Assessing latent inhibition deficits in youth at risk of conversion to psychosis

... Possible Applications of Latent Inhibition 9.1.1 Using Latent Inhibition to Differentiate “Converters” From “Non-converters” 37 9.1.2 Using Latent Inhibition Paradigms as a Mass-Screening Tool 9.2... studied in both healthy and clinical samples seems to have received relatively little attention from researchers working with at- risk populations: Latent Inhibition What is Latent Inhibition? Latent. .. schizophrenia are some of the most disabling disorders affecting mankind Marked by delusions, hallucinations, disorganization in thinking, cognitive deficits, or combinations of these symptoms, they are

ASSESSING LATENT INHIBITION DEFICITS IN YOUTH AT-RISK OF CONVERSION TO PSYCHOSIS JAMIE THONG YU JIN (B.Soc.Sci (Hons.)), NUS A THESIS SUBMITTED FOR THE DEGREE OF MASTER OF SOCIAL SCIENCES DEPARTMENT OF PSYCHOLOGY NATIONAL UNIVERSITY OF SINGAPORE 2012 Acknowledgements I would like to thank my supervisor, Dr Simon L. Collinson, for being willing to take me on as a student despite his busy schedule. Without his guidance and advice throughout the course of my studies it would not have been possible to finish this thesis. I am very grateful to Associate Professor Chong Siow Ann, Dr Mythily Subramaniam and Ms. Patricia T. Kin from the Institute of Mental Health Research Division. Without their continuous support, advice and understanding, I would not have been able to make it this far. They gave me the opportunity to serve as project lead on the Neurocognitive Core and out of these meetings emerged the idea for this thesis. I especially want to thank Dr Mythily for being willing to lend me her ears when I needed to discuss my thoughts and ideas with someone. Her comments were always insightful and helped to crystallize my thoughts. Dr. Attilio Rapisarda has been a great help to me in many ways in matters related to neurocognition. Thank you for being willing to discuss my thoughts and ideas no matter what topic they were on. I also thank Dr. Edimansyah Abdin who has been a great help to me in matters of statistics. The data used in this thesis was collected by all the team members of the Longitudinal Youth At-Risk Study. Despite the difficulties we encountered, we persevered. Thank you everyone for your effort. Special mentions go out to my friends the “Pansies” and the “Old People” for all the moments of laughter they provided. You guys are the best! i I want to thank Professor Richard S. E. Keefe from Duke University for his support and Mr. Michael Kraus for his work in programming the MATLAB script, and classifying participants’ strategies. I also want to thank Mr. Kraus for his assistance to me with regards to analysing the data. Finally, I thank the Lord Jesus Christ who has helped me to complete this thesis. It is His grace, strength and guidance that has brought me this far. Because of all He has done and all the people He has placed in my life, I am able to complete this work. Thank You Jesus for everything. All glory to God! ii Table of Contents Page Acknowledgements i Table of Contents iii Thesis summary vii List of Tables ix List of Figures x Chapters 1. Psychosis and Its Prodrome 1 2. Neurocognitive Function of Ultra High Risk Individuals 2 3. What is Latent Inhibition? 3 4. Latent Inhibition and Dopamine 5 5. Latent Inhibition, Dopamine, and Ultra High Risk Individuals 7 6. Study 1 8 6.1. Introduction 8 6.2. Method 11 6.2.1. Study Sample 11 6.2.2. Screening for Ultra High Risk Status 12 iii 7. 6.2.3. Administration of the LI Paradigm 13 6.2.4. Apparatus 14 6.2.5. Procedure 14 6.3. Statistical Analysis 15 6.4. Results 16 6.5. Discussion 16 Study 2 18 7.1. Introduction 18 7.2. Method 19 7.2.1. Study Sample, Ultra High Risk Screening and Administration of the Latent Inhibition Paradigm 8. 19 7.2.2. Apparatus 20 7.2.3. Procedure 20 7.3. Statistical Analysis 22 7.4. Results 22 7.5. Discussion 26 Study 3 28 8.1. 28 Introduction iv 8.2. Method 29 8.2.1. Study Sample, Ultra High Risk Screening and Administration of the Latent Inhibition Paradigm 9. 29 8.2.2. Breakdown of CAARMS Groupings 30 8.2.3. Apparatus 30 8.2.4. Procedure 31 8.3. Statistical Analysis 31 8.4. Results 32 8.5. Discussion 35 General Discussion 36 9.1. 37 Possible Applications of Latent Inhibition 9.1.1. Using Latent Inhibition to Differentiate “Converters” From “Non-converters” 37 9.1.2. Using Latent Inhibition Paradigms as a Mass-Screening Tool 9.2. 38 Possible Explanations for the Strategy Effect 39 9.2.1. 39 A Cultural Difference? 9.2.2. A Problem with the Design of the Paradigm? 40 v 9.3. 9.4. 9.5. 9.6. 10. Further Improvements to the Paradigm 41 9.3.1. An Issue of Pre-exposure Duration 42 9.3.2. An Issue of Task Brevity 42 Strengths 43 9.4.1. Extension of Current Knowledge 43 9.4.2. Generalizable Findings 43 Limitations 44 9.5.1. Small Sample Size 44 9.5.2. Possible Misclassification of Participants 45 Conclusion 45 References 46 vi Summary In recent years there has been much interest in the psychosis prodrome, the period that directly precedes the onset of psychotic illness. Past research has found that prodromal individuals demonstrate various neurocognitive deficits when compared to healthy individuals. Despite being well studied in patients with schizophrenia little or no research has examined Latent Inhibition (LI), a cognitive phenomenon where simple exposure to a stimulus without pairing to a consequence lowers the future associability of that stimulus to events, in at-risk individuals. As LI is sensitive to fluctuations in dopamine levels, it has potential as a method for detecting disrupted dopaminergic systems. This thesis describes the use of a novel LI paradigm to investigate LI deficits in individuals who have been identified to be at Ultra High Risk (UHR) of transition to psychosis. Three studies are described in this thesis. Study 1 attempted to validate the novel LI paradigm for use in an Asian population taking into account cigarette smoking as a mediating variable of dopaminergic level and thereby LI. Sixty healthy participants (30 smokers and 30 non smokers) were tested. No significant differences in reaction times were found between the Pre-Exposed (PE) and Non Pre-Exposed (NPE) conditions in either group. However, the data showed that some participants showed a LI effect regardless of smoking status. Study 2 was conducted to determine what influenced participants’ performance on the LI task. The LI paradigm was administered on 109 healthy participants, and their strategies in approaching the task were collected. The results show that the strategy reported by vii participants significantly influenced the results. Only when participants utilized one particular strategy (dubbed the Optimal strategy) did they show a LI effect. In Study 3, 52 participants who met UHR criteria were tested with the LI paradigm, and their strategy information was collected. The results showed that even in the group which utilized the “Optimal” strategy, there were no differences between PE and NPE reaction times. This indicated the absence of a LI effect. The findings are consistent with prior research on LI in individuals with schizophrenia, and the results provide support for the possibility of a disrupted dopaminergic system in UHR individuals. viii List of Tables Page 1. Sample Characteristics (Study 1) 2. Number of Participants for Each Strategy and Examples of 12 Responses (Study 2) 21 3. Mean PE and NPE Reaction Times and SDs for Each Strategy (Study 2) 25 4. Mean Differences, SDs and P-Values of Strategy Group for the PE 5. And NPE Conditions (Study 2) 25 Number of Participants for Each Strategy (Study 3) 31 ix List of Figures Page 1. Reaction Times for Each Strategy Type by Block and Condition (Study 2) 24 2. PE minus NPE Reaction Times by Strategy Type and Block (Study 2) 24 3. Venn Diagram Depicting CAARMS Groupings (Study 3) 30 4. Reaction Times by Block and Condition for Optimal and Ambiguous/None strategy types (Study 3) 5. 34 PE minus NPE Reaction Times by Block for Optimal and Ambiguous/None strategy types (Study 3) 34 x 1. Psychosis and Its Prodrome Psychotic disorders such as schizophrenia are some of the most disabling disorders affecting mankind. Marked by delusions, hallucinations, disorganization in thinking, cognitive deficits, or combinations of these symptoms, they are among the leading causes of disability worldwide. Psychotic disorders generate an enormous burden in the area of healthcare, with costs for schizophrenia alone being typically 1.5 to 3% of the total health budgets (Knapp & Razzouk, 2008). They also place burdens on many areas of society such as on the caregivers, social welfare systems, and police and court systems, and often result in premature mortality due to an increased risk of suicide and suicidal behaviors in patients (Harris, Burgess, Chant, Pirkirs & McGorry, 2008). These disorders, most notably schizophreniform illnesses, have traditionally been conceptualized as having a gradual onset (Keith & Matthews, 1991) but it is not until recent years that more knowledge about how to adequately characterize this period has emerged. Most psychotic disorders are preceded by a period where an individual undergoes alterations in behavior and functioning, and often experience symptoms of psychosis at an attenuated level (Yung & McGorry, 1996). For example, individuals in the pre-psychotic phase may experience frequent auditory hallucinations but these sounds remain indistinct and are not clear enough to be true hallucinations. They may also have experienced a significant decline in functioning in one or more areas such as in school, work or socially. This period may begin a number of years prior to a full-blown psychotic episode, and is referred to as the prodrome of psychosis. 