Anahit Mkrtchian’s research while affiliated with University College London and other places

Importance: To support treatment assignment, mechanistic biomarkers should be selectively sensitive to specific interventions. Here, we examine whether different components of reinforcement learning in humans satisfy this necessary precondition. We focus on pharmacological manipulations of dopamine and serotonin that form the backbone of first-line management of common mental illnesses such as depression and anxiety. Objective: To perform a meta-analysis of pharmacological manipulations of dopamine and serotonin and examine whether they show distinct causal effects on reinforcement learning components in healthy humans. Data Sources: Ovid MEDLINE/PubMed, Embase, and PsycInfo databases were searched for studies published between January 1, 1946 and January 19, 2023 (repeated April 9, 2024, and October 15, 2024) investigating dopaminergic or serotonergic effects on reward/punishment processes in healthy humans. Study Selection: Studies reporting randomized, placebo-controlled, dopaminergic or serotonergic manipulations on a behavioral outcome from a reward/punishment processing task in healthy humans were included. Data Extraction and Synthesis: Standardized mean difference (SMD) scores were calculated for the comparison between each drug (dopamine/serotonin) and placebo on a behavioral reward or punishment outcome and quantified in random-effects models for overall reward/punishment processes and four main subcategories. Study quality, moderators, heterogeneity, and publication bias were also assessed. Main Outcome(s) and Measure(s): Performance on reward/punishment processing tasks. Results: In total, 68 dopamine and 39 serotonin studies in healthy volunteers were included (Ndopamine=2452, Nplacebo=2432; Nserotonin=1364, Nplacebo=1393 participants). Dopamine increased overall reward (SMD=0.21; 95%CI [0.12 0.30]) but not punishment function (SMD=-0.09; 95%CI [-0.27,0.10]). Serotonin did not meaningfully affect overall punishment (SMD=0.22; 95%CI [-0.04,0.49]) or reward (SMD=0.01; 95%CI [-0.33,0.35]). Importantly, dopaminergic and serotonergic manipulations had distinct and selective effects on subcomponents. Dopamine affected reward learning/sensitivity (SMD=0.25; 95%CI [0.10,0.40]), reward discounting (SMD=-0.08; 95%CI [-0.14,-0.01]) and reward vigor (SMD=0.32; 95%CI [0.11,0.54]). By contrast, serotonin shaped punishment learning/sensitivity (SMD=0.32; 95%CI [0.05,0.59]), reward discounting (SMD=-0.35; 95%CI [-0.67,-0.02]), and aversive Pavlovian processes (within-subject studies only; SMD=0.36; 95%CI [0.20,0.53]). Conclusions and Relevance: Pharmacological manipulations of both dopamine and serotonin have measurable effects on reinforcement learning in humans. The selective effects on different components suggests that reinforcement learning tasks could form the basis of selective, mechanistically interpretable biomarkers to support treatment assignment.

Background Motivational dysfunction is a core feature of depression, and can have debilitating effects on everyday function. However, it is unclear which disrupted cognitive processes underlie impaired motivation, and whether impairments persist following remission. Decision-making concerning exerting effort to collect rewards offers a promising framework for understanding motivation, especially when examined with computational tools which can offer precise quantification of latent processes. Methods Effort-based decision-making was assessed using the Apple Gathering Task, in which participants decide whether to exert effort via a grip-force device to obtain varying levels of reward; effort levels were individually calibrated and varied parametrically. We present a comprehensive computational analysis of decision-making, initially validating our model in healthy volunteers (N=67), before applying it in a case-control study including current (N=41) and remitted (N=46) unmedicated depressed individuals, and healthy volunteers with (N=36) and without (N=57) a family history of depression. Results Four fundamental computational mechanisms that drive patterns of effort-based decisions, which replicated across samples, were identified: an overall bias to accept effort challenges; reward sensitivity; and linear and quadratic effort sensitivity. Traditional model-agnostic analyses showed that both depressed groups showed lower willingness to exert effort. In contrast with previous findings, computational analysis revealed that this difference was driven by lower effort acceptance bias, but not altered effort or reward sensitivity. Conclusions This work provides insight into the computational mechanisms underlying motivational dysfunction in depression. Lower willingness to exert effort could represent a trait-like factor contributing to symptoms, and might represent a fruitful target for treatment and prevention.

Computational models can offer mechanistic insight into cognition and therefore have the potential to transform our understanding of psychiatric disorders and their treatment. For translational efforts to be successful, it is imperative that computational measures capture individual characteristics reliably. To date, this issue has received little consideration. Here we examine the reliability of reinforcement learning and economic models derived from two commonly used tasks. Healthy individuals (N=50) completed a restless four-armed bandit and a calibrated gambling task twice, two weeks apart. Reward and punishment processing parameters from the reinforcement learning model showed fair-to-good reliability, while risk/loss aversion parameters from a prospect theory model exhibited good-to-excellent reliability. Both models were further able to predict future behaviour above chance within individuals. This prediction was better when based on participants’ own model parameters than other participants’ parameter estimates. These results suggest that reinforcement learning, and particularly prospect theory parameters, can be measured reliably to assess learning and decision-making mechanisms, and that these processes may represent relatively distinct computational profiles across individuals. Overall, these findings indicate the translational potential of clinically-relevant computational parameters for precision psychiatry.

