Progress in Neuro-Psychopharmacology & Biological Psychiatry 57 (2015) 60–68

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Progress in Neuro-Psychopharmacology & Biological Psychiatry

Dissociation of decision making under ambiguity and decision making under risk: A neurocognitive endophenotype candidate for obsessive– compulsive disorder Long Zhang a,b, Yi Dong c, Yifu Ji c, Chunyan Zhu b, Fengqiong Yu b, Huijuan Ma a,b, Xingui Chen a,b, Kai Wang a,b,⁎ a b c

Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China Laboratory of Neuropsychology, Anhui Medical University, Hefei, China Mental Health Center of Anhui Province, Hefei, China

a r t i c l e

i n f o

Article history: Received 1 April 2014 Received in revised form 5 September 2014 Accepted 18 September 2014 Available online 12 October 2014 Keywords: Decision making Game of Dice Task Iowa Gambling Task Neurocognitive endophenotype Obsessive–compulsive disorder

a b s t r a c t Evidence in the literature suggests that executive dysfunction is regarded as an endophenotype candidate for obsessive–compulsive disorder (OCD). Decision making is an important domain of executive function. However, few studies that have investigated whether decision making is a potential endophenotype for OCD have produced inconsistent results. Differences in the findings across these studies may be attributed to several factors: different study materials, comorbidity, medication, etc. There are at least two types of decision making that differ mainly in the degree of uncertainty and how much useful information about consequences and their probabilities are provided to the decision maker: decision making under ambiguity and decision making under risk. The aim of the present study was to simultaneously examine decision making under ambiguity as assessed by the Iowa Gambling Task (IGT) and decision making under risk as measured by the Game of Dice Task (GDT) in OCD patients and their unaffected first-degree relative (UFDR) for the first time. The study analyzed 55 medication-naïve, non-depressed OCD patient probands, 55 UFDRs of the OCD patients and 55 healthy matched comparison subjects (CS) without a family history of OCD with the IGT, the GDT and a neuropsychological test battery. While the OCD patients and the UFDRs performed worse than the CS on the IGT, they were unimpaired on the GDT. Our study supports the claim that decision making under ambiguity differs from decision making under risk and suggests that dissociation of decision making under ambiguity and decision making under risk may qualify to be a neurocognitive endophenotypes for OCD. © 2014 Elsevier Inc. All rights reserved.

1. Introduction Obsessive–compulsive disorder (OCD) is a phenotypically heterogeneous neuropsychiatric disorder, and there is strong evidence that genetic factors play an important role in the development of OCD. The level of monozygotic twin concordance is reported to be 63–87% (Hanna et al., 2005), and family studies show that the risk to firstdegree relatives of OCD patients is approximately five times that of the normal population (Nestadt et al., 2000). However, classical genetic

Abbreviations: ANOVA, analysis of variance; CS, comparison subjects; dlPFC, dorsolateral prefrontal cortex; DS, Digit Span; GDT, Game of Dice Task; HARS, Hamilton Anxiety Rating Scale; HDRS, Hamilton Depression Rating Scale; IGT, Iowa Gambling Task; OCD, obsessive– compulsive disorder; OFC, orbitofrontal prefrontal cortex; SCWT, Stroop Color Word Test; TMT, Trail Making Test; ToL, Tower of London; UFDR, unaffected first-degree relative; VF, Verbal Fluency; vmPFC, ventromedial prefrontal cortex; WCST, Wisconsin Card Sorting Test; Y-BOCS, Yale–Brown Obsessive–Compulsive Scale. ⁎ Corresponding author at: Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Jixi Road, Hefei, Anhui Province, China. Tel./fax: + 86 551 62923704. E-mail address: [email protected] (K. Wang).

http://dx.doi.org/10.1016/j.pnpbp.2014.09.005 0278-5846/© 2014 Elsevier Inc. All rights reserved.

linkage and association studies have not yet provided consistent results to identify the contributory genes involved in OCD (Nestadt et al., 2010), which leads to the exploration of other approaches to investigate the genetic basis of OCD, including searching for endophenotype. Endophenotypes are intermediate phenotypes that are not obvious or external but, rather, are microscopic and internal (Gottesman and Gould, 2003). To be more specific, endophenotypes are defined as heritable quantitative traits that are believed to be intermediate between disease phenotypes and the biological processes that underlie them (Reus and Freimer, 1997) and to be correlated with increased genetic risk for a disease, which could exist in both patients and clinically unaffected first-degree relative (UFDR) of patients (Kéri and Janka, 2004). The commonly used assessment measures available for endophenotype analysis include biochemical, neuroimaging, neuroanatomical, endocrinological, and neuropsychological methods. Putative endophenotypes must fulfill the following criteria: be associated with the disease in the population, be heritable, be state-independent, be co-segregated with the disease, and be found among the UFDR of patients at a higher rate than in the general population (Gottesman and Gould, 2003). From this perspective, neurocognitive impairments are

