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Enhanced disgust generalization in obsessive–compulsive disorder is related to insula and putamen hyperactivity

Published online by Cambridge University Press:  14 April 2025

Juntong Liu ,
Jinxia Wang ,
Yuchen Song ,
Benjamin Becker ,
Xianchao Ming  and
Yi Lei Open the ORCID record for Yi Lei [Opens in a new window]
Juntong Liu
Affiliation:
College of Psychology, Shenzhen University, Shenzhen, China Center for Neurogenetics, Shenzhen Institute of Neuroscience, Shenzhen, China
Jinxia Wang
Affiliation:
Institution for Brain and Psychological Science, Sichuan Normal University, Chengdu, China Center for Neurogenetics, Shenzhen Institute of Neuroscience, Shenzhen, China Faculty of Education and Psychology, University of Jyväskylä, Jyväskylä, Finland
Yuchen Song
Affiliation:
Institution for Brain and Psychological Science, Sichuan Normal University, Chengdu, China
Benjamin Becker
Affiliation:
State Key Laboratory of Brain and Cognitive Sciences, Department of Psychology, The University of Hong Kong, Hong Kong, China
Xianchao Ming
Affiliation:
School of Psychology, South China Normal University, Guangzhou, China
Yi Lei*
Affiliation:
Institution for Brain and Psychological Science, Sichuan Normal University, Chengdu, China Center for Neurogenetics, Shenzhen Institute of Neuroscience, Shenzhen, China
*
Corresponding author: Yi Lei; Email: leiyi821@vip.sina.com
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Abstract

Background

Compulsive cleaning is a characteristic symptom of a particular subtype of obsessive–compulsive disorder (OCD) and is often accompanied by intense disgust. While overgeneralization of threat is a key factor in the development of obsessive–compulsive symptoms, previous studies have primarily focused on fear generalization and have rarely examined disgust generalization. A systematic determination of the behavioral and neural mechanisms underlying disgust generalization in individuals with contamination concern is crucial for enhancing our understanding of OCD.

Method

In this study, we recruited 27 individuals with high contamination concerns and 30 individuals with low contamination concerns. Both groups performed a disgust generalization task while undergoing functional magnetic resonance imaging (fMRI).

Results

The results revealed that individuals with high contamination concern had higher disgust expectancy scores for the generalization stimulus GS4 (the stimulus most similar to CS+) and exhibited higher levels of activation in the left insula and left putamen. Moreover, the activation of the left insula and putamen were positively correlated with a questionnaire core of the ratings of disgust and also positively correlated with the expectancy rating of CS+ during the generalization stage.

Conclusion

Hyperactivation of the insula and putamen during disgust generalization neutrally mediates the higher degree of disgust generalization in subclinical OCD individuals. This study indicates that altered disgust generalization plays an important role in individuals with high contamination concerns and provides evidence of the neural mechanisms involved. These insights may serve as a basis for further exploration of the pathogenesis of OCD in the future.

Keywords

contamination concerndisgustacquisitiongeneralizationinsulaobsessive-complusive disorderputamenfMRIROIcontamination-related feardisgust learningconditioned responses

Information

Type
Original Article
Information
Psychological Medicine , Volume 55 , 2025 , e116
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Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2025. Published by Cambridge University Press

Introduction

Obsessive–compulsive disorder (OCD) is a disabling disorder characterized by intrusive obsessive thinking or/and repetitive compulsive behaviors (Diagnostic and statistical manual of mental disorders, 1994). It is estimated that around 2% of the world’s population suffers from OCD and that almost all affected individuals experience intrusive thoughts and strongly aversive emotional states and have a strongly increased risk for comorbid mental and physical disorders (Adam, Meinlschmidt, Gloster, & Lieb, Reference Adam, Meinlschmidt, Gloster and Lieb2012; Murphy et al., Reference Murphy, Timpano, Wheaton, Greenberg and Miguel2010; Ruscio, Stein, Chiu, & Kessler, Reference Ruscio, Stein, Chiu and Kessler2010). Patients with OCD present with various symptoms and behaviors such as contamination obsessions with washing/cleaning compulsions and doubting obsessions with checking compulsions (Matsunaga et al., Reference Matsunaga, Maebayashi, Hayashida, Okino, Matsui, Iketani and Stein2008). The boundaries between these OCD subtypes are not always clear, and many patients exhibit obsessions and compulsions from different subtypes (Starcevic & Brakoulias, Reference Starcevic and Brakoulias2008). However, contamination-related fears are one of the most common and prominent symptoms of OCD, affecting up to 50% of individuals with the disorder (referred to as contamination-based OCD or C-OCD) (Markarian et al., Reference Markarian, Larson, Aldea, Baldwin, Good, Berkeljon and McKay2010). Patients with this subtype experience an intense, irrational fear of germs, dirt, toxic substances or even mental contamination, which drives them to engage in excessive cleaning rituals or avoidance behaviors (Rachman, Reference Rachman2004).

Disgust is an emotional response to stimuli with contaminating properties, serving to protect an individual’s health from harmful substances (Mason & Richardson, Reference Mason and Richardson2010). In patients with compulsive cleaning behaviors, the onset of symptoms is often accompanied by a strong disgust for contamination (Bhikram, Abi-Jaoude, & Sandor, Reference Bhikram, Abi-Jaoude and Sandor2017). Consequently, disgust is considered a highly relevant emotion in obsessive–compulsive symptoms and may play a significant role in the emotional processing associated with the compulsive cleaning subtype (Armstrong & Olatunji, Reference Armstrong and Olatunji2017; Broderick, Grisham, & Weidemann, Reference Broderick, Grisham and Weidemann2013; Cisler, Brady, Olatunji, & Lohr, Reference Cisler, Brady, Olatunji and Lohr2010; Ludvik, Boschen, & Neumann, Reference Ludvik, Boschen and Neumann2015; Olatunji & Kim, Reference Olatunji and Kim2024). Given the co-occurrence of anxiety and OCD symptoms, as well as the hypothesis of shared etiopathological factors, exploring the relationship between dysregulations in Pavlovian fear conditioning and OCD symptoms is crucial for a deeper understanding of the disorder (Armstrong, McClenahan, Kittle, & Olatunji, Reference Armstrong, McClenahan, Kittle and Olatunji2014). However, research on generalization remains limited. Rouhani et al. (Reference Rouhani, Wimmer, Schneier, Fyer, Shohamy and Simpson2019) demonstrated that individuals with OCD exhibit impaired reward generalization but intact punishment generalization, suggesting domain-specific deficits in generalization rather than a broad impairment. This specificity warrants further investigation into the mechanisms underlying atypical generalization patterns in OCD. Previous studies have not observed excessive generalization in individuals with high compulsion which may be due to the fact that fear of physical harm is less relevant to OCD (Kaczkurkin & Lissek, Reference Kaczkurkin and Lissek2013). Additionally, disgust learning may play a key role in contamination-based OCD. The exaggerated perceptions of contamination in many OCD patients may stem from abnormal disgust learning processes (Armstrong & Olatunji, Reference Armstrong and Olatunji2017). Therefore, exploring the characteristics of disgust generalization in individuals with obsessive–compulsive cleaning is crucial for identifying its etiologic factors and warrants further investigation.

