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The motivational mechanisms driving the antidepressant effect of ketamine
Abstract
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.
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Dopamine in the prefrontal cortex can be disrupted in human disorders that affect cognitive function such as Parkinson's disease (PD), attention-deficit hyperactivity disorder (ADHD), and schizophrenia. Dopamine has a powerful effect on prefrontal circuits via the D1-type dopamine receptor (D1DR). It has been proposed that prefrontal dopamine has "inverted U-shaped" dynamics, with optimal dopamine and D1DR signaling required for peak cognitive function. However, the quantitative relationship between prefrontal dopamine and cognitive function is not clear. Here, we conducted a meta-analysis of published manipulations of prefrontal dopamine and the effects on working memory, a high-level executive function in humans, primates, and rodents that involves maintaining and manipulating information over seconds to minutes. We reviewed 646 articles and found that 75 studies met criteria for inclusion. Our quantification of effect sizes for dopamine, D1DRs, and behavior revealed a negative quadratic slope. This is consistent with the proposed inverted U-shape of prefrontal dopamine and D1DRs and working memory performance, explaining 10% of the variance. Of note, the inverted quadratic fit was much stronger for prefrontal D1DRs alone, explaining 26% of the variance, compared to prefrontal dopamine alone, explaining 10% of the variance. Taken together, these data, derived from a variety of manipulations and systems, demonstrate that optimal prefrontal dopamine signaling is linked with higher cognitive function. Our results provide insight into the fundamental dynamics of prefrontal dopamine, which could be useful for pharmacological interventions targeting prefrontal dopaminergic circuits, and into the pathophysiology of human brain disease. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Magnetic resonance imaging (MRI) has transformed our understanding of the human brain through well-replicated mapping of abilities to specific structures (for example, lesion studies) and functions1–3 (for example, task functional MRI (fMRI)). Mental health research and care have yet to realize similar advances from MRI. A primary challenge has been replicating associations between inter-individual differences in brain structure or function and complex cognitive or mental health phenotypes (brain-wide association studies (BWAS)). Such BWAS have typically relied on sample sizes appropriate for classical brain mapping⁴ (the median neuroimaging study sample size is about 25), but potentially too small for capturing reproducible brain–behavioural phenotype associations5,6. Here we used three of the largest neuroimaging datasets currently available—with a total sample size of around 50,000 individuals—to quantify BWAS effect sizes and reproducibility as a function of sample size. BWAS associations were smaller than previously thought, resulting in statistically underpowered studies, inflated effect sizes and replication failures at typical sample sizes. As sample sizes grew into the thousands, replication rates began to improve and effect size inflation decreased. More robust BWAS effects were detected for functional MRI (versus structural), cognitive tests (versus mental health questionnaires) and multivariate methods (versus univariate). Smaller than expected brain–phenotype associations and variability across population subsamples can explain widespread BWAS replication failures. In contrast to non-BWAS approaches with larger effects (for example, lesions, interventions and within-person), BWAS reproducibility requires samples with thousands of individuals.
Importance:
Computational psychiatry studies have investigated how reinforcement learning may be different in individuals with mood and anxiety disorders compared with control individuals, but results are inconsistent.
Objective:
To assess whether there are consistent differences in reinforcement-learning parameters between patients with depression or anxiety and control individuals.
Data sources:
Web of Knowledge, PubMed, Embase, and Google Scholar searches were performed between November 15, 2019, and December 6, 2019, and repeated on December 3, 2020, and February 23, 2021, with keywords (reinforcement learning) AND (computational OR model) AND (depression OR anxiety OR mood).
Study selection:
Studies were included if they fit reinforcement-learning models to human choice data from a cognitive task with rewards or punishments, had a case-control design including participants with mood and/or anxiety disorders and healthy control individuals, and included sufficient information about all parameters in the models.
Data extraction and synthesis:
Articles were assessed for inclusion according to MOOSE guidelines. Participant-level parameters were extracted from included articles, and a conventional meta-analysis was performed using a random-effects model. Subsequently, these parameters were used to simulate choice performance for each participant on benchmarking tasks in a simulation meta-analysis. Models were fitted, parameters were extracted using bayesian model averaging, and differences between patients and control individuals were examined. Overall effect sizes across analytic strategies were inspected.
Main outcomes and measures:
The primary outcomes were estimated reinforcement-learning parameters (learning rate, inverse temperature, reward learning rate, and punishment learning rate).
Results:
A total of 27 articles were included (3085 participants, 1242 of whom had depression and/or anxiety). In the conventional meta-analysis, patients showed lower inverse temperature than control individuals (standardized mean difference [SMD], -0.215; 95% CI, -0.354 to -0.077), although no parameters were common across all studies, limiting the ability to infer differences. In the simulation meta-analysis, patients showed greater punishment learning rates (SMD, 0.107; 95% CI, 0.107 to 0.108) and slightly lower reward learning rates (SMD, -0.021; 95% CI, -0.022 to -0.020) relative to control individuals. The simulation meta-analysis showed no meaningful difference in inverse temperature between patients and control individuals (SMD, 0.003; 95% CI, 0.002 to 0.004).
Conclusions and relevance:
The simulation meta-analytic approach introduced in this article for inferring meta-group differences from heterogeneous computational psychiatry studies indicated elevated punishment learning rates in patients compared with control individuals. This difference may promote and uphold negative affective bias symptoms and hence constitute a potential mechanistic treatment target for mood and anxiety disorders.
In order to develop effective treatments for anhedonia we need to understand its underlying neurobiological mechanisms. Anhedonia is conceptually strongly linked to reward processing, which involves a variety of cognitive and neural operations. This chapter reviews the evidence for impairments in experiencing hedonic response (pleasure), reward valuation and reward learning based on outcomes (commonly conceptualised in terms of "reward prediction error"). Synthesising behavioural and neuroimaging findings, we examine case-control studies of patients with depression and schizophrenia, including those focusing specifically on anhedonia. Overall, there is reliable evidence that depression and schizophrenia are associated with disrupted reward processing. In contrast to the historical definition of anhedonia, there is surprisingly limited evidence for impairment in the ability to experience pleasure in depression and schizophrenia. There is some evidence that learning about reward and reward prediction error signals are impaired in depression and schizophrenia, but the literature is inconsistent. The strongest evidence is for impairments in the representation of reward value and how this is used to guide action. Future studies would benefit from focusing on impairments in reward processing specifically in anhedonic samples, including transdiagnostically, and from using designs separating different components of reward processing, formulating them in computational terms, and moving beyond cross-sectional designs to provide an assessment of causality.
In this longitudinal study of children and adolescents with a documented history of maltreatment, we investigated the impact of maltreatment on behavioral and neural indices of effort-based decision making for reward and examined their associations with future internalizing symptoms. Thirty-seven children with a documented history of maltreatment (MT group) and a carefully matched group of 33 non-maltreated children (NMT group) aged 10–16, completed an effort-based decision-making task during functional magnetic resonance imaging (fMRI). Internalizing symptoms were assessed at baseline and again 18 months later. Computational models were implemented to extract individual estimates of reward and effort sensitivity, and neural signals during decision-making about different levels of reward and effort were analyzed. These were used to predict internalizing symptoms at follow-up. We identified lower effort-related activation in the anterior cingulate cortex (ACC), a prespecified region-of-interest, in the MT relative to the NMT group. No group differences were observed in the striatum, or in behavioral indices of reward and effort processing. Lower effort-related ACC activation significantly predicted elevated internalizing symptoms at follow-up in the MT group. These findings suggest that disrupted effort-related activation may index latent vulnerability to mental illness in children who have experienced maltreatment.
