Perceptual insensitivity to the modulation of interoceptive signals in depression, anxiety, and substance use disorders

A Computational Account of the Development and Evolution of Psychotic Symptoms

The mechanisms of psychotic symptoms such as hallucinations and delusions are often investigated in fully formed illness, well after symptoms emerge. These investigations have yielded key insights but are not well positioned to reveal the dynamic forces underlying symptom formation itself. Understanding symptom development over time would allow us to identify steps in the pathophysiological process leading to psychosis, shifting the focus of psychiatric intervention from symptom alleviation to prevention. We propose a model for understanding the emergence of psychotic symptoms within the context of an adaptive, developing neural system. We make the case for a pathophysiological process that begins with cortical hyperexcitability and bottom-up noise transmission, which engenders inappropriate belief formation via aberrant prediction error signaling. We argue that this bottom-up noise drives learning about the (im)precision of new incoming sensory information because of diminished signal-to-noise ratio, causing a compensatory relative overreliance on prior beliefs. This overreliance on priors predisposes to hallucinations and covaries with hallucination severity. An overreliance on priors may also lead to increased conviction in the beliefs generated by bottom-up noise and drive movement toward conversion to psychosis. We identify predictions of our model at each stage, examine evidence to support or refute those predictions, and propose experiments that could falsify or help select between alternative elements of the overall model. Nesting computational abnormalities within longitudinal development allows us to account for hidden dynamics among the mechanisms driving symptom formation and to view established symptoms as a point of equilibrium among competing biological forces.

Change starts with the body: Interoceptive appreciation mediates the effect of mindfulness training on behavior change - an effect moderated by depression severity

Mindfulness catalyzes health behavior change. Yet, interoception is dysregulated in depression, potentially impairing behavioral activation. We examined the mediating role of interoceptive appreciation, as measured by how much one trusts and listens to internal bodily signals, on behavior change. Primary care patients with depression, anxiety, or stress disorders related to chronic illness were randomized to Mindfulness Training for Primary Care (MTPC) using the Mindful Behavior Change curriculum or a low-dose mindfulness comparator. Participants (N = 274) completed the Multidimensional Assessment of Interoceptive Awareness (MAIA) at 0 and 8 weeks. At week 7, participants chose a health behavior action plan. During weeks 8-10, participants reported their action plan initiation (API) level. We investigated the effect of MTPC on API level (MTPC-API), the mediating role of interoceptive appreciation (Body Listening [MAIA-BL] + Trusting [MAIA-T]), and baseline depression severity as a moderator. MTPC had a significant direct effect on API. Interoceptive appreciation (MAIA-BL + MAIA-T) had a significant indirect effect on API (CI=0.15-0.56). Without depression (n = 76), MAIA-BL partially mediated MTPC-API (CI=0.02-0.87). With moderate-to-severe depression (n = 132), MAIA-T partially mediated MTPC-API (CI=0.01-0.85). Interoceptive appreciation helps people listen to motivating bodily signals. In depression, regaining body trust may be an important step on a mindful path towards change.

Interoceptive Processing in Substance Use Disorders (SUDs): From the Neuroanatomy to Insights from Computational Models and Predictive Coding Frameworks

Substance use disorders (SUDs) represent complex public health challenges characterized by a blend of genetic, cognitive, environmental, and psychosocial factors. This chapter explores the critical role of interoceptive processing - the internal sensing of physiological states - in the neurobiology and treatment of SUDs. Interoceptive dysfunctions are highlighted as central to the craving, emotional regulation, and decision-making processes that underpin addictive behaviors. The importance of the insula in sustaining drug use, particularly nicotine, underscores a broader involvement of interoceptive pathways in SUDs. Altered interoceptive processing is evident across various SUDs, where individuals demonstrate both a heightened sensitivity to drug-related cues and a diminished ability to process aversive stimuli, suggesting substantial neurobiological underpinnings that complicate treatment outcomes. Moreover, we delve into the theoretical and computational approach to understanding interoceptive processing in SUDs. This perspective utilizes a predictive coding framework, positing that the brain continuously generates and updates predictions about internal states based on sensory inputs. In SUDs, disruptions in this predictive mechanism can lead to inaccuracies in interoceptive perception, contributing significantly to the compulsive nature of drug-seeking behaviors and the challenges associated with treatment. We explore how computational models, such as Bayesian inference, provide insights into the interplay between expected and received interoceptive signals, highlighting the role of hyper-precise prior beliefs in the persistence of craving and impulsivity. This theoretical approach not only deepens our understanding of the neural and cognitive bases of addiction but also suggests novel intervention strategies. By recalibrating interoceptive predictions through targeted therapies, such as neuromodulation and mindfulness training, we can potentially restore the interoceptive accuracy, thereby offering new avenues for effective treatment of SUDs.

