Search

In generative modeling of neuroimaging data, such as dynamic causal modeling (DCM), one typically considers several alternative models, either to determine the most plausible explanation for observed data (Bayesian model selection) or to account for model uncertainty (Bayesian model averaging). Both procedures rest on estimates of the model evidence, a principled trade-off between model accuracy and complexity. In the context of DCM, the log evidence is usually approximated using variational Bayes. Although this approach is highly efficient, it makes distributional assumptions and is vulnerable to local extrema. This paper introduces the use of thermodynamic integration (TI) for Bayesian model selection and averaging in the context of DCM. TI is based on Markov chain Monte Carlo sampling which is asymptotically exact but orders of magnitude slower than variational Bayes. In this paper, we explain the theoretical foundations of TI, covering key concepts such as the free energy and its origins in statistical physics. Our aim is to convey an in-depth understanding of the method starting from its historical origin in statistical physics. In addition, we demonstrate the practical application of TI via a series of examples which serve to guide the user in applying this method. Furthermore, these examples demonstrate that, given an efficient implementation and hardware capable of parallel processing, the challenge of high computational demand can be overcome successfully. The TI implementation presented in this paper is freely available as part of the open source software TAPAS.

Psychiatry faces fundamental challenges with regard to mechanistically guided differential diagnosis, as well as prediction of clinical trajectories and treatment response of individual patients. This has motivated the genesis of two closely intertwined fields: (i) Translational Neuromodeling (TN), which develops “computational assays” for inferring patient-specific disease processes from neuroimaging, electrophysiological, and behavioral data; and (ii) Computational Psychiatry (CP), with the goal of incorporating computational assays into clinical decision making in everyday practice. In order to serve as objective and reliable tools for clinical routine, computational assays require end-to-end pipelines from raw data (input) to clinically useful information (output). While these are yet to be established in clinical practice, individual components of this general end-to-end pipeline are being developed and made openly available for community use. In this paper, we present the Translational Algorithms for Psychiatry-Advancing Science (TAPAS) software package, an open-source collection of building blocks for computational assays in psychiatry. Collectively, the tools in TAPAS presently cover several important aspects of the desired end-to-end pipeline, including: (i) tailored experimental designs and optimization of measurement strategy prior to data acquisition, (ii) quality control during data acquisition, and (iii) artifact correction, statistical inference, and clinical application after data acquisition. Here, we review the different tools within TAPAS and illustrate how these may help provide a deeper understanding of neural and cognitive mechanisms of disease, with the ultimate goal of establishing automatized pipelines for predictions about individual patients. We hope that the openly available tools in TAPAS will contribute to the further development of TN/CP and facilitate the translation of advances in computational neuroscience into clinically relevant c

It has been suspected that abnormalities in social inference (e.g., learning others' intentions) play a key role in the formation of persecutory delusions (PD). In this study, we examined the association between subclinical PD and social inference, testing the prediction that proneness to PD is related to altered social inference and beliefs about others' intentions. We included 151 participants scoring on opposite ends of Freeman's Paranoia Checklist (PCL). The participants performed a probabilistic advice-taking task with a dynamically changing social context (volatility) under one of two experimental frames. These frames differentially emphasised possible reasons behind unhelpful advice: (i) the adviser's possible intentions (dispositional frame) or (ii) the rules of the game (situational frame). Our design was thus 2 × 2 factorial (high vs. low delusional tendencies, dispositional vs. situational frame). We found significant group-by-frame interactions, indicating that in the situational frame high PCL scorers took advice less into account than low scorers. Additionally, high PCL scorers believed more frequently that incorrect advice was delivered intentionally and that such misleading behaviour was directed towards them personally. Overall, our results suggest that social inference in individuals with subclinical PD tendencies is shaped by negative prior beliefs about the intentions of others and is thus less sensitive to the attributional framing of adviser-related information. These findings may help future attempts of identifying individuals at risk for developing psychosis and understanding persecutory delusions in psychosis.

It has been suspected that abnormalities in social inference (e.g., learning others’ intentions) play a key role in the formation of persecutory delusions (PD). In this study, we examined the association between subclinical PD and social inference, testing the prediction that proneness to PD is related to altered social inference and beliefs about others’ intentions. We included 151 participants scoring on opposite ends of Freeman’s Paranoia Checklist (PCL). The participants performed a probabilistic advice-taking task with a dynamically changing social context (volatility) under one of two experimental frames. These frames differentially emphasized possible reasons behind unhelpful advice: (i) the adviser’s possible intentions (dispositional frame) or (ii) the rules of the game (situational frame). Our design was thus 2x2 factorial (high vs. low delusional tendencies, dispositional vs. situational frame). We found significant group-by- frame interactions, indicating that in the situational frame high PCL scorers took advice less into account than low scorers. Additionally, high PCL scorers believed more frequently that incorrect advice was delivered intentionally and that such misleading behaviour was directed towards them personally. Overall, our results suggest that social inference in individuals with subclinical PD tendencies is shaped by negative prior beliefs about the intentions of others and is thus less sensitive to the attributional framing of adviser-related information. These findings may help future attempts of identifying individuals at risk for developing psychosis and understanding persecutory delusions in psychosis.

Background This paper introduces a new paradigm that allows one to quantify the Bayesian beliefs evidenced by subjects during oculomotor pursuit. Subjects’ eye tracking responses to a partially occluded sinusoidal target were recorded non-invasively and averaged. These response averages were then analysed using dynamic causal modelling (DCM). In DCM, observed responses are modelled using biologically plausible generative or forward models – usually biophysical models of neuronal activity. New method Our key innovation is to use a generative model based on a normative (Bayes-optimal) model of active inference to model oculomotor pursuit in terms of subjects’ beliefs about how visual targets move and how their oculomotor system responds. Our aim here is to establish the face validity of the approach, by manipulating the content and precision of sensory information – and examining the ensuing changes in the subjects’ implicit beliefs. These beliefs are inferred from their eye movements using the normative model. Results We show that on average, subjects respond to an increase in the ‘noise’ of target motion by increasing sensory precision in their models of the target trajectory. In other words, they attend more to the sensory attributes of a noisier stimulus. Conversely, subjects only change kinetic parameters in their model but not precision, in response to increased target speed. Conclusions Using this technique one can estimate the precisions of subjects’ hierarchical Bayesian beliefs about target motion. We hope to apply this paradigm to subjects with schizophrenia, whose pursuit abnormalities may result from the abnormal encoding of precision.