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Department of Neuroscience, Brighton and Sussex Medical School, United Kingdom of Great Britain and Northern IrelandSussex Partnership NHS Foundation Trust, United Kingdom of Great Britain and Northern IrelandSussex Neuroscience, University of Sussex, United Kingdom of Great Britain and Northern Ireland
Department of Neuroscience, Brighton and Sussex Medical School, United Kingdom of Great Britain and Northern IrelandSussex Neuroscience, University of Sussex, United Kingdom of Great Britain and Northern Ireland
Department of Neuroscience, Brighton and Sussex Medical School, United Kingdom of Great Britain and Northern IrelandSchool of Humanities and Social Science, University of Brighton, United Kingdom of Great Britain and Northern Ireland
Department of Neuroscience, Brighton and Sussex Medical School, United Kingdom of Great Britain and Northern IrelandDepartment of Psychiatry, University of Oxford, United Kingdom of Great Britain and Northern Ireland
Department of Neuroscience, Brighton and Sussex Medical School, United Kingdom of Great Britain and Northern IrelandSchool of Psychology, University of Sussex, United Kingdom of Great Britain and Northern Ireland
Department of Neuroscience, Brighton and Sussex Medical School, United Kingdom of Great Britain and Northern IrelandSussex Neuroscience, University of Sussex, United Kingdom of Great Britain and Northern Ireland
1 Current: Department of Neuroscience, Brighton and Sussex Medical School, United Kingdom of Great Britain and Northern Ireland.
Jessica A. Eccles
Footnotes
1 Current: Department of Neuroscience, Brighton and Sussex Medical School, United Kingdom of Great Britain and Northern Ireland.
Affiliations
Department of Neuroscience, Brighton and Sussex Medical School, United Kingdom of Great Britain and Northern IrelandSussex Partnership NHS Foundation Trust, United Kingdom of Great Britain and Northern IrelandSussex Neuroscience, University of Sussex, United Kingdom of Great Britain and Northern Ireland
Department of Neuroscience, Brighton and Sussex Medical School, United Kingdom of Great Britain and Northern IrelandSchool of Psychology, University of Sussex, United Kingdom of Great Britain and Northern Ireland
Abnormalities in the regulation of physiological arousal and interoceptive processing are implicated in the expression and maintenance of specific psychiatric conditions and symptoms. We undertook a cross-sectional characterisation of patients accessing secondary mental health services, recording measures relating to cardiac physiology and interoception, to understand how physiological state and interoceptive ability relate transdiagnostically to affective symptoms.
Methods
Participants were patients (n = 258) and a non-clinical comparison group (n = 67). Clinical diagnoses spanned affective disorders, complex personality presentations and psychoses. We first tested for differences between patient and non-clinical participants in terms of cardiac physiology and interoceptive ability, considering interoceptive tasks and a self-report measure. We then tested for correlations between cardiac and interoceptive measures and affective symptoms. Lastly, we explored group differences across recorded clinical diagnoses.
Results
Patients exhibited lower performance accuracy and confidence in heartbeat discrimination and lower heartbeat tracking confidence relative to comparisons. In patients, greater anxiety and depression predicted greater self-reported interoceptive sensibility and a greater mismatch between performance accuracy and sensibility. This effect was not observed in comparison participants. Significant differences between patient groups were observed for heart rate variability (HRV) although post hoc differences were not significant after correction for multiple comparisons. Finally, accuracy in heartbeat tracking was significantly lower in schizophrenia compared to other diagnostic groups.
Conclusions
The multilevel characterisation presented here identified certain physiological and interoceptive differences associated with psychiatric symptoms and diagnoses. The clinical stratification and therapeutic targeting of interoceptive mechanisms is therefore of potential value in treating certain psychiatric conditions.
Our understanding of psychiatric conditions is often dominated by either neurochemical or psychological models; a dichotomy reflected in current treatments. However, more integrative approaches are emerging with increasing attention to the role of the body and the processing of bodily states in psychological health (
). Physiologically, interoceptive signalling is involved in coordinating homeostatic reflexes (e.g., control of blood pressure or glucose levels within a set range) and by guiding predictive (allostatic) autonomic and behavioural responses (e.g., preparing the body for action by increasing blood pressure and heart rate). Psychologically, interoceptive representations are proposed to underpin both motivational (e.g., hunger) and emotional (e.g., anxiety) feeling states (
). By extension, autonomic control and interoceptive signalling are implicated in the physical consequences of psychological challenges (e.g., stress-related cardiovascular morbidity) as well as in the psychological symptoms linked to poor physical health or allostatic overload (
Within psychiatry, as a basis of motivational drive and representations of bodily integrity, interoception is arguably in the foreground of eating disorders (
). Moreover, in contemporary models of consciousness, by supporting a coherent continuity of subjective self-experience, interoception is proposed to be fundamental to self-representation (or ‘biological self’) (
). Disrupted interoceptive functioning may thus manifest as disturbances of conscious selfhood, e.g., as symptoms of dissociation, depersonalisation, and related psychotic phenomena (
). If interoception is indeed central to psychological health, we need to understand its contributions to mental health conditions. Both the Research Domain Criteria initiative (RDoC) of the National Institute of Mental Health (USA) and the Hierarchal Taxonomy of Psychopathology (HiTOP) have proposed transdiagnostic biological taxonomies for mental illness, with a view toward better treatment targets. RDoC's major functional domains are negative valence, positive/reward valence, cognitive systems, systems for social processes (including self-representation) and arousal/modulatory systems (
). Interoceptive processing is arguably present across numerous domains within each of these taxonomies, representing a more fundamental construct that supports basic physiological regulation, motivation, emotional feelings, and self-representation. Here, we tested how accessible indices of physiological regulation (heart rate and heart rate variability) and aspects of interoception (heartbeat detection and self-reported sensitivity to bodily signals) relate to presentation, affective symptoms and diagnosis in patients accessing secondary psychiatric services.