Computational Mechanisms of Learning and Forgetting Differentiate Affective and Substance Use Disorders

Depression and anxiety are common, highly co-morbid conditions associated with a range of learning and decision-making deficits. While the computational mechanisms underlying these deficits have received growing attention, the transdiagnostic vs. diagnosis-specific nature of these mechanisms remains insufficiently characterized. Individuals with affective disorders (iADs; i.e., depression with or without co-morbid anxiety; N=168 and 74, respectively) completed a widely-used decision-making task. To establish diagnostic specificity, we also incorporated data from a sample of individuals with substance use disorders (iSUDs; N=147) and healthy comparisons (HCs; N=54). Computational modeling afforded separate measures of learning and forgetting rates, among other parameters. Compared to HCs, forgetting rates (reflecting recency bias) were elevated in both iADs and iSUDs (p = 0.007, η 2 = 0.022). In contrast, iADs showed faster learning rates for negative outcomes than iSUDs (p = 0.027, η 2 = 0.017), but they did not differ from HCs. Other model parameters associated with learning and information-seeking also showed suggestive relationships with early adversity and impulsivity. Our findings demonstrate distinct differences in learning and forgetting rates between iSUDs, iADs, and HCs, suggesting that different cognitive processes are affected in these conditions. These differences in decision-making processes and their correlations with symptom dimensions suggest that one could specifically develop interventions that target changing forgetting rates and/or learning from negative outcomes. These results pave the way for replication studies to confirm these relationships and establish their clinical implications.

Impulsivity, trauma history, and interoceptive awareness contribute to completion of a criminal diversion substance use treatment program for women

Introduction

In the US, women are one of the fastest-growing segments of the prison population and more than a quarter of women in state prison are incarcerated for drug offenses. Substance use criminal diversion programs can be effective. It may be beneficial to identify individuals who are most likely to complete the program versus terminate early as this can provide information regarding who may need additional or unique programming to improve the likelihood of successful program completion. Prior research investigating prediction of success in these programs has primarily focused on demographic factors in male samples.

Methods

The current study used machine learning (ML) to examine other non-demographic factors related to the likelihood of completing a substance use criminal diversion program for women. A total of 179 women who were enrolled in a criminal diversion program consented and completed neuropsychological, self-report symptom measures, criminal history and demographic surveys at baseline. Model one entered 145 variables into a machine learning (ML) ensemble model, using repeated, nested cross-validation, predicting subsequent graduation versus termination from the program. An identical ML analysis was conducted for model two, in which 34 variables were entered, including the Women's Risk/Needs Assessment (WRNA).

Results

ML models were unable to predict graduation at an individual level better than chance (AUC = 0.59 [SE = 0.08] and 0.54 [SE = 0.13]). Post-hoc analyses indicated measures of impulsivity, trauma history, interoceptive awareness, employment/financial risk, housing safety, antisocial friends, anger/hostility, and WRNA total score and risk scores exhibited medium to large effect sizes in predicting treatment completion (p < 0.05; ds = 0.29 to 0.81).

Discussion

Results point towards the complexity involved in attempting to predict treatment completion at the individual level but also provide potential targets to inform future research aiming to reduce recidivism.

