Are candidate neurocognitive endophenotypes of OCD present in paediatric patients? A systematic review

Journal Pre-proof Are Candidate Neurocognitive Endophenotypes of OCD Present in Paediatric Patients? A Systematic Review Aleya A. Marzuki, Ana Maria Frota Lisboa Pereira de Souza, Barbara J. Sahakian, Trevor W. Robbins

PII:

S0149-7634(19)30908-X

DOI:

https://doi.org/10.1016/j.neubiorev.2019.12.010

Reference:

NBR 3623

To appear in:

Neuroscience and Biobehavioral Reviews

Received Date:

4 October 2019

Revised Date:

1 December 2019

Accepted Date:

6 December 2019

Please cite this article as: Marzuki AA, Pereira de Souza AMFL, Sahakian BJ, Robbins TW, Are Candidate Neurocognitive Endophenotypes of OCD Present in Paediatric Patients? A Systematic Review, Neuroscience and Biobehavioral Reviews (2019), doi: https://doi.org/10.1016/j.neubiorev.2019.12.010

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Title Are Candidate Neurocognitive Endophenotypes of OCD Present in Paediatric Patients? A Systematic Review.

Authors Aleya A. Marzukiab* [email protected] Contact Number: +44(0)1223764422 Ana Maria Frota Lisboa Pereira de Souzaab [email protected]

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Barbara J. Sahakianc [email protected] Trevor W. Robbinsab [email protected]

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Bold indicates family name

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*Indicates corresponding author

Affiliations a

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Behavioural and Clinical Neuroscience Institute, University of Cambridge, CB2 3EL, Cambridge, UK Department of Psychology, Downing Site, University of Cambridge, CB2 3EB, Cambridge, UK

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Herchel Smith Building, Department of Psychiatry, University of Cambridge, CB2 0SZ Cambridge, UK

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b

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Highlights

Action monitoring, decision-making, and planning is affected in child-OCD.

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Patients show aberrant activation of cortico-fronto-striatal regions.

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Less evidence for cognitive flexibility, response inhibition, and memory deficits.

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Abstract To-date it has been difficult to ascertain the exact cognitive profile of childhood OCD as studies report variable results. Adult OCD research lately utilises the endophenotype approach; studying 1

cognitive traits that are present in both patients and their unaffected first-degree relatives, and are thought to lie closer to the genotype than the full-blown disorder. By observing whether candidate endopenotypes of adult OCD are present in child patients, we can determine whether the two subtypes show cognitive overlap. We conducted a systematic review of the paediatric OCD literature focussing on proposed neurocognitive endophenotypes of OCD: cognitive flexibility, response inhibition, memory, planning, decision-making, action monitoring, and reversal learning. We found that paediatric patients present robust increases in brain error related negativity associated with

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abnormal action monitoring, impaired decision-making under uncertainty, planning, and visual working memory, but there is less evidence for deficits in other cognitive domains. This implies that children with OCD show some cognitive similarities with adult patients, but other dysfunctions may

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only manifest later in the disorder trajectory.

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Keywords: Obsessive-compulsive disorder; paediatric; cognition; endophenotype; neuroimaging; development

1.

Introduction

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Obsessive-compulsive disorder (OCD) is a complex, heterogeneous neuropsychiatric disorder with multiple genetic, epigenetic, and environmental factors contributing to its development (Pauls et al.,

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2014). This is reflected in OCD having high comorbidity rates with other psychiatric disorders (Insel

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et al., 2010; Ruscio et al., 2010) and having distinct symptom dimensions which require tailored treatment (Mataix-Cols et al., 2002, 2005). Crucially, clinical presentation of the disorder appears to differ depending on age of onset (Kalra & Swedo, 2009; Mancebo et al., 2008; Taylor et al., 2011). To date, the distinctions between early-onset and late-onset OCD have not been satisfyingly explained. An extreme view is that the two should be simply classified as distinct psychiatric disorders as there is little developmental continuity from one subtype to the other (Farrell, et al., 2

2006; Geller et al., 2001). Furthermore, on average, only 41% of OCD diagnoses in childhood persist into adulthood (Stewart et al., 2004). Neuropsychological research into patients with early- vs lateonset OCD has indeed uncovered divergence in cognitive features of the subtypes (Gousśe et al., 2005; Hwang et al., 2007; Roth et al., 2005). Nonetheless, evidence has emerged suggesting that early-onset OCD in adults may not be equivalent to OCD seen in children. A comparative study reported more learning disabilities and lower rates of obsessions and compulsions in children with OCD contrasting with early-onset adults (Sobin et al., 2000). This highlights the necessity for

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research into how and why children with OCD differ from adults with OCD. A recent meta-analysis by Abramovitch et al. (2015) concluded that children with OCD show fewer to no deficits on cognitive domains including executive function, attention, memory and processing speed, which is

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in contrast to adults with OCD who tend to show widespread impairments across these domains

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(Abramovitch et al., 2013; Shin et al., 2014; Snyder et al., 2015).

Recently, studies have utilised the endophenotype approach to identify cognitive traits of clinical

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significance to OCD. An endophenotype is a heritable quantitative trait associated with increased genetic risk for a disorder (Chamberlain & Menzies, 2009). In order for a trait to be classified as an

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endophenotype, it must be associated with a specific disease or disorder in the population, be heritable, be “state independent” (manifest in an individual regardless of the disease being active),

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be inherited together with the disease, and occur in non-affected family members at a higher rate than the general population (Gottesman & Gould, 2003). Research has then explored possible

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endophenotypes of OCD by testing samples of OCD patients and their unaffected first-degree relatives (UFDRs).

Chamberlain and Menzies (2009) have proposed that the following neurocognitive domains serve as potential endophenotypes: motor inhibition, cognitive flexibility, decision-making, action monitoring, reversal learning (linked to orbitofrontal cortex (OFC) dysfunction) and memory. Empirical research has corroborated these claims, revealing impairments for patients and their 3

UFDRs in these domains (see Table 1 for empirical studies investigating endophenotypes of OCD in patients and their UFDRs). Adding to this list, impaired goal-directed planning has similarly been identified as a candidate neurocognitive endophenotype (Bey et al., 2018; Cavedini et al., 2010; Delorme et al., 2007; Li et al., 2012; Vaghi et al., 2017; Zhang et al., 2015). However, endophenotype OCD research seems limited to adult patients, making it difficult to generalise these findings to young people with OCD. Nonetheless, we can use these candidate markers to frame our understanding of the neuropsychological distinctions between paediatric and

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adult OCD. As endophenotypes are supposedly linked to genes underlying the disorder (Chamberlain & Menzies, 2009), we can deduce genetic overlap between the two developmental categories of OCD if they share these neurocognitive traits. There is slight evidence for a role of gene expression in

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OCD developmental subtypes; Bloch et al. (2008) reported that the long allele of the 5-HTTLPR

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polymorphism of the serotonin transporter gene is associated more with OCD in childhood compared to adulthood, while Taylor et al. (2011) found early onset OCD to be associated with greater

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prevalence in first degree relatives, suggesting onset is linked to different rates of heritability. To our knowledge, ours is the first systematic review to investigate cognitive features associated

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with paediatric OCD based on known candidate neurocognitive endophenotypes of the disorder. Past systematic reviews tended to include very few studies due to strict eligibility criteria. For example,

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Abramovitch et al. (2015) excluded studies with novel or adapted cognitive tasks while Brem et al. (2011)’s meta-analysis only included papers with neuroimaging data, culminating in only 11 and 15

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studies included respectively. We intended to acquire a more comprehensive scope of the paediatric OCD literature and hence incorporated studies using novel and adapted tasks, and a mixture of neuroimaging and cognitive/behavioural methods. Ultimately, we aimed to explore whether the following neurocognitive functions are impaired in paediatric OCD patients: inhibition, cognitive flexibility, decision-making, memory, planning, action monitoring, and reversal learning.

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2.

Method

We performed a systematic review of the literature in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines (Moher et al., 2016). Our protocol was registered on PROSPERO (registration number: CRD42019129636) in April 2019. 2.1 Inclusion/Exclusion Criteria

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Papers were included in the review if they fulfilled the following inclusion criteria: 1) published in a peer reviewed journal in the last 20 years, 2) contained at least one group of patients with a primary diagnosis of OCD, 3) included paediatric patients, and 4) measured one or more cognitive functions

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of interest using neuropsychological tasks that are specified to measure the cognitive functions in question. Among the reasons for papers to be excluded were having no healthy control group, only

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studying adult patients, measuring no neurocognitive domains of interest, studying only non-patient

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groups (e.g. subclinical OCD) and having duplicate data from other published studies already included in this review (Figure 1 outlines the selection process).

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2.2 Search Strategy

A systematic review of the relevant literature was performed using the Pubmed library, PsycINFO

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via EbscoHost, and Google Scholar. Citations were extracted from Google Scholar and stored using the Publish or Perish software (Harzing, 2007). Additional references were also included via scanning

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the reference lists of eligible studies and through hand searches. Eligible studies were included from the last 20 years up to the 7th of April 2019, which was the date of the last search performed. Authors 1 and 2 conducted the searches independently. Any differences in study inclusion were thoroughly discussed until an agreement was reached between both authors.

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The search terms for describing the disorder included “obsessive-compulsive disorder” and “OCD”, while the terms for describing the demographic of the sample included “adolescent”, “paediatric” and “children”. Our search also included the following terms for general neurocognition: “neurocognition”, “neuropsychological”, “executive function”, “cognition” and “cognitive. Search terms for specific neurocognitive functions were also used, namely: “inhibition”, “inhibitory control”, “cognitive flexibility”, “set-shifting”, “decision-making”, “memory”, “working memory”, “visuospatial memory”, “learning”, “error processing” “conflict monitoring”, “error monitoring”,

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“prediction error”, “feedback sensitivity”, “performance monitoring”, “error-related negativity”, “ERN”, “reversal learning” and “orbitofrontal cortex”. 2.3 Data Extraction

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Information extraction was conducted by author 1 and double checked by author 2. The following

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information was recorded: 1) participant characteristics (number of patients and controls, mean ages, age ranges, OCD severity scores, comorbid psychiatric disorders, medication taken by participants), cognitive

function(s)

measured,

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neuropsychological

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2)

tasks

used,

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neuroimaging/electrophysiology methods used (if any), and 5) key findings. If any papers were

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inaccessible, we contacted the authors of the papers retrieve them. All demographic information can

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be found in Table 2 while results from studies are summarised in Table 3.

3.

Results

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The systematic searches initially generated 4190 titles, which were reduced to 2921 following the removal of duplicates. One additional paper was found via hand searches. After scanning titles for relevance, 300 full-text articles were assessed for eligibility. In total, 43 papers were selected for this review. Several papers studied more than one cognitive function, for example Hybel et al. (2017) explored cognitive flexibility, response inhibition, memory and planning. The selected papers investigated cognitive flexibility (N=19), response inhibition (N=17), memory (N=15), action 6

monitoring (N=13), planning (N=5) and decision-making (N=7). There were no studies investigating reversal learning related to OFC functioning in paediatric OCD patients. All studies were crosssectional. Twenty-four studies comprised neuroimaging techniques, including functional magnetic resonance imaging (fMRI), electroencephalography (EEG), Near-Infrared Spectroscopy (NIRS) and Diffusion Tensor Imaging (DTI). Sample characteristics and results for each study are reported in Table 2 and Table 3 respectively. A summary of the studies’ neuroimaging findings is presented in

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Figure 2.

Study Population

The overall sample size was 1228 for patients with OCD and 1425 for healthy controls. Sample sizes

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ranged from 10 to 102 for patients and from 9 to 161 for controls. Four studies (Carlisi et al., 2017;

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Norman et al., 2018; Vloet et al., 2010; Woolley et al., 2008) included only males in their sample, whereas Fitzgerald et al. (2013) included only females. All other studies included mixed-gender

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samples. One study, (Baykal et al., 2014), did not report any gender information. Three studies (Carrasco et al., 2013a; Hanna et al., 2012, 2016) included OCD patients in remission. One study

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(Hanna et al., 2012) included patients with tic-related and non-tic related OCD. Carrasco et al. (2013b) included a sample of UFDRs of OCD patients alongside their patient and control samples.

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The mean ages of the patient and control samples respectively were 13.5 (±1.55) and 13.4 (±1.63).

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One study (Baykal et al., 2014) did not report the mean ages of their sample. Participant ages ranged from 6 to 19 years in all studies except one which included patients aged up to 21 years (Diwadkar et al., 2015). Six studies (Beers et al., 1999; Erhan et al., 2017; Fitzgerald et al., 2018; Wolff et al., 2017, 2018; Yamamuro et al., 2017) did not report the age ranges of their participants. The majority of studies reported comorbid psychiatric disorders in their sample of OCD patients (N=27). Thirteen studies reported no comorbidities, while 4 presented no information about 7

comorbidities (Baykal et al., 2014; Diwadkar et al., 2015; Hajcak et al., 2008; Wolff et al., 2018). Many studies also reported a proportion of their patient sample were receiving medication treatment (N=32). Two studies (Baykal et al., 2014; Diwadkar et al., 2015) provided no medication information. Thirty-three studies reported Children’s Yale Brown Obsessive-Compulsive Scale (CY-BOCS) scores. One study (Shin et al., 2014) reported the Child Leyton Obsessional Inventory score and one other (Carrasco et al., 2013a) reported the Schedule for Obsessive-Compulsive and Other

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Behavioural Syndromes score. Other studies (N=8) reported no OCD severity scores.

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3.2 Cognitive Flexibility (N=19)

Cognitive flexibility is defined as the ability to adapt one’s attention to different tasks, strategies and

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stimuli which are relevant, while simultaneously disengaging from irrelevant stimuli (Abramovitch

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& Cooperman, 2015; Diamond, 2013). Papers included in this review have studied this function using set-shifting paradigms, such as the the Wisconsin Card Sorting Task (WCST, Grant & Berg, 1948)

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and the Intra-Extra Dimensional Set Shift Task (ID/ED, Downes et al., 1989). and perceptual switching paradigms such as the Trail-Making Task B (TMT-B, Reitan & Wolfson, 1985) and task-

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switching tests. There are key differences between the two paradigms: set-shifting involves shifting attention between different cognitive sets by learning from feedback to follow a certain rule (e.g. sort

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based on colour or shape), while perceptual switching requires shifting between attending to different perceptual properties of stimuli (e.g focus on numbers instead of letters), usually after being cued to switch.

The WCST involves sorting cards based on a specific rule that can change from time to time (e.g. from colour to shape). Children with OCD generally show poor performance on the WCST; young patients tended to be more perseverative, commit more overall errors, and complete fewer categories 8

compared to healthy controls (Baykal et al., 2014; Shin et al., 2008; Taner et al., 2011). This indicates that children with OCD are less able to direct attention to task-relevant information. In addition, Andrés et al. (2007) found that paediatric patients also make more non-perseverative errors suggesting an issue with attention in general. Nonetheless, some included studies reported no OCDrelated impairment in children on the WCST (Beers et al., 1999; Geller et al. 2018; Gruner et al.,2011; Kodaira et al., 2012; Ornstein et al., 2010). Findings by Gruner et al. suggest that medication may affect performance on the task as they found that paediatric patients using Selective Serotonin

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Reuptake Inhibitors (SSRIs) completed fewer categories on the WCST while medication-naïve patients showed no impairment. There were no differences between medication and unmedicated patients on all other measures. Yet, Andrés et al. (2007) also checked for effects of medication but

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did not find any differences between medicated and unmedicated patients on the task.

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The ID/ED task dissociates the effects of intra-dimensional (ID) and extra-dimensional (ED) shifts which are implicit in the WCST (Rogers et al., 2000). ID shifts involve the formation of an attentional

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set towards a stimulus dimension (e.g. shapes), and then ‘shifting’ between test stimuli within the same dimension. ED shifts involve attending to a new stimulus dimension (e.g. lines) that was

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previously irrelevant. Gottwald et al. (2018) discovered that adolescents with OCD made more errors compared to controls in the pre-ED shift portion of the task, which includes discrimination and

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reversal learning (ID shifts), but not attentional set shifting (ED shifts). In contrast, Kim et al., (2018) reported that children with generalised anxiety disorder (GAD), and not OCD, showed poor learning

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in the pre-ED phase. However, when controlling for medication status, GAD children in Kim et al.’s study no longer had significantly more pre-ED errors than OCD children, suggesting an interaction between anxiety and medication on attentional learning. Different age ranges could also account for these differences as Kim et al. recruited children as young as 7 years old in their study, while Gottwald et al. only studied adolescents. Lastly, although Hybel et al. (2017) did not find a difference

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in ID/ED performance between OCD patients and controls they did not report pre- and post-ED scores in their analysis, so we are unable to draw firm conclusions from their findings. The TMT-B is a perceptual switching task requiring participants to alternate drawing lines between letters and numbers (e.g 1-A-2-B) (Salthouse, 2011). Studies in this review, aside from patients in Ornstein et al. (2010)’s study having slower reaction times, did not find any significant deficits on the TMT-B in children with OCD (Beers et al., 1999; Garcia-Delgar et al., 2018), and even studies that found OCD-related impairments on the WCST reported no significant performance deficits on

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the TMT-B (Andrés et al., 2007; Shin et al., 2008). This indicates that the WCST may be more sensitive to set-shifting deficits in OCD compared to the TMT-B. Nevertheless, Gruner et al. (2011), using DTI, uncovered brain white matter abnormalities in children with OCD pertaining to their task

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performance. Higher functional anisotrophy (more diffusion of water molecules along a tract) in the

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cingulum bundle, including the anterior cingulate cortex (ACC), was correlated with better performance on the TMT-B within only the patient group. It was inferred that this atypical diffusion

counterparts (Gruner et al., 2011).

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serves as a compensatory mechanism, allowing patients to perform similarly to their healthy

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Task switching tests are similar in implementation to the TMT-B, as they involve switching from attending to one feature of a stimulus (e.g. shape) to another (e.g. colour) when cued. Studies

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generally revealed no behavioural differences between paediatric patients and controls on such tasks (Britton et al., 2010; Wolley et al., 2008), however, Wolff et al. (2017) demonstrated that OCD

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patients had lower reaction times on memory-based switching (requires remembering when to switch tasks, e.g. switching between attending between ‘number’ and ‘shape’ every 15 trials) but not cuebased switching (switching attention when provided with a cue), suggesting patients’ cognitive flexibility deficits are modulated by working memory ability. Interestingly, in a separate study, adolescent patients were shown to be faster than controls at attending to previously abandoned mental sets during task switching (Wolff et al., 2018), suggesting that young OCD patients are impaired at 10

processing new information, but are able to flexibly reactivate old mental sets. Patients underactivated rostral brain regions, specifically the inferior frontal gyrus (Britton et al., 2010) and the inferior prefrontal cortices (Woolley et al., 2008) during task switching. Furthermore, OCD patients showed lower P1 event-related potential (ERP) amplitudes, as measured using EEG, during memory-based switching which corresponded to decreased activation in the right inferior frontal gyrus and temporal gyrus (Wolff et al., 2017).

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Summary Evidence from the WCST suggests that children with OCD may present cognitive inflexibility, however, it is uncertain whether medication status plays a role here. In spite of mixed behavioural

3.3 Response Inhibition (N=17)

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between patients and controls during task switching tests.

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results, studies employing neuroimaging reveal crucial prefrontal-cortical brain region differences

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Response inhibition refers to the ability to inhibit a pre-potent motor response, and impairment in this domain has been suggested to account for OCD patients being unable to stop repetitive rituals

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(Chamberlain et al., 2005). Tasks used to study inhibition in children with OCD include the Stroop Colour and Word Test (SCWT, Stroop, 1935), Stop-Signal Task (SST, Logan & Cowan, 1984),

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Go/No-Go (GNG) task, and the Continuous Performance Test (CPT). The SCWT entails reading different coloured words aloud and inhibition is measured via number of errors and reaction times when the word and the colour are incongruent (e.g. the word ‘purple’ written in the colour green). A few studies have found that children with OCD make more errors and have longer reaction times compared to healthy controls on the task (Baykal et al., 2014; Taner et al.,

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2011; Yamamuro et al., 2017). Furthermore, it was found that previously unmedicated patients still performed poorly on the task following treatment with SSRIs (Yamamuro et al., 2017). A majority of the studies, however, found no behavioural deficits on the SCWT (Andrés et al., 2007; Chang et al., 2007; Garcia-Delgar et al., 2018; Geller et al., 2018; Gruner et al., 2007; Ota et al., 2013). In fact, patients performed even better than controls in Beers et al.’s (1999) study. In spite of this, administration of this task alongside neuroimaging methods has shed light on key brain abnormalities in young OCD patients. Using functional near-infrared spectroscopy (fNIRs), patients displayed

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significantly low prefrontal haemodynamic activity during the SCWT (Ota et al., 2012; Yamamuro et al., 2017) even in the absence of performance impairments (Ota et al., 2012). Specifically, changes in oxyhaemoglobin levels occurred slowly in patients in the frontopolar region of the prefrontal

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cortex, an area that is implicated in higher order cognitive control (Boschin, Piekema, & Buckley,

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2015).