Interoceptive signals are generated throughout the body via mechanoreceptor and chemoreceptor activation of afferent pathways (
). Perceptual characteristics of interoceptive sensations are distinguished by afferent channel and signal strength. Signals are projected throughout the neuraxis (including the autonomic ganglia, spinal cord, medulla, pons hypothalamus, thalamus, basal ganglia, amygdala and hippocampus) via spinal and cranial nerves toward insular and cingulate cortices (
Despite the influence of interoceptive information throughout the body, a majority of literature to date has focused on cardiac signals. In particular, the baroreflex which maintains blood pressure through baroreceptor activation during cardiac systole produces interoceptive information in the form of heartbeat strength and timing and is strongly associated with heart rate variability (HRV) (
). Measured as the change in cardiac inter-beat intervals over time, it is an important feature of a dynamic and adaptive autonomic system, allowing for rapid anticipation, mitigation and response to changing environmental demands (
People often vary in how precisely they consciously perceive internal bodily sensations. Greater sensitivity to interoceptive feelings (measurable using questionnaires or behavioural tasks) may predict stronger emotional experiences. For example, interoceptive sensitivity—including both behavioural accuracy and self-report sensibility—is reportedly higher among anxiety and panic patients, but lower in depression (
A heartfelt response to Zimprich et al. (2020), and Ainley et al. (2020)’s commentaries: acknowledging issues with the HCT would benefit interoception research.
). Improving upon this, one influential framework distinguishes among the following interoceptive dimensions: self-report (questionnaire/confidence ratings), behavioural accuracy (performance accuracy on interoceptive tasks, e.g., heartbeat detection), and insight (a metacognitive measure detailing the correspondence between behavioural accuracy and self-report measures) (
). Discrepancy between self-report and behavioural accuracy measures of interoception may account for affective symptoms, and is a promising target for intervention (
). In a randomized controlled trial, training to enhance behavioural accuracy on cardiac interoception tasks decreased anxiety in autistic adults significantly more than an active control intervention (
The relevance of interoception to mental health goes far beyond the narrow view that it is primarily concerned with perception of visceral changes and performance accuracy on heartbeat detection (or related) tasks to encompass homeostatic and allostatic control, motivational drive, hormonal, metabolic, immune and gut-brain influences on mind and behaviour. Nevertheless, sensitivity to, and interpretation of, internal physiological responses remains relevant to certain patient groups. Importantly, in this context, the value of performance in the (easy-to-implement) heartbeat tracking task for understanding interoceptive influences (‘baseline/threshold’ individual differences) on psychopathology has been called into question many times, most recently in a meta-analysis (
). Therefore, the present study, measuring cardiac interoception via two heartbeat detection tasks in patients accessing generic secondary mental health services, makes an important and timely contribution.
With converging evidence now connecting psychological symptom expression to aspects of cardiac interoception, there is a need for systematic characterisation in clinical patients (
). Here we explored associations between measures of cardiac physiology and interoception and affective symptoms (depression and anxiety) in a group of representative patient and comparison participants.
1. Materials and methods
1.1 Research ethics, governance and study sample
The study was approved by the National Research Ethics Service (13/LO/1866MHRNA), and registered with the International Standard Randomized Controlled Trial Registry (ISRCTN13588109). Patients at least 18 years of age and accessing services for a recorded psychiatric diagnosis were recruited from secondary care mental health clinics, or self-referred from advertisement in primary care and community settings. The study was conducted between 2014 and 2019. Exclusion criteria included global cognitive impairment, neurological conditions, and alcohol intake on day of testing. Clinical diagnoses by psychiatrists were confirmed from medical records. An anxiety group, comprising generalised anxiety, social anxiety and panic disorder, was distinguished from posttraumatic stress, and obsessive-compulsive disorders (PTSD, OCD). In addition, patients with schizophrenia or paranoid schizophrenia were categorised separately from patients with schizoaffective disorder, psychosis with affective features, or unspecified psychosis.
Comparison participants, eligible adults with no formal mental health diagnosis were recruited through poster advertisements. Exclusion criteria were history of mental or systemic medical illness and medication affecting cardiovascular or cognitive function. Assessments took place in university facilities and hospital clinic rooms.
