Changes in Interoceptive Accuracy Related to Emotional Interference in Somatic Symptom Disorder

doi:10.21203/rs.3.rs-3109222/v1

Download PDF

Research Article

Changes in Interoceptive Accuracy Related to Emotional Interference in Somatic Symptom Disorder

https://doi.org/10.21203/rs.3.rs-3109222/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Objective: Patients with somatic symptom disorder (SSD) tend to have problems perceiving their bodily signals. We hypothesized that SSD patients would exhibit changes in interoceptive accuracy (IA), particularly when emotional processing is involved.

Methods: Twenty-three patients with SSD and 20 healthy controls were recruited. IA was assessed using the heartbeat perception task. The task was performed in the absence of stimuli as well as in the presence of emotional interference, i.e., photographs of faces with an emotional expression. IA were examined for correlation with measures related to their somatic symptoms, including resting-state heart rate variability (HRV).

Results: There was no significant difference in the absolute values of IA between patients with SSD and healthy controls, regardless of the condition. However, the degree of difference in IA without emotional interference and with neutral facial interference was greater in patients with SSD than in healthy controls (p=0.039). The IA of patients with SSD also showed a significant correlation with low-frequency HRV (p=0.004) and high-frequency HRV (p=0.007).

Conclusion: SSD patients showed more significant changes in IA when neutral facial interference was given. These results suggest that bodily awareness is more affected by emotionally ambiguous stimuli in SSD patients than in healthy controls.

emotional processing

heartbeat perception task

interoceptive awareness

somatic symptom disorder

The DSM-5’s proposed diagnostic criteria for somatic symptom disorder (SSD) include distressing somatic symptoms and related excessive thoughts, feelings, or behaviors [1]. Although the underlying mechanisms of somatic symptoms in SSD have not been fully clarified, altered perception of bodily signals has been suggested to play important role in the disorder’s pathophysiology [2]. Some previous studies of patients with somatoform disorder have reported excessive recognition of bodily signals related to somatic symptoms [3], whereas other such studies have indicated diminished perception of internal bodily sensations [4]. Although these results seem to be conflicting, the selective attentional shift from normative bodily signals to somatic symptoms has been presented as a possible explanation [5]. This abnormal attentional focus on somatic symptoms in SSD has been proposed as an automatic process performed according to previously formed memory structures (schemata) for somatization.

Interoceptive accuracy (IA), or the difference between subjective estimation and objective measurement of an internal bodily state, is an important indicator of one’s ability to appropriately perceive one’s bodily signals [6]. Behavioral tasks that measure how accurately a person can estimate their heart rate (the mental tracking paradigm) [7] and distinguish their heartbeat from external stimuli (the heartbeat discrimination paradigm) [8] have been used to assess IA. Previous studies using these tasks on patients with somatoform disorder showed that IA in these patients was similar to that of normal controls [9, 10]. However, in situations where other psychological factors were involved, the IA of individuals with somatization disorders tended to decline [4, 11]. We speculate that IA in SSD patients does not simply increase or decrease but is distorted by other psychological factors that may affect the patients’ schemata for somatization.

Somatization has long been regarded as a defensive mechanism in which intolerable emotional conflicts are converted into somatic complaints. Consistent with this concept, several previous studies have suggested that disturbance of emotional processing is one of the most crucial psychopathological factors in SSD [12]. Other studies have demonstrated that patients with somatoform disorders exhibited difficulties with emotional awareness [13] and recognition of facial emotions [14]. A recent study of SSD patients found that while SSD patients have difficulty recognizing their own emotions, they are more sensitive to the negative emotions of others [15]. The high comorbidity of somatoform disorder and depression also implies a strong association between somatization and emotional processing [16]. Taken together, we speculate that patients with SSD have difficulty processing emotional information and that these difficulties may substantially affect their clinical features. This view is supported by previous findings, which have shown that disturbed autonomic nervous system activity in SSD was more remarkable when emotional processing was engaged [11]. We also speculate that IA alteration is one of the core features related to the pathophysiology of SSD, which would be affected by whether or not emotional processing is involved. Previous studies indicating that IA is affected by emotional state and is related to the emotional regulation, such as reappraisal, support our speculation [17, 18].