L. Zhang et al. / Progress in Neuro-Psychopharmacology & Biological Psychiatry 57 (2015) 60–68

regarded to be the most promising candidate endophenotypes for many psychiatric disorders (Delorme et al., 2007). Moreover, measures of neurocognitive function are widely considered to be valuable endophenotypes in large part because of their demonstrated reliability and stability over time (Rund, 1998). Searching for candidate endophenotypes has been extensively applied to some psychiatric disorders, for instance, schizophrenia (Leppänen et al., 2008), mood disorders (Ancín et al., 2010), autism (Delorme et al., 2007), and attention deficit hyperactivity disorder (Albrecht et al., 2008). To date, few studies that have searched for endophenotypes of OCD mainly focused on neurocognitive functions. The first research that adopted this approach showed that OCD patients and their UFDR had deficits in motor inhibition (Chamberlain et al., 2007). Similar studies also found impairments in planning and working memory processes (Delorme et al., 2007), cognitive flexibility (Cavedini et al., 2010), volitional action generation (Kloft et al., 2013), performance monitoring (Riesel et al., 2011) and behavioral reversal (Viswanath et al., 2009) presented in both OCD probands and their UFDR. Moreover, to remove the effect of the drug treatment, a study by Rajender et al. (2011) reported impaired set-shifting and inhibitory control in patients with drug-naïve OCD and their UFDR. In light of the abovementioned results, we propose that certain domains of cognitive functions including cognitive flexibility, response inhibition and planning could be potential neurocognitive endophenotypes for OCD. Decision making is another important domain of cognitive functions. However, individuals with OCD frequently experience serious impairments in everyday decision making. That is, making decision appears to be dysfunctional in the clinical OCD setting in the context of obsessive doubting and uncertainty (Dittrich and Johansen, 2013). Some authors even regard decision making impairments to be the underlying cause of obsessive and compulsive symptoms and suggest that conceptualizing OCD as a disorder of decision making allows the application of novel approaches to measure symptom provocation and their elimination to further determine the neural mechanisms of OCD (Dittrich and Johansen, 2013; Sachdev and Malhi, 2005). Moreover, the conceptualization of OCD as a decision making disorder may lead to new approaches for the cognitive behavioral therapy of this disorder (Sachdev and Malhi, 2005). Therefore, neuropsychological studies on the decision making for OCD patients have received much attention (Boisseau et al., 2013; Starcke et al., 2009, 2010). Many studies in OCD patients have highlighted impaired decision making as potential vulnerability marker of the disorder, and researchers have suggested that the ritualistic behaviors related with OCD result from a detrimental sensitivity to immediate gains without proper judgments about long-term consequences of such behaviors (Cavedini et al., 2002). Such impairments in decision making may provide an endophenotype or an intermediate marker of brain dysfunction (Boisseau et al., 2013). However, the few studies that have investigated decision making in OCD patients and their UFDR have produced inconsistent results. The various study tasks used by researchers may account for this inconsistency. Two studies that used the Iowa Gambling Task suggested that deficits in decision making could qualify as a suitable endophenotype candidate for OCD (Cavedini et al., 2010; Viswanath et al., 2009), but another study that used the Cambridge Gamble task found that OCD patients and their UFDR showed intact decision making compared to normal controls (Chamberlain et al., 2007). To date, from a neuroscientific perspective, there are at least two types of decision making that differ in mainly the degree of uncertainty and how much useful information about consequences and their probabilities is provided to decision maker (Brand et al., 2006). In some situations, outcomes and probabilities are implicit, and the decision makers have to initially find some effective information and figure out the options' qualities by themselves by means of processing feedback of previous choices. This type of decision making is often termed decision making under ambiguity, which is usually measured with the Iowa Gambling Task (IGT; Bechara et al., 1994). In the IGT, participants have to maximize a fictitious amount of

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money by successively choosing cards from four different card decks. Participants do not know the amount of cards they need to choose or which card decks are disadvantageous (i.e., coupling large gains with even larger losses and leading to a negative overall balance in the long term) or advantageous (i.e., coupling small gains with even smaller losses and leading to a positive overall balance in the long term). Therefore, the possible choices are full of ambiguity and participants must learn to avoid the disadvantageous card decks using feedback from previous trials. In contrast to decision making under ambiguity, explicit information about the potential consequences of various choices and their probabilities are provided in some decision situations. This type of decision making is referred to as decision making under risk, which is usually measured with the Game of Dice Task (GDT; Brand et al., 2005). The GDT requires subjects to decide between different options that are explicitly related to a specific amount of gain/loss. Furthermore, winning probabilities are obvious and stable from the beginning of the task. Some options, which are related with high potential gains/losses, but low winning probabilities are risky; and other options, which are related with lower potential gains/losses, but higher winning probabilities are non-risky. Thus, subjects can estimate the risk related with each option and may apply strategies to maximize profit. So far, however, only one study has investigated decision making under ambiguity as measured by the IGT and decision making under risk as measured by the GDT in patients with OCD (Starcke et al., 2010). The study found that while OCD patients performed worse than comparison subjects on the IGT, they were unimpaired on the GDT. Meanwhile the study further emphasized dysfunctions of the orbitofrontal cortex (OFC), but suggested intact functioning of the dorsolateral prefrontal cortex (dlPFC) in patients with OCD (Starcke et al., 2010). The study also provided support for the notion that there is a fundamental distinction between decision making under ambiguity and decision making under risk (Clark et al., 2008). Previous studies have suggested that unimpaired IGT performance, in the sense of preferentially selecting the advantageous options, depends on intact functioning of the ventromedial prefrontal cortex (vmPFC)/OFC. Patients with vmPFC/OFC lesions (Bechara et al., 2000; Manes et al., 2002) showed deficits on the IGT. Even in a rat analogue of the IGT, rats with OFC lesions preferred to choose larger but more unpredictable rewards over smaller but more reliable rewards under conditions of uncertainty and ambiguity (Pais-Vieira et al., 2007). However, the dlPFC plays a major role in the GDT. Neuropsychological studies have found that subjects with compromised dlPFC function show impaired performance on the GDT (Brand et al., 2007; Delazer et al., 2007). Neuroimaging studies have demonstrated that decision making under risk as assessed by the GDT depends on the activation of the dlPFC (Labudda et al., 2008). Many functional imaging and morphometric magnetic resonance imaging studies of OCD have supported the notion that abnormalities in key gray matter regions, such as the OFC, thalamus, anterior cingulate cortex, and striatum play important roles in its pathophysiology (Alvarenga et al., 2012; Piras et al., 2013). In particular, the OFC plays a central role in most neurobiological models of OCD. These findings suggest that a dysfunctional cortico-striatothalamo-cortical circuitry contributes to the pathophysiology of OCD (Friedlander and Desrocher, 2006; Menzies et al., 2008). Furthermore, neuroimaging studies have identified abnormally reduced activation of the lateral OFC in OCD patients and their UFDR during reversal learning (Chamberlain et al., 2008). As for the dlPFC, studies on the potential involvement of the region in the pathophysiology of OCD are inconsistent. Although some research has shown abnormalities in the dlPFC activity of OCD patients (van den Heuvel et al., 2005), other studies have not yielded similar results (Abbruzzese et al., 1995; Whiteside et al., 2004). One important reason that the findings of dlPFC functioning in OCD patients are inconsistent is because the abnormal activation of the dlPFC, in many cases, is often related to special symptom dimensions. For example, there is a correlation between the “aggressive/ harm” dimension and a structural substrate encompassing the dlPFC,