1.2 Assessment of cardiac physiology and interoceptive dimensions
Heart Rate and Heart Rate Variability. Medical-grade pulse oximetry (Nonin Xpod® 3012LP with soft finger-mount (
) was used to record heart rate and measure heart rate variability (HRV). Pulse oximetry measures differences in light absorption of blood, based on oxygen levels. Each heartbeat sends oxygenated blood to the body, increasing the oxygen saturation signal at the finger. At rest, this produces an oscillatory signal with the same temporal resolution as the electrocardiogram (ECG) signal (
). Participants were asked to report the number of perceived heartbeats at rest over six randomized trials of length 25, 30, 35, 40, 45 and 50 s. Immediately after each trial, they rated their confidence in the accuracy of their response on a continuous visual analogue scale (VAS) ranging from 0 cm (“Total guess/No heartbeat awareness”) to 10 cm (“Complete confidence/Full perception of heartbeat”). Behavioural accuracy was quantified by comparing the number of reported heartbeats to recorded pulses via the following: 1 − (|nbeatsrecorded − nbeatsreported|)/((nbeatsrecorded + nbeatsreported)/2) (
). Scores were averaged across the six trials to produce a single accuracy and confidence value for each participant. Metacognitive insight (awareness) was computed as the Pearson correlation between accuracy and confidence values across trials (
). Each trial consisted of 10 auditory tones (440 Hz for 100 ms) presented either synchronously or asynchronously (delayed) relative to heartbeats. Synchronous tones were triggered at the rising edge of the pulse pressure wave. Asynchronous tones were presented after a 300 ms delay. Thus, adjusting for the average 250 ms delay between the ECG R-wave and arrival of the pressure wave at the finger (
). After each trial, participants judged if tones were synchronous or asynchronous relative to their heartbeats, then rated their confidence in the accuracy of their judgement on a continuous VAS ranging from 0 cm (“Total guess/No heartbeat awareness”) to 10 cm (“Complete confidence/Full perception of heartbeat”). They completed 40 trials over two sessions. Accuracy was calculated as the number of correct trials divided by the total number of trials (i.e. the proportion of correct trials). Confidence scores were averaged across trials to produce a single value. Metacognitive insight (awareness) was calculated as the area under the receiver operating characteristic (ROC) curve relating accuracy and confidence scores across trials (
Assessing body awareness and autonomic reactivity: factor structure and psychometric properties of the body perception questionnaire-short form (BPQ-SF).
) and Interoceptive Trait Prediction Error. Self-report interoceptive ‘sensibility’ was quantified from self-rating sensitivity to bodily sensations on the BPQ-awareness subscale. Interoceptive Trait Prediction Error (ITPE) (
) quantified ‘interoceptive surprise’ from discrepancy (on the z-score scale) between self-report (BPQ-awareness score) and behavioural (heartbeat tracking/discrimination accuracy) interoceptive measures (
). This is a 21-question self-report scale of symptoms associated with depression, e.g., level of feelings of worry, failure and disappointment. Scores are measured on a 4-point scale (0–3) with higher scores indicating more severity. Total scores have a maximum of 63 points. The BDI demonstrates high internal consistency and has alpha coefficients of 0.86 and 0.81 for psychiatric and non-psychiatric populations respectively (
). This is a 40-item self-report questionnaire measuring state (STAI-Y1; 20 items) and trait anxiety (STAI-Y2; 20 items). State anxiety measures in the moment positive and negative conditions such as feeling upset and feeling comfortable. Trait anxiety is measured using items relating to general personal tendencies, e.g., feeling calm, cool and collected or feeling that difficulties are piling up and cannot be overcome. A 4-point scale (from “Almost Never” to “Almost Always”) is used to rate all items and higher scores indicate greater anxiety. Total scores have a maximum of 80 points. The scale's internal consistency coefficients range from 0.86 to 0.95 (
Descriptive summaries of participant characteristics and baseline physiological, interoceptive and affect scores were carried out for each group (patients, comparison participants) and for all participants (patients + comparisons). Counts (n), percentages (%), mean (m) and standard deviations (±SD) were used. Participant characteristics included age, sex, Body Mass Index (BMI), and medication use indicated by antipsychotics (no/yes) and antidepressants (no/yes). Differences in patient characteristics were tested using Chi-Square or Fisher's Exact tests for categorical variables and Analysis of Variance (ANOVA) for continuous variables.
1.4.1 Between group differences on cardiac physiology and interoceptive dimensions
Between-group analyses were conducted using ANOVAs. Initial analyses compared patient with comparison participants on cardiac physiology and interoceptive dimensions. A second exploratory analysis looked at the effect of medication on the patient group by comparing participants using antipsychotics and/or antidepressants to those not using medication. A third analysis explored differences between patient diagnostic groups. To maintain the robustness of our comparisons, diagnostic groups with very small numbers (i.e., n < 10 were excluded from this subgroup analysis) as was the complex diagnostic category (see below) due to the inconclusive and heterogenous nature of the group.
1.4.2 Correlations between interoceptive and affective symptoms
Spearman's rank correlations, ρ(n), were used to test for relationships between physiological/interoceptive measures and affective symptoms in patient and comparison participants separately.
Each of the above analyses were repeated with age, sex and BMI included simultaneously to consider their potential effect as confounding covariates on physiology and interoception. This had the aim of increasing inferential precision and group balance on baseline factors. We did not impute for missing values present across the dataset. For all statistical tests, an alpha level of 0.05 was used.
2. Results
2.1 Participant characteristics, baseline physiology and affect
A total of 67 (17.9 %) comparison and 307 (82.1 %) patient participants were recruited to the study giving a grand total of 374 study participants. Comparison participants were aged 18–67 years, and patient participants were aged 18–65 years. Table 1A shows the participant characteristics and baseline scores. Patient diagnoses were depression (n = 59), generalised anxiety, social anxiety and phobic disorder (Anxiety, n = 29); dual diagnosis of anxiety and depression (Mixed A/D, n = 47); Emotionally Unstable/Borderline Personality Disorder (EUPD, n = 22); schizophrenia or paranoid schizophrenia (Schizophrenia, n = 19); diagnosis of schizoaffective disorder, affective psychosis or unspecified psychosis (Schizoaffective, n = 26); Obsessive Compulsive Disorder (n = 9); Post Traumatic Stress Disorder (n = 6); Anorexia Nervosa (n = 8); Autistic Spectrum Conditions (n = 6); Attention Deficit Hyperactivity Disorder (n = 4) and Complex inconclusive and unstated conditions (n = 16). Categories with n < 10 patients were excluded from between diagnostic group analyses and summary tables but were included in the total patient count. Patients were overall older (patient vs comparison participants, years mean ± SD: 38.9 ± 14.1 vs 35.0 ± 13.2, [F(1, 368) = 4.1, p=.04]) with greater BMI (kg/m2 26.4 ± 7.1 vs 23.0 ± 3.6, [F(1, 336) = 9.2, p=.003]). Of patients, 58 % (n = 176) were female, and 61 % of comparison participants (n = 41) were female; this was not a statistically significant difference (p=.79). Just under two-thirds (65.4 %) of patients took antipsychotic or antidepressant medication; no comparison participants were using medication.
