Beliefs, compulsive behavior and reduced confidence in control.

OCD has been conceptualized as a disorder arising from dysfunctional beliefs, such as overestimating threats or pathological doubts. Yet, how these beliefs lead to compulsions and obsessions remains unclear. Here, we develop a computational model to examine the specific beliefs that trigger and sustain compulsive behavior in a simple symptom-provoking scenario. Our results demonstrate that a single belief disturbance-a lack of confidence in the effectiveness of one's preventive (harm-avoiding) actions-can trigger and maintain compulsions and is directly linked to compulsion severity. This distrust can further explain a number of seemingly unrelated phenomena in OCD, including the role of not-just-right feelings, the link to intolerance to uncertainty, perfectionism, and overestimation of threat, and deficits in reversal and state learning. Our simulations shed new light on which underlying beliefs drive compulsive behavior and highlight the important role of perceived ability to exert control for OCD.

Practical challenges for precision medicine.

The prediction of individual treatment responses with machine learning faces hurdles.

SOMAScience: A Novel Platform for Multidimensional, Longitudinal Pain Assessment.

Chronic pain is one of the most significant health issues in the United States, affecting more than 20% of the population. Despite its contribution to the increasing health crisis, reliable predictors of disease development, progression, or treatment outcomes are lacking. Self-report remains the most effective way to assess pain, but measures are often acquired in sparse settings over short time windows, limiting their predictive ability. In this paper, we present a new mobile health platform called SOMAScience. SOMAScience serves as an easy-to-use research tool for scientists and clinicians, enabling the collection of large-scale pain datasets in single- and multicenter studies by facilitating the acquisition, transfer, and analysis of longitudinal, multidimensional, self-report pain data. Data acquisition for SOMAScience is done through a user-friendly smartphone app, SOMA, that uses experience sampling methodology to capture momentary and daily assessments of pain intensity, unpleasantness, interference, location, mood, activities, and predictions about the next day that provide personal insights into daily pain dynamics. The visualization of data and its trends over time is meant to empower individual users' self-management of their pain. This paper outlines the scientific, clinical, technological, and user considerations involved in the development of SOMAScience and how it can be used in clinical studies or for pain self-management purposes. Our goal is for SOMAScience to provide a much-needed platform for individual users to gain insight into the multidimensional features of their pain while lowering the barrier for researchers and clinicians to obtain the type of pain data that will ultimately lead to improved prevention, diagnosis, and treatment of chronic pain.

Interoception of breathing and its relationship with anxiety.

Interoception, the perception of internal bodily states, is thought to be inextricably linked to affective qualities such as anxiety. Although interoception spans sensory to metacognitive processing, it is not clear whether anxiety is differentially related to these processing levels. Here we investigated this question in the domain of breathing, using computational modeling and high-field (7 T) fMRI to assess brain activity relating to dynamic changes in inspiratory resistance of varying predictability. Notably, the anterior insula was associated with both breathing-related prediction certainty and prediction errors, suggesting an important role in representing and updating models of the body. Individuals with low versus moderate anxiety traits showed differential anterior insula activity for prediction certainty. Multi-modal analyses of data from fMRI, computational assessments of breathing-related metacognition, and questionnaires demonstrated that anxiety-interoception links span all levels from perceptual sensitivity to metacognition, with strong effects seen at higher levels of interoceptive processes.

TAPAS: An Open-Source Software Package for Translational Neuromodeling and Computational Psychiatry.

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 computational assays.

Computational Models of Interoception and Body Regulation.

To survive, organisms must effectively respond to the challenge of maintaining their physiological integrity in the face of an ever-changing environment. Preserving this homeostasis critically relies on adaptive behavior. In this review, we consider recent frameworks that extend classical homeostatic control via reflex arcs to include more flexible forms of adaptive behavior that take interoceptive context, experiences, and expectations into account. Specifically, we define a landscape for computational models of interoception, body regulation, and forecasting, address these models' unique challenges in relation to translational research efforts, and discuss what they can teach us about cognition as well as physical and mental health.

