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. 2025 Oct 16;20(1):nsaf107. doi: 10.1093/scan/nsaf107

Enhanced activation in the dorsolateral prefrontal cortex and inferior parietal lobule during recovery from body dissatisfaction

Yinying Hu 1,#, Xiaojun Cheng 2,#, Yuqi Liu 3, Yuting Hou 4, Liming Yue 5, Yafeng Pan 6,✉, Xiangping Gao 7,✉
PMCID: PMC12640241  PMID: 41099370

Abstract

Previous studies have examined the neural mechanisms of body dissatisfaction. This study aimed to investigate the neural basis of recovery from body dissatisfaction. Sixty-seven young women participated in this study, engaging in a fat talk—a conversation known to induce body dissatisfaction—followed by a discussion on non-body-related topics (referred to as the post-talk). Prior to the fat talk, participants had another conversation on non-body-related topics (the pre-talk). Brain activity in the prefrontal and fronto-parietal regions was measured using fNIRS throughout the conversations. The results showed that body dissatisfaction significantly increased after the fat talk compared to the pre-talk, confirming the successful induction of body dissatisfaction. During the post-talk, body dissatisfaction significantly decreased compared to the fat talk, indicating successful recovery. This recovery activated the dorsolateral prefrontal cortex (DLPFC) and inferior parietal lobule (IPL). Furthermore, DLPFC activation positively predicted the reduction of body dissatisfaction, while IPL activation correlated with impulse control. These findings underscore the role of the fronto-parietal network in recovery from body dissatisfaction. The results suggest that individuals suppress negative cognitive and emotional responses to body-related stimuli by inhibiting impulsive reactions, providing insights into the neural mechanisms of psychological resilience.

Keywords: body dissatisfaction, self-control, fronto-parietal network, fat talk, fNIRS

Introduction

Imagine the feeling when a casual comment from a friend about your weight sparks a wave of self-doubt, unleashing a stream of negative thoughts and feelings about your body. This experience, known as body dissatisfaction, is a global public health concern. It is especially common among girls and young women and is increasingly affecting men as well (Grogan 2016). It has been reported that body dissatisfaction not only takes a toll on mental health—contributing to issues like depression (Brechan and Kvalem 2015), distress (Duchesne et al. 2017), self-injurious thoughts and behaviours in clinical (Ren et al. 2024)—but also increases the risk of serious clinical conditions, such as eating disorders (Vartanian et al. 2018; Warnick et al. 2022). As such, a growing body of research has focused on the neural mechanisms underlying body dissatisfaction (Preston and Ehrsson 2016; Wang et al. 2019a). However, the neural basis of recovery from body dissatisfaction remains poorly understood. A deeper understanding of this process may be critical for developing effective prevention and intervention strategies.

The dual-systems model of self-regulation offers a compelling theoretical framework for understanding recovery from body dissatisfaction. This model posits two interacting systems: a ‘hot’ system, characterized by rapid, emotionally driven responses to salient stimuli, and a ‘cold’ system, which supports cognitive and emotional regulation through slower, more deliberative processing (Hofmann et al. 2009). Robust empirical support for this model has been found across self-regulation domains (Corbin et al. 2015; Gunn and Finn 2015; Lazuras et al. 2019; Li et al. 2019; Zheng et al. 2023). Neuroanatomically, the cold system is thought to be anchored in the dorsolateral prefrontal cortex (DLPFC), with contributions from the inferior parietal lobule (IPL). Together, these regions form part of a fronto-parietal control network implicated in executive functions central to adaptive self-regulation (Berkman et al. 2011; Heatherton and Wagner 2011), hypothetically supporting recovery from body dissatisfaction.

Previous neuroimaging studies have shown that the DLPFC and IPL as key hubs for emotion regulation (Phillips et al. 2008; Ellard et al. 2025), cognitive control (Turner et al. 2019; Pan et al. 2023), and body representation (Wagner et al. 2003; Castellini et al. 2013; Engelen et al. 2015). Specifically, the DLPFC is involved in core cognitive control functions, including working memory maintenance, goal setting, and response inhibition (Berkman et al. 2012). The IPL, in turn, supports attentional control and multimodal integration essential for goal-directed decision-making (Krönke et al. 2021). In the context of emotion regulation, DLPFC facilitates top-down modulation of limbic responses via diverse regulatory strategies (Phillips et al. 2008), while IPL is involved in interpreting affective cues and self-referential emotional processing (Morawetz et al. 2016). Regarding body representation, DLPFC may inhibit responses to salient or emotionally charged bodily stimuli (Castellini et al. 2013), while both DLPFC and IPL likely contribute to somatosensory integration in body perception (Engelen et al. 2015). Thus, these regions are well-positioned to support recovery from body dissatisfaction by enabling dynamic regulation of affective, cognitive, and embodied experiences.