Table 1ADescriptive summary of participant characteristics and baseline scores.
Notes: Data are mean, (SD), n = number of observations. Max n = maximum number in group. F = Female, M = Male, NB = non-binary, # = number, % = percentage, yrs. = years, kg = kilograms, bpm = beats-per-minute; ms = milliseconds; BMI = body mass index; HR = heart rate; HRV = heart rate variability; BDI = Beck Depression Inventory; STAI = Spielberger State and Trait Anxiety Inventory, Y1 = state, Y2 = trait.
2.2 Differences between patient and comparison participants on cardiac physiological and interoceptive dimensions
Distributions of heart rate and HRV are shown in Fig. 1A-1B and Table 1A. ANOVA results are displayed in Table 2. Overall, patients did not differ from comparison participants in heart rate but had lower HRV (patient vs comparison participants, ms: 51.6 ± 42.6 vs 70.8 ± 58.4; p=.003; Fig. 1B). However, this difference became non-significant when age, sex, and BMI were included as confounding covariates.
Fig. 1Physiological measures across comparisons and patients.
Distributions of cardiac physiology measures are shown for each group. (A) Heart rate in comparison and patient participants. (B) Heart rate variability in comparison and patient participants. (C) Heart rate in comparisons and patients by diagnostic group. (D) Heart rate variability in comparisons and patients by diagnostic group.
On the heartbeat tracking task, patients were significantly less confident than comparison participants (VAS 4.2 ± 2.6 vs 5.4 ± 1.9; p=.003; with covariates p = .001; see Fig. 2B and Table 2). There was also a significant group difference in performance accuracy (0.48 ± 0.37 vs 0.59 ± 0.3; p = .022; Fig. 2A) which became non-significant (p = .823) after adjustment. However, there was no group difference for metacognitive insight (Fig. 2C).
Fig. 2Performance of patients versus comparisons on heartbeat detection tasks.
Distributions of scores are shown for comparison and patient participants. Scores for behavioural performance accuracy (A, D), confidence ratings (B, E), and the correspondence between the two (metacognitive insight; C, F) are shown for the heartbeat tracking and heartbeat discrimination tasks.
On the heartbeat discrimination task, patients again showed lower confidence than comparison participants (patient vs comparison participants VAS 5.0 ± 2.4 vs 5.9 ± 1.8; p = .02), even after covariate adjustment (p=.01) (see Fig. 2E and Table 2). Patients' performance accuracy was also lower (patient vs comparison participants 0.52 ± 0.1 vs 0.57 ± 0.2; p=.03), even with covariate inclusion (p=.03; Fig. 2D). Again, groups did not differ in metacognitive insight on this task (Fig. 2F).
Self-rated interoceptive sensibility (BPQ-awareness) did not distinguish patient from comparison participants (112.2 ± 29 vs 114.5 ± 34; p=.64; Fig. 3A ). Similarly, there were no group differences in interoceptive trait prediction error, for either the heartbeat tracking (ITPE HBT: 0.04 ± 1.5 vs 0.09 ± 1.4; p = .865; Fig. 3B) or the heartbeat discrimination tasks (ITPE HBD: 0.01 ± 1.4 vs −0.23 ± 1.5; p = .32; Fig. 3C). Thus, patients showed reduced confidence and accuracy in judging their own cardiac sensations relative to comparison participants. These effects were not primarily attributable to differences between patient and comparison participants in physiology (HR or HRV), interoceptive sensibility (BPQ-awareness), metacognitive insight, or interoceptive trait prediction error.
Fig. 3Interoceptive assessment in comparisons and patients.
Distributions of scores are shown for each group. (A) Interoceptive sensibility (BPQ-awareness) scores for comparison and patient participants. (B-C) Interoceptive trait prediction error (ITPE) scores for comparison and patient participants in heartbeat tracking and discrimination tasks, respectively. (D) Interoceptive sensibility (BPQ-awareness) scores for comparisons and patient diagnostic groups. (E-F) ITPE scores for comparisons and patient diagnostic groups.
2.3 Correlations between cardiac physiology/interoceptive and affective symptoms
Patient depression (BDI) and state and trait anxiety scores (Fig. 4) were tested for correlations with cardiac physiology and interoception measures (Table 3). Select correlations are shown in Fig. 5. Depression symptoms were moderately to strongly associated with anxiety (STAI-Y1: ρ(306) = 0.52, p < .01; STAI-Y2: ρ(306) = 0.75, p < .01) and weakly associated with both increased interoceptive sensibility (BPQ-awareness: ρ(282) = 0.35, p < .01) and increased interoceptive trait prediction error (ITPE HBT: ρ(270) = 0.24, p < .01; ITPE HBD: ρ(270) = 0.16, p < .01).
Fig. 4Affective symptoms in patient and comparison participants.
