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

Test-retest reliability of reinforcement learning parameters

It has recently been suggested that parameter estimates of computational models can be used to understand individual differences at the process level. One area of research in which this approach, called computational phenotyping, has taken hold is computational psychiatry. One requirement for successful computational phenotyping is that behavior and parameters are stable over time. Surprisingly, the test-retest reliability of behavior and model parameters remains unknown for most experimental tasks and models. The present study seeks to close this gap by investigating the test-retest reliability of canonical reinforcement learning models in the context of two often-used learning paradigms: a two-armed bandit and a reversal learning task. We tested independent cohorts for the two tasks (N = 69 and N = 47) via an online testing platform with a between-test interval of five weeks. Whereas reliability was high for personality and cognitive measures (with ICCs ranging from .67 to .93), it was generally poor for the parameter estimates of the reinforcement learning models (with ICCs ranging from .02 to .52 for the bandit task and from .01 to .71 for the reversal learning task). Given that simulations indicated that our procedures could detect high test-retest reliability, this suggests that a significant proportion of the variability must be ascribed to the participants themselves. In support of that hypothesis, we show that mood (stress and happiness) can partly explain within-participant variability. Taken together, these results are critical for current practices in computational phenotyping and suggest that individual variability should be taken into account in the future development of the field.

Cortical similarities in psychiatric and mood disorders identified in federated VBM analysis via COINSTAC

Structural neuroimaging studies have identified a combination of shared and disorder-specific patterns of gray matter (GM) deficits across psychiatric disorders. Pooling large data allows for examination of a possible common neuroanatomical basis that may identify a certain vulnerability for mental illness. Large-scale collaborative research is already facilitated by data repositories, institutionally supported databases, and data archives. However, these data-sharing methodologies can suffer from significant barriers. Federated approaches augment these approaches by enabling access or more sophisticated, shareable and scaled-up analyses of large-scale data. We examined GM alterations using Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation, an open-source, decentralized analysis application. Through federated analysis of eight sites, we identified significant overlap in the GM patterns (n = 4,102) of individuals with schizophrenia, major depressive disorder, and autism spectrum disorder. These results show cortical and subcortical regions that may indicate a shared vulnerability to psychiatric disorders.

Updating Prospective Self-Efficacy Beliefs About Cardiac Interoception in Anorexia Nervosa: An Experimental and Computational Study

Patients with anorexia nervosa (AN) typically hold altered beliefs about their body that they struggle to update, including global, prospective beliefs about their ability to know and regulate their body and particularly their interoceptive states. While clinical questionnaire studies have provided ample evidence on the role of such beliefs in the onset, maintenance, and treatment of AN, psychophysical studies have typically focused on perceptual and 'local' beliefs. Across two experiments, we examined how women at the acute AN (N = 86) and post-acute AN state (N = 87), compared to matched healthy controls (N = 180) formed and updated their self-efficacy beliefs retrospectively (Experiment 1) and prospectively (Experiment 2) about their heartbeat counting abilities in an adapted heartbeat counting task. As preregistered, while AN patients did not differ from controls in interoceptive accuracy per se, they hold and maintain 'pessimistic' interoceptive, metacognitive self-efficacy beliefs after performance. Modelling using a simplified computational Bayesian learning framework showed that neither local evidence from performance, nor retrospective beliefs following that performance (that themselves were suboptimally updated) seem to be sufficient to counter and update pessimistic, self-efficacy beliefs in AN. AN patients showed lower learning rates than controls, revealing a tendency to base their posterior beliefs more on prior beliefs rather than prediction errors in both retrospective and prospective belief updating. Further explorations showed that while these differences in both explicit beliefs, and the latent mechanisms of belief updating, were not explained by general cognitive flexibility differences, they were explained by negative mood comorbidity, even after the acute stage of illness.

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.

Incorporating uncertainty within dynamic interoceptive learning

Introduction

Interoception, the perception of the internal state of the body, has been shown to be closely linked to emotions and mental health. Of particular interest are interoceptive learning processes that capture associations between environmental cues and body signals as a basis for making homeostatically relevant predictions about the future. One method of measuring respiratory interoceptive learning that has shown promising results is the Breathing Learning Task (BLT). While the original BLT required binary predictions regarding the presence or absence of an upcoming inspiratory resistance, here we extended this paradigm to capture continuous measures of prediction (un)certainty.

Methods

Sixteen healthy participants completed the continuous version of the BLT, where they were asked to predict the likelihood of breathing resistances on a continuous scale from 0.0 to 10.0. In order to explain participants' responses, a Rescorla-Wagner model of associative learning was combined with suitable observation models for continuous or binary predictions, respectively. For validation, we compared both models against corresponding null models and examined the correlation between observed and modeled predictions. The model was additionally extended to test whether learning rates differed according to stimuli valence. Finally, summary measures of prediction certainty as well as model estimates for learning rates were considered against interoceptive and mental health questionnaire measures.

Results

Our results demonstrated that the continuous model fits closely captured participant behavior using empirical data, and the binarised predictions showed excellent replicability compared to previously collected data. However, the model extension indicated that there were no significant differences between learning rates for negative (i.e. breathing resistance) and positive (i.e. no breathing resistance) stimuli. Finally, significant correlations were found between fatigue severity and both prediction certainty and learning rate, as well as between anxiety sensitivity and prediction certainty.

Discussion

These results demonstrate the utility of gathering enriched continuous prediction data in interoceptive learning tasks, and suggest that the updated BLT is a promising paradigm for future investigations into interoceptive learning and potential links to mental health.

A computationally informed distinction of interoception and exteroception

While interoception is of major neuroscientific interest, its precise definition and delineation from exteroception continue to be debated. Here, we propose a functional distinction between interoception and exteroception based on computational concepts of sensor-effector loops. Under this view, the classification of sensory inputs as serving interoception or exteroception depends on the sensor-effector loop they feed into, for the control of either bodily (physiological and biochemical) or environmental states. We explain the utility of this perspective by examining the perception of skin temperature, one of the most challenging cases for distinguishing between interoception and exteroception. Specifically, we propose conceptualising thermoception as inference about the thermal state of the body (including the skin), which is directly coupled to thermoregulatory processes. This functional view emphasises the coupling to regulation (control) as a defining property of perception (inference) and connects the definition of interoception to contemporary computational theories of brain-body interactions.