A computational account of the development and evolution of psychotic symptoms

The mechanisms of psychotic symptoms like hallucinations and delusions are often investigated in fully-formed illness, well after symptoms emerge. These investigations have yielded key insights, but are not well-positioned to reveal the dynamic forces underlying symptom formation itself. Understanding symptom development over time would allow us to identify steps in the pathophysiological process leading to psychosis, shifting the focus of psychiatric intervention from symptom alleviation to prevention. We propose a model for understanding the emergence of psychotic symptoms within the context of an adaptive, developing neural system. We will make the case for a pathophysiological process that begins with cortical hyperexcitability and bottom-up noise transmission, which engenders inappropriate belief formation via aberrant prediction error signaling. We will argue that this bottom-up noise drives learning about the (im)precision of new incoming sensory information because of diminished signal-to-noise ratio, causing an adaptive relative over-reliance on prior beliefs. This over-reliance on priors predisposes to hallucinations and covaries with hallucination severity. An over-reliance on priors may also lead to increased conviction in the beliefs generated by bottom-up noise and drive movement toward conversion to psychosis. We will identify predictions of our model at each stage, examine evidence to support or refute those predictions, and propose experiments that could falsify or help select between alternative elements of the overall model. Nesting computational abnormalities within longitudinal development allows us to account for hidden dynamics among the mechanisms driving symptom formation and to view established symptomatology as a point of equilibrium among competing biological forces.

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.

Heart activity perception: narrative review on the measures of the cardiac perceptual ability

Measures of cardiac perceptual ability (also called cardiac accuracy) are methods of cardiac interoception, the perception of bodily sensation related to heart activity. This narrative review aims to provide an overview of these methods. We differentiate between three main measurement types: (1) change detection, i.e., when the task is to notice the change in the heart rate; also called: heart rate perception tasks, (2) discrimination tasks, i.e., when the task is to compare the internal sensations with external signal(s); also called: heartbeat detection tasks and (3) tracking tasks, i.e., when the task is to follow and report heartbeats via tapping or counting. We describe some of the new methods under "mixed methods," as they share features with more than one of the large measurement types described above. Specific measures differ in various aspects, such as their focus (heart rhythm vs. single beats), their sensitivity to non-conscious sensations and the calculated indices (e.g., whether significance level by hypothesis test is provided). When a measure of cardiac perceptual ability is chosen, it is advisable to take its characteristics into consideration in light of the planned research.

Interoceptive technologies for psychiatric interventions: From diagnosis to clinical applications

Interoception-the perception of internal bodily signals-has emerged as an area of interest due to its implications in emotion and the prevalence of dysfunctional interoceptive processes across psychopathological conditions. Despite the importance of interoception in cognitive neuroscience and psychiatry, its experimental manipulation remains technically challenging. This is due to the invasive nature of existing methods, the limitation of self-report and unimodal measures of interoception, and the absence of standardized approaches across disparate fields. This article integrates diverse research efforts from psychology, physiology, psychiatry, and engineering to address this oversight. Following a general introduction to the neurophysiology of interoception as hierarchical predictive processing, we review the existing paradigms for manipulating interoception (e.g., interoceptive modulation), their underlying mechanisms (e.g., interoceptive conditioning), and clinical applications (e.g., interoceptive exposure). We suggest a classification for interoceptive technologies and discuss their potential for diagnosing and treating mental health disorders. Despite promising results, considerable work is still needed to develop standardized, validated measures of interoceptive function across domains and before these technologies can translate safely and effectively to clinical settings.

Neural circuit selective for fast but not slow dopamine increases in drug reward

The faster a drug enters the brain, the greater its addictive potential, yet the brain circuits underlying the rate dependency to drug reward remain unresolved. With simultaneous PET-fMRI we linked dynamics of dopamine signaling, brain activity/connectivity, and self-reported 'high' in 20 adults receiving methylphenidate orally (results in slow delivery) and intravenously (results in fast delivery) (trial NCT03326245). We estimated speed of striatal dopamine increases to oral and IV methylphenidate and then tested where brain activity was associated with slow and fast dopamine dynamics (primary endpoint). We then tested whether these brain circuits were temporally associated with individual 'high' ratings to methylphenidate (secondary endpoint). A corticostriatal circuit comprising the dorsal anterior cingulate cortex and insula and their connections with dorsal caudate was activated by fast (but not slow) dopamine increases and paralleled 'high' ratings. These data provide evidence in humans for a link between dACC/insula activation and fast but not slow dopamine increases and document a critical role of the salience network in drug reward.

Active learning impairments in substance use disorders when resolving the explore-exploit dilemma: A replication and extension of previous computational modeling results

BACKGROUND

Substance use disorders (SUDs) represent a major public health risk. Yet, our understanding of the mechanisms that maintain these disorders remains incomplete. In a recent computational modeling study, we found initial evidence that SUDs are associated with slower learning rates from negative outcomes and less value-sensitive choice (low "action precision"), which could help explain continued substance use despite harmful consequences.