Focus of attention modulates the heartbeat evoked potential.

Theoretical frameworks such as predictive coding suggest that the perception of the body and world - interoception and exteroception - involve intertwined processes of inference, learning, and prediction. In this framework, attention is thought to gate the influence of sensory information on perception. In contrast to exteroception, there is limited evidence for purely attentional effects on interoception. Here, we empirically tested if attentional focus modulates cortical processing of single heartbeats, using a newly-developed experimental paradigm to probe purely attentional differences between exteroceptive and interoceptive conditions in the heartbeat evoked potential (HEP) using EEG recordings. We found that the HEP is significantly higher during interoceptive compared to exteroceptive attention, in a time window of 524-620 ms after the R-peak. Furthermore, this effect predicted self-report measures of autonomic system reactivity. Our study thus provides direct evidence that the HEP is modulated by pure attention and suggests that this effect may provide a clinically relevant readout for assessing interoception.

Interoception and Mental Health: A Roadmap.

Interoception refers to the process by which the nervous system senses, interprets, and integrates signals originating from within the body, providing a moment-by-moment mapping of the body's internal landscape across conscious and unconscious levels. Interoceptive signaling has been considered a component process of reflexes, urges, feelings, drives, adaptive responses, and cognitive and emotional experiences, highlighting its contributions to the maintenance of homeostatic functioning, body regulation, and survival. Dysfunction of interoception is increasingly recognized as an important component of different mental health conditions, including anxiety disorders, mood disorders, eating disorders, addictive disorders, and somatic symptom disorders. However, a number of conceptual and methodological challenges have made it difficult for interoceptive constructs to be broadly applied in mental health research and treatment settings. In November 2016, the Laureate Institute for Brain Research organized the first Interoception Summit, a gathering of interoception experts from around the world, with the goal of accelerating progress in understanding the role of interoception in mental health. The discussions at the meeting were organized around four themes: interoceptive assessment, interoceptive integration, interoceptive psychopathology, and the generation of a roadmap that could serve as a guide for future endeavors. This review article presents an overview of the emerging consensus generated by the meeting.

Piercing of Consciousness as a Threshold-Crossing Operation.

Many decisions arise through an accumulation of evidence to a terminating threshold. The process, termed bounded evidence accumulation (or drift diffusion), provides a unified account of decision speed and accuracy, and it is supported by neurophysiology in human and animal models. In many situations, a decision maker may not communicate a decision immediately and yet feel that at some point she had made up her mind. We hypothesized that this occurs when an accumulation of evidence reaches a termination threshold, registered, subjectively, as an "aha" moment. We asked human participants to make perceptual decisions about the net direction of dynamic random dot motion. The difficulty and viewing duration were controlled by the experimenter. After indicating their choice, participants adjusted the setting of a clock to the moment they felt they had reached a decision. The subjective decision times (tSDs) were faster on trials with stronger (easier) motion, and they were well fit by a bounded drift-diffusion model. The fits to the tSDs alone furnished parameters that fully predicted the choices (accuracy) of four of the five participants. The quality of the prediction provides compelling evidence that these subjective reports correspond to the terminating process of a decision rather than a post hoc inference or arbitrary report. Thus, conscious awareness of having reached a decision appears to arise when the brain's representation of accumulated evidence reaches a threshold or bound. We propose that such a mechanism might play a more widespread role in the "piercing of consciousness" by non-conscious thought processes.

Computational Psychosomatics and Computational Psychiatry: Toward a Joint Framework for Differential Diagnosis.

This article outlines how a core concept from theories of homeostasis and cybernetics, the inference-control loop, may be used to guide differential diagnosis in computational psychiatry and computational psychosomatics. In particular, we discuss 1) how conceptualizing perception and action as inference-control loops yields a joint computational perspective on brain-world and brain-body interactions and 2) how the concrete formulation of this loop as a hierarchical Bayesian model points to key computational quantities that inform a taxonomy of potential disease mechanisms. We consider the utility of this perspective for differential diagnosis in concrete clinical applications.

