Computational Psychiatry: towards a mathematically informed understanding of mental illness

Computational Psychiatry aims to describe the relationship between the brain's neurobiology, its environment and mental symptoms in computational terms. In so doing, it may improve psychiatric classification and the diagnosis and treatment of mental illness. It can unite many levels of description in a mechanistic and rigorous fashion, while avoiding biological reductionism and artificial categorisation. We describe how computational models of cognition can infer the current state of the environment and weigh up future actions, and how these models provide new perspectives on two example disorders, depression and schizophrenia. Reinforcement learning describes how the brain can choose and value courses of actions according to their long-term future value. Some depressive symptoms may result from aberrant valuations, which could arise from prior beliefs about the loss of agency ('helplessness'), or from an inability to inhibit the mental exploration of aversive events. Predictive coding explains how the brain might perform Bayesian inference about the state of its environment by combining sensory data with prior beliefs, each weighted according to their certainty (or precision). Several cortical abnormalities in schizophrenia might reduce precision at higher levels of the inferential hierarchy, biasing inference towards sensory data and away from prior beliefs. We discuss whether striatal hyperdopaminergia might have an adaptive function in this context, and also how reinforcement learning and incentive salience models may shed light on the disorder. Finally, we review some of Computational Psychiatry's applications to neurological disorders, such as Parkinson's disease, and some pitfalls to avoid when applying its methods

Neuro-cognitive processes as mediators of psychological treatment effects

Psychological interventions are first-line treatments of depression. Despite a rich theoretical background, the mediators of treatment effects remain only partially understood: it has been difficult to precisely delineate the targets psychological interventions engage, and even more difficult to differentiate amongst the targets engaged by different psychological interventions. Here, we outline these issues and discuss a surprisingly understudied approach, namely the study of cognitive and computational tasks to measure psychological treatment targets. Such tasks benefit from substantial advances in cognitive neuroscience over the past two decades, and have excellent face validity. We discuss two candidate tasks for back-translation and conclude with a critical evaluation of potential problems associated with this neuro-cognitive approach

The effects of life stress and neural learning signals on fluid intelligence.

Fluid intelligence (fluid IQ), defined as the capacity for rapid problem solving and behavioral adaptation, is known to be modulated by learning and experience. Both stressful life events (SLES) and neural correlates of learning [specifically, a key mediator of adaptive learning in the brain, namely the ventral striatal representation of prediction errors (PE)] have been shown to be associated with individual differences in fluid IQ. Here, we examine the interaction between adaptive learning signals (using a well-characterized probabilistic reversal learning task in combination with fMRI) and SLES on fluid IQ measures. We find that the correlation between ventral striatal BOLD PE and fluid IQ, which we have previously reported, is quantitatively modulated by the amount of reported SLES. Thus, after experiencing adversity, basic neuronal learning signatures appear to align more closely with a general measure of flexible learning (fluid IQ), a finding complementing studies on the effects of acute stress on learning. The results suggest that an understanding of the neurobiological correlates of trait variables like fluid IQ needs to take socioemotional influences such as chronic stress into account

Prefrontal Glutamate Levels Predict Altered Amygdala-prefrontal Connectivity in Traumatized Youths

BACKGROUND:Neurobiological models of stress and stress-related mental illness, including post-traumatic stress disorder, converge on the amygdala and the prefrontal cortex (PFC). While a surge of research has reported altered structural and functional connectivity between amygdala and the medial PFC following severe stress, few have addressed the underlying neurochemistry.METHODS: We combined resting-state functional magnetic resonance imaging measures of amygdala connectivity with in vivo MR-spectroscopy (1H-MRS) measurements of glutamate in 26 survivors from the 2011 Norwegian terror attack and 34 control subjects.RESULTS: Traumatized youths showed altered amygdala-anterior midcingulate cortex (aMCC) and amygdala-ventromedial prefrontal cortex (vmPFC) connectivity. Moreover, the trauma survivors exhibited reduced levels of glutamate in the vmPFC which fits with the previous findings of reduced levels of Glx (glutamate + glutamine) in the aMCC (Ousdal et al.2017) and together suggest long-term impact of a traumatic experience on glutamatergic pathways. Importantly, local glutamatergic metabolite levels predicted the individual amygdala-aMCC and amygdala-vmPFC functional connectivity, and also mediated the observed group difference in amygdala-aMCC connectivity.CONCLUSIONS: Our findings suggest that traumatic stress may influence amygdala-prefrontal neuronal connectivity through an effect on prefrontal glutamate and its compounds. Understanding the neurochemical underpinning of altered amygdala connectivity after trauma may ultimately lead to the discovery of new pharmacological agents which can prevent or treat stress-related mental illness