This study aimed to examine whether patients with SSD struggle with the perception of bodily signals and, specifically, how this perception is impacted by emotional processing. We hypothesized that patients with SSD would show significant differences in IA when emotional processing was involved. To test our hypothesis, we compared the IA of SSD patients with that of healthy controls using the mental tracking paradigm. In particular, we selected facial interference as an emotional processing stimulus and observed the degree of change in IA after presenting the stimulus. Facial stimuli have been widely used in psychological tasks related to emotion recognition and emotional responding [19, 20]. We also speculated that IA, as measured through the mental tracking paradigm, could be significantly correlated with the clinical characteristics of SSD.

Participants

We recruited patients who were clinically diagnosed with SSD according to the DSM-V diagnostic criteria in a psychiatric outpatient clinic at Severance University Hospital in Seoul. Healthy controls were recruited through online advertising, flyers, and word of mouth. The exclusion criteria applied to all participants in this study were as follows: major psychiatric disorders other than SSD, low cognitive function that would cause difficulty in completing a self-reported questionnaire, medical problems that directly lead to physical symptoms, and current medical treatment with medication. A certified psychiatrist conducted interviews to confirm that the patient group had no major psychiatric disorders besides somatization disorder and the control group had no major psychiatric disorders. Finally, 17 female and 6 male SSD patients (age = 33.9 ± 10.1 years) and 14 female and 6 male healthy controls (age = 30.6 ± 8.1 years) were included in the study. The group of healthy controls also participated in our previous study using different behavioral tasks (dot-probe task) [21].

Measurement of Clinical Characteristics

All subjects were given several self-report rating scales to assess their clinical characteristics. The severity of their somatic symptoms was assessed through the somatization subscale of the Symptom Checklist-90-Revised (SCL-90-R) [22]. The amplification of somatosensory signal recognition was evaluated using the Somatosensory Amplification Scale (SSAS) [23]. The degree of difficulty in recognizing emotions was assessed using the Toronto Alexithymia Scale (TAS) [24]. The Center for Epidemiologic Studies Depression (CES_D) scale [25] was used to assess depressive symptoms, and the State-Trait Anxiety Inventory state instrument (STAIX_S) [26] was used to assess anxiety symptoms.

Resting-state heart rate variability (HRV) was also calculated for all subjects using a 5-minute electrocardiogram (ECG) measurement. The detailed methods of HRV measurement were described in our previous study of SSD patients [21]. We established frequency domains for the HRV parameters, i.e., high-frequency (HF)-HRV and low-frequency (LF)-HRV, and transformed the parameters with the natural logarithm.

Measurement of Interoceptive Accuracy

We used the mental tracking paradigm adjusted by Ehlers and Breuer [27] to measure the IA of this study’s subjects. This paradigm assesses whether the subjects can count their heartbeats (the heartbeat perception task) and estimate the passage of time (the time estimation task). Before the tasks were performed, each subject’s baseline heart rate was measured during a 3-minute rest period. In the heartbeat perception task, subjects were instructed to quietly focus on their bodily sensations and count their heartbeats without a direct measurement (e.g., taking their pulse) between two beeps. An ECG was conducted to calculate the actual heart rate after the task. In the time estimation task, the subjects were instructed to estimate how many seconds passed between the two beeps, and we prevented them from performing actions that could directly measure time. The time estimation task was performed to confirm that the subjects’ ability to recognize the flow of time was not a confounding variable in counting their heartbeats. The heartbeat perception task consisted of three trials that were run for 35, 25, and 45 seconds. The time estimation task consisted of three trials that were run for 23, 56, and 40 seconds. Heartbeat perception and time estimation error scores were calculated by subtracting the estimated value from the actual value and dividing the result by the actual value. The mean error scores for heartbeat perception and time estimation were calculated by averaging the values obtained from the three trials. The accuracy scores for heartbeat perception and time estimation were calculated by subtracting the mean error scores from 1 and converting them to percentages.