METHODS

Here we aimed to replicate and extend these results in a pre-registered study with a new sample of 168 individuals with SUDs and 99 healthy comparisons (HCs). We performed the same computational modeling and group comparisons as in our prior report (doi: 10.1016/j.drugalcdep.2020.108208) to confirm previously observed effects. After completing all pre-registered replication analyses, we then combined the previous and current datasets (N = 468) to assess whether differences were transdiagnostic or driven by specific disorders.

RESULTS

Replicating prior results, SUDs showed slower learning rates for negative outcomes in both Bayesian and frequentist analyses (partial η2=.02). Previously observed differences in action precision were not confirmed. Learning rates for positive outcomes were also similar between groups. Logistic regressions including all computational parameters as predictors in the combined datasets could differentiate several specific disorders from HCs, but could not differentiate most disorders from each other.

CONCLUSIONS

These results provide robust evidence that individuals with SUDs adjust behavior more slowly in the face of negative outcomes than HCs. They also suggest this effect is common across several different SUDs. Future research should examine its neural basis and whether learning rates could represent a new treatment target or moderator of treatment outcome.

The new measures of interoceptive accuracy: A systematic review and assessment

Conscious interoception, the perception of internal bodily states, is thought to contribute to fundamental human abilities (e.g., decision-making and emotional regulation). One of its most studied dimensions is interoceptive accuracy: the objective capacity to detect internal bodily signals. In the past few years, several labs across the world have started developing new tasks aimed at overcoming limitations inherent in classical measures of interoceptive accuracy. In this systematic review, we identified these tasks (since 2015) for the cardiac, respiratory, and gastrointestinal domains. For each identified task, we discuss their strengths and weaknesses, and make constructive suggestions for further improvement. In the general discussion, we discuss the (potentially elusive) possibility of reaching high validity in the measurement of interoceptive accuracy. We also point out that interoceptive accuracy may not be the most critical dimension for informing current theories, and we encourage researchers to investigate other dimensions of conscious interoception.

A multi-site German validation of the Interoceptive Accuracy Scale and its relation to psychopathological symptom burden

Altered interoception is thought to be implicated in the development of psychopathology. Recent proposals highlight the need to differentiate between dimensions of interoception to better understand its relation to mental health. Here, we validated a German version of the Interoceptive Accuracy Scale (IAS) and investigated the relationship between IAS scores and clinical outcomes, across seven samples from four research centers (N = 3462). The German IAS version was best explained by a one-factor structure that showed acceptable psychometric properties. We replicated previous findings showing a negative association between IAS scores and measures of alexithymia. IAS scores were negatively related to measures of clinical symptomatology (e.g., anxiety, depressive, and somatoform symptoms) and neurotic traits. These findings suggest that the German IAS is a reliable and valid instrument for subjective interoceptive accuracy. Results emphasize the importance of distinguishing between dimensions of interoception to understand its potential modulatory and protective role in psychopathology.

Active learning impairments in substance use disorders when resolving the explore-exploit dilemma: A replication and extension of previous computational modeling results

Background

Substance use disorders (SUDs) represent a major public health risk. Yet, our understanding of the mechanisms that maintain these disorders remains incomplete. In a recent computational modeling study, we found initial evidence that SUDs are associated with slower learning rates from negative outcomes and less value-sensitive choice (low "action precision"), which could help explain continued substance use despite harmful consequences.

Methods

Here we aimed to replicate and extend these results in a pre-registered study with a new sample of 168 individuals with SUDs and 99 healthy comparisons (HCs). We performed the same computational modeling and group comparisons as in our prior report (doi: 10.1016/j.drugalcdep.2020.108208) to confirm previously observed effects. After completing all pre-registered replication analyses, we then combined the previous and current datasets (N = 468) to assess whether differences were transdiagnostic or driven by specific disorders.

Results

Replicating prior results, SUDs showed slower learning rates for negative outcomes in both Bayesian and frequentist analyses (η 2 =.02). Previously observed differences in action precision were not confirmed. Logistic regressions including all computational parameters as predictors in the combined datasets could differentiate several specific disorders from HCs, but could not differentiate most disorders from each other.