The GNG task, SST, and CPT generally measure the ability to inhibit a motor action following visual

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or auditory cues. Zandt et al., (2009) found that young OCD patients committed more inhibitory errors on a version of the SST known as the Walk-Don’t-Walk, in which participants have to draw

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lines along a path with a pen until they hear a tone cueing them to stop. Furthermore, on an emotional GNG task, OCD patients made more false presses (errors of commission) on No-Go trials (Waters

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& Farrell, 2014). Likewise, patients in Baykal et al.’s (2014) study tended to make more errors of commission on the CPT. Woolley et al. (2008) reported that young boys with OCD underactivated

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the bilateral OFC, right thalamus, and basal ganglia whilst completing a ‘Stop Task’, revealing an inhibitory control-related dysfunction in the cortico-striatal-thalamo circuit. Other studies reported no OCD-specific performance or reaction time deficits on these tasks (Beers et al., 1999; Gooskens et al., 2018; Hybel et al., 2017; Ornstein et al., 2010; Shin et al., 2008). Summary

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While there is some evidence for an inhibitory control deficit in children with OCD, the majority of studies do not describe significant impairments. Brain regions that seem to be associated with inhibitory control in patients include the frontopolar cortex, OFC, thalamus, and basal ganglia.

3.4 Memory (N=15) Broadly, studies in this review have investigated the following domains of memory: working

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memory, and long-term verbal, non-verbal and visuospatial memory in children with OCD. Working memory involves temporary maintenance and manipulation of information and is thought to underlie broad cognitive impairments in many psychiatric disorders (Gold et al., 2018). Children

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with OCD appear to have intact verbal working memory functioning, demonstrated by their proficiency on digit span tests (Andrés et al., 2007; Geller et al., 2017; Hybel et al., 2017; Shin et al.,

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2008; Taner et al., 2011). Nonetheless, Geller et al. (2017) proposed that children with OCD only

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show impaired working memory when under time pressure, as they found that patients performed poorly on a timed arithmetic test but not on the (non-timed) digit span test. More unconventional

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tests of working memory have garnered interesting results; Chang et al. (2007) found patients performed poorly on a spatial working memory task known as the Finger Windows test (Sheslow &

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Adams, 1990), which requires participants to remember the sequential placement (by an examiner) of a pencil into a series of holes in a plastic card. Next, Wolff et al. (2017) administered a task

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switching paradigm where participants had to assess, on screen, whether a numerical target was smaller or greater than 5 or whether a target was even or odd. Patients performed worse on the working memory portion of the task which required remembering when to switch tasks. This is indicative of children with OCD having specific deficits in visual working memory tasks but exhibiting intact digit span.

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Studies assessing other memory domains show conflicting results. On the one hand, widespread memory impairments across verbal, non-verbal, and visuospatial memory tasks have been found in children with OCD (Andrés et al., 2007; Gottwald et al., 2018; Ornstein et al., 2010), but many papers report no OCD-significant deficits in these domains (Beers et al., 1999; Chang et al., 2007; Geller et al., 2018; Hybel et al., 2017; Kim et al., 2018; Shin et al., 2008). Garcia-Delgar et al.’s (2018) paper provides fascinating insight into these opposing findings. Researchers administered various memory tests to 61 youths with OCD and divided the sample into ‘selectively impaired’ and ‘globally

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preserved’ groups based on their task performance. Visuospatial and non-verbal memory were most affected in the ‘impaired’ group while the ‘preserved group’ exhibited comparable performance to controls, indicating that youths with OCD can display vastly different presentations of memory

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ability. These discrete profiles could not be explained by demographic and clinical factors as there were no differences between the two groups on age, gender, OCD severity and medication status.

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could undermine the reliability of these findings.

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Nonetheless, the ‘selectively impaired’ group comprised a very limited sample of 9 patients, which

Two functional magnetic resonance imaging (fMRI) studies have clarified the brain activation

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profiles of paediatric OCD associated with memory ability. Diwadkar et al. (2015) found OCDrelated aberrant activation of frontoparietal regions (dorsal prefrontal cortex (dPFC), parietal lobe,

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middle frontal gyrus) that was modulated by dorsal ACC (dACC) activity during high and low working memory demands. The hyper-modulation by the dACC was proposed to reflect the

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inefficiency of control-related networks in paediatric OCD. Another claim is that children with OCD fail to employ cognitive strategies when encoding information (Batistuzzo et al., 2015). When asked to use a semantic clustering strategy to solve a Verbal Episodic Memory test, children with OCD revealed decreased activity in the bilateral dorsomedial PFC, superior frontal gyrus, right middle frontal gyrus, inferior parietal lobe, superior and middle temporal gyri and putamen (Batistuzzo et al., 2015). Moreover, semantic clustering scores correlated with episodic memory scores in controls 14

but not patients. The authors concluded that altered neural mechanisms underlie strategy implementation in children with OCD, that could account for differences in memory ability reported in other papers. Summary Similar to other functions described, findings for a memory impairment in children with OCD are contentious. Patients are not impaired on tests of verbal working memory such as the digit span test,

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but they face difficulties with visual working memory paradigms and timed tasks. In addition, it was found that children with OCD can be divided into two discrete cognitive profiles, namely those with intact cognition and those with impaired visuospatial and verbal memory ability, which could explain disparities in the results. Finally, performance on various memory tasks in young patients is driven

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by atypical activation of frontal, parietal, and striatal regions.

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3.5 Decision-Making (N=7)

Decision-making tasks generally test whether participants can make favourable choices with the aim

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of achieving rewards, points or positive feedback. Three studies investigated decision-making using gambling tasks, namely the Iowa Gambling Task (IGT, Bechara et al., 1994) and the Cambridge

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Gambling Task (CGT, Rogers et al., 1999). On the IGT, subjects have to learn over time to select from decks of cards that are advantageous (offer points without high risk of losses). Kodaira et al.

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(2012) reported children with OCD made more disadvantageous selections on the IGT and correspondingly, Norman et al. (2018) found that OCD patients showed underactivation of the ventromedial OFC when choosing advantageous choices on the IGT relative to controls and patients with Attention Deficit Hyperactivity Disorder (ADHD). Authors inferred that this underactivity meant patients with OCD were impaired at both learning from rewards and using stochastic information to guide their choices. Modelling the data revealed that patients with OCD explored 15

non-optimal decks more than other groups, suggesting a lack of confidence in their choices (Norman et al., 2018). Combined, results point towards OCD patients being intolerant of uncertainty, which is connected to possible OFC dysfunction (Norman et al., 2018). However, when tested on the CGT, children with OCD showed analogous performance to controls (Hybel et al., 2017). This is likely due to task differences; the IGT relies on implicitly learning to favour the deck that offers smaller rewards but is less risky, while the CGT has no learning component as it explicitly informs participants of choice pay-offs, and is considered a more straightforward test of risky decision-making. Hence,

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children with OCD show impaired decision-making under uncertainty but intact decision-making under risk.

Two computational modelling papers reveal further evidence of uncertainty intolerance in children

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with OCD (Erhan et al., 2017; Hauser et al., 2017). Hauser et al. (2017) administered the Information

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Sampling Task (Clark, Robbins, Ersche, & Sahakian, 2006) requiring participants to guess whether the majority of (initially) hidden cards are green or yellow. Participants are allowed to turn over

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cards, one-by-one, to reveal the colour underneath before making their decisions. When not penalised for turning over cards, patients turned over more cards than controls and also made more accurate

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judgements. Bayesian modelling revealed that all participants felt increasing urgency to make a decision the more they turned over cards, but OCD patients discounted subjective costs of taking

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longer to make a decision, such as fatigue, impatience and time. Erhan et al. (2017) likewise employed a Dot Discrimination task, a low level perceptual decision-making task requiring

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participants to decide whether clusters of dots are moving towards the left or right on a screen, with varying noise levels. Modelling of this data showed that patients took longer to accumulate evidence, and the length of time between accumulating evidence and executing responses was increased compared to controls. Both studies concluded that decision-making thresholds are abnormally high in paediatric OCD patients, reflecting their desire to be as certain as possible before making choices.

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Moreover, two studies administering variations of delay discounting tasks (Carlisi et al., 2017; Vloet et al., 2010) further show that children with OCD do not take time/delays into account when making decisions. Children with OCD performed similarly to controls on these tasks requiring them to choose between receiving small rewards now or bigger rewards later, while children with ADHD made more impulsive decisions, preferring smaller immediate rewards (Carlisi et al., 2017; Vloet et al., 2010). In addition, Vloet et al. (2010) found that OCD patients were impaired on a task assessing implicit learning, offering more evidence that children with OCD perform poorly under uncertainty.

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Summary

Decision-making is only impaired in children with OCD when tasks offer ambiguous information or when they involve implicit learning (such as the IGT). This could be primarily driven by patients

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having poor tolerance for uncertainty.

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3.6 Planning (N=5)

Studies here utilised “Tower” tests to investigate planning, which typically involve moving beads or

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disks from peg to peg with the aim of matching a model pattern as quickly as possible. These tests are considered planning tasks as it is assumed that planning a course of action is required to solve

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the task problems efficiently (Riccio et al., 2004). The following tasks were reported to probe planning ability: Tower of London (TOL, Shallice, 1982), Tower of Hanoi (TOH, Hofstadter, 1985),

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Delis-Kaplan Executive Function System (D-KEFS) Tower Test (Delis et al., 2001) and Stockings of Cambridge (SOC, Owen et al., 1995). Compared to controls, children with OCD required more moves to solve the SOC (Kim et al., 2018) and D-KEFS Tower Test (Ornstein et al., 2010), and used more time to solve the TOL (Huyser et al., 2010), revealing impaired planning ability. On the contrary, Hybel et al. (2017) and Beers et al. (1999) found equivalent performance across controls and patients on the SOC and TOH. However, 17

Hybel et al. did not explicitly report latencies in their analysis of the SOC, so we are unable to infer from their findings whether patients are truly unimpaired on the task. Intriguingly, Huyser et al. (2010) uncovered evidence for planning dysfunction being a state, as opposed to a trait, feature of paediatric OCD. Brain abnormalities, namely underactivation in the left posterior dorsolateral prefrontal cortex (DLPFC) and right parietal cortex, during planning ceased to be significant in patients following cognitive behavioural therapy (CBT). Moreover, following treatment, patients were able to solve the task faster. This indicates that disrupted planning ability is

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linked to OCD severity. It is conceivable that Hybel et al.’s (2017) and Beers et al.’s (1999) results were insignificant due to their sample not having severe OCD symptomatology compared to other studies’, a notion strengthened by both studies recruiting non-medicated subjects (whose OCD may

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not have been sufficiently critical enough to warrant medication)

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Summary

Children with OCD were found to react slower and require more moves to solve planning tests. Slow

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planning is also associated with underactivation of the DLPFC and parietal cortex, which normalises following CBT. However, evidence is very limited as only 5 studies reviewed here have investigated

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planning.

3.7 Action Monitoring (N=13)

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OCD has been frequently associated with overactive action monitoring, the generation of inappropriate error-detection signals, that gives rise to feelings of “wrongness” (Nieuwenhuis et al., 2005). Action monitoring has been studied by examining an event-related potential component of the brain known as error-related negativity (ERN). ERN is generated by a fronto-central negative deflection that occurs 100ms after the execution of an incorrect response during a forced-choice reaction time task (Endrass et al., 2008). 18

Studies in this review explored action monitoring in paediatric OCD using a combination of electrophysiological measures (electroencephalogram, EEG) or fMRI, and reaction time tasks namely the Flanker task (Eriksen & Eriksen, 1974), Multisource Inference Task (MSIT, Bush & Shin, 2006), and the Simon’s task (Simon & Wolf, 1963). All EEG studies reported enhanced ERN in paediatric patients, uncorrelated with symptom severity, medication status, or presence of co-morbid disorders (Carrasco et al., 2013a, 2013b; Hajcak et al., 2008; Hanna et al., 2012; 2016; 2018; Liu et al., 2014). One study even showed that UFDRs of

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children with OCD also display increased ERN (Carrasco et al., 2013b), hence promoting overactive action monitoring as a plausible endophenotype of paediatric OCD. fMRI studies equally report OCD-related aberrant activation in key frontal brain regions following task errors or high conflict

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trials, namely the ACC/dorsal ACC (Fitzgerald et al., 2010; Huyser et al., 2011), posterior medial

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frontal cortex (MFC, Fitzgerald et al., 2010; 2018), and DLPFC (Fitzgerald et al., 2013). Neural activity underlying action monitoring was furthermore found to be unchanged following cognitive-

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behavioural treatment (Huyser et al., 2011), demonstrating the plausibility of this function as a trait marker.

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One study investigated how abnormal action monitoring may manifest behaviourally. Liu et al. (2012) proposed that behavioural adaptation to conflicts/errors can be assessed by observing post-

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error slowing and post-conflict adaptation during reaction time tasks. Post-error slowing involves slowing down responses after an error in an effort to reduce future errors, while post-conflict

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adaptation involves speeding up following a correct response to an incongruent trial which indicates that participants have learnt to attend to relevant information. Compared to controls, youths with OCD did not display either of these adaptive responses during the MSIT, demonstrating deficits in adjustment of cognitive control when environments are volatile. Summary

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Overactive action monitoring, operationalised as increased ERN and abnormal activation in specific frontal regions (i.e the ACC) in response to errors is a promising stable trait of paediatric OCD. Overactive monitoring could be linked to an inability to adapt behaviour following mistakes and/or demanding problems. 4.

Discussion

To our knowledge, this is the first systematic review to clarify whether neurocognitive functions thought to be endophenotypes of adult OCD also apply to children with OCD. We reviewed 43

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paediatric OCD studies (with a combined sample of 1228 patients and 1425 controls) exploring one or more of the following functions: cognitive flexibility, response inhibition, memory, planning, decision-making and action monitoring (we did not find any papers studying reversal learning related

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to OFC dysfunction). Our findings indicate preliminary evidence for overactive monitoring being a

will be discussed below. Neurocognitive features of childhood OCD

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4.1.1 Action Monitoring

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4.1

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robust feature of paediatric OCD. We also found evidence for impairment in other domains which

Error sensitivity appears to be a marked feature of childhood OCD with all studies exploring action

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monitoring in this review revealing increased ERN during errors/conflict trials on response-conflict tasks. These findings are concurrent with adult OCD literature as a very recent meta-analysis by

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Riesel (2019) including 38 studies with samples of both adults and paediatric patients revealed that ERN was significantly increased in patients with OCD with moderately high effect sizes. Studies in our review and that of Riesel (2019) reported that enhanced ERN was not related to symptom severity, medication, and administration of behavioural treatment, suggesting the feature is a vulnerability marker and not a reflection of the symptoms. Furthermore, studies in our review described brain areas within the cortico-fronto-striatal circuits that are associated with OCD (Maia 20

et al., 2008) to also be implicated in error monitoring in young patients, namely the dACC, the dorsomedial PFC and the dorsolateral PFC (Fitzgerald et al., 2010, 2013; Huyser et al., 2011). This points to the role of the ACC and PFC in paediatric OCD pathophysiology, specifically with regard to action monitoring. Remarkably, heightened action monitoring and structural abnormalities of the ACC can predict onset of the disorder; Longitudinal research revealed that preschool children with a combination of increased action monitoring and smaller right dACC volume (Gilbert et al., 2018) had a significantly greater likelihood of developing OCD compared to children without these

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features. All in all, this is indicative of abnormal action monitoring being a shared trait of both adult and paediatric OCD, as well as a highly plausible endophenotype of the disorder.

Research in adult patients demonstrates that ERN is linked to internally generated error signals

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(Endrass et al., 2010), that do not diminish even when the observable threat/error has subsided (Riesel

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et al., 2012). This may explain patients’ propensity for avoidance and increased checking in daily life, even when threats are not tangible (Weinberg et al., 2016). Extreme fears of things going wrong

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and harm to oneself or others is likely to be maintained by abnormal ACC activity in response to such triggers. Additionally, poor cognitive adaptability to difficult situations could also play a role.

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Research into trial-by-trial conflict adaptation has posited that healthy people generally adapt their behaviour following errors, usually by slowing down their responses (Danielmeier et al., 2011),

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supposedly to reduce the risk of future errors. By contrast, many ERN studies in our review, with the exception of Hanna et al. (2012;2016;2018), did not detect a behavioural deficit or post-error

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slowness associated with increased ERN in patients (Carrasco et al., 2013a; Carrasco et al., 2013b; Fitzgerald et al., 2010; 2013; 2018; Hajcak et al., 2008; Huyser et al., 2011; Liu et al., 2014). In contrast, Liu et al. (2012) found that children with OCD, counterintuitively, sped up responses following errors, revealing a failure to employ appropriate cognitive control to improve their performance. Poor cognitive control has been noted as a feature of adolescent OCD (Gottwald et al.,

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2018) and in real life could contribute to patients feeling that they are ill-equipped to handle stressful or triggering situations, leading to hypothetical coping mechanisms such as avoidance or checking. Ongoing investigation into what drives ERN, suggests that anxious apprehension, or rumination regarding future events, is linked to increased error signals (Moser et al., 2013). Hence, enhanced ERN in OCD is proposed to be driven by anxiety in patients, a notion strengthened by both OCD and GAD patients and their UFDRs presenting increased ERN amplitudes (Riesel et al., 2019). Thus, enhanced ERN has been labelled a transdiagnostic marker of both OCD and GAD (see Riesel, 2019

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for review). However, if ERN is linked to anxiety, and ERN is an endophenotype of OCD, is anxiety an equally important biomarker for OCD? Anxiety is typically thought to not play a causal role in OCD development (Gillan & Sahakian, 2015; Robbins, Vaghi, & Banca, 2019), as children with

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OCD tend to not attribute their compulsions to any specific fear or worry (Gillan & Sahakian, 2015;

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Karno et al., 1988, Swedo et al., 1989), and anxiety disorders are not often associated with childOCD to the same extent as adult-OCD (Mancebo et al., 2008). We suggest that, in children, enhanced

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ERN precedes the development of anxious thoughts or even worry, demonstrated by a longitudinal study showing increased ERN in pre-school children long before the onset of an anxiety disorder

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later in life (Meyer et al., 2015). Hence, an exaggerated ERN driving constant monitoring of one’s behaviour may in fact be causal of worry and anxiety. This highlights the utility of ERN as a

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transdiagnostic marker as it can predict an individual developing either GAD or OCD. Clinically, this implies that nullifying enhanced ERN in childhood would prevent onset of OCD later on. Indeed,

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GABA (inhibitory)-ergic medication has been found to reduce the ERN in healthy people (de Brujin et al., 2004), and more recently, dopaminergic medication normalised enlarged cingulate error prediction signals in adult OCD patients (Murray et al., 2017). Longitudinal research needs to be conducted to investigate whether such treatment can reduce risk of OCD in otherwise healthy individuals with enhanced ERN. 4.1.2 Decision-Making 22

Overactive action monitoring may underlie apparent unusual decision-making patterns in paediatric OCD patients. Studies in this review revealed that children with OCD only struggle with decisionmaking when facing uncertainty. Adult patients are similarly intolerant of uncertainty, as they have been found to avoid choices in a decision-making task where the payoffs were unstable (Pushkarskaya et al., 2015). Furthermore, children with OCD were found to gather more information before making decisions on information sampling tasks (Erhan et al., 2017; Hauser et al., 2017a) which is also found in adults with OCD (Banca et al., 2015), revealing that both age groups are more

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cautious in their decision-making. Overactive action monitoring may drive patients to fear ambiguous contexts where errors are prone to occur, resulting in this cautious behaviour. However, in spite of being cautious, children with OCD also tend to persist making disadvantageous selections

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on decision-making tasks (Norman et al., 2018). This could again be linked to abnormal prediction error signals in patients; as these signals are internally generated, patients disregard external feedback

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more than controls, demonstrated by a study showing ERN is enhanced regardless of the magnitude

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of an error or punishment (Endrass et al., 2010). Hence, they appear more impaired at using feedback to drive their actions, and indeed adolescent and adult patients alike have shown a greater tendency to switch choices on a probabilistic reversal learning task, regardless of outcome associated with the

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choice (i.e rewarding or punishing) (Hauser et al., 2017b). Paediatric patients are likely more exploratory as a result of their need to be as certain as possible before committing to a choice.