Interoceptive Accuracy Under Emotional Interference

To determine the effect of the emotional interference, we measured the subjects’ IA using the heartbeat perception task during emotional interference. Emotional facial photographs have been widely used in previous research for this purpose [28, 29]. Furthermore, the brain’s response to emotional faces is influenced more by the emotional information contained in the face rather than the attentional process [30]. We used facial photo stimuli from the Korean Facial Expressions of Emotion (KOFEE) photographs as emotional interference in the current study [31]. Five male and five female faces, each with happy, angry, and neutral expressions, were chosen from the set. We used photographs that did not show hair features or clothing to minimize the impact of physical appearance on the results. Emotional interference was presented as one of the facial photographs in the center of an otherwise black screen. During the heartbeat perception task with emotional interference, each subject was asked to focus on their body’s senses as they looked at the photographs. The heartbeat perception task consisted of three trials (35, 25, and 45 seconds) for each condition (happy, angry, and neutral expressions). At the end of each trial, subjects were asked to guess how many times their heart had beat, and their IA was calculated. The IA values of the three trials for each condition (happy, angry, and neutral faces) were averaged.

Statistical Analysis

Statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS), version 25.0 K (SPSS Inc., Chicago, IL, USA). P-values less than 0.05 were considered statistically significant. We applied independent t-tests and χ² tests to compare demographic data and clinical characteristics between patients with SSD and healthy controls. Differences in time estimates and IA between these groups were verified by independent t-tests. To analyze the effect of emotional interference on IA, the difference between the IA values of each interference condition and the IA value in the absence of the interference was calculated and compared between groups.

We then analyzed the correlation between IA (without interference) and clinical variables (SCL-90-R, SSAS, TAS, HF-HRV, LF-HRV) within each group (SSD patients, healthy controls). The partial correlation analysis controlled for CES_D and STAIX_S.

Demographic and Clinical Characteristics of Participants

Age and gender were not significantly different between the patients with SSD and controls. (Table 1). We found no significant differences in mean baseline heart rate between patients with SSD and healthy controls during the time estimation and heartbeat perception tasks. Patients with SSD scored significantly higher on somatic symptom severity and somatosensory amplification (somatization subscale of the SCL-90-R: p < 0.001; SSAS: p = 0.004). Alexithymia was significantly higher in patients with SSD than in the controls (TAS: p = 0.003), as were depression and anxiety symptoms (CES_D: p = 0.001; STAIX_S: p < 0.001). Patients with SSD had lower HRV parameters than healthy controls (LF-HRV: p = 0.016; HF-HRV: p = 0.030).

Table 1

Demographics and physiological and clinical variables of participants.
	SSD patients (n = 23)	Controls (n = 20)	Test	p-value
Demographic Variables
Sex (male), numbers (%)	6 (26.1)	6 (30.0)	χ² = 0.081	0.775
Age, years	33.9 ± 10.1	30.6 ± 8.1	1.178	0.246
Physiological Variables
Baseline Heart Rate, beats per minute	76.6 ± 11.9	73.7 ± 11.3	0.811	0.422
Heart Rate During Heartbeat Perception Task
Without Interference	77.6 ± 20.9	70.6 ± 7.7	1.503	0.144
With Happy Face Interference	79.0 ± 23.3	69.7 ± 7.5	1.545	0.132
With Angry Face Interference	77.6 ± 22.3	71.1 ± 7.9	1.305	0.202
With Neutral Face Interference	79.6 ± 23.2	72.9 ± 7.5	1.302	0.204
Clinical Variables
SCL	27.6 ± 10.4	17.1 ± 3.7	4.531	< 0.001
SSAS	22.8 ± 6.2	17.7 ± 4.1	3.099	0.004
TAS	54.4 ± 9.9	45.2 ± 9.1	3.157	0.003
CES_D	24.6 ± 6.3	18.8 ± 4.6	3.527	0.001
STAIX_S	55.1 ± 10.8	38.7 ± 10.0	5.168	< 0.001
Heart Rate Variability
Low Frequency	4.9 ± 1.0	5.6 ± 1.0	−2.502	0.016
High Frequency	5.0 ± 1.1	5.7 ± 1.0	−2.242	0.030
CES_D: Center for Epidemiologic Studies Depression scale, SCL-90-R: Symptom Checklist-90-Revised, SSAS: Somatosensory Amplification Scale, SSD: somatic symptom disorder, STAIX_S: State-Trait Anxiety Inventory state instrument, TAS: Toronto Alexithymia Scale
Data are expressed as mean ± standard deviation unless otherwise indicated.
All p-values were calculated with the independent t-test or χ² test.

