous work reporting that 
322 
regions of VLPFC demonstrated increased functional connectivity with amygdala in paranoid 
323 
patients compared with non-paranoid patients (Fan et al., 2021), our pilot tDCS study indicates 
324 
that VLPFC may be a potentially important treatment target for clinical levels of paranoia in 
325 
patients with SSD. As such, using tDCS to impact functioning of VLPFC not only offers 
326 
perspective regarding the neurobiological mechanisms of paranoid ideation by providing 
327 
evidence of a causal link between VLPFC activity and the experience of paranoia, but also holds 
328 
important implications for treating clinical levels of paranoia in patients with SSD. 
329 
 
330 
3. Project Outline, Goals, and Hypotheses 
331 
To address these issues, the current research aimed to investigate the effects of tDCS on 
332 
paranoid ideation and social functioning in individuals with SSD. Individuals who met 
333 
diagnostic criteria for schizophrenia or schizoaffective disorder, demonstrated clinically 
334 
significant levels of recent paranoia, and had no contraindications for neural stimulation (e.g., 
335 
presence or history of epilepsy or seizures) were recruited. Our proposed mechanistic model 
336 
suggests that either decreasing activation of the amygdala or increasing activation of prefrontal 
337 
regulatory regions (e.g., VLPFC) may reduce paranoia. However, given the amygdala’s 
338 
subcortical location, VLPFC was targeted in the present study as it is more feasibly modulated 
339 
via tDCS. A double-blind, crossover design was used to administer both active and sham 
340 
stimulation conditions to each participant to allow within-subject comparisons. The most widely 
341 
adopted and commonly used parameters were utilized during active stimulation to ensure safety 
342 
and efficacy (i.e., 2mA, 20min) (Brunoni et al., 2014; Gupta et al., 2018). To increase potential 
343 
efficacy, 2 sessions of stimulation were administered with a time interval of ≥3 hours (Bose et al., 
344 
2014; Brunelin et al., 2012) for each condition. We utilized a higher frequency here compared 
345 
with our pilot study (Springfield, Isa, et al., 2021) because the alleviation of clinical levels of 
346 
paranoia may require multiple sessions of stimulation (Cheng et al., 2020; Selvaraj et al., 2021), 
347 
which may increase efficacy as suggested by previous research (Cheng et al., 2020; Kim et al., 
348 
2019). Offline stimulations were employed because there are no tasks that can be used to train 
349 
the neural networks related to paranoid ideation (i.e., online stimulation) and previous literature 
350 
indicates that cognitive activity during tDCS may attenuate or even abolish the stimulation 
351 
effects (Horvath et al., 2014). Finally, in addition to measuring improvements in paranoia and 
352 

9 
 
social functioning in the lab setting, we also administered short daily surveys to participants 
353 
multiple times per day for 7 days before and after the 1st stimulation session, as well as after the 
354 
2nd stimulation session. These ecological momentary assessments (EMA) allow for capturing 
355 
baseline paranoia severity as well as any delayed effects of neural stimulation in the real world. 
356 
3.1 Aims and Hypotheses 
357 
The specific goals and hypotheses of this project are listed below: 
358 
Goal 1: To examine whether applying tDCS to VLPFC will reduce paranoid symptoms in SSD. 
359 
Hypothesis 1a: Patients with SSD will show reduced paranoia symptoms after the active VLPFC 
360 
stimulation condition relative to sham stimulation as measured by in-lab assessments (i.e., 
361 
decreased self-report paranoia, less attributional bias and hostility, more trust to strangers). 
362 
Hypothesis 1b: Patients with SSD will show reduced paranoia symptoms after the active VLPFC 
363 
stimulation condition relative to sham stimulation as measured by EMA assessments (i.e., 
364 
reduced feelings of paranoia in daily life). 
365 
Goal 2: To examine whether applying tDCS to VLPFC will improve social functioning in SSD. 
366 
Hypothesis 2a: Patients with SSD will show better social performance after the active VLPFC 
367 
stimulation condition relative to sham stimulation as measured by in-lab assessments (i.e., 
368 
improved self-report of social functioning). 
369 
Hypothesis 2b: Patients with SSD will show better social functioning after the active VLPFC 
370 
stimulation condition relative to sham stimulation as measured by EMA assessments (i.e., more 
371 
interactions with others, more positive experience in, and expectations of, daily social 
372 
interactions). 
373 
 
374 
4. Participants 
375 
A total of 62 participants (age = 18-64) diagnosed with schizophrenia or schizoaffective 
376 
disorder with current (in the past week) or recent (in the past month) paranoia were enrolled in 
377 
the study. Twelve participants withdrew (n = 4), were lost to follow-up (n = 7), or were 
378 
determined ineligible after the enrollment visit (n = 1), resulting in 50 participants completing 
379 
the whole procedure (see Table 1 in the main article). Non-completers did not differ 
380 
significantly from the completed sample in age (p = 0.813), gender (p = 0.693), race (p = 0.321), 
381 
ethnicity (p = 0.970), IQ (p = 0.322), years of education (p = 0.538), handedness (p = 0.861), or 
382 
CPZ-equivalent (p = 0.176). However, a higher proportion of non-completers were not taking 
383 
any medications for their mental disorders (nNot-taking medication = 8, 66.66% of the subsample) 
384 
compared to completers (nNot-taking medication = 3, 6.00% of the subsample; χ2 (1) = 24.41, p < 
385 
0.001), suggesting a lower adherence to treatment and possibly more severe psychopathological 
386 
symptoms in this population. Supporting this notion, more non-completers showed current 
387 
paranoia (ncurrent paranoia= 12, 100.00% of the subsample) compared to completers (ncurrent paranoia= 
388 
38, 76.00% of the subsample; χ2 (1) = 3.57, p = 0.059) at a marginally significant level. 
389 
4.1 Sample Size Estimation 
390 
In our pilot study using an undergraduate sample, a medium effect size was revealed in 
391 
the active stimulation group (Cohen’s d = 0.51), thus it was used for sample size estimation 
392 
using G*Power 3.1 (Faul et al., 2009) in the current study. For research goals 1 and 2, we 
393 
planned to use repeated-measures ANOVAs to test the tDCS effects on VLPFC on paranoia and 
394 
social functioning, and power analyses yielded a sample size of 46 in total (f = 0.25, power = 
395 

