From Computation to Clinic.

Theory-driven and data-driven computational approaches to psychiatry have enormous potential for elucidating mechanism of disease and providing translational linkages between basic science findings and the clinic. These approaches have already demonstrated utility in providing clinically relevant understanding, primarily via back translation from clinic to computation, revealing how specific disorders or symptoms map onto specific computational processes. Nonetheless, forward translation, from computation to clinic, remains rare. In addition, consensus regarding specific barriers to forward translation-and on the best strategies to overcome these barriers-is limited. This perspective review brings together expert basic and computationally trained researchers and clinicians to 1) identify challenges specific to preclinical model systems and clinical translation of computational models of cognition and affect, and 2) discuss practical approaches to overcoming these challenges. In doing so, we highlight recent evidence for the ability of computational approaches to predict treatment responses in psychiatric disorders and discuss considerations for maximizing the clinical relevance of such models (e.g., via longitudinal testing) and the likelihood of stakeholder adoption (e.g., via cost-effectiveness analyses).

Randomized Manipulation of Early Cognitive Experience Impacts Adult Brain Structure.

Does early exposure to cognitive and linguistic stimulation impact brain structure? Or do genetic predispositions account for the co-occurrence of certain neuroanatomical phenotypes and a tendency to engage children in cognitively stimulating activities? Low socioeconomic status infants were randomized to either 5 years of cognitively and linguistically stimulating center-based care or a comparison condition. The intervention resulted in large and statistically significant changes in brain structure measured in midlife, particularly for male individuals. These findings are the first to extend the large literature on cognitive enrichment effects on animal brains to humans, and to demonstrate the effects of uniquely human features such as linguistic stimulation.

Short-term mindfulness practice attenuates reward prediction errors signals in the brain.

Activity changes in dopaminergic neurons encode the ongoing discrepancy between expected and actual value of a stimulus, providing a teaching signal for a reward prediction process. Previous work comparing a cohort of long-term Zen meditators to controls demonstrated an attenuation of reward prediction signals to appetitive reward in the striatum. Using a cross-commodity design encompassing primary- and secondary-reward conditioning experiments, the present study asks the question of whether reward prediction signals are causally altered by mindfulness training in naïve subjects. Volunteers were randomly assigned to 8 weeks of mindfulness training (MT), active control training (CT), or a one-time mindfulness induction group (MI). We observed a decreased response to positive prediction errors in the putamen in the MT group compared to CT using both a primary and a secondary-reward experiment. Furthermore, the posterior insula showed greater activation to primary rewards, independently of their predictability, in the MT group, relative to CT and MI group. These results support the notion that increased attention to the present moment and its interoceptive features - a core component of mindfulness practice - may reduce predictability effects in reward processing, without dampening (in fact, enhancing) the response to the actual delivery of the stimulus.

Computational psychiatry: the brain as a phantastic organ.

In this Review, we discuss advances in computational neuroscience that relate to psychiatry. We review computational psychiatry in terms of the ambitions of investigators, emerging domains of application, and future work. Our focus is on theoretical formulations of brain function that put subjective beliefs and behaviour within formal (computational) frameworks-frameworks that can be grounded in neurophysiology down to the level of synaptic mechanisms. Understanding the principles that underlie the brain's functional architecture might be essential for an informed phenotyping of psychopathology in terms of its pathophysiological underpinnings. We focus on active (Bayesian) inference and predictive coding. Specifically, we show how basic principles of neuronal computation can be used to explain psychopathology, ranging from impoverished theory of mind in autism to abnormalities of smooth pursuit eye movements in schizophrenia.

What is value-accumulated reward or evidence?

Why are you reading this abstract? In some sense, your answer will cast the exercise as valuable-but what is value? In what follows, we suggest that value is evidence or, more exactly, log Bayesian evidence. This implies that a sufficient explanation for valuable behavior is the accumulation of evidence for internal models of our world. This contrasts with normative models of optimal control and reinforcement learning, which assume the existence of a value function that explains behavior, where (somewhat tautologically) behavior maximizes value. In this paper, we consider an alternative formulation-active inference-that replaces policies in normative models with prior beliefs about the (future) states agents should occupy. This enables optimal behavior to be cast purely in terms of inference: where agents sample their sensorium to maximize the evidence for their generative model of hidden states in the world, and minimize their uncertainty about those states. Crucially, this formulation resolves the tautology inherent in normative models and allows one to consider how prior beliefs are themselves optimized in a hierarchical setting. We illustrate these points by showing that any optimal policy can be specified with prior beliefs in the context of Bayesian inference. We then show how these prior beliefs are themselves prescribed by an imperative to minimize uncertainty. This formulation explains the saccadic eye movements required to read this text and defines the value of the visual sensations you are soliciting.

Active inference and agency: optimal control without cost functions.

This paper describes a variational free-energy formulation of (partially observable) Markov decision problems in decision making under uncertainty. We show that optimal control can be cast as active inference. In active inference, both action and posterior beliefs about hidden states minimise a free energy bound on the negative log-likelihood of observed states, under a generative model. In this setting, reward or cost functions are absorbed into prior beliefs about state transitions and terminal states. Effectively, this converts optimal control into a pure inference problem, enabling the application of standard Bayesian filtering techniques. We then consider optimal trajectories that rest on posterior beliefs about hidden states in the future. Crucially, this entails modelling control as a hidden state that endows the generative model with a representation of agency. This leads to a distinction between models with and without inference on hidden control states; namely, agency-free and agency-based models, respectively.