The Human Affectome

Over the last decades, theoretical perspectives in the interdisciplinary field of the affective sciences have proliferated rather than converged due to differing assumptions about what human affective phenomena are and how they work. These metaphysical and mechanistic assumptions, shaped by academic context and values, have dictated affective constructs and operationalizations. However, an assumption about the purpose of affective phenomena can guide us to a common set of metaphysical and mechanistic assumptions. In this capstone paper, we home in on a nested teleological principle for human affective phenomena in order to synthesize metaphysical and mechanistic assumptions. Under this framework, human affective phenomena can collectively be considered algorithms that either adjust based on the human comfort zone (affective concerns) or monitor those adaptive processes (affective features). This teleologically-grounded framework offers a principled agenda and launchpad for both organizing existing perspectives and generating new ones. Ultimately, we hope the Human Affectome brings us a step closer to not only an integrated understanding of human affective phenomena, but an integrated field for affective research.

Does Active Inference Provide a Comprehensive Theory of Placebo Analgesia?

Placebo interventions generate mismatches between expected pain and sensory signals from which pain states are inferred. Because we lack direct access to bodily states, we can only infer whether nociceptive activity indicates tissue damage or results from noise in sensory channels. Predictive processing models propose to make optimal inferences using prior knowledge given noisy sensory data. However, these models do not provide a satisfactory explanation of how pain relief expectations are translated into physiological manifestations of placebo responses. Furthermore, they do not account for individual differences in the ability to endogenously regulate nociceptive activity in predicting placebo analgesia. The brain not only passively integrates prior pain expectations with nociceptive activity to infer pain states (perceptual inference) but also initiates various types of actions to ensure that sensory data are consistent with prior pain expectations (active inference). We argue that depending on whether the brain interprets conflicting sensory data (prediction errors) as a signal to learn from or noise to be attenuated, the brain initiates opposing types of action to facilitate learning from sensory data or, conversely, to enhance the biasing influence of prior pain expectations on pain perception. Furthermore, we discuss the role of stress, anxiety, and unpredictability of pain in influencing the weighting of prior pain expectations and sensory data and how they relate to the individual ability to regulate nociceptive activity (endogenous pain modulation). Finally, we provide suggestions for future studies to test the implications of the active inference model of placebo analgesia.

Capnography-Assisted Learned, Monitored (CALM) breathing therapy for dysfunctional breathing in COPD: A bridge to pulmonary rehabilitation

BACKGROUND

Although dyspnea is a primary symptom of chronic obstructive pulmonary disease (COPD), its treatment is suboptimal. In both COPD and acute anxiety, breathing patterns become dysregulated, contributing to abnormal CO2, dyspnea, and inefficient recovery from breathing challenges. While pulmonary rehabilitation (PR) improves dyspnea, only 1-2% of patients access it. Individuals with anxiety who use PR have worse outcomes.

METHODS

We present the protocol of a randomized controlled trial designed to determine the feasibility and acceptability of a new, four-week mind-body intervention that we developed, called "Capnography-Assisted Learned, Monitored (CALM) Breathing," as an adjunct to PR. Eligible participants are randomized in a 1:1 ratio to either CALM Breathing program or Usual Care. CALM Breathing consists of 10 core, slow breathing exercises combined with real time biofeedback (of end-tidal CO2, respiratory rate, and airflow) and motivational interviewing. CALM Breathing promotes self-regulated breathing, linking CO2 changes to dyspnea and anxiety symptoms and targeting breathing efficiency and self-efficacy in COPD. Participants are randomized to CALM Breathing or a Usual Care control group.

RESULTS

Primary outcomes include feasibility and acceptability metrics of recruitment efficiency, participant retention, intervention adherence and fidelity, PR facilitation, patient satisfaction, and favorable themes from interviews. Secondary outcomes include breathing biomarkers, symptoms, health-related quality of life, six-minute walk distance, lung function, mood, physical activity, and PR utilization and engagement.

CONCLUSION

By disrupting the cycle of dyspnea and anxiety, and providing a needed bridge to PR, CALM Breathing may address a substantive gap in healthcare and optimize treatment for patients with COPD.

Placebo and Nocebo Effects as Bayesian-Brain Phenomena: The Overlooked Role of Likelihood and Attention

The Bayesian-brain framework applied to placebo responses and other mind-body interactions suggests that the effects on the body result from the interaction between priors, such as expectations and learning, and likelihood, such as somatosensorial information. Significant research in this area focuses on the role of the priors, but the relevance of the likelihood has been surprisingly overlooked. One way of manipulating the relevance of the likelihood is by paying attention to sensorial information. We suggest that attention can influence both precision and position (i.e., the relative distance from the priors) of the likelihood by focusing on specific components of the somatosensorial information. Two forms of attention seem particularly relevant in this framework: mindful attention and selective attention. Attention has the potential to be considered a "major player" in placebo/nocebo research, together with expectations and learning. In terms of application, relying on attentional strategies as "amplifiers" or "silencers" of sensorial information could lead to an active involvement of individuals in shaping their care process and health. In this contribution, we discuss the theoretical implications of these intuitions with the aim to provide a comprehensive framework that includes Bayesian brain, placebo/nocebo effects, and the role of attention in mind-body interactions.

Long COVID as a functional somatic symptom disorder caused by abnormally precise prior expectations during Bayesian perceptual processing: A new hypothesis and implications for pandemic response

This review proposes a model of Long-COVID where the constellation of symptoms are in fact genuinely experienced persistent physical symptoms that are usually functional in nature and therefore potentially reversible, that is, Long-COVID is a somatic symptom disorder. First, we describe what is currently known about Long-COVID in children and adults. Second, we examine reported "Long-Pandemic" effects that create a risk for similar somatic symptoms to develop in non-COVID-19 patients. Third, we describe what was known about somatization and somatic symptom disorder before the COVID-19 pandemic, and suggest that by analogy, Long-COVID may best be conceptualized as one of these disorders, with similar symptoms and predisposing, precipitating, and perpetuating factors. Fourth, we review the phenomenon of mass sociogenic (functional) illness, and the concept of nocebo effects, and suggest that by analogy, Long-COVID is compatible with these descriptions. Fifth, we describe the current theoretical model of the mechanism underlying functional disorders, the Bayesian predictive coding model for perception. This model accounts for moderators that can make symptom inferences functionally inaccurate and therefore can explain how to understand common predisposing, precipitating, and perpetuating factors. Finally, we discuss the implications of this framework for improved public health messaging during a pandemic, with recommendations for the management of Long-COVID symptoms in healthcare systems. We argue that the current public health approach has induced fear of Long-COVID in the population, including from constant messaging about disabling symptoms of Long-COVID and theorizing irreversible tissue damage as the cause of Long-COVID. This has created a self-fulfilling prophecy by inducing the very predisposing, precipitating, and perpetuating factors for the syndrome. Finally, we introduce the term "Pandemic-Response Syndrome" to describe what previously was labeled Long-COVID. This alternative perspective aims to stimulate research and serve as a lesson learned to avoid a repeat performance in the future.