10 
 
0.80, α = .05, correlation among repeated measures = 0.3) in order to detect a medium effect. 
396 
Given the potentially high retention rate due to the repeated-stimulation characteristics of this 
397 
study, we intentionally overrecruited. Recruitment continued until the end of January 2024, 
398 
aligning with the anticipated completion of our data collection period. As a result, a total of 63 
399 
participants consented, 50 of whom were randomized and completed the study. 
400 
4.2 Recruitment and Eligibility 
401 
The current sample was recruited from a database of previous participants in our lab and 
402 
via mental health agencies in the Dallas-Fort Worth region. Study fliers were placed in outpatient 
403 
clinics, and interested individuals either contacted study personnel directly or asked to be 
404 
contacted by the study team. This recruitment approach has been consistently used by our 
405 
research team supervised by Dr. Amy Pinkham, and has been found to yield a substantial number 
406 
of participants, respect patient privacy, and minimize the possibility of coercion. 
407 
Eligible participants were between the ages of 18 and 64 inclusively, had a diagnosis of 
408 
schizophrenia or schizoaffective disorder, and demonstrated either current (in the past week) or 
409 
recent (in the past month) paranoia. Participants were not asked to change any part of their 
410 
current treatment regimen, and all medications were accepted. Participants were required to have 
411 
been clinically stable for a minimum of 6 weeks with no dose changes >20% for a minimum of 
412 
two weeks. Exclusion criteria included: 1) presence or history of a pervasive developmental 
413 
disorder or mental retardation as defined by IQ < 70, 2) presence or history of neurological or 
414 
medical disorders that contraindicate neural stimulation (e.g. presence or history of epilepsy, 
415 
seizures, etc.), 3) demonstrating sensory limitations, including uncorrectable visual or hearing 
416 
impairments that interfere with assessment, 4) history of electroconvulsive therapy, 5) lack of 
417 
proficiency in English, and 6) substance use disorder not in remission in the past 6 months.  
418 
 
419 
5. Procedure 
420 
As shown in Figure 1 below, this was a double-blind, within-subjects, crossover design 
421 
comparing the effects of active vs. sham tDCS. Participants completed 3 long visits (i.e., 1 
422 
enrollment visit, 2 stimulation visits). The 2 stimulation visits were at least a week apart (with an 
423 
average of 14.58 days between the stimulation visits in the current study) to make sure that the 
424 
tDCS effects from the first visit dissipated, and the first simulation visit was expected to occur 7 
425 
days after the enrollment visit (average days = 13.59 days in the current study). 
426 

11 
 
 
427 
Figure 1. Flowchart of the study procedure 
428 
 
429 
6. Stimulation Protocol 
430 
6.1 Stimulation Procedure 
431 
After completing pre-stimulation assessments, participants started the first stimulation 
432 
procedure of the visit via neuroConn’s programable Direct Current stimulator. Electrodes were 
433 
affixed to the participant’s head to target the stimulation area as determined for each individual 
434 
via the international 10-20 EEG placement system with Modified Combinatorial Nomenclature. 
435 
For stimulating the right VLPFC, the anode was placed over F6 (Montreal Neurological Institute 
436 
coordinates: 44, 32, -12 (Pinkham, Hopfinger, Ruparel, et al., 2008), and the contralateral 
437 
homolog area (F5) was targeted for the stimulation of the left VLPFC (see Figure 2 for 10-20 
438 
EEG placement). Electrodes were placed identically for the control condition. Following the 
439 
neurostimulation procedure, participants were asked to remain seated in the chair and browse 
440 
through magazines quietly for 30 minutes to allow stimulation effects to stabilize (Samani et al., 
441 
2019). 
442 

12 
 
 
443 
Figure 2. 10-20 EEG placement 
444 
6.2 Counterbalance and Randomization 
445 
The active stimulation and sham control conditions were counterbalanced, meaning that 
446 
each participant had the same possibility of experiencing either the active or sham condition at 
447 
the first visit, and the other condition at the second visit. In the current study, 26 participants 
448 
received the active stimulation at their Visit 1, while the remaining 24 received it at their Visit 2. 
449 
This counterbalance procedure was achieved via the “study mode” preprogrammed feature of the 
450 
NeuroConn machine, which randomly assigned participants to either the active stimulation or 
451 
sham group in a double-blinded way. Specifically, each participant received a 5-digit code, 
452 
which was paired with one active stimulation code and one sham control code to ensure that each 
453 
participant did not receive two stimulation or control conditions. After study completion, each 
454 
participant’s study code for that given visit was revealed and recorded. The project manager 
455 
maintained the list containing the condition information corresponding to each visit during the 
456 
study in case the blind conditions needed to be revealed in real time (e.g., if participants reported 
457 
adverse effects during tDCS). 
458 
6.3 Stimulation Parameters 
459 
The total stimulation time of 40 minutes was divided into two twenty-minute sessions to 
460 
allow repeated stimulation, which may increase efficacy (Cheng et al., 2020; Kim et al., 2019). 
461 
The active stimulation included active current at 2mA, ramping up over 15 seconds, sustaining 
462 
for 20 minutes, and finally ramping down for 15 seconds. The dimensions of each electrode were 
463 
5 × 7 centimeters in size (35 cm2), which led to a current density of 0.5714 mA/cm2. For the 
464 
sham condition, electrodes were still attached for 20 minutes each session, but the actual 
465 
stimulation time was limited to 45 seconds per session, with 15 seconds of gradual stimulation, 
466 
leading up to 15 seconds of full stimulation, and finally 15 seconds of ramping down. This 
467 
limited stimulation mimicked the ramp up phase of active stimulation, making it more difficult 
468 
for participants to guess the condition. The machine also automatically performed impedance 
469 
control checks throughout the neurostimulation to ensure that both the participant and the 
470 
experimenter could not tell which condition was being administered. Following each session, 
471 