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Incidentally, this could explain why paediatric patients appear to struggle with instrumental learning

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(Gottwald et al., 2018) and, as an extension, show lower school attainment than healthy children (Piacentini et al., 2004). As children with OCD rely less on feedback and more on internally generated signals, they are less goal-directed and will be slower at rule- or pattern-learning (Vloet et al., 2010). 4.1.3 Planning

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As only 5 studies have explored planning ability in children with OCD, we cannot yet draw any strong conclusions. Nonetheless, the few studies have revealed patients require more moves and more time to solve planning tasks such as the TOL and SOC (Kim et al., 2018; Ornstein et al., 2010). Moreover, underactivation of the posterior dlPFC and right parietal cortex was linked to poor planning ability in paediatric patients (Huyser et al., 2010). These findings are analogous to adult research, as Vaghi et al. (2017) found adult patients were slower completing the TOL and likewise displayed hypoactivation of the right dlPFC. Vaghi et al (2017) also noted similar brain

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activation patterns in UFDRs of OCD patients, indicating impaired planning ability to be a possible endophenotype. Hypoactivation of this region indicates less recruitment of regions implicated in goal-directed control. Furthermore, slowness in planning may again be related to higher decision-

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making thresholds and the need to meticulously monitor performance. Interestingly, Huyser et al. (2010) reported that planning ability improves and brain activity normalises following cognitive-

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behavioural treatment (CBT) in children with OCD, which is different from adult patients whose

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planning deficits/slowness persist following both drug treatment (Nielen & Boer, 2003) and CBT (Kuelz et al., 2006). In fact, children with OCD appear to improve in many different cognitive domains following CBT (Andrés et al., 2008). It could be that the brains of children are more plastic,

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and are thus more susceptible to behavioural treatment effects, compared to adults who appear to have entrenched deficits. A study, demonstrating this possible plasticity, revealed structural brain

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improvements (increased parietal grey and white matter) in children with OCD after

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pharmacological treatment compared to baseline (Lazaro et al., 2008). Additionally, children may be better at applying techniques learnt from CBT to improve strategizing and planning compared to adult patients, but this has yet to be investigated. 4.1.4 Memory As for working memory, one study in our review found hyperactive frontoparietal recruitment in children with OCD during the N-Back test (Diwadkar et al., 2015), which has previously been shown 24

in adult patients and their UFDRS (de Vries et al., 2014), revealing that this possible endophenotype of adult OCD is also present in paediatric patients. Both the child and adult studies concluded this hyperactivity to be reflective of inefficient memory processing, particularly as overactivation is prominent at lower working memory loads (Diwadkar et al., 2015; de Vries et al., 2014). Yet, only one study, according to our systematic search findings, has investigated this in paediatric patients. Nonetheless, adults (Moritz et al., 2002; Tallis et al. 1999; Krishna et al., 2011; Demeter et al., 2013) and children (Andrés et al., 2007; Geller et al., 2017; Hybel et al., 2017; Shin et al., 2008; Taner et

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al., 2011) are largely unimpaired on the digit span test, further revealing that children are equivalent to adults in this domain. Hence, paediatric and adult patients seem to have intact verbal working memory, but show dysfunctional frontoparietal activity during visual working memory tasks such as

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the N-Back test.

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However, our results do not support a reliable non-verbal memory deficit in children with OCD. In two meta-analyses exploring cognition in adult OCD, the biggest effect sizes across studies were for

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underperformance in non-verbal memory tasks, such as the RCFT, (Abramovitch et al., 2013; Shin et al., 2014), and yet RCFT performance appears mostly intact in paediatric patients (Chang et al.,

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2007; Geller et al., 2017; Ornstein et al., 2010; Shin et al., 2008; Zandt et al., 2009). Non-verbal memory is defined as the ability to recall information that is unrelated to words, such as faces, sounds,

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and images. Adult patients hence appear to struggle with recall of abstract concepts, which is unrelated to a visuospatial deficit, as they are not impaired on the copy portion of the RCFT, and

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they perform normally on the computerised CANTAB Spatial Working Memory task (Chamberlain et al., 2006; Morein-Zamir et al., 2010). This non-verbal memory deficit in adults could be associated with poor organisational abilities and an inability to employ cognitive strategies (Chamberlain et al., 2006), and could similarly contribute to their poor planning. It is proposed that children with OCD, by contrast, only show deficits on such tasks when there is time pressure (Geller et al., 2017), suggesting a processing speed deficit instead of a memory problem. Nonetheless, memory issues 25

could still impact children with OCD. Evidence has emerged for the existence of a subset of young patients with visuospatial and non-verbal memory deficits, distinct from other paediatric patients who show normative performance (Garcia-Delgar et al., 2018). A theory is that young patients with these memory impairments grow up to become similarly impaired adults, while cognitively-intact patients are more likely to recover from OCD before adulthood. This is purely speculative and once again requires longitudinal research to investigate this further.

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4.1.5 Response Inhibition and Cognitive Flexibility Lastly, cognitive flexibility and response inhibition are widely studied functions in adults with OCD. Regarding flexibility, adults with OCD tend to show more ED errors on the ID/ED Task (Chamberlain et al., 2006, 2007; Vaghi et al., 2017; Watkins et al., 2005), and more perseverative

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errors on the WCST (WCST (Aigner et al., 2007; Bucci et al., 2007; Cavedini et al., 2010; Lucey et

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al., 1997; Okasha et al., 2000; Paast et al., 2016; Tukel et al., 2012). For inhibition, adults with OCD are impaired at inhibiting responses on the SST (Chamberlain et al., 2006; 2007; Menzies et al., 2007;

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Morein-Zamir et al., 2010; Penades et al., 2007) and make more errors of commission on the SCWT (Peles et al., 2014; Penades et al., 2004; Penades et al., 2007). There is less evidence for GNG and

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CPT deficits, but adult patients have been found to make more commission errors during a punishment version of the GNG task (Morein-Zamir et al., 2013). Evidence for a cognitive flexibility

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and inhibition deficit in children with OCD is much less clear. Some studies report impaired WCST performance (Andrés et al., 2007; Baykal et al., 2014; Shin et al., 2008; Taner et al., 2011) but the

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majority of studies report no differences between child patients and controls on a variety of setshifting tasks (Andrés et al., 2007; Beers et al., 1999; Garcia-Delgar et al., 2018; Geller et al., 2018; Gruner et al., 2011; Hybel et al., 2017; Kim et al., 2018; Kodaira et al., 2012; Ornstein et al., 2010; Shin et al., 2008). Equally, some studies investigating inhibition in children with OCD have found impairments on the SCWT (Baykal et al., 2014; Taner et al., 2011; Yamamuro et al., 2017), GNG task (Waters and Farrell, 2014), SST (Zandt et al., 2009- version of the SST called Walk-Don’t26

Walk) and the CPT (Baykal et al., 2014), but more studies reported no behavioural deficits on these tasks (Andrés et al., 2007; Beers et al., 1999; Chang et al., 2007; Garcia-Delgar et al., 2018; Geller et al., 2018; Gooskens et al., 2018; Gruner et al., 2011; Hybel et al., 2017; Ornstein et al., 2010; Ota et al., 2013; Shin et al., 2008). This implies a cognitive segregation between adult- and child-OCD. Gottwald et al. (2018) proposed that juvenile OCD is more associated with a learning impairment as opposed to a cognitive flexibility issue, as demonstrated by patients’ poor performance on the preED portion of the ID/ED task, revealing that they were unable to learn instrumental responses

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necessary to form an attentional set. In spite of these mixed cognitive flexibility and inhibition results, task-related brain patterns in paediatric patients mirror those of adult patients. Children with OCD (Britton et al., 2010; Woolley

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et al., 2008) and adults (Gu et al., 2008; Vaghi et al., 2017) alike display decreased activation of

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prefrontal cortices and the inferior frontal gyrus when completing set-shifting tasks. As for response inhibition, child OCD patients in partial remission show reduced activation in the bilateral OFC and

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dlPFC during an fMRI version of the SST (Woolley et al., 2008), which are areas also implicated in adult OCD patients (Menzies et al., 2007; Page et al., 2009). This suggests that behavioural deficits

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in cognitive flexibility and inhibition are only displayed later in the disorder trajectory, but brain areas underlying these deficits are already functionally abnormal in children with OCD.

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4.2 Why are there differences between developmental subtypes of OCD?

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Thus far, we have inferred overactive action monitoring, impaired decision-making, slow planning (associated with dlPFC underactivation), and increased frontoparietal activity during working memory, to be shared cognitive characteristics of child and adult-OCD. Yet, there is still equivocation over whether children with OCD show other memory, cognitive flexibility, and inhibition deficits to the same extent as adult patients. The accepted rationale for endophenotype research is that characteristics regarded as endophenotypes are less complex, and lie closer to the

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gene action than clinical phenotypes, which make them useful for identifying genetic polymorphisms (and familial factors) associated with risk for the disorder (Gottesman & Gould, 2003; Cannon & Keller, 2006). If, hypothetically, paediatric and adult patients do not share certain candidate endophenotypes, can we infer separate genetic mechanisms for each disorder subtype? Should the two subtypes be then classified as separate disorders? This is unlikely, as we have demonstrated that both subtypes still share some neurocognitive traits. In this section, we aim to explore reasons behind the differences in neurocognitive profiles.

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4.2.1 Endophenotypes

First, presentation of endophenotypes may vary according to developmental changes or changes in the environment, as phenotypes associated with specific genes can differ depending on random

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environmental factors (Visscher et al., 2008). This can account for neurocognitive endophenotypes

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possibly changing from childhood to adulthood. Evidence for this has been found via epigenetic research, which is the study of changes in gene function that do not involve DNA changes. Epigenetic

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changes, for example DNA methylation of a gene, can be driven by the environment (Feil & Fraga, 2012). In one study, children with OCD displayed higher DNA methylation levels in the serotonin

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transporter gene (SLC6A4) compared to adults with OCD (Grunblatt et al., 2017), suggesting potentially altered gene expression in child compared to adult patients. Age of patients also correlated

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with decreasing SCL6A4 DNA methylation levels, revealing that gene variation stabilises with age. As serotonin is an important regulator of mood (Young & Leyton, 2002) and cognition (Buhot, 1997;

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Clarke et al., 2004), these developmental-based methylation patterns may explain why clinical and cognitive phenotypes are different for child and adult patients. We suggest that the criteria for endophenotypes be more clearly defined, and be able to account for the differential effects of genes and environment on the trait. Kendler and Neale (2010) have proposed several possible models defining the relationship between genes, endophenotypes, and

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psychiatric disorders. The two standard models are the mediational model and the liability-index model. The liability-index model is pleiotropic, in that it assumes a common set of genes independently codes for the endophenotype and the psychiatric disorder (see Figure 4). This model assumes that changes in either the endophenotype or the psychiatric disorder do not result in changes in the other component. On the other hand, the mediational model (see Figure 4) assumes that any variation in the disorder is affected by variation in the endophenotype (and potentially vice versa). Both models account for endophenotypes occurring in UFDRs as well as patients. Kendler and Neale

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suggest that the true relationship between components is likely more complex and we should not rely on any one model; multivariate models may be more appropriate. Hypothetically, a trait that does not correlate with symptom severity and is more resistant to treatment effects, such as overactive

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action monitoring (Huyser et al., 2011), could be explained by the liability-index model, while a trait that does improve following treatment, such as planning (Huyser et al., 2010), could be explained by

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the mediational model. A more complex model was also proposed accounting for the effects of

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environmental risk factors on endophenotypes and psychiatric disorders (see Figure 5), which could explain why it seems that some endophenotypes alter with age in OCD. The potential utility of an endophenotype which has a mediational relationship with the psychiatric disorder may enable it to

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be a target for treatment. For instance, the finding that OCD treatment improves planning in children and not always in adults, highlights the importance of early detection of the disorder, as we can use

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these endophenotypes as risk markers. However, these models so far are merely theoretical, and

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empirical research is needed to define true models of endophenotypes, in order to understand their utility in detection, diagnosis, and treatment of a disorder. 4.2.2 Functional and Structural Brain Differences

Another explanation is that children with OCD have not yet acquired the cognitive deficits associated with their abnormal brain activity, which is apparent from studies showing aberrant frontostriatal activation during conflict monitoring (Fitzgerald et al., 2010, 2013; Huyser et al., 2011), 29

set-shifting (Britton et al., 2010; Wolley et al., 2008), response inhibition (Ota et al., 2012; Woolley et al., 2008; Yamamuro et al., 2017), working memory (Diwadkar et al., 2015), and decision-making (Norman et al., 2018) in the absence of significant underperformance on cognitive tasks. Several reviews have concluded equivalent altered brain activity between paediatric and adult OCD patients (Brem et al., 2012; Maia et al., 2008; Norman et al., 2016), specifically in regions within the corticostriato-thalamo-cortico loops including the OFC, ACC, PFC, and basal ganglia. These findings imply that endophenotypes of OCD may be neurobiological instead of cognitive, which accounts for neural

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markers, such as ERN, being reliably abnormal in young patients, but not cognitive markers. Perhaps dysfunction starts off predominantly neural in paediatric patients but eventually culminates in cognitive impairment later in life, for instance reduced ACC volume precedes performance

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monitoring issues and the development of OCD in young children (Gilbert et al., 2018). We suggest

rather than exclusively on behavioural cognition.

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endophenotype research should focus more on neural abnormalities underlying cognitive deficit,

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Additionally, it could be that children with OCD have neural compensatory mechanisms in place that are protective (at first) against impairment. For example, higher functional connectivity along a

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white matter tract in the ACC was proposed to allow paediatric patients to perform normally on setshifting and inhibition tasks (Gruner et al., 2011). And yet, some findings indicate inefficient

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processing in young patients; for example, when faced with difficult working memory tasks, they over-recruit frontal and parietal regions modulated by the dACC (Diwadkar et al., 2015). Moreover,

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even though patients can use encoding strategies similar to healthy children during memory tasks, they do not activate essential brain areas associated with memory (Batistuzzo et al., 2015). Perhaps patients can overcome these deficiencies in brain functioning early on, potentially with neural compensation, but these neural resources deplete with age, causing an age-related decline in cognitive function. More research is needed to affirm the relationship between age, cognition, and

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brain activity in OCD through conducting longitudinal studies exploring brain and cognitive development in the disorder. Fundamental brain differences between child and adult patients could further contribute to these separate cognitive profiles. Many of these distinctions are structural, which contrasts with neuroimaging studies that show similar activity between developmental subtypes. A review by Huyser et al. (2009) reported predominant involvement of the globus pallidus and thalamus in paediatric OCD compared to adults who typically show OFC and caudate abnormalities. Volumetric

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paediatric studies indeed show larger grey matter density in the OFC compared to age-matched controls (Szeszko et al., 2008) while adult studies often report bilateral reductions in OFC volumes in OCD patients (Hoexter et al., 2012; Rotge et al., 2009; Atmaca et al., 2006; Atmaca et al., 2007;

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Szeszko et al.,1999). Moreover, reduced caudate volume is seen in adults (Bartha et al., 1998; Ebert

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et al., 1997; Luxenberg et al., 1988; Robinson et al., 1995) but not in children (Rosenberg et al., 1997; Szesko et al., 2004; Maia et al., 2008). Normal or increased volume in these areas may be protective

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against cognitive deficit in children with OCD, as the OFC is implicated in response inhibition (Godefroy et al., 1996; Humberstone et al., 1997; Garavan et al., 1999; Rubia et al., 1999, Rubia et

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al., 2000; 2001b) and possibly reversal learning (Rejminse et al., 2006), while the caudate has been recently found to be one of the regions linked to cognitive flexibility in OCD (Vaghi et al., 2017).

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Conversely, a more recent meta-analysis has uncovered that adults and youth with OCD, in fact, have similar grey matter volumes in the striatum (enlarged) and PFC (reduced), but adult patients

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displayed smaller grey matter volumes in the ACC and greater grey matter volumes in the cerebellum compared to child patients (Hu et al., 2017). We speculate that perhaps paediatric brains develop to become similar to adult patients’ brains over time. However, there is a shortage of research exploring whether brain structure predicts OCD development, as well as neuroimaging research directly comparing child and adult patients. As a result, it is impossible for now to know whether these structural impairments are a consequence of OCD or vice versa, and whether child patients eventually 31

acquire the impairments with age. One resting state study has reported that children with OCD showed reduced connectivity from the dorsal striatum and thalamus to the rostral and dorsal ACC (Fitzgerald et al., 2011), which was absent in older adolescent and adult patients, providing some insight into how the brain develops with age in OCD. However, patient sample sizes in this study were small, with only 11 children and 18 adolescents compared to 31 adults. Neuroimaging research studying adults with early-onset vs late-onset OCD also show mixed results and utilise small subject samples (see Taylor et al., 2011 for review).

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4.2.3 Age-related Deficits

A further significant argument is that children with OCD may only appear unimpaired when compared with healthy children, who display age-related deficits on these tasks. Research has

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established that child and adolescent brain regions, including frontal, parietal, striatal, and thalamic

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areas, continue to develop into adulthood (Giedd et al., 1999; Luna et al., 2001). While these regions are still maturing, healthy children perform non-optimally as compared with adults on inhibitory

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control (Bunge, Dudukovic, Thomason, Vaidya, & Gabrieli, 2002; Luna et al., 2001; Luna et al., 2004), cognitive flexibility (Crone et al., 2004), goal-directed learning (Decker et al., 2013), and

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visuospatial and verbal memory tasks (Sowell et al., 2001) while adolescents specifically will engage in increased risky decision-making (see Blakemore & Robbins, 2012 for review). Thus it is possible

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that some OCD-related impairments only become pronounced in adulthood, as patients are compared

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to healthy adults who have matured frontal lobes and developed executive functioning abilities. 4.2.4 Sample Characteristics

Medication could also explain the differences between young and adult OCD patients. Several studies reported their paediatric patients to be on medication, and the age at which an individual receives medication may affect cognition. According to Mancebo et al. (2008), paediatric patients received medication treatment at 10.5 years while adult patients tended to receive treatment much 32

later at 27.1 years. Medication taken at a younger age perhaps interacts uniquely with brain neurochemistry compared to treatment received in adulthood. After all, in early childhood and adolescence, the brain undergoes significant developmental changes in cortical and subcortical regions (Foulkes & Blakemore, 2018; Giedd & Rapoport, 2010) and may be more susceptible to the effects of medication that alter neurotransmitter function such as selective serotonin reuptake inhibitors. These changes may manifest as the drug being more effective in treating symptoms in younger patients, as adult-onset patients in comparison to juveniles tend to experience longer

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latencies to drug treatment (Mancebo et al., 2008). Hence, young patients with OCD may appear to exhibit little cognitive impairment as a result of improved drug response compared to adult patients. Furthermore, children with OCD exhibit different patterns of comorbidity compared to adults with

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OCD, displaying higher prevalence of ADHD and tics (Kalra and Swedo, 2009). Most studies in this review reported samples of patients with various comorbidities, for instance ADHD, which may have

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influenced results. Paediatric patients with comorbid ADHD may show cognitive performance that

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is unrepresentative of OCD as studies have reported divergent cognitive presentation between youths with OCD and youths with ADHD (Carlisi et al., 2017; Norman et al., 2018; Vloet et al., 2010).

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A less thought-provoking, albeit crucial, reason for the disparity in results is that there are simply more studies involving adult patients and with larger samples compared to children, which makes it

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more likely to detect significant effects in adult studies. It is to be expected that the combined power of all paediatric literature is not large enough to detect true effects, explaining the low effect sizes

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reported by meta-analyses (Abramovitch et al., 2015). More recently, studies have started expanding their patient samples to include up to 50 (Hybel et al., 2017), 61 (Garcia-Delgar et al., 2018) and even over 100 participants (Geller et al., 2017), which is a vast improvement from earlier studies that only included samples of 10 to 15 patients (for instance Ornstein et al., 2010 and Woolley et al., 2008). Nevertheless, more research is needed with larger samples within the realm of paediatric OCD, in order to truly understand cognition in this population to the same extent as adult OCD. 33

4.3 Task differences

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Studies in the paediatric OCD literature exhibit varied results, despite exploring the same cognitive functions. Aside from differences including sample size, comorbidities, and medication usage, we propose that the disparity in results could be largely due to differences in the tasks used.

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4.3.1 Cognitive Flexibility

First, different set-shifting tasks probe different aspects of cognitive flexibility. The TMT-B has

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been described as a test of complex visual scanning with a motor component (Lezak, 1995; Shum,

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McFarland, & Bain, 1990). Motor speed and agility have been found to make a strong contribution to success on this task (Lamberty et al., 1994; Schear & Sato, 1989). This clarifies why children with OCD have been shown to be slower on the task (Ornstein et al., 2010), but otherwise perform well.