Pavlovian conditioning theory is a classic framework for studying generalization. Meanwhile, Pavlov’s disgust-conditioned reflex model has been successfully applied to determine the pathological mechanisms related to disgust in pollution-based OCD (Stein, Liu, Shapira, & Goodman, Reference Stein, Liu, Shapira and Goodman2001). In the laboratory, researchers repeatedly pair conditioned stimuli (CS) (For example, a sound of 300 Hz as CS+, a sound of 800 Hz as CS-) with unconditioned stimuli (US) that elicit disgust responses. When a disgust response occurs in response to the CS alone, it indicates that the individual has successfully completed the acquisition (Herry & Johansen, Reference Herry and Johansen2014). CS paired with the US are referred to as CS+, while those not paired with the US are termed as CS−. Generalization is said to occur when the individual also exhibits responses to the GS (Lissek et al., Reference Lissek, Bradford, Alvarez, Burton, Espensen-Sturges, Reynolds and Grillon2014). In generalization studies, after an individual has completed acquisition, they are presented with a series of generalized stimuli (GS,400 Hz, 500 Hz tone) that are similar to CS+. GSs are used to determine whether conditioned responses to a primary CS+ extend to similar stimuli. Assessing generalization is crucial for understanding how individuals apply learned responses to new but comparable contexts. These insights are essential for clarifying and managing psychological disorders involving conditioned reflexes, such as anxiety disorders and OCD. For instance, after experiencing food poisoning from spoiled seafood, a person may develop disgust toward foods with a similar smell or appearance – an adaptive response to minimize health risks. However, this disgust can irrationally generalize to all types of food and eating situations, leading to the avoidance of many safe foods and restaurants due to perceived danger. The closer a GS (For example, GS4, 400 Hz tone) is to the CS+ (a sound of 300 Hz) in terms of perceptual features, the more likely it is to elicit a similar disgust response. Because GS4 is very similar to the CS+, the conditioned response to GS4 would be high but slightly less than the response to the CS+. Whereas, GS1 (a lower similarity to CS+, 700 Hz tone) is considerably different from the CS+. As a result, GS1 elicits a much weaker fear response than GS4 because it is less similar to the CS+.

The neural mechanisms associated with disgust in healthy subjects and individuals with obsessive–compulsive tendencies have been explored in various studies. MRI studies have demonstrated that the experience of disgust is encoded in widespread brain systems, with the insula, amygdala, striatal and prefrontal regions being commonly identified (Gan et al., Reference Gan, Zhou, Li, Jiao, Jiang, Biswal and Becker2022). Additionally, in comparison to control subjects, the bilateral insula in OCD patients exhibited an increased response to disgusting images control subjects, a difference not observed with threatening images (Schienle et al., Reference Schienle, Schäfer, Hermann, Walter, Stark and Vaitl2006). Further findings suggest that the anterior insula plays a central role in modulating the contaminating obsessive–compulsive inhibition of responses to disgusting images (Berlin et al., Reference Berlin, Schulz, Zhang, Turetzky, Rosenthal and Goodman2015).

To date, studies on the neural basis of disgust generalization in individuals with OCD are scarce. Investigating the characteristics and neural basis of disgust generalization in individuals with OCD will not only enrich our understanding of the disorder but also provide new targets for clinical intervention. However, the heterogeneity of OCD symptoms and the complexity of the disorder’s etiology present several limitations when studying clinical samples. These limitations include variations in medication duration, comorbidities and other confounding factors. Therefore, utilizing nonclinical samples, specifically individuals with high yet subclinical levels of obsessive–compulsive tendencies, can facilitate more focused research on the underlying mechanisms of the disorders and subsequently guide the determination of novel treatment approaches(Kou et al., Reference Kou, Zhang, Zhou, Gao, Yao, Zhao and Becker2022; Wong & Beckers, Reference Wong and Beckers2021; Xin et al., Reference Xin, Zhou, Dong, Zhao, Yang, Wang and Becker2020).

In this study, we aim to examine the characteristics and neural basis of disgust generalization in individuals with high and low compulsive contamination concerns. To this end, we recruited participants with high and low compulsive contamination concerns. Both groups subsequently underwent Pavlovian disgust acquisition and disgust generalization with concomitant fMRI acquisition. Previous studies have shown that individuals with high compulsive contamination concerns often exhibit high levels of disgust sensitivity (Gurak Thayer et al., Reference Gurak Thayer, DeRiso, Warren, Wilson and Vance2021; Rickelt et al., Reference Rickelt, de Wit, Werf, Schruers, Marcelis, Vries and Heuvel2019). Meanwhile, the level of activation of the insula is closely related to disgust and closely associated with OCD symptoms (Li et al., Reference Li, Li, Li, Zou, Wang, Wu and Zhang2021). Consequently, we hypothesized that (1) compared to the low contamination concern group, the high compulsive contamination concern group would report a higher expectancy rating towards generalized stimuli and experience a higher level of disgust and that (2) the activation level of brain regions related to disgust (such as insula) would be higher in the group with high contamination concerns.

Method and materials

Participants

300 copies of the Padua Inventory-Contamination of obsessive–compulsive Disorder Subscale (PI-COWC) (Burns, Keortge, Formea, & Sternberger, Reference Burns, Keortge, Formea and Sternberger1996) were distributed through campus advertising to recruit student participants with high and low contamination concerns, based on a previous study (Armstrong & Olatunji, Reference Armstrong and Olatunji2017). The inclusion criteria for the high tendency group was a PI-COWC score greater than 13, while the low tendency group required a PI-COWC score less than 7. All participants had normal or corrected vision and had no history of psychiatric or neurological diseases (according to self-report). The above recruitment methods are consistent with the research of Wang et al. (Reference Wang, Becker, Wang, Ming, Lei and Wikgren2024), but the samples do not overlap (J. Wang et al., Reference Wang, Becker, Wang, Wang, Zhang, Mei and Lei2024). We used G-power 3.1 to estimate power for behavioral analyses using repeated measures, within-between interaction ANOVAs (two groups and six measurements) with an effect size of Cohen’s f = 0.25 (Kang, Reference Kang2015; Y. Wang et al., Reference Wang, Becker, Wang, Ming, Lei and Wikgren2024) and α = 0.05. A total of 28 participants were required based on imaging measures. After the initial examination, three subjects were excluded for failing to acquire which type of CS would be followed by disgust stimuli (Armstrong & Olatunji, Reference Armstrong and Olatunji2017), and one subject was excluded due to excessive head movement during the generalization phase. Therefore, a total of 57 subjects were included in the final analysis. Among them, there were 27 individuals (20.41 ± 1.45, female = 24) in the high-compulsive contamination concern group and 30 individuals (19.80 ± 1.16, female = 23) in the low-compulsive contamination concern group. Participants provided informed consent before the study and received compensation upon completion. The study was approved by the Ethics Committee of Sichuan Normal University.

Experimental design

The study was divided into two phases: the acquisition phase and the generalization phase. The acquisition phase included two stimulus categories (CS+ and CS−), while the generalization phase included six stimulus categories (CS−, GS1, GS2, GS3, GS4 and CS+).

Experimental materials

Circles of different sizes were used as experimental materials (Lissek et al., Reference Lissek, Biggs, Rabin, Cornwell, Alvarez, Pine and Grillon2008). Specifically, ten circles with diameters increasing in 20% increments from 5.08 cm to 14.22 cm were used, with either the largest or smallest circle serving as the CS+. The assignment of the CS+ was counterbalanced between subjects. See Supplementary Figure S1. for the sample stimulus.

See more descriptions about the US in supplementary materials.

Scales

Each subject completed the following self-report scale (see more information in supplementary materials):

  1. (1) Padua Inventory Contamination of obsessive–compulsive Disorder Subscale (PI-COWC): The Padua Inventory is a self-report measure of obsessive–compulsive and compulsive symptoms (Burns, Keortge, Formea, & Sternberger, Reference Burns, Keortge, Formea and Sternberger1996), where each question assesses the degree to which obsessive–compulsive symptoms interfere with the individual’s life, using a 5-point scale ranging from 0 (absent) to 4 (severe). assessment. The present study used its pollution OCD subscale of 10 questions for the degree of obsessive compulsiveness in terms of pollution (in the current sample: Cronbach’s ɑ = 0.94).

  2. (2) The Revised obsessive–compulsive Inventory (OCI-R): The revised OCI-R measures the main symptoms of OCD on six dimensions: checking, cleaning, ordering, hoarding, obsessing and neutralizing (Foa et al., Reference Foa, Huppert, Leiberg, Langner, Kichic and Hajcak2002), with each question assessed in terms of frequency of occurrence and degree of distress, with the frequency of occurrence using a 5-point scale of 0 (never) − 4 (almost always) on a 5-point scale (in the current sample: Cronbach’s ɑ = 0.91), and the level of distress was assessed using a 5-point scale of 0 (not at all distressing) − 4 (extremely distressing) (in the current sample: Cronbach’s ɑ = 0.94).