Objective
Consistent evidence suggests residual depressive symptomology are the strongest predictors of depression relapse following cognitive-behavioral therapy (CBT) and antidepressant medications (ADM's). Psychometric network models help detecting and understanding central symptoms that remain post-treatment, along with their complex co-occurrences. However, individual psychometric network studies show inconsistent findings. This systematic review and IPD network analysis aimed to estimate and compare the symptom network structures of residual depressive symptoms following CBT, ADM's, and their combination.
Methods
PsycINFO, PsycArticles, and PubMed were systematically searched through October 2020 for studies that have assessed individuals with major depression at post-treatment receiving either CBT and/or ADM's (venlafaxine, escitalopram, mirtazapine). IPD was requested from eligible samples to estimate and compare residual symptom psychometric network models post-CBT and post-ADM's.
Results
In total, 25 from 663 eligible samples, including 1,389 patients qualified for the IPD. Depressed mood and anhedonia were consistently central residual symptoms post-CBT and post-ADM's. For CBT, fatigue-related and anxiety symptoms were also central post-treatment. A significant difference in network structure across treatments (CBT vs. ADM) was observed for samples measuring depression severity using the MADRS. Specifically, stronger symptom occurrences were present amongst lassitude-suicide post-CBT (vs. ADM's) and amongst lassitude-inability to feel post-ADM's (vs. CBT). No significant difference in global strength was observed across treatments.
Conclusions
Core major depression symptoms remain central across treatments, strategies to target these symptoms should be considered. Anxiety and fatigue related complaints also remain central post-CBT. Efforts must be made amongst researchers, institutions, and journals to permit sharing of IPD.
Systematic Review Registration: A protocol was prospectively registered on PROSPERO (CRD42020141663; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=141663).
The Effort-Expenditure for Rewards Task (EEfRT) has gained validity evidence from several studies. However, various modifications have been applied to the original version, which have never been compared systematically. In Study 1, we tested 120 healthy participants to directly compare two versions of the EEfRT. In Study 2, we tested a larger sample of 394 healthy participants to further examine the original EEfRT. We replicated the split-half reliability of both task versions. However, self-reported personality traits (e.g., trait BAS) correlated with only some task performance parameters in Study 1, which did not replicate for the original EEfRT in Study 2. Our results indicate complex and sometimes inconsistent relations between different personality traits, task properties, and reward attributes.
Exploration reduces uncertainty about the environment and improves the quality of future decisions, but at the cost of provisional uncertain and suboptimal outcomes. Although anxiety promotes intolerance to uncertainty, it remains unclear whether and by which mechanisms anxiety relates to exploratory decision-making. We use a dynamic three-armed-bandit task and find that higher trait-anxiety is associated with increased exploration, which in turn harms overall performance. We identify two distinct behavioral sources: first, decisions made by anxious individuals are guided toward reduction of uncertainty; and second, decisions are less guided by immediate value gains. These findings are similar in both loss and gain domains, and further demonstrate that an affective trait relates to exploration and results in an inverse-U-shaped relationship between anxiety and overall performance. Additional imaging data (fMRI) suggests that normative anxiety correlates negatively with the representation of expected-value in the dorsal-anterior-cingulate-cortex, and in contrast, positively with the representation of uncertainty in the anterior-insula. We conclude that a trade-off between value-gains and uncertainty-reduction entails maladaptive decision-making in individuals with higher normal-range anxiety.
Anhedonia constitutes one of the main symptoms of depressive episode. It correlates with suicidality and significantly effects the quality of patient's lives. Available treatments are not sufficient against this group of symptoms. Ketamine is a novel, rapid acting strategy for treatment resistant depression. Here we report the change in symptoms of anhedonia measured by Snaith-Hamilton Pleasure Scale as an effect of eight ketamine infusions as an add-on treatment in 42 patients with treatment resistant depression. We also determined the effect of this change on the severity of depressive symptoms measured by Inventory for Depression Symptomatology-Self Report 30-Item (IDS-SR 30). We have observed statistically significant decrease in the level of anhedonia during ketamine treatment. After adjusting for potential confounders we have found that significant reduction in Snaith-Hamilton Pleasure Scale (SHAPS) after each infusion and 1 week post treatment was observed only among patients who did not use benzodiazepines. The reduction in symptoms of anhedonia mediates the antidepressive effect of ketamine. The results need replication in a larger randomized placebo controlled trial.
Concepts of cognitive control (CC) and executive function (EF) are defined in terms of their relationships with goal-directed behavior versus habits and controlled versus automatic processing, and related to the functions of the prefrontal cortex (PFC) and related regions and networks. A psychometric approach shows unity and diversity in CC constructs, with 3 components in the most commonly studied constructs: general or common CC and components specific to mental set shifting and working memory updating. These constructs are considered against the cellular and systems neurobiology of PFC and what is known of its functional neuroanatomical or network organization based on lesioning, neurochemical, and neuroimaging approaches across species. CC is also considered in the context of motivation, as “cool” and “hot” forms. Its Common CC component is shown to be distinct from general intelligence ( g ) and closely related to response inhibition. Impairments in CC are considered as possible causes of psychiatric symptoms and consequences of disorders. The relationships of CC with the general factor of psychopathology (p) and dimensional constructs such as impulsivity in large scale developmental and adult populations are considered, as well as implications for genetic studies and RDoC approaches to psychiatric classification.
Importance
Major depressive disorder is prevalent and impairing. Parsing neurocomputational substrates of reinforcement learning in individuals with depression may facilitate a mechanistic understanding of the disorder and suggest new cognitive therapeutic targets.
Objective
To determine associations among computational model–derived reinforcement learning parameters, depression symptoms, and symptom changes after treatment.
Design, Setting, and Participants
In this mixed cross-sectional–cohort study, individuals performed reward and loss variants of a probabilistic learning task during functional magnetic resonance imaging at baseline and follow-up. A volunteer sample with and without a depression diagnosis was recruited from the community. Participants were assessed from July 2011 to February 2017, and data were analyzed from May 2017 to May 2021.
Main Outcomes and Measures
Computational model–based analyses of participants’ choices assessed a priori hypotheses about associations between components of reward-based and loss-based learning with depression symptoms. Changes in both learning parameters and symptoms were then assessed in a subset of participants who received cognitive behavioral therapy (CBT).
Results
Of 101 included adults, 69 (68.3%) were female, and the mean (SD) age was 34.4 (11.2) years. A total of 69 participants with a depression diagnosis and 32 participants without a depression diagnosis were included at baseline; 48 participants (28 with depression who received CBT and 20 without depression) were included at follow-up (mean [SD] of 115.1 [15.6] days). Computational model–based analyses of behavioral choices and neural data identified associations of learning with symptoms during reward learning and loss learning, respectively. During reward learning only, anhedonia (and not negative affect or arousal) was associated with model-derived learning parameters (learning rate: posterior mean regression β = −0.14; 95% credible interval [CrI], −0.12 to −0.03; outcome sensitivity: posterior mean regression β = 0.18; 95% CrI, 0.02 to 0.37) and neural learning signals (moderation of association between striatal prediction error and expected value signals: t97 = −2.10; P = .04). During loss learning only, negative affect (and not anhedonia or arousal) was associated with learning parameters (outcome shift: posterior mean regression β = −0.11; 95% CrI, −0.20 to −0.01) and disrupted neural encoding of learning signals (association with subgenual anterior cingulate prediction error signals: r = −0.28; P = .005). Symptom improvement following CBT was associated with normalization of learning parameters that were disrupted at baseline (reward learning rate: posterior mean regression β = 0.15; 90% CrI, 0.001 to 0.41; loss outcome shift: posterior mean regression β = 0.42; 90% CrI, 0.09 to 0.77).