13 
 
inquiries into the participants’ perceptions of the suspected condition showed that their accuracy 
472 
in identifying the correct condition was below chance levels (47%), supporting the efficacy of 
473 
the blinding procedures. 
474 
 
475 
Figure 3. Computational simulation of tDCS-induced current density. (A) shows the 
476 
montage location in the study (1 = right VLPFC, 2 = left VLPFC). (B) reveals the distribution of 
477 
the current density estimated based on the stimulation location and parameters.  
478 
 
479 
6.4 Current Modeling 
480 
To better characterize the current distribution during the stimulation, current modelling 
481 
was conducted for the active condition. This computational simulation utilized a finite element 
482 
model derived from MRI, implemented through the Comets toolbox (Jung et al., 2013) in Matlab 
483 
(http://www.COMETStool.com). Figure 3 illustrates a detailed three-dimensional (3D) 
484 
numerical analysis of the current density generated by tDCS based on the montage applied in this 
485 
study. It reveals that our targeted VLPFC is one of the regions receiving the most intense 
486 
stimulation, along with the dorsal medial PFC and a portion of the DLPFC. 
487 
 
488 
7. Assessments 
489 
7.1 Enrollment Visit 
490 
At the enrollment visit, consent was obtained from participants, and demographic 
491 
information was also collected. Paranoia screening and diagnostic interviews were performed to 
492 
determine eligibility. Current medications were reported by the patients, and they were asked to 
493 

14 
 
remain on a stable dose of medication throughout their participation in the study unless 
494 
specifically recommended by their physician. After that, participants completed daily EMA 
495 
surveys for 7 days to assess baseline paranoia level and social functioning in daily life. They then 
496 
attended the first stimulation visit. At both stimulation visits, the Positive and Negative 
497 
Syndrome Scale (PANSS)  (Kay et al., 1987) was administered by trained research personnel to 
498 
assess the full range of symptoms in individuals with SSD. Daily EMA surveys between 
499 
Stimulation Visit 1 and 2, as well as after Stimulation Visit 2, were also sent to participants to 
500 
monitor any changes in paranoia and social functioning after tDCS in the participant’s day-to-
501 
day life. 
502 
7.2 Stimulation Visits: Pre-stimulation Assessments 
503 
Trait Paranoia. The Paranoia Scale (PS) (Fenigstein & Vanable, 1992) is a self-report 
504 
measure to assess trait paranoia. This measure contains 20 items (e.g., “Someone has it in for 
505 
me”, “It is safer to trust no one”) and is measured on a five-point Likert scale (1 = not at all 
506 
applicable, 5 = extremely applicable). Scores range from 20-100, with higher scores indicating 
507 
higher paranoia. The PS demonstrated good reliability and validity in previous work on patients 
508 
with schizophrenia (Pinkham et al., 2012; Tiernan et al., 2014). In the current study, PS showed 
509 
excellent internal consistency in both Stimulation Visit 1 (Cronbach’s α = 0.941) and Stimulation 
510 
Visit 2 (Cronbach’s α = 0.930). 
511 
State Paranoia. The State Social Paranoia Scale (SSPS) (Freeman et al., 2007) was used 
512 
to examine paranoia levels immediately before and at the conclusion of stimulation within each 
513 
visit. Participants indicated how much they agreed with each of 20 statements (e.g., “Someone 
514 
was hostile towards me”, “Someone was trying to isolate me”) using a five-point Likert scale (1 
515 
= do not agree, 5 = totally agree). Additionally, the SSPS contains three subscales, measuring 
516 
paranoid, neutral, and positive thinking. Scores range from 20-100, and higher scores represent 
517 
higher state paranoid ideation. Acceptable reliability and validity have been demonstrated in 
518 
previous research in patients with psychosis (Freeman et al., 2015; Veling et al., 2016). In the 
519 
current study, the total scale (Visit 1: Cronbach’s αpre = 0.819, Cronbach’s αpost = 0.840; Visit 2: 
520 
Cronbach’s αpre = 0.798, Cronbach’s αpost = 0.880) and the persecution subscale scale (Visit 1: 
521 
Cronbach’s αpre = 0.924, Cronbach’s αpost = 0.926; Visit 2: Cronbach’s αpre = 0.903, Cronbach’s 
522 
αpost = 0.911) showed outstanding internal consistency. The neutral thinking (Visit 1: Cronbach’s 
523 
αpre = 0.750, Cronbach’s αpost = 0.457; Visit 2: Cronbach’s αpre = 0.603, Cronbach’s αpost = 0.803) 
524 
and positive thinking (Visit 1: Cronbach’s αpre = 0.706, Cronbach’s αpost = 0.731; Visit 2: 
525 
Cronbach’s αpre = 0.611, Cronbach’s αpost = 0.853) subscales showed mostly acceptable internal 
526 
consistency. 
527 
State Anxiety. Considering that anxiety has been consistently identified as a construct 
528 
that overlaps with paranoia (Martin & Penn, 2001; Startup et al., 2007), we also measured state 
529 
anxiety to examine potential changes after tDCS. The State subscale of State-Trait Anxiety 
530 
Inventory (STAI-S) includes 20 items measuring current feelings (e.g., “I am tense”, “I feel 
531 
nervous”) on a four-point Likert scale (1 = not at all, 4 = very much so; scores range 20-80) 
532 
(Spielberger, 2010), with higher scores indicating higher anxiety at this moment. The STAI-S 
533 
demonstrated excellent internal consistency in both visits (Visit 1: Cronbach’s αpre = 0.879, 
534 
Cronbach’s αpost = 0.869; Visit 2: Cronbach’s αpre = 0.847, Cronbach’s αpost = 0.908). 
535 
7.3 Assessments Between the Two Stimulation Sessions  
536 
As noted, participants were asked to sit quietly for 30 minutes after each stimulation 
537 