1 In medicine, a prodrome is a group of symptoms that serve as a precursor to the full manifestation of an illness. Similarly, the psychosis prodrome is defined as “the period of change from pre-morbid functioning, including various mental state features, to the time of onset of frank psychotic features” (Yung et al., 1998, S23). If symptomatology becomes more severe, then an individual will be considered to have ‘converted’ to psychosis. However, in many instances these individuals actually recover from this period of lowered functioning without experiencing a full-blown psychotic attack, with rates of transition found to be as low as 16% in recent years (Yung et al., 2008). Furthermore, other than the attenuated psychotic symptoms, the behavioral symptoms that accompany the prodrome are non-specific; they are common to other disorders such as depression or anxiety related issues such as a disturbance in sleep or being unwilling to leave the house. These reasons, together with the stigma associated with the illness, have led to such individuals being described as being at Ultra-High Risk (UHR) of psychosis and the mental state which they are said to have is termed the At-Risk Mental State, or ARMS. This is defined as a mental state that confers an elevated risk of developing a psychotic disorder in the near future (Yung et al., 2005). 2. Neurocognitive Function of Ultra High Risk individuals In recent years, much effort has gone into investigating the neurocognitive functioning of at-risk individuals. Neurocognitive dysfunction is an established feature of schizophrenia, with many studies finding that patients with schizophrenia show deficits in a wide array of domains such as attention, motor coordination, learning and memory, executive function, and spatial abilities (Heinrichs & Zalkanis, 2 1998; Keefe et al., 2004). Similar deficits have also been detected in patients experiencing their first episode of psychosis and in non-psychotic first-degree relatives of patients with schizophrenia (Birket et al., 2007; Kurtz, 2005; Heaton et al., 2001; Bilder et al., 2000), as well as individuals who have schizotypal personality disorder (Mitropoulou et al., 2005; Roitman et al., 2000; Bergman et al., 1996; Roitman et al., 1997). As such, there is expectation that measuring the neurocognitive functioning of UHR individuals would provide a reliable method of identifying imminent transition to psychosis. Recent studies have focused on domains such as verbal memory, verbal fluency, motor speed, sustained attention, executive function, speed of processing and spatial working memory in UHR individuals. There is evidence that these individuals experience neurocognitive deficits in these domains (Bartók et al., 2005; Brewer et al., 2005; Brewer et al., 2006; Cosway et al., 2000; Francey et al., 2005, Gschwandtner et al., 2003; Hambrecht et al., 2002; Hawkins et al., 2004; Keefe et al., 2006; Lencz et al., 2006; Wood et al., 2003; Bertisch et al., 2007; Birkett et al., 2007; Niemdam et al., 2006; Özgürdal et al., 2009). Thus far however, one area which is very well studied in both healthy and clinical samples seems to have received relatively little attention from researchers working with at-risk populations: Latent Inhibition. 3. What is Latent Inhibition? Latent Inhibition (LI) is a cognitive phenomenon where simple exposure to a stimulus without pairing to a consequence lowers the future associability of that stimulus to events. Take for example, a doorbell that randomly produces a buzzing 3 noise every once in a while. Only the sound of the bell is predictive of the presence of someone at the door, but the buzzing noise is not. The people living in the house will eventually become accustomed to the buzzing noise and learn to ignore it. Subsequently, if the buzzing noise were to become predictive of an event (say it becomes magically able to predict the presence of a salesman at the door) it will take longer to associate the previously irrelevant buzzing noise with the appearance of a salesman at the door. It may not even be noticed by the inhabitants. Had the inhabitants not previously heard this buzzing noise, they would have associated it much faster with the appearance of a salesman. This phenomenon has been studied extensively in animals, and has contributed to the development of animal learning theory (Mackintosh, 1983; Pearce & Hall, 1980; Wagner, 1976). It has been observed in numerous mammalian species including humans (Lubow & Moore, 1959; Lubow & Gerwitz, 1995) and is thought to function via attentional mechanisms. When an organism has already been exposed to a stimulus that has no overt relation to any consequence, attention to said stimulus from the organism will become attenuated in the future. Subsequent processing of that stimulus is impeded, resulting in a disruption in learning about that stimulus. The organism will hence take longer to learn any new associations involving that stimulus. Latent inhibition is thought to exist because it provides an evolutionary advantage. It promotes stimulus selectivity by causing the organism to avoid paying attention to stimuli previously learnt as less important, allowing them to direct its limited attentional resources to stimuli that it has previously learnt to be more important, thereby promoting faster learning (Lubow, 1989). 4 There are many methods of demonstrating LI in animals and humans using classical and instrumental conditioning procedures, such as passive and active avoidance, conditioned emotional response, taste and olfaction aversion, and discrimination learning (Weiner, 2003). Nevertheless, all of them make use of the same basic structure: a pre-exposure phase followed by a test phase. In the preexposure phase the test subject undergoes repeated exposure to a stimulus (termed the pre-exposed (PE) stimulus) such as a flash of light or a sound. This PE stimulus is not paired with any sort of consequence. After the pre-exposure phase comes the test phase, where a stimulus that the test subject has not experienced before (the non pre-exposed (NPE) stimulus) and the PE stimulus are paired with a test stimulus (in animals, this is a reinforcer while in humans this can be the objective) over a number of trials. The test subject’s performance is assessed by examining some behavioural index of conditioned responding such as reaction time or accuracy. The test subject’s performance on the PE stimulus and the NPE stimulus is then compared. LI is said to be observed when there is poorer performance on the former compared to the latter. 4. Latent Inhibition and Dopamine As LI is thought to function via attentional processes, there has been much interest in studying this phenomenon in disorders where deficits of attention are prominent, one of which is schizophrenia. Many theories of schizophrenia suggest an attentional deficit as a central feature of the disease (e.g. McGhie & Chapman, 1961; Frith, 1979; Nuechterlein & Dawson, 1984, Anscombe, 1987; Hemsley, 1994) and have argued that this deficit causes an inability to ignore irrelevant or unimportant 5 stimuli such that they become abnormally salient, leading to the development of hallucinations and paranoia. Some researchers have also suggested LI as the main mechanism by which the symptoms of schizophrenia develop, and have proposed the LI model of schizophrenia (Solomon et al., 1981; Weiner, Lubow & Feldon, 1981; Weiner, Lubow & Feldon, 1984; Weiner, 2003). Evidence for these theories comes mainly from research on the effects of dopamine agonists and antagonists on animals as well as humans. Latent Inhibition appears to be sensitive to disruptions in the dopaminergic system, becoming elevated or reduced depending on the level of dopamine in the brain. Studies with rats show that administration of dopamine agonists such as Damphetamine causes an abolishment of LI (Solomon et al., 1981; Solomon & Staton, 1982; Weiner, Lubow, & Feldon, 1981; Weiner, Lubow & Feldon, 1984). Conversely, administration of dopamine antagonists reverses and even strengthens the LI effect (Christison, Atwater, Dunn, & Kilts, 1988; Solomon et al., 1981; Weiner & Feldon, 1987; Weiner, Feldon, & Katz, 1987). In humans, administration of amphetamine (Gray, Pickering, Hemsley, Dawling & Gray, 1992; Swerdlow et al., 2003) causes LI to be abolished, and Lubow and Gewirtz (1995) report a study on the elderly and Parkinson’s patients – both groups have decreased levels of dopamine in their system (Cote & Crutcher, 1991; Pradhan, 1980) – where LI in increased compared to young, normal participants. Also, dopamine pathways in the nucleus accumbens play a key role in LI (Weiner, 2003), suggesting that LI is at least mediated by the dopaminergic system. Hence, this phenomenon seems to be sensitive to changes in the dopaminergic system and has potential as a measure of the relative health of the system. 6 5. Latent Inhibition, Dopamine and Ultra High Risk Individuals Given the link between LI and dopamine, it is likely that LI deficits in UHR individuals will be observed. The dopamine hypothesis of schizophrenia states that the symptoms of the disease are due to dopaminergic dysregulation in the system. It is now in the third iteration, proposed by Howes and Kapur, (2009), and it explicitly links dopamine dysregulation to psychosis rather than to schizophrenia as a whole. It is also known that substances that increase dopamine such as amphetamine and cannabis can induce psychotic symptoms in otherwise healthy people (Angrist, Sathanathan, Wilk and Gershon, 1974; D’Souza, Cho, Perry & Krystal, 2004; Krystal et al., 2005). The fact that many UHR individuals experience attenuated psychotic symptoms hints at a disrupted dopaminergic system, though not to the same extent as patients with psychosis. Further support for finding a LI deficit in UHR individuals comes from recent evidence that found links between dysfunction of the dopaminergic midbrain and psychosis (Murray et al., 2008), the same region implicated in dopaminergic disruptions in LI (Solomon & Staton, 1982; Weiner, 2003). Furthermore, while patients with schizophrenia show a number of attentional deficits (e.g. Nuechterlein & Dawson, 1984; Harris, Minassian & Perry, 2007), reduced LI is only observed in floridly psychotic patients who have not yet received medication (Baruch, Hemsley & Gray, 1988; Gray, Hemsley & Gray, 1992). Once they have been medicated, there is a restoration of LI (Baruch, Hemsley, & Gray, 1988) or even an increase (Rascle et al., 2001) suggesting that the observed pattern of LI changes in schizophrenia is due to the fluctuation in dopamine levels, and not the disorder itself. In addition, those high in schizotypy also have reduced LI (e.g. Gray & Snowden, 2005), as well as increased 7 levels of dopamine in their system (Abi-Dargham et al., 2004; Soliman et al., 2008). It no surprise then that it is the positive dimension of schizotypy (analogous to the positive psychotic symptoms) that is most associated with reduced LI (Gray & Snowden, 2005). Based on the accumulated evidence, it is hence reasonable to expect to observe reduced LI in UHR individuals as well. This thesis describes a series of experiments performed to examine the pattern of LI deficits in UHR individuals. The central hypothesis is that UHR individuals will have reduced LI, and the aim is to demonstrate this through a series of experiments. Study 1 and 2 describe the selection, validation and subsequent modification of the paradigm used to measure LI, while Study 3 details the actual experiment that is performed with UHR individuals. 