Conclusions and Relevance
In this study, the mapping of reinforcement learning components to symptoms of major depression revealed mechanistic features associated with these symptoms and points to possible learning-based therapeutic processes and targets.
Objective:
To examine if cognitive restructuring (CR), behavioral activation (BA), and cognitive-behavioral therapy (CBT) result in differential effects in the treatment of adult depression.
Method:
We extracted randomized controlled trials (RCTs) from a database updated yearly from PubMed, PsycINFO, Embase, and Cochrane Library. Network and pairwise meta-analyses were conducted to investigate the effects of CR, BA, and CBT delivered in a face-to-face individual format, compared with waiting list (WL) and care-as-usual (CAU), on adult depression. The primary outcome was a standardized mean difference (SMD) in posttreatment depression severity. Tolerability of treatments and depression severity at follow-up were also assessed.
Results:
A total of 45 studies with 3,382 participants were included. There was no evidence of a difference in effectiveness between CR, BA, and CBT. All three interventions were superior to CAU; SMD 0.57, 95% confidence interval [CI 0.08-1.07]; 0.52 [0.34-0.71]; 0.44 [0.28-0.60], respectively and WL 1.20 [0.69-1.70]; 1.15 [0.90-1.40]; 1.07 [0.87-1.26]. No difference in tolerability was found (risk ratio [RR] vs. CAU: 1.01 [0.04-22.81], 0.84 [0.63-1.11], and 0.96 [0.76-1.21], respectively). Metaregression and sensitivity analyses did not produce material differences.
Conclusions:
Results suggest that CR or BA alone and their combination (CBT) may be effective interventions in comparison to WL and CAU in the treatment of adult depression. There was no evidence suggesting differences in effectiveness among the three treatments. More research is needed to derive conclusions about the performance of CR. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Reinforcement learning (RL) is a concept that has been invaluable to fields including machine learning, neuroscience, and cognitive science. However, what RL entails differs between fields, leading to difficulties when interpreting and translating findings. After laying out these differences, this paper focuses on cognitive (neuro)science to discuss how we as a field might overinterpret RL modeling results. We too often assume — implicitly — that modeling results generalize between tasks, models, and participant populations, despite negative empirical evidence for this assumption. We also often assume that parameters measure specific, unique (neuro)cognitive processes, a concept we call interpretability, when evidence suggests that they capture different functions across studies and tasks. We conclude that future computational research needs to pay increased attention to implicit assumptions when using RL models, and suggest that a more systematic understanding of contextual factors will help address issues and improve the ability of RL to explain brain and behavior.
Objective. Simultaneous electroencephalography-functional magnetic resonance imaging (EEG-fMRI) recordings offer a high spatiotemporal resolution approach to study human brain and understand the underlying mechanisms mediating cognitive and behavioral processes. However, the high susceptibility of EEG to MRI-induced artifacts hinders a broad adaptation of this approach. More specifically, EEG data collected during fMRI acquisition are contaminated with MRI gradients and ballistocardiogram artifacts, in addition to artifacts of physiological origin. There have been several attempts for reducing these artifacts with manual and time-consuming pre-processing, which may result in biasing EEG data due to variations in selecting steps order, parameters, and classification of artifactual independent components. Thus, there is a strong urge to develop a fully automatic and comprehensive pipeline for reducing all major EEG artifacts. In this work, we introduced an open-access toolbox with a fully automatic pipeline for reducing artifacts from EEG data collected simultaneously with fMRI (refer to APPEAR). Approach. The pipeline integrates average template subtraction and independent component analysis to suppress both MRI-related and physiological artifacts. To validate our results, we tested APPEAR on EEG data recorded from healthy control subjects during resting-state (n= 48) and task-based (i.e. event-related-potentials (ERPs); n= 8) paradigms. The chosen gold standard is an expert manual review of the EEG database. Main results. We compared manually and automated corrected EEG data during resting-state using frequency analysis and continuous wavelet transformation and found no significant differences between the two corrections. A comparison between ERP data recorded during a so-called stop-signal task (e.g. amplitude measures and signal-to-noise ratio) also showed no differences between the manually and fully automatic fMRI-EEG-corrected data. Significance. APPEAR offers the first comprehensive open-source toolbox that can speed up advancement of EEG analysis and enhance replication by avoiding experimenters’ preferences while allowing for processing large EEG-fMRI cohorts composed of hundreds of subjects with manageable researcher time and effort.
Anhedonia, a pronounced reduction in interest or pleasure in any of life’s daily activities, is a cardinal symptom of major depression. In this Perspective article, we synthesise the recent evidence from rodent, monkey and human neuroimaging literature to highlight how the habenula, a small evolutionarily conserved subcortical structure located in the midbrain, may orchestrate the behavioural expression of anhedonia across fronto-mesolimbic networks. We then review how this circuitry can be modulated by ketamine, an NMDA receptor antagonist with rapid antidepressant properties. We propose that experimental paradigms founded in reinforcement learning and value-based decision-making can usefully probe this network and thereby help elucidate the mechanisms underlying ketamine’s rapid antidepressant action.
People often make the difficult decision to try new options (exploration) and forego immediate rewards (exploitation). Novelty-seeking is an adaptive solution to this explore-exploit dilemma that has been studied using targeted recordings in monkeys, but our understanding of the neural computations supporting novelty-seeking in humans is limited. Here, we show homologous computations supporting novelty-seeking across humans and monkeys, and reveal a previously unidentified cortico-subcortical architecture mediating explore-exploit behavior in humans.
Background
Ketamine as an antidepressant improves anhedonia as early as 2h post-infusion. These drug effects are thought to be exerted via actions on reward-related brain areas—yet, these actions remain largely unknown. Our study investigates ketamine’s effects during the anticipation and receipt of an expected reward, after the psychotomimetic effects of ketamine have passed, when early antidepressant effects are reported.
Methods
We examined ketamine’s effects during the anticipation and receipt of expected rewards on pre-defined brain areas, namely the dorsal and ventral striatum, the ventral tegmental area, the amygdala and the insula. We have recruited 37 male and female participants who remitted from depression and were free from symptoms and antidepressant treatments at the time of the scan. Participants were scanned, 2h after drug administration, in a double-blind cross over design (ketamine:0.5mg/kg and placebo) while performing a monetary reward task.
Results
A significant main effect of ketamine, across all ROIs, was observed during the anticipation and feedback phases of win and no-win trials. The drug effects were particularly prominent in the nucleus accumbens and putamen, which showed increased activation upon the receipt of smaller rewards compared to neutral. The levels of (2R,6R)-HNK, 2h post-infusion, significantly correlated with the activation observed in the ventral tegmental area for that contrast.
Conclusions
These findings demonstrate that ketamine can produce detectable changes in reward-related brain areas, 2h after infusion, which occur without symptom changes and support the idea that ketamine might improve reward-related symptoms via modulation of response to feedback.
