News

Publications 2020

  • doi preprint pdf Advances in the computational understanding of mental illness
  • Huys QJM, Browning MB, Paulus MP and Frank MJ
  • Neuropsychopharmacology In Press
  • Computational psychiatry is a rapidly growing field attempting to translate advances in computational neuroscience and machine learning into improved outcomes for patients suffering from mental illness. It encompasses both data-driven and theory-driven efforts. Here, recent advances in theory-driven work are reviewed. We argue that the brain is a computational organ. As such, an understanding of the illnesses arising from it will require a computational framework. The review divides work up into three theoretical approaches that have deep mathematical connections: dynamical systems, Bayesian inference and reinforcement learning. We discuss both general and specific challenges for the field, and suggest ways forward.

  • doi pdf A Comparison of 'Pruning' During Multi-Step Planning in Depressed and Healthy Individuals
  • Faulkner P, Huys QJM, Renz D, Eshel N, Pilling S, Dayan P and Roiser JP
  • bioRxiv 2020.05.08.084509
  • Humans routinely formulate plans in domains so complex that even the most powerful computers are taxed. To do so, they seem to avail themselves of many strategies and heuristics that efficiently simplify, approximate, and hierarchically decompose hard tasks into simpler subtasks. Theoretical and cognitive research has revealed several such strategies; however, little is known about their establishment, interaction, and efficiency. Here, we use model-based behavioral analysis to provide a detailed examination of the performance of human subjects in a moderately deep planning task. We find that subjects exploit the structure of the domain to establish subgoals in a way that achieves a nearly maximal reduction in the cost of computing values of choices, but then combine partial searches with greedy local steps to solve subtasks, and maladaptively prune the decision trees of subtasks in a reflexive manner upon encountering salient losses. Subjects come idiosyncratically to favor particular sequences of actions to achieve subgoals, creating novel complex actions or "options."
  • doi pdf How representative are neuroimaging samples? Large-scale evidence for trait anxiety differences between MRI and behaviour-only research participants.
  • Charpentier C, Faulkner P, Pool E, Ly V, Tollenaar MS, Kluen LM, Fransen A, Yamamori Y, Lally N, Mkrtchian A, Valton V, Huys QJM, Morrow K, Krenz V, Kalbe F, Cremer A, Zerbes G, Kausche FM, Wanke N, Giarrizzo A, Pulcu E, Murphy A, Kaltenboeck A, Browning M, Paul LK, Cools R, Roelofs K, Pessoa L, Harmer C, Chase HW, Grillon C, Schwabe L, Roiser J, Robinson O and O'Doherty J
  • PsyArXiv
  • Over the past three decades, MRI has become a key tool to study how cognitive processes are implemented in the human brain. However, the question of whether participants recruited into MRI 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, if a behavioural effect discovered in a non-imaging context does not replicate in a neuroimaging environment. Here, we tested the hypothesis, motivated by preliminary findings (n=272), that MRI study participants differ from behaviour-only study participants on one fundamental individual difference variable: trait anxiety. Analysing a large-scale dataset drawn from multiple institutions (n=3317) and controlling for possible confounding variables, we found robust support for lower trait anxiety in MRI study participants, consistent with a sampling bias. Distributions of trait anxiety scores differed most markedly when psychiatric screening was minimal. Our findings highlight the need for surveying trait anxiety at recruitment and for appropriate screening procedures, in an attempt to mitigate this bias.
  • preprint Stimulation of the vagus nerve reduces learning in a go/no-go reinforcement learning task
  • Kuehnel A, Teckentrup V, Neuser MP, Huys QJM, Burrasch C, Walter M, Kroemer NB
  • European Neuropsychopharmacology (2020)
  • When facing decisions to approach rewards or to avoid punishments, we often figuratively go with our gut, and the impact of metabolic states such as hunger on motivation are well documented. However, whether and how vagal feedback signals from the gut influence instrumental actions is unknown. Here, we investigated the effect of non-invasive transcutaneous vagus nerve stimulation (tVNS) vs. sham (randomized cross-over design) on approach and avoidance behavior using an established go/no-go reinforcement learning paradigm (Guitart-Masip et al., 2012) in 39 healthy, participants after an overnight fast. First, mixed-effects logistic regression analysis of choice accuracy showed that tVNS acutely impaired decision-making, p = .045. Computational reinforcement learning models identified the cause of this as a reduction in the learning rate through tVNS (## = -0.092, pboot = .002), particularly after punishment (##Pun = -0.081, pboot = .012 vs. ##Rew = -0.031, p = .22). However, tVNS had no effect on go biases, Pavlovian response biases or response time. Hence, tVNS appeared to influence learning rather than action execution. These results highlight a novel role of vagal afferent input in modulating reinforcement learning by tuning the learning rate according to homeostatic needs.