6. Study 1 6.1. Introduction The LI paradigm used in the following experiments derives from SchmidtHansen, Kilcross and Honey (2009). Originally designed by Evans, Gray and Snowden (2007), the paradigm is a short computer-based task consisting of a string of letters presented on screen, with one letter serving as the target stimulus. Participants are instructed to respond to the target stimulus and to attempt predict the appearance of the target stimulus by responding one letter prior to the target stimulus. Two letters reliably predict the target stimulus; one that is previously presented in the sequence (the PE stimulus) and one that is not shown until later in the sequence (the NPE stimulus). In their studies, Evans et al. (2007) found that participants were significantly slower in responding to the target stimulus when it came after the PE 8 stimulus compared to when it came after the NPE stimulus, which is interpreted as an LI effect. Schmidt-Hansen et al. (2009) replicated the study on participants with high scores on schizotypy and obtained similar results even when they reduced the number of pre-exposures to half of the original study. This paradigm has the advantage of being short (the task takes roughly 7 minutes to complete) and easy to administer, and it also avoids the proble ms associated with having a masking task. A masking task in the paradigm complicates interpretation of the results as it makes it difficult to determine if the observed effect is due to LI or some other cognitive phenomenon altogether, such as negative priming (McLaren & Graham, 1998). In addition, some studies have shown that complexity of the masking task and the associated load it places on attention can modulate the magnitude of the LI effect, leading one to question if the observed effect is actually due to the task demands instead of LI (Braunstein-Bercovitz & Lubow, 1998a; Braunstein-Bercovitz & Lubow, 1998b). Furthermore, it becomes difficult to make comparisons across studies as different paradigms may make use of different sorts masking tasks, with different associated attentional loads and possibly with different cognitive effects. Hence, opting for a task that does not require a masking task avoids these difficulties. This study aimed to validate this paradigm for use in a local population as both Evans et al. (2007) and Schmidt-Hansen et al. (2009) mainly used British psychology undergraduates for their studies. Developments in cultural neuroscience have increasingly highlighted the importance of detecting and adjusting for cultural differences between populations, as evidence has shown that culture has an effect on numerous cognitive processes, and these effects are present for both lower-level 9 and higher-order processes (Ames & Fiske, 2010). For example, a study by Hedden, Ketay, Aron, Markus and Gabrieli (2008) tasked participants with making judgments about patterns shown on a screen while either ignoring the visual context or taking the visual context into account. They found that East Asians showed greater activation in prefrontal and parietal attention regions associated with attentional control for the former condition rather than for the latter, while Americans showed the opposite pattern. This finding substantiates the claim that less attentional processing is required for culturally preferred modes of attention, and this fits with findings that showed reduced attentional activation when one is performing tasks that one is well practiced in (Milham, Banich, Claus, & Cohen, 2003). Other studies have shown that individuals have better performance speed and accuracy when performing a culturally congruent (context dependent versus contextually independent) task (Masuda & Nisbett, 2001; Masuda & Nisbett, 2006). Hence, while the chosen paradigm is relatively simple, it first needed to be validated for use in the local, Asian context of Singapore. In this study, we compared the PE and NPE reaction times of smoking and non-smoking participants. Comparing smokers to non-smokers provides a method of testing the sensitivity of the paradigm to disruptions in LI due to alteration of dopamine levels. Previous research has shown that administration of nicotine reduces LI (Allan et al., 1995; Joseph et al., 1993; Della Casa, Hofer & Feldon, 1999; Della Casa, Hofer, Weiner & Feldon, 1999), and it is known that nicotine promotes the release of dopamine in the nucleus accumbens (Pierce & Kumaresan, 2006) mainly via direct stimulation of nicotinic receptors on dopaminergic terminals (Di 10 Chiara, 2000; Picciotto, 2003). This elevation of dopamine levels is l ikely to be the cause of the disruption in LI that is observed following the administration of nicotine. In their study Evans et al. (2007) showed that smokers had reduced LI while non-smokers did not. The same pattern of results was expected. In other words, non-smoking participants in the study would respond faster to the target stimulus following the presentation of the NPE stimulus compared to the PE stimulus, whereas participants who smoke would show no difference in reaction times between the two conditions. 6.2. Method 6.2.1. Study sample Sample characteristics of the two groups are shown in Table 1. Sixty participants (40 male and 20 female) between the age of 14 to 28 (mean age = 21.88, SD = 3.52) participated in the study. Forty were of Chinese ethnicity, 12 were of Malay ethnicity and 8 were of Indian ethnicity. All participants were recruited from the general public via word of mouth and print advertisements, as well as via internal email advertisements sent to staff of the Institute of Mental Health (IMH); Singapore’s state funded tertiary psychiatric hospital. IMH currently serves as the main treatment centre for patients with psychiatric disorders across the spectrum. All participants were recruited as part of a larger study that aimed to investigate risk factors leading to transition to psychosis. 11 Table 1: Sample Characteristics (Study 1) Smokers Non-smokers 22.40 (3.27) 21.37 (3.74) 23 (76.7) 17 (56.7) Chinese 20 20 Malay 6 6 Indian 4 4 Years of smoking, years (SD) 6.50 (3.63) NA Mean sticks per day, n (SD) 6.93 (3.86) NA Age, years (SD) Male, n(%) Half of the participants were smokers, with an average of 6.50 (SD = 3.63) years of smoking and who smoked an average of 6.93 (SD = 3.86) sticks per day. There were no differences in age between the smoking and non-smoking groups, t (58) = 1.141, p = .259. Although there were more males in the smoking group compared to the non-smoking group, the difference was not significant, Pearson’s Chi-Square = 2.700, p = .100. The distribution of ethnicities in both groups was equal. 6.2.2. Screening for Ultra High Risk status All participants in the study were screened by a trained psychologist with the Comprehensive Assessment for At-Risk Mental States (CAARMS) (Yung et al., 2006) on a separate day prior to them performing the neurocognitive battery. The CAARMS is a semi-structured interview designed to identify UHR individuals and it contains questions pertaining to the intensity and frequency of positive psychotic symptoms that a person has experienced in the last one year (Yung et al., 2005). There are 3 12 types of UHR groups and each has its own criteria: (1) Vulnerability group, those with a first-degree relative with a diagnosed psychotic disorder or those who have been diagnosed with schizotypal personality disorder (2) Attenuated Psychotic Symptoms group, which indicates the presence of psychotic symptoms either at subthreshold intensity or frequency and (3) Brief Limited Intermittent Psychotic Symptoms (BLIPS) group, describing a history of brief psychotic episodes that spontaneously resolve within the span of a week. In addition, all 3 groups also require an individual to have experienced a significant disruption in social and/or occupational functioning for at least 1 month within the last 12 months or persistent low functioning for at least 12 months as measured on the Social and Occupational Functioning Scale (Goldman, Skodol & Lave, 1992). UHR individuals may be in multiple groups, depending on the pattern of their symptomatology and whether they have any genetic vulnerability. For example, an individual may have a first-degree relative with schizophrenia, experience attenuated psychotic symptoms and experience a drop in functioning. In this case, this individual fulfills the criteria for both Group 1 and 2. A more detailed description of the CAARMS and the grouping criteria can be found in Yung, Phillips & McGorry (2004). None of the participants in this study met UHR criteria. 6.2.3. Administration of the Latent Inhibition Paradigm The LI task was performed as part of a battery of neurocognitive tests that took between 2 and 2.5 hours to complete. Typically, it was administered 35 – 50 minutes into the test battery, depending on how fast each participant completed the prior neurocognitive tasks. Each participant received a total of SGD100 as 13 inconvenience fees upon completion of the entire study visit, which comprised the CAARMS screening and the neurocognitive battery. The CAARMS screening was done on a separate date from the neurocognitive battery, usually one day to a week prior to the administration of the battery. 