Understanding the brain requires us to answer both what the brain does, and how it does it. Using a series of examples, I make the case that behavior is often more useful than neuroscientific measurements for answering the first question.¹ Moreover, I show that even for “how” questions that pertain to neural mechanism, a well-crafted behavioral paradigm can offer deeper insight and stronger constraints on computational and mechanistic models than do many highly challenging (and very expensive) neural studies. I conclude that purely behavioral research is essential for understanding the brain—especially its cognitive functions—contrary to the opinion of prominent funding bodies and some scientific journals, who erroneously place neural data on a pedestal and consider behavior to be subsidiary.
Reinforcement Learning (RL) has revolutionized the cognitive and brain sciences, explaining behavior from simple conditioning to problem solving, across the life span, and anchored in brain function. However, discrepancies in results are increasingly apparent between studies, particularly in the developmental literature. To better understand these, we investigated to which extent parameters generalize between tasks and models, and capture specific and uniquely interpretable (neuro)cognitive processes. 291 participants aged 8-30 years completed three learning tasks in a single session, and were fitted using state-of-the-art RL models. RL decision noise/exploration parameters generalized well between tasks, decreasing between ages 8-17. Learning rates for negative feedback did not generalize, and learning rates for positive feedback showed intermediate generalizability, dependent on task similarity. These findings can explain discrepancies in the existing literature. Future research therefore needs to carefully consider task characteristics when relating findings across studies, and develop strategies to computationally model how context impacts behavior.
Highlights
Efforts in computational cognitive modeling often assume that Reinforcement Learning (RL) modeling parameters will generalize between studies and models, but this is not well established.
We empirically investigate whether RL parameters generalize between three tasks and models, using a large developmental dataset and a within-participant design.
We find that RL decision noise/exploration parameters generalize fairly well, but RL learning rates do not.
Our data support previous conclusions that decision noise/exploration decreases during development (ages 8-17), but suggests that claims about learning rate development cannot be generalized.
Ketamine, classical psychedelics and sleep deprivation are associated with rapid effects on depression. Interestingly, these interventions also have common psychotomimetic actions, mirroring aspects of psychosis such as an altered sense of self, perceptual distortions and distorted thinking. This raises the question whether these interventions might be acute antidepressants through the same mechanisms that underlie some of their psychotomimetic effects. That is, perhaps some symptoms of depression can be understood as occupying the opposite end of a spectrum where elements of psychosis can be found on the other side. This review aims at reviewing the evidence underlying a proposed continuum hypothesis of psychotomimetic rapid antidepressants, suggesting that a range of psychotomimetic interventions are also acute antidepressants as well as trying to explain these common features in a hierarchical predictive coding framework, where we hypothesise that these interventions share a common mechanism by increasing the flexibility of prior expectations. Neurobiological mechanisms at play and the role of different neuromodulatory systems affected by these interventions and their role in controlling the precision of prior expectations and new sensory evidence will be reviewed. The proposed hypothesis will also be discussed in relation to other existing theories of antidepressants. We also suggest a number of novel experiments to test the hypothesis and highlight research areas that could provide further insights, in the hope to better understand the acute antidepressant properties of these interventions.
The subdivisions of the anterior cingulate cortex (ACC) – including subgenual, perigenual and dorsal zones – are implicated in the etiology, pathogenesis and treatment of major depression. We review an emerging body of evidence which suggests that changes in ACC activity are critically important in mediating the antidepressant effects of ketamine, the prototypical member of an emerging class of rapidly acting antidepressants. Infusions of ketamine induce acute (over minutes) and post-acute (over hours to days) modulations in subgenual and perigenual activity, and importantly, these changes can correlate with antidepressant efficacy. The subgenual and dorsal zones of the ACC have been specifically implicated in ketamine’s anti-anhedonic effects. We emphasize the synergistic relationship between neuroimaging studies in humans and brain manipulations in animals to understand the causal relationship between changes in brain activity and therapeutic efficacy. We conclude with circuit-based perspectives on ketamine’s action: first, related to ACC function in a central network mediating affective pain, and second, related to its role as the anterior node of the default mode network.
Escaping aversive stimuli is essential for complex organisms, but prolonged exposure to stress leads to maladaptive learning. Stress alters neuronal activity and neuromodulatory signaling in distributed networks, modifying behavior. Here we describe changes in dopaminergic neuron activity and signaling following aversive learning in a learned helplessness paradigm in mice. A single dose of ketamine suffices to restore escape behavior after aversive learning. Dopaminergic neuron activity in the ventral tegmental area (VTA) systematically varies across learning, correlating with future sensitivity to ketamine treatment. Ketamine's effects are blocked by chemogenetic inhibition of dopamine signaling. Rather than directly altering the activity of dopaminergic neurons, ketamine appears to rescue dopamine dynamics through actions in the medial prefrontal cortex (mPFC). Chemogenetic activation of Drd1 receptor positive mPFC neurons mimics ketamine's effects on behavior. Together, our data link neuromodulatory dynamics in mPFC-VTA circuits, aversive learning, and the effects of ketamine.
Depressed patients often do not respond to the first antidepressant prescribed, resulting in sequential trials of different medications. Personalised medicine offers a means of reducing this delay; however, the clinical effectiveness of personalised approaches to antidepressant treatment has not previously been tested. We assessed the clinical effectiveness of using a predictive algorithm, based on behavioural tests of affective cognition and subjective symptoms, to guide antidepressant treatment. We conducted a multicentre, open-label, randomised controlled trial in 913 medication-free depressed patients. Patients were randomly assigned to have their antidepressant treatment guided by a predictive algorithm or treatment as usual (TaU). The primary outcome was the response of depression symptoms, defined as a 50% or greater reduction in baseline score of the QIDS-SR-16 scale, at week 8. Additional prespecified outcomes included symptoms of anxiety at week 8, and symptoms of depression and functional outcome at weeks 8, 24 and 48. The response rate of depressive symptoms at week 8 in the PReDicT (55.9%) and TaU (51.8%) arms did not differ significantly (odds ratio: 1.18 (95% CI: 0.89–1.56), P = 0.25). However, there was a significantly greater reduction of anxiety in week 8 and a greater improvement in functional outcome at week 24 in the PReDicT arm. Use of the PReDicT test did not increase the rate of response to antidepressant treatment estimated by depressive symptoms but did improve symptoms of anxiety at week 8 and functional outcome at week 24. Our findings indicate that personalisation of antidepressant treatment may improve outcomes in depressed patients.
Background
Influential theories predict that antidepressant medication and psychological therapies evoke distinct neural changes.
Aims
To test the convergence and divergence of antidepressant- and psychotherapy-evoked neural changes, and their overlap with the brain's affect network.
Method
We employed a quantitative synthesis of three meta-analyses ( n = 4206). First, we assessed the common and distinct neural changes evoked by antidepressant medication and psychotherapy, by contrasting two comparable meta-analyses reporting the neural effects of these treatments. Both meta-analyses included patients with affective disorders, including major depressive disorder, generalised anxiety disorder and panic disorder. The majority were assessed using negative-valence tasks during neuroimaging. Next, we assessed whether the neural changes evoked by antidepressants and psychotherapy overlapped with the brain's affect network, using data from a third meta-analysis of affect-based neural activation.
Results
Neural changes from psychotherapy and antidepressant medication did not significantly converge on any region. Antidepressants evoked neural changes in the amygdala, whereas psychotherapy evoked anatomically distinct changes in the medial prefrontal cortex. Both psychotherapy- and antidepressant-related changes separately converged on regions of the affect network.