Conclusions

These results provide robust evidence that individuals with SUDs have more difficulty adjusting behavior in the face of negative outcomes than HCs. They also suggest this effect is common across several different SUDs. Future research should examine its neural basis and whether learning rates could represent a new treatment target or moderator of treatment outcome.

The impact of traumatic childhood experiences on interoception: disregarding one's own body

BACKGROUND

Deficient interoception, the processing and perception of internal bodily signals, has been discussed as a mechanism underlying various mental disorders. First results indicate a mediating role of interoception in the interplay of traumatic childhood experiences and adult mental disorders. Traumatic childhood experiences may hinder the adequate processing, integration, and trust in bodily signals that are important in order to understand and regulate own needs and emotions, thereby increasing the vulnerability for mental disorders. However, an overarching study investigating alterations in different interoceptive measures and trauma-related disorders as well as their mediating role between early trauma and emotion dysregulation is still missing.

METHODS

One hundred thirty-six individuals with varying levels of traumatic childhood experiences who either had a current diagnosis of major depression, posttraumatic stress disorder, or somatic symptom disorder, or no mental disorder, took part in a multidimensional assessment of interoceptive processes, including interoceptive accuracy, sensibility, and awareness. Kruskal-Wallis tests were used to compare groups regarding interoceptive processes and associations with traumatic childhood experiences and emotion dysregulation were analyzed with Spearman correlations. Furthermore, mediation analyses were computed to examine and compare interoceptive processes as potential mediators between traumatic childhood experiences and emotion dysregulation.

RESULTS

Only body dissociation, a measure for interoceptive sensibility, was significantly reduced in individuals with a current mental disorder. Body dissociation was also the only interoceptive measure significantly associated with traumatic childhood experiences and emotion dysregulation and the only significant mediator in the relationship between traumatic childhood experiences and emotion dysregulation across groups.

CONCLUSION

Results suggest body dissociation, but not other interoceptive measures, as an important feature linking traumatic childhood experiences to current emotion dysregulation, an important transdiagnostic feature. As body dissociation refers to a habitual non-attendance or disregard of interoceptive signals, integrative therapeutic interventions could help affected individuals to overcome difficulties in emotion perception and regulation.

TRIAL REGISTRATION

The general study design was preregistered; see the German Clinical Trials Register (DRKS-ID: DRKS00015182). This study's analysis plan was not preregistered.

Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review

BACKGROUND

Artificial intelligence (AI) is giving rise to a revolution in medicine and health care. Mental health conditions are highly prevalent in many countries, and the COVID-19 pandemic has increased the risk of further erosion of the mental well-being in the population. Therefore, it is relevant to assess the current status of the application of AI toward mental health research to inform about trends, gaps, opportunities, and challenges.

OBJECTIVE

This study aims to perform a systematic overview of AI applications in mental health in terms of methodologies, data, outcomes, performance, and quality.

METHODS

A systematic search in PubMed, Scopus, IEEE Xplore, and Cochrane databases was conducted to collect records of use cases of AI for mental health disorder studies from January 2016 to November 2021. Records were screened for eligibility if they were a practical implementation of AI in clinical trials involving mental health conditions. Records of AI study cases were evaluated and categorized by the International Classification of Diseases 11th Revision (ICD-11). Data related to trial settings, collection methodology, features, outcomes, and model development and evaluation were extracted following the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) guideline. Further, evaluation of risk of bias is provided.

RESULTS

A total of 429 nonduplicated records were retrieved from the databases and 129 were included for a full assessment-18 of which were manually added. The distribution of AI applications in mental health was found unbalanced between ICD-11 mental health categories. Predominant categories were Depressive disorders (n=70) and Schizophrenia or other primary psychotic disorders (n=26). Most interventions were based on randomized controlled trials (n=62), followed by prospective cohorts (n=24) among observational studies. AI was typically applied to evaluate quality of treatments (n=44) or stratify patients into subgroups and clusters (n=31). Models usually applied a combination of questionnaires and scales to assess symptom severity using electronic health records (n=49) as well as medical images (n=33). Quality assessment revealed important flaws in the process of AI application and data preprocessing pipelines. One-third of the studies (n=56) did not report any preprocessing or data preparation. One-fifth of the models were developed by comparing several methods (n=35) without assessing their suitability in advance and a small proportion reported external validation (n=21). Only 1 paper reported a second assessment of a previous AI model. Risk of bias and transparent reporting yielded low scores due to a poor reporting of the strategy for adjusting hyperparameters, coefficients, and the explainability of the models. International collaboration was anecdotal (n=17) and data and developed models mostly remained private (n=126).