Interoceptive Accuracy Measurements

We found no significant differences in the estimation of time or perception of heartbeats between patients with SSD and healthy controls in the absence of interference stimuli (time estimation: p = 0.421; heart rate estimation: p = 0.987; Table 2). The heartbeat perception task with facial interference also showed no significant difference in IA between patients with SSD and healthy controls (happy face: p = 0.112; angry face: p = 0.163; neutral face: p = 0.091). However, the degree of difference in IA without emotional interference and with neutral facial interference was greater in patients with SSD than in healthy controls (p = 0.039). Moreover, the degree of difference in IA in the absence of facial interference and in the presence of happy or angry facial interference showed a trend-level difference between patients with SSD and healthy controls (happy face: p = 0.065; angry face: p = 0.071).

Table 2

Interoceptive accuracy in the heartbeat perception tasks.
	SSD patients (n = 23)	Controls (n = 17)	Test	p-value
Time Estimation Task, %	66.5 ± 17.5	71.1 ± 19.6	−0.812	0.421
Heartbeat Perception Task
Without Interference (R), %	57.1 ± 20.6	57.2 ± 24.7	−0.017	0.987
With Happy Face Interference (H), %	46.7 ± 17.9	56.9 ± 23.1	−1.623	0.112
With Angry Face Interference (A), %	53.1 ± 17.8	62.6 ± 24.5	−1.427	0.163
With Neutral Face Interference (N), %	46.7 ± 18.5	57.5 ± 22.2	−1.733	0.091
Difference Between H and R, %	−10.4 ± 19.7	−0.3 ± 14.1	−1.897	0.065
Difference Between A and R, %	−4.0 ± 16.8	5.3 ± 16.0	−1.854	0.071
Difference Between N and R, %	−10.4 ± 19.9	0.3 ± 10.8	−2.131	0.039
SSD: somatic symptom disorder
Data are expressed as mean ± standard deviation.
All p-values were calculated with the independent t-test.

Correlation Analysis

LF-HRV and HF-HRV showed a significant positive correlation in both groups (patients with SSD: r = 0.766, p < 0.001; healthy controls: r = 0.656, p < 0.001; Table 3). In patients with SSD, lower IA was significantly correlated with lower HRV parameters (LF-HRV: r = 0.614, p = 0.003; HF-HRV: r = 0.604, p = 0.004). There was no significant correlation between IA and HRV parameters in healthy controls.

Table 3

Partial correlation matrix for interoceptive accuracy and clinical variables (the Center for Epidemiologic Studies Depression scale and State-Trait Anxiety Inventory state instrument were controlled).
Variables	IA	SCL	SSAS	TAS	LF	HF
SSD patients (n = 23)
IA	1
SCL	0.044	1
SSAS	0.086	0.033	1
TAS	0.288	0.067	0.125	1
LF	0.614**	0.153	−0.244	0.062	1
HF	0.604**	0.259	−0.021	0.072	0.766***	1
Controls (n = 20)
IA	1
SCL	0.238	1
SSAS	−0.195	−0.434	1
TAS	−0.259	−0.236	0.170	1
LF	0.238	−0.035	−0.435	0.023	1
HF	0.379	0.035	−0.191	0.181	0.656**	1
HF: high frequency, IA: interoceptive accuracy, LF: low frequency, SCL-90-R: Symptom Checklist-90-Revised, SSAS: Somatosensory Amplification Scale, SSD: somatic symptom disorder, TAS: Toronto Alexithymia Scale

This study compared the body perception of SSD patients with that of healthy controls by using the mental tracking paradigm to measure IA. There was no statistically significant difference in IA between the SSD patients and healthy controls regardless of the absence or presence of emotional interference. These results are consistent with previous studies that showed similar IA between SSD patients and healthy controls [10]. However, regarding the extent to which facial interference affected IA, the difference in IA in the absence of interference and in the presence of neutral facial interference was more pronounced in SSD patients than in healthy controls. Previous studies have reported that neutral face photographs may be perceived as containing emotions in the experimental environment [32]. Therefore, our current findings are partially consistent with our hypothesis that patients with SSD have difficulty recognizing bodily signals when emotional processing is involved.