6.2.4. Apparatus All stimuli were identical to those used by Schmidt-Hansen et al. (2009), and were presented on a 17 inch Dell laptop. The stimuli were programmed in MATLAB version 7.8.0.347 (R2009a) (The MathWorks, Inc., 2009). Participants underwent the procedure one at a time. 6.2.5. Procedure The paradigm consists of two phases, a pre-exposure phase and a test phase consisting of two blocks. In the pre-exposure phase, the PE letter was presented 10 times. Interspersed between each presentation of the PE letter were the filler letters, each presented a total of 14 times in a pseudo-random sequence. The sequence was constrained by the rule that no stimulus should be presented consecutively. The test phase immediately followed the pre-exposure phase with no break in between. Roughly in the middle of the test phase is a string of 26 filler letters, marking the transition from the first block of the test to the second block. In each block the target stimulus, the letter X, was presented 12 times. Interspersed with the presentation of X were the filler letters (each presented an average of 27 times per block); the NPE letter and the PE letter (both presented 8 times per block). The NPE and PE letters directly preceded the X a total of 8 times each block, while the filler 14 letters directly preceded the target 2 times each block. Each letter is presented in black on a white background for 1000ms with no inter-stimulus interval and the order of presentation of all stimuli was fixed for all participants. When the task is started, the screen displays the words “Look for X”. Participants are then read the following instructions: This is a reaction time test which lasts for about 7 minutes. In this task I want you to watch the sequence of letters. Your task is to try to predict when the letter ‘‘X’’ is going to appear. If you think that you know when the ‘‘X’’ will appear, then you can press the spacebar early in the sequence. Alternatively, press as quickly as you can when you see the ‘‘X’’. There may be more than one rule that predicts the ‘‘X’’. Please try to be as accurate as you can, but do not worry about the occasional error. Participants are then asked if they understand what is required of them, and any questions they might have are answered. The instructions to try to predict the letter X by pressing spacebar one letter before it appears were then reiterated to ensure that participants knew that they could respond before the X was actually presented on screen. The whole procedure lasted up to 8 minutes. 6.3. Statistical Analysis Statistical analysis was conducted using SPSS Statistics 17.0 for PC. Group differences for age were analyzed using independent samples t-tests, and gender 15 distribution was analyzed using Pearson’s chi-square test. Reaction time data was analyzed using a mixed 2x2x2 (Condition, Block, Group) ANOVA. 6.4. Results There were no significant interactions between any of the variables, all p > .05. No main effects of condition or group were observed, all p > .05. There was a main effect of block, F (1, 58) = 9.907, p = .003. Pairwise comparisons with Bonferroni correction indicated that for both groups, Block 2 mean reaction times were significantly lower than those in Block 1 (M = 42.32, SD = 13.45, p = .003). This indicated that participants were learning about the test as they underwent the procedure, and were able to respond quicker to the appearance of the X during the second block. 6.5. Discussion The results did not support the hypotheses of the study. No significant difference was found in reaction times between the PE and NPE conditions in the non-smoking or smoking group. This was contrary to Evans et al. (2007)’s findings where only smokers showed the above pattern, while non-smokers showed a clear LI effect. It seems that this paradigm was unable to elicit a LI effect as the non-smokers were expected to show faster reaction times in the NPE condition while at the same time the smokers would show no difference between the PE and NPE conditions. Based on the results of this study, it was therefore not possible to verify the efficacy of the paradigm in showing the LI effect in healthy participants. 16 One possible explanation for the absence of the LI effect was that participants were not performing the task seriously or were unable to concentrate as the LI task was usually administered almost 45 minutes into the neurocognitive test battery. Thus, they could have suffered from fatigue or boredom while performing the LI task. However, this was ruled out as both groups responded to the X significantly faster in block 2. This was similar to the findings of Schmidt-Hansen et al. (2009); showing that participants were focused on performing the task well, and thus they showed improvement in their reaction times in block 2. Therefore, some other factor was at play in influencing the pattern of the results. There were a number of participants who spontaneously voiced their concerns at the end of the task, asking if there was something wrong with the task. Specifically, they stated that they did not notice any way to predict the appearance of the X despite trying their best. This happened frequently enough that this feedback was noted as something to look into subsequently. Due to the results of this study, a subsequent inspection of the frequency distribution of block 2 PE minus NPE scores showed that the PE minus NPE reaction time data was roughly split into 3 blocks: strongly negative, close to zero and strongly positive. This was true for both smokers and non-smokers. These findings corresponded to a reverse LI effect, no LI effect and a LI effect respectively. Hence in this study participants were actually showing 3 different types of cognitive effects on the LI task regardless of smoking status. It is possible that some participants, being unable to notice any pattern between the NPE or PE stimulus and the X, started focusing only on responding whenever the X appeared and ignoring all other letters. Indeed, part of the 17 instructions to participants is to simply respond as quickly as they can when they see the X if they are unable to detect any pattern. Thus, participants could have been primed to fall back to the above strategy if they were unable to observe any relations between the PE/NPE stimuli and the X. These participants would show little or no difference between PE and NPE reaction times as they did not make use of the preceding letters in their approach to the task, and they correspond to the group with PE minus NPE reaction times that are close to zero. Another group seemed to focus on the PE stimulus while neglecting the NPE stimulus, leading to more negative PE minus NPE reaction times. The third group focused on both the PE and NPE stimulus and showed a LI effect, leading to the observed positive PE minus NPE reaction times. For this study, we did not find a difference in LI between smokers and nonsmokers. However, an interesting pattern emerged whereby some participants did show a LI effect regardless of smoking status. A more systematic investigation was necessary in order to determine what was influencing participants’ performance on the task. The next study details our investigation of this issue. 7. Study 2 7.1. Introduction In the previous study, participants showed three distinct types of results on the LI task regardless of smoking status. One type of result corresponded to a LI effect, leading to the conclusion that a more systematic investigation was needed in order to determine the cause of this pattern of results, and to elucidate if this 18 paradigm could actually elicit the LI effect after taking into account the influencing factor. The next hypothesis was that the pattern of results would depend on the type of approach a participant had in performing this task. It was predicted that reaction times would differ depending on how they allocated their attention to the various stimuli. In other words, the type of strategy participants used would moderate the way subjects performed the task and their subsequent reaction times on both the PE and NPE conditions. Thus, the second hypothesis was that detecting LI using this paradigm would be contingent on whether a participant utilized a strategy that takes into account both the NPE and PE stimuli in predicting X. This study also attempted to investigate if, after taking into account type of strategy, this paradigm could replicate the results of Evans et al. (2007). To investigate these hypotheses the first study was repeated with a minor addition to the procedure. 7.2. Method 7.2.1. Study Sample, Ultra High Risk Screening and Administration of the Latent Inhibition Paradigm One hundred and nine participants (64 male and 45 female) between the age of 14 to 29 (mean age = 21.68, SD = 3.96) were recruited for the study. Seventy four were of Chinese ethnicity, 18 were of Malay ethnicity, 11 were of Indian ethnicity and 6 were of mixed parentage or were from other Asian ethnicities such as Burmese/Myanmese. Those of mixed/other Asian ethnicities all had been immersed in the local culture for at least a few years, hence they were assumed to be relatively similar culturally to the rest of the ethnic groups. There were a total of 21 smokers in 19 the study (mean age = 20. 81, SD = 4.14). On average, they smoked 4.67 (SD = 3.32) sticks per day, and had been smoking for a mean of 4.81 (SD = 3.75) years. There were no differences in age between smokers and non-smokers, t (29.11) = 1.08, p = .288. Similar to Study 1, all participants were recruited from the general public via word of mouth and print advertisements, as well as via internal email advertisements sent to staff of the Institute of Mental Health. All participants were recruited as part of a larger study that aimed to investigate risk factors leading to transition to psychosis, and were screened with the CAARMS to determine UHR status. They received the LI task as part of the same neurocognitive battery as described in Study 1. None of the participants met criteria for UHR status. All participants received a total of SGD100 as inconvenience fees upon completion of the entire study visit. 