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and there is a positive relationship between the scores in the “sexual/ religious” dimension and gray matter volume within the right dlPFC (Alvarenga et al., 2012; Lázaro et al., 2014). It is also important to point out that some neuroimaging studies have found that while OCD patients show activation of areas such as the OFC and anterior cingulate cortex during provocation of obsessive–compulsive symptoms, they only show task-related hyperactivity in dlPFC compared with healthy subjects (de Vries et al., 2013; Nakao et al., 2009). For example, during a functional magnetic resonance imaging trial using the N-back task, the OCD patients showed a significantly greater activation pattern in several regions including the dlPFC (Nakao et al., 2009). As we know, OCD is related to impairments in neuropsychological and neuroimaging studies, but the results are inconsistent across studies. These inconsistencies can be attributed to methodological issues such as variation in the medication status of the patients because most of the studies have been carried out in drug-treated patients with OCD (Narayanaswamy et al., 2013). In a meta-analysis study, Kuelz et al. (2004) found that medicated OCD patients have worse performance on information processing tests and the Wisconsin Card Sorting Test compared to unmedicated OCD patients and emphasized the importance of carrying out studies on OCD patients without medication. Furthermore, many studies showed that the results of neuropsychological studies of OCD are potentially confounded by comorbidity with depression (Basso et al., 2001; Moritz et al., 2001). To our best knowledge, no study has simultaneously examined decision making under ambiguity and decision making under risk in the UFDR of patients with OCD and OCD probands. However, it is worth mentioning that Starcke et al. (2010) found dissociation between decision making under ambiguity and decision making under risk in OCD patients. Therefore, the aim of the current study was to replicate this in a medication-naïve and non-depressed OCD group and to extend this by adding relatives to consider its utility as an endophenotype. Accordingly, we investigated OCD patients, their UFDR and healthy controls with the IGT, the GDT and a neuropsychological test battery. In correspondence with the above-mentioned studies, the following assumption was made: compared to healthy controls, patients with OCD and their UFDR were expected to perform worse on the IGT due to functional abnormalities in the OFC and to be unimpaired on the GDT given the relatively intact functioning of their dlPFC.

OCD were recruited by advertisements and leaflets or by word of mouth among college students and the local community. They were matched for age, gender, and education with the UFDR. The exclusion criteria were current or past diagnosis of another psychiatric disorder, neurological illness, head injury, drug or alcohol abuse, gambling addiction, or having serious medical illness. In total, 66 relatives were contacted. Of these, eight denied participation, and three did not complete the IGT or the Wisconsin Card Sorting Test; therefore, these relatives were excluded from participation. All of the subjects were examined using the Mini International Neuropsychiatric Inventory, which is a wellvalidated screening instrument for axis I disorders (Sheehan et al., 1998). Obsessive–compulsive symptom severity was assessed by the Y-BOCS. The 14-item Hamilton Anxiety Rating Scale (HARS; Hamilton, 1959) and the 17-item HDRS were used to assess the current anxiety and depressive symptoms of the OCD patients. All of the subjects gave their written informed consent to participate, and the local Ethics Committee approved the study.

2. Methods

2.3. Decision tasks

2.1. Subjects

2.3.1. Iowa Gambling Task Decision making under ambiguity was measured by using the Chinese computerized version of the IGT (Bechara et al., 2000). In the task, participants are asked to select one card from four decks of cards, deck A, B, C or D each time. After each selection, they are awarded a specified amount of fictitious money. However, on a fixed but unpredictable schedule, they may lose a specific amount of money. The subjects were informed to gain as much money as possible with a starting capital (¥2000) in 100 trials. The selection of a card from deck A or B produces high immediate gains but even higher losses that occur unpredictably at certain times. The selection of a card from deck C or D is followed by small immediate gains but even smaller losses. In short, decks A and B are considered to be disadvantageous and result in a negative outcome in the long run; decks C and D are regarded to be advantageous decks and lead to a positive outcome in the long run. The subjects were told that some decks are better than other decks and that they can switch between the four decks. The gain or loss after each selection and the changed money are shown on the screen. For the analysis of the IGT performance, we calculated the total netscore by subtracting the frequency of disadvantageous selections from the frequency of advantageous selections. The 100 trials were divided into 5 blocks of 20 card selections, and the netscore of each block was calculated to investigate whether decision making changed over time.