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In contrast, the WCST requires searching and learning from feedback to drive action. The WCST may also be a more complex task, tapping into visual working memory functions as participants have

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to keep in mind 3 different rules, namely shapes, colours, and numbers, and switch between rules

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accordingly, while the TMT-B only requires shifting attention between numbers and letters. Hence, more paediatric OCD studies have found WCST deficits compared to the TMT-B (Andrés et al., 2007; Baykal et al., 2014; Shin et al., 2008; Taner et al., 2011). The ID/ED task is unique as it isolates the effects of ID and ED shifts, which is crucial as there is evidence for separate cortical and subcortical pathways underlying each type of shift (Rogers et al., 2000). Only 3 studies to date have administered the task to children with OCD and compared them to healthy controls. It appears that

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children with OCD make more errors on the pre-ED phase of the task (Gottwald et al., 2018), suggesting they have difficulties at the learning stages of forming an attentional set. This could explain why some studies find children with OCD make more overall errors on the WCST (Baykal et al., 2014; Shin et al., 2008; Taner et al., 2011) and not just perseverative errors which would specifically indicate a cognitive flexibility issue. 4.3.2 Planning

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For planning, not all ‘Tower’ tasks investigate this function similarly. While the goals for the TOH and TOL are identical, they are tapping into different psychological processes. The SOC is an isomorph of the TOL as both provide explicit restrictions on the numbers of moves and require participants to match a pattern based on colours of discs/balls. In turn, the DKEFS Tower Test and

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TOH both involve arranging discs based on colour AND size (larger discs cannot be on top of smaller

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discs) with no specified limit to the number of moves. The TOH and its isomorphs are considered learning tasks (Schiff & Vakil, 2015) as performance is at first generally poor but improves over time

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as a function of practice, likely a reflection of the implicit/procedural learning required. Variations of the TOL, in contrast, are not as dependent on implicit learning to solve, and may be more

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straightforward tests of goal-directed planning. Furthermore, there is very little shared variance between tasks, with 84% performance variance unshared between the TOH and TOL (Welsh et al.,

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1999), and only 22% shared between the TOL and D-KEFS Tower Task (Larochette et al., 2009). Our findings reveal that children with OCD are impaired on both skill learning on the D-KEFS

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(Ornstein et al., 2010), and goal-directed planning on the SOC and TOL (Huyser et al., 2010; Kim et al., 2018). As a result of these task distinctions, similar results (e.g. requiring more moves to solve the task) on different Tower tasks do not necessarily translate to the same type of cognitive impairment. 4.3.3 Response Inhibition

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Within the domain of response inhibition, the SCWT differs from other standard inhibition tasks as it measures interference control, that is the ability to attend to one feature (i.e the colour of a word) while simultaneously blocking out conflicting information (i.e the word itself). Young patients may present difficulties with the task (Baykal et al., 2014; Taner et al., 2011; Yamamuro et al., 2017) as a result of their abnormal action monitoring when processing conflicting information. The GNG and SST are more standard response inhibition tasks, measuring action restraint and action cancellation respectively (Eagle, Bari, and Robbins, 2008). Healthy adolescents and children tend to show

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underperformance on these inhibition tasks (Bunge et al., 2002; Luna et al., 2001), which could account for most studies finding no differences on the tasks between paediatric patients and healthy children (Beers et al., 1999; Gooskens et al., 2018; Hybel et al., 2017; Ornstein et al., 2010; Shin et

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al., 2008). Hence, the SCWT may be more sensitive in uncovering paediatric OCD-specific deficits,

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compared to other response inhibition tasks. 4.3.4 Other Functions

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Briefly, as it has been described elsewhere in this review, decision-making tasks with a learning component, such as the IGT, are more sensitive to deficits in children with OCD (Kodaira et al.,

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2012) compared to decision-making tasks, such as the CGT, where choice pay-offs are explicit and do not have to be learnt (Hybel et al., 2017). Lastly, visual working memory tasks (Chang et al.,

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2007; Wolff et al., 2017) and timed tasks (Geller et al., 2017) are more likely to also reveal significant working memory impairments in children with OCD compared to digit span tests (Andrés et al.,

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2007; Geller et al., 2017; Hybel et al., 2017; Shin et al., 2008; Taner et al., 2011). In sum, it is important to be aware of the conclusions that can be drawn about neuropsychological dysfunction in psychiatric patient groups when using certain cognitive tasks. Many of the tasks tap into various cognitive functions, making it hard to isolate impairments specific to a disorder. Furthermore, some tasks that claim to be measuring one function may indeed be measuring a

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completely different construct, for example the TOH measures procedural learning and not necessarily planning. There is, therefore, a need for a greater precision in defining the cognitive components of tasks in order to study specific cognitive processes.

4.4 Further Research

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In order to truly affirm whether these proposed neurocognitive endophenotypes of OCD are present in children with OCD, we need far more research studying child patients and their UFDRs. Furthermore, more resources should be channelled into investigating whether these traits are

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associated with any candidate genes, in order to establish how the endophenotypes interact with genes, the environment, and the disorder. For instance, Iacono et al. (2014) linked several candidate

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psychophysiological endophenotypes of substance-use disorder, schizophrenia, and mood disorders

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such as electrodermal activity, P3-event related potential amplitude, and eye tracking performance to single nucleotide polymorphisms (SNPs) in the genotype. An interdisciplinary approach is ideal,

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combining standard UFDR studies with various genetic approaches, including behavioural genetics (twin studies), imaging genetics, molecular genetics, and Genome Wide Association studies

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(GWAS). This will enable us to identify which model(s), as proposed by Kendler and Neale (2010), best suit the endophenotypes being studied.

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We have also established the crucial need for more longitudinal research, exploring how brain and cognitive function change across the lifespan in OCD, which will enhance our understanding of reasons for neurocognitive differences between child- and adult-OCD.

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5. Conclusions This review aimed to explore the generalisability of candidate neurocognitive endophenotypes of adult-OCD to children with the disorder. Research thus far has categorised paediatric OCD as being associated with 1) abnormal action monitoring quantified via high ERN amplitudes, 2) an intolerance of uncertainty measured using implicit decision-making tasks, 3) possible impaired planning ability

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and 4) hyperactivity of frontoparietal regions during working memory tasks, which is similar in presentation to adult OCD. There is less clarity regarding the other domains, namely cognitive flexibility, inhibition, other types of memory, and reversal learning associated with OFC dysfunction. Neuroimaging methods used alongside cognitive tasks have unveiled comparable brain patterns of

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activity between children and adults with OCD. These results point towards paediatric OCD sharing

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some distinct neurocognitive features with adult OCD but more neuroimaging and cognitive research using larger samples of children with OCD and their unaffected relatives, as well as longitudinal

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studies, is needed to address gaps in the knowledge. Functional brain abnormalities in the absence of cognitive deficit in child patients implies that early detection of the disorder is crucial, and that

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treatment needs to delivered early on in the disorder trajectory to potentially impede cognitive

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decline.

Acknowledgements None to declare. Funding

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This research project did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. AMFLPS is supported via the Cambridge Coordination for the Improvement of Higher Education Personnel (CAPES) PhD Scholarship. BJS receives funding from the NIHR Cambridge Biomedical Research Centre (Mental Health Theme) and the Wallitt Foundation and Eton College. TWR is supported by a Wellcome Trust Senior Investigator award 104631/Z/14/Z. AAM is also supported as a research assistant via the Wellcome Trust Senior

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Investigator Award awarded to TWR.

Declaration of Competing Interest

AAM and AMFLPS both report no conflicts of interest. TWR receives research grants from

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GlaxoSmithKline and Shionogi & Co; consulting fees from Unilever, Greenfield Bioventures and Cassava Inc; and both consultancy fees and royalties (for CANTAB) from Cambridge Cognition,

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as well as editorial honoraria from Springer Verlag and Elsevier. BJS consults for Cambridge

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ur

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Cognition, Greenfield BioVentures and Cassava Sciences.

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References Abramovitch, A., Abramowitz, J. S., & Mittelman, A. (2013). The neuropsychology of adult obsessive-compulsive disorder: A meta-analysis. Clinical Psychology Review. https://doi.org/10.1016/j.cpr.2013.09.004 Abramovitch, A., Abramowitz, J. S., Mittelman, A., Stark, A., Ramsey, K., & Geller, D. A.

ro of

(2015). Research Review: Neuropsychological test performance in pediatric obsessive-compulsive disorder - a meta-analysis. Journal of Child Psychology and Psychiatry and Allied Disciplines. https://doi.org/10.1111/jcpp.12414

-p

Abramovitch, A., & Cooperman, A. (2015). The cognitive neuropsychology of obsessive-

compulsive disorder: A critical review. Journal of Obsessive-Compulsive and Related

re

Disorders, 5, 24-36.

lP

Aigner, M., Sachs, G., Bruckmüller, E., Winklbaur, B., Zitterl, W., Kryspin-Exner, I., ... & Katschnig, H. (2007). Cognitive and emotion recognition deficits in obsessive–

na

compulsive disorder. Psychiatry Research, 149(1-3), 121-128. Andrés, S., Boget, T., Lázaro, L., Penadés, R., Morer, A., Salamero, M., & Castro-Fornieles,

ur

J. (2007). Neuropsychological Performance in Children and Adolescents with Obsessive-Compulsive Disorder and Influence of Clinical Variables. Biological

Jo

Psychiatry. https://doi.org/10.1016/j.biopsych.2006.07.027

Atmaca, M., Yildirim, H., Ozdemir, H., Tezcan, E., & Poyraz, A. K. (2007). Volumetric MRI study of key brain regions implicated in obsessive–compulsive disorder. Progress in neuro-psychopharmacology and Biological Psychiatry, 31(1), 46-52. Atmaca, M., Yildirimb, H., Ozdemirb, H., Aydinb, A., Tezcana, E., & Ozlera, S. (2006). 40

Volumetric MRI assessment of brain regions in patients with refractory obsessive– compulsive disorder. Progress in neuro-psychopharmacology and Biological Psychiatry, 30(6), 1051-1057. Bartha, R., Stein, M. B., Williamson, P. C., Drost, D. J., Neufeld, R. W., Carr, T. J., ... & Siddiqui, A. R. (1998). A short echo 1H spectroscopy and volumetric MRI study of the corpus striatum in patients with obsessive-compulsive disorder and comparison

ro of

subjects. American Journal of Psychiatry, 155(11), 1584-1591. Batistuzzo, M. C., Balardin, J. B., Martin, M. D. G. M., Hoexter, M. Q., Bernardes, E. T., Borcato, S., … Miotto, E. C. (2015). Reduced prefrontal activation in pediatric patients with obsessive-compulsive disorder during verbal episodic memory

re

https://doi.org/10.1016/j.jaac.2015.06.020

-p

encoding. Journal of the American Academy of Child and Adolescent Psychiatry.

Baykal, S., Karabekiroglu, K., Senses, A., Karakurt, M. N., Calik, T., & Yüce, M. (2014).

lP

Neuropsychological and Clinical Profiles of Children and Adolescents Diagnosed with Childhood Obsessive Compulsive Disorder. Noro Psikiyatri Arsivi.

na

https://doi.org/10.5152/npa.2014.6862

ur

Bechara, A., Damasio, A. R., Damasio, H., & Anderson, S. W. (1994). Insensitivity to future consequences following damage to human prefrontal cortex. Cognition, 50(1-3), 7-15.

Jo

Beers, S. R., Rosenberg, D. R., Dick, E. L., Williams, T., O’Hearn, K. M., Birmaher, B., & Ryan, C. M. (1999). Neuropsychological study of frontal lobe function in psychotropic-naive children with obsessive-compulsive disorder. American Journal of Psychiatry, 156(5), 777–779. https://doi.org/10.1176/ajp.156.5.777 Bey, K., Kaufmann, C., Lennertz, L., Riesel, A., Klawohn, J., Heinzel, S., … Wagner, M. (2018). Impaired planning in patients with obsessive-compulsive disorder and 41

unaffected first-degree relatives: Evidence for a cognitive endophenotype. Journal of Anxiety Disorders. https://doi.org/10.1016/j.janxdis.2018.05.009 Bloch, M. H., Landeros‐ Weisenberger, A., Sen, S., Dombrowski, P., Kelmendi, B., Coric, V., ... & Leckman, J. F. (2008). Association of the serotonin transporter polymorphism and obsessive‐ compulsive disorder: Systematic review. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 147(6), 850-858.

ro of

Boschin, E. A., Piekema, C., & Buckley, M. J. (2015). Essential functions of primate frontopolar cortex in cognition. Proceedings of the National Academy of Sciences, 112(9), E1020-E1027.

-p

Brem, S., Hauser, T. U., Iannaccone, R., Brandeis, D., Drechsler, R., & Walitza, S. (2012). Neuroimaging of cognitive brain function in paediatric obsessive compulsive

re

disorder: A review of literature and preliminary meta-analysis. Journal of Neural

lP

Transmission. https://doi.org/10.1007/s00702-012-0813-z Britton, J. C., Rauch, S. L., Rosso, I. M., Killgore, W. D. S., Price, L. M., Ragan, J., …

na

Stewart, S. E. (2010). Cognitive inflexibility and frontal-cortical activation in pediatric obsessive-compulsive disorder. Journal of the American Academy of Child

ur

and Adolescent Psychiatry. https://doi.org/10.1016/j.jaac.2010.05.006 Bucci, P., Galderisi, S., Catapano, F., Di Benedetto, R., Piegari, G., Mucci, A., & Maj, M.

Jo

(2007). Neurocognitive indices of executive hypercontrol in obsessive–compulsive disorder. Acta Psychiatrica Scandinavica, 115(5), 380-387.

Bunge, S. A., Dudukovic, N. M., Thomason, M. E., Vaidya, C. J., & Gabrieli, J. D. (2002). Immature frontal lobe contributions to cognitive control in children: evidence from fMRI. Neuron, 33(2), 301-311.

42

Buhot, M. C. (1997). Serotonin receptors in cognitive behaviors. Current opinion in neurobiology, 7(2), 243-254. Bush, G., & Shin, L. M. (2006). The Multi-Source Interference Task: an fMRI task that reliably activates the cingulo-frontal-parietal cognitive/attention network. Nature protocols, 1(1), 308. Cannon, T. D., & Keller, M. C. (2006). Endophenotypes in the genetic analyses of mental

ro of

disorders. Annu. Rev. Clin. Psychol., 2, 267-290. Carlisi, C. O., Norman, L., Murphy, C. M., Christakou, A., Chantiluke, K., Giampietro, V., ... & Rubia, K. (2017). Comparison of neural substrates of temporal discounting between

-p

youth with autism spectrum disorder and with obsessive-compulsive disorder. Psychological medicine, 47(14), 2513-2527.

re

Carrasco, M., Harbin, S. M., Nienhuis, J. K., Fitzgerald, K. D., Gehring, W. J., & Hanna, G.

lP

L. (2013). Increased error-related brain activity in youth with obsessive-compulsive disorder and unaffected siblings. Depression and Anxiety.

na

https://doi.org/10.1002/da.22035

Carrasco, M., Hong, C., Nienhuis, J. K., Harbin, S. M., Fitzgerald, K. D., Gehring, W. J., &

ur

Hanna, G. L. (2013). Increased error-related brain activity in youth with obsessivecompulsive disorder and other anxiety disorders. Neuroscience Letters.

Jo

https://doi.org/10.1016/j.neulet.2013.02.017

Cavedini, P., Zorzi, C., Piccinni, M., Cavallini, M. C., & Bellodi, L. (2010). Executive Dysfunctions in Obsessive-Compulsive Patients and Unaffected Relatives: Searching for a New Intermediate Phenotype. Biological Psychiatry. https://doi.org/10.1016/j.biopsych.2010.02.012

43

Chamberlain, S. R., Blackwell, A. D., Fineberg, N. A., Robbins, T. W., & Sahakian, B. J. (2005). The neuropsychology of obsessive compulsive disorder: The importance of failures in cognitive and behavioural inhibition as candidate endophenotypic markers. Neuroscience and Biobehavioral Reviews. https://doi.org/10.1016/j.neubiorev.2004.11.006 Chamberlain, S. R., Fineberg, N. A., Blackwell, A. D., Robbins, T. W., & Sahakian, B. J. (2006). Motor inhibition and cognitive flexibility in obsessive-compulsive disorder

ro of

and trichotillomania. American Journal of Psychiatry, 163(7), 1282-1284.

Chamberlain, S. R., & Menzies, L. (2009). Endophenotypes of obsessive-compulsive disorder: Rationale, evidence and future potential. Expert Review of

-p

Neurotherapeutics. https://doi.org/10.1586/ern.09.36

re

Chamberlain, S. R., Menzies, L., Hampshire, A., Suckling, J., Fineberg, N. A., Del Campo, N., … Sahakian, B. J. (2008). Orbitofrontal dysfunction in patients with obsessive-

lP

compulsive disorder and their unaffected relatives. Science.

na

https://doi.org/10.1126/science.1154433

Chamberlain, S. R. S. R., Fineberg, N. A. N. A., Menzies, L. A. L. A., Blackwell, A. D.,

ur

Bullmore, E. T. E. T., Robbins, T. W. T. W., & Sahakian, B. J. B. J. (2007). Impaired Cognitive Flexibility and Motor Inhibition in Unaffected First-Degree Relatives of

Jo

Patients With Obsessive-Compulsive Disorder. American Journal of Psychiatry, 164, 335–338. https://doi.org/10.1176/ajp.2007.164.2.335

Chang, S. W., McCracken, J. T., & Piacentini, J. C. (2007). Neurocognitive correlates of child obsessive compulsive disorder and Tourette syndrome. Journal of Clinical and Experimental Neuropsychology. https://doi.org/10.1080/13825580600966383 Clarke, H. F., Dalley, J. W., Crofts, H. S., Robbins, T. W., & Roberts, A. C. (2004). 44

Cognitive inflexibility after prefrontal serotonin depletion. Science, 304(5672), 878880. Clark, L., Robbins, T. W., Ersche, K. D., & Sahakian, B. J. (2006). Reflection impulsivity in current and former substance users. Biological psychiatry, 60(5), 515-522. Crone, E. A., Richard Ridderinkhof, K., Worm, M., Somsen, R. J., & Van Der Molen, M. W. (2004). Switching between spatial stimulus–response mappings: a developmental

ro of

study of cognitive flexibility. Developmental science, 7(4), 443-455. Danielmeier, C., Eichele, T., Forstmann, B. U., Tittgemeyer, M., & Ullsperger, M. (2011).

Posterior medial frontal cortex activity predicts post-error adaptations in task-related

-p

visual and motor areas. Journal of Neuroscience, 31(5), 1780-1789.

Decker, J. H., Otto, A. R., Daw, N. D., & Hartley, C. A. (2016). From creatures of habit to

re

goal-directed learners: Tracking the developmental emergence of model-based

lP

reinforcement learning. Psychological science, 27(6), 848-858. De Vries, F. E., De Wit, S. J., Cath, D. C., Van Der Werf, Y. D., Van Der Borden, V., Van

na

Rossum, T. B., … Van Den Heuvel, O. A. (2014). Compensatory frontoparietal activity during working memory: An endophenotype of obsessive-compulsive

ur

disorder. Biological Psychiatry. https://doi.org/10.1016/j.biopsych.2013.11.021 De Wit, S. J., De Vries, F. E., Van Der Werf, Y. D., Cath, D. C., Heslenfeld, D. J., Veltman,

Jo

E. M., … Van Den Heuvel, O. A. (2012). Presupplementary motor area hyperactivity during response inhibition: A candidate endophenotype of obsessive-compulsive disorder. American Journal of Psychiatry. https://doi.org/10.1176/appi.ajp.2012.12010073

Delis, D. C., Kramer, J. H., Kaplan, E., & Holdnack, J. (2004). Reliability and validity of the

45

Delis-Kaplan Executive Function System: an update. Journal of the International Neuropsychological Society, 10(2), 301-303. Delorme, R., Goussé, V., Roy, I., Trandafir, A., Mathieu, F., Mouren-Siméoni, M. C., … Leboyer, M. (2007). Shared executive dysfunctions in unaffected relatives of patients with autism and obsessive-compulsive disorder. European Psychiatry. https://doi.org/10.1016/j.eurpsy.2006.05.002

ro of

De Bruijn, E. R., Hulstijn, W., Verkes, R. J., Ruigt, G. S., & Sabbe, B. G. (2004). Druginduced stimulation and suppression of action monitoring in healthy volunteers. Psychopharmacology, 177(1-2), 151-160.

-p

Demeter, G., Racsmany, M., Csigo, K., Harsanyi, A., Nemeth, A., & Doeme, L. (2013).