In this study, only the effect of neutral facial interference on IA was significant; the effects of happy or angry facial interference on IA were insignificant. Previous research has found that neutral faces were interpreted as ambiguous stimuli containing emotion rather than as stimuli without emotion [32]. Therefore, it is more affected by the emotional process of the subject who recognizes it [33]. Patients with somatoform disorder have also been found unable to accurately distinguish emotions from facial expressions [14]. This helps to explain why, in this study, the emotional processing difficulty of SSD patients was more pronounced when they were given emotionally ambiguous, i.e., neutral, facial interference. We speculate that the patients with SSD in this study experienced altered perception of their bodily signals due to the emotional interference caused by the neutral facial photographs. These speculations are consistent with the psychoanalytic explanations of somatization that individuals with somatization are unable to adequately deal with emotional information [12].

Additionally, the results of this study can be interpreted from the perspective of trustworthiness. We automatically form trustworthiness of other individuals through bottom-up emotion-attention interactions from facial features [34]. Previous studies reported that identifying trustworthiness from neutral facial stimuli is related to the function of the autonomic nervous system [35, 36]. SSD patients have been found to show alterations of the autonomic nervous system and low levels of trustworthiness [15]. Taken together, the distinct changes in IA for neutral face interference in this study may have been influenced by the differences in the propensity of trustworthiness readings from facial stimuli of SSD. This interpretation could be tested through further studies with psychological experimental designs involving trustworthiness.

SSD patients in this study showed lower HRV than the controls, suggesting that they experienced autonomic nervous system alterations. The HRV of SSD patients showed a significant correlation with IA. These results are consistent with recent studies that have reported the relationship between HRV and IA [37]. According to the neurovisceral integration model, HRV is known to reflect not only autonomic nervous system activity but also connectivity with the prefrontal cortex, such as the ventromedial prefrontal cortex or the orbitofrontal cortex [38]. The prefrontal cortical network, centered on the ventromedial prefrontal cortex, plays a major role in the regulation of interoceptive signals [39]. Taken together, the results of this study suggest that SSD patients’ prefrontal cortical network alterations may be related to their IA. In addition, previous studies have reported on the close associations between IA, HRV, empathy and emotion recognition [40–43]. Therefore, the correlation between IA and HRV specifically found in SSD patients supports that the pathophysiology of SSD is associated with IA and emotional processing. Future research, including investigation of emotional processing such as empathy and emotion recognition, will help to further expand the results of this study.

There are several limitations to consider when interpreting our findings. First, this study had few participants, making it difficult to derive significant, generalizable results. However, the SSD patients were relatively homogenous with no other major psychiatric illnesses, and the patient and control groups were demographically well matched. Second, this study measured IA using only one paradigm. Although the tasks used in this study proved useful in several previous studies, many complex factors could affect one’s ability to recognize one’s bodily sensations. Using a variety of tools, including the heartbeat discrimination paradigm, will help reinforce our results. Third, because the same paradigm was repeatedly performed, the results may have been impacted by the learning effect rather than the task condition alone.

In conclusion, patients with SSD showed more pronounced IA changes compared with healthy controls when neutral facial interference was given. The results of this study suggest that the perception of bodily signals in patients with SSD is not simply increased or decreased as a whole but may be influenced by various psychological factors, including emotionally ambiguous interference. Our current findings imply that disturbed emotional processing may contribute to the development and maintenance of somatic symptoms in SSD.

Acknowledgements

Not applicable.

Author contributions

SJK and JIK conceived and designed the study. JC and JIK recruited participants. DL analyzed data and drafted the manuscript. SJK and Y-CJ provided critical revision of the manuscript and important intellectual content. All authors had full access to all data in the study, take responsibility for the integrity of the data and the accuracy of the data analysis, and critically reviewed and approved the final version of this manuscript for publication.

Funding

This study was funded by a National Research Foundation of Korea (NRF) grant funded by the Korean government (NRF-2019R1A2C1084611).