The impact of traumatic stress on Pavlovian biases

BACKGROUND: Disturbances in Pavlovian valuation systems are reported to follow traumatic stress exposure. However, motivated decisions are also guided by instrumental mechanisms, but to date the effect of traumatic stress on these instrumental systems remain poorly investigated. Here, we examine whether a single episode of severe traumatic stress influences flexible instrumental decisions through an impact on a Pavlovian system. METHODS: Twenty-six survivors of the 2011 Norwegian terror attack and 30 matched control subjects performed an instrumental learning task in which Pavlovian and instrumental associations promoted congruent or conflicting responses. We used reinforcement learning models to infer how traumatic stress affected learning and decision-making. Based on the importance of dorsal anterior cingulate cortex (dACC) for cognitive control, we also investigated if individual concentrations of Glx (=glutamate + glutamine) in dACC predicted the Pavlovian bias of choice. RESULTS: Survivors of traumatic stress expressed a greater Pavlovian interference with instrumental action selection and had significantly lower levels of Glx in the dACC. Across subjects, the degree of Pavlovian interference was negatively associated with dACC Glx concentrations. CONCLUSIONS: Experiencing traumatic stress appears to render instrumental decisions less flexible by increasing the susceptibility to Pavlovian influences. An observed association between prefrontal glutamatergic levels and this Pavlovian bias provides novel insight into the neurochemical basis of decision-making, and suggests a mechanism by which traumatic stress can impair flexible instrumental behaviours

Cognitive Bias in Ambiguity Judgements:Using Computational Models to Dissect the Effects of Mild Mood Manipulation in Humans

Positive and negative moods can be treated as prior expectations over future delivery of rewards and punishments. This provides an inferential foundation for the cognitive (judgement) bias task, now widely-used for assessing affective states in non-human animals. In the task, information about affect is extracted from the optimistic or pessimistic manner in which participants resolve ambiguities in sensory input. Here, we report a novel variant of the task aimed at dissecting the effects of affect manipulations on perceptual and value computations for decision-making under ambiguity in humans. Participants were instructed to judge which way a Gabor patch (250ms presentation) was leaning. If the stimulus leant one way (e.g. left), pressing the REWard key yielded a monetary WIN whilst pressing the SAFE key failed to acquire the WIN. If it leant the other way (e.g. right), pressing the SAFE key avoided a LOSS whilst pressing the REWard key incurred the LOSS. The size (0-100 UK pence) of the offered WIN and threatened LOSS, and the ambiguity of the stimulus (vertical being completely ambiguous) were varied on a trial-by-trial basis, allowing us to investigate how decisions were affected by differing combinations of these factors. Half the subjects performed the task in a 'Pleasantly' decorated room and were given a gift (bag of sweets) prior to starting, whilst the other half were in a bare 'Unpleasant' room and were not given anything. Although these treatments had little effect on self-reported mood, they did lead to differences in decision-making. All subjects were risk averse under ambiguity, consistent with the notion of loss aversion. Analysis using a Bayesian decision model indicated that Unpleasant Room subjects were ('pessimistically') biased towards choosing the SAFE key under ambiguity, but also weighed WINS more heavily than LOSSes compared to Pleasant Room subjects. These apparently contradictory findings may be explained by the influence of affect on different processes underlying decision-making, and the task presented here offers opportunities for further dissecting such processes

(Mis)computation in Computational Psychiatry

An adequate explication of miscomputation should do justice to the practices involved in the computational sciences. As relevant practices outside computer science have been overlooked, I begin to fill this gap by distinguishing different notions of miscomputation in computational psychiatry. I argue that a satisfactory explication of miscomputation in computational psychiatry should be grounded in the semantic view of computation, rather than in the mechanistic view. To the extent my argument is convincing, we should reconsider the adequacy of the mechanistic view of computation for illuminating some methodological and explanatory practices in computational cognitive neuroscience, as well as for individuating biological computing systems