Taken together, the dual-systems model of self-regulation provides a useful framework for understanding recovery from body dissatisfaction, highlighting the interplay between affective reactivity and higher-order regulatory control. In line with this perspective, accumulating evidence points to the critical involvement of the DLPFC and IPL in emotion regulation, cognitive control, and body representation—processes that are central to recovery from body dissatisfaction (e.g. Zhao et al. 2023; Hu et al. 2024). Accordingly, we hypothesized that recovery from body dissatisfaction would be characterized by enhanced activity in the DLPFC and IPL. Furthermore, we hypothesized that activity in these regions would reliably predict the effectiveness of body dissatisfaction recovery, as their heightened activation has reliably been linked to successful self-regulation (Cieslik et al. 2015; Emmerling et al. 2017).

To test these hypotheses, we used functional near-infrared spectroscopy (fNIRS) to measure DLPFC and IPL activity in a sample of young adult women during a three-phase conversational task. Participants were asked to engage in a fat talk with an experimenter, which was preceded and followed by discussions on neutral topics (e.g. travel, dormitory decoration). We expected an initial increase and subsequent reduction in body dissatisfaction levels, accompanied by increased activation in the DLPFC and IPL during the recovery phase. Finally, based on previous research showing a central hub role of DLPFC for self-regulation (McClure and Bickel 2014), we carried out an exploratory analysis to assess functional interactions between the DLPFC and other ROIs (e.g. IPL) during recovery from body dissatisfaction.

Methods

Participants

A total of 67 female college students (M ± SD = 19.612 ± 0.904 years) were recruited through campus advertisements to participate in the study, with a mean BMI of 20.976 ± 2.893. The sample size was determined through an a priori power analysis using the pwr package in R, based on the data from our previous study on the induction of body dissatisfaction (Hu et al. 2025). According to the obtained effect size (d = 0.35), setting the threshold to 80% sensitivity (power = 0.80) and a significant level of alpha = 0.05 yielded a required sample size of 65. To account for potential participant attrition or unusable data, we recruited slightly more participants than the minimum required. Participants’ height (in meters) and weight (in kilograms) were recorded prior to the experiment, and body mass index (BMI) was calculated using the standard formula: BMI = weight (kg)/[height (m)]2. All participants were right-handed, had normal vision or corrected-to-normal vision, and had no history of neurological or psychiatric conditions. Informed consent was obtained from all participants, who were compensated with 35 RMB upon completing the experiment. This study received ethical approval from the Human Participants Ethics Committee of Shanghai Normal University (2022-069).

Measurements

To assess participants’ trait-level self-control, they completed the Self-Control Scale prior to the experiment. The scale, originally developed by Tangney, Baumeister, and Boone (2004) and later adapted into Chinese by Tan and Guo (2008), consists of 19 items. This revised version evaluates five key dimensions: impulse control, healthy habits, resistance to temptation, work focus, and moderation of leisure activities. The scale has demonstrated strong reliability and validity, with a Cronbach’s α coefficient of 0.860. Participants respond using a 5-point Likert scale, where higher scores indicate greater self-control. The scale has demonstrated strong psychometric properties, with a Cronbach’s α of 0.860 in previous research. In this study, internal consistency was similarly good (Cronbach’s alpha = 0.866; 95% CI [0.815, 0.908]), indicating satisfactory reliability. Participants responded on a 5-point Likert scale, with higher scores reflecting greater self-control.

During the experiment, participants were asked to complete the Body Image State Scale after each conversation. This scale was used to assess participants’ body dissatisfaction (Cash et al. 2002), focusing on thoughts related to appearance, body shape, weight, attractiveness, and comparisons with one’s typical state and others. The scale consists of six items (e.g. ‘I feel my body looks attractive at the moment,’ ‘I feel my body is too large right now’) and employs a 9-point Likert scale, with lower scores indicating higher body dissatisfaction. In this study, the scale was administered on three occasions corresponding to three conversational conditions. The scale showed high internal consistency at each time point: pre-test talk, Cronbach’s α  =  0.871, 95% CI [0.871, 0.914]; fat talk, Cronbach’s α  =  0.916, 95% CI [0.879, 0.944]; and post-test talk, Cronbach’s α  =  0.934, 95% CI [0.905, 0.956]. To assess the test–retest reliability of the scale across the three administrations, we calculated the intraclass correlation coefficient (ICC), which was 0.881 (95% CI [0.829, 0.921], P < 0.001), indicating excellent temporal stability.