  • doi Opportunities for emotion and mental health research in the resource-rationality framework
  • Russek EM, Moran R, McNamee D, Reiter A, Liu Y, Dolan RJ and Huys QJM
  • Behav Brain Sci (2020) 43:e21
  • We discuss opportunities in applying the resource-rationality framework toward answering questions in emotion and mental health research. These opportunities rely on characterization of individual differences in cognitive strategies; an endeavor that may be at odds with the normative approach outlined in the target article. We consider ways individual differences might enter the framework and the translational opportunities offered by each.
  • doi Canonical correlation analysis for identifying biotypes of depression
  • Mihalik A, Adams RA, Huys QJM
  • Biological Psychiatry CNNI (2020) 5(5): 478-480
  • n old question is challenging novel methods: do diagnoses such as schizophrenia and depression contain subgroups of patients? And might such subgroups be identifiable by neurobiological means, and have differential responses to therapies? If so, then long-awaited biomarkers for psychiatric diagnosis and therapeutics might be found.
  • doi Computational mechanisms of effort and reward decisions in depression and their relationship to relapse after antidepressant discontinuation
  • Berwian IM, Wenzel J, Collins AGE, Seifritz E, Stephan KE, Walter H, Huys QJM
  • JAMA Psychiatry (2020)
  • IMPORTANCE Nearly 1 in 3 patients with major depressive disorder who respond to antidepressants relapse within 6 months of treatment discontinuation. No predictors of relapse exist to guide clinical decision-making in this scenario. OBJECTIVES To establish whether the decision to invest effort for rewards represents a persistent depression process after remission, predicts relapse after remission, and is affected by antidepressant discontinuation. DESIGN, SETTING, AND PARTICIPANTS This longitudinal randomized observational prognostic study in a Swiss and German university setting collected data from July 1, 2015, to January 31, 2019, from 66 healthy controls and 123 patients in remission from major depressive disorder in response to antidepressants prior to and after discontinuation. Study recruitment took place until January 2018. EXPOSURE Discontinuation of antidepressants. MAIN OUTCOMES AND MEASURES Relapse during the 6 months after discontinuation. Choice and decision times on a task requiring participants to choose how much effort to exert for various amounts of reward and the mechanisms identified through parameters of a computational model. RESULTS A total of 123 patients (mean [SD] age, 34.5 [11.2] years; 94 women [76%]) and 66 healthy controls (mean [SD] age, 34.6 [11.0] years; 49 women [74%]) were recruited. In the main subsample, mean (SD) decision times were slower for patients (n = 74) compared with controls (n = 34) (1.77 [0.38] seconds vs 1.61 [0.37] seconds; Cohen d = 0.52; P = .02), particularly for those who later relapsed after discontinuation of antidepressants (n = 21) compared with those who did not relapse (n = 39) (1.95 [0.40] seconds vs 1.67 [0.34] seconds; Cohen d = 0.77; P < .001). This slower decision time predicted relapse (accuracy = 0.66; P = .007). Patients invested less effort than healthy controls for rewards (F1,98 = 33.970; P < .001). Computational modeling identified a mean (SD) deviation from standard drift-diffusion models that was more prominent for patients than controls (patients, 0.67 [1.56]; controls, -0.71 [1.93]; Cohen d = 0.82; P < .001). Patients also showed higher mean (SD) effort sensitivity than controls (patients, 0.31 [0.92]; controls, -0.08 [1.03]; Cohen d = 0.51; P = .05). Relapsers differed from nonrelapsers in terms of the evidence required to make a decision for the low-effort choice (mean [SD]: relapsers, 1.36 [0.35]; nonrelapsers, 1.17 [0.26]; Cohen d = 0.65; P = .02). Group differences generally did not reach significance in the smaller replication sample (27 patients and 21 controls), but decision time prediction models from the main sample generalized to the replication sample (validation accuracy = 0.71; P = .03). CONCLUSIONS AND RELEVANCE This study found that the decision to invest effort was associated with prospective relapse risk after antidepressant discontinuation and may represent a persistent disease process in asymptomatic remitted major depressive disorder. Markers based on effort-related decision-making could potentially inform clinical decisions associated with antidepressant discontinuation.
  • doi pdf The relationship between resting-state functional connectivity, antidepressant discontinuation and depression relapse
  • Berwian IM, Wenzel J, Kuehn L, Schnuerer I, Kasper L, Veer IM, Seifritz E, Stephan KE, Walter H, Huys QJM
  • bioRxiv
  • Background: The risk of relapsing into depression after stopping antidepressants is high, but no established predictors exist. Resting-state functional magnetic resonance imaging (rsfMRI) measures may help predict relapse and identify the mechanisms by which relapses occur. Method: rsfMRI data were acquired from healthy controls and from patients with remitted major depressive disorder on antidepressants who were intent on discontinuing their medication. Patients went on to discontinue their antidepressants, were assessed a second time either before or after discontinuation and followed up for six months to assess relapse. A seed-based functional connectivity analysis was conducted focusing on the left subgenual anterior cingulate cortex and left posterior cingulate cortex. Seeds in the amygdala and dorsolateral prefrontal cortex were explored. Results: 44 healthy controls (age: 33.8 (10.5), 73% female) and 84 patients (age: 34.23 (10.8), 80% female) were included in the analysis. 29 patients went on to relapse and 38 remained well. Seed-based analysis failed to reveal differences in functional connectivity between patients and controls; and between relapsers and non-relapsers. Although overall there was no effect of antidepressant discontinuation, amongst non-relapsers discontinuation resulted in an increased functional connectivity between the right dorsolateral prefrontal cortex and the parietal cortex. Conclusion: No abnormalities in resting-state functional connectivity were detected in remitted patients on antidepressant medication. Resilience to relapse after open-label antidepressant discontinuation was associated with changes in the connectivity between the dorsolateral prefrontal cortex and the posterior default mode network.
  • doi pdf Low predictive power of clinical features for relapse prediction after antidepressant discontinuation in a naturalistic setting
  • Berwian IM, Wenzel J, Kuehn L, Schnuerer I, Seifritz E, Stephan KE, Walter H, Huys QJM
  • bioRxiv
  • Background: The risk of relapse after antidepressant medication (ADM) discontinuation is high. Predictors of relapse could guide clinical decision-making, but are yet to be established. Method: We assessed demographic and clinical variables in a longitudinal observational study before antidepressant discontinuation. State-dependent variables were re-assessed either after discontinuation or before discontinuation after a waiting period. Relapse was assessed during six months after discontinuation. We applied logistic general linear models in combination with least absolute shrinkage and selection operator and elastic nets to avoid overfitting in order to identify predictors of relapse and estimated their generalisability using cross-validation. Results: The final sample included 104 patients (age: 34.86 (11.1), 77% female) and 57 healthy controls (age: 34.12 (10.6), 70% female). 36% of the patients experienced a relapse. Treatment by a general practitioner increased the risk of relapse. Although within-sample statistical analyses suggested reasonable sensitivity and specificity, out-of-sample prediction of relapse was at chance level. Residual symptoms increased with discontinuation, but did not relate to relapse. Conclusion: and Relevance Demographic and standard clinical variables appear to carry little predictive power and therefore are of limited use for patients and clinicians in guiding clinical decision-making.
  • doi Psychiatric Illnesses as Disorders of Network Dynamics
  • Durstewitz D, Huys QJM, Koppe G
  • Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. In Press.
  • This review provides a dynamical systems perspective on psychiatric symptoms and disease, and discusses its potential implications for diagnosis, prognosis, and treatment. After a brief introduction into the theory of dynamical systems, we will focus on the idea that cognitive and emotional functions are implemented in terms of dynamical systems phenomena in the brain, a common assumption in theoretical and computational neuroscience. Specific computational models, anchored in biophysics, for generating different types of network dynamics, and with a relation to psychiatric symptoms, will be briefly reviewed, as well as methodological approaches for reconstructing the system dynamics from observed time series (like fMRI or EEG recordings). We then attempt to outline how psychiatric phenomena, associated with schizophrenia, depression, PTSD, ADHD, phantom pain, and others, could be understood in dynamical systems terms. Most importantly, we will try to convey that the dynamical systems level may provide a central, hub-like level of convergence which unifies and links multiple biophysical and behavioral phenomena, in the sense that diverse biophysical changes can give rise to the same dynamical phenomena and, vice versa, similar changes in dynamics may yield different behavioral symptoms depending on the brain area where these changes manifest. If this assessment is correct, it may have profound implications for the diagnosis, prognosis, and treatment of psychiatric conditions, as it puts the focus on dynamics. We therefore argue that consideration of dynamics should play an important role in the choice and target of interventions.