Eredeti közlemény intact short-term memory and impaired executive functions in

re

obsessive compulsive disorder. Ideggyogy Sz, 66(1-2), 35-41.

lP

Den Braber, A., Van ’T Ent, D., Cath, D. C., Wagner, J., Boomsma, D. I., & De Geus, E. J. C. (2010). Brain activation during cognitive planning in twins discordant or

na

concordant for obsessive-compulsive symptoms. Brain. https://doi.org/10.1093/brain/awq229

ur

Diamond, A. (2013). Executive functions. Annual review of psychology, 64, 135-168. Diwadkar, V. A., Burgess, A., Hong, E., Rix, C., Arnold, P. D., Hanna, G. L., & Rosenberg,

Jo

D. R. (2015). Dysfunctional Activation and Brain Network Profiles in Youth with Obsessive-Compulsive Disorder: A Focus on the Dorsal Anterior Cingulate during Working Memory. Frontiers in Human Neuroscience. https://doi.org/10.3389/fnhum.2015.00149

Downes, J. J., Roberts, A. C., Sahakian, B. J., Evenden, J. L., Morris, R. G., & Robbins, T.

46

W. (1989). Impaired extra-dimensional shift performance in medicated and unmedicated Parkinson's disease: evidence for a specific attentional dysfunction. Neuropsychologia, 27(11-12), 1329-1343. Eagle, D. M., Bari, A., & Robbins, T. W. (2008). The neuropsychopharmacology of action inhibition: cross-species translation of the stop-signal and go/no-go tasks. Psychopharmacology, 199(3), 439-456.

ro of

Ebert, D., Speck, O., König, A., Berger, M., Hennig, J., & Hohagen, F. (1997). 1H-magnetic resonance spectroscopy in obsessive-compulsive disorder: evidence for neuronal loss in the cingulate gyrus and the right striatum. Psychiatry Research: Neuroimaging,

-p

74(3), 173-176.

Endrass, T., Klawohn, J., Schuster, F., & Kathmann, N. (2008). Overactive performance

re

monitoring in obsessive-compulsive disorder: ERP evidence from correct and

lP

erroneous reactions. Neuropsychologia, 46(7), 1877-1887. Erhan, C., Bulut, G. Ç., Gökçe, S., Ozbas, D., Turkakin, E., Dursun, O. B., ... & Balcı, F.

na

(2017). Disrupted latent decision processes in medication-free pediatric OCD patients. Journal of affective disorders, 207, 32-37.

ur

Eriksen, B. A., & Eriksen, C. W. (1974). Effects of noise letters upon the identification of a target letter in a nonsearch task. Perception & psychophysics, 16(1), 143-149.

Jo

Farrell, L., Barrett, P., & Piacentini, J. (2006). Obsessive–compulsive disorder across the developmental trajectory: clinical correlates in children, adolescents and adults. Behaviour Change, 23(2), 103-120. Feil, R., & Fraga, M. F. (2012). Epigenetics and the environment: emerging patterns and implications. Nature reviews genetics, 13(2), 97.

47

Fitzgerald, K. D., Liu, Y., Johnson, T. D., Moser, J. S., Marsh, R., Hanna, G. L., & Taylor, S. F. (2018). Development of Posterior Medial Frontal Cortex Function in Pediatric Obsessive-Compulsive Disorder. Journal of the American Academy of Child and Adolescent Psychiatry. https://doi.org/10.1016/j.jaac.2018.02.016 Fitzgerald, K. D., Liu, Y., Stern, E. R., Welsh, R. C., Hanna, G. L., Monk, C. S., … Taylor, S. F. (2013). Reduced error-related activation of dorsolateral prefrontal cortex across pediatric anxiety disorders. Journal of the American Academy of Child and

ro of

Adolescent Psychiatry. https://doi.org/10.1016/j.jaac.2013.09.002

Fitzgerald, K. D., Stern, E. R., Angstadt, M., Nicholson-Muth, K. C., Maynor, M. R., Welsh, R. C., … Taylor, S. F. (2010). Altered function and connectivity of the medial frontal

-p

cortex in pediatric obsessive-compulsive disorder. Biological Psychiatry.

re

https://doi.org/10.1016/j.biopsych.2010.08.018

Fitzgerald, K. D., Welsh, R. C., Stern, E. R., Angstadt, M., Hanna, G. L., Abelson, J. L., &

lP

Taylor, S. F. (2011). Developmental alterations of frontal-striatal-thalamic connectivity in obsessive-compulsive disorder. Journal of the American Academy of

na

Child and Adolescent Psychiatry. https://doi.org/10.1016/j.jaac.2011.06.011

ur

Foulkes, L., & Blakemore, S. J. (2018). Studying individual differences in human adolescent brain development. Nature neuroscience, 21(3), 315.

Jo

Garavan, H., Ross, T. J., & Stein, E. A. (1999). Right hemispheric dominance of inhibitory control: an event-related functional MRI study. Proceedings of the National Academy of Sciences, 96(14), 8301-8306.

Garcia-Delgar, B., de la Serna, E., Lera, S., Varela, E., Andrés-Perpiñá, S., & Lázaro, L. (2018). Neuropsychological functioning in pediatric obsessive-compulsive disorder: One size does not fit all. Journal of Obsessive-Compulsive and Related Disorders. 48

https://doi.org/10.1016/j.jocrd.2017.12.005 Geller, D. A., Abramovitch, A., Mittelman, A., Stark, A., Ramsey, K., Cooperman, A., … Stewart, S. E. (2018). Neurocognitive function in paediatric obsessive-compulsive disorder. World Journal of Biological Psychiatry. https://doi.org/10.1080/15622975.2017.1282173 Giedd, J. N., & Rapoport, J. L. (2010). Structural MRI of pediatric brain development: what

ro of

have we learned and where are we going?. Neuron, 67(5), 728-734. Gilbert, K. E., Barclay, M. E., Tillman, R., Barch, D. M., & Luby, J. L. (2018). Associations of Observed Performance Monitoring During Preschool With Obsessive-Compulsive

-p

Disorder and Anterior Cingulate Cortex Volume Over 12 Years. JAMA Psychiatry. https://doi.org/10.1001/jamapsychiatry.2018.1805

re

Gillan, C. M., & Sahakian, B. J. (2015). Which is the driver, the obsessions or the

lP

compulsions, in OCD?. Neuropsychopharmacology, 40(1), 247. Godefroy, O., Lhullier, C., & Rousseaux, M. (1996). Non-spatial attention disorders in

na

patients with frontal or posterior brain damage. Brain, 119(1), 191-202. Gold, J. M., Barch, D. M., Feuerstahler, L. M., Carter, C. S., MacDonald III, A. W., Ragland,

ur

J. D., ... & Luck, S. J. (2018). Working memory impairment across psychotic

Jo

disorders. Schizophrenia bulletin, 45(4), 804-812. Gooskens, B., Bos, D. J., Mensen, V. T., Shook, D. A., Bruchhage, M. M. K., Naaijen, J., … Durston, S. (2018). No evidence of differences in cognitive control in children with autism spectrum disorder or obsessive-compulsive disorder: An fMRI study. Developmental Cognitive Neuroscience. https://doi.org/10.1016/j.dcn.2018.11.004 Gottwald, J., de Wit, S., Apergis-Schoute, A. M., Morein-Zamir, S., Kaser, M., Cormack, F., 49

… Sahakian, B. J. (2018). Impaired cognitive plasticity and goal-directed control in adolescent obsessive–compulsive disorder. Psychological Medicine. https://doi.org/10.1017/S0033291717003464 Gottesman, I. I., & Gould, T. D. (2003). The endophenotype concept in psychiatry: etymology and strategic intentions. American Journal of Psychiatry, 160(4), 636-645. Gousśe, V., Delorme, R., Chabane, N., Perez-Diaz, F., Flavie, M., Mouren-Simeoni, M. C., &

ro of

Leboyer, M. (2005). [Is age at onset associated with executive dysfunction in obsessive-compulsive disorder?]. Encephale, 31(6 Pt 1), 666–671.

Grant, D. A., & Berg, E. (1948). A behavioral analysis of degree of reinforcement and ease of

experimental psychology, 38(4), 404.

-p

shifting to new responses in a Weigl-type card-sorting problem. Journal of

re

Grünblatt, E., Marinova, Z., Roth, A., Gardini, E., Ball, J., Geissler, J., ... & Walitza, S.

lP

(2018). Combining genetic and epigenetic parameters of the serotonin transporter gene in obsessive-compulsive disorder. Journal of psychiatric research, 96, 209-217.

na

Gruner, P., Vo, A., Ikuta, T., Mahon, K., Peters, B. D., Malhotra, A. K., ... & Szeszko, P. R. (2012). White matter abnormalities in pediatric obsessive-compulsive

ur

disorder. Neuropsychopharmacology, 37(12), 2730. Gruner, P., & Pittenger, C. (2017). Cognitive inflexibility in Obsessive-Compulsive Disorder.

Jo

Neuroscience. https://doi.org/10.1016/j.neuroscience.2016.07.030

Gu, B. M., Park, J. Y., Kang, D. H., Lee, S. J., Yoo, S. Y., Jo, H. J., … Kwon, J. S. (2008). Neural correlates of cognitive inflexibility during task-switching in obsessivecompulsive disorder. Brain, 131(1), 155–164. https://doi.org/10.1093/brain/awm277 Hajcak, G., Franklin, M. E., Foa, E. B., & Simons, R. F. (2008). Increased error-related brain 50

activity in pediatric obsessive-compulsive disorder before and after treatment. The American Journal of Psychiatry. https://doi.org/10.1176/appi.ajp.2007.07010143 Hanna, G. L., Carrasco, M., Harbin, S. M., Nienhuis, J. K., Larosa, C. E., Chen, P., … Gehring, W. J. (2012). Error-related negativity and tic history in pediatric obsessivecompulsive disorder. Journal of the American Academy of Child and Adolescent Psychiatry. https://doi.org/10.1016/j.jaac.2012.06.019

ro of

Hanna, G. L., Liu, Y., Isaacs, Y. E., Ayoub, A. M., Brosius, A., Salander, Z., … Gehring, W. J. (2018). Error-related brain activity in adolescents with obsessive-compulsive disorder and major depressive disorder. Depression and Anxiety.

-p

https://doi.org/10.1002/da.22767

Hanna, G. L., Liu, Y., Isaacs, Y. E., Ayoub, A. M., Torres, J. J., O’Hara, N. B., & Gehring,

re

W. J. (2016). Withdrawn/Depressed Behaviors and Error-Related Brain Activity in Youth With Obsessive-Compulsive Disorder. Journal of the American Academy of

lP

Child and Adolescent Psychiatry. https://doi.org/10.1016/j.jaac.2016.06.012

na

Harzing, A. W. (2007). Publish or perish [computer software]. Available from. Hauser, T. U., Iannaccone, R., Dolan, R. J., Ball, J., Hättenschwiler, J., Drechsler, R., ... &

ur

Brem, S. (2017). Increased fronto-striatal reward prediction errors moderate decision

Jo

making in obsessive–compulsive disorder. Psychological medicine, 47(7), 1246-1258. Hauser, T. U., Moutoussis, M., Iannaccone, R., Brem, S., Walitza, S., Drechsler, R., ... & Dolan, R. J. (2017). Increased decision thresholds enhance information gathering performance in juvenile obsessive-compulsive disorder (OCD). PLoS computational biology, 13(4), e1005440. Hoexter, M. Q., de Souza Duran, F. L., D'alcante, C. C., Dougherty, D. D., Shavitt, R. G.,

51

Lopes, A. C., ... & Miguel, E. C. (2012). Gray matter volumes in obsessivecompulsive disorder before and after fluoxetine or cognitive-behavior therapy: a randomized clinical trial. Neuropsychopharmacology, 37(3), 734. Hofstadter, Douglas R. (1985). Metamagical Themas : Questing for the Essence of Mind and Pattern. New York: Basic Books. ISBN 978-0-465-04540-2. Hu, X., Du, M., Chen, L., Li, L., Zhou, M., Zhang, L., ... & Gong, Q. (2017). Meta-analytic

ro of

investigations of common and distinct grey matter alterations in youths and adults with obsessive-compulsive disorder. Neuroscience & Biobehavioral Reviews, 78, 91103.

-p

Humberstone, M., Sawle, G. V., Clare, S., Hykin, J., Coxon, R., Bowtell, R., ... & Morris, P. G. (1997). Functional magnetic resonance imaging of single motor events reveals

re

human presupplementary motor area. Annals of Neurology: Official Journal of the

lP

American Neurological Association and the Child Neurology Society, 42(4), 632-637. Huyser, C., Veltman, D. J., de Haan, E., & Boer, F. (2009). Paediatric obsessive-compulsive

na

disorder, a neurodevelopmental disorder?. Evidence from neuroimaging. Neuroscience and Biobehavioral Reviews.

ur

https://doi.org/10.1016/j.neubiorev.2009.01.003 Huyser, C., Veltman, D. J., Wolters, L. H., De Haan, E., & Boer, F. (2010). Functional

Jo

magnetic resonance imaging during planning before and after cognitive-behavioral therapy in pediatric obsessive-compulsive disorder. Journal of the American Academy of Child and Adolescent Psychiatry. https://doi.org/10.1016/j.jaac.2010.08.007

Huyser, C., Veltman, D. J., Wolters, L. H., De Haan, E., & Boer, F. (2011). Developmental aspects of error and high-conflict-related brain activity in pediatric obsessivecompulsive disorder: A fMRI study with a Flanker task before and after CBT. Journal 52

of Child Psychology and Psychiatry and Allied Disciplines. https://doi.org/10.1111/j.1469-7610.2011.02439.x Hybel, K. A., Mortensen, E. L., Lambek, R., Thastum, M., & Thomsen, P. H. (2017). Cool and Hot Aspects of Executive Function in Childhood Obsessive-Compulsive Disorder. Journal of Abnormal Child Psychology. https://doi.org/10.1007/s10802016-0229-6

ro of

Iacono, W. G. (2018). Endophenotypes in psychiatric disease: Prospects and challenges. Genome Medicine. https://doi.org/10.1186/s13073-018-0526-5

Iacono, W. G., Malone, S. M., & Vrieze, S. I. (2017). Endophenotype best practices.

-p

International Journal of Psychophysiology. https://doi.org/10.1016/j.ijpsycho.2016.07.516

re

Iacono, W. G., Vaidyanathan, U., Vrieze, S. I., & Malone, S. M. (2014). Knowns and

lP

unknowns for psychophysiological endophenotypes: Integration and response to commentaries. Psychophysiology, 51(12), 1339-1347.

na

Kalra, S. K., & Swedo, S. E. (2009). Children with obsessive-compulsive disorder: are they just “little adults”?. The Journal of clinical investigation, 119(4), 737-746.

ur

Karno, M., Golding, J. M., Sorenson, S. B., & Burnam, M. A. (1988). The epidemiology of obsessive-compulsive disorder in five US communities. Archives of general

Jo

psychiatry, 45(12), 1094-1099.

Kim, K. L., Christensen, R. E., Ruggieri, A., Schettini, E., Freeman, J. B., Garcia, A. M., … Dickstein, D. P. (2018). Cognitive performance of youth with primary generalized anxiety disorder versus primary obsessive-compulsive disorder. Depression and Anxiety. https://doi.org/10.1002/da.22848

53

Kendler, K. S., & Neale, M. C. (2010). Endophenotype: a conceptual analysis. Molecular psychiatry, 15(8), 789. Kodaira, M., Iwadare, Y., Ushijima, H., Oiji, A., Kato, M., Sugiyama, N., … Saito, K. (2012). Poor performance on the Iowa gambling task in children with obsessivecompulsive disorder. Annals of General Psychiatry. https://doi.org/10.1186/1744859X-11-25

ro of

Kortte, K. B., Horner, M. D., & Windham, W. K. (2002). The trail making test, part B: cognitive flexibility or ability to maintain set?. Applied neuropsychology, 9(2), 106109.

-p

Kuelz, A., Riemann, D., Halsband, U., Vielhaber, K., Unterrainer, J., Kordon, A., &

Voderholzer, U. (2006). Neuropsychological impairment in obsessive-compulsive

re

disorder—Improvement over the course of cognitive behavioral treatment. Journal of

lP

Clinical and Experimental Neuropsychology, 28(8), 1273-1287. Krishna, R., Udupa, S., George, C. M., Kumar, K. J., Viswanath, B., Kandavel, T., ... &

na

Reddy, Y. J. (2011). Neuropsychological performance in OCD: a study in medicationnaïve patients. Progress in Neuro-Psychopharmacology and Biological Psychiatry,

ur

35(8), 1969-1976.

Lázaro, L., Caldú, X., Junqué, C., Bargalló, N., Andrés, S., Morer, A., & Castro-Fornieles, J.

Jo

(2008). Cerebral activation in children and adolescents with obsessive-compulsive disorder before and after treatment: A functional MRI study. Journal of Psychiatric Research. https://doi.org/10.1016/j.jpsychires.2007.12.007

Lamberty, G. J., Putnam, S. H., Chatel, D. M., & Bieliauskas, L. A. (1994). Derived Trail Making Test indices: A preliminary report. Neuropsychiatry, Neuropsychology, & Behavioral Neurology. 54

Larochette, A. C., Benn, K., & Harrison, A. G. (2009). Executive functioning: A comparison of the Tower of LondonDX and the D-KEFS Tower Test. Applied Neuropsychology, 16(4), 275-280. Lezak, M. D. (1995). Neumpsychologicalassessment (3rd cd.). Li, B., Sun, J. H., Li, T., & Yang, Y. C. (2012). Neuropsychological study of patients with obsessive-compulsive disorder and their parents in China: Searching for potential

ro of

endophenotypes. Neuroscience Bulletin. https://doi.org/10.1007/s12264-012-1262-2 Liu, Y., Gehring, W. J., Weissman, D. H., Taylor, S. F., & Fitzgerald, K. D. (2012). Trial-bytrial adjustments of cognitive control following errors and response conflict are

https://doi.org/10.3389/fpsyt.2012.00041

-p

altered in pediatric obsessive compulsive disorder. Frontiers in Psychiatry.

re

Liu, Y., Hanna, G. L., Carrasco, M., Gehring, W. J., & Fitzgerald, K. D. (2014). Altered

lP

relationship between electrophysiological response to errors and gray matter volumes in an extended network for error-processing in pediatric obsessive-compulsive

na

disorder. Human Brain Mapping. https://doi.org/10.1002/hbm.22240 LJ, N., CO, C., Christakou, A., CM, M., Chantiluke, K., Giampietro, V., … Rubia, K. (2018).

ur

Frontostriatal Dysfunction During Decision Making in AttentionDeficit/Hyperactivity Disorder and Obsessive-Compulsive Disorder. Biological

Jo

Psychiatry. Cognitive Neuroscience and Neuroimaging. https://doi.org/10.1016/j.bpsc.2018.03.009

Logan, G. D., & Cowan, W. B. (1984). On the ability to inhibit thought and action: A theory of an act of control. Psychological review, 91(3), 295. Lucey, J. V., Burness, C. E., Costa, D. C., Gacinovic, S., Pilowsky, L. S., Ell, P. J., ... &

55

Kerwin, R. W. (1997). Wisconsin Card Sorting Task (WCST) errors and cerebral blood flow in obsessive‐ compulsive disorder (OCD). British Journal of Medical Psychology, 70(4), 403-411. Luna, B., Garver, K. E., Urban, T. A., Lazar, N. A., & Sweeney, J. A. (2004). Maturation of cognitive processes from late childhood to adulthood. Child development, 75(5), 1357-1372.

ro of

Luna, B., Thulborn, K. R., Munoz, D. P., Merriam, E. P., Garver, K. E., Minshew, N. J., ... & Sweeney, J. A. (2001). Maturation of widely distributed brain function subserves cognitive development. Neuroimage, 13(5), 786-793.

-p

Luxenberg, J. S., Swedo, S. E., Flament, M. F., Friedland, R. P., Rapoport, J., & Rapoport, S. I. (1988). Neuroanatomical abnormalities in obsessive-compulsive disorder detected

re

with quantitative X-ray computed tomography. The American journal of psychiatry.

lP

Mancebo, M. C., Garcia, A. M., Pinto, A., Freeman, J. B., Przeworski, A., Stout, R., … Rasmussen, S. A. (2008). Juvenile-onset OCD: Clinical features in children,

na

adolescents and adults. Acta Psychiatrica Scandinavica, 118(2), 149–159. https://doi.org/10.1111/j.1600-0447.2008.01224.x

ur

Maia, T. V., Cooney, R. E., & Peterson, B. S. (2008). The neural bases of obsessive– compulsive disorder in children and adults. Development and Psychopathology,

Jo

20(04), 1251. https://doi.org/10.1017/S0954579408000606

Menzies, L., Achard, S., Chamberlain, S. R., Fineberg, N., Chen, C. H., Del Campo, N., … Bullmore, E. (2007). Neurocognitive endophenotypes of obsessive-compulsive disorder. Brain, 130(12), 3223–3236. https://doi.org/10.1093/brain/awm205 Meyer, A., Proudfit, G. H., Torpey-Newman, D. C., Kujawa, A., & Klein, D. N. (2015).