During the experiment, participants also completed a subset of six items from the Positive and Negative Affect Schedule (PANAS) to assess transient emotional states. The PANAS has been shown to reliably capture both general and momentary affective experiences under varying time-frame instructions (Watson et al. 1988; Egloff et al. 2003; Watson and Levin-Aspenson 2018). Drawing on previous findings that fat talk increases negative emotions and decreases happiness (Gapinski et al. 2003; Hu et al. 2025), we selected six items to represent distinct emotional dimensions: happiness, sadness, fear, disgust, anger, and anxiety. Participants rated each emotion using a percentage scale, with higher scores indicating greater emotional intensity. Questionnaire order was randomized to mitigate order effects. Given that individual emotions may fluctuate independently over time (Egloff et al. 2003, we analyzed each item separately rather than computing a composite score. The six items were administrated at three time points (pre-talk, fat talk, post-talk), and test–retest reliability was assessed using ICCs. Five items (happiness, sadness, anger, fear, and anxiety) demonstrated good to excellent temporal consistency (ICC = 0.755–0.944). The item ‘disgust’ showed moderate reliability (ICC = 0.592), acceptable for exploratory research.

Experimental paradigm

To induce body dissatisfaction, participants were asked to engage in the fat talk with a female confederate. Previous research has shown that such discussions can induce or exacerbate body dissatisfaction (Salk and Engeln-Maddox 2011; Engeln et al. 2013). Therefore, fat talk served as the experimental manipulation to activate a state of body dissatisfaction. During this conversation, the confederate posed a series of appearance-focused questions, such as: ‘Which part of your body are you most satisfied with?’, ‘Which part are you least satisfied with?’, and ‘If you could change one thing about your body, what would it be?’ These prompts were designed to elicit negative self-evaluations and encourage participants to reflect critically on their physical appearance.

To assess changes before and after the induction of body dissatisfaction, participants were also asked to engage in conversations with the confederate on topics unrelated to body image (e.g. travel or dormitory), both prior to and following the fat talk. These neutral discussions served as control conditions to isolate the effects of the fat talk. In the travel-related conversation, the confederate asked questions like, ‘Which scenic spots do you prefer? How do you plan your travel itinerary? What factors do you consider when mapping out your travel route?’ For the dormitory-related discussion, the confederate asked, ‘What style of interior design do you find most appealing? How do you organize your living space? What wallpapers would you recommend?’

Procedures

As participants entered the laboratory, they were introduced to a female confederate and took part in a two-minute warm-up to familiarize themselves with one another. Each participant and the experimenter were strangers to one another and had no prior acquaintance. Then they were then given a 3-minute resting period, during which they were instructed to remain as still as possible and relax.

Subsequently, participants commenced the formal experimental procedure, which involved engaging in three conversations with the confederate: a pre-talk, a fat talk, and a post-talk (Fig. 1). Each conversation began with the confederate asking questions, and participants were required to respond and elaborate in detail. Since research suggests that just three to five minutes of fat talk is enough to induce body dissatisfaction (Stice et al. 2003), each conversation was timed to last five minutes. Importantly, each participant discussed topics related to travel, body, and dormitory. The order of the travel and dormitory discussions was randomized to ensure a balanced sequence. After each conversation, participants were given a few minutes to complete several questionnaires, which are described in detail in Section 2.2.

Figure 1.

Figure 1.

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Experimental procedures and settings. (A) Participants were instructed to engage in conversations on three topics with a confederate. (B) During the conversations, their brain activity was recorded using fNIRS, with 40 channels (represented as grey dots).

Data acquisition

During each conversation, brain data from the prefrontal and fronto-parietal regions of the participants were recorded using the NIRSport device, manufactured by Hanxiang Company, with data collected at a sampling rate of 7.6 Hz. Two 4 × 4 probe boards, each containing 16 emitters and 16 detectors, were placed on the participant’s head. Together, the boards provided 40 measurement channels, with a 3-cm distance between the optodes (Fig. 1). The alignment of the fNIRS channels with cortical measurement points was achieved through the virtual registration method (Tsuzuki et al. 2007), which has been validated in multi-subject studies exploring anatomical brain correlations. The potential MNI coordinates for each channel were determined using the NIRSport device’s built-in software (NIRSite). Brain regions corresponding to these channels were identified using the Automated Anatomical Labeling atlas (Tzourio-Mazoyer et al. 2002).

Data analysis

Subjective data were analyzed using a one-way repeated measures ANOVA, with the three conditions being the pre-talk, fat talk, and post-talk. This analysis focused on scores for body dissatisfaction, positive and negative emotional states after each conversation. If a significant main effect was observed, post hoc comparisons were performed to assess differences between the conditions.