A computational psychiatry approach identifies how alpha-2A noradrenergic agonist Guanfacine affects feature-based reinforcement learning in the macaque

[EN] Noradrenaline is believed to support cognitive flexibility through the alpha 2A noradrenergic receptor (a2A-NAR) acting in prefrontal cortex. Enhanced flexibility has been inferred from improved working memory with the a2A-NA agonist Guanfacine. But it has been unclear whether Guanfacine improves specific attention and learning mechanisms beyond working memory, and whether the drug effects can be formalized computationally to allow single subject predictions. We tested and confirmed these suggestions in a case study with a healthy nonhuman primate performing a feature-based reversal learning task evaluating performance using Bayesian and Reinforcement learning models. In an initial dose-testing phase we found a Guanfacine dose that increased performance accuracy, decreased distractibility and improved learning. In a second experimental phase using only that dose we examined the faster feature-based reversal learning with Guanfacine with single-subject computational modeling. Parameter estimation suggested that improved learning is not accounted for by varying a single reinforcement learning mechanism, but by changing the set of parameter values to higher learning rates and stronger suppression of non-chosen over chosen feature information. These findings provide an important starting point for developing nonhuman primate models to discern the synaptic mechanisms of attention and learning functions within the context of a computational neuropsychiatry framework.This research was supported by grants from the Canadian Institutes of Health Research (CIHR), the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Ontario Ministry of Economic Development and Innovation (MEDI). We thank Dr. Hongying Wang for invaluable help with drug administration and animal careHassani, SA.; Oemisch, M.; Balcarras, M.; Westendorff, S.; Ardid-Ramírez, JS.; Van Der Meer, MA.; Tiesinga, P.... (2017). A computational psychiatry approach identifies how alpha-2A noradrenergic agonist Guanfacine affects feature-based reinforcement learning in the macaque. Scientific Reports. 7:1-19. https://doi.org/10.1038/srep40606S1197Arnsten, A. F., Wang, M. J. & Paspalas, C. D. Neuromodulation of thought: flexibilities and vulnerabilities in prefrontal cortical network synapses. Neuron 76, 223–239 (2012).Arnsten, A. F. & Dudley, A. G. Methylphenidate improves prefrontal cortical cognitive function through alpha2 adrenoceptor and dopamine D1 receptor actions: Relevance to therapeutic effects in Attention Deficit Hyperactivity Disorder. Behav Brain Funct 1, 2 (2005).Clark, K. L. & Noudoost, B. The role of prefrontal catecholamines in attention and working memory. Front Neural Circuits 8, 33 (2014).Wang, M. et al. Neuronal basis of age-related working memory decline. Nature 476, 210–213 (2011).Wang, M. et al. Alpha2A-adrenoceptors strengthen working memory networks by inhibiting cAMP-HCN channel signaling in prefrontal cortex. Cell 129, 397–410 (2007).Aston-Jones, G. & Cohen, J. D. An integrative theory of locus coeruleus-norepinephrine function: adaptive gain and optimal performance. Annu Rev Neurosci 28, 403–450 (2005).Yu, A. J. & Dayan, P. Uncertainty, neuromodulation, and attention. Neuron 46, 681–692 (2005).Mather, M., Clewett, D., Sakaki, M. & Harley, C. W. Norepinephrine ignites local hot spots of neuronal excitation: How arousal amplifies selectivity in perception and memory. Behav Brain Sci, 1–100, doi: 10.1017/S0140525X15000667 (2015).Amemiya, S. & Redish, A. D. Manipulating Decisiveness in Decision Making: Effects of Clonidine on Hippocampal Search Strategies. J Neurosci 36, 814–827 (2016).Doya, K. Metalearning and neuromodulation. Neural Netw 15, 495–506 (2002).Uhlen, S., Muceniece, R., Rangel, N., Tiger, G. & Wikberg, J. E. Comparison of the binding activities of some drugs on alpha 2A, alpha 2B and alpha 2C-adrenoceptors and non-adrenergic imidazoline sites in the guinea pig. Pharmacology & toxicology 76, 353–364 (1995).Mao, Z. M., Arnsten, A. F. & Li, B. M. Local infusion of an alpha-1 adrenergic agonist into the prefrontal cortex impairs spatial working memory performance in monkeys. Biological psychiatry 46, 1259–1265 (1999).Arnsten, A. F. & Goldman-Rakic, P. S. Analysis of alpha-2 adrenergic agonist effects on the delayed nonmatch-to-sample performance of aged rhesus monkeys. Neurobiol Aging 11, 583–590 (1990).Franowicz, J. S. & Arnsten, A. F. The alpha-2a noradrenergic agonist, guanfacine, improves delayed response performance in young adult rhesus monkeys. Psychopharmacology 136, 8–14 (1998).Caetano, M. S. et al. Noradrenergic control of error perseveration in medial prefrontal cortex. Frontiers in Integrative Neuroscience 6, 125 (2012).Kim, S., Bobeica, I., Gamo, N. J., Arnsten, A. F. & Lee, D. Effects of alpha-2A adrenergic receptor agonist on time and risk preference in primates. Psychopharmacology 219, 363–375 (2012).Seu, E., Lang, A., Rivera, R. J. & Jentsch, J. D. Inhibition of the norepinephrine transporter improves behavioral flexibility in rats and monkeys. Psychopharmacology 202, 505–519 (2009).Kawaura, K., Karasawa, J., Chaki, S. & Hikichi, H. Stimulation of postsynapse adrenergic alpha2A receptor improves attention/cognition performance in an animal model of attention deficit hyperactivity disorder. Behav Brain Res 270, 349–356 (2014).Aoki, C., Go, C. G., Venkatesan, C. & Kurose, H. Perikaryal and synaptic localization of alpha 2A-adrenergic receptor-like immunoreactivity. Brain Res 650, 181–204 (1994).Barth, A. M., Vizi, E. S., Zelles, T. & Lendvai, B. Alpha2-adrenergic receptors modify dendritic spike generation via HCN channels in the prefrontal cortex. J Neurophysiol 99, 394–401 (2008).Ji, X. H., Ji, J. Z., Zhang, H. & Li, B. M. Stimulation of alpha2-adrenoceptors suppresses excitatory synaptic transmission in the medial prefrontal cortex of rat. Neuropsychopharmacology 33, 2263–2271 (2008).Yi, F., Liu, S. S., Luo, F., Zhang, X. H. & Li, B. M. Signaling mechanism underlying alpha2A -adrenergic suppression of excitatory synaptic transmission in the medial prefrontal cortex of rats. Eur J Neurosci 38, 2364–2373 (2013).Engberg, G. & Eriksson, E. Effects of alpha 2-adrenoceptor agonists on locus coeruleus firing rate and brain noradrenaline turnover in N-ethoxycarbonyl-2-ethoxy-1,2-dihydroquinoline (EEDQ)-treated rats. Naunyn-Schmiedeberg’s archives of pharmacology 343, 472–477 (1991).Jakala, P. et al. Guanfacine, but not clonidine, improves planning and working memory performance in humans. Neuropsychopharmacology 20, 460–470 (1999).Jakala, P. et al. Guanfacine and clonidine, alpha 2-agonists, improve paired associates learning, but not delayed matching to sample, in humans. Neuropsychopharmacology 20, 119–130 (1999).Muller, U. et al. Lack of effects of guanfacine on executive and memory functions in healthy male volunteers. Psychopharmacology 182, 205–213 (2005).Scahill, L. et al. A placebo-controlled study of guanfacine in the treatment of children with tic disorders and attention deficit hyperactivity disorder. The American journal of psychiatry 158, 1067–1074 (2001).Huys, Q. J. M., Maia, T. V. & Frank, M. J. Computational psychiatry as a bridge from neuroscience to clinical applications. Nat Neurosci 19, 404–413 (2016).Stephan, K. E. et al. Computational neuroimaging strategies for single patient predictions. NeuroImage in press (2015).Arnsten, A. F., Cai, J. X. & Goldman-Rakic, P. S. The alpha-2 adrenergic agonist guanfacine improves memory in aged monkeys without sedative or hypotensive side effects: evidence for alpha-2 receptor subtypes. J Neurosci 8, 