Distributions of subjective symptoms scores are shown for each group. (A) Depression scores in comparison and patient participants. (B) State anxiety scores in comparison and patient participants. (C) Trait anxiety scores in comparison and patient participants. (D) Depression scores in comparisons and patient diagnostic groups. (E) State anxiety scores in comparisons and patient diagnostic groups. (F) Trait anxiety scores in comparisons and patient diagnostic groups.
Scatterplots of interoceptive measures (y-axes) vs affective symptoms (x-axes) with distribution densities for each measure. All correlations are significant with the exception of those indicated as not significant (‘not sig’). Correlation values are shown in Table 3. BDI = Beck Depression Inventory; STAI = Spielberger State and Trait Anxiety Inventory, Y1 = state, Y2 = trait; HBD aware = metacognitive insight (correspondence between accuracy and confidence) on heartbeat discrimination task; BPQ-awareness = Porges' Body Perception Questionnaire, awareness subsection (measure of interoceptive sensibility); ITPE = Interoceptive Trait Prediction Error.
Similar to depression symptoms, anxiety symptoms were strongly associated with each other (ρ(306) = 0.64, p < .01) and weakly associated with both increased interoceptive sensibility and increased interoceptive trait prediction error. Trait anxiety was also weakly associated with metacognitive insight in heartbeat discrimination (STAI-Y2: ρ(284) = 0.19, p < .01). We found no associations between affective symptoms and either physiology or heartbeat detection performance accuracy in patient participants.
To determine if relationships between affective symptoms and cardiac physiology/interoception existed for comparison participants, we again tested for correlations among these data. Here, depression symptoms were only significantly correlated with state anxiety after covariate inclusion (STAI-Y1: ρ(43) = 0.279, p= .07 vs ρ(43) = 0.424, p < .01). They were also moderately correlated with trait anxiety (STAI-Y2: ρ(43) = 0.535, p < .001). However, depression symptoms were not associated with cardiac physiology or interoception measures in comparison participants.
Anxiety symptoms in comparison participants were moderately associated with each other (ρ(65) = 0.579, p < .001), and state anxiety was moderately associated with decreased self-report confidence in heartbeat discrimination performance (STAI-Y1: ρ(43) = −0.324, p= .034). State anxiety was also moderately correlated with heart rate (STAI-Y1: ρ(43) = 0.302, p = .049), but this relationship was lost after covariate adjustment (ρ(43) = 0.08, p = .63). Thus, in comparison participants only state anxiety was associated with confidence in heartbeat discrimination such that lesser anxiety predicted greater confidence. Otherwise, anxiety symptoms did not relate to cardiac physiology or interoception measures.
2.4 Medication effects
We tested for differences in patients' physiological and interoceptive measures between those who were using antipsychotic medication only (n = 59; 20 %) or not, those using antidepressants only (n = 75; 25 %) or not, and those using both antidepressants and antipsychotics (n = 58, 20 %) or not. Just over a third (n = 102; 35 %) of patients were not using either. Significant findings were limited to: (1) Marginally higher heart rate in people on both antidepressants and antipsychotics, (both vs neither; bpm 77.6 ± 12.7 vs 72.1 ± 10.9 [F(1, 275) = 10.4, p = .001]; with covariates [F(1, 260) = 8.57, p = .004]); (2) Significantly higher HRV in patients on antidepressants only (RMSSD: ms: 63.7 ± 51.0 vs 48.1 ± 38.3 [F(1, 267) = 7.2, p = .008 with covariates F(1, 253 = 8.02, p = .005)]); and (3) Significantly lower HRV in patients on both antidepressants and antipsychotics (RMSSD: ms: 37.4 ± 26.5 vs 55.7 ± 44.7 [F(1, 267) = 7.9, p = .005, with covariates F(1, 253 = 6.6, p = .011)]).
2.5 Differences between patient diagnostic groups on cardiac and interoceptive measures
We tested for differences in physiological and interoceptive measures between groups of patients categorised according to recorded clinical diagnoses. Diagnostic groups explored were depression (n = 59), anxiety disorder (n = 29), mixed anxiety & depression (n = 47), bipolar disorder (n = 56), emotionally unstable/borderline personality disorder (n = 22), schizoaffective disorder (n = 26) and schizophrenia (n = 19) (see Methods and Table 1A, Table 1B).
Table 1BInteroceptive measures across groups.
