Computational neuroimaging strategies for single patient predictions

AbstractNeuroimaging increasingly exploits machine learning techniques in an attempt to achieve clinically relevant single-subject predictions. An alternative to machine learning, which tries to establish predictive links between features of the observed data and clinical variables, is the deployment of computational models for inferring on the (patho)physiological and cognitive mechanisms that generate behavioural and neuroimaging responses. This paper discusses the rationale behind a computational approach to neuroimaging-based single-subject inference, focusing on its potential for characterising disease mechanisms in individual subjects and mapping these characterisations to clinical predictions. Following an overview of two main approaches – Bayesian model selection and generative embedding – which can link computational models to individual predictions, we review how these methods accommodate heterogeneity in psychiatric and neurological spectrum disorders, help avoid erroneous interpretations of neuroimaging data, and establish a link between a mechanistic, model-based approach and the statistical perspectives afforded by machine learning

Confidence and psychosis: a neuro-computational account of contingency learning disruption by NMDA blockade.

A state of pathological uncertainty about environmental regularities might represent a key step in the pathway to psychotic illness. Early psychosis can be investigated in healthy volunteers under ketamine, an NMDA receptor antagonist. Here, we explored the effects of ketamine on contingency learning using a placebo-controlled, double-blind, crossover design. During functional magnetic resonance imaging, participants performed an instrumental learning task, in which cue-outcome contingencies were probabilistic and reversed between blocks. Bayesian model comparison indicated that in such an unstable environment, reinforcement learning parameters are downregulated depending on confidence level, an adaptive mechanism that was specifically disrupted by ketamine administration. Drug effects were underpinned by altered neural activity in a fronto-parietal network, which reflected the confidence-based shift to exploitation of learned contingencies. Our findings suggest that an early characteristic of psychosis lies in a persistent doubt that undermines the stabilization of behavioral policy resulting in a failure to exploit regularities in the environment.FV was supported by the Groupe Pasteur Mutualité. RG was supported by the Fondation pour la Recherche Médicale and the Fondation Bettencourt Schueller. SP is supported by a Marie Curie Intra-European fellowship (FP7-PEOPLE-2012-IEF). AF was supported by National Health and Medical Research Council grants (IDs : 1050504 and 1066779) and an Australian Research Council Future Fellowship (ID: FT130100589). This work was supported by the Wellcome Trust and the Bernard Wolfe Health Neuroscience Fund.This is the final version of the article. It first appeared from the Nature Publishing Group via http://dx.doi.org/10.1038/mp.2015.7

Adults with autism overestimate the volatility of the sensory environment.

Insistence on sameness and intolerance of change are among the diagnostic criteria for autism spectrum disorder (ASD), but little research has addressed how people with ASD represent and respond to environmental change. Here, behavioral and pupillometric measurements indicated that adults with ASD are less surprised than neurotypical adults when their expectations are violated, and decreased surprise is predictive of greater symptom severity. A hierarchical Bayesian model of learning suggested that in ASD, a tendency to overlearn about volatility in the face of environmental change drives a corresponding reduction in learning about probabilistically aberrant events, thus putatively rendering these events less surprising. Participant-specific modeled estimates of surprise about environmental conditions were linked to pupil size in the ASD group, thus suggesting heightened noradrenergic responsivity in line with compromised neural gain. This study offers insights into the behavioral, algorithmic and physiological mechanisms underlying responses to environmental volatility in ASD

Pharmacological Fingerprints of Contextual Uncertainty

Successful interaction with the environment requires flexible updating of our beliefs about the world. By estimating the likelihood of future events, it is possible to prepare appropriate actions in advance and execute fast, accurate motor responses. According to theoretical proposals, agents track the variability arising from changing environments by computing various forms of uncertainty. Several neuromodulators have been linked to uncertainty signalling, but comprehensive empirical characterisation of their relative contributions to perceptual belief updating, and to the selection of motor responses, is lacking. Here we assess the roles of noradrenaline, acetylcholine, and dopamine within a single, unified computational framework of uncertainty. Using pharmacological interventions in a sample of 128 healthy human volunteers and a hierarchical Bayesian learning model, we characterise the influences of noradrenergic, cholinergic, and dopaminergic receptor antagonism on individual computations of uncertainty during a probabilistic serial reaction time task. We propose that noradrenaline influences learning of uncertain events arising from unexpected changes in the environment. In contrast, acetylcholine balances attribution of uncertainty to chance fluctuations within an environmental context, defined by a stable set of probabilistic associations, or to gross environmental violations following a contextual switch. Dopamine supports the use of uncertainty representations to engender fast, adaptive responses. \ua9 2016 Marshall et al

Cortical parcellation based on structural connectivity: A case for generative models

One of the major challenges in systems neuroscience is to identify brain networks and unravel their significance for brain function –this has led to the concept of the ‘connectome’. Connectomes are currently extensively studied in large-scale international efforts at multiple scales, and follow different definitions with respect to their connections as well as their elements. Perhaps the most promising avenue for defining the elements of connectomes originates from the notion that individual brain areas maintain distinct (long-range) connection profiles. These connectivity patterns determine the areas’ functional properties and also allow for their anatomical delineation and mapping. This rationale has motivated the concept of connectivity-based cortex parcellation. In the past ten years, non-invasive mapping of human brain connectivity has led to immense advances in the development of parcellation techniques and their applications. Unfortunately, many of these approaches primarily aim for confirmation of well-known, existing architectonic maps and, to that end, unsuitably incorporate prior knowledge and frequently build on circular argumentation. Often, current approaches also tend to disregard the specific apertures of connectivity measurements, as well as the anatomical specificities of cortical areas, such as spatial compactness, regional heterogeneity, inter-subject variability, the multi-scaling nature of connectivity information, and potential hierarchical organisation. From a methodological perspective, however, a useful framework that regards all of these aspects in an unbiased way is technically demanding. In this commentary, we first outline the concept of connectivity-based cortex parcellation and discuss its prospects and limitations in particular with respect to structural connectivity. To improve reliability and efficiency, we then strongly advocate for connectivity-based cortex parcellation as a modelling approach; that is, an approximation of the data based on (model) parameter inference. As such, a parcellation algorithm can be formally tested for robustness –the precision of its predictions can be quantified and statistics about potential generalization of the results can be derived. Such a framework also allows the question of model constraints to be reformulated in terms of hypothesis testing through model selection and offers a formative way to integrate anatomical knowledge in terms of prior distributions