  3. (3) State–Trait Anxiety Inventory (STAI): The State–Trait Anxiety Inventory (STAI) consists of two 20-item self-report scales (Sydeman, Reference Sydeman, Zeigler-Hill and Shackelford2018). The SAI is used to measure the intensity of anxiety at a particular moment in time, and the TAI asks people to describe their overall feelings based on how often they experience specific anxiety symptoms (in the current sample: Cronbach’s ɑ = 0.97).

  4. (4) Disgust Sensitivity Scale (DS-R): The Disgust Sensitivity Scale was used to assess the sensitivity of subjects’ responses to disgusting stimuli (Olatunji et al., Reference Olatunji, Williams, Tolin, Abramowitz, Sawchuk, Lohr and Elwood2007). The scale categorizes disgust into three dimensions: core disgust, animal remainder and interpersonal contamination-based disgust and consists of 27 items, two of which are lie-detector questions, and all items were rated on a 0–4 scale, with higher ratings representing greater sensitivity (in the current sample: Cronbach’s ɑ = 0.89).

Experimental procedure

The formal experiment consisted of three phases: the habituation phase, the disgust acquisition phase and the disgust generalization phase performing magnetic resonance scanning in all three stages. The three stages are carried out continuously.

During the habituation phase, the CS+, CS− and each of the four GSs were presented 20 times in a pseudo-randomized order, ensuring that no stimulus appeared consecutively more than twice (Y. Wang et al., Reference Wang, Becker, Wang, Ming, Lei and Wikgren2024). This resulted in a total of 120 trials during this stage. During each time, CS was presented for 4 seconds and followed by a fixation for 2.4–4.8 seconds as inter-trail intervals. Subjects were only required to passively view and familiarize themselves with each graphic. The entire stage takes about 15 minutes.

In the acquisition phase, a randomly selected fixation point was displayed for 2.4–4.8 seconds, followed by the presentation of a CS alone for 800 ms. Participants were then instructed to provide their ratings within 3200 ms, during which the CS remained on the screen. In total, each CS was presented for 4000 ms. The CS was presented in a pseudorandomized order so that no more than two of the same CS appeared consecutively. Subjects were required to rate the likelihood of a disgusting picture following CS on a scale of 1 to 9. Participants adjusted the position of a square on the screen by pressing a button, with the square’s final position reflecting their expected level of unconditional stimulation. A disgusting picture (US) appeared and lasted for 1 second after the CS+, while no disgusting picture followed the CS−. Subsequently, a fixation point appears for 2.4–4.8 seconds (ITI). Each type of CS was presented 15 times, with an 80% reinforcement rate for CS+, resulting in a total of 30 trials. At the end of the phase, subjects rated the two types of CS on a disgusting scale of 1 to 9, see Figure 1a. After the end of this phase, the researcher informed the subject to take a short break and then entered the generalization phase. The entire stage takes about 5 minutes.

Figure 1.

Experimental procedure diagram of acquisition phase and generalization phase. (a) In acquisition phase, subjects were required to score the likelihood of a disgusting stimulus following different stimuli. (b) In the generalization phase only a small probability of following the aversive pictures after CS+, subjects need to rate the generalized stimuli according to the laws learned in the acquisition phase.

The generalization phase was similar to the acquisition phase. Random fixation points were first presented for 2.4–4.8 seconds, followed by the stimulus. Subjects rated the likelihood that the stimulus would be followed by a disgusting picture. During this phase, CS+ appeared 15 times, accompanied by the US five times to prevent premature extinction of disgust to CS+ (Wang, Sun, Becker, & Lei, Reference Wang, Sun, Becker and Lei2022). The CS− and each GS were presented 10 times, resulting in a total of 65 trials. The order of stimulus presentation was pseudo-randomized, with no more than two consecutive occurrences of the same type of stimulus. No rest during the entire generalization process. Consistent with the acquisition phase, subjects rated the two types of CS and each GS retrospectively on a disgusting scale after the end of the generalization phase, see Figure 1b. The entire stage takes about 10 minutes.

fMRI data acquisition

The fMRI data for this experiment were acquired using a Siemens Prisma 3.0 T MRI scanner (Siemens, Erlangen, Germany) (see more information in supplementary materials).

Data analysis

Analysis of behavioral data

A 2 (group: high/low obsessive–compulsive tendency) × 2 (stimulus: CS+/CS−) repeated-measures ANOVA was conducted to analyze the expected ratings of subjects during the acquisition phase. Similarly, a 2 (group: high/low obsessive–compulsive tendency) × 2 (stimulus: CS+/CS−) repeated-measures ANOVA was performed on the disgust ratings for the different CSs during the acquisition phase.

To examine changes in ratings across the time course of the generalization phase, each trial was included in the analysis. We performed linear mixed models (LMM, “lme4”) in R v.4.1.1 (R Development Core Team, 2012) for the behavioral analyses on prediction ratings during the generalization stage. To account for individual differences and variations across multiple levels of each variable, a 2 (group: high/low contamination concern) × 6 (stimuli: CS-/GS1/GS2/GS3/GS4/CS+) × 10 (trials: 1/2/3/4/5/6/7/8/9/10) linear mixed model analysis was conducted. Finally, a 2 (group: high/low contamination concern) × 6 (stimuli: CS-/GS1/GS2/GS3/GS4/CS+) repeated-measures ANOVA was performed on the disgusting scores during the generalization phase. For all analyses, a significance level of p < 0.05 was defined. In case of violation of sphericity, Greenhouse–Geisser epsilon (GG-ε) and uncorrected degrees of freedom are reported (Ahrens et al., Reference Ahrens, Pauli, Reif, Mühlberger, Langs, Aalderink and Wieser2016).

Analysis of fMRI data

fMRI data preprocessing

fMRI data preprocessing was conducted using MATLAB version 2021b (The MathWorks Inc., Natick, USA) and DPABI (RRID), an open-source software package derived from DPARSF and REST (source code available at http://rfmri.org/DPABI). See more information in supplementary materials.

First-level and second-level analysis

First-Level Analysis: After completing preprocessing, first-level analyses were conducted using the SPM12 toolbox in MATLAB2019a (Wellcome Department of Imaging Neuroscience, London; http://www.fl.ion.ucl.ac.uk/spm). A general linear model (GLM) was applied to create contrasts for each CSs and GSs during the generalization phase (with CS+ in the generalization phase excluding trials accompanied by US). MATLAB-based code was used to batch-process these analyses for efficiency across subjects.

Second-Level Analyses: Using the CON files generated from the first-level analyses, second-level analyses were performed with the SPM12 toolkit. For the generalization phase, a 2 (group: high/low contamination concern) × 6 (stimuli: CS-/GS1/GS2/GS3/GS4/CS+) repeated-measures ANOVA was performed. Then, we combined GS1/GS2, GS3/GS4 to perform a 2 (group: high/low contamination concern) × 3 (stimuli: GS1GS2/GS3GS4/CS+) repeated-measures ANOVA. Additionally, exploratory independent samples t-tests were conducted for each stimulus type (CS-/GS1/GS2/GS3/GS4/CS+) during the generalization phase to investigate differences in brain activation between the two groups in response to various stimuli.