15 
 
session to enable the stimulation to work thoroughly throughout the neural networks. Next, they 
538 
completed several assessments of basic cognitive function and social cognitive ability/bias that 
539 
have been generally linked to psychotic symptoms and paranoia but that were not of primary 
540 
importance and therefore not reported here. Relationships among paranoia, social decision-
541 
making, and aggression were also explored in this segment for secondary analyses. Since these 
542 
analyses were not the main focus of the project, they are not included in the main text (details of 
543 
all measures administered between stimulation sessions can be found in Appendix A). 
544 
Participants were offered a 30-minute break before the second stimulation session of that visit. 
545 
The only measure relevant to the main goal of this study is overall social functioning as detailed 
546 
below:  
547 
Birchwood Social Functioning Scale (SFS; Stimulation Visit 1 and 2). The SFS 
548 
(Birchwood et al., 1990) measures 7 domains of social adjustment based on self-reports (range = 
549 
0-223, Cronbach’s αvisit 1 = 0.915, Cronbach’s αvisit 2 = 0.897), including social 
550 
engagement/withdrawal (range = 0-15; Cronbach’s αvisit 1 = 0.422, Cronbach’s αvisit 2 = 0.561), 
551 
interpersonal communication (range = 0-9; Cronbach’s αvisit 1 = 0.426, Cronbach’s αvisit 2 = 0.269), 
552 
prosocial activities (range = 0-66; Cronbach’s αvisit 1 = 0.879, Cronbach’s αvisit 2 = 0.815), 
553 
recreation activities (range = 0-45; Cronbach’s αvisit 1 = 0.684, Cronbach’s αvisit 2 = 0.749), 
554 
independence-competence (range = 0-39; Cronbach’s αvisit 1 = 0.911, Cronbach’s αvisit 2 = 0.934), 
555 
independence-performance (range = 0-39; Cronbach’s αvisit 1 = 0.915, Cronbach’s αvisit 2 = 0.920), 
556 
and employment (single item; range = 0-10). A higher score on each subscale indicates better 
557 
functioning in the respective social domain, while a higher overall score signifies better social 
558 
functioning in general. The SFS was originally developed for schizophrenia patients and has 
559 
been widely used to measure social functioning outcomes in this population (Birchwood et al., 
560 
1990; Burns & Patrick, 2007).  
561 
6.7 Stimulation Visits: Post-stimulation Assessments 
562 
State Paranoia and State Anxiety. SSPS and STAI-S were administered as described in 
563 
the pre-stimulation assessments.  
564 
Ambiguous Intentions Hostility Questionnaire (AIHQ). The AIHQ is a widely used 
565 
measure to assess hostile attributional style in ambiguous situations (Combs et al., 2007). It is 
566 
selected as one of the primary outcome measures because hostile perception is a crucial 
567 
component of paranoia (Combs et al., 2009; Zaytseva et al., 2013), and there is a robust, 
568 
medium-to-large association between paranoia and hostile attributional bias measured by AIHQ 
569 
(r fluctuates at around 0.3-0.5) (An et al., 2010; Combs et al., 2009; Klein et al., 2018; Langdon 
570 
et al., 2013). Five ambiguous scenarios were presented to participants, and they gave reasons 
571 
why each situation happened, indicated how much they thought that people did this to them on 
572 
purpose, how angry they would be, how much they would blame others, and what they would do 
573 
about it. Responses to the first and last questions were recorded verbatim for later coding, and 
574 
responses to questions 2-4 were rated on Likert scales (1-6 for if the behavior was on purpose 
575 
and 1-5 for how angry they were and how much they would blame others). These three Likert 
576 
scale items were averaged to form the blaming attribution subscale of AIHQ, which showed 
577 