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Ethics approval and consent to participate

The protocols used in this study were approved by the Institutional Review Board at Severance Hospital, Yonsei University, South Korea, and all subjects were provided detailed explanations of the study and signed an informed consent form before participating. This study was conducted in accordance to the relevant guidelines and regulations.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Association AP. Diagnostic and statistical manual of mental disorders (DSM-5®). American Psychiatric Pub; 2013.
Rief W, Barsky AJ. Psychobiological perspectives on somatoform disorders. Psychoneuroendocrinology. 2005;30(10):996–1002.
Barsky AJ. Amplification, somatization, and the somatoform disorders. Psychosomatics. 1992;33(1):28–34.
Bogaerts K, Van Eylen L, Li W, Bresseleers J, Van Diest I, De Peuter S, et al. Distorted symptom perception in patients with medically unexplained symptoms. J Abnorm Psychol. 2010;119(1):226.
Brown RJ. Psychological mechanisms of medically unexplained symptoms: an integrative conceptual model. Psychol Bull. 2004;130(5):793.
Garfinkel SN, Seth AK, Barrett AB, Suzuki K, Critchley HD. Knowing your own heart: distinguishing interoceptive accuracy from interoceptive awareness. Biol Psychol. 2015;104:65–74.
Schandry R. Heart beat perception and emotional experience. Psychophysiology. 1981;18(4):483–8.
Brener J, Kluvitse. Heartbeat detection: Judgement of the simultaneity of external stimuli and heartbeats. Psychophysiology. 1988;25(5):554–61.
Mussgay L, Klinkenberg N, Rüddel H. Heart beat perception in patients with depressive, somatoform, and personality disorders. J Psychophysiol. 1999;13(1):27.
Schaefer M, Egloff B, Witthöft M. Is interoceptive awareness really altered in somatoform disorders? Testing competing theories with two paradigms of heartbeat perception. J Abnorm Psychol. 2012;121(3):719.
Pollatos O, Herbert BM, Wankner S, Dietel A, Wachsmuth C, Henningsen P, et al. Autonomic imbalance is associated with reduced facial recognition in somatoform disorders. J Psychosom Res. 2011;71(4):232–9.
Waller E, Scheidt CE. Somatoform disorders as disorders of affect regulation: a development perspective. Int Rev psychiatry. 2006;18(1):13–24.
Duddu V, Isaac M, Chaturvedi S. Alexithymia in somatoform and depressive disorders. J Psychosom Res. 2003;54(5):435–8.
Pedrosa Gil F, Ridout N, Kessler H, Neuffer M, Schoechlin C, Traue HC et al. Facial emotion recognition and alexithymia in adults with somatoform disorders. Depress Anxiety. 2009;26(1).
Erkic M, Bailer J, Fenske SC, Schmidt SN, Trojan J, Schröder A, et al. Impaired emotion processing and a reduction in trust in patients with somatic symptom disorder. Clin Psychol Psychother. 2018;25(1):163–72.
Löwe B, Spitzer RL, Williams JB, Mussell M, Schellberg D, Kroenke K. Depression, anxiety and somatization in primary care: syndrome overlap and functional impairment. Gen Hosp Psychiatry. 2008;30(3):191–9.
Pollatos O, Traut-Mattausch E, Schandry R. Differential effects of anxiety and depression on interoceptive accuracy. Depress Anxiety. 2009;26(2):167–73.
Lischke A, Pahnke R, Mau-Moeller A, Jacksteit R, Weippert M. Sex-specific relationships between interoceptive accuracy and emotion regulation. Front Behav Neurosci. 2020;14:67.
Adolphs R. Recognizing emotion from facial expressions: psychological and neurological mechanisms. Behav Cogn Neurosci Rev. 2002;1(1):21–62.
Mauss IB, Robinson MD. Measures of emotion: A review. Cogn Emot. 2009;23(2):209–37.
Lee D, Kim SJ, Cheon J, Hwang EH, Jung Y-c, Kang JI. Characteristics of autonomic activity and reactivity during rest and emotional processing and their clinical correlations in somatic symptom disorder. Psychosom Med. 2018;80(8):690–7.
Derogatis LR, Unger R. Symptom checklist-90‐revised. Wiley Online Library; 2010.
Barsky AJ, Wyshak G, Klerman GL. The somatosensory amplification scale and its relationship to hypochondriasis. J Psychiatr Res. 1990;24(4):323–34.
Bagbya-C RM. The Revised Toronto Alexithymia Scale: some reliability, validity, and normative data. 1992.