Well-established animal models of depression have described a proximal relationship between stress and central nervous system (CNS) inflammation - a relationship mirrored in the peripheral inflammatory biomarkers of individuals with depression. Evidence also suggests that stress-induced proinflammatory states can contribute to the neurobiology of treatment-resistant depression. Interestingly, ketamine, a rapid-acting antidepressant, can partially exert its therapeutic effects via anti-inflammatory actions on the hypothalamic-pituitary-adrenal (HPA) axis, the kynurenine pathway or by cytokine suppression. Further investigations into the relationship between ketamine, inflammation and stress could provide insight into ketamine's unique therapeutic mechanisms and stimulate efforts to develop rapid-acting, anti-inflammatory-based antidepressants.

Ketamine is a rapidly-acting antidepressant and has shown to be effective in depressed individuals who have previously failed to benefit from other available treatments. An important question is how ketamine works. Addressing this might help inform more targeted and efficient treatments in the future. The aim of this thesis was to examine the neural, cognitive, and computational mechanisms underpinning the antidepressant response to ketamine in treatment-resistant depression. The work has specifically focused on motivational processing, since ketamine is particularly effective in alleviating symptoms of anhedonia, which are thought to be related to impaired reward-related function. Following a general introduction (Chapter 1), the first experimental chapter (Chapter 2) focuses on identifying suitable reward and punishment tasks for repeated testing in a clinical trial. Test retest properties of various tasks are explored in healthy individuals, assessed by both traditional measures of task performance (e.g., accuracy) and computational parameters. Chapter 3 outlines a pilot simultaneous EEGfMRI study in healthy individuals probing the neural dynamics of the motivation to exert cognitive effort, an important but understudied process in depression. The third study (Chapter 4) uses resting-state fMRI to examine how ketamine modulates fronto-striatal circuitry, which is known to drive motivational behaviour, in depressed and healthy individuals. The final experimental chapter (Chapter 5) examines which cognitive and computational measures of motivational processing (using tasks identified in Chapter 2) change following a single dose of ketamine compared to placebo in depression, using a crossover design. Based on preliminary findings, it is tentatively proposed that ketamine might affect reward processing by enhancing fronto-striatal circuitry functional connectivity, as well as by increasing exploratory behaviours, and possibly punishment learning rates. The general discussion (Chapter 6) discusses these findings in relation to contemporary models of anhedonia and antidepressant action, considering both the limitations of the work presented and possible future directions.

Background: Computational models can offer mechanistic insight into cognition and therefore have the potential to transform our understanding of psychiatric disorders and their treatment. For translational efforts to be successful, it is imperative that computational measures capture individual characteristics reliably. To date, this issue has received little consideration. Methods: Here we examine the reliability of canonical reinforcement learning and economic models derived from two commonly used tasks. Healthy individuals (N=50) completed a restless four-armed bandit and a calibrated gambling task twice, two weeks apart. Results: Reward and punishment processing parameters from the reinforcement learning model showed fair-to-good reliability, while risk/loss aversion parameters from a prospect theory model exhibited good-to-excellent reliability. Both models were further able to predict future behaviour above chance within individuals. Conclusions: These results suggest that reinforcement learning, and particularly prospect theory measures, represent relatively reliable decision-making mechanisms, which are also unique across individuals, indicating the translational potential of clinically-relevant computational parameters for precision psychiatry.

Ketamine improves motivation-related symptoms in depression but simultaneously elicits similar symptoms in healthy individuals, suggesting that it might have different effects in health and disease. This study examined whether ketamine affects the brain's fronto-striatal system, which is known to drive motivational behavior. The study also assessed whether inflammatory mechanisms-which are known to influence neural and behavioral motivational processes-might underlie some of these changes. These questions were explored in the context of a double-blind, placebo-controlled, crossover trial of ketamine in 33 individuals with treatment-resistant major depressive disorder (TRD) and 25 healthy volunteers (HVs). Resting-state functional magnetic resonance imaging (rsfMRI) was acquired 2 days post-ketamine (final sample: TRD n = 27, HV n = 19) and post-placebo (final sample: TRD n = 25, HV n = 18) infusions and was used to probe fronto-striatal circuitry with striatal seed-based functional connectivity. Ketamine increased fronto-striatal functional connectivity in TRD participants toward levels observed in HVs while shifting the connectivity profile in HVs toward a state similar to TRD participants under placebo. Preliminary findings suggest that these effects were largely observed in the absence of inflammatory (C-reactive protein) changes and were associated with both acute and sustained improvements in symptoms in the TRD group. Ketamine thus normalized fronto-striatal connectivity in TRD participants but disrupted it in HVs independently of inflammatory processes. These findings highlight the potential importance of reward circuitry in ketamine's mechanism of action, which may be particularly relevant for understanding ketamine-induced shifts in motivational symptoms.

Over the past three decades, functional MRI (fMRI) has become key to study how cognitive processes are implemented in the human brain. However, the question of whether participants recruited into fMRI studies differ from participants recruited into other study contexts has received little to no attention. This is particularly pertinent when effects fail to generalize across study contexts: for example, a behavioural effect discovered in a non-imaging context not replicating in a neuroimaging environment. Here, we tested the hypothesis, motivated by preliminary findings (n = 272), that fMRI participants differ from behaviour-only participants on one fundamental individual difference variable: trait anxiety. Analysing trait anxiety scores and possible confounding variables from healthy volunteers across multiple institutions (n = 3317), we found robust support for lower trait anxiety in fMRI study participants, consistent with a sampling or self-selection bias. The bias was larger in studies that relied on phone screening (compared to full in-person psychiatric screening), recruited at least partly from convenience samples (compared to community samples), and in pharmacology studies. Our findings highlight the need for surveying trait anxiety at recruitment and for appropriate screening procedures or sampling strategies to mitigate this bias.