56

Increased error-related brain activity in children predicts the onset of anxiety disorders three years later. Journal of Abnormal Psychology, 124, 266-274. Moher, D., Stewart, L., & Shekelle, P. (2016). Implementing PRISMA-P: recommendations for prospective authors. Morein-Zamir, S., Craig, K. J., Ersche, K. D., Abbott, S., Muller, U., Fineberg, N. A., ... & Robbins, T. W. (2010). Impaired visuospatial associative memory and attention in

modulation. Psychopharmacology, 212(3), 357-367.

ro of

obsessive compulsive disorder but no evidence for differential dopaminergic

Morein-Zamir, S., Papmeyer, M., Pertusa, A., Chamberlain, S. R., Fineberg, N. A., Sahakian,

-p

B. J., ... & Robbins, T. W. (2014). The profile of executive function in OCD hoarders and hoarding disorder. Psychiatry research, 215(3), 659-667.

re

Moritz, S., Birkner, C., Kloss, M., Jahn, H., Hand, I., Haasen, C., & Krausz, M. (2002).

lP

Executive functioning in obsessive–compulsive disorder, unipolar depression, and schizophrenia. Archives of Clinical Neuropsychology, 17(5), 477-483.

na

Moser, J., Moran, T., Schroder, H., Donnellan, B., & Yeung, N. (2013). On the relationship between anxiety and error monitoring: a meta-analysis and conceptual framework.

ur

Frontiers in human neuroscience, 7, 466. Murray, G. K., Knolle, F., Ersche, K. D., Craig, K. J., Abbott, S., Shabbir, S. S., ... &

Jo

Robbins, T. W. (2019). Dopaminergic drug treatment remediates exaggerated cingulate prediction error responses in obsessive-compulsive disorder. Psychopharmacology, 1-12.

Nielen, M. M. A., & Den Boer, J. A. (2003). Neuropsychological performance of OCD patients before and after treatment with fluoxetine: evidence for persistent cognitive

57

deficits. Psychological Medicine, 33(5), 917-925. Nieuwenhuis, S., Nielen, M. M., Mol, N., Hajcak, G., & Veltman, D. J. (2005). Performance monitoring in obsessive-compulsive disorder. Psychiatry research, 134(2), 111-122. Norman, L. J., Carlisi, C., Lukito, S., Hart, H., Mataix-Cols, D., Radua, J., & Rubia, K. (2016). Structural and functional brain abnormalities in attention-deficit/hyperactivity disorder and obsessive-compulsive disorder: a comparative meta-analysis. JAMA

ro of

psychiatry, 73(8), 815-825. Norman, L. J., Carlisi, C. O., Christakou, A., Murphy, C. M., Chantiluke, K., Giampietro, V., ... & Rubia, K. (2018). Frontostriatal dysfunction during decision making in attention-

-p

deficit/hyperactivity disorder and obsessive-compulsive disorder. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 3(8), 694-703.

re

Okasha, A., Rafaat, M., Mahallawy, N., Nahas, G. E., Dawla, A. S. E., Sayed, M., & Kholi,

lP

S. E. (2000). Cognitive dysfunction in obsessive‐ compulsive disorder. Acta Psychiatrica Scandinavica, 101(4), 281-285.

na

Okazaki, K., Yamamuro, K., Iida, J., Ota, T., Nakanishi, Y., Matsuura, H., … Kishimoto, T. (2018). Intra-individual variability across cognitive task in drug-naïve pediatric

ur

patients with obsessive compulsive disorder. Psychiatry Research. https://doi.org/10.1016/j.psychres.2018.04.024

Jo

Ornstein, T. J., Arnold, P., Manassis, K., Mendlowitz, S., & Schachar, R. (2010). Neuropsychological performance in childhood OCD: A preliminary study. Depression and Anxiety, 27(4), 372–380. https://doi.org/10.1002/da.20638 Ota, T., Iida, J., Sawada, M., Suehiro, Y., Yamamuro, K., Matsuura, H., … Kishimoto, T. (2013). Reduced prefrontal hemodynamic response in pediatric obsessive-compulsive

58

disorder as measured by near-infrared spectroscopy. Child Psychiatry and Human Development. https://doi.org/10.1007/s10578-012-0323-0 Owen, A. M., Sahakian, B. J., Hodges, J. R., Summers, B. A., Polkey, C. E., & Robbins, T. W. (1995). Dopamine-dependent frontostriatal planning deficits in early Parkinson's disease. Neuropsychology, 9(1), 126. Ozcan, H., Ozer, S., & Yagcioglu, S. (2016). Neuropsychological, electrophysiological and

ro of

neurological impairments in patients with obsessive compulsive disorder, their healthy siblings and healthy controls: Identifying potential endophenotype(s). Psychiatry Research. https://doi.org/10.1016/j.psychres.2016.04.013

-p

Paast, N., Khosravi, Z., Memari, A. H., Shayestehfar, M., & Arbabi, M. (2016). Comparison of cognitive flexibility and planning ability in patients with obsessive compulsive

re

disorder, patients with obsessive compulsive personality disorder, and healthy

lP

controls. Shanghai archives of psychiatry, 28(1), 28.

Page, L. A., Rubia, K., Deeley, Q., Daly, E., Toal, F., Mataix-Cols, D., … Murphy, D. G. M.

na

(2009). A functional magnetic resonance imaging study of inhibitory control in obsessive-compulsive disorder. Psychiatry Research - Neuroimaging.

ur

https://doi.org/10.1016/j.pscychresns.2009.05.002 Peles, E., Weinstein, A., Sason, A., Adelson, M., & Schreiber, S. (2014). Stroop task among

Jo

patients with obsessive-compulsive disorder (OCD) and pathological gambling (PG) in methadone maintenance treatment (MMT). CNS spectrums, 19(6), 509-518.

Penadés, R., Catalán, R., Andrés, S., Salamero, M., & Gastó, C. (2005). Executive function and nonverbal memory in obsessive-compulsive disorder. Psychiatry research, 133(1), 81-90.

59

Penades, R., Catalan, R., Rubia, K., Andres, S., Salamero, M., & Gasto, C. (2007). Impaired response inhibition in obsessive compulsive disorder. European Psychiatry, 22(6), 404-410. Piacentini, J., Bergman, R. L., Keller, M., & McCracken, J. (2003). Functional impairment in children and adolescents with obsessive-compulsive disorder. Journal of child and adolescent psychopharmacology, 13(2, Supplement 1), 61-69.

ro of

Pushkarskaya, H., Tolin, D., Ruderman, L., Kirshenbaum, A., Kelly, J. M. L., Pittenger, C., & Levy, I. (2015). Decision-making under uncertainty in obsessive-compulsive disorder. Journal of Psychiatric Research.

-p

https://doi.org/10.1016/j.jpsychires.2015.08.011

Rajender, G., Bhatia, M. S., Kanwal, K., Malhotra, S., Singh, T. B., & Chaudhary, D. (2011).

re

Study of neurocognitive endophenotypes in drug-naïve obsessive-compulsive disorder patients, their first-degree relatives and healthy controls. Acta Psychiatrica

lP

Scandinavica. https://doi.org/10.1111/j.1600-0447.2011.01733.x

na

Reitan, R. M., & Wolfson, D. (1985). The Halstead-Reitan neuropsychological test battery: Theory and clinical interpretation (Vol. 4). Reitan Neuropsychology.

ur

Remijnse, P. L., Nielen, M. M., van Balkom, A. J., Cath, D. C., van Oppen, P., Uylings, H. B., & Veltman, D. J. (2006). Reduced orbitofrontal-striatal activity on a reversal

Jo

learning task in obsessive-compulsive disorder. Archives of general psychiatry, 63(11), 1225-1236.

Riccio, C. A., Wolfe, M. E., Romine, C., Davis, B., & Sullivan, J. R. (2004). The Tower of London and neuropsychological assessment of ADHD in adults. Archives of Clinical Neuropsychology, 19(5), 661-671.

60

Riesel, A. (2019). The erring brain: Error‐ related negativity as an endophenotype for OCD—A review and meta‐ analysis. Psychophysiology, 56(4), e13348. Riesel, A., Endrass, T., Kaufmann, C., & Kathmann, N. (2011). Overactive error-related brain activity as a candidate endophenotype for obsessive-compulsive disorder: Evidence from unaffected first-degree relatives. American Journal of Psychiatry. https://doi.org/10.1176/appi.ajp.2010.10030416

ro of

Riesel, A., Kathmann, N., & Endrass, T. (2014). Overactive performance monitoring in obsessive–compulsive disorder is independent of symptom expression. European

Archives of Psychiatry and Clinical Neuroscience. https://doi.org/10.1007/s00406-

-p

014-0499-3

Riesel, A., Klawohn, J., Grützmann, R., Kaufmann, C., Heinzel, S., Bey, K., ... & Kathmann,

re

N. (2019). Error-related brain activity as a transdiagnostic endophenotype for obsessive-compulsive disorder, anxiety and substance use disorder. Psychological

lP

medicine, 49(7), 1207-1217.

na

Riesel, A., Richter, A., Kaufmann, C., Kathmann, N., & Endrass, T. (2015). Performance monitoring in obsessive-compulsive undergraduates: Effects of task difficulty. Brain

ur

and Cognition. https://doi.org/10.1016/j.bandc.2015.05.00 Riesel, A., Weinberg, A., Endrass, T., Kathmann, N., & Hajcak, G. (2012). Punishment has a

Jo

lasting impact on error‐ related brain activity. Psychophysiology, 49(2), 239-247.

Robbins, T. W., Gillan, C. M., Smith, D. G., de Wit, S., & Ersche, K. D. (2012). Neurocognitive endophenotypes of impulsivity and compulsivity: Towards dimensional psychiatry. Trends in Cognitive Sciences. https://doi.org/10.1016/j.tics.2011.11.009

61

Robbins, T. W., Vaghi, M. M., & Banca, P. (2019). Obsessive-compulsive disorder: puzzles and prospects. Neuron, 102(1), 27-47. Robinson, D., Wu, H., Munne, R. A., Ashtari, M., Alvir, J. M. J., Lerner, G., ... & Bogerts, B. (1995). Reduced caudate nucleus volume in obsessive-compulsive disorder. Archives of General Psychiatry, 52(5), 393-398. Rogers, R. D., Andrews, T. C., Grasby, P. M., Brooks, D. J., & Robbins, T. W. (2000).

ro of

Contrasting cortical and subcortical activations produced by attentional-set shifting and reversal learning in humans. Journal of cognitive neuroscience, 12(1), 142-162.

Rogers, R. D., Owen, A. M., Middleton, H. C., Williams, E. J., Pickard, J. D., Sahakian, B. J.,

-p

& Robbins, T. W. (1999). Choosing between small, likely rewards and large, unlikely rewards activates inferior and orbital prefrontal cortex. Journal of Neuroscience,

re

19(20), 9029-9038.

lP

Rosenberg, D. R., Keshavan, M. S., O'Hearn, K. M., Dick, E. L., Bagwell, W. W., Seymour, A. B., ... & Birmaher, B. (1997). Frontostriatal measurement in treatment-naive

824-830.

na

children with obsessive-compulsive disorder. Archives of general psychiatry, 54(9),

ur

Roth, R. M., Milovan, D., Baribeau, J., & O’Connor, K. (2005). Neuropsychological functioning in early- and late-onset obsessive-compulsive disorder. The Journal of

Jo

Neuropsychiatry and Clinical Neurosciences, 17(2), 208–213. https://doi.org/10.1176/jnp.17.2.208

Rotge, J. Y., Dilharreguy, B., Aouizerate, B., Martin-Guehl, C., Guehl, D., Jaafari, N., ... & Burbaud, P. (2009). Inverse relationship between thalamic and orbitofrontal volumes in obsessive–compulsive disorder. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 33(4), 682-687. 62

Rubia, K., Overmeyer, S., Taylor, E., Brammer, M., Williams, S. C. R., Simmons, A., ... & Bullmore, E. T. (2000). Functional frontalisation with age: mapping neurodevelopmental trajectories with fMRI. Neuroscience & Biobehavioral Reviews, 24(1), 13-19. Rubia, K., Overmeyer, S., Taylor, E., Brammer, M., Williams, S. C., Simmons, A., & Bullmore, E. T. (1999). Hypofrontality in attention deficit hyperactivity disorder during higher-order motor control: a study with functional MRI. American Journal of

ro of

Psychiatry, 156(6), 891-896.

Rubia, K., Russell, T., Overmeyer, S., Brammer, M. J., Bullmore, E. T., Sharma, T., ... & Taylor, E. (2001). Mapping motor inhibition: conjunctive brain activations across

-p

different versions of go/no-go and stop tasks. Neuroimage, 13(2), 250-261.

lP

Intelligence, 39(4), 222-232.

re

Salthouse, T. A. (2011). What cognitive abilities are involved in trail-making performance?.

Schear, J. M., & Sato, S. D. (1989). Effects of visual acuity and visual motor speed and

25-32.

na

dexterity on cognitive test performance. Archives of Clinical Neuropsychology, 4(1),

ur

Schiff, R., & Vakil, E. (2015). Age differences in cognitive skill learning, retention and transfer: The case of the Tower of Hanoi Puzzle. Learning and Individual Differences,

Jo

39, 164-171.

Segalas, C., Alonso, P., Real, E., Garcia, A., Minambres, A., Labad, J., ... & Menchon, J. M. (2010). Memory and strategic processing in first-degree relatives of obsessive compulsive patients. Psychological medicine, 40(12), 2001-2011. Shallice, T. (1982). Specific impairments of planning. Philosophical Transactions of the

63

Royal Society of London. B, Biological Sciences, 298(1089), 199-209. Sheslow, D., & Adams, W. (2003). Wide Range Assessment of Memory and Learning (WRAML). NCS Pearson. Shin, M. S., Choi, H., Kim, H., Hwang, J. W., Kim, B. N., & Cho, S. C. (2008). A study of neuropsychological deficit in children with obsessive-compulsive disorder. European Psychiatry. https://doi.org/10.1016/j.eurpsy.2008.03.010

ro of

Shin, N. Y., Lee, T. Y., Kim, E., & Kwon, J. S. (2014). Cognitive functioning in obsessivecompulsive disorder: A meta-analysis. Psychological Medicine. https://doi.org/10.1017/S0033291713001803

-p

Silk, T., Chen, J., Seal, M., & Vance, A. (2013). White matter abnormalities in pediatric obsessive-compulsive disorder. Psychiatry Research - Neuroimaging.

re

https://doi.org/10.1016/j.pscychresns.2013.04.003

lP

Simon, J. R., & Wolf, J. D. (1963). Choice reaction time as a function of angular stimulusresponse correspondence and age. Ergonomics, 6(1), 99-105.

na

Snyder, H. R., Kaiser, R. H., Warren, S. L., & Heller, W. (2015). Obsessive-compulsive disorder is associated with broad impairments in executive function: A meta-

ur

analysis. Clinical Psychological Science, 3(2), 301-330.

Jo

Sobin, C., Blundell, M. L., & Karayiorgou, M. (2000). Phenotypic differences in early-and late-onset obsessive-compulsive disorder. Comprehensive psychiatry, 41(5), 373-379.

Sowell, E. R., Delis, D., Stiles, J., & Jernigan, T. L. (2001). Improved memory functioning and frontal lobe maturation between childhood and adolescence: a structural MRI study. Journal of the International Neuropsychological Society, 7(3), 312-322. Stewart, S. E., Geller, D. A., Jenike, M., Pauls, D., Shaw, D., Mullin, B., & Faraone, S. V. 64

(2004). Long‐ term outcome of pediatric obsessive–compulsive disorder: a meta‐ analysis and qualitative review of the literature. Acta Psychiatrica Scandinavica, 110(1), 4-13. Stroop, J. R. (1935). Studies of interference in serial verbal reactions. Journal of experimental psychology, 18(6), 643. Swedo, S. E., Rapoport, J. L., Leonard, H., Lenane, M., & Cheslow, D. (1989). Obsessive-

ro of

compulsive disorder in children and adolescents: clinical phenomenology of 70 consecutive cases. Archives of General Psychiatry, 46(4), 335-341.

Szeszko, P. R., Bilder, R. M., Lencz, T., Pollack, S., Alvir, J. M. J., Ashtari, M., ... &

-p

Lieberman, J. A. (1999). Investigation of frontal lobe subregions in first-episode schizophrenia. Psychiatry Research: Neuroimaging, 90(1), 1-15.

re

Szeszko, P. R., Christian, C., MacMaster, F., Lencz, T., Mirza, Y., Taormina, S. P., ... &

lP

Rosenberg, D. R. (2008). Gray matter structural alterations in psychotropic drug-naive pediatric obsessive-compulsive disorder: an optimized voxel-based morphometry

na

study. American Journal of Psychiatry, 165(10), 1299-1307. Szeszko, P. R., MacMillan, S., McMeniman, M., Chen, S., Baribault, K., Lim, K. O., ... &

ur

Moore, G. J. (2004). Brain structural abnormalities in psychotropic drug-naive pediatric patients with obsessive-compulsive disorder. American Journal of

Jo

Psychiatry, 161(6), 1049-1056.

Tallis, F., Pratt, P., & Jamani, N. (1999). Obsessive compulsive disorder, checking, and nonverbal memory: a neuropsychological investigation. Behaviour Research and Therapy, 37(2), 161-166. Taner, Y. I., Bakar, E. E., & Oner, O. (2011). Impaired executive functions in paediatric

65

obsessive-compulsive disorder patients. Acta Neuropsychiatrica, 23(6), 272-281. Taylor, S. (2011). Early versus late onset obsessive-compulsive disorder: Evidence for distinct subtypes. Clinical Psychology Review. https://doi.org/10.1016/j.cpr.2011.06.007 Tezcan, D., Tumkaya, S., & Bora, E. (2017). Reversal learning in patients with obsessivecompulsive disorder (OCD) and their unaffected relatives: Is orbitofrontal dysfunction

https://doi.org/10.1016/j.psychres.2017.03.001

ro of

an endophenotype of OCD? Psychiatry Research, 252, 231–233.

Tükel, R., Gürvit, H., Ertekin, B. A., Oflaz, S., Ertekin, E., Baran, B., ... & Atalay, F. (2012).

-p

Neuropsychological function in obsessive-compulsive disorder. Comprehensive psychiatry, 53(2), 167-175.

re

Vaghi, M. M., Hampshire, A., Fineberg, N. A., Kaser, M., Brühl, A. B., Sahakian, B. J., …

lP

Robbins, T. W. (2017). Hypoactivation and Dysconnectivity of a Frontostriatal Circuit During Goal-Directed Planning as an Endophenotype for Obsessive-

na

Compulsive Disorder. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 2(8), 655–663. https://doi.org/10.1016/j.bpsc.2017.05.005

ur

Visscher, P. M., Hill, W. G., & Wray, N. R. (2008). Heritability in the genomics era— concepts and misconceptions. Nature reviews genetics, 9(4), 255.

Jo

Viswanath, B., Janardhan Reddy, Y. C., Kumar, K. J., Kandavel, T., & Chandrashekar, C. R. (2009). Cognitive endophenotypes in OCD: A study of unaffected siblings of probands with familial OCD. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 33(4), 610–615. https://doi.org/10.1016/j.pnpbp.2009.02.018 Vloet, T. D., Marx, I., Kahraman-Lanzerath, B., Zepf, F. D., Herpertz-Dahlmann, B., &

66

Konrad, K. (2010). Neurocognitive performance in children with ADHD and OCD. Journal of Abnormal Child Psychology, 38(7), 961-969. Waters, A. M., & Farrell, L. J. (2014). Response inhibition to emotional faces in childhood obsessive-compulsive disorder. Journal of Obsessive-Compulsive and Related Disorders. https://doi.org/10.1016/j.jocrd.2013.12.004 Wechsler, D. (1949). Wechsler intelligence scale for children.

ro of

Welsh, M. C., Satterlee-Cartmell, T., & Stine, M. (1999). Towers of Hanoi and London: Contribution of working memory and inhibition to performance. Brain and cognition, 41(2), 231-242.

-p

Wolff, N., Buse, J., Tost, J., Roessner, V., & Beste, C. (2017). Modulations of cognitive flexibility in obsessive compulsive disorder reflect dysfunctions of perceptual

lP

https://doi.org/10.1111/jcpp.12733

re

categorization. Journal of Child Psychology and Psychiatry and Allied Disciplines.