The raw brain data were preprocessed using the Homer3 toolbox (Huppert et al. 2009). For each channel, raw optical intensity data were converted into changes in optical density, which were then used to calculate oxygenated and deoxygenated hemoglobin concentrations via the modified Beer-Lambert law. Motion artifacts from head movements were corrected using Correlation-based Signal Improvement (Cui et al. 2010). To further reduce global and physiological noise, wavelet transform denoising techniques were applied (Duan et al. 2018). After preprocessing, all brain data were considered valid. Oxygenated hemoglobin concentrations (HbO) were selected for statistical analysis, as they are the most sensitive indicator of regional cerebral blood flow and exhibit a strong correlation with the BOLD signal in fMRI (Hoshi 2007; Cui et al. 2012).

To compare brain activity across different conditions, a general linear model (GLM) was used to compute parameter estimates, incorporating the effects of the condition, a mean, and a linear trend. The resulting estimates, represented as beta values, reflected brain activation levels across different channels for each participant during each condition (ie, the pre-talk, fat talk, and post-talk). To define the regions of interest (ROIs) covered by the fNIRS channels, all channels were projected onto the cortical surface based on their MNI coordinates from the scalp, using an automatic anatomical labeling method (Tzourio-Mazoyer et al. 2002). Channels sharing the same fNIRS source were averaged, resulting in 16 ROIs centered around the source locations: (1) Orbitofrontal Cortex (OFC), with left (Channels 1, 2) and right (Channels 21, 22) sides; (2) Superior Frontal Cortex (SFC), with left (Channels 3, 4) and right (Channels 23, 24) sides; (3) Middle Frontal Cortex (MFC), with left (Channels 5, 8, 9, 10) and right (Channels 25, 28, 29, 30) sides; (4) Inferior Frontal Cortex (IFC), with left (Channels 6, 7, 11) and right (Channels 26, 27, 31) sides; (5) DLPFC, with left (Channels 12, 13) and right (Channels 32, 33) sides; (6) IPL, with left (Channels 14, 16) and right (Channels 34, 36) sides; (7) Superior Parietal Lobule (SPL), with left (Channels 15, 19) and right (Channels 35, 39) sides; (8) Angular Gyrus, with left (Channels 17, 18, 20) and right (Chan nels 37, 38, 40) sides. Channels within each ROI were averaged prior to further analysis. A one-way repeated measures ANOVA was then conducted on the beta values of each ROI. To account for the simultaneous data collection from multiple spatially adjacent channels (ie, 16 ROI), a false discovery rate (FDR) correction (P < 0.050) was applied. ROIs that remained significant after FDR correction underwent post hoc analysis. Additionally, to examine whether brain activation could predict subjective reports, Pearson correlation was used to assess the relationship between brain activation and subjective ratings.

To further validate the reliability of the results, the Behavioral Partial Least Squares (PLS) analysis was conducted to examine the significant relationships between brain activity and subjective report across different conditions. PLS, a multivariate, data-driven method, is commonly used in neuroimaging research to explore brain-behavior associations by relating two sets of data (Krishnan et al. 2011). This approach allows for the identification of patterns in the covariance between brain signals across channels, in addition to assessing their mean effects. One key advantage of PLS is that it does not require multiple comparison corrections, as it involves only a single test to evaluate the statistical significance of the multivariate pattern against a null distribution. Additionally, PLS is data-driven, minimizing the influence of researcher bias, making it particularly well-suited for examining the reliability of the data.

The brain data consisted of the beta values extracted from the GLM for each condition and each participant, while the behavioral data consisted of body dissatisfaction scores for each condition and each participant. The PLS analysis begins by constructing a covariance matrix between the subjective variable (ie, body dissatisfaction scores) and the brain signal (ie, beta values for each ROI). This matrix is then subjected to singular value decomposition, which generates orthogonal latent variables (LVs) that explain the maximal covariance between the brain and behavioral data. Typically, the number of LVs is equal to the number of experimental conditions or behavioral variables. Each LV contains three components: (1) a diagonal matrix of singular values representing the variance explained by each LV, (2) the singular vector of brain saliences, which indicates which brain variables best capture the relationship between brain and behavior, and (3) the singular vector of behavioral saliences, which represents the behavioral variables most strongly associated with the brain activity. To assess statistical significance, 10,000 permutation tests were performed for each LV, and 10,000 bootstrapped samples were generated to calculate the 95% confidence intervals for the mean correlation between brain activity and subjective report for each condition and channel. The reliability of the brain-behavior relationship was evaluated using bootstrap ratios, which measure the consistency of the contribution of each channel to the LV. Channels with bootstrap ratios greater than +2 or less than −2 were considered statistically significant, as these ratios can be interpreted as z-scores (Meidenbauer et al. 2021; Li et al. 2024).