Allostatic Self-efficacy: A Metacognitive Theory of Dyshomeostasis-Induced Fatigue and Depression.

This paper outlines a hierarchical Bayesian framework for interoception, homeostatic/allostatic control, and meta-cognition that connects fatigue and depression to the experience of chronic dyshomeostasis. Specifically, viewing interoception as the inversion of a generative model of viscerosensory inputs allows for a formal definition of dyshomeostasis (as chronically enhanced surprise about bodily signals, or, equivalently, low evidence for the brain's model of bodily states) and allostasis (as a change in prior beliefs or predictions which define setpoints for homeostatic reflex arcs). Critically, we propose that the performance of interoceptive-allostatic circuitry is monitored by a metacognitive layer that updates beliefs about the brain's capacity to successfully regulate bodily states (allostatic self-efficacy). In this framework, fatigue and depression can be understood as sequential responses to the interoceptive experience of dyshomeostasis and the ensuing metacognitive diagnosis of low allostatic self-efficacy. While fatigue might represent an early response with adaptive value (cf. sickness behavior), the experience of chronic dyshomeostasis may trigger a generalized belief of low self-efficacy and lack of control (cf. learned helplessness), resulting in depression. This perspective implies alternative pathophysiological mechanisms that are reflected by differential abnormalities in the effective connectivity of circuits for interoception and allostasis. We discuss suitably extended models of effective connectivity that could distinguish these connectivity patterns in individual patients and may help inform differential diagnosis of fatigue and depression in the future.

A Bayesian perspective on magnitude estimation.

Our representation of the physical world requires judgments of magnitudes, such as loudness, distance, or time. Interestingly, magnitude estimates are often not veridical but subject to characteristic biases. These biases are strikingly similar across different sensory modalities, suggesting common processing mechanisms that are shared by different sensory systems. However, the search for universal neurobiological principles of magnitude judgments requires guidance by formal theories. Here, we discuss a unifying Bayesian framework for understanding biases in magnitude estimation. This Bayesian perspective enables a re-interpretation of a range of established psychophysical findings, reconciles seemingly incompatible classical views on magnitude estimation, and can guide future investigations of magnitude estimation and its neurobiological mechanisms in health and in psychiatric diseases, such as schizophrenia.

A model-based analysis of impulsivity using a slot-machine gambling paradigm.

Impulsivity plays a key role in decision-making under uncertainty. It is a significant contributor to problem and pathological gambling (PG). Standard assessments of impulsivity by questionnaires, however, have various limitations, partly because impulsivity is a broad, multi-faceted concept. What remains unclear is which of these facets contribute to shaping gambling behavior. In the present study, we investigated impulsivity as expressed in a gambling setting by applying computational modeling to data from 47 healthy male volunteers who played a realistic, virtual slot-machine gambling task. Behaviorally, we found that impulsivity, as measured independently by the 11th revision of the Barratt Impulsiveness Scale (BIS-11), correlated significantly with an aggregate read-out of the following gambling responses: bet increases (BIs), machines switches (MS), casino switches (CS), and double-ups (DUs). Using model comparison, we compared a set of hierarchical Bayesian belief-updating models, i.e., the Hierarchical Gaussian Filter (HGF) and Rescorla-Wagner reinforcement learning (RL) models, with regard to how well they explained different aspects of the behavioral data. We then examined the construct validity of our winning models with multiple regression, relating subject-specific model parameter estimates to the individual BIS-11 total scores. In the most predictive model (a three-level HGF), the two free parameters encoded uncertainty-dependent mechanisms of belief updates and significantly explained BIS-11 variance across subjects. Furthermore, in this model, decision noise was a function of trial-wise uncertainty about winning probability. Collectively, our results provide a proof of concept that hierarchical Bayesian models can characterize the decision-making mechanisms linked to the impulsive traits of an individual. These novel indices of gambling mechanisms unmasked during actual play may be useful for online prevention measures for at-risk players and future assessments of PG.