Conclusions
This supports the notion of treatment-specific brain effects of antidepressants and psychotherapy. Both treatments induce changes in the affect network, but our results suggest that their effects on affect processing occur via distinct proximal neurocognitive mechanisms of action.
http://www.annualreviews.org/eprint/VH4BGBCTZ2HTFA9RMXGX/full/10.1146/annurev-clinpsy-072120-014126
The therapeutic onset of traditional antidepressants is delayed by several weeks, and many depressed patients fail to respond to treatment altogether. In contrast, subanesthetic ketamine can rapidly alleviate symptoms of depression within hours of a single administration, even in patients who are considered treatment-resistant. Ketamine is thought to exert these effects by restoring the integrity of neural circuits that are compromised in depression. This hypothesis stems in part from preclinical observations that ketamine can strengthen synaptic connections by increasing glutamate-mediated neurotransmission and promoting rapid neurotrophic factor release. An improved understanding of how ketamine, and other novel rapid-acting antidepressants, give rise to these processes will help foster future therapeutic innovation. Here, we review the history of antidepressant treatment advances that preceded the ketamine discovery, critically examine mechanistic hypotheses for how ketamine may exert its antidepressant effects, and discuss the impact this knowledge has had on ongoing drug discovery efforts.
Ketamine produces immediate antidepressant effects and has inspired research into next-generation treatments. Ketamine also has short term dissociative effects, in which individuals report altered consciousness and perceptions of themselves and their environment. However, whether ketamine’s dissociative side effects are necessary for its antidepressant effects remains unclear. This perspective examines the relationship between dissociative effects and acute and longer-lasting antidepressant response to ketamine and other N-methyl-D-aspartate (NMDA) receptor antagonists. Presently, the literature does not support the conclusion that dissociation is necessary for antidepressant response to ketamine. However, further work is needed to explore the relationship between dissociation and antidepressant response at the molecular, biomarker, and psychological levels.
Background:
Anhedonia, the loss of pleasure in previously rewarding activities, is a prominent feature of major depressive disorder (MDD) and often resistant to first-line antidepressant treatment. A paucity of translatable cross-species tasks to assess subdomains of anhedonia, including reward learning, presents a major obstacle to the development of effective therapeutics. One assay of reward learning characterized by orderly behavioral and pharmacological findings in both humans and rats is the Probabilistic Reward Task (PRT). In this computerized task, subjects make discriminations across numerous trials in which correct responses to one alternative are rewarded more often (rich) than correct responses to the other (lean). Healthy control subjects reliably develop a response bias to the rich alternative. However, participants with MDD as well as rats exposed to chronic stress typically exhibit a blunted response bias.
Methods:
The present studies validated a touchscreen-based PRT for the marmoset, a small nonhuman primate with considerable translational value. First, probabilistic reinforcement contingencies were parametrically examined. Next, the effects of ketamine (1.0-10.0 mg/kg), an FDA-approved rapid-acting antidepressant, and phencyclidine (0.01-0.1 mg/kg), a pharmacologically similar NMDA receptor antagonist with no known antidepressant efficacy, were evaluated.
Results:
Increases in the asymmetry of rich:lean probabilistic contingencies produced orderly increases in response bias. Consistent with their respective clinical profiles, ketamine, but not phencyclidine, produced dose-related increases in response bias at doses that did not reduce task discriminability.
Conclusions:
Collectively, these findings confirm task and pharmacological sensitivity in the marmoset, which may be useful in developing medications to counter anhedonia across neuropsychiatric disorders.
Explore-exploit decisions require us to trade off the benefits of exploring unknown options to learn more about them, with exploiting known options, for immediate reward. Such decisions are ubiquitous in nature, but from a computational perspective, they are notoriously hard. There is therefore much interest in how humans and animals make these decisions and recently there has been an explosion of research in this area. Here we provide a biased and incomplete snapshot of this field focusing on the major finding that many organisms use two distinct strategies to solve the explore-exploit dilemma: a bias for information (‘directed exploration’) and the randomization of choice (‘random exploration’). We review evidence for the existence of these strategies, their computational properties, their neural implementations, as well as how directed and random exploration vary over the lifespan. We conclude by highlighting open questions in this field that are ripe to both explore and exploit.
Cognitive effort is described as aversive, and people will generally avoid it when possible. This aversion to effort is believed to arise from a cost–benefit analysis of the actions available. The comparison of cognitive effort against other primary aversive experiences, however, remains relatively unexplored. Here, we offered participants choices between performing a cognitively demanding task or experiencing thermal pain. We found that cognitive effort can be traded off for physical pain and that people generally avoid exerting high levels of cognitive effort. We also used computational modelling to examine the aversive subjective value of effort and its effects on response behaviours. Applying this model to decision times revealed asymmetric effects of effort and pain, suggesting that cognitive effort may not share the same basic influences on avoidance behaviour as more primary aversive stimuli such as physical pain.
In the field of behavioral decision-making, “loss aversion” is a behavioral phenomenon in which individuals show a higher sensitivity to potential losses than to gains. Conversely, “risk averse” individuals have an enhanced sensitivity/aversion to options with uncertain consequences. Here we examine whether hypomania or negative symptoms predict the degree of these choice biases. We chose to study these two symptom dimensions because they present a common theme across many syndromes with compromised decision-making. In our exploratory study, we employed a non-clinical sample to dissociate the hypomanic from negative symptom dimension regarding choice behavior. We randomly selected a sample of 45 subjects from a student population (18–37 years) without self-reported psychiatric diagnoses (n = 835). We stratified them based on percentiles into a low hypomania/low negative symptoms (n = 15), a hypomania (n = 15), and a negative symptoms group (n = 15) using the hypomanic personality scale (HPS-30) and community assessment of psychic experiences (CAPE). Participants completed a loss aversion task consisting of forced binary choices between a monetary gamble and a riskless choice without gain or loss. We found a reduced loss aversion in participants with higher negative symptoms. In addition, risk aversion was reduced in participants with higher hypomania and negative symptoms compared to low hypomania/negative symptoms. This study adds to the understanding of underlying psychological mechanisms of loss and risk aversion. Given the partially opposing nature of hypomania and negative symptoms, further work is needed to examine whether they affect loss and risk aversion via dissociable mechanisms.
Importance
Dysfunctional reward processing is a leading candidate mechanism for the development of certain depressive symptoms, such as anhedonia. However, to our knowledge, there has not yet been a systematic assessment of whether and to what extent depression is associated with impairments on behavioral reward-processing tasks.
Objective
To determine whether depression is associated with impairments in reward-processing behavior.
Data Sources
The MEDLINE/PubMed, Embase, and PsycInfo databases were searched for studies that investigated reward processing using performance on behavioral tasks by individuals with depression and nondepressed control groups, published between January 1, 1946, and August 16, 2019.
Study Selection
Studies that contained data regarding performance by depressed and healthy control groups on reward-processing tasks were included in the systematic review and meta-analysis.
Data Extraction and Synthesis
Summary statistics comparing performance between depressed and healthy groups on reward-processing tasks were converted to standardized mean difference (SMD) scores, from which summary effect sizes for overall impairment in reward processing and 4 subcomponent categories were calculated. Study quality, heterogeneity, replicability-index, and publication bias were also assessed.
Main Outcome and Measures
Performance on reward-processing tasks.