Influence of vmPFC on dmPFC predicts valence-guided belief formation

When updating beliefs about their future prospects, people tend to disregard bad news. By combining fMRI with computational and dynamic causal modeling, we identified neurocircuitry mechanisms underlying this optimism bias to test for valence-guided belief formation. In each trial of the fMRI task, participants (n = 24, 10 male) estimated the base rate (eBR) and their risks of experiencing negative future events, were confronted with the actual BR, and finally had the opportunity to update their initial self-related risk estimate. We demonstrated an optimism bias by revealing greater belief updates in response to good over bad news (i.e., learning that the actual BR is lower or higher than expected) while controlling for confounds (estimation error and personal relevance of the new information). Updating was favorable when the final belief about risks improved (or at least did not worsen) relative to the initial risk estimate. This valence of updating was encoded by the ventromedial prefrontal cortex (vmPFC) associated with the valuation of rewards. Within the updating circuit, the vmPFC filtered the incoming signal in a valence-dependent manner and influenced the dorsomedial prefrontal cortex (dmPFC). Both the valence-encoding activity in the vmPFC and its influence on the dmPFC predicted individual magnitudes of the optimism bias. Our results indicate that updating was biased by the motivation to maximize desirable beliefs, mediated by the influence of the valuation system on further cognitive processing. Therefore, although it provides the very basis for human reasoning, belief formation is essentially distorted to promote desired conclusions.SIGNIFICANCE STATEMENT The question of whether human reasoning is biased by desires and goals is crucial for everyday social, professional, and economic decisions. How much our belief formation is influenced by what we want to believe is, however, still debated. Our study confirms that belief updates are indeed optimistically biased. Critically, the bias depends on the recruitment of the brain valuation system and the influence of this system on neural regions involved in reasoning. These neurocircuit interactions support the notion that the motivation to maximize pleasant beliefs reinforces those cognitive processes that are most likely to yield the desired conclusion

Neuro-computational account of how mood fluctuations arise and affect decision making

Fluctuations in mood are known to affect our decisions. Here the authors propose and validate a model of how mood fluctuations arise through a slow integration of positive and negative feedback and report the resulting key changes in brain activity that modulate our decision making

Choose, rate or squeeze: Comparison of economic value functions elicited by different behavioral tasks

A standard view in neuroeconomics is that to make a choice, an agent first assigns subjective values to available options, and then compares them to select the best. In choice tasks, these cardinal values are typically inferred from the preference expressed by subjects between options presented in pairs. Alternatively, cardinal values can be directly elicited by asking subjects to place a cursor on an analog scale (rating task) or to exert a force on a power grip (effort task). These tasks can vary in many respects: they can notably be more or less costly and consequential. Here, we compared the value functions elicited by choice, rating and effort tasks on options composed of two monetary amounts: one for the subject (gain) and one for a charity (donation). Bayesian model selection showed that despite important differences between the three tasks, they all elicited a same value function, with similar weighting of gain and donation, but variable concavity. Moreover, value functions elicited by the different tasks could predict choices with equivalent accuracy. Our finding therefore suggests that comparable value functions can account for various motivated behaviors, beyond economic choice. Nevertheless, we report slight differences in the computational efficiency of parameter estimation that may guide the design of future studies

Neural encoding of food and monetary reward delivery

Different types of rewards such as food and money can similarly drive our behavior owing to shared brain processes encoding their subjective value. However, while the value of money is abstract and needs to be learned, the value of food is rooted in the innate processing of sensory properties and nutritional utilization. Yet, the actual consumption of food and the receipt of money have never been directly contrasted in the same experiment, questioning what unique neural processes differentiate those reward types. To fill this gap, we examined the distinct and common neural responses to the delivery of food and monetary rewards during fMRI.In a novel experimental approach, we parametrically manipulated the subjective value of food and monetary rewards by modulating the quantities of administered palatable milkshake and monetary gains. The receipt of increasing amounts of milkshake and money recruited the ventral striatum and the ventromedial prefrontal cortex, previously associated with value encoding. Notably, the consumption and the subsequent evaluation of increasing quantities of milkshake relative to money revealed an extended recruitment of brain regions related to taste, somatosensory processing, and salience. Moreover, we detected a decline of reward encoding in the ventral tegmental area, nucleus accumbens, and vmPFC, indicating that these regions may be susceptible to time-dependent effects upon accumulation of food and money rewards.Relative to monetary gains, the consumption and evaluation of palatable milkshakes engaged complex neural processing over and above value tracking, emphasizing the critical contribution of taste and other sensory properties to the processing of food rewards. Furthermore, our results highlight the need to closely monitor metabolic states and neural responses to the accumulation of rewards to pinpoint the mechanisms underlying time-dependent dynamics of reward-related processing