Central Autonomic Network Alterations in Anorexia Nervosa Following Peripheral Adrenergic Stimulation

BACKGROUND

Anorexia nervosa (AN) is characterized by low body weight, disturbed eating, body image disturbance, anxiety, and interoceptive dysfunction. However, the neural processes underlying these dysfunctions in AN are unclear. This investigation combined an interoceptive pharmacological probe, the peripheral β-adrenergic agonist isoproterenol, with resting-state functional magnetic resonance imaging to examine whether individuals with AN relative to healthy comparison participants show dysregulated neural coupling in central autonomic network brain regions.

METHODS

Resting-state functional magnetic resonance imaging was performed in 23 weight-restored female participants with AN and 23 age- and body mass index-matched healthy comparison participants before and after receiving isoproterenol infusions. Whole-brain functional connectivity (FC) changes were examined using central autonomic network seeds in the amygdala, anterior insular cortex, posterior cingulate cortex, and ventromedial prefrontal cortex after performing physiological noise correction procedures.

RESULTS

Relative to healthy comparison participants, adrenergic stimulation caused widespread FC reductions in the AN group between central autonomic network regions and motor, premotor, frontal, parietal, and visual brain regions. Across both groups, these FC changes were inversely associated with trait anxiety (State-Trait Anxiety Inventory-Trait), trait depression (9-item Patient Health Questionnaire), and negative body image perception (Body Shape Questionnaire) measures, but not with changes in resting heart rate. These results were not accounted for by baseline group FC differences.

CONCLUSIONS

Weight-restored females with AN show a widespread state-dependent disruption of signaling between central autonomic, frontoparietal, and sensorimotor brain networks that facilitate interoceptive representation and visceromotor regulation. Additionally, trait associations between central autonomic network regions and these other brain networks suggest that dysfunctional processing of interoceptive signaling may contribute to affective and body image disturbance in AN.

Interoceptive and metacognitive facets of fatigue in multiple sclerosis

Numerous disorders are characterised by fatigue as a highly disabling symptom. Fatigue plays a particularly important clinical role in multiple sclerosis (MS) where it exerts a profound impact on quality of life. Recent concepts of fatigue grounded in computational theories of brain-body interactions emphasise the role of interoception and metacognition in the pathogenesis of fatigue. So far, however, for MS, empirical data on interoception and metacognition are scarce. This study examined interoception and (exteroceptive) metacognition in a sample of 71 persons with a diagnosis of MS. Interoception was assessed by prespecified subscales of a standard questionnaire (Multidimensional Assessment of Interoceptive Awareness [MAIA]), while metacognition was investigated with computational models of choice and confidence data from a visual discrimination paradigm. Additionally, autonomic function was examined by several physiological measurements. Several hypotheses were tested based on a preregistered analysis plan. In brief, we found the predicted association of interoceptive awareness with fatigue (but not with exteroceptive metacognition) and an association of autonomic function with exteroceptive metacognition (but not with fatigue). Furthermore, machine learning (elastic net regression) showed that individual fatigue scores could be predicted out-of-sample from our measurements, with questionnaire-based measures of interoceptive awareness and sleep quality as key predictors. Our results support theoretical concepts of interoception as an important factor for fatigue and demonstrate the general feasibility of predicting individual levels of fatigue from simple questionnaire-based measures of interoception and sleep.

The role of pharmacogenomics in precision psychiatry

The field of psychiatry is facing an important paradigm shift in the provision of clinical care and mental health service organization toward personalization and integration of multimodal data science. This approach, termed precision psychiatry, aims at identifying subgroups of patients more prone to the development of a certain phenotype, such as symptoms or severe mental disorders (risk detection), and/or to guide treatment selection. Pharmacogenomics and computational psychiatry are two fundamental tools of precision psychiatry, which have seen increasing levels of integration in clinical settings. Here we present a brief overview of these two applications of precision psychiatry in clinical settings.

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).

Synapses, predictions, and prediction errors: A neocortical computational study of MDD using the temporal memory algorithm of HTM

INTRODUCTION

Synapses and spines play a significant role in major depressive disorder (MDD) pathophysiology, recently highlighted by the rapid antidepressant effect of ketamine and psilocybin. According to the Bayesian brain and interoception perspectives, MDD is formalized as being stuck in affective states constantly predicting negative energy balance. To understand how spines and synapses relate to the predictive function of the neocortex and thus to symptoms, we used the temporal memory (TM), an unsupervised machine-learning algorithm. TM models a single neocortical layer, learns in real-time, and extracts and predicts temporal sequences. TM exhibits neocortical biological features such as sparse firing and continuous online learning using local Hebbian-learning rules.

METHODS

We trained a TM model on random sequences of upper-case alphabetical letters, representing sequences of affective states. To model depression, we progressively destroyed synapses in the TM model and examined how that affected the predictive capacity of the network. We found that the number of predictions decreased non-linearly.

RESULTS

Destroying 50% of the synapses slightly reduced the number of predictions, followed by a marked drop with further destruction. However, reducing the synapses by 25% distinctly dropped the confidence in the predictions. Therefore, even though the network was making accurate predictions, the network was no longer confident about these predictions.

DISCUSSION

These findings explain how interoceptive cortices could be stuck in limited affective states with high prediction error. Connecting ketamine and psilocybin's proposed mechanism of action to depression pathophysiology, the growth of new synapses would allow representing more futuristic predictions with higher confidence. To our knowledge, this is the first study to use the TM model to connect changes happening at synaptic levels to the Bayesian formulation of psychiatric symptomatology. Linking neurobiological abnormalities to symptoms will allow us to understand the mechanisms of treatments and possibly, develop new ones.

The (un)learning of social functions and its significance for mental health

Social interactions are dynamic, context-dependent, and reciprocal events that influence prospective strategies and require constant practice and adaptation. This complexity of social interactions creates several research challenges. We propose a new framework encouraging future research to investigate not only individual differences in capacities relevant for social functioning and their underlying mechanisms, but also the flexibility to adapt or update one's social abilities. We suggest three key capacities relevant for social functioning: (1) social perception, (2) sharing emotions or empathizing, and (3) mentalizing. We elaborate on how adaptations in these capacities may be investigated on behavioral and neural levels. Research on these flexible adaptations of one's social behavior is needed to specify how humans actually "learn to be social". Learning to adapt implies plasticity of the relevant brain networks involved in the underlying social processes, indicating that social abilities are malleable for different contexts. To quantify such measures, researchers need to find ways to investigate learning through dynamic changes in adaptable social paradigms and examine several factors influencing social functioning within the three aformentioned social key capacities. This framework furthers insight concerning individual differences, provides a holistic approach to social functioning, and may improve interventions for ameliorating social abilities in patients.