Results
The final data set comprised 48 case-control studies (1387 healthy control individuals and 1767 individuals with major depressive disorder). The mean age was 37.85 years and 58% of the participants were women. These studies used tasks assessing option valuation (n = 9), reward bias (n = 6), reward response vigor (n = 12), reinforcement learning (n = 20), and grip force (n = 1). Across all tasks, depression was associated with small to medium impairments in reward-processing behavior (SMD = 0.345; 95% CI, 0.209-0.480). When examining reward-processing subcomponent categories, impairment was associated with tasks assessing option valuation (SMD = 0.309; 95% CI, 0.147-0.471), reward bias (SMD = 0.644; 95% CI, 0.270-1.017), and reinforcement learning (SMD = 0.352; 95% CI, 0.115-0.588) but not reward response vigor (SMD = 0.083; 95% CI, −0.144 to 0.309). The medication status of the major depressive disorder sample did not explain any of the variance in the overall effect size. There was significant between-study heterogeneity overall and in all subcomponent categories other than option valuation. Significant publication bias was identified overall and in the reinforcement learning category.
Conclusions and Relevance
Relative to healthy control individuals, individuals with depression exhibit reward-processing impairments, particularly for tests of reward bias, option valuation, and reinforcement learning. Understanding the neural mechanisms driving these associations may assist in designing novel interventions.
Dorsolateral prefrontal cortex (DLPFC) is well‐known for its role in exerting mental work, however the contribution of DLPFC for deciding whether or not to engage in effort remains unknown. Here, we assessed the causal role of DLPFC in effort‐based decision making. We disrupted functioning of DLPFC with noninvasive brain stimulation before participants repeatedly decided whether to exert mental effort in a working memory task. We found the same DLPFC subregion involved in mental effort exertion to influence also effort‐based decisions: First, it enhanced effort discounting, suggesting that DLPFC may signal the capacity to successfully deal with effort demands. Second, a novel computational model integrating the costs of enduring effort into the effort‐based decision process revealed that DLPFC disruption reduced fatigue after accumulated effort exertion, linking DLPFC activation with fatigue. Together, our findings indicate that in effort‐based decisions DLPFC represents the capacity to exert mental effort and the updating of this information with enduring time‐on‐task, informing theoretical accounts on the role of DLPFC in the motivation to exert mental effort and the fatigue arising from it.
In this editorial for the collection on complexity in mental health research, we introduce and summarize the inaugural contributions to this collection: a series of theoretical, methodological, and empirical papers that aim to chart a path forward for investigating mental health in all its complexity. A central theme emerges from these contributions: if we are to make genuine progress in explaining, predicting, and treating mental illness, we must study the systems from which psychopathology emerges. As the articles in this collection make clear, the systems that give rise to psychopathology encompass a host of components across biological, psychological, and social levels of analysis, intertwined in a web of complex interactions. The task of advancing our understanding of these systems will be a challenging one. Yet, this challenge presents a unique opportunity. From physics to ecology, there is a rapidly evolving body of interdisciplinary research dedicated to investigating complex systems. This work provides clear guidance for psychiatric research, opportunities for collaboration, and a set of tools and concepts from which we can draw in our efforts to understand mental health, helping us move toward our ultimate aim of improving the prevention and treatment of psychopathology.
A better understanding of suicidal ideation (SI), including patterns of SI, may help elucidate links between depression, SI, and suicidal behavior. This study sought to identify trajectories of SI in a large, community-based clinical trial of participants with major depressive disorder (MDD) and to investigate the relationships between these trajectories and predictors of interest, including anxiety and anhedonia. A longitudinal latent class analysis was conducted in 3923 participants enrolled in Level 1 of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study of citalopram for the treatment of MDD. An unconditional latent class analysis was conducted using SI at study weeks 0, 2, 4, 6, and 9 as the indicators. A multinomial regression was then conducted with SI trajectory as the outcome and anhedonia, severity of depressive symptoms, atypical depression, anxiety, history of suicide attempt, history of substance abuse, history of trauma, and other covariates as the predictors. Four SI trajectories were identified: 1) variable SI; 2) little-to-no SI; 3) persistent SI; and 4) improving SI. Compared to the little-to-no SI trajectory, those with more severe anhedonia were more likely to experience persistent SI, while those with more severe anxiety were more likely to experience improving SI. Factors that distinguish SI trajectories, such as anxiety and anhedonia, may be critical targets for intervention or profiles for prognosis.
Anhedonia, characterized by a lack of motivation, interest, or ability to experience pleasure, is a prominent symptom of depression and other psychiatric disorders and has been associated with poor response to standard therapies. One pathophysiologic pathway receiving increased attention for its potential role in anhedonia is inflammation and its effects on the brain. Exogenous administration of inflammatory stimuli to humans and laboratory animals has reliably been found to affect neurotransmitters and neurocircuits involved in reward processing, including the ventral striatum and ventromedial prefrontal cortex, in association with reduced motivation. Moreover, a rich literature including meta-analyses describes increased inflammation in a significant proportion of patients with depression and other psychiatric illnesses involving anhedonia, as evident by elevated inflammatory cytokines, acute phase proteins, chemokines, and adhesion molecules in both the periphery and central nervous system. This endogenous inflammation may arise from numerous sources including stress, obesity or metabolic dysfunction, genetics, and lifestyle factors, many of which are also risk factors for psychiatric illness. Consistent with laboratory studies involving exogenous administration of peripheral inflammatory stimuli, neuroimaging studies have further confirmed that increased endogenous inflammation in depression is associated with decreased activation of and reduced functional connectivity within reward circuits involving ventral striatum and ventromedial prefrontal cortex in association with anhedonia. Here, we review recent evidence of relationships between inflammation and anhedonia, while highlighting translational and mechanistic work describing the impact of inflammation on synthesis, release, and reuptake of neurotransmitters like dopamine and glutamate that affects circuits to drive motivational deficits. We will then present insight into novel pharmacological strategies that target either inflammation or its downstream effects on the brain and behavior. The meaningful translation of these concepts through appropriately designed trials targeting therapies for psychiatric patients with high inflammation and transdiagnostic symptoms of anhedonia is also discussed.
Objectives: Anhedonia is a common, persistent, and disabling phenomenon in patients with major depressive disorder (MDD) and bipolar depression (BD). This study was conducted to investigate the comparative effectiveness of repeated ketamine infusions in treating anhedonia in Chinese individuals suffering from MDD and BD.
Methods: Ninety-seven individuals suffering from MDD (n=77) or BD (n=20) were treated with six intravenous infusions of ketamine (0.5 mg/kg) administered over 40 min. Anhedonia was measured through the Montgomery–Åsberg Depression Rating Scale (MADRS). The antianhedonic response and remission were defined as ≥ 50% and ≥ 75% reduction in MADRS anhedonia subscale score one day after the sixth infusion, respectively.
Results: Anti-anhedonic response and remission rates after the sixth ketamine infusion were 48.5% (95% confidence interval=38.3-58.6) and 30.9% (95% confidence interval=21.6-40.3), respectively. When compared to baseline, a significant reduction in the MADRS anhedonia subscale score was observed at 4 h after the first infusion and was maintained with repeated infusions at any time point (all Ps<0.05). The anti-anhedonic effect of ketamine did not differ between the MDD and BD groups.
Conclusion: This preliminary study found that repeated ketamine infusions appeared to be effective at rapidly ameliorating anhedonia, with similar efficacy in MDD and BD.