Radloff LS. The CES-D scale: A self-report depression scale for research in the general population. Appl Psychol Meas. 1977;1(3):385–401.
Spielberger CD, Gorsuch RL, Lushene RE. Manual for the state-trait anxiety inventory. 1970.
Ehlers A, Breuer P. Increased cardiac awareness in panic disorder. J Abnorm Psychol. 1992;101(3):371–82.
Fales CL, Barch DM, Rundle MM, Mintun MA, Snyder AZ, Cohen JD, et al. Altered emotional interference processing in affective and cognitive-control brain circuitry in major depression. Biol Psychiatry. 2008;63(4):377–84.
Sabatinelli D, Fortune EE, Li Q, Siddiqui A, Krafft C, Oliver WT, et al. Emotional perception: meta-analyses of face and natural scene processing. NeuroImage. 2011;54(3):2524–33.
Vuilleumier P, Armony JL, Driver J, Dolan RJ. Effects of attention and emotion on face processing in the human brain: an event-related fMRI study. Neuron. 2001;30(3):829–41.
Park JY, Oh JM, Kim SY, Lee M, Lee C, Kim BR, et al. Korean facial expressions of emotion (KOFEE). Seoul, Korea: Section of Affect & Neuroscience, Institute of Behavioral Science in Medicine, Yonsei University College of Medicine.; 2011.
Lee E, Kang JI, Park IH, Kim J-J, An SK. Is a neutral face really evaluated as being emotionally neutral? Psychiatry Res. 2008;157(1):77–85.
Albohn DN, Brandenburg JC, Adams RB. Perceiving emotion in the “neutral” face: a powerful mechanism of person perception. In. The social nature of emotion expression: Springer; 2019. p. 25–47.
Weymar M, Ventura-Bort C, Wendt J, Lischke A. Behavioral and neural evidence of enhanced long-term memory for untrustworthy faces. Sci Rep. 2019;9(1):19217.
Lischke A, Junge M, Hamm AO, Weymar M. Enhanced processing of untrustworthiness in natural faces with neutral expressions. Emotion. 2018;18(2):181.
Wendt J, Weymar M, Junge M, Hamm AO, Lischke A. Heartfelt memories: Cardiac vagal tone correlates with increased memory for untrustworthy faces. Emotion. 2019;19(1):178.
Lischke A, Pahnke R, Mau-Moeller A, Weippert M. Heart rate variability modulates interoceptive accuracy. Front NeuroSci. 2021;14:612445.
Thayer JF, Lane RD. Claude Bernard and the heart–brain connection: Further elaboration of a model of neurovisceral integration. Neurosci Biobehavioral Reviews. 2009;33(2):81–8.
Hurliman E, Nagode JC, Pardo JV. Double dissociation of exteroceptive and interoceptive feedback systems in the orbital and ventromedial prefrontal cortex of humans. J Neurosci. 2005;25(18):4641–8.
Lischke A, Weippert M, Mau-Moeller A, Jacksteit R, Pahnke R. Interoceptive accuracy is associated with emotional contagion in a valence-and sex-dependent manner. Soc Neurosci. 2020;15(2):227–33.
Lischke A, Pahnke R, Mau-Moeller A, Behrens M, Grabe HJ, Freyberger HJ, et al. Inter-individual differences in heart rate variability are associated with inter-individual differences in empathy and alexithymia. Front Psychol. 2018;9:229.
Quintana DS, Guastella AJ, Outhred T, Hickie IB, Kemp AH. Heart rate variability is associated with emotion recognition: Direct evidence for a relationship between the autonomic nervous system and social cognition. Int J Psychophysiol. 2012;86(2):168–72.
Lischke A, Lemke D, Neubert J, Hamm AO, Lotze M. Inter-individual differences in heart rate variability are associated with inter-individual differences in mind-reading. Sci Rep. 2017;7(1):1–6.

No competing interests reported.

Download PDF

Submission checks completed at journal
30 Jun, 2023
First submitted to journal
26 Jun, 2023

You are reading this latest preprint version

Changes in Interoceptive Accuracy Related to Emotional Interference in Somatic Symptom Disorder

Status:

Version 1

Abstract

Introduction

Methods

Participants

Measurement of Clinical Characteristics

Measurement of Interoceptive Accuracy

Interoceptive Accuracy Under Emotional Interference

Statistical Analysis

Results

Demographic and Clinical Characteristics of Participants

Interoceptive Accuracy Measurements

Correlation Analysis

Discussion

Declarations

References

Additional Declarations

Status:

Version 1