Allostasis as a core feature of hierarchical gradients in the human brain

This paper integrates emerging evidence from two broad streams of scientific literature into one common framework: (a) hierarchical gradients of functional connectivity that reflect the brain's large-scale structural architecture (e.g., a lamination gradient in the cerebral cortex); and (b) approaches to predictive processing and one of its specific instantiations called allostasis (i.e., the predictive regulation of energetic resources in the service of coordinating the body's internal systems). This synthesis begins to sketch a coherent, neurobiologically inspired framework suggesting that predictive energy regulation is at the core of human brain function, and by extension, psychological and behavioral phenomena, providing a shared vocabulary for theory building and knowledge accumulation.

In the Body’s Eye: The computational anatomy of interoceptive inference

A growing body of evidence highlights the intricate linkage of exteroceptive perception to the rhythmic activity of the visceral body. In parallel, interoceptive inference theories of affective perception and self-consciousness are on the rise in cognitive science. However, thus far no formal theory has emerged to integrate these twin domains; instead, most extant work is conceptual in nature. Here, we introduce a formal model of cardiac active inference, which explains how ascending cardiac signals entrain exteroceptive sensory perception and uncertainty. Through simulated psychophysics, we reproduce the defensive startle reflex and commonly reported effects linking the cardiac cycle to affective behaviour. We further show that simulated ‘interoceptive lesions’ blunt affective expectations, induce psychosomatic hallucinations, and exacerbate biases in perceptual uncertainty. Through synthetic heart-rate variability analyses, we illustrate how the balance of arousal-priors and visceral prediction errors produces idiosyncratic patterns of physiological reactivity. Our model thus offers a roadmap for computationally phenotyping disordered brain-body interaction.

Understanding interactions between the brain and the body has become a topic of increased interest in computational neuroscience and psychiatry. A particular question here concerns how visceral, homeostatic rhythms such as the heart beat influence sensory, affective, and cognitive processing. To better understand these and other oscillatory brain-body interactions, we here introduce a novel computational model of interoceptive inference in which a synthetic agent’s perceptual beliefs are coupled to the rhythm of the heart. Our model both helps to explain emerging empirical data indicating that perceptual inference depends upon beat-to-beat heart rhythms, and can be used to better quantify intra- and inter-individual differences in heart-brain coupling. Using proof-of-principle simulations, we demonstrate how future empirical works could utilize our model to better understand and stratify disorders of interoception and brain-body interaction.

Functional neuroimaging in psychiatry and the case for failing better

Psychiatric disorders encompass complex aberrations of cognition and affect and are among the most debilitating and poorly understood of any medical condition. Current treatments rely primarily on interventions that target brain function (drugs) or learning processes (psychotherapy). A mechanistic understanding of how these interventions mediate their therapeutic effects remains elusive. From the early 1990s, non-invasive functional neuroimaging, coupled with parallel developments in the cognitive neurosciences, seemed to signal a new era of neurobiologically grounded diagnosis and treatment in psychiatry. Yet, despite three decades of intense neuroimaging research, we still lack a neurobiological account for any psychiatric condition. Likewise, functional neuroimaging plays no role in clinical decision making. Here, we offer a critical commentary on this impasse and suggest how the field might fare better and deliver impactful neurobiological insights.

Rational inattention in mice

Behavior exhibited by humans and other organisms is generally inconsistent and biased and, thus, is often labeled irrational. However, the origins of this seemingly suboptimal behavior remain elusive. We developed a behavioral task and normative framework to reveal how organisms should allocate their limited processing resources such that sensory precision and its related metabolic investment are balanced to guarantee maximal utility. We found that mice act as rational inattentive agents by adaptively allocating their sensory resources in a way that maximizes reward consumption in previously unexperienced stimulus-reward association environments. Unexpectedly, perception of commonly occurring stimuli was relatively imprecise; however, this apparent statistical fallacy implies "awareness" and efficient adaptation to their neurocognitive limitations. Arousal systems carry reward distribution information of sensory signals, and distributional reinforcement learning mechanisms regulate sensory precision via top-down normalization. These findings reveal how organisms efficiently perceive and adapt to previously unexperienced environmental contexts within the constraints imposed by neurobiology.

Effect of Pain Reprocessing Therapy vs Placebo and Usual Care for Patients With Chronic Back Pain: A Randomized Clinical Trial

IMPORTANCE

Chronic back pain (CBP) is a leading cause of disability, and treatment is often ineffective. Approximately 85% of cases are primary CBP, for which peripheral etiology cannot be identified, and maintenance factors include fear, avoidance, and beliefs that pain indicates injury.

OBJECTIVE

To test whether a psychological treatment (pain reprocessing therapy [PRT]) aiming to shift patients' beliefs about the causes and threat value of pain provides substantial and durable pain relief from primary CBP and to investigate treatment mechanisms.

DESIGN, SETTING, AND PARTICIPANTS

This randomized clinical trial with longitudinal functional magnetic resonance imaging (fMRI) and 1-year follow-up assessment was conducted in a university research setting from November 2017 to August 2018, with 1-year follow-up completed by November 2019. Clinical and fMRI data were analyzed from January 2019 to August 2020. The study compared PRT with an open-label placebo treatment and with usual care in a community sample.

INTERVENTIONS

Participants randomized to PRT participated in 1 telehealth session with a physician and 8 psychological treatment sessions over 4 weeks. Treatment aimed to help patients reconceptualize their pain as due to nondangerous brain activity rather than peripheral tissue injury, using a combination of cognitive, somatic, and exposure-based techniques. Participants randomized to placebo received an open-label subcutaneous saline injection in the back; participants randomized to usual care continued their routine, ongoing care.

MAIN OUTCOMES AND MEASURES

One-week mean back pain intensity score (0 to 10) at posttreatment, pain beliefs, and fMRI measures of evoked pain and resting connectivity.