CONCLUSIONS

These significant shortcomings, alongside the lack of information to ensure reproducibility and transparency, are indicative of the challenges that AI in mental health needs to face before contributing to a solid base for knowledge generation and for being a support tool in mental health management.

The unique face of comorbid anxiety and depression: increased interoceptive fearfulness and reactivity

Anxiety and depression commonly co-occur, yet the underlying brain and behavioral processes are poorly understood. Here we examined the hypothesis that individuals with comorbid anxiety and depression would show increased fearful reactivity to an aversive interoceptive perturbation relative to depressed-only individuals. One-hundred and eighty anxious and/or depressed participants from the Tulsa 1000 study completed multi-level behavioral or functional magnetic resonance imaging assessments of interoception and nociception including breath-hold and cold-pressor challenges, and heartbeat perception and interoceptive attention tasks. One-hundred and four individuals with comorbid depression and anxiety disorders (Dep+Anx) were propensity matched with 52 individuals with depression-only (Dep). Data were analyzed using mixed-effects linear regression. The Dep+Anx group showed significantly greater self-reported fear of suffocation during breath holding (Wilcoxon r = 0.23) and reduced cold pain tolerance (R ² = 0.027) signified by hand removal during immersion. However, these groups did not differ with respect to neutrally-valenced behavioral indices of heartbeat perception or neural indices of interoceptive attention. Individuals with comorbid depression and anxiety, vs. those with only depression, show increased respiratory fearfulness and nociceptive reactivity during perturbations of these signals, whilst showing similar interoceptive awareness in the absence of perturbation. Our findings suggest that individuals with comorbid anxiety and depression process aversive interoceptive and nociceptive signals differently than those with depression alone, providing support for a process model of increased threat sensitivity and hyperarousal in anxious depression.

Seeing Through Each Other's Hearts: Inferring Others' Heart Rate as a Function of Own Heart Rate Perception and Perceived Social Intelligence

Successful social interactions require a good understanding of the emotional states of other people. This information is often not directly communicated but must be inferred. As all emotional experiences are also imbedded in the visceral or interoceptive state of the body (i.e., accelerating heart rate during arousal), successfully inferring the interoceptive states of others may open a window into their emotional state. But how well can people do that? Here, we replicate recent results showing that people can discriminate between the cardiac states (i.e., the resting heartrate) of other people by simply looking at them. We further tested whether the ability to infer the interoceptive states of others depends on one's own interoceptive abilities. We measured people's performance in a cardioception task and their self-reported interoceptive accuracy. Whilst neither was directly associated to their ability to infer the heartrate of another person, we found a significant interaction. Specifically, overestimating one's own interoceptive capacities was associated with a worse performance at inferring the heartrate of others. In contrast, underestimating one's own interoceptive capacities did not have such influence. This pattern suggests that deficient beliefs about own interoceptive capacities can have detrimental effects on inferring the interoceptive states of other people.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42761-022-00151-4.

The Hybrid Excess and Decay (HED) model: an automated approach to characterising changes in the photoplethysmography pulse waveform

Photoplethysmography offers a widely used, convenient and non-invasive approach to monitoring basic indices of cardiovascular function, such as heart rate and blood oxygenation. Systematic analysis of the shape of the waveform generated by photoplethysmography might be useful to extract estimates of several physiological and psychological factors influencing the waveform. Here, we developed a robust and automated method for such a systematic analysis across individuals and across different physiological and psychological contexts. We describe a psychophysiologically-relevant model, the Hybrid Excess and Decay (HED) model, which characterises pulse wave morphology in terms of three underlying pressure waves and a decay function. We present the theoretical and practical basis for the model and demonstrate its performance when applied to a pharmacological dataset of 105 participants receiving intravenous administrations of the sympathomimetic drug isoproterenol (isoprenaline). We show that these parameters capture photoplethysmography data with a high degree of precision and, moreover, are sensitive to experimentally-induced changes in interoceptive arousal within individuals. We conclude by discussing the possible value in using the HED model as a complement to standard measures of photoplethysmography signals.