Combining symbolic cues with sensory input and prior experience in an iterative bayesian framework.

Perception and action are the result of an integration of various sources of information, such as current sensory input, prior experience, or the context in which a stimulus occurs. Often, the interpretation is not trivial hence needs to be learned from the co-occurrence of stimuli. Yet, how do we combine such diverse information to guide our action? Here we use a distance production-reproduction task to investigate the influence of auxiliary, symbolic cues, sensory input, and prior experience on human performance under three different conditions that vary in the information provided. Our results indicate that subjects can (1) learn the mapping of a verbal, symbolic cue onto the stimulus dimension and (2) integrate symbolic information and prior experience into their estimate of displacements. The behavioral results are explained by to two distinct generative models that represent different structural approaches of how a Bayesian observer would combine prior experience, sensory input, and symbolic cue information into a single estimate of displacement. The first model interprets the symbolic cue in the context of categorization, assuming that it reflects information about a distinct underlying stimulus range (categorical model). The second model applies a multi-modal integration approach and treats the symbolic cue as additional sensory input to the system, which is combined with the current sensory measurement and the subjects' prior experience (cue-combination model). Notably, both models account equally well for the observed behavior despite their different structural assumptions. The present work thus provides evidence that humans can interpret abstract symbolic information and combine it with other types of information such as sensory input and prior experience. The similar explanatory power of the two models further suggest that issues such as categorization and cue-combination could be explained by alternative probabilistic approaches.

Iterative Bayesian estimation as an explanation for range and regression effects: a study on human path integration.

Systematic errors in human path integration were previously associated with processing deficits in the integration of space and time. In the present work, we hypothesized that these errors are de facto the result of a system that aims to optimize its performance by incorporating knowledge about prior experience into the current estimate of displacement. We tested human linear and angular displacement estimation behavior in a production-reproduction task under three different prior experience conditions where samples were drawn from different overlapping sample distributions. We found that (1) behavior was biased toward the center of the underlying sample distribution, (2) the amount of bias increased with increasing sample range, and (3) the standard deviation for all conditions was linearly dependent on the mean reproduced displacements. We propose a model of bayesian estimation on logarithmic scales that explains the observed behavior by optimal fusion of an experience-dependent prior expectation with the current noisy displacement measurement. The iterative update of prior experience is modeled by the formulation of a discrete Kalman filter. The model provides a direct link between Weber-Fechner and Stevens' power law, providing a mechanistic explanation for universal psychophysical effects in human magnitude estimation such as the regression to the mean and the range effect.

Fast MR parameter mapping using k-t principal component analysis.

Quantification of magnetic resonance parameters plays an increasingly important role in clinical applications, such as the detection and classification of neurodegenerative diseases. The major obstacle that remains for its widespread use in clinical routine is the long scanning times. Therefore, strategies that allow for significant decreases in scan time are highly desired. Recently, the k-t principal component analysis method was introduced for dynamic cardiac imaging to accelerate data acquisition. This is done by undersampling k-t space and constraining the reconstruction of the aliased data based on the k-t Broad-use Linear Acquisition Speed-up Technique (BLAST) concept and predetermined temporal basis functions. The objective of this study was to investigate whether the k-t principal component analysis concept can be adapted to parameter quantification, specifically allowing for significant acceleration of an inversion recovery fast imaging with steady state precession (TrueFISP) acquisition. We found that three basis functions and a single training data line in central k-space were sufficient to achieve up to an 8-fold acceleration of the quantification measurement. This allows for an estimation of relaxation times T(1) and T(2) and spin density in one slice with sub-millimeter in-plane resolution, in only 6 s. Our findings demonstrate that the k-t principal component analysis method is a potential candidate to bring the acquisition time for magnetic resonance parameter mapping to a clinically acceptable level.