Neurocognitive Mechanisms of Learning and Decision-Making in Adolescent-OCD: A Computational Approach

Early-onset obsessive-compulsive disorder (OCD) is substantially less researched than adult-OCD, resulting in prevalent equivocation surrounding the neurocognitive profile of child-OCD. Research into this area is pivotal as population studies report that youths with OCD struggle significantly in academic settings. In the General Introduction of this thesis, I reviewed existing literature and found that strikingly, young patients do not show impairment on features that are considered both hallmarks of adult OCD and tightly linked to disorder symptomatology, such as response inhibition and cognitive flexibility. Among the characteristics that are thought to be present in children and adolescents with OCD are abnormal decision-making under uncertainty and impaired learning, and I decided to focus on these features as they may be driving poor academic attainment in young people with the disorder. In addition, I sought to investigate other cognitive processes that have not been well-researched in adolescent-OCD but are found to be robustly altered in adult OCD such as goal directed/model-based reasoning, meta-cognition, and feedback sensitivity. I aimed to delineate these various processes using a battery of suitably complex cognitive tasks. Moreover, I highlighted that majority of past studies fail to find differences between young patients and controls due to behavioural signatures being too subtle to be uncovered by standard statistical analyses. Hence, I employed computational modelling of cognitive task data to disentangle latent decision-making processes displayed by adolescents with OCD. In Chapter 2, I modelled data from the Wisconsin Card Sorting task, a frequently used paradigm of cognitive flexibility, and confirmed that youths with OCD show equivalent performance on the task to controls. Only patients on serotonergic medication showed increased response latencies and a tendency to make unique errors (choosing a deck associated with no rule present on the test card). Next, in Chapter 3, I sought to understand instrumental and Pavlovian learning, and whether adolescents with OCD show increased punishment sensitivity on a novel aversive Pavlovian-to Instrumental Transfer paradigm. Once again, patient performance was equivalent to that of controls. Hence, the remaining chapters were dedicated to probing behaviour on probabilistic paradigms. In Chapter 4, I formally investigated model-based and model-free learning using a well-validated two step decision-making task, and fit a reinforcement learning drift diffusion model to both choice and reaction time data. Patients showed increased exploration on the task as well as faster and more erratic decisions compared to controls. Nonetheless, model-based learning was equivalent between groups. In the penultimate chapter, I demonstrate on a predictive-inference task that patients with OCD update their choices more frequently compared to controls independent of prediction error magnitude. Finally, in Chapter 6, I administered a probabilistic reversal learning paradigm to a large sample of 50 adolescent patients and 53 matched controls. Standard analyses revealed a significant reversal learning deficit in patients with OCD, wherein they displayed more errors and a lower propensity to repeat choices following positive feedback during the post-reversal phase. Crucially, computational modelling revealed striking group differences where adolescents with OCD displayed elevated reward learning and lower punishment learning, increased exploration, and decreased perseveration compared to controls. In the General Discussion, I emphasise that atypical learning and decision-making in adolescent-OCD are more pronounced on probabilistic tasks, where task environments are more volatile. Results are partly discussed in the context of the uncertainty model of OCD, where subjective feelings of doubt experienced by patients drive compulsive behaviours such as checking and certainty-seeking in daily life, alongside excessive exploration on probabilistic tasks. I also consider various explanations for cognitive distinctions between adult- and adolescent OCD. More general implications of the findings are discussed for understanding OCD in the context of adolescent development and for treatment/support strategies.WELLCOME TRUST (104631/Z/14/Z

Compulsive Avoidance in Youths and Adults with Obsessive-Compulsive Disorder: An Aversive Pavlovian-To-Instrumental Transfer Study

Background Compulsive behaviour is often triggered by Pavlovian cues. Assessing how Pavlovian cues drive instrumental behaviour in obsessive-compulsive disorder (OCD) is therefore crucial to understand how compulsions develop and are maintained. An aversive Pavlovian-to-Instrumental transfer (PIT) paradigm, particularly one involving avoidance/cancellation of negative outcomes, can enable such investigation and has not previously been studied in clinical-OCD. Methods Forty-one participants diagnosed with OCD (21 adults; 20 youths) and 44 controls (21 adults; 23 youths) completed an aversive PIT task. Participants had to cancel unpleasant noises by learning response-outcome (instrumental) and stimulus-outcome (Pavlovian) associations. We then assessed whether Pavlovian cues elicited specific instrumental avoidance responses (specific PIT) and induced general instrumental avoidance (general PIT). We investigated whether task learning and confidence indices influenced PIT strength differentially between groups. Results Urge to avoid unpleasant noises and preference for safe over unsafe stimuli influenced specific and general PIT respectively in OCD, while PIT in controls was more influenced by confidence in instrumental and Pavlovian learning. However, there was no overall group difference in PIT performance, although youths with OCD showed weaker specific PIT than youth controls. Conclusions In OCD, implicit motivational factors, but not learnt knowledge, contribute to the successful integration of aversive Pavlovian and instrumental cues. This implies that compulsive avoidance may be driven by these automatic processes. Youths with OCD show deficits in specific PIT, suggesting cue integration impairments are only apparent in adolescence. These findings are clinically relevant as they emphasise the importance of targeting such implicit motivational processes when treating OCD