RESULTS

At baseline, 151 adults (54% female; mean [SD] age, 41.1 [15.6] years) reported mean (SD) pain of low to moderate severity (mean [SD] pain intensity, 4.10 [1.26] of 10; mean [SD] disability, 23.34 [10.12] of 100) and mean (SD) pain duration of 10.0 (8.9) years. Large group differences in pain were observed at posttreatment, with a mean (SD) pain score of 1.18 (1.24) in the PRT group, 2.84 (1.64) in the placebo group, and 3.13 (1.45) in the usual care group. Hedges g was -1.14 for PRT vs placebo and -1.74 for PRT vs usual care (P < .001). Of 151 total participants, 33 of 50 participants (66%) randomized to PRT were pain-free or nearly pain-free at posttreatment (reporting a pain intensity score of 0 or 1 of 10), compared with 10 of 51 participants (20%) randomized to placebo and 5 of 50 participants (10%) randomized to usual care. Treatment effects were maintained at 1-year follow-up, with a mean (SD) pain score of 1.51 (1.59) in the PRT group, 2.79 (1.78) in the placebo group, and 3.00 (1.77) in the usual care group. Hedges g was -0.70 for PRT vs placebo (P = .001) and -1.05 for PRT vs usual care (P < .001) at 1-year follow-up. Longitudinal fMRI showed (1) reduced responses to evoked back pain in the anterior midcingulate and the anterior prefrontal cortex for PRT vs placebo; (2) reduced responses in the anterior insula for PRT vs usual care; (3) increased resting connectivity from the anterior prefrontal cortex and the anterior insula to the primary somatosensory cortex for PRT vs both control groups; and (4) increased connectivity from the anterior midcingulate to the precuneus for PRT vs usual care.

CONCLUSIONS AND RELEVANCE

Psychological treatment centered on changing patients' beliefs about the causes and threat value of pain may provide substantial and durable pain relief for people with CBP.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT03294148.

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.

Regenerative Virtual Therapy: The Use of Multisensory Technologies and Mindful Attention for Updating the Altered Representations of the Bodily Self

The term “regenerative medicine” (RM) indicates an emerging trend in biomedical sciences that aims at replacing, engineering, or regenerating human cells, tissues, or organs to restore or establish normal function. So far, the focus of RM has been the physical body. Neuroscience, however, is now suggesting that mental disorders can be broadly characterized by a dysfunction in the way the brain computes and integrates the representations of the inner and outer body across time [bodily self-consciousness (BSC)]. In this perspective, we proposed a new kind of clinical intervention, i.e., “Regenerative Virtual Therapy” (RVT), which integrates knowledge from different disciplines, from neuroscience to computational psychiatry, to regenerate a distorted or faulty BSC. The main goal of RVT was to use technology-based somatic modification techniques to restructure the maladaptive bodily representations behind a pathological condition. Specifically, starting from a Bayesian model of our BSC (i.e., body matrix), we suggested the use of mindful attention, cognitive reappraisal, and brain stimulation techniques merged with high-rewarding and novel synthetic multisensory bodily experience (i.e., a virtual reality full-body illusion in sync with a low predictabIlity interoceptive modulation) to rewrite a faulty experience of the body and to regenerate the wellbeing of an individual. The use of RVT will also offer an unprecedented experimental overview of the dynamics of our bodily representations, allowing the reverse-engineering of their functioning for hacking them using advanced technologies.

Toward the unity of pathological and exertional fatigue: A predictive processing model

Fatigue is a common experience in both health and disease. Yet, pathological (i.e., prolonged or chronic) and transient (i.e., exertional) fatigue symptoms are traditionally considered distinct, compounding a separation between interested research fields within the study of fatigue. Within the clinical neurosciences, nascent frameworks position pathological fatigue as a product of inference derived through hierarchical predictive processing. The metacognitive theory of dyshomeostasis (Stephan et al., 2016) states that pathological fatigue emerges from the metacognitive mechanism in which the detection of persistent mismatches between prior interoceptive predictions and ascending sensory evidence (i.e., prediction error) signals low evidence for internal generative models, which undermine an agent’s feeling of mastery over the body and is thus experienced phenomenologically as fatigue. Although acute, transient subjective symptoms of exertional fatigue have also been associated with increasing interoceptive prediction error, the dynamic computations that underlie its development have not been clearly defined. Here, drawing on the metacognitive theory of dyshomeostasis, we extend this account to offer an explicit description of the development of fatigue during extended periods of (physical) exertion. Accordingly, it is proposed that a loss of certainty or confidence in control predictions in response to persistent detection of prediction error features as a common foundation for the conscious experience of both pathological and nonpathological fatigue.

Inference on homeostatic belief precision

Interoception and homeostatic/allostatic control are intertwined branches of closed-loop brain-body interactions (BBI). Given their importance in mental and psychosomatic disorders, establishing computational assays of BBI represents a clinically important but methodologically challenging endeavor. This technical note presents a novel approach, derived from a generic computational model of homeostatic/allostatic control that underpins (meta)cognitive theories of affective and psychosomatic disorders. This model views homeostatic setpoints as probability distributions ("homeostatic beliefs") whose parameters determine regulatory efforts and change dynamically under allostatic predictions. In particular, changes in homeostatic belief precision, triggered by anticipated threats to homeostasis, are thought to alter cerebral regulation of bodily states. Here, we present statistical procedures for inferring homeostatic belief precision from measured bodily states and/or regulatory (action) signals. We analyze the inference problem, derive two alternative estimators of homeostatic belief precision, and apply our method to simulated data. Our proposed approach may prove useful for assessing BBI in individual subjects.

Gut inference: A computational modelling approach

Neurocomputational theories have hypothesized that Bayesian inference underlies interoception, which has become a topic of recent experimental work in heartbeat perception. To extend this approach beyond cardiac interoception, we describe the application of a Bayesian computational model to a recently developed gastrointestinal interoception task completed by 40 healthy individuals undergoing simultaneous electroencephalogram (EEG) and peripheral physiological recording. We first present results that support the validity of this modelling approach. Second, we provide a test of, and confirmatory evidence supporting, the neural process theory associated with a particular Bayesian framework (active inference) that predicts specific relationships between computational parameters and event-related potentials in EEG. We also offer some exploratory evidence suggesting that computational parameters may influence the regulation of peripheral physiological states. We conclude that this computational approach offers promise as a tool for studying individual differences in gastrointestinal interoception.

Levels of Emotional Awareness: Theory and Measurement of a Socio-Emotional Skill

Emotional awareness is the ability to conceptualize and describe one's own emotions and those of others. Over thirty years ago, a cognitive-developmental theory of emotional awareness patterned after Piaget's theory of cognitive development was created as well as a performance measure of this ability called the Levels of Emotional Awareness Scale (LEAS). Since then, a large number of studies have been completed in healthy volunteers and clinical populations including those with mental health or systemic medical disorders. Along the way, there have also been further refinements and adaptations of the LEAS such as the creation of a digital version in addition to further advances in the theory itself. This review aims to provide a comprehensive summary of the evolving theoretical background, measurement methods, and empirical findings with the LEAS. The LEAS is a reliable and valid measure of emotional awareness. Evidence suggests that emotional awareness facilitates better emotion self-regulation, better ability to navigate complex social situations and enjoy relationships, and better physical and mental health. This is a relatively new but promising area of research in the domain of socio-emotional skills. The paper concludes with some recommendations for future research.