7.2.2. Apparatus All stimuli used were identical to that used in Study 1 and were presented on a 17 inch Dell laptop. 7.2.3. Procedure The procedure was similar to that of Study 1 with one minor addition. At the end of the LI paradigm, participants were asked the following two questions: (1) “What was your strategy for doing that task?” and (2) “Was there anything you did to try to respond more quickly when the X came up?” Participants’ responses to 20 these two questions were recorded verbatim and were subsequently coded based on the content of the responses to the two questions. Participants’ responses to the two questions could be code d according to one of five different types of strategies: 1) Favouring the PE stimulus, where participants only noticed the PE stimulus reliably predicting X, 2) Favouring the NPE stimulus, where participants only noticed the NPE stimulus reliably predicti ng X, 3) Optimal, where participants realized that both the PE and NPE reliably predicted X, 4) Irrelevant, where participants utilized an irrelevant strategy such as counting the number of letters between the appearance of X, and 5) Ambiguous/None, where participants either denied utilizing a strategy or gave a vague response, such as “I looked at the letters that came before X”. The majority of participants belonged to the Optimal group or the Ambiguous/None group, with only small numbers in the other groups. Table 2 summarizes the number of participants under each strategy type. Table 2: Number of Participants for Each Strategy and Examples of Responses (Study 2) Strategy type Number Examples Favours PE 5 No, it comes follow the letter 'S' Favours NPE 7 When the letter H appears, I know the letter X comes next Optimal 38 Pattern was H and S before the X. Based on reaction time Irrelevant 10 Count the number of letters between the X Ambiguous/None 49 No, just waited for X to appear. I didn't anticipate anything 21 7.3. Statistical Analysis Statistical analysis was conducted using SPSS Statistics 17.0 for PC. Due to unequal sample sizes, group differences in PE and NPE reaction times were analyzed with ANOVA with Tamhane’s T2 as the post-hoc test. For this analysis, PE and NPE reaction times were combined across the two blocks and the mean was used. To investigate if the paradigm could elicit LI after taking strategy into account, reaction time data was analyzed using 2x2x2 mixed (Condition, Block, Group) ANOVA with post-hoc pairwise comparisons using Bonferroni’s correction. Due to insufficient numbers in some of the groups, the ANOVA was restricted to the Optimal and the Ambiguous/None strategy groups. A 2x2 (Condition, Block) repeated measures ANOVA was conducted on only the smokers in the Optimal group to attempt to replicate Evans et al. (2007)’s results. 7.4. Results Table 3 shows the mean reaction times for the PE and NPE conditions for each strategy type. Figure 1 shows the PE and NPE reaction times for each strategy type, separated by block and condition, while Figure 2 shows the PE minus NPE reaction time data for each strategy. Analysis of the data was complicated by the low numbers in some of the groups. Nevertheless, clear trends were still observable. The Ambiguous/None group, which was followed by the largest number of participants, tended to have no differences between the PE and NPE conditions. In all the other groups the reaction times for the PE and NPE conditions were differe nt. True to its description, the Favours PE strategy resulted in a trend where PE reaction times were faster than NPE reaction times across both blocks. On the other hand, 22 the Favours NPE, Irrelevant and Optimal strategies tended to have lower NPE reaction times than PE reaction times, with the Favours NPE strategy resulting in dramatically lower NPE reaction times than all other strategies. The observed trends correspond to the pattern of results obtained in Study 1, where the PE minus NPE scores of participants were either very negative, close to zero or very positive. A summary of the comparisons is presented in Table 4. There were group differences in PE reaction times, F (4, 104) = 10.32, p < .001. Post-hoc tests revealed that the Optimal group had significantly lower PE reaction times than the Ambiguous group, mean difference = 213.63, SD = 37.61, p < .001. No other group differences were observed for PE reaction times, all p > .05. For NPE reaction times, there was an effect of group, F (4, 104) = 21.3 46, p < .001. Post-hoc tests showed that the Favours NPE group had significantly faster reaction times than the Ambiguous/None, Irrelevant and Favours PE groups, all p < .05, but not the Optimal group, p = .374. Additionally, the Optimal group had significantly faster NPE reaction times than the Ambiguous/None and Favours PE groups, both p < .05, but not the Irrelevant group, p = .400. No other differences were found, all p > .05. 23 450 350 Mean RT 250 Ambiguous/None Favours NPE 150 Favours PE Irrelevant Optimal 50 -50 Block 1 PE Block 1 NPE Block 2 PE Block 2 NPE -150 Figure 1: Reaction Times for Each Strategy Type by Block and Condition (Study 2) 550 450 350 Mean RT 250 Block 1 PE-NPE 150 Block 2 PE-NPE 50 -50 Ambiguous /None Favours NPE Favours PE Irrelevant Optimal -150 -250 Figure 2: PE minus NPE Reaction Times by Strategy Type and Block (Study 2) 24 Table 3: Mean PE and NPE Reaction Times and SDs for Each Strategy (Study 2) Mean PE Reaction Times Mean NPE Reaction Times (SD) (SD) Ambiguous/None 419.21 (122.25) 415.09 (135.22) Favours NPE 342.56 (171.54) -62.11 (132.01) Favours PE 223.36 (101.73) 381.95 (129.36) Irrelevant 392.01 (153.38) 261.90 (239.23) Optimal 205.58 (205.33) 78.08 (253.02) Strategy Table 4: Mean Differences, SDs and P-Values of Strategy Group for the PE and NPE Conditions (Study 2) Pre-Exposed Condition Mean Group Comparisons Non Pre-Exposed Condition Mean difference P-Value difference (SD) P-Value (SD) Optimal vs. Favours NPE -136.98 (72.89) .616 140.19 (64.61) .374 Optimal vs. Favours PE -17.78 (56.38) 1.000 -303.87 (70.93) .021 Optimal vs. Irrelevant -186.43 (58.84) .051 -183.83 (86.07) .400 Optimal vs. Ambiguous/None -216.63 (37.61) < .001 -337.01 (45.36) < .001 Favours NPE vs. Favours PE 119.20 (79.21) .833 -444.06 (76.40) .003 Favours NPE vs. Irrelevant -49.45 (80.97) 1.000 -324.01 (90.63) .029 Favours NPE vs. Ambiguous/None -76.65 (67.15) .968 -477.21 (53.51) < .001 Favours PE vs. Irrelevant -168.65 (66.50) .237 120.05 (95.24) .927 Favours PE vs. Ambiguous/None -195.85 (48.73) .088 -33.14 (60.99) 1.000 25 The mixed ANOVA revealed no significant interaction between Block, Condition, and Group, F (1, 85) = 0.167, p = .684. However, significant interactions were observed between Block and Group, F (1, 85) = 19.16, p < .001, and Condition and Group, F (1, 85) = 13.45, p < .001. Post-hoc comparisons of Block and Group showed that in the Optimal group was there an improvement in reaction times across the blocks, mean difference = 110.82, SD = 21.76, p < .001, while there was none in the Ambiguous/None group, p = .404. Post-hoc comparisons of Condition and Group showed that there were significant differences in reaction times between the two conditions in the Optimal group, with faster NPE reaction times compared to PE reaction times, mean difference = 127.50, SD = 25.25, p < .001. However, there were no differences between the two conditions in the Ambiguous/None group, p = .854. There were a total of 5 smokers who utilized the Optimal strategy. The ANOVA revealed that in this sub-group, there was no significant interaction between Block or Condition, F (1, 4) = 0.047, p = .839. There were also no significant main effects of Block, F (1, 4) = 6.18, p = .068; or Condition, F (1, 4) = 3.86, p = .121. Thus, the smokers who utilized the Optimal strategy did not show a significant improvement in reaction times across blocks, and did not show a LI effect. 7.5. Discussion In this study, the type of strategy reported by participants had a significant influence on both PE and NPE reaction times. Evidence of an LI effect was observed in the Optimal group, but not the Ambiguous/None group. These findings thus lend support to the hypotheses that strategy type influences reaction time, and that 26 detecting a LI effect with this paradigm is contingent on the participant being able to utilize a so-called “Optimal” strategy, where the participant takes note of the predictive reliability of both the PE and NPE stimuli. Once strategy type was taken into account, it was possible to replicate the results of Evans et al. (2007) on smokers. The smokers who utilized the Optimal strategy did not show LI. While the interpretation and generalizabilty of the results was limited by the small numbers in some of the groups, clear trends of the effect of strategy on PE and NPE reaction times could still be observed. For example, despite the lack of numbers in the Favours PE and Favours NPE groups, it is strikingly clear that these two strategies produce very different results, as observed in Figures 1 and 2. Furthermore, despite the reduced power, the results still reached significance. Hence, although this study suffers from insufficient numbers in some of the groups, there remains strong support for the first hypothesis. This study showed that the selected LI paradigm can indeed be used to detect LI in healthy participants, once strategy is taken into account. The Optimal group showed a clear LI effect with NPE reaction times being lower than PE reaction times. In contrast, those in the Ambiguous/None group showed no difference between the two conditions. This follows from prior conjecture regarding the way these participants responded to the demands of the task in that they tended to ignore all letters that appeared on the screen except for the X. These participants would then show neither an effect of LI, nor an effect of learning on their reaction times, and this is supported by the results. In conclusion, this paradigm demonstrates the ability to detect LI in healthy participants and the LI deficit in smokers once strategy is taken into account. The 27 next step was to administer this paradigm on individuals at risk of psychosis and determine presence or absence of LI in this group, and that is what Study 3 addresses. 