Research data supporting "Association of Environmental Uncertainty With Altered Decision-making and Learning Mechanisms in Youths With Obsessive-Compulsive Disorder"

In the 'PRL' folder, there is a 'core' and a 'support' folder. The raw data are saved in 'core' as 'probrev_forstan.csv'. The data in this file are collected from 103 adolescents (50 with OCD and 53 without) who completed a probabilistic reversal learning task (details in paper). The file contains information about subject ID (patient_num), trials, chosen stimulus per trial (chosen_stim), whether stimulus 1 was chosen (stim1_chosen), whether subject chose correct stimulus (chosen_correctly), task feedback (1 - positive, 0 - negative; fdbk), group (1 - Control, 0 - OCD). This data are in a format ready to undergo computational modelling. To run the models, use the Runner.R file in the 'core' folder. The models themselves are in .stan format. Next, in the 'WCST' folder, the raw data (in the wcst_data.txt) file are obtained from 73 adolescents (27 with OCD 46 without) who completed a Wisconsin Card Sorting Task (details in paper). Relevant columns in the wcst_data file are subject ID (subnum), trial (trial), whether the card chosen matches the test card on colour (corr_col), shape (corr_shape) and number (corr_name), group (0 -OCD, 1 - CTL), whether card was chosen correctly (corr). The main script for fitting the computational models to data is WCST_JAGS_2021.R. Models themselves are in .txt files (files that end in _dnormsd_groupdiff).Wellcome Trust grant 104631/Z/14/Z

Error- and inhibitory-related brain activity associated with political ideology: A multi-site replication study

The relationship between political ideology and brain activity has captured the fascination of scientists and the public alike. Using approaches from cognitive neuroscience to provide insights into deeply held and personal beliefs requires careful navigation, with the application of robust methods that generate replicable results. A hallmark study in this area from Amodio et al. (2007) reported that brain components reflective of conflict monitoring and inhibition (namely the ERN [error-related negativity] and N2) are heightened in individuals who self-identify as liberal compared to conservative. While the study is highly influential and well-cited in the scientific literature, no direct replications of their findings exist and as such, this work was selected as a target replication for the #EEGManyLabs initiative. This cross-cultural multi-site study (N=320) will conduct a thorough replication of the Amodio et al. (2007) study, strictly adhering to the original protocol, namely by administering a Go/No-Go task with simultaneous EEG recording and a one-item scale asking participants to rate the extent to which they are liberal or conservative. We will supplement the original study with new measures that may better correspond to political identity in non-US contexts, such as religiosity, dogmatism, and traditionalism. In line with the original study, we will conduct correlational analyses between self-identified liberalism and ERN/N2 amplitudes. In addition, Bayesian linear regressions will be used to provide robust estimates of the strength of association between other components of political ideology and electrophysiological signals

Revisiting the Neurocognitive Correlates of the Behavioral Inhibition and Activation Systems

The Behavioral Inhibition System (BIS) and the Behavioral Activation System (BAS) are cornerstones of neurobehavioral research. Personality scales have been developed to capture the behavioral and motivational tendencies associated with these systems, and many studies have attempted to link these scales with basic neurocognitive processes. The results, however, have been inconclusive. Here, we aim to replicate a seminal study on this topic by Amodio et al. (2008), in which the authors used a Go/No-Go task to test the association of the trait BIS with cognitive control and the BAS trait with approach tendency. The authors found significant correlations that were mutually exclusive from each other; BAS did not correlate with measures of cognitive control, and BIS did not correlated with measures of approach tendency. Despite the paper’s high citation frequency and influence on the field, there has been no direct replication to date. These factors motivated the inclusion of this study in the #EEGManyLabs project, an international community-driven effort to replicate influential EEG results and this registered report forms a part of this initiative. Following the original study, a Go/No-Go experiment will be performed with a total of 320 participants across eight replicating labs. EEG will be recorded both during the experiment and in an eight-minute resting period. Target variables are the amplitude of the N2 during a successfully inhibited response, the amplitude of the error-related negativity (ERN) after an erroneous response, left frontal asymmetry (LFA) during rest, and trait BIS/BAS measured by the Carver and White questionnaire. Both Pearson’s and Spearman rank sum correlations, as well as regression analyses will be used to test the hypotheses that trait BIS is associated with ERN and N2 amplitudes, and that trait BAS is associated with LFA during rest