Disrupted Dorsal Mid-Insula Activation During Interoception Across Psychiatric Disorders

OBJECTIVE

Maintenance of bodily homeostasis relies on interoceptive mechanisms in the brain to predict and regulate bodily state. While altered neural activation during interoception in specific psychiatric disorders has been reported in many studies, it is unclear whether a common neural locus underpins transdiagnostic interoceptive differences.

METHODS

The authors conducted a meta-analysis of neuroimaging studies comparing patients with psychiatric disorders with healthy control subjects to identify brain regions exhibiting convergent disrupted activation during interoception. Bibliographic, neuroimaging, and preprint databases through May 2020 were searched. A total of 306 foci from 33 studies were extracted, which included 610 control subjects and 626 patients with schizophrenia, bipolar or unipolar depression, posttraumatic stress disorder, anxiety, eating disorders, or substance use disorders. Data were pooled using a random-effects model implemented by the activation likelihood estimation algorithm. The preregistered primary outcome was the neuroanatomical location of the convergence of peak voxel coordinates.

RESULTS

Convergent disrupted activation specific to the left dorsal mid-insula was found (Z=4.47, peak coordinates: -36, -2, 14; volume: 928 mm3). Studies directly contributing to the cluster included patients with bipolar disorder, anxiety, major depression, anorexia, and schizophrenia, assessed with task probes including pain, hunger, and interoceptive attention. A series of conjunction analyses against extant meta-analytic data sets revealed that this mid-insula cluster was anatomically distinct from brain regions involved in affective processing and from regions altered by psychological or pharmacological interventions for affective disorders.

CONCLUSIONS

These results reveal transdiagnostic, domain-general differences in interoceptive processing in the left dorsal mid-insula. Disrupted mid-insular activation may represent a neural marker of psychopathology and a putative target for novel interventions.

A Developmental Framework for Embodiment Research: The Next Step Toward Integrating Concepts and Methods

Embodiment research is at a turning point. There is an increasing amount of data and studies investigating embodiment phenomena and their role in mental processing and functions from across a wide range of disciplines and theoretical schools within the life sciences. However, the integration of behavioral data with data from different biological levels is challenging for the involved research fields such as movement psychology, social and developmental neuroscience, computational psychosomatics, social and behavioral epigenetics, human-centered robotics, and many more. This highlights the need for an interdisciplinary framework of embodiment research. In addition, there is a growing need for a cross-disciplinary consensus on level-specific criteria of embodiment. We propose that a developmental perspective on embodiment is able to provide a framework for overcoming such pressing issues, providing analytical tools to link timescales and levels of embodiment specific to the function under study, uncovering the underlying developmental processes, clarifying level-specific embodiment criteria, and providing a matrix and platform to bridge disciplinary boundaries among the involved research fields.

Computational models of the "active self" and its disturbances in schizophrenia

The notion that self-disorders are at the root of the emergence of schizophrenia rather than a symptom of the disease, is getting more traction in the cognitive sciences. This is in line with philosophical approaches that consider an enactive self, constituted through action and interaction with the environment. We thereby analyze different definitions of the self and evaluate various computational theories lending to these ideas. Bayesian and predictive processing are promising approaches for computational modeling of the "active self". We evaluate their implementation and challenges in computational psychiatry and cognitive developmental robotics. We describe how and why embodied robotic systems provide a valuable tool in psychiatry to assess, validate, and simulate mechanisms of self-disorders. Specifically, mechanisms involving sensorimotor learning, prediction, and self-other distinction, can be assessed with artificial agents. This link can provide essential insights to the formation of the self and new avenues in the treatment of psychiatric disorders.

Long-term stability of computational parameters during approach-avoidance conflict in a transdiagnostic psychiatric patient sample

Maladaptive behavior during approach-avoidance conflict (AAC) is common to multiple psychiatric disorders. Using computational modeling, we previously reported that individuals with depression, anxiety, and substance use disorders (DEP/ANX; SUDs) exhibited differences in decision uncertainty and sensitivity to negative outcomes versus reward (emotional conflict) relative to healthy controls (HCs). However, it remains unknown whether these computational parameters and group differences are stable over time. We analyzed 1-year follow-up data from a subset of the same participants (N = 325) to assess parameter stability and relationships to other clinical and task measures. We assessed group differences in the entire sample as well as a subset matched for age and IQ across HCs (N = 48), SUDs (N = 29), and DEP/ANX (N = 121). We also assessed 2-3 week reliability in a separate sample of 30 HCs. Emotional conflict and decision uncertainty parameters showed moderate 1-year intra-class correlations (.52 and .46, respectively) and moderate to excellent correlations over the shorter period (.84 and .54, respectively). Similar to previous baseline findings, parameters correlated with multiple response time measures (ps < .001) and self-reported anxiety (r = .30, p < .001) and decision difficulty (r = .44, p < .001). Linear mixed effects analyses revealed that patients remained higher in decision uncertainty (SUDs, p = .009) and lower in emotional conflict (SUDs, p = .004, DEP/ANX, p = .02) relative to HCs. This computational modelling approach may therefore offer relatively stable markers of transdiagnostic psychopathology.

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.

Be still my heart: Cardiac regulation as a mode of uncertainty reduction

Decreased heart rate (HR) and variability (HRV) are well-established correlates of attention; however, the functional significance of these dynamics remains unclear. Here, we investigate whether attention-related cardiac modulation is sensitive to different varieties of uncertainty. Thirty-nine adults performed a binocular rivalry-replay task in which changes in visual perception were driven either internally (in response to constant, conflicting stimuli; rivalry) or externally (in response to physically alternating stimuli; replay). Tonic HR and high-frequency HRV linearly decreased as participants progressed from resting-state baseline (minimal visual uncertainty) through replay (temporal uncertainty) to rivalry (temporal uncertainty and ambiguity). Time-resolved frequency estimates revealed that cardiac deceleration was sustained throughout the trial period and modulated by ambiguity, novelty, and switch rate. These findings suggest cardiac regulation during active attention may play an instrumental role in uncertainty reduction.