ROI analysis

Based on previous research and the results of the second-order analysis in this study, region of interest (ROI) analysis focuses on exploring the differences in activation levels of the insula and putamen between the two groups (Gan et al., Reference Gan, Zhou, Li, Jiao, Jiang, Biswal and Becker2022; Gan et al., Reference Gan, Zhou, Xu, Liu, Zhang, Zheng and Becker2024; Wang, Sun, Becker, & Lei, Reference Wang, Sun, Becker and Lei2022). The masks of insula (left and right) and putamen (left and right) were created using the AAL atlas (Tzourio-Mazoyer et al., Reference Tzourio-Mazoyer, Landeau, Papathanassiou, Crivello, Etard, Delcroix and Joliot2002), implemented in WFU Pickatlas (Maldjian, Laurienti, Kraft, & Burdette, Reference Maldjian, Laurienti, Kraft and Burdette2003). The mean percent signal changes within each ROI were extracted using the Marsbar toolbox (Brett et al., Reference Brett, Johnsrude and Owen2002) and subsequently exported to SPSS for analysis. The ROIs were then analyzed separately using a two-level group analysis, with group as a between-subjects variable and stimulus type as a within-subjects variable, employing a 2 (group: high/low contamination concern) × 6 (stimuli: CS-/GS1/GS2/GS3/GS4/CS+) repeated-measures ANOVA.

Results

Demographic results

Independent samples t-test was done for the age of subjects and the scores of each questionnaire in the two groups, and the statistical results and the gender comparison between the two groups are shown in Table 1. Among them, there is a significant difference between the two groups in the shares of PI-COWC, DSR and OCIR, indicating that the grouping is effective.

Table 1.

Scores of PI-COWC, DSR, OCI-R, SAI, TAI and gender comparison between two groups

Note: M, mean; SD, standard deviation; N, number; STAI-T, Trait Anxiety Inventory; STAI-S, State Anxiety Inventory; PI-COWC, Padua Inventory-Contamination of obsessive-compulsive Disorder Subscale; DSR, The Disgust Scale-Revised; OCIR, The Obsessive–Compulsive Inventory-Revised; OCIR washing, The Washing Subscale of the Obsessive–Compulsive Inventory-Revised; OCIR obsessing, The Obsessing Subscale of the Obsessive–Compulsive Inventory-Revised; OCIR hoarding, The Hoarding Subscale of the Obsessive–Compulsive Inventory-Revised; OCIR ordering, The Ordering Subscale of the Obsessive–Compulsive Inventory-Revised; OCIR neutralizing, The Neutralizing Subscale of the Obsessive–Compulsive Inventory-Revised; OCIR checking, The Checking Subscale of the Obsessive–Compulsive Inventory-Revised; HCC, high contamination concern; LCC, low contamination concern.

*p < 0.05 **p < 0.01 ***p < 0.001

Behavioral results

Acquisition

A 2 (group: high/low contamination concern) × 2 (stimulus: CS+/CS-) ANOVA on the expected ratings of the two groups during the acquisition phase revealed a significant main effect of stimulus type [F (1,55) = 976.70, p < 0.001, ηp2 = 0.947]. Post hoc analyses indicated that participants rated CS+ (M ± SD = 7.82 ± 0.09) significantly higher than CS− (M ± SD = 2.10 ± 0.11), demonstrating successful acquisition of the experimental rule (see Figure 2a). However, the differences between groups were not significant, nor was the interaction between group and stimulus type.

Figure 2.

Behavioral results during the acquisition stage. (a) Acquisition phase rating scores for both groups of subjects. There were no between-group differences in the pattern of acquisition for both groups. (b) Disgust ratings to stimuli in both groups during the acquisition phase, subjects in the high compulsive tendency group had higher CS+ disgust ratings. Error bars reflect ± standard deviation (SD). ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001.

In a 2 (group: high/low contamination concern) × 2 (stimulus: CS+/CS−) repeated-measures ANOVA on the disgust ratings of the different CSs during the acquisition phase (The data of the two individuals was not recorded), a significant main effect of stimulus type was observed [F (1,53) = 288.70, p < 0.001, ηp2 = 0.845]. Further analysis revealed that CS+ was rated significantly higher in disgust (M ± SD = 7.14 ± 0.29) compared to CS− (M ± SD = 1.60 ± 0.18). A borderline significant interaction between group and stimulus type was also detected [F (1,53) = 3.51, p = 0.066, ηp2 = 0.062]. Simple effects analysis showed that high-compulsive tendency participants rated CS+ significantly higher in disgust (M ± SD = 7.77 ± 0.42) compared to low-compulsive tendency participants (M ± SD = 6.52 ± 0.40; p = 0.035). The main effect of the group was not significant (see Figure 2b).

Generalization

The results of the linear mixed model analysis revealed a nonsignificant interaction among group, stimulus type and trial. However, there was a significant interaction between group and stimulus type [χ2 (54) = 165, p < 0.001, ηp2 = 0.01]. Simple effects analysis showed that participants in the high obsessive–compulsive tendency group rated CS+ significantly higher than low obsessive–compulsive tendency group [t (94.4) = 3.52, p = 0.007]. A similar pattern was observed for GS4, the stimulus most similar to CS+ [t (94.4) = 2.05, p = 0.0429], indicating that individuals with high contamination concern are more sensitive to disgusting stimuli. Additionally, the group × trial interaction was significant [χ2 (50) = 187.5, p < 0.001, ηp2 = 0.006]. Simple effects analysis revealed that the high obsessive–compulsive tendency group had significantly higher ratings in the early phase, specifically during the first trial [t (133) = 2.47, p = 0.0147] and the fourth trial [t (133) = 2.20, p = 0.03], see Figure 3.

Figure 3.

Subjects’ expectancy ratings of different types of stimuli during the generalization phase. (a) Shows the ratings of the two groups of subjects for different stimulus types. (b) Demonstrates the difference in overall scores between the two groups of subjects across trials. Error bars reflect ± Standard deviation (SD).

In a 2 (group: high/low contamination concern) × 6 (stimulus: CS+/CS-/GS1/GS2/GS3/GS4) repeated-measures ANOVA on the disgust ratings of the different stimuli during the generalization phase (The data of the one individual was not recorded), a significant main effect of stimulus type was observed [F (5,270) = 143.07, p < 0.001, ηp2 = 0.726]. Post hoc analyses revealed that CS+ was rated highest in disgust (M ± SD = 6.94 ± 0.30), and the ratings from high to low are GS4 (M ± SD = 4.68 ± 0.32), GS3 (M ± SD = 2.62 ± 0.25), GS2 (M ± SD = 1.80 ± 0.17), GS1 (M ± SD = 1.34 ± 0.10) and CS− (M ± SD = 1.30 ± 0.10). The main effect of the group was not significant, and there was no significant interaction.

fMRI results

Results of whole-brain analysis of the generalization phase

The interaction between group and stimulus type was entered into the flexible factorial design module to examine the main effect of stimulus type. Whole-brain analysis revealed no significant group-by-stimulus interaction. See more information in supplementary materials.

To further investigate differences in brain activation during the disgust generalization phase between individuals with high and low contamination concerns when exposed to various stimuli, independent samples t-tests were conducted for each GS. The results indicated that the high contamination concern group exhibited significantly higher engagement of the right putamen and left anterior insula (FWE-corrected, p < 0.05) under the GS4 condition, which is the most similar to CS+. The activated brain regions are detailed in Table 2.

Table 2.

Brain regions by Independent sample t-test under GS4 conditions

ROI results

A significant main effect of stimulus type was observed in the left insula [F (5,275) = 13.80, p < 0.001, ηp2 = 0.201]. Post hoc analyses revealed that both groups exhibited the highest level of activation in this region when exposed to CS+ (M ± SD = 0.53 ± 0.05). Additionally, the interaction between the group and stimulus type was significant [F (5,275) = 2.70, p = 0.021, ηp2 = 0.047]. Simple effects analyses indicated that the left insula activation was significantly greater in the high-contamination concern group than in the low-contamination concern group when exposed to GS4 (p = 0.018). There is no significant difference between groups.

A significant main effect of stimulus type was observed in the left putamen region [F (4.26,233.92) = 3.19, p = 0.012, ηp2 = 0.055]. Simple effects analysis revealed that both groups exhibited the highest level of activation in this region when exposed to CS+ (M ± SD = 0.32 ± 0.037). The main effect of the group was also significant [F (1,55) = 7.78, p = 0.007, ηp2 = 0.124], with post hoc analyses indicating that the high contamination concern group (M ± SD = 0.34 ± 0.04) had significantly greater activation levels in the left putamen compared to the low contamination concern group (M ± SD = 0.17 ± 0.042). Furthermore, a significant group × stimulus type interaction was found [F (4.25,233.92) = 3.07, p = 0.015, ηp2 = 0.053]. Simple effects analyses showed significantly greater activation in the left putamen in the high contamination concern group compared to the low contamination concern group when exposed to GS4 (p < 0.001) and GS3 (p = 0.023) and CS+(p = 0.021).