Anhedonia - a common feature of depression and other neuropsychiatric disorders - encompasses a reduction in the subjective experience and anticipation of rewarding events, and a reduction in the motivation to seek out such events. The presence of anhedonia often predicts or accompanies treatment resistance, and as such better interventions and treatments are important. Yet the mechanisms giving rise to anhedonia are not well understood. In this chapter, we briefly review existing computational conceptualisations of anhedonia. We argue that they are mostly descriptive and fail to provide an explanatory account of why anhedonia may occur. Working within the framework of reinforcement learning, we examine two potential computational mechanisms that could give rise to anhedonic phenomena. First, we show how anhedonia can arise in multi-dimensional drive-reduction settings through a trade-off between different rewards or needs. We then generalise this in terms of model-based value inference and identify a key role for associational belief structure. We close with a brief discussion of treatment implications of both of these conceptualisations. In summary, computational accounts of anhedonia have provided a useful descriptive framework. Recent advances in reinforcement learning suggest promising avenues by which the mechanisms underlying anhedonia may be teased apart, potentially motivating novel approaches to treatment.
Anhedonia, a loss of interest or pleasure in activities, is a transdiagnostic symptom that characterizes many individuals suffering from depression and anxiety. Most psychological interventions are designed to decrease negative affect rather than increase positive affect, and are largely ineffective for reducing anhedonia. More recently, affective neuroscience has been leveraged to inform treatments for anhedonia by targeting aspects of the Positive Valence Systems, including impairments in reward anticipation, reward responsiveness, and reward learning. In this chapter, we review the efficacy of treatments and, when possible, highlight links to reward constructs. Augmented behavioral approaches and targeted cognitive interventions designed to target reward anticipation, responsiveness, and learning show preliminary efficacy in reducing anhedonia, while there is a relative lack of treatments that target positive emotion regulation and reward devaluation. In addition to developing treatments that address these targets, the field will benefit from establishing standardized measurement of anhedonia across units of analysis, mapping mechanisms of change onto aspects of reward processing, and examining anhedonia outcomes in the long-term.
Anhedonia, or reward system dysfunction, is associated with poorer treatment outcomes among depressed individuals. The role of anhedonia in treatment engagement, however, has not yet been explored. We review research on components of reward functioning impaired in depression, including effort valuation, reward anticipation, initial responsiveness, reward learning, reward probability, and reward delay, highlighting potential barriers to treatment engagement associated with these components. We then propose interventions to improve treatment initiation and continuation by addressing deficits in each component of reward functioning, focusing on modifications of existing evidence-based interventions to meet the needs of individuals with heightened anhedonia. We describe potential settings for these interventions and times at which they can be delivered during the process of referring individuals to mental health treatment, conducting intakes or assessments, and providing treatment. Additionally, we note the
advantages of using screening processes already in place in primary care, workplace, school, and online settings to identify individuals with heightened anhedonia who may benefit from these interventions. We conclude with suggestions for future research on the impact of anhedonia on treatment engagement and the efficacy of interventions to
address it.
Major depressive disorder (MDD) is characterized by behavioral and neural abnormalities in processing both rewarding and aversive stimuli, which may impact motivational and affective symptoms. Learning paradigms have been used to assess reinforcement encoding abnormalities in MDD and their association with dysfunctional incentive-based behavior, but how the valence and context of information modulate this learning is not well understood. To address these gaps, we examined responses to positive and negative reinforcement across multiple temporal phases of information processing. While undergoing functional magnetic resonance imaging (fMRI), 47 participants (23 unmedicated, predominantly medication-naïve participants with MDD and 24 demographically-matched HC participants) completed a probabilistic, feedback-based reinforcement learning task that allowed us to separate neural activation during motor response (choice) from reinforcement feedback and monetary outcome across two independent conditions: pursuing gains and avoiding losses. In the gain condition, MDD participants showed overall blunted learning responses (prediction error) in the dorsal striatum when receiving monetary outcome, and reduced responses in ventral striatum for positive, but not negative, prediction error. The MDD group showed enhanced sensitivity to negative information, and symptom severity was associated with better behavioral performance in the loss condition. These findings suggest that striatal responses during learning are abnormal in individuals with MDD but vary with the valence of information.
The prefrontal cortex (PFC) has emerged as one of the regions most consistently impaired in major depressive disorder (MDD). Although functional and structural PFC abnormalities have been reported in both individuals with current MDD as well as those at increased vulnerability to MDD, this information has not translated into better treatment and prevention strategies. Here, we argue that dissecting depressive phenotypes into biologically more tractable dimensions – negative processing biases, anhedonia, despair-like behavior (learned helplessness) – affords unique opportunities for integrating clinical findings with mechanistic evidence emerging from preclinical models relevant to depression, and thereby promises to improve our understanding of MDD. To this end, we review and integrate clinical and preclinical literature pertinent to these core phenotypes, while emphasizing a systems-level approach, treatment effects, and whether specific PFC abnormalities are causes or consequences of MDD. In addition, we discuss several key issues linked to cross-species translation, including functional brain homology across species, the importance of dissecting neural pathways underlying specific functional domains that can be fruitfully probed across species, and the experimental approaches that best ensure translatability. Future directions and clinical implications of this burgeoning literature are discussed.
Exogenous administration of inflammatory stimuli to humans and laboratory animals and chronic endogenous inflammatory states lead to motivational deficits and ultimately anhedonia, a core and disabling symptom of depression present in multiple other psychiatric disorders. Inflammation impacts neurotransmitter systems and neurocircuits in subcortical brain regions including the ventral striatum, which serves as an integration point for reward processing and motivational decision-making. Many mechanisms contribute to these effects of inflammation, including decreased synthesis, release and reuptake of dopamine, increased synaptic and extrasynaptic glutamate, and activation of kynurenine pathway metabolites including quinolinic acid. Neuroimaging data indicate that these inflammation-induced neurotransmitter effects manifest as decreased activation of ventral striatum and decreased functional connectivity in reward circuitry involving ventral striatum and ventromedial prefrontal cortex. Neurocircuitry changes in turn mediate nuanced effects on motivation that include decreased willingness to expend effort for reward while maintaining the ability to experience reward. Taken together, the data reveal an inflammation-induced pathophysiologic phenotype that is agnostic to diagnosis. Given the many mechanisms involved, this phenotype represents an opportunity for development of novel and/or repurposed pharmacological strategies that target inflammation and associated cellular and systemic immunometabolic changes and their downstream effects on the brain. To date, clinical trials have failed to capitalize on the unique nature of this transdiagnostic phenotype, leaving the field bereft of interpretable data for meaningful clinical application. However, novel trial designs incorporating established targets in the brain and/or periphery using relevant outcome variables (e.g., anhedonia) are the future of targeted therapy in psychiatry. SIGNIFICANCE STATEMENT: Emerging understanding of mechanisms by which peripheral inflammation can affect the brain and behavior has created unprecedented opportunities for development of pharmacological strategies to treat deficits in motivation including anhedonia, a core and disabling symptom of depression well represented in multiple psychiatric disorders. Mechanisms include inflammation and cellular and systemic immunometabolism and alterations in dopamine, glutamate, and kynurenine metabolites, revealing a target-rich environment that nevertheless has yet to be fully exploited by current clinical trial designs and drugs employed.
Objective:
Adolescent depression is prevalent and is associated with significant morbidity and mortality. Although intravenous ketamine has shown efficacy in adult treatment-resistant depression, its efficacy in pediatric populations is unknown. The authors conducted an active-placebo-controlled study of ketamine’s safety and efficacy in adolescents.