8. Study 3 8.1. Introduction The previous study showed that the LI paradigm can detect LI once strategy is taken into account. In this study the sample was expanded to include individuals who are at-risk of conversion to psychosis (i.e. the CAARMS positive individuals). As mentioned, evidence shows that dopamine dysregulation is linked to both psychosis (Howes & Kapur, 2009) and reduced LI (Baruch, Hemsley & Gray, 1988; Gray, Hemsley & Gray, 1992), and that the same regions are implicated in both LI disruptions and psychosis (Murray et al., 2008; Solomon & Staton, 1982; Weiner, 2003). Thus, it is expected that UHR individuals would show reduced LI. It was hypothesized that, even after taking strategy type into account, CAARMS positive individuals would fail to show any LI effect. As such, the Optimal and Ambiguous groups were expected show a similar pattern of results when comparing reaction times between the PE and NPE conditions. 8.2. Method 8.2.1. Study Sample, Ultra High Risk Screening and Administration of the Latent Inhibition Paradigm Fifty two participants who met UHR criteria (35 male and 17 female) between the age of 15 to 29 (mean age = 21.92, SD = 3.736) were recruited for the study. 28 Thirty eight were of Chinese ethnicity, 9 were of Malay ethnicity, 3 were of Indian ethnicity and 2 more were of mixed ethnicities. While these two participants were of mixed Asian and Western ancestry, they had been living in Singapore for all their lives, and hence they were assumed to be culturally similar. There were 12 smokers in the sample, with a mean age of 21.58 (SD = 3.75), and they smoked a mean of 7.75 (SD = 4.81) sticks per day. They had been smoking for an average of 6.00 (SD = 3.95) years. Smokers were not significantly different in age from the non-smokers, t (18.21) = 0.357, p = .725. Similar to studies 1 and 2, participants were recruited from the general public via word of mouth and print advertisements, as well as via internal email advertisements sent to staff of the Institute of Mental Health. Additionally, some participants were patient referrals from psychiatrists in the Support for Wellness Achievement Program, a treatment program for UHR individuals, and referrals from relatives who were receiving treatment for psychiatric disorders (for example patients with schizophrenia who have children) at the hospital’s outpatient clinic. All participants were screened for medication; those who were taking anti -psychotics (either typical or atypical) were not considered for the study. All participants were recruited as part of a larger study that aimed to investigate risk factors leading to transition to psychosis, and were screened with the CAARMS to determine UHR status. They received the LI task as part of the same neurocognitive battery as described in studies 1 and 2. All participants received a total of SGD100 as inconvenience fees upon completion of the entire study visit. 29 8.2.2. Breakdown of the CAARMS Groupings There were 10 participants who were in group 1, 34 participants in group 2 and 1 in group 3. Six participants were in both group 1 and 2, and 1 participant was in all 3 groups. There were no participants in both groups 1 and 3, or in both groups 2 and 3. Figure 3 shows a Venn diagram depicting the CAARMS groupings of the participants. Figure 3: Venn diagram depicting CAARMS Groupings (Study 3) 8.2.3. Apparatus All stimuli used were identical to that used in studies 1 and 2 and were presented on a 17 inch Dell laptop. 30 8.2.4. Procedure The procedure was identical to that of Study 2. Similar to what we observed in Study 2, the two largest groups were the “Ambiguous/None” and the “Optimal” groups. Table 5 summarizes the number of participants under each strategy type. Table 5: Number of Participants for Each Strategy (Study 3) Strategy type Number Favours PE 0 Favours NPE 2 Optimal 19 Irrelevant 2 Ambiguous/None 29 8.3. Statistical Analysis Statistical analysis was conducted using SPSS Statistics 17.0 for PC. In the analysis, the Favours NPE and Irrelevant groups were excluded due to low numbers. The analysis focused only on the Optimal and Ambiguous/None groups. To investigate if there was an LI effect in the CAARMS positive group, reaction time data for the Optimal and Ambiguous/None groups was analyzed using 2x2x2 (Condition, Block, Group) mixed ANOVA with post-hoc pairwise comparisons using Bonferroni’s correction. To determine if smoking status would have any effects on the results, a 2x2 (Condition, Group) repeated measures ANOVA was conducted on the reaction time data from smokers in the two strategy groups. 31 8.4. Results Figure 4 shows the PE and NPE reaction times for the Optimal and Ambiguous/None groups, while Figure 5 shows PE minus NPE reaction times. Similar to the findings in Study 2, in the CAARMS positive the type of strategy used still affected a participants’ performance on the task. As seen in Figure 5, PE minus NPE reaction time for the two groups across the two blocks was in opposite directions. Although the absolute difference was not very large, it was still a noticeable trend. The repeated measures ANOVA showed that there were no significant interactions between Condition, Block and Group, F (1, 46) = 3.82, p = .057. There was no significant interaction between Block and Condition, F (1, 46) = 0.118, p = .732, or between Group and Condition, F (1, 46) = 0.277, p = .601. Also, no main effect of Condition was found, F (1, 46) = 0.209, p = .649. The results indicated that, even in the Optimal group there was no difference in reaction times between PE and NPE stimuli, lending support for the hypothesis that no LI would be observed even when taking strategy into account. There was a significant interaction between Group and Block, F (1, 46) = 8.30, p = .007. Post-hoc analysis indicated that participants in the Optimal group responded faster in block 2 than in block 1, mean difference = 156.73, SD = 47.65, p = < .001. This indicated that participants in the Optimal group were learning as the task proceeded, and were able to respond faster as the task went on. In contrast, participants in the Ambiguous/None group failed to show any learning. As seen in Figure 4, reaction times were much faster overall in the Optimal group than in the Ambiguous/None group. Between-group comparisons revealed that this difference was significant, F (1, 46) = 26.95, p < .001, with mean difference 32 for PE reaction times across blocks = 218.81, SD = 47.56, and mean difference for NPE reaction times across blocks = 233.72, SD = 44.03, both p < .001. There were 6 smokers in the Ambiguous/None group and 5 in the Optimal group. In the smokers who utilized the Optimal strategy, there were no interactions and no main effects of Block or Condition, all p > .05. However, there was a significant interaction between Block and Condition in the smokers who utilized the Ambiguous/None strategy, F (1, 5) = 13.36, p = .015. Pairwise comparisons revealed that in Block 1, NPE reaction times were significantly slower than PE reaction times, mean difference = 46.40, SD = 12.70, p = .015. This difference was not present in Block 2, p = .932. 33 450 400 350 Mean RT 300 250 Ambiguous/None 200 Optimal 150 100 50 0 Block 1 PE Block 1 NPE Block 2 PE Block 2 NPE Figure 4: Reaction Times by Block and Condition for Optimal and Ambiguous/None strategy types (Study 3) 30 20 10 Mean RT 0 Block 1 PE-NPE -10 Block 2 PE-NPE Ambiguous/None Optimal -20 -30 -40 -50 Figure 5: PE minus NPE Reaction Times by Block for Optimal and Ambiguous/None strategy types (Study 3) 34 8.5. Discussion In this study, in UHR individuals the type of strategy reported by participants had a significant influence on both PE and NPE reaction times. Overall reaction times across conditions were faster in the Optimal group than in the Ambiguous/None group. However, in contrast to non-UHR participants, no evidence of a LI effect was present in the Optimal group. Thus, even after taking strategy into account UHR individuals did not show a LI effect on this paradigm. The hypothesis was thus supported. In addition, smokers in the Ambiguous/None group were slower to respond to the NPE stimulus in the first block. However, the practical significance of this effect is questionable as the difference between the two conditions is very small (only 46.40 ms). An interesting result is that PE minus NPE reaction times for the UHR group showed a different pattern than non-UHR individuals in Study 2. In this study PE minus NPE reaction times were negative in block 1 and positive in block 2 for the Ambiguous/None group, with the Optimal group showing the opposite pattern. In comparison, non-UHR individuals did not show this interesting “flip” effect; both groups in Study 2 had a consistent pattern in both blocks. This may indicate that UHR individuals perform the task differently from non-UHR individuals for other strategy types other than the Optimal strategy. However, further studies that show data from the other strategy types are needed before any conclusions can be made. In summary, this study demonstrates the absence of LI in UHR individuals even in those using a strategy that has previously been shown to be able to detect LI in non-UHR individuals. By extension, this supports the possibility that the dopaminergic system in UHR individuals is disrupted, as LI is sensitive to dopamine 35 dysfunction. In addition, the findings confirmed that strategy influences reaction times, although there was insufficient data for the Favours NPE, Favours PE and Irrelevant strategy types. It would have been interesting to compare the pattern of results across strategy types to see if it is similar or different to that observed in non UHR individuals. Thus, while this study had a relatively good sample size, future studies with larger sample sizes would be advantageous. 