The study groups comprised 55 pairs of OCD patient probands (each at least has one unaffected sibling) and the UFDR of the identified OCD probands and 55 healthy comparison subjects (CS). The patients were recruited from the outpatients of the Mental Health Center of Anhui Province in Hefei, China. The OCD subjects were included in the study if they 1) met the DSM-IV-TR (APA, 2000) diagnostic criteria for OCD, 2) had never been treated with any psychiatric medication, 3) had a Yale–Brown Obsessive Compulsive Scale (Y–BOCS; Goodman et al., 1989) total severity score ≥ 16, and 4) had at least six years of school education. The subjects were excluded if they 1) met any other DSM-IV axis I diagnosis including lifetime history of depression or 2) had a 17-item Hamilton Depression Rating Scale (HDRS; Hamilton, 1960) score N 7 (Hamilton, 1960). Eight patients were excluded because they withdrew from the study or underwent psychotherapy. Drugnaïve patients were defined as those patients who had never been treated with any psychiatric medications before enrolling in the study. This information was obtained by taking detailed treatment history from the patient and at least one immediate family member (Krishna et al., 2011). To eliminate the effect of the differing neurodevelopmental stages of the subjects, only unaffected siblings of the probands with OCD were recruited into the UFDR group. The CS without a known family history of

2.2. Neuropsychological background tests All of the subjects performed a neuropsychological test battery. The Stroop Color Word Test (SCWT; Spreen and Strauss, 1998) was used to measure response inhibition and interference susceptibility. The Trail Making Test (TMT; Reitan, 1992) was administered to assess motor speed (TMT A), cognitive flexibility and selective attention (TMT B). Verbal short-term memory and verbal working memory were tested by the Digit Span test (DS; Wechsler, 1987). Verbal Fluency tests (VF; Troyer et al., 1997) were also administered. The letter fluency test asked the subjects to name as many Chinese characters as possible whose initial consonant is the letter D in 1 min, and the category fluency test asked the subjects to produce words that belong to a given semantic category (animals) within 1 min. Additionally, we employed two tests for the assessment of executive functions and information processing: the Wisconsin Card Sorting Test (WCST; Heaton et al., 1993) for the assessment of organization and set-shifting; the Tower of London test (ToL; Shallice, 1982) for the assessment of planning called the “lookahead” function.

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2.3.2. Game of Dice Task The computerized GDT (Brand et al., 2005) was used to assess decision making under risk. In the task, the participants are asked to throw a virtual die, and their goal is to maximize their gains with a fictitious starting capital (€1000) in 18 trials by choosing one of four alternatives. Before each throw, the individuals must guess which number will be thrown or which combination of numbers will include the number that will be thrown. Each option is associated with different winning probabilities and specific gain/loss: a 1000€ gain/loss with a winning probability of 1:6 for a single number; a 500€ gain/loss with a winning probability of 2:6 for a combination of two numbers (“1, 2” or “3, 4” or “5, 6”); a 200€ gain/loss with a winning probability of 3:6 for a combination of three numbers (“1, 2, 3” or “4, 5, 6”); a 100€ gain/loss with a winning probability of 4:6 for a combination of four numbers (“1, 2, 3, 4” or “2, 3, 4, 5” or “3, 4, 5, 6”). The former two with lower winning probability are grouped into risky decisions, and the latter two with higher winning probability are grouped into non-risky decisions. For analysis, we calculated a netscore by subtracting the number of risky choices from the number of non-risky choices and analyzed how often each alternative was chosen. The rules and amounts of gains and losses are explicitly introduced before the task and are shown on the screen. Additionally, the gain or loss, the changed capital, and the number of remaining die throws are presented on the screen after each choice.

2.4. Statistical analysis SPSS 16.0 was used to perform all of the statistical analyses. All of the variables were tested for normal distribution with the Kolmogorov– Smirnov Test separately for the three groups. There were no significant deviations from the normal distribution for the IGT scores, the GDT scores, or for most of the neuropsychological variables. Thus, parametric methods were used (one-way analysis of variance and analysis of variance with repeated measures) for these variables. In addition, because the Y-BOCS total scores, HDRS scores, and HARS scores were not normatively distributed, we also used a Spearman correlation analysis to examine the correlation between the neuropsychological test scores and between the severity of symptoms. The threshold of statistical significance was set at p b 0.05.

3. Results 3.1. Demographic and clinical characteristics of the sample The demographic characteristics of the subjects are shown in Table 1. No differences were found between the OCD, UFDR and CS groups for age, years of education or sex. There were significant differences on the mean Y–BOCS scores. The OCD patients had significant higher Y–BOCS scores than the UFDR and the CS, F(2,162) = 965.22, p b 0.001.