To complement the brain activation analysis, we examined functional connectivity among four task-relevant ROIs (that showed significant brain activation): the left DLPFC, left IPL, right DLPFC, and right IFC. Functional connectivity was assessed by computing Pearson correlations between the HbO signals of each ROI pair. These four ROIs yielded six unique connection pairs. For each pair, a one-way repeated-measures ANOVA was conducted across experimental conditions to evaluate connectivity differences. To control for multiple comparisons, p-values were adjusted using FDR correction method.

To further explore the directional flow of neural information between the ROIs (e.g. DLPFC → IPL and IPL → DLPFC), we performed Granger causality analysis (GCA) using the Multivariate Granger Causality Toolbox in MATLAB (Barnett and Seth 2014). GCA estimates the extent to which one time series can predict another, beyond what can be predicted by its own past values, quantified as a log-likelihood ratio. GCA was applied to preprocessed HbO time series obtained from each participant dyad during the conversation phase. All time series were normalized to have zero mean prior to model fitting. The optimal model order was determined using the Akaike Information Criterion, with the maximum order set to 20. Multivariate vector autoregressive (VAR) models were estimated using locally weighted regression, and the full autocovariance sequence was computed from the VAR parameters. For each ROI pair, bidirectional Granger causality values were extracted and entered into a two-way repeated-measures ANOVA with condition and direction as within-subject factors.

Results

Validation of the induction and disengagement of fat talk

The ANOVA revealed a significant main effect of Condition on body dissatisfaction [F(2,132) = 7.743, P < 0.001, partial η2 = 0.105]. Further comparisons revealed that body dissatisfaction increased following the fat talk compared to the pre-talk, and decreased after the post-talk (33.224 ± 0.947 vs. 31.731 ± 1.008 vs. 33.552 ± 1.080; ts > 2.989, Ps < 0.004, Cohen’s ds > 0.365; Fig. 2). There was no significant difference between the pre- and post-talk scores (t = 0.673, p = 0.503). These results suggest that fat talk effectively induced body dissatisfaction in participants, but this effect gradually waned over time, reflecting the self-recovery from fat talk.

Figure 2.

Figure 2.

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Subjective Results. After each conversation condition, participants reported body dissatisfaction and associated levels of happiness and anxiety. Participants experienced decreased body satisfaction and happiness during the fat talk compared to the pre-talk, but these experiences lessened in the post-talk. Participants also reported increased anxiety from the pre-talk to the fat talk, but decreased in the post-talk. The error bars indicated standard errors. *P < 0.05, **P < 0.01, ***P < 0.001.

The ANOVA also revealed a significant main effect of Condition on happiness, sadness, and anxiety (Fs > 3.861, Ps < 0.024, partial η2 > 0.055). Further comparisons indicated that participants’ happiness significantly decreased after the fat talk compared to the pre-talk, and then significantly increased following the post-talk (70.522 ± 2.020 vs. 62.970 ± 2.943 vs. 71.209 ± 2.443; ts > 4.080, Ps < 0.001, Cohen’s ds > 0.498; Fig. 2). No significant differences on happiness was found between the pre- and post-talks (t = 0.387, p = 0.700). Anxiety exhibited a similar trend, with levels rising during the fat talk and falling afterward (20.119 ± 21.864 vs. 23.582 ± 24.490 vs. 18.955 ± 23.586; ts > 2.244, Ps < 0.028, Cohen’s ds > 0.273; Fig. 2). Sadness significantly decreased after the post-talk compared to the pre-talk and fat talk (6.403 ± 10.596 vs. 6.746 ± 9.758 vs. 4.388 ± 7.215; ts > 2.611, Ps < 0.022, Cohen’s ds > 0.319). There was no significant main effect of Condition was found for disgust, anger, or fear (Fs < 2.850, Ps > 0.061). These findings indicate that for healthy participants, the experience of happiness and anxiety changes significantly with the content of the conversation, and is particularly responsive to the recovery of body dissatisfaction.

Enhanced brain activity during post-talk

A one-way ANOVA revealed a significant main effect of Condition across several ROIs, including the right IFC, bilateral DLPFC, and left IPL (Fs > 5.194, corrected Ps < 0.050, partial η2 > 0.073; Fig. 3).

Figure 3.

Figure 3.