Quantifying mechanisms of cognition with an experiment and modeling ecosystem

Although there have been major strides toward uncovering the neurobehavioral mechanisms involved in cognitive functions like memory and decision making, methods for measuring behavior and accessing latent processes through computational means remain limited. To this end, we have created SUPREME (Sensing to Understanding and Prediction Realized via an Experiment and Modeling Ecosystem): a toolbox for comprehensive cognitive assessment, provided by a combination of construct-targeted tasks and corresponding computational models. SUPREME includes four tasks, each developed symbiotically with a mechanistic model, which together provide quantified assessments of perception, cognitive control, declarative memory, reward valuation, and frustrative nonreward. In this study, we provide validation analyses for each task using two sessions of data from a cohort of cognitively normal participants (N = 65). Measures of test-retest reliability (r: 0.58-0.75), stability of individual differences (ρ: 0.56-0.70), and internal consistency (α: 0.80-0.86) support the validity of our tasks. After fitting the models to data from individual subjects, we demonstrate each model's ability to capture observed patterns of behavioral results across task conditions. Our computational approaches allow us to decompose behavior into cognitively interpretable subprocesses, which we can compare both within and between participants. We discuss potential future applications of SUPREME, including clinical assessments, longitudinal tracking of cognitive functions, and insight into compensatory mechanisms.

Greater decision uncertainty characterizes a transdiagnostic patient sample during approach-avoidance conflict: a computational modelling approach

BACKGROUND

Imbalances in approach-avoidance conflict (AAC) decision-making (e.g., sacrificing rewards to avoid negative outcomes) are considered central to multiple psychiatric disorders. We used computational modelling to examine 2 factors that are often not distinguished in descriptive analyses of AAC: decision uncertainty and sensitivity to negative outcomes versus rewards (emotional conflict).

METHODS

A previously validated AAC task was completed by 478 participants, including healthy controls (n = 59), people with substance use disorders (n = 159) and people with depression and/or anxiety disorders who did not have substance use disorders (n = 260). Using an active inference model, we estimated individual-level values for a model parameter that reflected decision uncertainty and another that reflected emotional conflict. We also repeated analyses in a subsample (59 healthy controls, 161 people with depression and/or anxiety disorders, 56 people with substance use disorders) that was propensity-matched for age and general intelligence.

RESULTS

The model showed high accuracy (72%). As further validation, parameters correlated with reaction times and self-reported task motivations in expected directions. The emotional conflict parameter further correlated with self-reported anxiety during the task (r = 0.32, p < 0.001), and the decision uncertainty parameter correlated with self-reported difficulty making decisions (r = 0.45, p < 0.001). Compared to healthy controls, people with depression and/or anxiety disorders and people with substance use disorders showed higher decision uncertainty in the propensity-matched sample (t = 2.16, p = 0.03, and t = 2.88, p = 0.005, respectively), with analogous results in the full sample; people with substance use disorders also showed lower emotional conflict in the full sample (t = 3.17, p = 0.002).

LIMITATIONS

This study was limited by heterogeneity of the clinical sample and an inability to examine learning.

CONCLUSION

These results suggest that reduced confidence in how to act, rather than increased emotional conflict, may explain maladaptive approach-avoidance behaviours in people with psychiatric disorders.

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.

A Bayesian computational model reveals a failure to adapt interoceptive precision estimates across depression, anxiety, eating, and substance use disorders

Recent neurocomputational theories have hypothesized that abnormalities in prior beliefs and/or the precision-weighting of afferent interoceptive signals may facilitate the transdiagnostic emergence of psychopathology. Specifically, it has been suggested that, in certain psychiatric disorders, interoceptive processing mechanisms either over-weight prior beliefs or under-weight signals from the viscera (or both), leading to a failure to accurately update beliefs about the body. However, this has not been directly tested empirically. To evaluate the potential roles of prior beliefs and interoceptive precision in this context, we fit a Bayesian computational model to behavior in a transdiagnostic patient sample during an interoceptive awareness (heartbeat tapping) task. Modelling revealed that, during an interoceptive perturbation condition (inspiratory breath-holding during heartbeat tapping), healthy individuals (N = 52) assigned greater precision to ascending cardiac signals than individuals with symptoms of anxiety (N = 15), depression (N = 69), co-morbid depression/anxiety (N = 153), substance use disorders (N = 131), and eating disorders (N = 14)-who failed to increase their precision estimates from resting levels. In contrast, we did not find strong evidence for differences in prior beliefs. These results provide the first empirical computational modeling evidence of a selective dysfunction in adaptive interoceptive processing in psychiatric conditions, and lay the groundwork for future studies examining how reduced interoceptive precision influences visceral regulation and interoceptively-guided decision-making.

Better Safe Than Sorry: A Common Signature of General Vulnerability for Psychopathology

Several labels, such as neuroticism, negative emotionality, and dispositional negativity, indicate a broad dimension of psychopathology. However, largely separate, often disorder-specific research lines have developed that focus on different cognitive and affective characteristics that are associated with this dimension, such as perseverative cognition (worry, rumination), reduced autobiographical memory specificity, compromised fear learning, and enhanced somatic-symptom reporting. In this article, we present a theoretical perspective within a predictive-processing framework in which we trace these phenotypically different characteristics back to a common underlying “better-safe-than-sorry” processing strategy. This implies information processing that tends to be low in sensory-perceptual detail, which allows threat-related categorical priors to dominate conscious experience and for chronic uncertainty/surprise because of a stagnated error-reduction process. This common information-processing strategy has beneficial effects in the short term but important costs in the long term. From this perspective, we suggest that the phenomenally distinct cognitive and affective psychopathological characteristics mentioned above represent the same basic processing heuristic of the brain and are only different in relation to the particular type of information involved (e.g., in working memory, in autobiographical memory, in the external and internal world). Clinical implications of this view are discussed.

Aberrant computational mechanisms of social learning and decision-making in schizophrenia and borderline personality disorder

Psychiatric disorders are ubiquitously characterized by debilitating social impairments. These difficulties are thought to emerge from aberrant social inference. In order to elucidate the underlying computational mechanisms, patients diagnosed with major depressive disorder (N = 29), schizophrenia (N = 31), and borderline personality disorder (N = 31) as well as healthy controls (N = 34) performed a probabilistic reward learning task in which participants could learn from social and non-social information. Patients with schizophrenia and borderline personality disorder performed more poorly on the task than healthy controls and patients with major depressive disorder. Broken down by domain, borderline personality disorder patients performed better in the social compared to the non-social domain. In contrast, controls and major depressive disorder patients showed the opposite pattern and schizophrenia patients showed no difference between domains. In effect, borderline personality disorder patients gave up a possible overall performance advantage by concentrating their learning in the social at the expense of the non-social domain. We used computational modeling to assess learning and decision-making parameters estimated for each participant from their behavior. This enabled additional insights into the underlying learning and decision-making mechanisms. Patients with borderline personality disorder showed slower learning from social and non-social information and an exaggerated sensitivity to changes in environmental volatility, both in the non-social and the social domain, but more so in the latter. Regarding decision-making the modeling revealed that compared to controls and major depression patients, patients with borderline personality disorder and schizophrenia showed a stronger reliance on social relative to non-social information when making choices. Depressed patients did not differ significantly from controls in this respect. Overall, our results are consistent with the notion of a general interpersonal hypersensitivity in borderline personality disorder and schizophrenia based on a shared computational mechanism characterized by an over-reliance on beliefs about others in making decisions and by an exaggerated need to make sense of others during learning specifically in borderline personality disorder.