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3.2. Neuropsychological assessment The neuropsychological tasks performance in the three groups are shown in Table 2. Significant differences between the three groups were present on the ToL, the SCWT and the TMT. According to the LSD test, both patients with OCD and their UFDR were found to spend significantly more time compared to the CS on 4-move problems (p b 0.05; p b 0.01, respectively) and 5-move problems (p b 0.001; p b 0.01, respectively), OCD patients performed worse than the UFDR and the CS on naming word color (p b 0.05; p b 0.05, respectively), and OCD patients but not the UFDR performed worse than the CS on TMT B (p b 0.01; p N 0.05, respectively). Therefore, we are concerned with the performance of the three groups on only the ToL in the “Discussion” section. 3.3. Decision making on the IGT A one-way analysis of variance (ANOVA) with group as the betweensubjects factor was performed to examine the IGT netscore: the CS had a significant higher netscore than the OCD patients and the UFDR, F(2,162) = 11.45, p b 0.001 (CS vs. OCD, p b 0.001; CS vs. UFDR, p b 0.001; OCD vs. UFDR, p N 0.05). In the IGT, the change curve of the netscore indicates the change of decision strategy over the task. As shown in Fig. 1, the netscore of the CS group markedly increased over the task, which indicates that they turned to advantageous choices. However, the increase of the netscore of the OCD and the UFDR group were not obvious (were always negative), which indicates that they maintained the preference for disadvantageous choices. To examine the IGT performance in more detail, a repeated measures ANOVA was performed with block as the within-subjects factor and group as the between-subjects factor. There were significant main effects for group, F(2,162) = 3.88, p b 0.05, which indicates that the CS group performed better than the OCD and UFDR groups, and for block, F(4,648) = 2.55, p b 0.05, which indicates that the IGT performance increased over time. Comparisons of each of the two groups showed various patterns of performance over the IGT between the OCD patients and the CS (group effect: F(1,108) = 15.20, p b 0.001; block effect: F(4,432) = 5.68, p b 0.001; group by block interaction: F(4,432) = 7.35, p b 0.001) as well as between the UFDR and the CS (group effect: F(1,108) = 17.28, p b 0.001; block effect: F(4,432) = 6.14, p b 0.001; group by block interaction: F(4,432) = 5.25, p b 0.001). There was no difference between the OCD patients and the UFDR (group effect: F(1,108) = 0.40, p = 0.530; block effect: F(4,432) = 1.10, p = 0.354; group by block interaction: F(4,432) = 1.12, p = 0.347). A one-way ANOVA was performed with group as the withinsubjects factor was performed. The decision process had a significant influence on the netscore of the CS group, F(4,648) = 8.95, p b 0.001. According to the LSD test, the netscores of the CS group between block 3 to block 5 were significant higher than for block 1 (ps b 0.01), and the netscores of the CS group between block 4 to block 5 were significant higher than for block 2 (ps b 0.01). However, the decision process had

Table 1 Demographic characteristics of the sample [M(S.D.)].

Age (years) Education (years) Sex (male/female) Y–BOCS Age of onset of OCD (years) Duration of OCD (years) HARS HDRS

OCD (n = 55)

UFDR (n = 55)

CS (n = 55)

F

p

26.51 (7.84) 12.96 (2.56) 22/33 22.67 (4.84) 22.69 (7.97) 4.33 (3.66) 13.56 (4.25) 6.11 (0.81)

28.42 (7.37) 11.93 (2.60) 26/29 1.85 (1.92)

27.85 (7.32) 12.44 (2.28) 24/31 0.00 (0.00)

0.94 2.40 0.59a 965.22

0.394 0.094 0.741 b0.001⁎

Abbreviations: CS, comparison subjects; HARS, Hamilton Anxiety Rating Scale; HDRS, Hamilton Depression Rating Scale; OCD, obsessive–compulsive disorder; S.D., standard deviation; UFDR, unaffected first-degree relative; Y–BOCS, Yale–Brown Obsessive–Compulsive Scale. a χ2, df = 2. ⁎ Significant differences between the three groups were found.

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Table 2 Results of the neuropsychological tasks in the OCD, the UFDR, and the CS group [M(S.D.)]. OCD (n = 55)

UFDR (n = 55)

CS (n = 55)

F

p

Effect sizea

SCWT Color (s) Word color (s) Interference (s)

13.34 (1.52) 22.72 (3.05) 9.38 (3.41)

13.27 (1.25) 21.24 (3.66) 7.97 (3.53)

12.80 (1.54) 21.12 (3.39) 8.32 (3.51)

2.29 3.85 2.45

0.105 0.023⁎ 0.090

0.35/0.34 0.50/0.03 0.32/0.10

TMT TMT A (s) TMT B (s) TMTA–TMT B (s)

35.39 (5.71) 73.74 (7.54) 38.34 (9.34)

34.48 (6.09) 71.66 (9.80) 37.18 (10.10)

33.94 (5.71) 69.00 (9.36) 35.06 (9.46)

0.88 3.87 1.64

0.419 0.023⁎ 0.198

0.25/0.09 0.58/0.28 0.35/0.22

9.11 (1.69) 6.07 (1.39)

8.75 (1.58) 5.85 (1.33)

9.36 (1.71) 6.15 (1.46)

1.93 0.65

0.149 0.523

0.15/0.37 0.06/0.21

Verbal Fluency Letter fluency Category fluency

12.38 (2.71) 15.53 (2.09)

11.67 (2.33) 16.29 (1.92)

11.36 (2.05) 16.25 (2.25)

2.65 2.34

0.074 0.100

0.42/0.14 0.33/0.02

Tower of Londonb 2-Move (s) 4-Move (s) 5-Move (s)

3.78 (0.59) 15.77 (2.97) 30.81 (4.54)

3.75 (0.75) 16.04 (2.81) 30.07 (8.17)

3.52 (0.65) 14.43 (2.31) 26.57 (5.50)

2.55 5.59 7.20

0.081 0.005⁎ 0.001⁎

0.42/0.33 0.51/0.63 0.84/0.50

WCST Total errors Perseverative response Perseverative errors

46.65 (22.54) 55.64 (27.89) 30.60 (19.22)