  • Abnormal Reward Valuation and Event-Related Connectivity in Unmedicated Major Depressive Disorder
  • Rupprechter S, Stankevicius A, Huys QJM, Seriès P and Steele JD
  • Psychological Medicine (2020)
  • Background. Experience of emotion is closely linked to valuation. Mood can be viewed as a bias to experience positive or negative emotions and abnormally biased subjective reward valuation and cognitions are core characteristics of major depression. Methods. Thirty-four unmedicated subjects with major depressive disorder and controls estimated the probability that fractal stimuli were associated with reward, based on passive observations, so they could subsequently choose the higher of either their estimated fractal value or an explicitly presented reward probability. Using model-based fMRI, we estimated each subject's internal value estimation, with psychophysiological interaction analysis used to examine event-related connectivity, testing hypotheses of abnormal reward valuation and cingulate connectivity in depression. Results. Reward value encoding in the hippocampus and rostral anterior cingulate was abnormal in depression. In addition, abnormal decision-making in depression was associated with increased anterior mid-cingulate activity and a signal in this region encoded the difference between the values of the two options. This localised decision-making and its impairment to the anterior mid-cingulate cortex consistent with theories of cognitive control. Notably, subjects with depression had significantly decreased event-related connectivity between the anterior mid-cingulate cortex and rostral cingulate regions during decision-making, implying impaired communication between the neural substrates of expected value estimation and decision-making in depression. Conclusions. Our findings support the theory that abnormal neural reward valuation plays a central role in MDD. To the extent that emotion reflects valuation, abnormal valuation could explain abnormal emotional experience in MDD, reflect a core pathophysiological process and be a target of treatment.