In addition, significant differences were observed in the activation levels of the right putamen between the two groups [F (1,55) = 5.77, p = 0.02, ηp2 = 0.095]. Post hoc analyses indicate that the high contamination concern group (M ± SD = 0.31 ± 0.054) had significantly greater activation levels in the left putamen compared to the low contamination concern group (M ± SD = 0.15 ± 0.051), see Figure 4.

Figure 4.

Activation of the left insula and Bilateral putamen. (a) Activation of left insula between two groups. (b) Activation of left putamen between two groups. (c) Activation of right putamen between two groups. Error bars reflect ± Standard deviation (SD). p < 0.05, p < 0.01, p < 0.001.

Correlation between behavioral and brain responses

Based on the above results, we conducted a correlation analysis on the entire sample to investigate the correlation between brain activation, scale scores and behavioral data (Xu et al., Reference Xu, Dai, Liu, Chen, Xin, Zhou and Becker2020; Xu et al., Reference Xu, Dai, Chen, Liu, Xin, Zhou and Becker2021). The results showed that the activation of left insula and putamen were positively correlated with the scale scores of the subjects and also positively correlated with the expectancy rating of CS+ during the generalization stage (see more details in supplementary material).

Discussion

The present study investigated the cognitive neural mechanisms underlying disgust generalization in individuals with high and low compulsive contamination concerns. Results from the acquisition phase indicated that both groups successfully learned disgusting associations, with no significant between-group differences in US expectancy, consistent with previous findings (Armstrong & Olatunji, Reference Armstrong and Olatunji2017). However, disgusting scores were significantly higher in the high-contamination concern group compared to the low-contamination concern group. During the generalization phase, behavioral data revealed that individuals with high contamination concern exhibited higher anticipatory ratings for disgusting stimuli, particularly for CS+ and GS4. Furthermore, fMRI data indicated that individuals with high contamination concern showed significantly greater activation in the left insula when exposed to GS4, as well as increased activation in the putamen. These activation reactions are correlated with scale scores and behavioral data.

During the acquisition phase, no significant difference was observed between the two groups in terms of anticipation scores. However, a notable difference emerged in disgust scores, with the high contamination concern group exhibiting significantly greater disgust compared to the low contamination concern group. This suggests that individuals with high contamination concern exhibit a stronger response to negative stimuli, aligning with their heightened sensitivity to such stimuli, as documented in prior studies (Olatunji et al., Reference Olatunji, Ebesutani, David, Fan and McGrath2011; Olatunji et al., Reference Olatunji, Ebesutani, Kim, Riemann and Jacobi2017). In the generalization phase, the high contamination concern group reported higher expectancy ratings for the CS+ and GS4. This finding supports the notion that one of the core cognitive dysfunctions in OCD is an exaggerated perception of threat (Olatunji et al., Reference Olatunji, Williams, Tolin, Abramowitz, Sawchuk, Lohr and Elwood2007). The tendency of individuals with OCD to overreact to stimuli may be attributed to the generalization of responses from threat cues to neutral or safe stimuli. The findings demonstrate heightened reactivity in individuals with high compulsive contamination concerns during the generalization phase, providing further evidence for this theory.

One potential explanation for this phenomenon is that individuals with high contamination concerns are more sensitive to stimuli that resemble disgusting cues, leading them to respond similarly to the generalized stimuli in a proactive and self-protective manner (Struyf, Zaman, Vervliet, & Van Diest, Reference Struyf, Zaman, Vervliet and Van Diest2015). Alternatively, the similarity of GS4 to the CS+ may heighten attention and vigilance in individuals with high contamination concerns, resulting in elevated appraisal and awareness. The two-phase model suggests that disgusting stimuli first capture attention, but are then increasingly avoided, potentially leading to heightened sensitivity to stimuli similar to the CS+ (Fink-Lamotte, Svensson, Schmitz, & Exner, Reference Fink-Lamotte, Svensson, Schmitz and Exner2022; Knowles, Cox, Armstrong, & Olatunji, Reference Knowles, Cox, Armstrong and Olatunji2019).

Regarding brain activation, the ROI analysis revealed that individuals with high-contamination concerns exhibited increased activation in the left insula during the generalization phase when presented with GS4. The insula is a critical brain region for disgust learning, playing a central role in interoception and the processing of disgust (Berlin et al., Reference Berlin, Schulz, Zhang, Turetzky, Rosenthal and Goodman2015; Song et al., Reference Song, Jung, Jang, Kim, Shim, Park and Kwon2011). Additionally, studies have shown that serotonin (5-HT) within the insula is involved in regulating anticipatory disgust, a critical component of disgust learning (Limebeer, Rock, Sharkey, & Parker, Reference Limebeer, Rock, Sharkey and Parker2018; Tuerke, Limebeer, Fletcher, & Parker, Reference Tuerke, Limebeer, Fletcher and Parker2012).

Furthermore, research has indicated that the insula exhibits greater activation intensity during disgust learning compared to fear learning, highlighting its distinct role in conditioned learning processes (Klucken et al., Reference Klucken, Schweckendiek, Koppe, Merz, Kagerer, Walter and Stark2012; Klucken et al., Reference Klucken, Schweckendiek, Merz, Vaitl and Stark2013). The findings of this study extend this understanding by demonstrating the insula’s role in disgust generalization, particularly in individuals with high contamination concern, thereby providing insights into the neural mechanisms underlying this phenomenon.

In addition to the insula, ROI analyses revealed that the bilateral putamen exhibited higher levels of activation in individuals with high contamination concerns. Numerous studies have demonstrated that viewing disgusting images increases neural activity in the basal ganglia, particularly the caudate nucleus and putamen (Gan et al., Reference Gan, Zhou, Li, Jiao, Jiang, Biswal and Becker2022; Gan et al., Reference Gan, Zhou, Xu, Liu, Zhang, Zheng and Becker2024). This activation also intensifies when subjects recall or re-experience the disgusting event, suggesting that the basal ganglia, including the putamen, play a crucial role in the processing of disgusting experiences (Fitzgerald et al., Reference Fitzgerald, Posse, Moore, Tancer, Nathan and Phan2004), and the circuitry connecting the basal ganglia nodes with prefrontal regions are critically involved in inhibitory control (Zhuang et al., Reference Zhuang, Xu, Zhou, Yao, Zheng, Zhou and Becker2021; Zhuang et al., Reference Zhuang, Qiao, Xu, Yao, Chen, Zheng and Becker2023). Recent research has further indicated that enhanced activation of the putamen is associated with an individual’s ability to recognize disgust-inducing faces, highlighting the importance of putamen in processing disgust-related emotions (Labuschagne et al., Reference Labuschagne, Poudel, Kordsachia, Wu, Thomson, Georgiou-Karistianis and Stout2018). These findings align with this study’s observation that the high-contamination concern group exhibited a stronger response to disgust stimuli and activation in the putamen. Consequently, these results together emphasize the importance of the putamen and insula disgust processing in general as well as in pathological alterations of disgust.

Our findings suggest that individuals with high contamination concerns exhibit greater disgust generalization and heightened neural activity in regions such as the insula and putamen. This heightened excessive generalization may contribute to the difficulty of treating disgust in therapeutic settings. According to the “law of contagion,” when an object comes into contact with something perceived as contaminated, it is believed to retain that contamination indefinitely. For example, if a clean spoon falls onto a floor considered dirty, it is seen as permanently contaminated, regardless of any cleaning attempts afterwards (Rozin, Millman, & Nemeroff, Reference Rozin, Millman and Nemeroff1986). Since individuals with OCD tend to excessively generalize disgust to GS, incorporating these GS alongside the original CS in exposure therapy may enhance therapeutic outcomes. Gradually introducing both the CS and similar GS in controlled exposure sessions can help patients reduce their overgeneralized disgust responses and better manage their reactions to a wider range of stimuli, potentially improving the effectiveness of treatment for contamination-related fears.