Association Between Changes in Post-hospital Cardiac Symptoms and Changes in Acute Coronary Syndrome-Induced Symptoms of Post-traumatic Stress

Background

After acute coronary syndrome (ACS), one in eight patients develops clinically significant symptoms of Post-traumatic stress disorder (PTSD). We hypothesized that changes in cardiac symptoms from 3 to 12 months after ACS are associated with changes in ACS-induced PTSD symptoms.

Methods

At 3 (n = 154) and/or 12 months (n = 106) post-ACS, patients (n = 156, mean age 59 years, 85% men) completed a clinical interview assessing chest tightness/pain (at rest and/or during exertion), heartbeat symptoms (heart palpitations, racing of heart, heart stumbling or skipping a beat) and PTSD symptoms during the prior 4 weeks. Random mixed regression models examined the association between the onset (or remission) from 3 to 12 months in cardiac symptoms with changes in PTSD symptoms, adjusting for a range of potential predictors of ACS-induced PTSD symptoms.

Results

The onset of chest tightness/pain [estimate = 0.588, 95% confidence interval: 0.275, 0.090; p &lt; 0.001] and of heartbeat symptoms [0.548 (0.165, 0.931); p = 0.005] from 3 to 12 months was independently associated with an increase in total PTSD symptoms. There were also independent associations between the onset of chest tightness/pain and heartbeat symptoms with an increase in PTSD symptom clusters. Specifically, the onset of chest tightness/pain showed associations with an increase in re-experiencing [0.450 (0.167, 0.733); p = 0.027] and avoidance/numbing [0.287 (0.001, 0.574); p = 0.049]. The onset of heartbeat symptoms showed associations with an increase in re-experiencing [0.392 (0.045, 0.739); p = 0.002], avoidance/numbing [0.513 (0.161, 0.864); p = 0.004] and hyperarousal [0.355 (0.051, 0.659); p = 0.022]. An increase in the total number of cardiac symptoms (score range 0–6) was also associated with an increase in total PTSD symptoms [0.343 (0.202, 0.484); p &lt; 0.001]. Psychotherapy in the post-hospital period moderated the association between the change in heartbeat symptoms and the change in total PTSD symptoms [−0.813 (−1.553, −0.074); p = 0.031 for interaction]; the association between the onset of heart beat symptoms and an increase in total PTSD symptoms was weaker in patients who attended psychotherapy [0.437 (−0.178, 1.052); p = 0.16] than in those who did not [0.825 (0.341, 1.309); p &lt; 0.001].

Conclusion

Changes in cardiac symptoms between 3 and 12 months after hospitalization are associated with changes in ACS-induced PTSD symptoms. ClinicalTrials.gov #NCT01781247.

Piloting the Update: The Use of Therapeutic Relationship for Change - A Free Energy Account

We apply the Free Energy Principle (FEP) to cognitive behavioral therapy (CBT). FEP describes the basic functioning of the brain as a predictive organ and states that any self-organizing system that is in equilibrium with its environment must minimize its free energy. Based on an internal model of the world and the self, predictions-so-called priors-are created, which are matched with the information input. The sum of prediction errors corresponds to the Free Energy, which must be minimized. Internal models can be identified with the cognitive-affective schemas of the individual that has become dysfunctional in patients. The role of CBT in this picture is to help the patient update her/his priors. They have evolved in learning history and no longer provide adaptive predictions. We discuss the process of updating in terms of the exploration-exploitation dilemma. This consists of the extent to which one relies on what one already has, i.e., whether one continues to maintain and "exploit" one's previous priors ("better safe than sorry") or whether one does explore new data that lead to an update of priors. Questioning previous priors triggers stress, which is associated with increases in Free Energy in short term. The role of therapeutic relationship is to buffer this increase in Free Energy, thereby increasing the level of perceived safety. The therapeutic relationship is represented in a dual model of affective alliance and goal attainment alliance and is aligned with FEP. Both forms of alliance support exploration and updating of priors. All aspects are illustrated with the help of a clinical case example.