Key Publications

  • blog doi pdf Dissociating neural learning signals in human sign- and goal-trackers
  • Schad DJ, Rapp MA, Garbusow M, Nebe S, Sebold M, Obst E, Sommer C, Deserno L, Rabovsky M, Friedel E, Romanczuk-Seiferth N, Wittchen H-U, Zimmermann US, Walter H, Sterzer P, Smolka MN, Schlagenhauf F, Heinz A, Dayan P, Huys QJM
  • Nat. Hum. Behav. (2020) 4:201-214
  • Individuals differ in how they learn from experience. In Pavlovian conditioning paradigms, where cues predict reinforcer delivery at a different goal location, some animals--so-called sign-trackers--come to approach the cue, whereas others, called `goal-trackers', approach the goal. In sign-trackers, model-free phasic dopaminergic reward prediction errors underlie learning, which renders stimuli `wanted'. `Goal-trackers' do not rely on dopamine for learning and are thought to use model-based learning. We demonstrate this double dissociation in male humans using eye-tracking, pupillometry and fMRI informed by computational models. We show that only sign- trackers exhibit a neural reward prediction error signal. Only for them is gaze and pupil dilation guided by model-free value. Goal-trackers exhibit a stronger model-based neural state prediction error signal. Only for them do model-based constructs determine gaze and pupil dilation. As sign-tracking may be a vulnerability factor for impulsive and addictive behavior, these findings have implications for mental health.
  • doi pdf A formal valuation framework for emotions and their control
  • Huys QJM and Renz D
  • Biological Psychiatry (2017) 82:413--420
  • Computational psychiatry attempts to apply mathematical and computational techniques to help improve psychiatric care. Here, we consider formal valuation accounts of emotion. The flexibility of emotional responses and the nature of appraisals suggest the need for a model-based framework for emotions. Resource limitations make plain model-based valuation impossible, and require strategies to apportion cognitive resources adaptively. We argue that emotions can implement such approximations by restricting the range of behaviours and states considered. We consider the processes that guide the deployment of the emotional approximations, discerning between innate, model-free, heuristic and model-based controllers. A focus on complex model-based decisions reveals the necessity for strategies to deal with the complexity of the problems. Emotions may provide such approximations, and this framework may provide a principled approach to examining them.
  • doi pdf A Roadmap for the Development of Applied Computational Psychiatry
  • Paulus MP, Huys QJM and Maia T
  • Biological Psychiatry: Cognitive Neuroscience and Neuroimaging (2016) 1(5):386-392
  • Background: Computational psychiatry is a burgeoning field that utilizes mathematical approaches to investigate psychiatric disorders, derive quantitative predictions, and integrate data across multiple levels of description. Computational psychiatry has already led to many new insights into the neurobehavioral mechanisms that underlie several psychiatric disorders, but its usefulness from a clinical standpoint is only now starting to be considered. Methods: Examples of computational psychiatry are highlighted, and a phase-based pipeline for the development of clinical computational- psychiatry applications is proposed, similar to the phase-based pipeline used in drug development. It is proposed that each phase has unique endpoints and deliverables, which will be important milestones to move tasks, procedures, computational models, and algorithms from the laboratory to clinical practice. Results: Application of computational approaches should be tested on healthy volunteers in Phase I, transitioned to target populations in Phase IB and Phase IIA, and thoroughly evaluated using randomized clinical trials in Phase IIB and Phase III. Successful completion of these phases should be the basis of determining whether computational models are useful tools for prognosis, diagnosis, or treatment of psychiatric patients. Conclusions: A new type of infrastructure will be necessary to implement the proposed pipeline. This infrastructure should consist of groups of investigators with diverse backgrounds collaborating to make computational psychiatry relevant for the clinic.