However, this study still has some limitations. On the one hand, besides brain activation data, this study did not measure other physiological indicators such as heart rate and sympathetic skin response. Future research can explore multiple physiological indicators in combination. On the other hand, the participants in this study were drawn from a nonclinical population; therefore, further research is required to determine whether these findings can be generalized to clinical populations.

In summary, this study investigated the characteristics and neural mechanisms of disgust generalization in individuals with high and low contamination concerns. We found that individuals with high contamination concerns exhibited greater generalization and increased activation in the insula and putamen in response to GS4, which closely resembles CS+. These results enhance our understanding of the mechanisms underlying disgust generalization in individuals with contamination concern and provide a foundation for further exploration of obsessive–compulsive symptoms.

Supplementary material

The supplementary material for this article can be found at http://doi.org/10.1017/S0033291725000728.

Data availability statement

All data are available upon reasonable request.

Acknowledgements

This work was supported by STI 2030—Major Projects [2022ZD021090], National Natural Science Foundation of China [NSFC32271142] and Ministry of Education Key Projects of Philosophy and Social Sciences Research [grant number 21JZD063].

Author contribution

Juntong Liu: Formal analysis; software; visualization; writing – original draft. Jinxia Wang: Conceived and designed the experiments; writing – review and editing. Yuchen Song: Conceived and designed the experiments; performed the experiments. Benjamin Becker: Conceptualization; writing – review and editing. Xianchao Ming: Formal analysis. Yi Lei: Conceptualization; funding acquisition; supervision. Juntong Liu and Jinxia Wang are Co-first Authors.

Competing interests

The authors declare none.

Ethics statement

The research was approved by the Medicine Ethics Committee of Sichuan Normal University.