16 
 
outstanding internal consistency in both visits (Cronbach’s αvisit 1 = 0.905, Cronbach’s αvisit 2 = 
578 
0.901). For the open-ended questions, hostility and aggression biases were rated by three 
579 
independent, trained raters using a five-point Likert scale (1 = not hostile/aggressive at all, 5 = 
580 
very hostile/aggressive). Inter-rater reliability was computed by ICC among all the raters, with 
581 
ICC > .80 being acceptable (ICC = 0.892 in the present study). Thus, the AIHQ yielded three 
582 
summary scores, the Blaming Score, the Hostility Bias Score, and the Aggression Bias Score.  
583 
Scrambled-sentences task (SST): In the SST (Wenzlaff, 1993), participants reordered 
584 
strings of scrambled words to create sentences of either paranoid or nonparanoid meanings by 
585 
selecting 5 out of the 6 provided words. For example, participants could reorder the words from 
586 
“Winner, am, born, I, loser, a” to “I am a born winner” (positive interpretation) or “I am a born 
587 
loser” (negative interpretation). They were given 4 min to finish as many as possible. The total 
588 
number of paranoid sentences was divided by the total number of completed sentences and then 
589 
multiplied by 100 to generate a percentage bias score, with a greater percentage indicating a 
590 
higher level of paranoid interpretation bias. Previous research has demonstrated moderate-to-
591 
strong correlations between SST scores and paranoia (r fluctuates at around 0.4-0.6) (Savulich et 
592 
al., 2015, 2017). 
593 
Hostility Scale of the Personality Inventory for DSM-5 (PID-5-HS):  PID-5-HS 
594 
(Krueger et al., 2012) contains 10 self-report items (e.g., “I snap at people when they do little 
595 
things that irritate me”) assessing pathological hostility on a 4-point scale (0 = very false or often 
596 
false, 3 = very true or often true), with higher total scores indicating more hostility (range = 0-
597 
30). PID-5-HS showed a high correlation with AIHQ blame score (r = 0.47) in previous research 
598 
(Buck et al., 2016). In the current study, the internal consistency of PID-5-HS was excellent 
599 
(Cronbach’s αvisit 1 = 0.902, Cronbach’s αvisit 2 = 0.913). 
600 
Trustworthiness Task. The trustworthiness task requires participants to indicate how 
601 
much they trust each of 60 grayscale facial stimuli along a 7-point Likert scale (-3 = very 
602 
untrustworthy, 3 = very trustworthy) (Adolphs et al., 1998). Total scores were calculated by 
603 
averaging across responses and hence varied from -3 to +3, with higher values indicating a 
604 
greater tendency to trust others. There is a small-to-medium association between trustworthiness 
605 
scores and paranoid ideation (r fluctuates between -0.1 and -0.3) (Buck et al., 2016; Klein et al., 
606 
2018).  
607 
 
608 
8. Ecological Momentary Assessments 
609 
EMA surveys were administered for a total of 21 days – 7 days prior to simulation visit 1, 
610 
7 days after stimulation visit 1, and 7 days after stimulation visit 2. Participants received 3 
611 
randomly timed surveys assessing current paranoid symptoms and social interactions each day. 
612 
The surveys were sent in the morning, mid-day, and evening, with random initiation at time 
613 
intervals ≥ 2 hours. Participants were allowed to respond to the survey within 1 hour after the 
614 
survey was sent. This protocol is in line with previous work and was demonstrated to yield ideal 
615 
intra-day variation and compliance (Jones, Moore, Depp, et al., 2021; Jones, Moore, Pinkham, et 
616 
al., 2021). Each survey queried the frequency of auditory hallucinations and paranoid ideation 
617 
along a 7-point scale, as well as the emotions associated with and perceptions of social 
618 
interactions since the last survey (e.g., did you interact with anyone since your last survey?). 
619 
Affective states since the past alarm were rated on a 7-point scale using the following items: 
620 
sad/depressed, excited, irritated, happy, belongingness, and burden to others. Expectations 
621 

17 
 
regarding subsequent interpersonal situations were also rated on a 7-point scale (e.g., how much 
622 
interest do you have in interacting with people for the rest of the day?). Appendix B contains the 
623 
complete list of EMA questions (i.e., paranoia: Q1; social functioning: Q3, Q4, Q5, Q6, Q7, Q14, 
624 
Q15). Adherence was monitored remotely via a secure platform, and participants who missed 3 
625 
consecutive surveys were contacted by research personnel to identify and resolve any issues. 
626 
Compensation of the EMA periods was determined by the actual number of surveys completed 
627 
to encourage adherence.  
628 
A total of 47 (out of 50) participants had full data across three EMA periods. One 
629 
participant did not have a phone and chose not to participate in the EMA protocol. Two 
630 
participants completed baseline EMA but lost their phones afterwards, and thus chose not to 
631 
complete the two remaining EMA periods. EMA compliance in the current study was above 80% 
632 
across the three periods (EMA1 = 82.74%, EMA2 = 83.38%, EMA3 = 80.04%).    
633 
 