  • doi pdf Computational psychiatry as a bridge from neuroscience to clinical applications
  • Huys QJM*, Maia T* and Frank MJ
  • Nature Neuroscience (2016) 19(3):404--413
    Commentary in Scientific American
  • Translating advances in neuroscience into benefits for patients with mental illness presents enormous challenges because it involves both the most complex organ----the brain---and its interaction with a similarly complex environment. Dealing with such complexities demands powerful techniques. Computational psychiatry combines multiple levels and types of computation with multiple types of data in an effort to improve understanding, prediction, and treatment of mental illness. Computational psychiatry, broadly defined, encompasses two complementary approaches: data-driven and theory-driven. Data-driven approaches apply machine-learning methods to high-dimensional data to improve classification of disease, predict treatment outcomes, or improve treatment selection. These approaches are generally agnostic as to the underlying mechanisms. Theory-driven approaches, in contrast, use models that instantiate prior knowledge of, or explicit hypotheses about, such mechanisms, possibly at multiple levels of analysis and abstraction. We review recent advances in both approaches, with an emphasis on clinical applications, and highlight the utility of combining them.

  • doi pdf Depression: A Decision-Theoretic Analysis
  • Huys QJM, Daw ND and Dayan P
  • Annu Rev Neurosci (2015), 38:1-23
  • The manifold symptoms of depression are a common and often transient feature of healthy life that are likely to be adaptive in difficult circumstances. It is when these symptoms enter a seemingly self-propelling spiral that the maladaptive features of a disorder emerge. We examine this malignant transformation from the perspective of the computational neuroscience of decision making, investigating how dysfunction of the brain's mechanisms of evaluation might lie at its heart. We start by considering the behavioural implications of pessimistic evaluations of decision variables. We then provide a selective review of work suggesting how such pessimism might arise via specific failures of the mechanisms of evaluation or state estimation. Finally, we analyze ways that miscalibration between the subject and environment may be self-perpetuating. We employ the formal framework of Bayesian decision theory as a foundation for this study, showing how most of the problems arise from one of its broad algorithmic facets, namely model-based reasoning.
  • doi pdf The interplay of approximate planning strategies
  • Huys QJM, Lally N, Faulkner P, Eshel N, Seifritz E, Gershman SJ, Dayan P and Roiser JP
  • PNAS (2015), 112(10):3098-3103
    Commentary by Daniel, Schuck & Niv
  • Humans routinely formulate plans in domains so complex that even the most powerful computers are taxed. To do so, they seem to avail themselves of many strategies and heuristics that efficiently simplify, approximate, and hierarchically decompose hard tasks into simpler subtasks. Theoretical and cognitive research has revealed several such strategies; however, little is known about their establishment, interaction, and efficiency. Here, we use model-based behavioral analysis to provide a detailed examination of the performance of human subjects in a moderately deep planning task. We find that subjects exploit the structure of the domain to establish subgoals in a way that achieves a nearly maximal reduction in the cost of computing values of choices, but then combine partial searches with greedy local steps to solve subtasks, and maladaptively prune the decision trees of subtasks in a reflexive manner upon encountering salient losses. Subjects come idiosyncratically to favor particular sequences of actions to achieve subgoals, creating novel complex actions or "options."