References

Adam, Y., Meinlschmidt, G., Gloster, A. T., & Lieb, R. (2012). Obsessive-compulsive disorder in the community: 12-month prevalence, comorbidity and impairment. Social Psychiatry and Psychiatric Epidemiology, 47(3), 339–349. https://doi.org/10.1007/s00127-010-0337-5CrossRefGoogle ScholarPubMed
Ahrens, L. M., Pauli, P., Reif, A., Mühlberger, A., Langs, G., Aalderink, T., & Wieser, M. J. (2016). Fear conditioning and stimulus generalization in patients with social anxiety disorder. Journal of Anxiety Disorders, 44, 36–46. https://doi.org/10.1016/j.janxdis.2016.10.003CrossRefGoogle ScholarPubMed
Armstrong, T., McClenahan, L., Kittle, J., & Olatunji, B. O. (2014). Don’t look now! Oculomotor avoidance as a conditioned disgust response. Emotion, 14(1), 95–104. https://doi.org/10.1037/a0034558CrossRefGoogle ScholarPubMed
Armstrong, T., & Olatunji, B. O. (2017). Pavlovian disgust conditioning as a model for contamination-based OCD: Evidence from an analogue study. Behaviour Research and Therapy, 93, 78–87. https://doi.org/10.1016/j.brat.2017.03.009CrossRefGoogle Scholar
Berlin, H. A., Schulz, K. P., Zhang, S., Turetzky, R., Rosenthal, D., & Goodman, W. (2015). Neural correlates of emotional response inhibition in obsessive-compulsive disorder: A preliminary study. Psychiatry Research, 234(2), 259–264. https://doi.org/10.1016/j.pscychresns.2015.09.019CrossRefGoogle ScholarPubMed
Bhikram, T., Abi-Jaoude, E., & Sandor, P. (2017). OCD: Obsessive-compulsive … disgust? The role of disgust in obsessive-compulsive disorder. Journal of Psychiatry & Neuroscience, 42(5), 300–306. https://doi.org/10.1503/jpn.160079CrossRefGoogle ScholarPubMed
Brett, M., Johnsrude, I. S., & Owen, A. M. (2002). The problem of functional localization in the human brain. Nat Rev Neurosci, 3(3), 243–9. https://doi.org/10.1038/nrn756CrossRefGoogle ScholarPubMed
Broderick, J., Grisham, J. R., & Weidemann, G. (2013). Disgust and fear responding in contamination-based obsessive-compulsive disorder during pictorial exposure. Behavior Therapy, 44(1), 27–38. https://doi.org/10.1016/j.beth.2012.05.005CrossRefGoogle ScholarPubMed
Burns, G. L., Keortge, S. G., Formea, G. M., & Sternberger, L. G. (1996). Revision of the Padua inventory of obsessive compulsive disorder symptoms: Distinctions between worry, obsessions, and compulsions. Behaviour Research and Therapy, 34(2), 163–173. https://doi.org/10.1016/0005-7967(95)00035-6CrossRefGoogle ScholarPubMed
Cisler, J. M., Brady, R. E., Olatunji, B. O., & Lohr, J. M. (2010). Disgust and obsessive beliefs in contamination-related OCD. Cognitive Therapy and Research, 34(5), 439–448. https://doi.org/10.1007/s10608-009-9253-yCrossRefGoogle ScholarPubMed
Diagnostic and statistical manual of mental disorders (1994). (4th ed.). American Psychiatric Publishing, Inc.Google Scholar
Fink-Lamotte, J., Svensson, F., Schmitz, J., & Exner, C. (2022). Are you looking or looking away? Visual exploration and avoidance of disgust- and fear-stimuli: An eye-tracking study. Emotion, 22(8), 1909–1918. https://doi.org/10.1037/emo0000993CrossRefGoogle ScholarPubMed
Fitzgerald, D. A., Posse, S., Moore, G. J., Tancer, M. E., Nathan, P. J., & Phan, K. L. (2004). Neural correlates of internally-generated disgust via autobiographical recall: A functional magnetic resonance imaging investigation. Neuroscience Letters, 370(2–3), 91–96. https://doi.org/10.1016/j.neulet.2004.08.007CrossRefGoogle ScholarPubMed
Foa, E. B., Huppert, J. D., Leiberg, S., Langner, R., Kichic, R., Hajcak, G. et al. (2002). Obsessive-Compulsive Inventory: Developmentof a short version. Psychological Assessment, 14, 485–496.CrossRefGoogle Scholar
Gan, X., Zhou, F., Xu, T., Liu, X., Zhang, R., Zheng, Z., … Becker, B. (2024). A neurofunctional signature of subjective disgust generalizes to oral distaste and socio-moral contexts. Nature Human Behaviour, 8(7), 1383–1402. https://doi.org/10.1038/s41562-024-01868-xCrossRefGoogle ScholarPubMed
Gan, X., Zhou, X., Li, J., Jiao, G., Jiang, X., Biswal, B., … Becker, B. (2022). Common and distinct neurofunctional representations of core and social disgust in the brain: Coordinate-based and network meta-analyses. Neuroscience & Biobehavioral Reviews, 135, 104553. https://doi.org/10.1016/j.neubiorev.2022.104553CrossRefGoogle ScholarPubMed
Gurak Thayer, K., DeRiso, M., Warren, J., Wilson, A., & Vance, S. (2021). What therapists need to know about the treatment of OCD when disgust is part of the clinical presentation. Journal of Affective Disorders Reports, 6, 100209. https://doi.org/10.1016/j.jadr.2021.100209CrossRefGoogle Scholar
Herry, C., & Johansen, J. P. (2014). Encoding of fear learning and memory in distributed neuronal circuits. Nature Neuroscience, 17(12), 1644–1654. https://doi.org/10.1038/nn.3869CrossRefGoogle ScholarPubMed
Kaczkurkin, A. N., & Lissek, S. (2013). Generalization of conditioned fear and obsessive-compulsive traits. Journal of Psychology & Psychotherapy, 7, 3.Google ScholarPubMed
Kang, H. (2015). Sample size determination for repeated measures design using G Power software. Anesthesia and Pain Medicine, 10, 6–15. https://doi.org/10.17085/apm.10.1.6CrossRefGoogle Scholar
Klucken, T., Schweckendiek, J., Koppe, G., Merz, C. J., Kagerer, S., Walter, B., … Stark, R. (2012). Neural correlates of disgust- and fear-conditioned responses. Neuroscience, 201, 209–218. https://doi.org/10.1016/j.neuroscience.2011.11.007CrossRefGoogle ScholarPubMed
Klucken, T., Schweckendiek, J., Merz, C. J., Vaitl, D., & Stark, R. (2013). Dissociation of neuronal, electrodermal, and evaluative responses in disgust extinction. Behavioral Neuroscience, 127(3), 380–386. https://doi.org/10.1037/a0032331CrossRefGoogle ScholarPubMed
Knowles, K. A., Cox, R. C., Armstrong, T., & Olatunji, B. O. (2019). Cognitive mechanisms of disgust in the development and maintenance of psychopathology: A qualitative review and synthesis. Clinical Psychology Review, 69, 30–50. https://doi.org/10.1016/j.cpr.2018.06.002CrossRefGoogle ScholarPubMed
Kou, J., Zhang, Y., Zhou, F., Gao, Z., Yao, S., Zhao, W., … Becker, B. (2022). Anxiolytic effects of chronic intranasal oxytocin on neural responses to threat are dose-frequency dependent. Psychotherapy and Psychosomatics, 91(4), 253–264. https://doi.org/10.1159/000521348CrossRefGoogle ScholarPubMed
Labuschagne, I., Poudel, G., Kordsachia, C., Wu, Q., Thomson, H., Georgiou-Karistianis, N., & Stout, J. C. (2018). Oxytocin selectively modulates brain processing of disgust in Huntington’s disease gene carriers. Progress in Neuro-Psychopharmacology & Biological Psychiatry, 81, 11–16. https://doi.org/10.1016/j.pnpbp.2017.09.023CrossRefGoogle ScholarPubMed
Li, Z., Li, Y., Li, X., Zou, F., Wang, Y., Wu, X., … Zhang, M. (2021). The spontaneous brain activity of disgust: Perspective from resting state fMRI and resting state EEG. Behavioural Brain Research, 403, 113135. https://doi.org/10.1016/j.bbr.2021.113135CrossRefGoogle ScholarPubMed
Limebeer, C. L., Rock, E. M., Sharkey, K. A., & Parker, L. A. (2018). Nausea-induced 5-HT release in the interoceptive insular cortex and regulation by monoacylglycerol lipase (MAGL) Inhibition and Cannabidiol. eNeuro, 5(4), ENEURO.0256-18.2018. https://doi.org/10.1523/ENEURO.0256-18.2018CrossRefGoogle ScholarPubMed
Lissek, S., Biggs, A. L., Rabin, S. J., Cornwell, B. R., Alvarez, R. P., Pine, D. S., & Grillon, C. (2008). Generalization of conditioned fear-potentiated startle in humans: Experimental validation and clinical relevance. Behaviour Research and Therapy, 46(5), 678–687. https://doi.org/10.1016/j.brat.2008.02.005CrossRefGoogle ScholarPubMed
Lissek, S., Bradford, D. E., Alvarez, R. P., Burton, P., Espensen-Sturges, T., Reynolds, R. C., & Grillon, C. (2014). Neural substrates of classically conditioned fear-generalization in humans: A parametric fMRI study. Social Cognitive and Affective Neuroscience, 9(8), 1134–1142. https://doi.org/10.1093/scan/nst096CrossRefGoogle ScholarPubMed
Ludvik, D., Boschen, M. J., & Neumann, D. L. (2015). Effective behavioural strategies for reducing disgust in contamination-related OCD: A review. Clinical Psychology Review, 42, 116–129. https://doi.org/10.1016/j.cpr.2015.07.001CrossRefGoogle ScholarPubMed
Maldjian, J. A., Laurienti, P. J., Kraft, R. A., & Burdette, J. H. (2003). An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets. Neuroimage, 19(3), 1233–1239. https://doi.org/10.1016/s1053-8119(03)00169-1CrossRefGoogle ScholarPubMed
Markarian, Y., Larson, M. J., Aldea, M. A., Baldwin, S. A., Good, D., Berkeljon, A., … McKay, D. (2010). Multiple pathways to functional impairment in obsessive-compulsive disorder. Clinical Psychology Review, 30(1), 78–88. https://doi.org/10.1016/j.cpr.2009.09.005CrossRefGoogle ScholarPubMed
Mason, E. C., & Richardson, R. (2010). Looking beyond fear: The extinction of other emotions implicated in anxiety disorders. Journal of Anxiety Disorders, 24(1), 63–70. https://doi.org/10.1016/j.janxdis.2009.08.007CrossRefGoogle ScholarPubMed
Matsunaga, H., Maebayashi, K., Hayashida, K., Okino, K., Matsui, T., Iketani, T., … Stein, D. J. (2008). Symptom structure in Japanese patients with obsessive-compulsive disorder. American Journal of Psychiatry, 165(2), 251–253. https://doi.org/10.1176/appi.ajp.2007.07020340CrossRefGoogle ScholarPubMed
Murphy, D. L., Timpano, K. R., Wheaton, M. G., Greenberg, B. D., & Miguel, E. C. (2010). Obsessive-compulsive disorder and its related disorders: A reappraisal of obsessive-compulsive spectrum concepts. Dialogues in Clinical Neuroscience, 12(2), 131–148. https://doi.org/10.31887/DCNS.2010.12.2/dmurphyCrossRefGoogle ScholarPubMed
Olatunji, B. O., Ebesutani, C., David, B., Fan, Q., & McGrath, P. B. (2011). Disgust proneness and obsessive-compulsive symptoms in a clinical sample: Structural differentiation from negative affect. Journal of Anxiety Disorders, 25(7), 932–938. https://doi.org/10.1016/j.janxdis.2011.05.006CrossRefGoogle Scholar
Olatunji, B. O., Ebesutani, C., Kim, J., Riemann, B. C., & Jacobi, D. M. (2017). Disgust proneness predicts obsessive-compulsive disorder symptom severity in a clinical sample of youth: Distinctions from negative affect. Journal of Affective Disorders, 213, 118–125. https://doi.org/10.1016/j.jad.2017.02.017CrossRefGoogle Scholar
Olatunji, B. O., & Kim, J. (2024). Examining reciprocal relations between disgust proneness and OCD symptoms: A four-wave longitudinal study. Journal of Behavior Therapy and Experimental Psychiatry, 82, 101907. https://doi.org/10.1016/j.jbtep.2023.101907CrossRefGoogle ScholarPubMed
Olatunji, B. O., Williams, N. L., Tolin, D. F., Abramowitz, J. S., Sawchuk, C. N., Lohr, J. M., & Elwood, L. S. (2007). The disgust scale: Item analysis, factor structure, and suggestions for refinement. Psychological Assessment, 19(3), 281–297. https://doi.org/10.1037/1040-3590.19.3.281CrossRefGoogle ScholarPubMed
Rachman, S. (2004). Fear of contamination. Behaviour Research and Therapy, 42(11), 1227–1255. https://doi.org/10.1016/j.brat.2003.10.009CrossRefGoogle ScholarPubMed
Rickelt, J., de Wit, S., Werf, Y., Schruers, K., Marcelis, M., Vries, F., & Heuvel, O. (2019). Emotional processing and disgust sensitivity in OCD patients with and without contamination-type obsessive-compulsive symptoms – An fMRI study. Journal of Obsessive-Compulsive and Related Disorders, 22, 100443. https://doi.org/10.1016/j.jocrd.2019.100443CrossRefGoogle Scholar
Rouhani, N., Wimmer, G. E., Schneier, F. R., Fyer, A. J., Shohamy, D., Simpson, H. B. (2019). Impaired generalization of reward but not loss in obsessive-compulsive disorder. Depress Anxiety, 36(2), 121–129. https://doi.org/10.1002/da.22857CrossRefGoogle Scholar
Rozin, P., Millman, L., & Nemeroff, C. (1986). Operation of the laws of sympathetic magic in disgust and other domains. Journal of Personality and Social Psychology, 50, 703–712. https://doi.org/10.1037/0022-3514.50.4.703CrossRefGoogle Scholar
Ruscio, A. M., Stein, D. J., Chiu, W. T., & Kessler, R. C. (2010). The epidemiology of obsessive-compulsive disorder in the National Comorbidity Survey Replication. Molecular Psychiatry, 15(1), 53–63. https://doi.org/10.1038/mp.2008.94CrossRefGoogle ScholarPubMed
Schienle, A., Schäfer, A., Hermann, A., Walter, B., Stark, R., & Vaitl, D. (2006). fMRI responses to pictures of mutilation and contamination. Neuroscience Letters, 393(2–3), 174–178. https://doi.org/10.1016/j.neulet.2005.09.072CrossRefGoogle ScholarPubMed
Song, A., Jung, W. H., Jang, J. H., Kim, E., Shim, G., Park, H. Y., … Kwon, J. S. (2011). Disproportionate alterations in the anterior and posterior insular cortices in obsessive-compulsive disorder. PLoS One, 6(7), e22361. https://doi.org/10.1371/journal.pone.0022361CrossRefGoogle ScholarPubMed
Starcevic, V., & Brakoulias, V. (2008). Symptom subtypes of obsessive-compulsive disorder: Are they relevant for treatment? Australian & New Zealand Journal of Psychiatry, 42(8), 651–661. https://doi.org/10.1080/00048670802203442CrossRefGoogle ScholarPubMed
Stein, D. J., Liu, Y., Shapira, N. A., & Goodman, W. K. (2001). The psychobiology of obsessive-compulsive disorder: How important is the role of disgust? Current Psychiatry Reports, 3(4), 281–287. https://doi.org/10.1007/s11920-001-0020-3CrossRefGoogle Scholar
Struyf, D., Zaman, J., Vervliet, B., & Van Diest, I. (2015). Perceptual discrimination in fear generalization: Mechanistic and clinical implications. Neuroscience & Biobehavioral Reviews, 59, 201–207. https://doi.org/10.1016/j.neubiorev.2015.11.004CrossRefGoogle ScholarPubMed
Sydeman, S. (2018). State-Trait Anxiety Inventory. In: Zeigler-Hill, V., Shackelford, T. (eds) Encyclopedia of Personality and Individual Differences. Springer, Cham. https://doi.org/10.1007/978-3-319-28099-8_950-1Google Scholar
Tuerke, K. J., Limebeer, C. L., Fletcher, P. J., & Parker, L. A. (2012). Double dissociation between regulation of conditioned disgust and taste avoidance by serotonin availability at the 5-HT(3) receptor in the posterior and anterior insular cortex. Journal of Neuroscience, 32(40), 13709–13717. https://doi.org/10.1523/jneurosci.2042-12.2012CrossRefGoogle ScholarPubMed
Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., … Joliot, M. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage, 15(1), 273–289. https://doi.org/10.1006/nimg.2001.0978CrossRefGoogle ScholarPubMed
Wang, J., Becker, B., Wang, Y., Ming, X., Lei, Y., & Wikgren, J. (2024). Conceptual-level disgust conditioning in contamination-based obsessive-compulsive disorder. Psychophysiology, e14637. https://doi.org/10.1111/psyp.14637CrossRefGoogle ScholarPubMed
Wang, J., Sun, X., Becker, B., & Lei, Y. (2022). Common and separable behavioral and neural mechanisms underlie the generalization of fear and disgust. Progress in Neuro-Psychopharmacology & Biological Psychiatry, 116, 110519. https://doi.org/10.1016/j.pnpbp.2022.110519CrossRefGoogle ScholarPubMed
Wang, Y., Becker, B., Wang, J., Wang, Y., Zhang, L., Mei, Y., … Lei, Y. (2024). Exaggerated sensitivity to threat and reduced medial prefrontal engagement during threat generalization in reactive aggressive adolescents. Neuroimage, 294, 120645. https://doi.org/10.1016/j.neuroimage.2024.120645CrossRefGoogle ScholarPubMed
Wong, A. H. K., & Beckers, T. (2021). Trait anxiety is associated with reduced typicality asymmetry in fear generalization. Behaviour Research and Therapy, 138, 103802. https://doi.org/10.1016/j.brat.2021.103802CrossRefGoogle ScholarPubMed
Xin, F., Zhou, X., Dong, D., Zhao, Z., Yang, X., Wang, Q., … Becker, B. (2020). Oxytocin Differentially modulates amygdala responses during top-down and bottom-up aversive anticipation. Advanced Science (Weinheim), 7(16), 2001077. https://doi.org/10.1002/advs.202001077Google ScholarPubMed
Xu, X., Dai, J., Chen, Y., Liu, C., Xin, F., Zhou, X., … Becker, B. (2021). Intrinsic connectivity of the prefrontal cortex and striato-limbic system respectively differentiate major depressive from generalized anxiety disorder. Neuropsychopharmacology, 46(4), 791–798. https://doi.org/10.1038/s41386-020-00868-5CrossRefGoogle ScholarPubMed
Xu, X., Dai, J., Liu, C., Chen, Y., Xin, F., Zhou, F., … Becker, B. (2020). Common and disorder-specific neurofunctional markers of dysregulated empathic reactivity in major depression and generalized anxiety disorder. Psychotherapy and Psychosomatics, 89(2), 114–116. https://doi.org/10.1159/000504180CrossRefGoogle ScholarPubMed
Zhuang, Q., Qiao, L., Xu, L., Yao, S., Chen, S., Zheng, X., … Becker, B. (2023). The right inferior frontal gyrus as pivotal node and effective regulator of the basal ganglia-thalamocortical response inhibition circuit. Psychoradiology, 3, kkad016. https://doi.org/10.1093/psyrad/kkad016CrossRefGoogle ScholarPubMed
Zhuang, Q., Xu, L., Zhou, F., Yao, S., Zheng, X., Zhou, X., … Becker, B. (2021). Segregating domain-general from emotional context-specific inhibitory control systems – ventral striatum and orbitofrontal cortex serve as emotion-cognition integration hubs. Neuroimage, 238, 118269. https://doi.org/10.1016/j.neuroimage.2021.118269CrossRefGoogle ScholarPubMed
Figure 0