9. General Discussion In the first of a series of 3 studies, an attempt was made to validate a recently developed paradigm by Evans et al. (2007) and Schmidt-Hansen et al. (2009) for use in a local Asian context. No evidence of a LI effect in healthy non-smoking participants was found with the paradigm, and a deeper investigation on the influence of participants’ self-reported strategy in performing the task was conducted. Despite the lack of numbers in some of the groups, it was discovered that strategy type had a significant influence on the pattern of observed results. More specifically, only in participants that reported using one particular strategy (the so-called “Optimal” strategy) was a LI effect observed. Subsequently, in UHR individuals, there was no evidence of a LI effect even in participants who reported using the “Optimal” strategy. This was in contrast to what had been observed in nonUHR individuals. The findings of these studies are consistent with prior research on LI in schizophrenia, and the hypothesis that these individuals have a disrupted dopaminergic system. Previous research has shown that LI deficits are only observed in patients with schizophrenia who are in the acute phase of the illness (e.g. Baruch, 36 Hemsley & Gray, 1988; Gray, Hemsley & Gray, 1992). In these patients, the level of dopamine in their system has not yet been stabilized by anti -psychotic medication. If UHR individuals also have abnormal levels of dopamine, a LI deficit would be expected as well. This was indeed the case in Study 3, as none of the UHR individuals in that study had received any form of anti-psychotic medication. 9.1. Possible Applications of Latent Inhibition Before LI is applied outside of an experimental setting more studies are required, preferably with large longitudinally followed up samples of UHR individuals, so that the specificity, sensitivity and diagnostic accuracy of LI can be determined. Notwithstanding this caveat, in this section a number of possible applications of LI are discussed. 9.1.1. Using Latent Inhibition to Differentiate “Converters” From “Non-converters” A logical extension would be to investigate the utility of LI deficits in predicting those who convert from UHR status to full-blown psychosis, and those who subsequently recover without a psychotic breakthrough as there is much interest in accurate prediction of conversion to psychosis (e.g. Velthhorst et al., 2009; Cannon et al., 2008; Francey, Jackson, Phillips, Wood, Yung & McGorry, 2005). Taking LI into account may provide additional accuracy in predicting conversion as there could be a difference between the groups in the level of LI deficit observed. However, a different LI paradigm would be required as the one used in these studies is unable to determine the extent of the LI deficit. 37 9.1.2. Using Latent Inhibition Paradigms as a Mass-Screening Tool Another extension would be to see if the detection of LI deficits can be used as a quick and simple pre-screening tool for dopaminergic dysfunction. Since both LI deficits and psychotic symptoms are related to a disrupted dopaminergic system, a paradigm to detect LI may have some utility in distinguishing between UHR and healthy individuals. As it is, the tool that was used to identify at-risk individuals in this series of studies, the CAARMS, can be quite lengthy to administer. Alternative rating scales like the Bonn Scale for the Assessment of Basic Symptoms (Gross, Huber, Klosterkotter & Linz, 1987) and the Scale of Prodromal Symptoms (Miller et al., 2003) are also lengthy, time consuming and require comprehensive training to administer accurately. This makes them unsuitable for use in situations where time is limited, and for mass screenings, for example in schools. If one were to administer a LI paradigm as a pre-screener, it could result in savings of time and resources. Of course, it is insufficient to rely on absence or presence of LI to determine an individual’s clinical status; it should be used in tandem with a proper screening tool by trained personnel. However, since it is simple and quick to administer, a LI paradigm is suitable as a rough, first layer of screening to determine if an individual requires a more detailed assessment or not. Whether or not the paradigm used in these studies is able to be used as such a pre screener is questionable as the result obtained is very much dependent on the type of strategy used by the participant. What is uncertain is why there such an effect in the first place. 38 9.2. Possible Explanations for the Strategy Effect 9.2.1. A Cultural Difference? In their studies, neither Evans et al. (2007) nor Schmidt-Hansen et al. (2009) required the use of follow up questions to probe the sort of strategy that participants used. Both groups were able to use this paradigm to demonstrate a strong LI effect in their studies. The present studies however, would have failed to find evidence of any LI effects in healthy individuals had they not taken into account what strategy they used. This raises the question as to why an additional step was required to obtain results that would answer our main hypothesis. One possibility is that this is evidence of a cultural difference – previous samples consisted largely of psychology undergraduates from British colleges (although Evans et al. (2007) did recruit participants from outside college). While neither group gave a breakdown of the ethnic composition of their samples, it can be assumed that most of their participants were people who have been heavily immersed in Western culture. In contrast, the sample used in the studies described here was a mix of Asian ethnicities. Despite the urban, globalized nature of Singapore, it remains very Asian in character and culture. Thus, one possibility is that the way one approaches this LI paradigm is heavily influenced by the cultural background of the individual. It is possible that some participants in the present studies were simply not used to performing neurocognitive tasks involving strings of letters appearing in turn on a computer screen, in contrast to the British psychology undergraduates who are likely to have had prior exposure to such tasks. It is known that automaticity increases with experience (Cohen, Servan-Schreiber, & McClelland, 1992), hence 39 some participants in the present studies could have been forced to rely more on conscious attentional processes as they were not very used to these sorts of tasks. Additionally, participants could have thought that all the letters were independent of each other, causing them to try and process the stimuli independent of context, that is, independent of the letters that appear before and after the current one. As shown by Hedden, Ketay, Aron, Markus and Gabrieli (2008), East Asians showed greater activation in regions associated with attentional control for context independent processing, while Americans show the reverse effect. Thus, the participants in the present studies could have ended up having to rely more on conscious rather than automatic processing, which interferes with the LI process. This would explain the difference between the findings of these studies and that of Evans et al. (2007) and Schmidt-Hansen et al. (2009). 9.2.2. A Problem with the Design of the Paradigm? One possible reason for the use of different strategies could be due to how a participant interprets the instructions. Of relevance is the sentence that reads “Alternatively, press as quickly as you can when you see the ‘X’”. This alternative in the instructions, a sort of “escape clause” in cases where they cannot figure out the pattern of the appearance of ‘X’, may actually have encouraged participants to prematurely stop looking for the relationship between the PE and NPE stimuli and the ‘X’, and default to focusing only on the appearance of the ‘X’ regardless of any inherent patterns. This may explain why the most common strategy type is the Ambiguous/None type, although further research to investigate if this is indeed the reason for the difference in our sample is required. 40 A way to improve the paradigm could be to change the instructions and attempt to force participants to use the Optimal strategy. One way to accomplish this is by removing the “escape clause” and stating explicitly that they have to identify 2 letters that predict the appearance of ‘X’ as well as respond quickly to ‘X’, as that influences performance on the task. Participants are then forced to keep trying to identify a pattern instead of just responding to the appearance of the ‘X’. This should reduce the number of participants in the Ambiguous/None group. Explicitly stating the number of letters to identify that predict ‘X’ should reduce the number of participants in the Favours PE, Favours NPE and Irrel evant groups, as they will be aware that they need to look out for 2 letters instead of just focusing on 1. These modifications should thus increase the number of those using the Optimal strategy with little or no influence on the LI effect, as the LI process happens outside of conscious awareness (Lubow, 1989). Hence, even if they are conscious of the requirement to identify 2 letters, they should still subconsciously screen out the pre exposed stimulus and still show an effect of LI. 9.3. Further Improvements to the Paradigm This paradigm was chosen as it is short and easy to administer. However, there are some problems associated with the brevity of the task, which may have resulted in the appearance of the Favours PE and Favours NPE strategies. Here, some improvements to the paradigm are suggested. 41 9.3.1. An Issue of Pre-exposure Duration The paradigm used in these studies used 10 pre-exposures followed by the test phase. It is possible that this number of pre-exposures was too short to induce LI in some of the participants such as in the Favours PE group. In this group, participants ended up focusing more on the pre-exposed stimulus while neglecting the NPE stimulus. Increasing the number of pre-exposures before the start of the test phase would allow LI to be induced in these participants, potentially resulting in these participants utilizing a different strategy. 