Wolff, N., Giller, F., Buse, J., Roessner, V., & Beste, C. (2018). When repetitive mental sets

na

increase cognitive flexibility in adolescent obsessive-compulsive disorder. Journal of Child Psychology and Psychiatry and Allied Disciplines.

ur

https://doi.org/10.1111/jcpp.12901

Jo

Woolley, J., Heyman, I., Brammer, M., Frampton, I., McGuire, P. K., & Rubia, K. (2008). Brain activation in paediatric obsessive compulsive disorder during tasks of inhibitory control. The British Journal of Psychiatry : The Journal of Mental Science, 192(1), 25–31. https://doi.org/10.1192/bjp.bp.107.036558 Y., I. T., E., E. B., & O., O. (2011). Impaired executive functions in paediatric obsessivecompulsive disorder patients. Acta Neuropsychiatrica.

67

Yamamuro, K., Ota, T., Iida, J., Kishimoto, N., Nakanishi, Y., & Kishimoto, T. (2017a). Persistence of impulsivity in pediatric and adolescent patients with obsessive– compulsive disorder. Psychiatry and Clinical Neurosciences, 71(1), 36–43. https://doi.org/10.1111/pcn.12465 Yamamuro, K., Ota, T., Nakanishi, Y., Matsuura, H., Okazaki, K., Kishimoto, N., … Kishimoto, T. (2015). Event-related potentials in drug-naïve pediatric patients with

https://doi.org/10.1016/j.psychres.2015.09.026

ro of

obsessive-compulsive disorder. Psychiatry Research.

Young, S. N., & Leyton, M. (2002). The role of serotonin in human mood and social

interaction: insight from altered tryptophan levels. Pharmacology Biochemistry and

-p

Behavior, 71(4), 857-865.

re

Zandt, F., Prior, M., & Kyrios, M. (2009). Similarities and differences between children and adolescents with autism spectrum disorder and those with obsessive compulsive

lP

disorder: Executive functioning and repetitive behaviour. Autism.

na

https://doi.org/10.1177/1362361308097120 Zhang, J., Yang, X., & Yang, Q. (2015). Neuropsychological dysfunction in adults with

ur

early-onset obsessive-compulsive disorder: The search for a cognitive endophenotype. Revista Brasileira de Psiquiatria. https://doi.org/10.1590/1516-4446-2014-1518

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Zhang, L., Dong, Y., Ji, Y., Tao, R., Chen, X., Ye, J., … Wang, K. (2015). Trait-related decision making impairment in obsessive-compulsive disorder: Evidence from decision making under ambiguity but not decision making under risk. Scientific Reports. https://doi.org/10.1038/srep17312 Zhang, L., Dong, Y., Ji, Y., Zhu, C., Yu, F., Ma, H., … Wang, K. (2015). Dissociation of decision making under ambiguity and decision making under risk: A neurocognitive 68

endophenotype candidate for obsessive-compulsive disorder. Progress in NeuroPsychopharmacology and Biological Psychiatry.

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https://doi.org/10.1016/j.pnpbp.2014.09.005

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Figure Captions

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Figure 1: Screening and Selection Process for Publications

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Figure 2: Summary of neuroimaging findings from studies, focusing on 5 key brain regions within the frontostriatal and frontoparietal circuits. Box colours and brain area colours correspond to each other. ↓ represents hypoactivation and ↑ represents hyperactivation (comparing patients to healthy controls). Each arrow is equivalent to one study. Abbreviations, PFC: Prefrontal cortex, OFC: orbitofrontal cortex, ACC: anterior cingulate cortex, fNIRS: functional near-infrared spectroscopy, MFC: Medial frontal cortex, EEG: Electroencephalogram

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Figure 3: Enhanced ERN and prediction error signals are proposed to modulate (to an extent)

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cognition, alongside clinical symptoms in paediatric OCD. ERN: Error Related Negativity.

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Figure 4: Endophenotype models reproduced (with permission) from Kendler and Neale (2010). Top – Liability-Index Model; gene independently codes for endophenotype and disorder. Changes to one component will not affect the other. Bottom – Mediational Model; gene effects on endophenotype and disorder are no longer independent. Changes to the endophenotype can change presentation of

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the psychiatric disorder.

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Figure 5: An adaptation of the original multivariate model proposed by Kendler and Neale (2010) to account for possible environmental effects on genes and the mediational effect of an

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endophenotype on the disorder. Endophenotype 1 is independent from the disorder, while Endophenotype 2 has a mediational relationship with the disorder. Both are influenced by

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environmental and genetic factors. The environment also has an effect on the genotype, to account for findings from epigenetic research. Our additions are the arrows coloured in red.

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Table 1 Proposed Neurocognitive Endophenotypes, Associated Tasks, and Studies Endophenotype

Tasks

Authors



Iowa Gambling Task

Ozcan et al., 2016; Viswanath et al., 2009; Zhang et al., 2015a;

Planning

 

Tower of London Tower of Hanoi

Bey et al., 2018; Cavedini et al., 2010; Delorme et al., 2007; Li et al., 2012; Vaghi et al., 2017; Zhang, Yang, & Yang, 2015

Action Monitoring



Flanker Task

Riesel, Endrass, Kaufmann, & Kathmann, 2011; Riesel et al., 2019

Inhibition

 

Stroop Colour Word Test Stop Signal Task

Memory (working, verbal, and nonverbal)

   

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Decision-Making

Digit Span N-Back Test Rey Complex Figure Test Rey Auditory Verbal Learning Test Figural Memory Test Logical Memory Test (from Wechsler Memory Scale)

De Vries et al., 2014; Li et al., 2012; Ozcan et al., 2016; Rajender et al., 2011; Segalas et al., 2010

Probabilistic reversal learning task Delayed Alternation Test

Chamberlain et al., 2008; Tezcan et al., 2017; Viswanath et al., 2009

Wisconsin Card Sorting Task Trail Making Task-B

Cavedini et al., 2010; Chamberlain et al., 2007; Ozcan et al., 2016; Rajender et al., 2011

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 

Reversal-Learning (either behavioural or associated with orbitofrontal cortex dysfunction)



Cognitive Flexibility







Chamberlain et al., 2007; de Wit et al., 2007; Menzies et al., 2007; Zhang et al., 2015

74

Intra-Extra Dimensional Set Shift Test

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

75

Demographic and Clinical Information of Subjects from Each Study No. of Patients

No. of Controls

Age Range

Ages

Y-BOCS

Hybel et al. (2017)

50 (15 male)

50 (15 male)

7-17 years

Patients: 25.34 13.1(2.35) (2.97) Controls: 13.1(2.38)

Wolff et al. (2017)

25 (13 male)

25 (11 male)

Comorbidities

Medication

pr

Author (Year)

oo

f

Table 2

22 (14 males)

Comments

ID/ED Shift TMT-B CGT SST Flanker task Spatial Working Memory Task Spatial Span SOC

N/A

Compared to a sample of anxiety patients.

Patients: 22.07(7.1 13.5(2.35) 8) Controls: 13.4 (1.98)

None

All unmedicate d

-Cognitive Flexibility -Working memory

Task Switching paradigm

EEG/E RP/Sour ce Localisa tion

-

Patients: 14.1 (2.01) Controls: 14.5 (2.01)

No information

SSRIs (4), ADHD medication (Stratter, 2)

-Cognitive Flexibility

Backwards Inhibition Task (Similar to Task Switching but with 3 different cues)

EEG/E RP/sour ce localisat ion

-

e-

-Cognitive flexibility -DecisionMaking -Inhibition -Working memory

na l No inform ation

Neuroi maging Method

All unmedicate d

Pr 20 (10 males)

Neuropsycho logical Task(s)

Separation Anxiety (1) Phobia (4); Social Phobia (1) GAD(3) ODD(1)

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Wolff et al. (2018)

No inform ation

Cognitive Function(s) Studied

15.71 (8.79)

76

6-17 years

Patients: 11.4 (3.05)

20.9 (5.04)

Unknown medication (61)

-Cognitive Flexibility -Inhibition -Working memory -Nonverbal memory

WCST Stroop Test Digit Span (from WISCIII) RCFT Arithmetic scaled score

N/A

Andre s et al. (2007)

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Pr

e-

Controls: 11.6 (3.03)

MDD Lifetime (46); MDD Current (20) Bipolar (16); dysthymia (9); Panic disorder (12); specific phobia (27); GAD (44); social phobia (15); ADHD(42); PTSD (2); Tourette’s (30); Pervasive Developmenta l Disorder (4); ODD (46)

f

161 (92 male)

oo

102 (57 male)

pr

Geller et al. (2018)

35 (18 male)

35 (18 male)

7-18 years

Patients: 27.7 13.8(2.78) (5.34) Controls: 13.8(2.74)

Data from 64 patients and 62 controls available for WCST; 99 patients and 78 controls for Stroop task; and 93 patients and 68 controls for RCFT. Did not exclude controls with psychiatric disorders (except OCD) to allow for a more ecologically valid control sample. Significance was set at p<.01

None

SSRIs (16)

-Cognitive Flexibility -Inhibition -Verbal Memory

WCST TMT-B Stroop Test

N/A

Significance was set at p<.01

77

WMS-III Logical Memory Test Visual Reproduction Test RAVLT

SSRIs (12)

-Cognitive Flexibility -Inhibition

WCST TMT-B Stroop Test

Diffusio n Tensor Imaging

Combined TMT-B and Stroop test into one score to measure inhibition and cognitive control.

None

SSRIs/antip sychotics (6)

-Cognitive Flexibility -Inhibition Visuospatia l Memory

WCST TMT-B Continuous Performance Task RCFT

N/A

Compared data with 25 patients with ADHD and 21 patients with tic disorder.

GAD(2); Specific Phobia (2); Agoraphobia (1); MDD(2); Depression-

SSRIs (12), Tricyclics (3)

-Cognitive Flexibility

Novel Switching Task

fMRI

-

23(12 males)

9-17 years

Patients: 14.3(2.1) Controls: 14.2(2.2)

Shin et al. (2008)

17 (11 males)

23 (20 males)

6-16 years

Patients: 12.1(3.00) Controls: 10.1(2.35)

15(9 males)

20 (13 males)

MDD(4); Social Anxiety (2); Panic Disorder (2); ADHD (5)

Used Leyton Obsession al Inventory (Child version). Score: 32.7 (2.00)

na l

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Britton et al. (2010)

26.9 (4.48)

e-

23(13 males)

Pr

Gruner et al. (2012)

pr

oo

f

Visuospatia l Memory

10-17 years

Patients: 15.4(5.7) 13.5(2.40) Controls: 13.6(2.40)

78

15 (11 males)

Ornste in et al. (2010)

14 (4 males)

24 (11 males)

9-17 years

Patients: 12.6(2.6) Controls: 11.9(2.7)

20.8(8.4)

f

GAD (6); Depression (4)

SSRI(1)

pr

16 (11 males)

na l

Pr

e-

Chang et al. (2007)

oo

NOS(1); Tourette's (1); ADHD (2)

Patients: 19.3(5.24) Tourette’s (2) 12.9(1.89) GAD (2) Controls: 12.8(2.50)

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8-16 years

Unknown medication (3)

-Cognitive Flexibility -Inhibition -Working Memory -Spatial Working Memory Visuospatia l Memory -Verbal Memory

Object Alternation Test Stroop Test Digit Span from WISCIII RCFT Finger Windows CAVLT

N/A

-

-Cognitive Flexibility -Inhibition -Planning -Working Memory -Spatial Working Memory -Verbal Memory Visuospatia l Memory

WCST TMT-B SST CAVLT Spatial Span Task from WISC-III N-Back Test RCFT D-KEFS Tower Test

N/A

-

79

No inform ation

Patients: 12.3 (2.90) Controls: 12.2 (2.90)

No informati on

Gottw ald et al. (2018)

36(11 males)

36(11 males)

12-19 years

Patients: 16.6 (1.90) Controls: 16.6 (2.10)

25.1(5.0)

Kim et al. (2018)

28 (15 males)

65 (27 males)

7-17 years

Patients: No 12.6 informati (2.50) on Controls: 13.0(2.90)

None

f

21 (12 males)

All unmedicate d

-Cognitive Flexibility -Inhibition -Working Memory -Planning

WCST TMT-B GNG Task WISC-III Digit Span CAVLT TOH Stroop Test

N/A

-

SSRIs (23)

-Cognitive Flexibility -Visual Memory

ID/ED Shift Task PRM Task PAL Task

N/A

-

Psychotropi cs (18)

-Cognitive Flexibility -Spatial Working Memory -Visual Memory -Planning

ID/ED Shift Task PRM Task Spatial Span Task SOC

N/A

Compared OCD sample to patients with GAD

oo

21 (12 males)

e-

pr

Beers et al. (1999)

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na l

Pr

None

ADHD (3); GAD (9); Social Anxiety Disorder (7); Tourette's (7)

Of these 18, SSRIs (7), alpha agonist (1), antiepileptic (1), antipsychotic (1), stimulant (1)

80

f Baykal et al. (2014)

35 (no gender informat ion)

35 (no gender informat ion)

11-17 years

Patients: 15.3 (2.10) Controls: 15.6 (1.90)

18.6 (6.9)

oo

40 (21 males)

Anxiety disorders (19), ADHD (10), Tic Disorders (6), affective disorders (6), eating disorders (5)

SSRIs (39), SRIs (11), in 13 of these 50, antipsychotics were combined

-Cognitive Flexibility -Inhibition -Working Memory -VerbalMemory -NonVerbal memory Visuospatia l Memory

Stroop Test Digit Span Logical Memory Test of WMS-III Visual Reproduction Test (WMSIII), RCFT TMT-B

N/A

Separated patient group into two clusters: globally Preserved group (n=52) and selectively Impaired (n=9) group. There were no demographic and clinical differences between the two groups.

No information

-Cognitive Flexibility -Inhibition

WCST Continuous Performance Task Stroop Test

N/A

-

pr

61 (33 males)

na l

Pr

e-

Garcia Delgar et al. (2018)

No age informati on

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8-15 years

No informati on

25 comorbid (tic disorder, ADHD, pervasive anxiety disorder, phobia, oppositional defiant disorder, social anxiety disorder,

81

13.117.8 years

Patients: 15.7 (1.50) Controls: 15.0 (1.10)

Carlisi et al. (2017)

20 (all male)

20 (all male)

11-17 years

Patients: 15.7 (1.40) Controls: 15.3 (1.80)

15.3 (9.6)

Specific Phobia (2); ADHD (2); Conduct Disorder (1); Other childhood emotional disorders (2); Tic Disorder (1)

f SSRIs (8); neuroleptic s (2)

-DecisionMaking

Information Sampling Task

N/A

-

pr

16 (8 males)

e-

16 (13 males)

Pr

Hauser et al. (2017)

oo

MDD, enuresis)

Antidepress ants (4)

-DecisionMaking

Temporal Discounting Task

fMRI

Compared to 29 patients with ADHD

Patients: No 11.6(2.70) informati Controls: on 11.3(2.80)

None

All unmedicate d

-Cognitive Flexibility -Inhibition -Working Memory

WCST Stroop Test WISC-R Digit Span

N/A

-

na l

None

Jo ur

Taner et al. (2011)

22.3 (5.8)

20 (8 males)

20 (10 males)

7-16 years

82

9 (all male)

12-17 years

Patients: 14.3(1.7) Controls: 14.5(1.1)

20.5(8.3)

None

Ota et al. (2013)

12 (6 males)

12 (6 males)

9-14 years

Patients: 11.6 (2.07) Controls: 11.4 (1.50)

22.1 (4.87)

No comorbid Axis-1 Disorders

Waters and Farrell (2014)

12 (6 males)

15 (7 males)

9-12 years

Patients: 9.2 (1.2) Controls: 10.1 (1.1)

SSRIs (8)

-Cognitive Flexibility -Inhibition

Switch Task Stop Task Motor Stroop Test

fMRI

-

All unmedicati ed

-Inhibition

Stroop Test

Nearinfared Spectros copy

-

GAD (7) Social Phobia (3) Specific Phobia (2)

SSRIs (4)

-Inhibition

Emotional GNG Task

None

SSRIs (4) Unknown (2)

-Inhibition Visuospatia l Memory -Cognitive Flexibility -Planning

Walk Don’t Walk RCFT ConceptGeneration Test (Child version)

N/A

Compared OCD and control group with 19 Autism Spectrum Disorder patients.

-Inhibition

Stroop Test

Nearinfared

Compared performance

f

10 (all male)

Pr

e-

pr

oo

Wooll ey et al. (2008)

21.4(8.9) Or

They reported two different severity scores for their patient sample using the CYBOCS.

Yama muro

17(8 male)

18 (6 male)

12 (5 male)

15 (8 male)

7-16 years

Patients: 12.3 (2.17) Controls: 11.9 (2.7)

Jo ur

Zandt et al. (2009)

na l

20.2 (8.25)

No informati on

Patients: Pre-SSRI: None 13.5(3.26) 26.0(3.52)

83

All unmedicate d

f

Controls: Post12.5(3.02) SSRI: 15.5(7.51)

oo

No inform ation

pr

et al. (2017)

Spectros copy

before medication and after being treated with SSRIs during 3 year follow-up.

16 (7 males)

53 (29 males)

8-12 years

Patients: 17.38(7.7) ADHD (2) 10.9 (1.47) Controls: 10.8(1.15)

Antidepress ants (7) Antipsychotics (1) Both (1)

-Inhibition

SST

fMRI

Compared with 26 autism spectrum patients

Batistu zzo et al. (2015)

25 (14 males)

25 (14 males)

8.117.5 years

Patients: 12.7(2.6) Controls: 12.2(2.4)

10 patients received no medication for 15 days before study and 15 patients were completely medicationnaïve.

-Verbal memory

Verbal Episodic Memory Test

fMRI

-

Pr

e-

Goosk ens et al. (2008)

Jo ur

na l

27.2(5.0)

ADHD(9) GAD (7) ODD (5) MDD (4) Separation Anxiety (4) Skin picking (4) Tic disorders (3) Social Phobia (2) PTSD (1) Trichotilloman ia (1) Body Dysmorphic Disorder (1)

84

f 25 (9 males)

9-19 years

16 (9.4)

Patients: 14.0 (2.52) Controls: 13.7 (2.85)

25.0 (5.08)

Jo ur

Norma n et al. (2018)

20 (all male)

20 (all male)

12-18 years

No information

Patients: 15.8 (1.43) Controls:

oo

25 (9 males)

Patients: 17.2 (3.33) Controls: 17.4 (3.14)

No information

-Verbal Working Memory

N-Back Memory Test

fMRI

-

All -Planning unmedicate d at time of study but 5 patients had history of medication: SSRIs (2) Risperione (2) Methylphe nidate + dexamphet amine +atomoxeti ne (1)

TOL

fMRI

-

SSRIs (4) SSRIs+Ris peridone (1)

Iowa Gambling Task

fMRI

-

pr

Huyse r et al. (2010)

11-21 years

e-

27 (18 males)

SAD (4) Social Phobia (16) Specific Phobia (16) GAD (8) PTSD (4) Depression (8) Dysthymia (4) ADHD (8) ODD (4) Tic Disorder (8)

Pr

18 (11 males)

na l

Diwad kar et al. (2015)

22.3(5.97) None

-DecisionMaking

85

22 (12 males)

22 (12 males)

10-15 years

Patients: 13.6 (1.84) Controls: 13.5 (1.72)

22.4(6.2)

Vloet et al. (2010)

20 (all male)

25 (all male)

10-18 years

Patients: 14.2 (2.3) Controls: 14.0 (1.6)

Erhan et al. (2017)

21 (no gender informat ion)

23 (no gender informat ion)

26 (8 males)

27 (14 males)

SSRIs (8) Tricyclics (2)

-DecisionMaking

Iowa Gambling Task

N/A

-

Compulsi ons: 6.31 (3.88) Obsession s: 9.8 (5.52)

Tic Disorder (2) Anxiety Disorder (4) ODD/CD (2)

SSRIs (10)

-DecisionMaking

Delay Aversion Task

N/A

Compared to 20 patients with ADHD.

e-

Pr

na l No inform ation

Patients: 12.00 (1.90) Controls: 12.5 (1.14)

No informati on

None

All medication free for at least 6 months before study

-DecisionMaking

Dot Motion N/A Discriminatio n Task

-

8-16 years

Patients: 12.7 (2.2) Controls: 12.4(2.2)

Schedule for Obsessive -

History of GAD/SAD (7)

SSRIs (11)

-Action Monitoring

Flanker Task

17 had a current diagnosis of OCD while 9

Jo ur

Carras co et al.