People suffering from psychiatric disorders frequently experience difficulties in social interaction, such as an impaired ability to use social signals to build representations of others and use these to guide behavior. Compuational models of learning and decision-making enable the characterization of individual patterns in learning and decision-making mechanisms that may be disorder-specific or disorder-general. We employed this approach to investigate the behavior of healthy participants and patients diagnosed with depression, schizophrenia, and borderline personality disorder while they performed a probabilistic reward learning task which included a social component. Patients with schizophrenia and borderline personality disorder performed more poorly on the task than controls and depressed patients. In addition, patients with borderline personality disorder concentrated their learning efforts more on the social compared to the non-social information. Computational modeling additionally revealed that borderline personality disorder patients showed a reduced flexibility in the weighting of newly obtained social and non-social information when learning about their predictive value. Instead, we found exaggerated learning of the volatility of social and non-social information. Additionally, we found a pattern shared between patients with borderline personality disorder and schizophrenia who both showed an over-reliance on predictions about social information during decision-making. Our modeling therefore provides a computational account of the exaggerated need to make sense of and rely on one’s interpretation of others’ behavior, which is prominent in both disorders.

Skipping a Beat: Heartbeat-Evoked Potentials Reflect Predictions during Interoceptive-Exteroceptive Integration

Several theories propose that emotions and self-awareness arise from the integration of internal and external signals and their respective precision-weighted expectations. Supporting these mechanisms, research indicates that the brain uses temporal cues from cardiac signals to predict auditory stimuli and that these predictions and their prediction errors can be observed in the scalp heartbeat-evoked potential (HEP). We investigated the effect of precision modulations on these cross-modal predictive mechanisms, via attention and interoceptive ability. We presented auditory sequences at short (perceived synchronous) or long (perceived asynchronous) cardio-audio delays, with half of the trials including an omission. Participants attended to the cardio-audio synchronicity of the tones (internal attention) or the auditory stimuli alone (external attention). Comparing HEPs during omissions allowed for the observation of pure predictive signals, without contaminating auditory input. We observed an early effect of cardio-audio delay, reflecting a difference in heartbeat-driven expectations. We also observed a larger positivity to the omissions of sounds perceived as synchronous than to the omissions of sounds perceived as asynchronous when attending internally only, consistent with the role of attentional precision for enhancing predictions. These results provide support for attentionally modulated cross-modal predictive coding and suggest a potential tool for investigating its role in emotion and self-awareness.

The effects of intravenous iron supplementation on fatigue and general health in non-anemic blood donors with iron deficiency: a randomized placebo-controlled superiority trial

We investigated whether intravenous iron supplementation improves fatigue and general health in non-anemic repeat adult blood donors with iron deficiency (ferritin ≤ 50 µg/L). Of 1,487 potentially eligible participants, 203 were randomly assigned to a single intravenous dose of 800 mg iron-carboxymaltose and 202 to placebo; 393 participants completed the trial. At 6 to 8 weeks after intervention, self-rated mean fatigue scores (numeric rating scale from 1-10, primary outcome) were 3.9 ± 1.8 in the iron supplementation group and 4.0 ± 2.2 in the placebo group, showing no group difference (p = 0.819). Pre-specified subgroup analyses of gender, ferritin < 25 µg/L and fatigue ≥ 4 points, as well as exploratory analyses of lower ferritin cut-offs did not reveal any between-group differences. In terms of secondary outcomes, the mean differences were 114.2 µg/L for ferritin (95% CI 103.1-125.3) and 5.7 g/L for hemoglobin (95% CI 4.3-7.2) with significantly higher values in the iron supplementation group. No group differences were observed for different measures of general well-being and other clinical and safety outcomes. Intravenous iron supplementation compared with placebo resulted in increase of ferritin and hemoglobin levels in repeat blood donors with low iron stores, yet had no effect on fatigue and general well-being.

Vagus Nerve Stimulation as a Gateway to Interoception

The last two decades have seen a growing interest in the study of interoception. Interoception can be understood as a hierarchical phenomenon, referring to the body-to-brain communication of internal signals, their sensing, encoding, and representation in the brain, influence on other cognitive and affective processes, and their conscious perception. Interoceptive signals have been notoriously challenging to manipulate in experimental settings. Here, we propose that this can be achieved through electrical stimulation of the vagus nerve (either in an invasive or non-invasive fashion). The vagus nerve is the main pathway for conveying information about the internal condition of the body to the brain. Despite its intrinsic involvement in interoception, surprisingly little research in the field has used Vagus Nerve Stimulation to explicitly modulate bodily signals. Here, we review a range of cognitive, affective and clinical research using Vagus Nerve Stimulation, showing that it can be applied to the study of interoception at each level of its hierarchy. This could have considerable implications for our understanding of the interoceptive dimension of cognition and affect in both health and disease, and lead to development of new therapeutic tools.

Network changes in patients with phobic postural vertigo

INTRODUCTION

Functional dizziness comprises a class of dizziness disorders, including phobic postural vertigo (PPV), that cause vestibular symptoms in the absence of a structural organic origin. For this reason, functional brain mechanisms have been implicated in these disorders.

METHODS

Here, functional network organization was investigated in 17 PPV patients and 18 healthy controls (HCs) during functional magnetic resonance imaging with a visual motion stimulus, data initially collected and described by Popp et al. (2018). Graph theoretical measures (degree centrality [DC], clustering coefficient [CC], and eccentricity) of 160 nodes within six functional networks were compared between HC and PPV patients during visual motion and static visual patterns.

RESULTS

Graph theoretical measures analyzed during the static condition revealed significantly different DC in the default-mode, sensorimotor, and cerebellar networks. Furthermore, significantly different group differences in network organization changes between static visual and visual motion stimulation were observed. In PPV, DC and CC showed a significantly stronger increase in the sensorimotor network during visual stimulation, whereas cerebellar network showed a significantly stronger decrease in DC.

CONCLUSION

These results suggest that the altered visual motion processing seen in PPV patients may arise from a modified state of sensory and cerebellar network connectivity.