39.73 (22.49) 47.58 (28.31) 25.18 (19.27)

46.95 (21.29) 56.55 (27.29) 29.87 (17.07)

1.88 1.73 1.38

0.156 0.181 0.254

0.03/0.33 0.03/0.32 0.04/0.26

Digit Span (DS) DS forward DS backward

Abbreviations: CS, comparison subjects; OCD, obsessive–compulsive disorder; SCWT, Stroop Color Word Test; S.D., standard deviation; TMT, Trail Making Test; UFDR, unaffected first-degree relative; WCST, Wisconsin Card Sorting Test. a Effect size: small effect, ≤0.30; medium effect, 0.31–0.50; large effect, N0.50. The former is the result of the comparison between the OCD group and the CS group. The latter is the result of the comparison between the UFDR group and the CS group. b The time required to complete the Tower of London was given. ⁎ Significant differences between the three groups were found.

no significant effect on the netscores of the OCD and UFDR groups (ps N 0.05). Single comparisons of performances on the five blocks between groups indicated significant netscore differences in block 3, F(2,162) = 3.11, p b 0.05, and block 4, F(2,162) = 10.82, p b 0.001, and block 5, F(2,162) = 13.91, p b 0.001 (Table 3). 3.4. Decision making on the GDT A one-way ANOVA with group as the between-subjects factor was performed to examine the GDT netscores. In contrast to the IGT, there was not a significant difference between the netscores of the three groups for the GDT, F(2,162) = 1.29, p = 0.277 (CS vs. OCD, p = 0.150; CS vs. UFDR, p b 0.909; OCD vs. UFDR, p = 0.185). An analysis of variance was carried out with repeated measures with choice as the within-subjects factor and group as the between-subjects factor. There was a significant main effect for choice, F(3,486) = 23.17, p b 0.001, but no significant

main effect for group, F(2,162) = 0.22, p = 0.800, and no significant interaction between choice and group, F(3,486) = 0.02, p = 0.980, which indicates that there were no differences between the three groups on the GDT. None of the single comparisons in the various choices reached significance between the groups (ps N 0.05) (Fig. 2 and Table 3). We examined the use of negative feedback (losses) after the decision of a risky option to choose a non-risky option in the next trial, but only those participants who chose a risky option and received negative feedback at least once during the GDT could be included. Thus, a total of 142 subjects were included. The three groups did not differ in the use of negative feedback, F(2,139) = 0.02, p = 0.981. The use of negative feedback was significantly associated with the GDT netscore (OCD: r = 0.71, p b 0.001; UFDR: r = 0.82, p b 0.001; CS: r = 0.79, p b 0.001). We also examined the use of positive feedback (gains) after the decision of a non-risky option to choose a non-risky option again, but only those participants who chose a non-risky option and received positive feedback at least once during the GDT could be included. Thus, the analysis was based on the data of 157 subjects. There was not a significant difference between the three groups in the use of positive feedback, F(2,154) = 0.87, p = 0.420 (Table 3). The use of positive feedback was also significantly associated with the GDT netscore (OCD: r = 0.75, p b 0.001; UFDR: r = 0.85, p b 0.001; CS: r = 0.71, p b 0.001).

3.5. Spearman correlation analysis between the severity of symptoms and the test performance indexes

Fig. 1. Mean netscores of the five blocks in the IGT for the OCD patients, the UFDR and the HC. Means ± SEMs are shown, ⁎p b 0.05 for differences between the three groups.

To check for the influence of the severity of symptoms on the neurocognitive performance indexes, Spearman correlation analyses were conducted between the Y–BOCS total scores, the HDRS scores as well as between the HARS scores and the scores on the neuropsychological tests. No significant correlations were found within groups (ps N 0.11).

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Table 3 Decision making performance of the OCD, the UFDR, and the CS group [M(S.D.)].

IGT Block1 Block2 Block3 Block4 Block5 Netscore GDT One number Two numbers Three numbers Four numbers Netscore Use of negative feedbackb (%) Use of positive feedbackc (%)

OCD (n = 55)

UFDR (n = 55)

CS (n = 55)

F

p

Effect sizea

−2.04 (5.08) −1.09 (4.94) −0.47 (6.48) −1.96 (6.39) −2.44 (5.48) −8.00 (18.05)

−1.67 (5.03) −0.58 (4.65) −1.42 (5.51) −2.07 (5.50) −0.47 (6.27) −6.22 (10.64)

−2.33 (5.12) −0.51 (4.96) 1.45 (6.45) 2.73 (6.59) 3.71 (6.90) 5.13 (17.08)

0.23 0.24 3.11 10.82 13.91 11.45

0.795 0.791 0.047⁎ b0.001⁎ b0.001⁎ b0.001⁎

0.06/0.13 0.12/0.01 0.30/0.48 0.72/0.79 0.99/0.63 0.75/0.80

2.00 (3.19) 5.58 (4.02) 5.82 (3.32) 4.60 (4.44) 2.91 (10.62) 53.86 (35.85) 57.71 (34.92)

1.47 (2.85) 4.80 (3.61) 6.15 (3.57) 5.58 (4.23) 5.45 (9.68) 52.45 (41.13) 59.64 (33.27)

1.60 (2.43) 4.96 (3.83) 6.20 (3.80) 5.24 (4.63) 5.67 (9.74) 53.73 (41.04) 65.85 (32.11)

0.52 0.64 0.18 0.69 1.29 0.02 0.87

0.597 0.528 0.832 0.501 0.277 0.981 0.420

0.14/0.05 0.16/0.04 0.11/0.01 0.14/0.08 0.27/0.02 0.01/0.03 0.24/0.19

Abbreviations: CS, comparison subjects; GDT, Game of Dice Task; IGT, Iowa Gambling Task; OCD, obsessive–compulsive disorder; S.D., standard deviation; UFDR, unaffected first-degree relative. a Effect size: small effect, ≤0.30; medium effect, 0.31–0.50; large effect, N0.50. The former is the result of the comparison between the OCD group and the CS group. The latter is the result of the comparison between the UFDR group and the CS group. b Sample size of three group (OCD: n = 47; UFDR: n = 48; CS: n = 47). c Sample size of three group (OCD: n = 55; UFDR: n = 48; CS: n = 54). ⁎ Significant differences between the three groups were found.