4287–4298 (1988).Callado, L. F. & Stamford, J. A. Alpha2A- but not alpha2B/C-adrenoceptors modulate noradrenaline release in rat locus coeruleus: voltammetric data. Eur J Pharmacol 366, 35–39 (1999).Millan, M. J. et al. Cognitive dysfunction in psychiatric disorders: characteristics, causes and the quest for improved therapy. Nature reviews. Drug discovery 11, 141–168 (2012).Niv, Y. et al. Reinforcement learning in multidimensional environments relies on attention mechanisms. J Neurosci 35, 8145–8157 (2015).Balcarras, M., Ardid, S., Kaping, D., Everling, S. & Womelsdorf, T. Attentional Selection Can Be Predicted by Reinforcement Learning of Task-relevant Stimulus Features Weighted by Value-independent Stickiness. J Cogn Neurosci 28, 333–349 (2016).Redish, A. D., Jensen, S., Johnson, A. & Kurth-Nelson, Z. Reconciling reinforcement learning models with behavioral extinction and renewal: implications for addiction, relapse, and problem gambling. Psychol Rev 114, 784–805 (2007).Nassar, M. R. et al. Rational regulation of learning dynamics by pupil-linked arousal systems. Nat Neurosci 15, 1040–1046 (2012).O’Reilly, J. X. et al. Dissociable effects of surprise and model update in parietal and anterior cingulate cortex. Proc Natl Acad Sci USA 110, 3660–3669 (2013).Shenhav, A., Botvinick, M. M. & Cohen, J. D. The expected value of control: an integrative theory of anterior cingulate cortex function. Neuron 79, 217–240 (2013).Womelsdorf, T. & Everling, S. Long-Range Attention Networks: Circuit Motifs Underlying Endogenously Controlled Stimulus Selection. Trends Neurosci 38, 682–700 (2015).Yang, Y. et al. Nicotinic alpha7 receptors enhance NMDA cognitive circuits in dorsolateral prefrontal cortex. Proc Natl Acad Sci USA 110, 12078–12083 (2013).Aston-Jones, G., Rajkowski, J. & Cohen, J. Role of locus coeruleus in attention and behavioral flexibility. Biological psychiatry 46, 1309–1320 (1999).Cole, B. J. & Robbins, T. W. Forebrain norepinephrine: role in controlled information processing in the rat. Neuropsychopharmacology 7, 129–142 (1992).Dalley, J. W., Cardinal, R. N. & Robbins, T. W. Prefrontal executive and cognitive functions in rodents: neural and neurochemical substrates. Neuroscience and biobehavioral reviews 28, 771–784 (2004).Devauges, V. & Sara, S. J. Activation of the noradrenergic system facilitates an attentional shift in the rat. Behav Brain Res 39, 19–28 (1990).Connor, D. F., Arnsten, A. F., Pearson, G. S. & Greco, G. F. Guanfacine extended release for the treatment of attention-deficit/hyperactivity disorder in children and adolescents. Expert opinion on pharmacotherapy 15, 1601–1610 (2014).Sallee, F. R. et al. Guanfacine extended release in children and adolescents with attention-deficit/hyperactivity disorder: a placebo-controlled trial. J Am Acad Child Adolesc Psychiatry 48, 155–165 (2009).Steere, J. C. & Arnsten, A. F. The alpha-2A noradrenergic receptor agonist guanfacine improves visual object discrimination reversal performance in aged rhesus monkeys. Behav Neurosci 111, 883–891 (1997).Doya, K. Modulators of decision making. Nat Neurosci 11, 410–416 (2008).Wang, X. J. & Krystal, J. H. Computational psychiatry. Neuron 84, 638–654 (2014).Wiecki, T. V. et al. A Computational Cognitive Biomarker for Early-Stage Huntington’s Disease. PLoS One 11, e0148409, doi: 10.1371/journal.pone.0148409 (2016).Huys, Q. J., Pizzagalli, D. A., Bogdan, R. & Dayan, P. Mapping anhedonia onto reinforcement learning: a behavioural meta-analysis. Biol Mood Anxiety Disord 3, 12 (2013).Gershman, S. J. & Niv, Y. Learning latent structure: carving nature at its joints. Curr Opin Neurobiol 20, 251–256 (2010).Voon, V. et al. Disorders of compulsivity: a common bias towards learning habits. Mol Psychiatry 20, 345–352 (2015).Maia, T. V. & Frank, M. J. From reinforcement learning models to psychiatric and neurological disorders. Nature