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Brain activity. Heat maps illustrating the effects of the Condition (ie, the pre-talk, fat talk, and post-talk) across all participants. Enhanced activation was observed in the left dorsolateral prefrontal cortex (DLPFC) and left inferior parietal lobule (IPL) during the post-talk compared to the fat talk. Additionally, activation in the right DLPFC and inferior frontal cortex (IFC) was greater during the pre-talk than during the fat talk. The error bars indicated standard errors. *P < 0.05, **P < 0.01, ***P < 0.001.

For the right IFC, further comparisons showed that activation during the pre-talk was significantly higher than during fat talk and post-talk (0.526 ± 0.697 vs. 0.213 ± 0.477 vs. 0.287 ± 0.516; ts > 2.434, Ps < 0.019, Cohen’s ds > 0.297). No significant difference was found between the fat talk and post-talk conditions (t = 0.998, p = 0.322).

For the bilateral DLPFC, further comparisons revealed that the activation at the left DLPFC during fat talk was significantly lower than during both pre-talk and post-talk (0.224 ± 0.367 vs. 0.066 ± 0.341 vs. 0.194 ± 0.339; ts > 3.061, Ps < 0.004, Cohen’s ds > 0.374). There was no significant difference between the pre-talk and post-talk (t = 0.708, p = 0.481). For the right DLPFC, a similar trend was observed, though the comparison between the fat talk and post-talk did not reach significance (0.380 ± 0.410 vs. 0.189 ± 0.401 vs. 0.303 ± 0.412).

For the left IPL, further comparisons indicated that the activation during post-talk was significantly higher than that during the fat talk, and the difference from the pre-talk approached significance (−0.121 ± 0.219 vs. −0.147 ± 0.281 vs. 0.004 ± 0.389; ts > 2.264, Ps < 0.027, Cohen’s ds > 0.277). No significant difference was observed between pre-talk and fat talk (t = 0.630, p = 0.531).

Brain activity predicted body dissatisfaction during Post-Talk

To investigate whether significant brain activity in areas such as the right IFC, bilateral DLPFC, and left IPL could predict body dissatisfaction, the correlation between brain activity in these regions and body dissatisfaction was calculated using Pearson’s correlation. The results revealed that activation in the left DLPFC was positively correlated with body satisfaction in the post-talk (r = 0.381, p = 0.001). Specifically, stronger activation in the left DLPFC during the recovery phase was associated with lower body dissatisfaction. This correlation was not observed during other phases (rs < 0.231, Ps > 0.050). Additionally, no significant correlation was found between body dissatisfaction scores and brain activity in the right IFC or left IPL during the pre-talk, fat talk, or post-talk phases rs < 0.206, Ps > 0.050). However, correlation analysis revealed a significant positive correlation between left IPL activation in the post-talk and impulse control scores (r = 0.278, p = 0.022), suggesting the role of the IPL in impulse control.

PLS analysis was conducted to further examine the brain activity that predicts body dissatisfaction across conditions (ie, the pre-talk, fat talk, and post-talk). Result showed that the first latent variable from the analysis was significant and explained 91% of the cross block covariance (p = 0.003; Fig. 4). LVs 2 and 3 in this analysis were not significant (all p > 0.988). For the significant LV 1, six ROIs (left DLPFC, right OFC, bilateral SFC, and bilateral IFC) showed three-condition dependent changes in the relationship between brain activity and body dissatisfaction. All of these significant ROIs had bootstrap ratios < –2, indicating the direction of the brain-behaviour relationship was the same across all eight channels. Additionally, the left DLPFC exhibited the highest bootstrap ratio (BSR = −3.655). Increased brain activity in the left DLPFC was positively associated with reduced body dissatisfaction during the post-talk, marginally associated with body dissatisfaction during the fat talk, and unrelated to body dissatisfaction in the pre-talk. In summary, these findings suggest that the metabolic demands on the DLPFC play a crucial role in recovery from body dissatisfaction.

Figure 4.

Figure 4.

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PLS results. (A) The relationship between body dissatisfaction and brain activity separately by condition. Error bars are 95% confidence intervals around the mean correlation. (B) Scatterplots showing the correlation between brain activity and body dissatisfaction at the left DLPFC with the largest BSR, separated by condition. Pearson’s correlation (r) and its associated p-value are presented.

Functional and effective connectivity during Post-Talk

Regarding functional connectivity, the ANOVA revealed no significant main effect of Condition across any of the six ROI pairs after FDR correction (Fs < 4.992, corrected Ps > 0.050).