4. Discussion The main result of the current study is that there is a clear dissociation of decisions under implicit versus explicit conditions in patients with OCD and their UFDR. In accordance with our predictions, the present study revealed that, while both the medication-naïve, non-depressed OCD probands and the UFDR had impairments in decision making under ambiguity, they were unimpaired in decision making under risk. Additionally, the UFDR and OCD probands showed deficits in planning. To our best knowledge, this is the first study which simultaneously examined decision making under ambiguity and decision making under risk in the UFDR of patients with OCD and OCD probands. On the IGT, the OCD patients chose the disadvantageous choices more frequently than the CS group. This result principally replicates the findings by Starcke et al. (2010). More significantly, the UFDR showed a similar behavior pattern on the IGT to that seen in patients with OCD. At the beginning of the IGT, all of the subjects preferred the decks that contain high immediate gains. Soon, the CS seemed to realize

Fig. 2. Mean frequency of each single alternative in the GDT for the OCD patients, the UFDR and the HC. [Bars indicate the standard error of the mean.]

that the high immediate gains were accompanied by higher losses and gradually turned to the advantageous choices. However, the OCD and the UFDR did not greatly change their selection strategy. They seemed to be unable to develop a favorable long-term strategy and preferred options with higher immediate gains and higher unpredictable penalties over options with smaller immediate gains and smaller penalties. In the study by Starcke et al. (2010), it was found that OCD patients also showed impairments in a simple feedback processing task, which was designed to directly assess the acquisition of simple feedback associations. Moreover, correlations between the IGT performance and feedback processing were found in the control subjects, but not in the patient group. According to Starcke et al., subjects' IGT performance was dependent on the feedback processing, i.e., learning from the feedback of previous selections. Without contingency learning and evaluation via the feedback associations, the underlying design rules involved in the IGT cannot be found and the subjects may prefer the disadvantaged decks (Starcke et al., 2010). In recent years, many researchers have regarded the IGT as a measurement of “contingency learning” and have suggested that contingency learning ability is related to performance on the IGT (Dymond et al., 2010; Fellows and Farah, 2005). This notion is based on the fact that initial selections from some decks (i.e., Deck B and D) produce reward, followed by loss after several selections. Intact contingency learning abilities can be considered a basis for profitable long-range decision making processes in the IGT because stimulus–selection–outcome associations must be identified and learned appropriately to identify the selections that will lead to the greatest gains and the fewest losses (Vanes et al., 2014). The OFC, in particular, has been frequently implicated in contingency learning (Tsuchida et al., 2010). Human imaging studies have found that the OFC is involved in relearning and re-evaluating contingencies (Windmann et al., 2006). Many human lesion studies have shown that lesions to the OFC are related with a deficit in the relearning of contingencies. Such lesions are associated with alterations in the learning of associations between choice selections and reward outcomes, manifested chiefly by perseverative behavior to previously rewarded stimuli following the reversal of reinforcement contingencies (Hornak et al., 2004; Klanker et al., 2013). Similarly, OCD patients and their UFDR have both structural and functional abnormalities within the OFC (Chamberlain et al., 2008) and, therefore, it is not difficult to understand their successive choices of the disadvantaged decks in the IGT in the current study.