Neuroscience 14, 154–162 (2011).Adams, R. A., Huys, Q. J. M. & Roiser, J. P. Computational Psychiatry: towards a mathematically informed understanding of mental illness. Journal of Neurology, Neurosurgery, and Psychiatry 87, 53–63 (2015).Schlagenhauf, F. et al. Striatal dysfunction during reversal learning in unmedicated schizophrenia patients. NeuroImage 89, 171–180 (2014).Harlé, K. M. et al. Bayesian neural adjustment of inhibitory control predicts emergence of problem stimulant use. Brain 138, 3413–3426 (2015).Zhang, J. et al. Different decision deficits impair response inhibition in progressive supranuclear palsy and Parkinson’s disease. Brain 139, 161–173 (2016).Frank, M. J. et al. fMRI and EEG Predictors of Dynamic Decision Parameters during Human Reinforcement Learning. Journal of Neuroscience 35, 485–494 (2015).Smith, A. C. & Brown, E. N. Estimating a state-space model from point process observations. Neural Comput 15, 965–991 (2003).Wilson, R. C. & Niv, Y. Inferring relevance in a changing world. Frontiers in human neuroscience 5, 189 (2011).Rämä, P. et al. Medetomidine, atipamezole, and guanfacine in delayed response performance of aged monkeys. Pharmacology Biochemistry and Behavior 55, 415–422 (1996).Arnsten, A. F. T. & Contant, T. A. Alpha-2 adrenergic agonists decrease distractibility in aged monkeys performing the delayed response task. Psychopharmacology 108, 159–169 (1992).O’Neill, J. et al. Effects of guanfacine on three forms of distraction in the aging macaque. Life Sciences 67, 877–885 (2000).Wang, M., Ji, J.-Z. & Li, B.-M. The α2A-Adrenergic Agonist Guanfacine Improves Visuomotor Associative Learning in Monkeys. Neuropsychopharmacology 29, 86–92 (2004).Witte, E. a. & Marrocco, R. T. Alteration of brain noradrenergic activity in rhesus monkeys affects the alerting component of covert orienting. Psychopharmacology 132, 315–323 (1997).Decamp, E., Clark, K. & Schneider, J. S. Effects of the alpha-2 adrenoceptor agonist guanfacine on attention and working memory in aged non-human primates. European Journal of Neuroscience 34, 1018–1022 (2011)

Conductance Ratios and Cellular Identity

Recent experimental evidence suggests that coordinated expression of ion channels plays a role in constraining neuronal electrical activity. In particular, each neuronal cell type of the crustacean stomatogastric ganglion exhibits a unique set of positive linear correlations between ionic membrane conductances. These data suggest a causal relationship between expressed conductance correlations and features of cellular identity, namely electrical activity type. To test this idea, we used an existing database of conductance-based model neurons. We partitioned this database based on various measures of intrinsic activity, to approximate distinctions between biological cell types. We then tested individual conductance pairs for linear dependence to identify correlations. Contrary to experimental evidence, in which all conductance correlations are positive, 32% of correlations seen in this database were negative relationships. In addition, 80% of correlations seen here involved at least one calcium conductance, which have been difficult to measure experimentally. Similar to experimental results, each activity type investigated had a unique combination of correlated conductances. Finally, we found that populations of models that conform to a specific conductance correlation have a higher likelihood of exhibiting a particular feature of electrical activity. We conclude that regulating conductance ratios can support proper electrical activity of a wide range of cell types, particularly when the identity of the cell is well-defined by one or two features of its activity. Furthermore, we predict that previously unseen negative correlations and correlations involving calcium conductances are biologically plausible