Impact of serotonergic medication on interoception in major depressive disorder

Unmedicated individuals with major depressive disorder (MDD) show abnormal interoception, but it is unclear whether antidepressant treatment via serotonergic medication alters this relationship. The current cross-sectional study examined associations between neural and behavioral indices of interoceptive processing and chronic serotonergic medication administration in MDD. 47 selective serotonin reuptake inhibitor (SSRI)-medicated MDD (MDD-SSRI) individuals were propensity-matched with 48 unmedicated current MDD (MDD-UnMed) and 41 healthy comparison (HC) participants on demographics including age, sex, body mass index, education, as well as on dimensional scales of symptom severity including depression and anxiety. All participants completed an interoceptive attention task during functional magnetic resonance imaging, and a behavioral heartbeat tapping task under three conditions: Guessing, No Guessing, and Breath Hold. Relative to HC, both MDD groups: (1) exhibited lower mid-insula, amygdala, putamen, and caudate activation during interoceptive versus exteroceptive attention; and (2) showed poorer heartbeat tapping performance during the Breath Hold condition. However, the MDD-SSRI group reported higher intensity ratings of heartbeat and stomach sensations than MDD-UnMed and HC during the interoceptive attention task. These findings suggest that the attenuated patterns of neural activation observed in depressed individuals during interoceptive attention are not ameliorated by the chronic administration of serotonergic medications. However, amplified interoceptive sensation ratings suggest a potential impact of chronic serotonergic medication on conscious experiences of internal body states. Future investigations will need to determine the extent to which serotonergic medications acutely influence interoceptive processing, and whether such changes play a role in therapeutic responses during treatment initiation.

Alcohol use and interoception - A narrative review

Interoception, defined as the ability to perceive and interpret body signals, may play an important role in alcohol use disorder (AUD). Earlier studies suggested an association between interoception impairment and known risk factors for AUD (e.g., alexithymia, emotion dysregulation, impulsivity, pain). Neurobiological studies show that the neurotoxicity of alcohol affects various elements of the interoceptive system (especially the insula) at structural and functional levels, with differential short/long term impacts. Conversely, primary interoceptive impairments may promote alcohol consumption and foster the evolution towards addiction. Despite convincing evidence demonstrating that interoception impairment may be an important contributor to the development and course of AUD, only a few studies directly evaluated interoceptive abilities in AUD. The research shows that interoceptive accuracy, the objective component of interoception, is lower in AUD individuals, and is correlated with craving and emotion dysregulation. Interoceptive sensibility is in turn higher in AUD individuals compared to healthy controls. Moreover, there is evidence that therapy focused on improving the ability to sense signals from the body in addiction treatment is effective. However, important methodological limitations in interoceptive measures persist, and it is therefore necessary to further investigate the associations between interoception and AUD.

Osteopathy and Mental Health: An Embodied, Predictive, and Interoceptive Framework

Globally, mental and musculoskeletal disorders present with high prevalence, disease burden, and comorbidity. In order to improve the quality of care for patients with persistent physical and comorbid mental health conditions, person-centered care approaches addressing psychosocial factors are currently advocated. Central to successful person-centered care is a multidisciplinary collaboration between mental health and musculoskeletal specialists underpinned by a robust therapeutic alliance. Such a collaborative approach might be found in osteopathy, which is typically utilized to treat patients with musculoskeletal disorders but may arguably also benefit mental health outcomes. However, research and practice exploring the reputed effect of osteopathy on patients with mental health problems lack a robust framework. In this hypothesis and theory article, we build upon research from embodied cognition, predictive coding, interoception, and osteopathy to propose an embodied, predictive and interoceptive framework that underpins osteopathic person-centered care for individuals with persistent physical and comorbid mental health problems. Based on the premise that, for example, chronic pain and comorbid depression are underlined by overly precise predictions or imprecise sensory information, we hypothesize that osteopathic treatment may generate strong interoceptive prediction errors that update the generative model underpinning the experience of pain and depression. Thus, physical and mental symptoms may be reduced through active and perceptual inference. We discuss how these theoretical perspectives can inform future research into osteopathy and mental health to reduce the burden of comorbid psychological factors in patients with persistent physical symptoms and support person-centered multidisciplinary care in mental health.

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.