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Contrary to the IGT, performances on the GDT showed no significant differences between the three groups. Several studies have shown that performance on the GDT is correlated with executive functions such as set-shifting, cognitive flexibility and categorization as measured by the WCST. There is further evidence that poor decision making in the GDT is linked to deficits in executive function (Brand et al., 2009; Euteneuer et al., 2009). Furthermore, several studies have related impaired decision making in the GDT to poor capacities to advantageously utilize feedback processing (Brand et al., 2007; Delazer et al., 2007). In the current study, the performances on the WCST did not differ between the OCD patients, the UFDR and the CS. Additionally, the three groups of participants showed no significant differences on processing feedback, using a loss (negative feedback) after a risky decision to choose a safe option or using a gain (positive feedback) after a non-risky decision to choose a safe option again. In other words, the three groups showed similar decision strategies or patterns of performance. Previous studies have suggested that deficits in decision making may qualify as an endophenotype candidate for OCD. However, the current study went a step further to simultaneously assess decision making under ambiguity and decision making in risky situations, and showed that dissociation of decision making under ambiguity and decision making under risk was a more appropriate potential neurocognitive endophenotype for OCD. Another issue needs to be further discussed. Although the feedback processing implicated in the IGT seems to be impaired in the OCD patients, the feedback processing involved in the GDT is intact. Indeed, this apparent inconsistency has been discussed in many articles (Brand, 2008; Starcke et al., 2009). In fact, there is a difference between the feedback processing implicated in the IGT and the feedback processing involved in the GDT. While the subject has no effective information (winning probability and the number of gains or losses) about the feedback in the IGT, explicit information about the potential consequences of various choices and their probabilities are provided in the GDT. Therefore, the subjects may have different psychological expectations for the consequences and may have different psychological reactions even under the condition of receiving the same feedback. Furthermore, a study has provided evidence that the feedback processing implicated in both tasks is different in the OCD patients according to the skin conductance responses (SCRs) (Starcke et al., 2009). The somatic marker hypothesis postulates that decision making under ambiguity is guided by somatic markers. The healthy participants showed high elevations after disadvantageous selections and after losses (namely, infrequent large loss resulted in the highest SCRs), but these effects were not observed in the OCD patients. The patients' performances were equal to those of the healthy participants in the GDT, and the SCR patterns were consistent with the behavioral data (Starcke et al., 2009). Another finding is related to this issue. The two tasks share two important components: feedback processing and executive functions. However, there is at least one difference between the IGT and the GDT. While using the feedback is more important than executive functions in the IGT for determining the rules, executive functions seem to be more important for comprehending the explicit rules and forming and utilizing some appropriate strategies in the GDT (Brand, 2008). Therefore, we were not surprised with the result that the OCD patients had impaired performance in the IGT but had intact performance in the GDT. Additionally, the OCD patients and their UFDR had deficits in planning, which is in agreement with the findings of Delorme et al. (2007) and Rajender et al. (2011). Another study that used the Tower of Hanoi test, which is analogous to the ToL test, showed similar results (Cavedini et al., 2010). Our study demonstrated that deficits in planning may represent a neurocognitive endophenotype for OCD. Functional neuroimaging studies have provided evidence that planning activity is correlated with frontal–striatal circuitry and especially the dlPFC and that task complexity is associated with the level of activity of the same

regions (Schall et al., 2003; van den Heuvel et al., 2003). In the present study, the OCD patients showed planning deficits on the ToL test, but this task is dlPFC dependent. How can this be reconciled with the hypothesis that the dlPFC is not affected, which we assumed is supported by the intact performance on the GDT. The association between dlPFC functioning and OCD has been a topic of debate. Changes in dlPFC activity have also been observed but have not been consistently replicated. Neuropsychological and neuroimaging studies emphasize dysfunctions of the OFC (Friedlander and Desrocher, 2006; Menzies et al., 2008), but the findings of dlPFC functioning are inconsistent (van den Heuvel et al., 2005; Whiteside et al., 2004). In this regard, we assumed that dlPFC functioning is unimpaired in OCD patients in the current study. Although the patients' performance on the GDT and the WCST were intact, the OCD patients showed planning deficits on the ToL test in the present study. The three tasks are associated with executive functioning and are primarily dependent on the dlPFC functioning (Labudda et al., 2008; Lie et al., 2006; Schall et al., 2003). Therefore, these findings are not consistent with our hypothesis, and we cannot hastily conclude whether dlPFC functioning in OCD patients is impaired. Further work is required to examine these inconsistent results more closely and to determine their causes. In addition, the WCST is of particularly note. The OCD patients and their UFDR in our study performed as well as the CS on the WCST in accordance with the study by Viswanath et al. (2009) but not in accordance with the study by Cavedini et al. (2010) or the study by Rajender et al. (2011). In a study with a large sample of twins, Kremen et al. (2007) found that the polychoric intrapair correlations for six WCST scores were relatively low between monozygotic (MZ) and dizygotic (DZ) twins and that there were no significant MZ–DZ differences, which signifies that whether the WCST is a good choice for use as a neurocognitive endophenotype test deserves further study. Additionally, there were no significant correlations between the Y–BOCS scores and the neurocognitive performances. The result implies that the neurocognitive impairments observed are more due to trait factors than state factors (Roopesh et al., 2013). Therefore, impairments in decision making under ambiguity and planning appear to be state-independent, and we will do more work in remitted OCD patients in the future to test whether these impairments are trait markers. Some similar studies have found that deficits in set shifting and inhibition are present in both symptomatic and remitted OCD patients (Bannon et al., 2006; Rao et al., 2008). Some limitations of the study should be considered. First, the current design for the endophenotype model did not include twins, and the similarity between the OCD patients and their UFDR could be a function of genes and/or the environment. Thus, our findings cannot be attributed to genes or a shared environment in particular. Second, achieving the IGT successfully has been suggested to depend on emotional processing supported by physiological measurement during task performance (Cavedini et al., 2012; Starcke et al., 2009). The issue whether feedback associations in our study are also regulated by emotions should be investigated in further studies measuring subjects' emotional reactivity during the task (through skin conductance responses, heart rate, or pupil dilation). Our study of medication-naïve, non-depressed OCD patients and unaffected siblings of OCD probands suggests that dissociation of decision making under ambiguity and decision making under risk and planning deficits are potential endophenotype markers for OCD. Further work is required to confirm our findings by using methods related to genomics and frontal–striatal circuits abnormalities (structural, functional, neurochemical, and electrophysiological). Researchers should also pay attention to evaluating the clinical consequences of planning deficits. Among the UFDR of patients who share similar endophenotypes, some eventually develop into patients with overt symptoms, but others do not. In future studies, researchers should seek not only the similarities but also the differences between OCD patients and their UFDR in neurocognitive domains.

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Acknowledgments This work was supported by the Natural Science Foundation of China (91232717, 31000503, 31100812). We are deeply grateful to all the patients, their relatives, and healthy controls who participated in this research. We are also grateful to Dr. Matthias Brand for providing the GDT.

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