Group
Possible range
HBT acc (−1–1)
HBT conf (0−10)
HBT aware (−1–1)
HBD acc (0–1)
HBD conf (0–10)
HBD aware (0–1)
BPQ aware (45–225)
ITPE HBT
ITPE HBD
Comparison
(67)
0.59 (0.3) 67
5.44 (1.9) 43
0.17 (0.5) 43
0.57 (0.2) 43
5.88 (1.8) 43
0.54 (0.1) 43
114.5 (33.9) 42
0.09 (1.4) 42
−0.2 (1.5) 42
Depression
(59)
0.52 (0.3) 56
4.16 (2.7) 55
0.20 (0.4) 54
0.53 (0.1) 56
4.64 (2.6) 56
0.53 (0.1) 55
108.2 (29.6) 54
−0.2 (1.4) 52
−0.2 (1.6) 52
Anxiety
(29)
0.44 (0.4) 29
4.04 (2.5) 28
0.04 (0.5) 27
0.50 (0.1) 29
5.32 (2.3) 29
0.53 (0.2) 29
116.8 (27.6) 28
0.3 (1.6) 28
0.3 (1.5) 28
Mixed A/D
(47)
0.55 (0.4) 42
3.72 (2.4) 42
0.27 (0.6) 39
0.51 (0.2) 43
4.15 (2.4) 42
0.53 (0.1) 41
119.3 (28.3) 42
0.2 (1.6) 38
0.2 (1.3) 39
Bipolar
(56)
0.47 (0.3) 55
4.51 (2.2) 55
0.18 (0.6) 55
0.52 (0.1) 55
5.43 (2.1) 54
0.53 (0.1) 54
113.3 (31.2) 50
0.09 (1.4) 50
0.03 (1.4) 50
EUPD
(22)
0.47 (0.4) 20
4.43 (3.1) 21
0.30 (0.5) 20
0.55 (0.1) 20
5.04 (2.4) 21
0.55 (0.1) 20
121.8 (28.6) 21
0.5 (1.3) 19
0.2 (1.3) 19
Schizoaffective
(26)
0.52 (0.3) 23
3.91 (2.6) 24
0.15 (0.5) 23
0.52 (0.2) 23
5.10 (2.3) 24
0.52 (0.1) 23
96.6 (22.3) 25
−0.6 (1.1) 22
−0.6 (1.4) 22
Schizophrenia
(19)
0.15 (0.44) 17
3.85 (2.6) 17
0.23 (0.6) 16
0.50 (0.1) 17
4.78 (2.9) 17
0.49 (0.1) 17
112.9 (22.2) 17
0.9 (1.3) 16
0.1 (1.0) 16
Notes: Data are mean, (SD), n. HBT = heart beat tracking task; HBD = heartbeat discrimination task; acc = accuracy; conf = confidence; aware = awareness; BPQ = Porges Body Perception Questionnaire; ITPE = Interoceptive Trait Prediction Error. *Missing data.
Distributions of cardiac and interoception measures by diagnostic group are shown alongside the comparison group for visual comparison only in Fig. 1, Fig. 3, Fig. 6. ANOVAs indicated that among patients there were statistically significant between-group differences in HRV [F(6, 228) = 3.3, p=.004; with covariates F(6, 217) = 2.4, p=.03]. No post-hoc results were significant after Tukey multiple comparison correction when considering covariates, although differences between anxiety and bipolar groups and between anxiety and emotionally unstable/borderline groups trended toward significance (p = .067 and p = .096, respectively). In general, decreased HRV characterised patients with diagnoses of bipolar disorder, emotionally unstable/borderline personality disorder, schizoaffective disorder, and schizophrenia, relative to anxiety and depression groups (and comparison group; Table 1A, Fig. 1D).
Fig. 6Performance of patients by diagnostic group on heartbeat detection tasks
Distributions of scores are shown for each group. Scores for behavioural performance accuracy, confidence ratings, and the correspondence between the two (metacognitive insight) are shown for the heartbeat tracking task (A-C) and the heartbeat discrimination task (D-F).
Groups differed in behavioural performance accuracy on the heartbeat tracking task [F(6, 235) = 2.8, p=.01; after covariate inclusion F(6, 223) = 2.2, p=.04]. This effect was primarily driven by schizophrenia patients exhibiting lower scores (Table 1B, Fig. 6A). Post-hoc Tukey's tests for multiple comparisons found that the mean accuracy score was different between depression and schizophrenia groups (p = .030; 95 % CI = [0.018, 0.643]), between mixed anxiety/depression and schizophrenia groups (p = .018; 95 % CI = [0.037, 0.692]), and between bipolar and schizophrenia groups (p = .049; 95 % CI = [4.1 × 10−4, 0.620]).
Groups also differed in self-report interoceptive sensibility (BPQ-awareness, F(6, 230) = 2.4, p=.03), although significance was lost after covariate adjustment (F(6, 219) = 1.78, p = .10). This effect was primarily driven by schizoaffective patients exhibiting lower sensibility (BPQ-awareness) scores compared to mixed anxiety/depression patients (96.6 ± 22.3 vs 119.3 ± 28.3, t(65) = 3.18, ptukey=0.027) and EUPD patients (96.6 ± 22.3 vs 121.8 ± 28.6, t(44) = 3.01, ptukey=0.046; Table 1B, Fig. 3D). A separate t-test revealed that schizoaffective patients also exhibited lower sensibility compared to schizophrenia patients (96.6 ± 22.3 vs 112.9 ± 22.2, t(40) = 2.33, p = .025). Relatedly, groups also differed in interoceptive trait prediction error on the heartbeat tracking task (F(6, 218) = 2.6, p=.02), but again this difference was not significant after covariate consideration (F(6, 207) = 1.96, p=.07). This effect was primarily driven by schizophrenia patients exhibiting greater interoceptive trait prediction error on the task compared to schizoaffective patients (0.9 ± 1.3 vs −0.6 ± 1.1, t(36) = 3.24, ptukey = 0.023; Table 1B, Fig. 3E). Self-reported confidence in heartbeat detection performance and metacognitive interoceptive awareness of heartbeat did not discriminate clinical groups (Fig. 6B, E, C, and F).