634 
9. Statistical Analyses Plan 
635 
For all tests, a conventional threshold of α = 0.05 was used. While opting not to correct 
636 
for multiple comparisons may permit alpha inflation, we did not want to risk missing any 
637 
potential therapeutic effects in this treatment study by increasing the likelihood of Type II errors. 
638 
Therefore, in line with a large body of existing literature, findings that passed the significance 
639 
level of α = 0.05 are presented. 
640 
Hypothesis 1a: To test the reduction in paranoia measured in the lab (active vs. sham 
641 
stimulation), repeated-measures ANOVAs were performed for self-reported state paranoia 
642 
measured both before and after the stimulation, while paired-samples t-tests were performed for 
643 
paranoia-related outcomes measured only after stimulation (i.e., attributional bias and hostility, 
644 
trust to strangers).  
645 
Hypothesis 1b: Multilevel modeling (MLM) with maximum likelihood robust estimation was 
646 
used to test the differences in daily feelings of paranoia (i.e., Q1) across the three EMA periods 
647 
(EMA-baseline vs. EMA-active vs. EMA-sham), with the condition of the EMA period being 
648 
dummy-coded. 
649 
Hypothesis 2a: To test the improvement in social functioning measured in the lab (active vs. 
650 
sham stimulation), paired-samples t-tests were performed for self-reported social functioning 
651 
measured only after the stimulation. 
652 
Hypothesis 2b: MLM with maximum likelihood robust estimation was used to test the 
653 
differences in daily social functioning (i.e., Q3, Q4, Q5, Q6, Q7, Q14, Q15) across the three 
654 
EMA periods (EMA-baseline vs. EMA-active vs. EMA-sham), with the condition of the EMA 
655 
period being dummy-coded.  
656 
Notably, one of the daily social functioning questions (i.e., Q3: Since the past alarm, who 
657 
have you interacted with?) has a different score range. It is quantified as the frequency of social 
658 
interactions, where a higher score indicates more interactions across a broader range of people 
659 
categories (range = 0-5). In contrast, the remaining six items adhere to a score range of 1-7. 
660 
Therefore, we performed MLM separately for Q3 (named as social interaction frequency) and for 
661 
the average of items Q4, Q5, Q6, Q7, Q14, and Q15 (named as social interaction motivation and 
662 
experience) for parsimony. 
663 
 
664 
10. Participant Payment 
665 

18 
 
Participants were compensated $20 for completing the enrollment visit, $100 for each of 
666 
the two in-lab stimulation visits, and up to $16 for each of the 7-day EMA periods (three EMA 
667 
periods in total), totaling a maximum of $268. Participants who enrolled in the study but were 
668 
found ineligible during screening were still compensated $20. If participants stopped taking part 
669 
in the study or were withdrawn by the research team, they received $5 for each 20 minutes 
670 
completed during the lab visits. For example, if they completed 1 hour of the study, they were 
671 
paid $15. Compensation for each EMA period was determined by the number of surveys 
672 
completed, with $0.75 paid for each survey except for the final survey, which was worth $1 (to 
673 
allow for an even amount of $16 per EMA period if all 21 surveys were completed). Parking on 
674 
campus was provided for participants. 
675 
 
676 
11. Risk and Benefits Assessments 
677 
11.1 Potential Risks 
678 
It is possible that participants may feel tired, fatigued or anxious during the research 
679 
procedure; however frequent breaks were built into the lab portion of the study to reduce this 
680 
possibility. Additionally, participants were informed that they may take a break or terminate their 
681 
participation at any time. The most common side effect of tDCS neurostimulation is skin redness 
682 
just beneath the electrode. This effect usually wears off within one hour. Other potential risks 
683 
associated with tDCS include: 
684 
1. Itching at the electrode location; typically lasting less than an hour; 
685 
2. A small percentage of participants may experience mild dizziness upon stimulation onset. 
686 
After stimulation, this only lasts a few seconds and has no effect on balance; 
687 
3. Occasionally, temporary skin damage may occur beneath the electrode. This results in a week-
688 
long darkening of the skin. At the planned current, the size of these marks, should they occur, is 
689 
only a few millimeters, posing a low risk; 
690 
4. After neurostimulation and the waiting period, participants may feel drowsy, but this effect 
691 
dissipates within one hour. 
692 
Finally, whenever information is gathered, there is a potential risk of loss of 
693 
confidentiality. This cannot be guaranteed, but every effort has been done to protect the privacy 
694 
of participants’ information. Participants were assigned a random number that was used to 
695 
identify their data, and the file linking the number to the participants’ email addresses and 
696 
birthdates was stored separately from the data they provide. This file will be erased upon 
697 
completion of the study. All data is stored in locked filing cabinets in secure offices at UTD, as 
698 
well as on password-protected/encrypted computers at UTD, and only study personnel who have 
699 
received extensive training in methods to protect confidentiality have access to the data. 
700 
11.2 Potential Benefits 
701 
It is possible that neurostimulation may reduce paranoid ideation and/or improve social 
702 
functioning for participants after active stimulation. However, the effects of tDCS are often 
703 
temporary, and we do not anticipate that such a short-term stimulation will have a lasting effect. 
704 
If this study is effective, subsequent research can investigate the ideal frequency and duration of 
705 
neurostimulation to deliver direct, long-lasting benefits. 
706 
This project may lay the foundation for distributing large-scale, easy-to-deliver 
707 
standardized treatments for patients. Additionally, this study’s use of ecological momentary 
708 
assessments (EMA) will facilitate a better understanding of how treatment effects can be 
709 

19 
 
observed in, and impacted by, real-world social situations, thereby laying the groundwork for 
710 
elucidating the duration of real-world improvements and developing potential long-term 
711 
treatment gains for individuals with schizophrenia. 
 