Methods:
In this proof-of-concept randomized, double-blind, single-dose crossover clinical trial, 17 adolescents (ages 13–17) with a diagnosis of major depressive disorder received a single intravenous infusion of either ketamine (0.5 mg/kg over 40 minutes) or midazolam (0.045 mg/kg over 40 minutes), and the alternate compound 2 weeks later. All participants had previously tried at least one antidepressant medication and met the severity criterion of a score >40 on the Children’s Depression Rating Scale–Revised. The primary outcome measure was score on the Montgomery-Åsberg Depression Rating Scale (MADRS) 24 hours after treatment.
Results:
A single ketamine infusion significantly reduced depressive symptoms 24 hours after infusion compared with midazolam (MADRS score: midazolam, mean=24.13, SD=12.08, 95% CI=18.21, 30.04; ketamine, mean=15.44, SD=10.07, 95% CI=10.51, 20.37; mean difference=−8.69, SD=15.08, 95% CI=−16.72, −0.65, df=15; effect size=0.78). In secondary analyses, the treatment gains associated with ketamine appeared to remain 14 days after treatment, the latest time point assessed, as measured by the MADRS (but not as measured by the Children’s Depression Rating Scale–Revised). A significantly greater proportion of participants experienced a response to ketamine during the first 3 days following infusion as compared with midazolam (76% and 35%, respectively). Ketamine was associated with transient, self-limited dissociative symptoms that affected participant blinding, but there were no serious adverse events.
Conclusions:
In this first randomized placebo-controlled clinical trial of intravenous ketamine in adolescents with depression, the findings suggest that it is well tolerated acutely and has significant short-term (2-week) efficacy in reducing depressive symptoms compared with an active placebo.
Anhedonia is one of the core symptoms of major depressive disorder (MDD), which is often inadequately treated by traditional antidepressants. The modern framework of anhedonia extends the definition from impaired consummatory pleasure or interest in rewards to a broad spectrum of deficits that impact functions such as reward anticipation, approach motivation, effort expenditure, reward valuation, expectation, and reward-cue association learning. Substantial preclinical and clinical research has explored the neural basis of reward deficits in the context of depression, and has implicated mesocorticolimbic reward circuitry comprising the nucleus accumbens, ventral pallidum, ventral tegmental area, amygdala, hippocampus, anterior cingulate, insula, orbitofrontal cortex, and other prefrontal cortex regions. Dopamine modulates several reward facets including anticipation, motivation, effort, and learning. As well, serotonin, norepinephrine, opioids, glutamate, Gamma aminobutyric acid (GABA), and acetylcholine are also involved in anhedonia, and medications targeting these systems may also potentially normalize reward processing in depression. Unfortunately, whereas reward anticipation and reward outcome are extensively explored by both preclinical and clinical studies, translational gaps remain in reward motivation, effort, valuation, and learning, where clinical neuroimaging studies are in the early stages. This review aims to synthesize the neurobiological mechanisms underlying anhedonia in MDD uncovered by preclinical and clinical research. The translational difficulties in studying the neural basis of reward are also discussed.
In the opening chapters of this volume, we outlined a series of challenges facing psychiatry, as well as a description of its various promises, and suggested that taking a computational perspective could potentially illuminate a way forward. In this concluding chapter, we revisit these challenges and promises, in the context of what transpired at this Ernst Strüngmann Forum, to highlight the connections between the various themes raised. In particular, we will bring out the points of agreement and disagreement between the discussion groups and the chapters that arose from those discussions. We conclude with a description of the efforts, current and ongoing, to bring the potential synergy between psychiatry and computational neuroscience emphasized in this volume to a reality in the scientific and clinical arenas.
Background
Anhedonia is a symptom associated with poorer outcomes in depression treatment, including resistance to treatment, higher functional impact and suicidality. Few drugs are known to adequately treat anhedonia in both unipolar and bipolar depression. The NMDA antagonist ketamine has been demonstrated to be effective in rapidly ameliorating anhedonia in depressive episodes. The main aim of present study is to evaluate the anti-anhedonic effect of esketamine, the S-enantiomer of ketamine recently approved for treatment-resistant depression, in unipolar and bipolar depression.
Methods
70 patients with unipolar or bipolar depression were treated with 6 weekly subcutaneous esketamine infusions (0.5-1mg/kg). Anhedonia was measured through MADRS item 8 before and 24h after each infusion.
Results
A significant reduction in anhedonia severity was observed (p<0.0001) after 6 infusions. The effect was statistically significant 24h after the first infusion (p<0.001) in both unipolar and bipolar groups and increased with repeated infusions. Anti-anhedonic effect of esketamine did not differ between groups.
Limitations
This is an open-label, real-world study. Lack of blinding and of a placebo arm may limit the interpretation of findings.
Conclusion
Although preliminary, present findings suggest that repeated subcutaneous esketamine infusions are effective for the treatment of anhedonia in both unipolar and bipolar depressed patients. These results need to be confirmed through replication in larger double-blinded controlled trials.
The cognitive enhancing effects of methylphenidate are well established, but the mechanisms remain unclear. We recently demonstrated that methylphenidate boosts cognitive motivation by enhancing the weight on the benefits of a cognitive task in a manner that depended on striatal dopamine. Here, we considered the complementary hypothesis that methylphenidate might also act by changing the weight on the opportunity cost of a cognitive task, that is, the cost of foregoing alternative opportunity. To this end, 50 healthy participants (25 women) completed a novel cognitive effort-discounting task that required choices between task and leisure. They were tested on methylphenidate, placebo, as well as the selective D2-receptor agent sulpiride, the latter to strengthen inference about dopamine receptor selectivity of methylphenidate’s effects. Furthermore, they also underwent an [18F]DOPA PET scan to quantify striatal dopamine synthesis capacity. Methylphenidate boosted choices of cognitive effort over leisure across the group, and this effect was greatest in participants with more striatal dopamine synthesis capacity. The effects of sulpiride did not reach significance. This study strengthens the motivational account of methylphenidate’s effects on cognition, and suggests that methylphenidate reduces the cost of mental labor by increasing striatal dopamine.
Background
Anhedonia is a trans-diagnostic, multidimensional phenotype that mediates patient outcomes and suicidality. Convergent evidence suggests that ketamine may be effective in targeting measures of anhedonia in adults with treatment resistant depression (TRD).
Methods
This retrospective, post-hoc analysis included 203 (x̄ = 45 ± 14.6 years of age) patients receiving four infusions of intravenous (IV) ketamine at a community-based clinic. The primary outcome measure was change in anhedonia severity, as measured by the Snaith-Hamilton Pleasure Scale (SHAPS). Secondary measures sought to determine if improvement on the SHAPS mediated the effect of repeated IV ketamine infusions on symptoms of depression and suicidal ideations, as measured by the Quick Inventory for Depression Symptomatology-Self Report 16-Item (QIDS-SR16) and anxiety, as measured using the Generalized Anxiety Scale 7 (GAD-7).
Results
After adjusting for age, sex and baseline depression severity, there was a statistically significant reduction in symptoms of anhedonia with IV ketamine treatment (F (2, 235.6) = 316, p < 0.001). Improvements in depressive symptoms, suicidal ideation, and anxiety symptoms with repeated-dose IV ketamine were significantly partially mediated by reduction in anhedonic severity. Moreover, the combination of number of infusions received and change in anhedonic severity accounted for 26% of the variance in depressive score improvements.
Limitations
This is a post-hoc analysis of retrospective data and lacks a control group.
Conclusion
Ketamine was effective in improving measures of anhedonia in this large, well-characterized community-based sample of adults with TRD. Improvements in anhedonia also partially mediated the significant improvement in depressive symptoms, suicidality, and anxiety.