9.3.2. An Issue of Task Brevity Data for the Favours NPE group shows a marked discrepancy between NPE and PE reaction times, with NPE reaction times very much faster than PE reaction times. These participants were obviously not paying special attention to the PE stimulus, as their reaction times in the PE condition were similar to when the ‘X’ when it was preceded by filler letters. It is possible that this effect was also evidence of a LI effect in the participants, but the paradigm was too short to properly capture the effect. When there is a LI effect, the PE stimulus is treated the same as random stimuli while the NPE stimulus receives the greater part of attention. Eventually though, the participant would end up noticing that the PE stimulus is also useful for the task at hand, and attention to the PE stimulus is no longer attenuated. However, if the task is too short (like in the case of this paradigm) these participants would not have noticed that the PE stimulus was also a useful predictor before the task ended; producing the results obtained in these studies. Extending the test phase of this paradigm should allow it to adequately capture the LI effect in cases such as these , 42 as it will provide a longer window for participants to exhibit a LI effect. As stated previously, a major reason this paradigm was chosen was because it was brief. Nevertheless, what this thesis demonstrates is that the task may possibly be a little too brief in its current form, and extending the test phase may be warranted. 9.4. Strengths 9.4.1. Extension of Current Knowledge A strength of this thesis is that it extends previous research on LI deficits to a novel population, those in the putatively prodromal state of psychosis. In doing so, it generates new knowledge on the presence of a LI deficit in this population and provides evidence for a disrupted dopaminergic system in these individuals. It has also demonstrated the possible utility of a LI paradigm in serving as a pre -screening procedure before conducting a time consuming, detailed screening. 9.4.2. Generalizable Findings In all of the studies, community based samples were selected that are relatively representative of the general population in Singapore. This sample is relatively heterogeneous; the participants have varying levels of education, socioeconomic status and are in different stages of life. Some are married and with children while others are just entering their teenage years. This is more reflective of the general population and is more realistic and relevant to the overall aim of the study, which is to investigate LI in UHR individuals. Hence, while the inhomogeneity of our sample makes it difficult to exclude the effect of potential confounds, it is 43 more reflective of the actual population of interest (UHR individuals) and makes the findings relatively more generalizable. 9.5. Limitations 9.5.1. Small Sample Size A limitation of these studies is the small sample size in the group that is of most interest, namely the Optimal group. The small sample size also reduced the ability to adequately investigate other patterns of interest, such as reaction times in the Favours NPE group. Nevertheless, even with the small sample size it was possible to obtain results that supported the hypotheses. Furthermore, the sample size was already quite large, given that it is very difficult to identify and recruit UHR individuals. Also, compared to other studies on at-risk individuals the sample size of Study 3 is relatively large. Most studies of UHR individuals that have large numbers have either run for an extended period (such as the ORYGEN group, e.g. Yung et al., 2008), or are a pooled sample from multiple sites, such as the North American Prodrome Longitudinal Study (NAPLS) group. Indeed, prior NAPLS studies have used a sample size of 291 participants (Goals – North American Prodrome Longitudinal Study (NAPLS), 2011) pooled from 8 centres. On average a single centre provided 36 participants, thus in comparison the sample size of 52 UHR individuals (out of which 48 were used for the study) from a single site in Study 3 is actually larger. Nevertheless, future research with a large sample size would be good to investigate the effects of strategy on reaction time in UHR individuals. 44 9.5.2. Possible Misclassifications of Participants Grouping of participant into strategy type was done mainly on the basis of information from the 2 questions asked at the end of the procedure. No follow up questions were used to probe more deeply into the strate gies reported by the participant. While this method may be reliable for some participants, there is a possibility that not all participants were able to accurately recall and express what they did during the task. That the LI process happens outside of conscious awareness probably contributed to the difficulty of some of the participants in expressing what strategy they used. As such, some misclassification may have occurred. Nevertheless, the data that was obtained was still able to show that self-reported strategy strongly affects the reaction times in different conditions. 9.6. Conclusion In summary, this thesis presents a series of studies detailing the investigation of LI in individuals at-risk of conversion to psychosis with a novel paradigm. It first study showed that the strategy that a participant uses when performing the task has a strong influence on the detection of LI using this paradigm. Subsequently, it showed that there is indeed a LI deficit present in UHR individuals, once strategy is taken into account. Through these investigations, support was provided for the role of the disrupted dopaminergic system in the LI deficit. Future investigations on whether this phenomenon would be useful in distinguishing between those who convert to psychosis and those who do not are suggested. 45 10. References Abi-Dargham, A., Kegeles, L. 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Can we predict the onset of first-episode psychosis in a high-risk group? International Clinical Psychopharmacology, 13 (suppl 1), S23-S30. 57 [...]... studied in both healthy and clinical samples seems to have received relatively little attention from researchers working with at- risk populations: Latent Inhibition 3 What is Latent Inhibition? Latent Inhibition (LI) is a cognitive phenomenon where simple exposure to a stimulus without pairing to a consequence lowers the future associability of that stimulus to events Take for example, a doorbell that randomly... regardless of smoking status One type of result corresponded to a LI effect, leading to the conclusion that a more systematic investigation was needed in order to determine the cause of this pattern of results, and to elucidate if this 18 paradigm could actually elicit the LI effect after taking into account the influencing factor The next hypothesis was that the pattern of results would depend on the type of. .. related issues such as a disturbance in sleep or being unwilling to leave the house These reasons, together with the stigma associated with the illness, have led to such individuals being described as being at Ultra-High Risk (UHR) of psychosis and the mental state which they are said to have is termed the At- Risk Mental State, or ARMS This is defined as a mental state that confers an elevated risk of. .. dopamine pathways in the nucleus accumbens play a key role in LI (Weiner, 2003), suggesting that LI is at least mediated by the dopaminergic system Hence, this phenomenon seems to be sensitive to changes in the dopaminergic system and has potential as a measure of the relative health of the system 6 5 Latent Inhibition, Dopamine and Ultra High Risk Individuals Given the link between LI and dopamine,... likely that LI deficits in UHR individuals will be observed The dopamine hypothesis of schizophrenia states that the symptoms of the disease are due to dopaminergic dysregulation in the system It is now in the third iteration, proposed by Howes and Kapur, (2009), and it explicitly links dopamine dysregulation to psychosis rather than to schizophrenia as a whole It is also known that substances that increase... developing a psychotic disorder in the near future (Yung et al., 2005) 2 Neurocognitive Function of Ultra High Risk individuals In recent years, much effort has gone into investigating the neurocognitive functioning of at- risk individuals Neurocognitive dysfunction is an established feature of schizophrenia, with many studies finding that patients with schizophrenia show deficits in a wide array of domains... of symptoms that serve as a precursor to the full manifestation of an illness Similarly, the psychosis prodrome is defined as “the period of change from pre-morbid functioning, including various mental state features, to the time of onset of frank psychotic features” (Yung et al., 1998, S23) If symptomatology becomes more severe, then an individual will be considered to have ‘converted’ to psychosis. .. paradigm would be contingent on whether a participant utilized a strategy that takes into account both the NPE and PE stimuli in predicting X This study also attempted to investigate if, after taking into account type of strategy, this paradigm could replicate the results of Evans et al (2007) To investigate these hypotheses the first study was repeated with a minor addition to the procedure 7.2 Method... auditory hallucinations but these sounds remain indistinct and are not clear enough to be true hallucinations They may also have experienced a significant decline in functioning in one or more areas such as in school, work or socially This period may begin a number of years prior to a full-blown psychotic episode, and is referred to as the prodrome of psychosis 1 In medicine, a prodrome is a group of. .. the future Subsequent processing of that stimulus is impeded, resulting in a disruption in learning about that stimulus The organism will hence take longer to learn any new associations involving that stimulus Latent inhibition is thought to exist because it provides an evolutionary advantage It promotes stimulus selectivity by causing the organism to avoid paying attention to stimuli previously learnt