712 

20 
 
APPENDIX A: SUPPLEMENTARY MEASURES 
713 
MATRICS Consensus Cognitive Battery (MCCB; Stimulation Visit 1). The MCCB 
714 
(Nuechterlein et al., 2008) was used to capture cognitive function, including processing speed 
715 
(Trail Making Test: Part A, Animal Naming), working memory (Letter-Number Span), and 
716 
verbal learning (Hopkin’s Verbal Learning Test-Revised). Previous research has found impaired 
717 
cognitive profiles in patients with schizophrenia (Kern et al., 2011).  
718 
Wisconsin Card Sorting Test (WCST; Stimulation Visit 1). The WCST (Berry et al., 
719 
2015; Nelson, 1976) was used to assess executive function (e.g., planning, set changing capacity). 
720 
In this task, 4 stimulus cards and 64 response cards were presented on the computer. Participants 
721 
were asked to match each response card to one of the stimulus cards according to one of three 
722 
possible criteria (i.e., color, shape, or number). Immediately following each sort, participants 
723 
were informed if they were correct or incorrect. After 10 correct sorts, the sorting rule changed, 
724 
and participants had to discover a new rule. A subsequent string of 10 correct responses triggered 
725 
another rule change, and this continued until all 64 cards were sorted. The number of 
726 
categories/criteria achieved, total errors, and perseverative errors (i.e., when the card is sorted 
727 
according to a category concept that the participant was told was erroneous) were calculated. 
728 
Previous research has found that more perseverative errors were made by schizophrenia patients 
729 
with paranoid ideations compared to healthy controls (Berry et al., 2015). 
730 
The Fast and Slow Thinking Questionnaire (FaST; Stimulation Visit 1). The FaST 
731 
(Hardy et al., 2020) contains 10 items measuring fast and slow thinking biases when having 
732 
paranoid thoughts on a 5-point scale (1 = not at all, 5 = totally; range = 10-50), with higher 
733 
scores indicating higher proneness for fast or slow thinking. This measure was included because 
734 
previous research has suggested the involvement of fast and slow thinking biases in paranoia in 
735 
both clinical and general populations (Garety et al., 2021; Hardy et al., 2020). 
736 
Childhood Trauma Questionnaire (CTQ; Stimulation Visit 1). Given that trauma has 
737 
previously been associated with paranoia (Freeman et al., 2002; Freeman & Fowler, 2009), we 
738 
used the CTQ Short Form (Bernstein et al., 2003) to assess childhood trauma and control for it. 
739 
The CTQ Short Form contains 28 items (e.g., “People in my family said hurtful or insulting 
740 
things to me”, “I believed that I was physically abused”) on a five-point scale (1 = never true, 5 = 
741 
very often true). Scores range from 28-140, with higher scores indicating higher level of 
742 
childhood trauma. 
743 
Penn Emotion Recognition Task (ER-40; Stimulation Visit 1 and 2). The ER-40 was 
744 
used to assess emotion recognition ability (Kohler et al., 2003). Forty color photographs of faces 
745 
expressing one of 4 basic emotions (happiness, sadness, anger, fear) or a neutral expression were 
746 
presented to the participants. After viewing each face, they were asked to choose the correct 
747 
emotion. The dependent variable consists of the accuracy score ranging from 0 to 40, with a 
748 
higher score indicating higher emotion recognition ability. Impaired ER-40 performance was 
749 
observed in patients with schizophrenia (Butler et al., 2009), but inconclusive results were 
750 
reported in studies on individuals with prominent paranoid ideation. Previous research found 
751 
nonsignificant differences in ER-40 performance between paranoid and non-paranoid patients 
752 
with schizophrenia (Pinkham, Harvey, et al., 2016); however, individuals with subclinical 
753 
paranoia showed reduced accuracy in identifying negative emotions compared to individuals 
754 
with low paranoia (Combs et al., 2013). Some studies also reported that paranoid patients are 
755 
more likely to misinterpret neutral facial expressions as angry and threatening (Pinkham et al., 
756 

21 
 
2011; Tso et al., 2015).  
757 
Bell Lysaker Emotion Recognition Test (BLERT; Stimulation Visit 1 and 2). The 
758 
BLERT (Bell et al., 1997) measures the ability to recognize seven emotions: happiness, sadness, 
759 
fear, disgust, surprise, anger, or no emotion. Twenty-one videos (each one lasts ~10 seconds) of 
760 
a single male actor were presented, which include facial, verbal, paraverbal, and body movement 
761 
information. Participants were asked to view these segments and identify the emotion shown. 
762 
The total number of correct identifications was used to reflect performance (range = 0-21), with a 
763 
higher score indicating better emotion recognition ability. The BLERT has good psychometric 
764 
properties in both clinical and general populations (Pinkham, Penn, et al., 2016), and has 
765 
demonstrated high sensitivity and specificity in differentiating individuals with varying degrees 
766 
of social cognitive deficits (Hajdúk et al., 2018).  
767 
Hinting task (Stimulation Visit 1 and 2). The Hinting task was used to assess the ability 
768 
to infer the true intent of interpersonal conversations (Corcoran et al., 1995). Ten short passages 
769 
were read aloud, each of which is a social interaction between two characters and ends with one 
770 
of the characters providing a hint. Participants were asked to indicate what they think the 
771 
character truly meant. Responses are scored in real-time, and if a correct response is not given 
772 
after the first hint, a second hint is provided. Each item is thus scored as either 0 = incorrect 
773 
response, 1= correct response after second hint, or 2 = correct response after first hint. Difficulty 
774 
in mentalizing/Theory of Mind (ToM) as measured by the Hinting task has been linked to more 
775 
auditory hallucinations in patients with psychosis and more paranoid-like experiences in non-
776 
psychotic first-degree relatives (Versmissen et al., 2008). Performance on the hinting task also 
777 
moderated the relationship between paranoia and social functioning, as revealed by a study of 88 
778 
adult patients with SSD, which found a negative correlation between paranoia and social 
779 
functioning in patients with hinting scores in the bottom 78th percentile, but such a correlation 
780 
became nonsignificant for patients with hinting scores in the top 22nd percentile (Phalen et al., 
781 
2017). 
782 
Residential mobility history (Stimulation Visit 1). Residential mobility is the frequency 
783 
that an individual moves from one place to another (Oishi, 2010). Residential mobility has been 
784 
found to be associated with a greater desire to expand one's social network (Oishi et al., 2013), 
785 
but also with poorer emotional and behavioral outcomes (Jelleyman & Spencer, 2008) and lower 
786 
well-being (Oishi & Schimmack, 2010). Therefore, to investigate the relationship between 
787 
residential mobility and paranoia, we collected data on the history of residential mobility in this 
788 
study. Participants were asked to indicate the number of times that they have moved from one 
789 
city/town to another since birth, followed by a list of all cities/towns to which they have moved 
790 
and their ages when they moved (Oishi, 2010). 
791 
Social norm violation task (SNVT; Stimulation Visit 2). Recent studies investigating 
792 
how paranoia affects social decision-making found that paranoia was associated with decreased 
793 
cooperation and increased punishment towards partners in economic games (Raihani et al., 2021; 
794 
Raihani & Bell, 2018). In this study, we measured social norm violation perception to test its role 
795 
in paranoia and aggression. In this social norm violation task (Mu et al., 2015), participants rated 
796 
the appropriateness of 48 norm-violating behaviors (e.g., “John is talking on his cell phone in the 
797 
movie theater”) on a 7-point scale (1 = strongly inappropriate, 7 = strongly appropriate). All 
798 
items were reverse-coded, and higher scores reflect being more critical towards the violation. 
799 
The Moralism Scale (MS; Stimulation Visit 2). The MS (Janoff-Bulman et al., 2009) 
800 