For effective connectivity, the ANOVA revealed no significant main effects (ie, Condition and Direction) or interaction (ie, Condition × Direction) for the connection between the left DLPFC and IPL (Fs < 0.001, Ps > 0.266). For the other five ROI pairs, a significant main effect of Direction was observed (Fs > 29.405, Ps < 0.001, partial η2s > 0.308), indicating asymmetry in information flow between these regions. The dominant information flow included left DLPFC → right IFC, left DLPFC → right DLPFC, left IPL → right IFC, left IPL → right DLPFC, and right DLPFC → right IFC (as compared to their reverse directions). However, neither the main effect of Condition nor the Condition × Direction interaction reached significance (Fs < 2.134, Ps > 0.123).

Discussion

This study aimed to investigate the neural mechanisms underlying recovery from body dissatisfaction using fNIRS. Body dissatisfaction significantly increased following fat talk compared to pre-talk levels, confirming the successful induction of dissatisfaction. However, in the post-talk, body dissatisfaction decreased to levels similar to those before the fat talk, indicating successful recovery. At the neural level, increased activity was observed in both the DLPFC and IPL during the post-talk. Furthermore, DLPFC activation was positively associated with body dissatisfaction, while IPL activation correlated with impulse control. These results emphasize the role of the fronto-parietal network in recovery from body dissatisfaction.

Our subjective results, including scores of body dissatisfaction, positive and negative emotions, align with previous research, indicating a strong connection between engaging in fat talk and increased body dissatisfaction. Studies have shown that fat talk often leads to or exacerbates body image issues, such as heightened body surveillance, dissatisfaction, body shame, eating disorder symptoms, and perceived sociocultural pressure (Arroyo and Harwood 2012; Arroyo 2014; Corning et al. 2014; Jones et al. 2014; Arroyo and Andersen 2016; Mills and Fuller-Tyszkiewicz 2016; Linder and Daniels 2018). The findings of this study further support that the public opinion about body shape in the social environment can trigger the corresponding negative psychological experience (Lin and Soby 2016). Importantly, we also found that following disengagement from fat talk, body dissatisfaction of healthy participants decreased and returned to baseline levels, reflecting a pattern of self-recovery. Moreover, both accompanying positive and anxious emotions were restored, collectively indicating the success of this recovery process. Future studies could complement self-report data with physiological and behavioral indicators of emotion, such as electrocardiogram-based heart rate variability (Michels and Hamers 2023), facial expression coding (Kohler et al. 2004), or automated sentiment analysis of speech content (Semeraro et al. 2025), to provide a more nuanced understanding of affective dynamics during body image regulation.

We observed increased activation in the left DLPFC during the post-talk, which showed a positive correlation with body dissatisfaction levels. This observation is consistent with previous research highlighting the DLPFC’s role in processing body image and eating behaviours. For example, reduced levels of body dissatisfaction have been positively correlated with activation in the left DLPFC during body image processing (Gao et al. 2016). Moreover, activation in the left DLPFC is associated with impulse control in women with a greater tendency toward restrictive eating (Dong et al. 2016) and the inhibition of food consumption (Hare et al. 2009; Aso et al. 2016). Additionally, stimulating the DLPFC has been shown to enhance inhibitory control and decision-making in individuals with eating disorders (Guillaume et al. 2018), as well as to reduce core symptoms of anorexia nervosa (McClelland et al. 2016) and disordered eating behaviours in bulimia patients (Kekic et al. 2017). Generally, the DLPFC is crucial for cognitive control, with improved performance being associated with heightened activation in this region (Emmerling et al. 2017). Thus, the DLPFC activation observed in this study likely reflects the suppression of negative emotions and automatic beliefs related to body shape and the desire for slimness.

We also observed increased activation in the left IPL during the post-talk, which showed a positive correlation with impulse control. This finding aligns with previous research suggesting that the IPL plays a critical role in executive control (Lin et al. 2014). For instance, enhanced performance in a spatial working memory task was observed following IPL stimulation (Yamanaka et al. 2010). A meta-analysis has consistently shown IPL activation across task-switching paradigms (Jiao et al. 2022). Furthermore, prior studies have highlighted the IPL’s involvement in inhibitory control, where reduced activation is linked to impaired inhibitory control in tasks such as Go-NoGo, Stroop (Luijten et al. 2015), and stop-signal tasks (Hu et al. 2014). However, importantly, as noted in the introduction, that IPL may also play a complementary role in the perception and integration of emotional and bodily information. For example, individuals with anorexia nervosa may exhibit biased self-body image recognition and estimations of body size, which are associated with reduced volume in the IPL and other medial parietal regions (Gaudio and Quattrocchi 2012). Future research should further investigate the role of the IPL in recovery from body dissatisfaction.