Tic disorders (3) Trichotilloman ia (3) Stuttering (1) Somatization disorder (1)

pr

Kodair a et al. (2012)

oo

f

15.2 (1.99)

EEG (ERN/C RN)

86

oo

f

had a past diagnosis with minimal OCD symptoms (not reaching criteria for OCD diagnosis) at the time of the experiment. Amplitudes were calculated for electrodes FCz and Cz.

pr

Compulsi ve and Other Behaviora l Syndrome s used. Score: 5.7(3.7)

Pr

e-

(2013a )

44 (22 males)

10-19 years

Patients (tic related): 13.6 (3.5) Patients: (non ticrelated) 13.9 (2.3) Controls: 13.9(2.3)

Tic OCD: 21.1(5.8) Non-tic OCD: 15.2(8.9)

na l

Tic related: 9 (6 males) Non-tic related: 35 (14 males)

Jo ur

Hanna et al. (2012)

ADHD (1) Tic disorder (9)

SSRIs (16)

-Action Monitoring

Flanker Task

EEG (ERN/C RN)

-Tic related and non-tic related OCD were compared. -29 had a current diagnosis of OCD; 15 had a past diagnosis. ERN readings from electrodes FCz and CZ were obtained.

87

8-19 years

Patients: 12.9 (3.0) Controls: 14.1 (3.1)

18.7(9.6)

Fitzger ald et al. (2018)

51 (24 males)

51 (28 males)

8-19

Patients :14.2 (2.8) Controls : 14.1 (3.2)

17.8 (7.4)

f

25 (all female)

Separation anxiety disorder (1) GAD (3) Specific phobia (1) Panic Disorder (2) Dysthymia (2) Tics (1)

SSRIs (1)

Anxiety disorders (27) Tic Disorders (18) Subclinical Depression (13) ADHD (9) Impulse Control Disorder (4) Developmenta l Coordination Disorder (1)

SSRIs (33) Guanfacine (1) Seven SSRItreated patients were also taking aripiprazole (1) quetiapine (1), risperidone (1), buspirone (1), guanfacine (1), methylphen idate (1) or risperidone plus

-Action Monitoring

MSIT

fMRI

-

-Action monitoring

MSIT

fMRI

Comorbidity information includes all patients screened and not just the ones included in the study:

oo

21 (all female)

Jo ur

na l

Pr

e-

pr

Fitzger ald et al. (2013)

88

89 (20 males)

13-18 years

Patients: 15.9(1.8) Controls: 16.2 (1.8)

OCD + MDD: 22.4(6.6) OCDMDD: 14.2 (8.6)

MDD (14)

SSRIs (25)

-Action Monitoring

Flanker Task

EEG (ERP)

pr

53 (14 males)

25 (9 males)

8-19 years

Pr

25 (9 males)

Patients: 25.0(5.08) Anxiety 14.0(2.52) disorders (12) Controls: Affective 13.7 Disorder (3) (2.85) ADHD/ODD( 3) Tic disorder (2)

None at the time of data collection

-Action Monitoring

Flanker Task

fMRI

Studied brain activity during action monitoring before and after patients received CBT.

SSRIs (7)

-Action monitoring

Flanker Task

EEG (ERP) fMRI

-Data from electrode Cz were loed at. -MRI was conducted after EEG some months (5) later or before for

Liu et al. (2014)

Jo ur

na l

Huyse r et al. (2011)

20 (10 males)

20 (12 males)

10-19 years

Patients: 15.2(9.30) GAD (2) 14.0(2.20) Depressive Controls: disorder not 14.6(2.40) otherwise (this is specified (4) age at Specific ERP) phobia (1) Panic disorder (1)

Compared data to 36 MDD patients. Separated OCD patients with MDD and without into separate groups.

e-

Hanna et al. (2018)

oo

f

methylphen idate (1).

89

18 (8 males)

8-17 years

Patients: 13.3 (2.80) Controls: 11.9 (2.60)

Hanna et al. (2016)

80 (31 males)

80 (36 males)

8-18 years

Patients: 13.5 (3.00) Controls: 13.6 (3.00)

25.6 (5.10)

No information

SSRIs or Tricyclics or NDRI (13)

-Action Monitoring

Simon’s Task

EEG (ERP)

Readings taken from Fz, Cz, and Pz, but final measure came from Fz. Compared action monitoring/E RN before CBT treatment and after.

SSRIs (34)

-Action Monitoring

Flanker Task

EEG (ERP)

-54 had a current diagnosis of OCD and 26 a past diagnosis no longer reaching criterion for OCD. -ERN recorded from Cz and FCz. Focus was on Cz.

SSRIs (16)

-Action Monitoring

Flanker Task

EEG (ERP)

Compared patients to

pr

18 (13 males)

Pr

e-

Hajcak et al. (2008)

oo

f

some patients.

Jo ur

na l

16.1(9.40) 61 had a history of at least 1 other axis 1 disorder

Carras co et

40 (18 males)

10-17 years

Patients: 16.0(8.90) Tic disorder 13.9(2.40) (8)

90

control and unaffected siblings. 11 of the siblings were related to patients in this study, while others had siblings with OCD who did not take part in the study. -25 patients had a current diagnosis while 15 had a past diagnosis with minimal current OCD symptoms. ERN readings taken from FCz and Cz.

f

Trichotilloman ia (2) ADHD (2) Separation anxiety disorder (6) Panic disorder (1) Specific phobia (6) Social Phobia (7) Agoraphobia (1) GAD (7) History of MDD (4)

oo

Siblings: 13.9(2.40) Controls: 13.8(2.30)

pr

Siblings of OCD patients: 19 (13 males) Controls : 40 (20 males)

Fitzger ald et al. (2010)

Jo ur

na l

Pr

e-

al. (2013b )

18 (6 males)

18 (6 males)

8-18 years

Patients: 16.1 13.9 (7.30) (2.60) Controls: 14.1(2.60)

Separation anxiety disorder (5) GAD (1) Anxiety disorder not otherwise specified (3)

SSRIs (12)

-Action Monitoring

MSIT

fMRI

91

48 (22 males)

8-19 years

Patients: 18.1 14.0 (7.50) (3.10) Controls: 13.9(3.10)

f oo

Separation anxiety disorder (4) GAD (2) anxiety NOS (2) Depressive disorder NOS (5) MDD (2) Tic disorder (6)

e-

48 (22 males)

SSRIs or Benzodiaza pine (24)

-Action Monitoring

MSIT

N/A -

Pr

Liu et al. (2012)

pr

Depression NOS (2) Tic disorder (2)

Jo ur

na l

Non-integer values have been rounded off to 3 significant figures. Abbreviations: OCD, Obsessive-Compulsive Disorder; ID/ED, IntradimensionalExtradimensional; SST, Stop Signal Task; CGT, Cambridge Gambling Task; SOC, Stockings of Cambridge; TMT-B, Trail Making Task-B; RCFT, Rey’s Complex Figure Task; WCST, Wisconsin Card Sorting Task; RAVLT, Rey’s Auditory Verbal Learning Task; CPT, Continuous Performance Task; CAVLT, California Auditory Verbal Learning Task; D-KEFS, Delis-Kaplan Executive Function System; GNG, Go/No-Go; WISC-III, Wechsler Intelligence Scale for Children - III; WISC-R, Wechsler Intelligence Scale for Children - Revised; PRM, Pattern Recognition Memory; PAL, Paired Associates Learning; SOC, Stockings of Cambridge; CY-BOCS, Child Yale-Brown Obsessive Compulsive Scale; WMS-III, Wechsler’s Memory Scale-III; IGT, Iowa Gambling Task; OFC, Orbitofrontal Cortex; SSRI, Selective Serotonin Reuptake Inhibitor; SRI, Serotonin Reuptake Inhibitor; NDRI, Noradrenaline Reuptake Inhibitor; fMRI, Functional Magnetic Resonance Imaging; EEG, Electroencephalogram; ERP, Event Related Potential; CRN, Correct-Related Negativity; ERN, ErrorRelated Negativity; MSIT, Multisource Inference Task; MDD, Major Depressive Disorder; PTSD, Post Traumatic Stress Disorder; GAD, Generalised Anxiety Disorder; ADHD, Attentional Deficit Hyperactivity Disorder; ODD, Oppositional Defiant Disorder; SAD, Separation Anxiety Disorder; CD, Conduct Disorder; NOS, Not otherwise specified; OC, Obsessive-Compulsive; CBCL, Child Behavior Checklist; CBT, Cognitive Behavioural Therapy; N/A, Not Applicable.

92

oo

f

Table 3 Results from Each Study Cognitive Flexibility

Inhibition

Decision-Making

Hybel et al. (2017)

ID/ED n.s TMT-B n.s.

SST n.s Flanker n.s.

CGT n.s.

Wolff et al. (2017)

Cue-based switching task n.s EEG ↓P1 amplitudes during memorybased switching compared to memory-based repetition. This was associated with ↓ activation in right inferior frontal gyrus and temporal gyrus.

-

Memory

Action Monitoring

Planning

Comments

Spatial Working Memory Task n.s. Spatial Span task n.s

Flanker task n.s.

SOC n.s.

-

↑ RTs during memory-based compared to cue-based switching on Task Switching test.

-

-

-

-

-

-

-

Pr

e-

pr

Author (Year)

Jo ur

na l

-

-

Wolff et al. (2018)

-

Backwards Inhibition version of Task Switching Test:

93

oo

f

↓ backward inhibition effect

Gruner et al. (2012)

e-

-

Pr

Stroop Test n.s

↑ nonperseverative errors on WCST. TMT-B n.s.

Stroop Test n.s

-

Jo ur

Andres et al. (2007)

WCST n.s

na l

Geller et al. (2018)

pr

EEG ↑P1 amplitude during backwards inhibition, associated with ↑ activation in right inferior frontal gyrus.

↓ categories completed on

Stroop Test n.s.

↓ Arithmetic test scores ↓ RCFT delay accuracy

-

-

-

-

-

Logical Memory Test performance was similar to controls when controlled for depression scores

-

-

-Patients exhibited ↑ FA compared to

Digit span n.s. ↓delayed recall on Logical Memory test ↓immediate recall on Visual Reproduction Test ↓ delayed and immediate recall on RCFT RAVLT n.s. Digit Span n.s.

-

-

94

WCST for medicated patients. ↑ functional anisotropy (FA) in the left dorsal cingulum bundle correlated with better performance on the TMT-B among patients.

↑FA in the left dorsal cingulum bundle was correlated with better performance on the Stroop Test among patients.

↑perseverative errors and overall errors on WCST ↓categories completed on WCST.

CPT n.s

-

Working Memory Test n.s. RCFT n.s

-

-

-

Jo ur

-

-

-

-

-

Shin et al. (2008)

na l

Pr

e-

pr

oo

f

controls in 4 white matter pathways: left dorsal cingulum bundle, splenium of the corpus callosum, right corticospinal tract, and left inferior fronto-occipital fasciculus. -Global executive functioning score (combined score of all tasks) correlated with higher FA in the left dorsal cingulum bundle among patients.

TMT-B n.s.

Britton et al. (2010)

↑RTs during mixed trials on Task Switching test

-

↓inferior frontal gyrus activation

95

Stroop n.s

oo

Object Alternation Test n.s.

↓ performance on Finger Windows Test.

-

-

-

-

-

↑ moves to solve the DKEFS Tower test.

-

TOH n.s.

-

-

-

pr

Chang et al. (2007)

f

during set shifting trials

↑ RTs on the TMT-B.

SST n.s

TMT-B n.s

Gottwald et al. (2018)

↑pre-ED errors on the ID/ED task

↑ intrusions from List 2 during the free recall phase on the CAVLT RCFT n.s. Spatial Span Test n.s. N-back n.s.

↑performance on the GNG task ↑ performance on the Stroop test

-

WISC-III Digit Span n.s. CAVLT n.s.

-

-

↓immediate and delayed recall on Pattern Recognition Memory Test.

Jo ur

WCST n.s

na l

WCST n.s

Beers et al. (1999)

-

Pr

Ornstein et al. (2010)

e-

RCFT n.s. Digit Span n.s. CAVLT n.s

-

96

ID/ED n.s

-

-

PRM test n.s Spatial Span Task n.s

f -

-

-

-

Jo ur

na l

Pr

Huyser et al. (2010)

e-

pr

Kim et al. (2018)

oo

↑errors on the PAL test.

-

-

On the SOC:

-

↑moves on levels 3 and 5 ↑ RTs on levels 3 and 4 ↑ RTs before cognitivebehavioural treatment

CY-BOCS scores were significantly correlated with changes in BOLD activity in the left Neuroimaging dorsolateral PFC. ↓activation in the left Brain activation and posterior behavioural dorsolateral differences between PFC and the patients and controls right parietal disappeared following cortex before cognitive-behavioural treatment. treatment. ↑activation on dorsolateral PFC, left dorsal ACC and right dorsomedial PFC due to increasing task load.

97

f Stroop test n.s.

oo

TMT-B n.s

-

↓performance on non-verbal memory (RCFT and WMS-III Visual test) for the Selectively Impaired patient group.

-

-

-

-

-

-

-

e-

pr

GarciaDelgar et al. (2018)

On the Stroop Test ↑ reaction-times

-

WMS-III Verbal Test n.s.

na l

On the WCST ↑ errors overall ↑perseverative errors ↑ reaction-times ↓ categories completed

On the CPT ↑ errors of commission and omission

Jo ur

Baykal et al. (2014)

Pr

Digit Span n.s.

Kodaira et al. (2012)

WCST n.s

-

↑disadvantageous card selections on the IGT

-

-

-

-

Taner et al. (2011)

On the WCST ↑ errors overall ↑perseverative errors ↓ categories completed

On the Stroop test ↑ errors overall ↑ reaction-times

-

Digit span n.s. Arithmetic test n.s.

-

-

-

98

Ota et al. (2013)

Neuroimaging ↓activation in right and left inferior parietal and superior temporal cortices, reaching into precentral and inferior prefrontal cortices, bilateral vermis, and right hemisphere of the cerebellum.

Stroop Test n.s.

-

-

-

-

-

Pr

e-

pr

Neuroimaging Stop Task ↓activation in right and left OFC, right thalamus, and basal ganglia. Neuroimaging Stroop test ↓activation in the right and left cerebellar vermis and right middle temporal gyrus.

-

f

Stop Task n.s.

oo

Switch Task n.s.

na l

Woolley et al. (2008)

Stroop test n.s.

-

-

-

-

-

-

-

-

-

-

RCFT n.s

-

-

-

Jo ur

Neuroimaging ↓Oxyhemoglobin changes in the PFC

Waters and Farrell (2014)

-

↑errors of commission on No Go trials, independently of stimuli type.

Zandt et al. (2009)

99

↓performance on the Stroop test even after SSRI treatment.

f

-

-

Pr

Stop Signal Task n.s.

-

-

-

-

-

-

-

Verbal episodic memory test n.s.

-

-

-

na l

-

-

e-

Neuroimaging ↓concentrations of oxyhaemoglobin in the frontopolar region of the anterior PFC. Gooskens et al. (2018)

-

oo

↓performance on Walk Don’t Walk task.

pr

Yamamuro et al. (2017)

Concept Generation Task n.s.

Jo ur

Neuroimaging Brain activation differences using fMRI n.s.

Batistuzzo et al. (2015)

-

-

-

Neuroimaging ↓ dorsomedial PFC activity

100

-

No information about N-Back Test performance

-

f

-

-

-

oo

-

Neuroimaging ↑activation in the frontal and parietal regions. Activation of frontal, parietal, and striatal regions were modulated by the dorsal ACC during high and low working memory loads.

-

Norman et al. (2018)

-

-

Delay Aversion task n.s

-

-

-

-

-

IGT n.s.

-

-

-

-

Jo ur

Vloet et al. (2010)

na l

Pr

e-

pr

Diwadkar et al. (2015)

Neuroimaging ↓activation in the ventromedial OFC and ventral striatum during advantageous choices, ↓ activity in medial PFC to losses and ↓ activity in left

101

-

-

Dot Discrimination Task: ↑reaction-times ↓ drift rates ↑ decision thresholds after erroneous decisions

-

-

-

-

Hauser et al. (2017)

-

-

Information Sampling Task: ↑points won ↑ information gathering (cards turned) ↑decision thresholds

-

-

-

Number of points won primarily driven by the decreasing condition. Information gathering correlated with selfreport indecisiveness

Carlisi et al. (2017)

-

-

-

-

-

na l

Pr

e-

pr

Erhan et al. (2017)

oo

f

putamen/caudate to wins.

Jo ur

-

Temporaldiscounting n.s. Neuroimaging ↓ activation to delayed vs. immediate choices (ventromedial/lateral OFC, medial/inferior PFC, cerebellum, posterior cingulate, and precuneus) ↓ activation to immediate vs.

102

-

-

oo

pr

-

-

-

-

Jo ur

Hanna et al. (2012)

na l

Pr

e-

Carrasco et al. (2013a)

f

delayed choices (ACC, ventromedial PFC, left caudate, bilateral temporal, inferior parietal)

Fitzgerald et al. (2013)

-

-

-

Flanker Task n.s.

-

Anxiety patients without OCD also showed increased ERN amplitude.

-

-

-

-

EEG ↑ERN amplitude, independent of OCD symptom severity -CBCL anxiety score correlated most with ERN amplitude.

-

↑errors for the non-tic OCD patients. ↑ERN amplitude

-

-

MSIT n.s. Neuroimaging ↓activation of the dorsolateral PFC during

103

-

-

-

-

-

e-

pr

-

-

Jo ur

Huyser et al. (2011)

-

-

-

MSIT n.s.

-

-

-

-

-

Error monitoring unaffected by CBT treatment.

Neuroimaging ↑error-related activation of the posterior MFC. ↑errors on Flanker Task EEG ↑ERN amplitude in OCD patients compared to controls, but not compared to MDD patients.

na l

Hanna et al. (2018)

-

Pr

Fitzgerald et al. (2018)

oo

f

errors on the MSIT, but not during conflict processing.

-

Flanker task n.s Neuroimaging ↑activation in the ACC during error trials ↑activation in the insula

104

-

-

-

-

-

Hanna et al. (2016)

Carrasco et al. (2013b)

-

-

na l

-

Jo ur

Hajcak et al. (2008)

Pr

e-

pr

Liu et al. (2014)

oo

f

during high conflict trials.

-

-

-

-

-

Flanker Task n.s.

-

ERN amplitude correlated with OC scores (measured via CBCL) in healthy children, but not in patients.

-

ERN in patients unaffected by CBT treatment.

-

ERN was significantly associated with the Withdrawn/Depressed scale scores of the CBCL.

-

ERN amplitude independent of OC symptom severity.

Neuroimaging/ EEG ↑ERN ERN amplitude was correlated with grey matter density in the posterior MFC and OFC. Simon’s Task n.s. EEG ↑ERN amplitude

-

↑errors on Flanker Task EEG ↑ERN amplitude

-

-

Flanker task n.s.

105

-

-

-

e-

-

Jo ur

na l

Pr

Fitzgerald et al. (2010)

pr

oo

f

↑ERN amplitude for patients and unaffected siblings compared to controls. MSIT n.s. Neuroimaging ↑activation of the MFC, from the posterior MFC (especially dorsal ACC) to the ventral MFC during interference processing.

-

Patients exhibited reduced functional connectivity between the dorsal ACC and the right anterior operculum, as well as reduced connectivity between ventral MFC and posterior cingulate cortex.

During errors, ↑activation in the vMFC.

↓RTs following errors on the MSIT during incongruent trials All results represent patients compared to healthy controls. ↑, increased, ↓, decreased, n.s., no significant difference. Dashed line (-) in row indicates that the study did not investigate that specific cognitive function. Liu et al. (2012)

-

-

-

-

106

Jo ur

na l

Pr

e-

pr

oo

f

Abbreviations: OCD, Obsessive-Compulsive Disorder; ID/ED, Intradimensional-Extradimensional; SST, Stop Signal Task; CGT, Cambridge Gambling Task; SOC, Stockings of Cambridge; TMT-B, Trail Making Task-B; RT, Reaction Time; RCFT, Rey’s Complex Figure Task; WCST, Wisconsin Card Sorting Task; RAVLT, Rey’s Auditory Verbal Learning Task; FA, Functional Anisotrophy; CPT, Continuous Performance Task; CAVLT, California Auditory Verbal Learning Task; D-KEFS, Delis-Kaplan Executive Function System; GNG, Go/No-Go; WISC-III,; PRM, Pattern Recognition Memory; PAL, Paired Associates Learning; SOC, Stockings of Cambridge; CY-BOCS, Child Yale-Brown Obsessive Compulsive Scale; PFC, Prefrontal cortex; ACC, Anterior Cingulate Cortex; WMS-III, Wechsler’s Memory Scale-III; IGT, Iowa Gambling Task; OFC, Orbitofrontal Cortex; SSRI, Selective Serotonin Reuptake Inhibitor; fMRI, Functional Magnetic Resonance Imaging; EEG, Electroencephalogram; MSIT, Multisource Inference Task; MFC, Medial Frontal Cortex; MDD, Major Depressive Disorder; OC, Obsessive-Compulsive; CBCL, Child Behavior Checklist; CBT, Cognitive Behavioural Therapy.

107