22 
 
was used to further measure the perception of prescriptive (i.e., did not do the good thing, for 
801 
example, did not help others) and proscriptive (i.e., do the bad thing, for example, cheat or steal) 
802 
moral violation in the current study. The MS contains 20 scenarios where the character is 
803 
considering conducting a certain moral (violation) behavior or not (10 items for prescriptive 
804 
morality and 10 for proscriptive morality). Participants indicated whether the character in each 
805 
situation should or should not do the behavior on a 9-point scale (1 = feel very strongly he/she 
806 
should not do, 9 = feel very strongly he/she should). Scores were calculated for prescriptive and 
807 
proscriptive (will be reverse-coded) morality, respectively, with higher scores indicating a higher 
808 
weight has been assigned to prescriptive or proscriptive morality.  
809 
Aggression Questionnaire (AQ; Stimulation Visit 2). The AQ includes 29 items 
810 
assessing aggressive behavior and tendencies across 4 dimensions: physical aggression, verbal 
811 
aggression, anger, and hostility (Buss & Perry, 1992). It is rated on a 5-point scale ranging from 
812 
1 (extremely uncharacteristic of me) to 5 (extremely characteristic of me), with higher scores 
813 
indicating higher levels of aggression. In previous research, a significant positive correlation was 
814 
found between persecutory ideation and aggression reported via AQ in both individuals with 
815 
SSD and the general community (van Dongen et al., 2011). 
816 
Novaco Anger Scale and Provocation Inventory (NAS-PI; Stimulation Visit 2). The 
817 
NAS-PI is another self-reported measure examining anger reactions in everyday life (Novaco, 
818 
2003). The first part of NAS-PI contains 48 items tapping in to 3 dimensions: cognitive, arousal, 
819 
and behavioral. Participants rated each item along a 3-point scale (1 = never true, 3 = always 
820 
true), with higher scores indicating higher levels of aggression tendency. The second part 
821 
contains 25 items depicting provoking situations, and participants rated how angry each of them 
822 
makes them feel on a 4-point scale (1 = not at all angry, 4 = very angry), with higher scores 
823 
indicating higher anger disposition. Good psychometric properties have been demonstrated 
824 
among individuals with psychotic disorders (Novaco, 2003), and previous research has found 
825 
that NAS-PI was positively associated with the suspiciousness item of PANSS in individuals 
826 
with psychotic disorders (Bucci et al., 2013)
827 

23 
 
APPENDIX B: EMA QUESTIONS 
828 
Q1. Since the past alarm, how much have you had thoughts that you really can't trust other 
people? 
(1 = not at all, 7 = very much) 
Q2. Since the past alarm, how much have you been bothered by voices?  
(1 = not at all, 7 = very much) 
Q3. Since the past alarm, who have you interacted with? Select all that apply. 
[haven’t interacted with others; friends; spouse, partner, or other family; with roommates, co-
workers, or others you know; with strangers; other people (please specify)] 
Q4. How much interest or motivation did you have interacting with others since the last 
alarm? 
(1 = not at all, 7 = very much) 
Q5. How much pleasure or enjoyment did you feel in these interactions?  
(1 = not at all, 7 = very much) 
Q6. How did you feel toward others in the interactions?  
(1 = on guard or threatened; 7 = trusting or warm) 
Q7. What do you think others were thinking about you?  
(1 = unlikeable or inferior; 7 = likeable or capable) 
Q8. Since the past alarm, how much have you felt sad or depressed?  
(1 = not at all, 7 = extremely) 
Q9. Since the past alarm, how much have you felt energized or excited?  
(1 = not at all, 7 = extremely) 
Q10. Since the past alarm, how much have you been feeling like you belong or fit with 
others in your life?  
(1 = not at all, 7 = extremely) 
Q11. Since the past alarm, how much have you felt irritated or upset?  
(1 = not at all, 7 = extremely) 
Q12. Since the past alarm, how much have you felt that you were a burden on others?  
(1 = not at all, 7 = extremely) 
Q13. Since the past alarm, how much have you felt happy?  
(1 = not at all, 7 = extremely) 
Q14. How much interest or motivation do you have in interacting with others later today?   
(1 = not at all, 7 = very much) 
Q15. How much do you want to avoid others later today?   
(1 = not at all, 7 = very much) 
 
 
829 

24 
 
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