Drawing on the dual-process model, we interpret the activation of the DLPFC and IPL as reflecting the suppression of impulsive responses triggered by negative emotions, thereby facilitating recovery from body dissatisfaction. Based on this framework, we further examined functional and effective connectivity between the DLPFC and IPL; however, no significant connectivity was observed. Previous research has consistently identified the DLPFC as a central hub for self-regulation, with converging evidence linking its connectivity with prefrontal and parietal regions to goal maintenance, emotion regulation, and reduced procrastination through enhanced top-down control (Hare et al. 2009; McClure and Bickel 2014; Morawetz et al. 2016; Gao et al. 2021). The lack of significant connectivity in our findings may be due to the relatively low cognitive demand of our task, which might not have been sufficient to recruit coordinated engagement of frontoparietal networks. In line with this explanation, developmental research suggest that under less demanding conditions, cognitive control in children aresearchged 9–11 primarily relies on intra-DLPFC connectivity, while more complex reasoning in older adolescents (12–18 years) increasingly engages DLPFC–IPL interactions (Wendelken et al. 2016). Future research should further examine DLPFC–IPL connectivity during recovery from body dissatisfaction using tasks with higher cognitive or emotional complexity.

This study has several limitations. First, the current work examined the recovery from body dissatisfaction only in young women. Previous studies have shown that body dissatisfaction, influenced by social values, has been a global public health issue, prevalent among adolescents (e.g. 24–46% in U.S. girls and 12–25% in U.S. boys; Wang et al. 2019b) and adults (e.g. 11–72% in U.S. women and 8–61% in U.S. men; Fiske et al. 2014). Future research should explore the recovery from body dissatisfaction in a larger, more diverse sample that includes individuals of both genders across different age groups. Second, our ROIs were limited to the prefrontal and fronto-parietal regions, as measured by fNIRS. However, other brain regions, such as the posterior cingulate cortex and the insular cortex, have also been implicated in body dissatisfaction (Preston and Ehrsson 2016) and cognitive control (Kelley et al. 2015). It would be valuable to investigate additional brain regions in future studies using fMRI, which offers relatively higher spatial resolution than fNIRS (Boas et al. 2004). Finally, according to objectification theory, body dissatisfaction is a key psychological mechanism of self-objectification (Fredrickson and Roberts 1997). Evidence also suggests that the greater the degree of self-objectification, the higher the degree of body dissatisfaction (Teng et al. 2019). Future research could investigate whether individuals with higher levels of self-objectification have more difficulty recovering from body dissatisfaction and exhibit decreased activity in the DLPFC and IPL.

Conclusion

This study found that the recovery from body dissatisfaction in young women relies on enhanced activation of the DLPFC. This increased activation primarily serves to suppress negative cognitions and emotions associated with body dissatisfaction. The DLPFC’s inhibitory effect occurs with the IPL, which moderates impulsive responses triggered by negative body-related cognitions. These findings highlight the role of the fronto-parietal network in recovery from body dissatisfaction. The results suggest that individuals manage negative cognitive and emotional reactions to body-related stimuli by inhibiting impulsive responses, shedding light on the neural mechanisms underlying psychological resilience.

Acknowledgements

We would like to thank Jianmei Shi, Qi Zhou, Mei Fu, and Zhennan Liu for their assistance in data collection.

Contributor Information

Yinying Hu, School of Psychology, Shanghai Normal University, Shanghai, China.

Xiaojun Cheng, School of Psychology, Shenzhen University, Shenzhen, China.

Yuqi Liu, Department of Endocrinology and Metabolism, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.

Yuting Hou, School of Psychology, Shanghai Normal University, Shanghai, China.

Liming Yue, School of Psychology, Shanghai Normal University, Shanghai, China.

Yafeng Pan, Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China.

Xiangping Gao, School of Psychology, Shanghai Normal University, Shanghai, China.

Author contributions

Yinying Hu (Conceptualization [Equal], Formal analysis [Equal], Investigation [Equal], Writing—original draft [Equal], Writing—review & editing [Equal]), Xiaojun Cheng (Writing—original draft [Equal], Writing—review & editing [Equal]), Yuqi Liu (Writing—original draft [Equal], Writing—review & editing [Equal]), Yuting Hou (Writing—original draft [Equal]), Liming Yue (Data curation [Equal], Formal analysis [Equal]), and Yafeng Pan (Formal analysis [Equal], Writing—original draft [Equal], Writing—review & editing [Equal]), Xiangping Gao (Conceptualization [Equal], Supervision [Equal], Writing—review & editing [Equal])

Conflict of interest

The authors declare no competing interests.

Funding

This work was supported by the Fundamental Research Funds for the Central Universities (No. 226-2025-00127), the National Natural Science Foundation of China (Nos. 62577047, 62207025 and 62337001), and the Zhejiang Provincial Natural Science Foundation of China (No. LMS25C090002) to Y.P.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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