Figure 1. Experimental procedure diagram of acquisition phase and generalization phase. (a) In acquisition phase, subjects were required to score the likelihood of a disgusting stimulus following different stimuli. (b) In the generalization phase only a small probability of following the aversive pictures after CS+, subjects need to rate the generalized stimuli according to the laws learned in the acquisition phase.

Figure 1

Table 1. Scores of PI-COWC, DSR, OCI-R, SAI, TAI and gender comparison between two groups

Figure 2

Figure 2. Behavioral results during the acquisition stage. (a) Acquisition phase rating scores for both groups of subjects. There were no between-group differences in the pattern of acquisition for both groups. (b) Disgust ratings to stimuli in both groups during the acquisition phase, subjects in the high compulsive tendency group had higher CS+ disgust ratings. Error bars reflect ± standard deviation (SD). ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001.

Figure 3

Figure 3. Subjects’ expectancy ratings of different types of stimuli during the generalization phase. (a) Shows the ratings of the two groups of subjects for different stimulus types. (b) Demonstrates the difference in overall scores between the two groups of subjects across trials. Error bars reflect ± Standard deviation (SD).

Figure 4

Table 2. Brain regions by Independent sample t-test under GS4 conditions

Figure 5

Figure 4. Activation of the left insula and Bilateral putamen. (a) Activation of left insula between two groups. (b) Activation of left putamen between two groups. (c) Activation of right putamen between two groups. Error bars reflect ± Standard deviation (SD). p < 0.05, p < 0.01, p < 0.001.

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