3. Discussion
In a representative sample of patients using mental health services in the UK, we characterised interoception as the processing and representation of internal bodily physiology (
We found that patients differed from comparison participants in cardiac physiology (HRV), interoceptive behavioural performance accuracy and self-report trial-by-trial confidence, exhibiting reduced HRV, accuracy and confidence. However, after adjustment for age, gender and BMI, these statistically significant differences were only maintained for confidence and behavioural accuracy in the heartbeat discrimination task. Across patients, self-report interoception paralleled affective symptoms: greater interoceptive sensibility (BPQ-awareness) and greater interoceptive trait prediction error (ITPE; i.e., divergence of interoceptive sensibility from behavioural accuracy (
)) was associated with elevated anxiety and depression symptoms. In addition, trait anxiety was weakly associated with metacognitive insight (correspondence between behavioural and self-reported sensitivity to interoceptive signals). These relationships were not observed in the comparison group, suggesting that self-report interoception in particular may be a potential mechanism and therapeutic target for affective symptoms (especially anxiety) in these clinical populations. This is supported by recent work in which interoceptive sensibility and ITPE were reduced alongside anxiety in a clinical trial using interoceptive training to target anxiety in autistic adults (
). This work suggests that specific symptoms in particular groups may be targetable through interoceptive training and even more heuristic tasks, leading to validated symptomatic improvement through interoceptive modification, even in comparison to active control conditions. Importantly, the BPQ-awareness questionnaire taps into different elements of interoception, namely, how aware one is of bodily signals, how often they are aware of bodily signals, and how accurately they perceive bodily signals (
). Thus, scores can differ depending on how participants interpret the questions. In the present study, patients and comparisons did not differ in terms of their BPQ-awareness scores and patient groups did not differ after consideration of covariates. The lack of difference could be due to a commonality of BPQ-awareness interpretation across groups revealing a lack of clinical difference, or it could result from differences in individual interpretation, potentially reflected in the spread of scores. Future studies should therefore provide clearer instructions and assess individual interpretations in order to improve the clarity of findings.
In contrast, affective symptoms showed limited transdiagnostic association with cardiac physiology among patients, despite the established coupling between perseverative cognition (e.g., worry and rumination) and reduced HRV (
). Here, we observed no significant relationships between HRV and anxiety/depression symptom severity. Interestingly, we did observe HRV to be lower in diagnoses other than depression and/or anxiety disorder (see below) (
Our data also demonstrates differences in interoception between psychiatric diagnoses. First, our findings extend previous reports of markedly reduced HRV in patients with emotionally unstable/borderline personality disorder (
), with psychosis being the primary diagnostic criteria. Given that psychosis is often present in both emotionally unstable and bipolar disorder, this could explain the reduced HRV observed in these groups. Lower HRV has been found to correspond to increased overall and negative symptom severity (e.g., reduced emotional expression) (
). Because HRV indexes the modulation of perception of emotional and sensory cues, less heart rate responsivity may reflect vulnerability to dissociative states and depersonalisation. Trends toward faster mean heart rate and lower heartbeat tracking accuracy suggest more pervasive interoceptive differences in schizophrenia. They also hint at potentially elevated sympathetic activity in this group. Patients with schizophrenia and schizoaffective disorder were further differentiated by the schizoaffective patients significantly under-reporting sensibility to bodily sensations (
). The clinical distinction between schizophrenia and schizoaffective disorder is rarely examined in research studies, favouring instead a broader diagnosis of psychosis. Although psychotic phenomena suggest disrupted self-representation (
), our study's focus on transdiagnostic relationships between interoception and affective symptoms meant that we did not quantify psychotic and dissociative symptoms. However, antipsychotic medication and illness duration did not provide a compelling account for interoceptive differences in schizophrenia. Thus, our exploratory findings motivate further research to characterise symptoms of schizophrenia and schizoaffective disorder with attention to interoceptive profiles (
Heartbeat detection tasks seek to quantify stable individual differences in sensitivity to cardiac sensations. Typically, the heartbeat counting task gives a spread of accuracy scores, while the more challenging heartbeat discrimination task produces a more binomial distribution (i.e., at chance, or above chance). Nevertheless, these tasks have recognised psychometric limitations (
). Actual heart rate, knowledge of one's average heart rate, and the ability to estimate time, can influence performance accuracy, particularly on heartbeat tracking. The perceived signals themselves may be ‘quasi-interoceptive’, i.e., somatosensory correlates of the (visceral afferent) signalling of internal physiology (
). Notwithstanding, heartbeat detection tasks remain relevant to inferences about how bodily sensations influence emotional states. For example, in non-clinical populations, heartbeat detection ability has been associated with increased anxiety symptoms, yet attenuated depressive symptoms (
). Moreover, the relevance of heartbeat detection task performance accuracy to measures of psychiatric symptoms has been called into question by a recent meta-analysis of studies, many involving clinical patients with affective disorders (
). While reduced interoceptive accuracy is reported in patients with schizophrenia, and replicated in our present study, previous work demonstrates that the presence of positive symptoms correlates with better heartbeat detection accuracy (
). Within our study, patient participants performed worse than comparison participants on the heartbeat discrimination task, and though among patient groups performance accuracy was broadly equivalent, schizophrenia patients tended to perform worse. While interoceptive methods can be further optimised for patient stratification, we demonstrate reliable implementation of heartbeat detection tasks within clinical settings.
In psychiatry, interoception is often an indirect target of treatment. Medications influence interoceptive processes; e.g., peripheral cardiovascular arousal is suppressed by beta-blockers, while monoaminergic drugs (from stimulants in ADHD, to antidepressant/anxiolytic SNRIs) target central neuromodulatory pathways governing central arousal and descending autonomic control. Trials repurposing antihypertensive drugs, e.g., Losartan (
Human extinction learning is accelerated by an angiotensin antagonist via ventromedial prefrontal cortex and its connections with basolateral amygdala.
) promise alternative treatment approaches. Non-pharmacological therapies also exploit interoceptive mechanisms. These include physical interventions, notably vagus nerve stimulation (
A 12-week integrative exercise program improves self-reported mindfulness and interoceptive awareness in war veterans with posttraumatic stress symptoms.
). The therapeutic utility of each of these approaches can be optimised through better mechanistic understanding of interoceptive processing on the individual level. Arguably, the efficacy of these treatments rests in their indirect targeting of interoceptive processes through (neuro)physiological and/or interoceptive pathways (
). These pathways effect changes in the body, including neural modulation and autonomic processing, often upregulating or downregulating the sensation and perception of bodily signals. For mental health conditions, this can lead to attenuation of symptoms. In the long term, effective recalibration of internal signalling can lead to recovery.
Moving forward, there is great need for new methods which patients can perform without undue burden (e.g., consisting of manageable numbers of trials in engaging tasks designed to limit fatigue) and tapping into specific aspects relevant to each individual patient's condition. Examples of such approaches include implicit measures such as heartbeat evoked potentials (
). The explicit techniques presented here have the added advantage of being adaptable to remote settings, allowing patients to participate in therapy from a location of their choice. These approaches are especially important considering the growing need for practical, flexible, and effective mental health treatments.
Outside of cardiac interoception, there is a growing understanding of the influence of additional interoceptive signals including respiration, gastric activity, and immune system activation on mental health. For example, respiratory studies have shown that slow, nasal breathing improves cognitive functioning and positive affect through stimulation of parasympathetic mechanisms (e.g., increased HRV; (
)). Gastric activation in relation to resting-state and task-based neural activity conveys information about hunger, satiety, and disgust as well as electrochemical signals reflecting inflammatory and endocrine responses (
). Thus, it has both specific and broad impacts on emotional and behavioural states. Interestingly, unlike measures of respiratory interoceptive accuracy, measures of gastric interoception have been found to correlate with heartbeat detection accuracy (
). More globally, both acute and chronic immune system activity and inflammation result in a variety of ‘sickness behaviours,’ including fatigue, anhedonia, social withdrawal and irritability, symptoms often associated with depression (
). Thus, the landscape of interoceptive processes is broad, and the use of interoceptive assessments and treatments (if appropriate) should be tailored to each individual's specific mental health condition and symptom(s). With the continued development of techniques and evidence-based practices, the interoceptive framework holds much promise for advancements in personalised healthcare. In the meantime, there is evidence that cardiac interoceptive awareness translates across other modalities (
), suggesting that cardiac methods may be useful for a variety of conditions.
An additional consideration for the present study is the fact that it was conducted under resting conditions. Importantly, most psychiatric conditions are characterised by allostatic and homeostatic dysfunction, with different conditions showing sensitivity to the effects of different interoceptive signals (
). As interoception serves not only to inform the brain of the body-state, but to also enhance allostatic and homeostatic regulation, there is a need for more studies which explicitly perturb both allostasis and homeostasis, assessing interoceptive dysfunction across a fuller range of functionality (
Interoceptive fear conditioning to an inspiratory load using 20% CO2 inhalation as an unconditioned stimulus.
in: Paper presented at the 12th Annual Meeting of the International Society for the Advancement of Respiratory Psychophysiology (ISARP), Hamburg, Germany, Location: Hamburg, Germany. 2005
). However, comparison of these methods to more commonly used resting-state methods would be useful, as well as consideration of these and future methods for particular psychiatric conditions.
This study follows a rising call for interoceptive processes to be considered foundational to psychiatric conditions (
). We show the feasibility of a multilevel characterisation of interoception in patients spanning diagnostic categories. Our findings reveal transdiagnostic interoceptive profiles linked to affective symptoms and suggest interoceptive measures may differentiate certain patients by diagnosis. Notably, there are potentially selective differences in patients with schizophrenia that merit further investigation. Interoception thereby offers emerging targets for therapeutic intervention in psychiatric conditions.
Funding
The study was funded by an Advanced Grant from the European Research Council to HDC: Cardiac control of Fear in the Brain CCFIB 324150. Generous funding was also provided by the Sackler Centre for Consciousness Science.
CRediT authorship contribution statement
All authors except AMJ, SPS and LQ contributed to the design of the study. FM, DLE, CGvdP, HHB carried out data collection and management with trained clinical research coordinators employed by Sussex Partnership NHS Foundation Trust. SNG and HDC wrote the first manuscript draft. SPS analysed data and produced figs. HDC, SPS, SNG, JAE, LQ and AMJ contributed to the final manuscript. All authors read and approved the final manuscript.
Data availability
Data are shared as supplementary material
Acknowledgements
We thank the participants for their integral contributions to the study. Recruitment and data collection to the study was facilitated by Sussex Partnership NHS Foundation Trust Research & Development Department and the input of three Clinical Research Coordinators. A preprint of an earlier version of this work is available on the medRxiv: doi:https://doi.org/10.1101/19012393 and preprints with The Lancet: https://doi.org/10.2139/ssrn.3487844.
Assessing body awareness and autonomic reactivity: factor structure and psychometric properties of the body perception questionnaire-short form (BPQ-SF).
A heartfelt response to Zimprich et al. (2020), and Ainley et al. (2020)’s commentaries: acknowledging issues with the HCT would benefit interoception research.
A 12-week integrative exercise program improves self-reported mindfulness and interoceptive awareness in war veterans with posttraumatic stress symptoms.
Interoceptive fear conditioning to an inspiratory load using 20% CO2 inhalation as an unconditioned stimulus.
in: Paper presented at the 12th Annual Meeting of the International Society for the Advancement of Respiratory Psychophysiology (ISARP), Hamburg, Germany, Location: Hamburg, Germany. 2005
Human extinction learning is accelerated by an angiotensin antagonist via ventromedial prefrontal cortex and its connections with basolateral amygdala.
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