Brain Changes Underlying Irritable Bowel Syndrome Symptom Improvement in Response to Mindfulness‐Based Stress Reduction.

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Learn more: PMC Disclaimer | PMC Copyright Notice Neurogastroenterol Motil . 2026 Apr 12;38(4):e70315. doi: 10.1111/nmo.70315 Search in PMC Search in PubMed View in NLM Catalog Add to search Brain Changes Underlying Irritable Bowel Syndrome Symptom Improvement in Response to Mindfulness‐Based Stress Reduction Jennifer S Labus Jennifer S Labus 1 Oppenheimer Center for Neurobiology of Stress and Resilience, Vatche and Tamar Manoukian Division of Digestive Diseases, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA 2 Brain Research Institute UCLA, Gonda (Goldschmied) Neuroscience and Genetics Research Center, University of California Los Angeles, Los Angeles, California, USA Find articles by Jennifer S Labus 1, 2, ✉ , Guistinna Tun Guistinna Tun 1 Oppenheimer Center for Neurobiology of Stress and Resilience, Vatche and Tamar Manoukian Division of Digestive Diseases, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA Find articles by Guistinna Tun 1 , Paul W Savoca Paul W Savoca 1 Oppenheimer Center for Neurobiology of Stress and Resilience, Vatche and Tamar Manoukian Division of Digestive Diseases, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA Find articles by Paul W Savoca 1 , Desiree R Delgadillo Desiree R Delgadillo 1 Oppenheimer Center for Neurobiology of Stress and Resilience, Vatche and Tamar Manoukian Division of Digestive Diseases, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA Find articles by Desiree R Delgadillo 1 , Kevin Antony Kevin Antony 1 Oppenheimer Center for Neurobiology of Stress and Resilience, Vatche and Tamar Manoukian Division of Digestive Diseases, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA Find articles by Kevin Antony 1 , Cathy Liu Cathy Liu 1 Oppenheimer Center for Neurobiology of Stress and Resilience, Vatche and Tamar Manoukian Division of Digestive Diseases, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA Find articles by Cathy Liu 1 , Suzanne R Smith Suzanne R Smith 1 Oppenheimer Center for Neurobiology of Stress and Resilience, Vatche and Tamar Manoukian Division of Digestive Diseases, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA Find articles by Suzanne R Smith 1 , J Greg Serpa J Greg Serpa 3 Veterans Health Administration Office of Patient Centered Care and Cultural Transformation, Washington, DC, USA Find articles by J Greg Serpa 3 , Jean Stains Jean Stains 1 Oppenheimer Center for Neurobiology of Stress and Resilience, Vatche and Tamar Manoukian Division of Digestive Diseases, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA Find articles by Jean Stains 1 , Bruce Naliboff Bruce Naliboff 1 Oppenheimer Center for Neurobiology of Stress and Resilience, Vatche and Tamar Manoukian Division of Digestive Diseases, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA Find articles by Bruce Naliboff 1 , Kirsten Tillisch Kirsten Tillisch 1 Oppenheimer Center for Neurobiology of Stress and Resilience, Vatche and Tamar Manoukian Division of Digestive Diseases, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA Find articles by Kirsten Tillisch 1 Author information Article notes Copyright and License information 1 Oppenheimer Center for Neurobiology of Stress and Resilience, Vatche and Tamar Manoukian Division of Digestive Diseases, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA 2 Brain Research Institute UCLA, Gonda (Goldschmied) Neuroscience and Genetics Research Center, University of California Los Angeles, Los Angeles, California, USA 3 Veterans Health Administration Office of Patient Centered Care and Cultural Transformation, Washington, DC, USA * Correspondence: Jennifer S. Labus ( [email protected] ) ✉ Corresponding author. Revised 2026 Mar 2; Received 2025 Aug 6; Accepted 2026 Mar 23; Issue date 2026 Apr. © 2026 The Author(s). Neurogastroenterology & Motility published by John Wiley & Sons Ltd. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. PMC Copyright notice PMCID: PMC13071138  PMID: 41968401 ABSTRACT Background Although the efficacy of mindfulness‐based interventions for improvements in IBS symptoms has been demonstrated, the specific neural mechanisms contributing to these changes remain unclear. Methods A single arm interventional design was employed to explore functional brain network changes underpinning IBS symptom improvement in response to an 8‐week mindfulness‐based stress reduction (MBSR) intervention. Differences in brain resting state functional connectivity (RSFC) changes between 32 MBSR Responders and 16 Nonresponders with IBS after MBSR were computed using general linear models. An elastic‐net regression model determined which RSFC changes were most impactful in determining MBSR responder status. Integrative association analyses elucidated the relationships between RSFC and symptom changes post MBSR. Key Results Following MBSR, Responders compared to Nonresponders showed large effect size decreases in IBS symptom severity and increased mindfulness. Responder status was associated with widespread changes in RSFC. In particular, decreases in the intrinsic connectivity of the default mode network along with decreased cognitive control‐somatomotor network connectivity after MBSR contributed 56% variance explained in IBS symptom severity improvement. Decreases in these two connections were associated with extensive increases in connectivity between salience, default mode, somatomotor, control, and dorsal attention networks that were further linked to reductions in comorbid psychological symptoms. Conclusions and Inferences Overall, Responders compared to Nonresponders showed widespread RSFC changes suggesting a shift from a predominantly inward, ruminative focus to one characterized by more externally focused attention to the body and the environment. The results suggest that MBSR may improve IBS symptom severity via impact on the brain's RSFC. Keywords: association network analyses, brain functional connectivity, irritable bowel syndrome, mindfulness, responder analyses Key Points In participants with irritable bowel syndrome (IBS) who show decreased symptom severity after mindfulness‐based stress reduction, brain regions communicate differently with each other when at rest. These changes in brain communication are consistent with a shift from inward and overthinking to paying more attention and noticing things happening in the body and environment. The study suggests MBSR improves IBS symptoms by changing communication between brain regions at rest. Mindfulness‐based stress reduction improves IBS symptoms by changing communication between brain regions at rest. 1. Introduction Irritable Bowel Syndrome (IBS) is a chronic visceral pain disorder characterized by remittent abdominal pain associated with altered bowel habits related to defecation, change in frequency of stool, and/or change in the appearance of stool [ 1 ]. The lack of consistently effective medical treatments for IBS drives high rates of healthcare cost, high utilization of resources, and poor quality of life [ 2 , 3 , 4 ]. In the United States, IBS imposes an economic burden with direct medical costs ranging between $1.7 billion and $10 billion and indirect costs of nearly $20 billion [ 5 ]. Furthermore, IBS has higher comorbidity with other chronic physical conditions [ 6 ] and often exists in comorbidity with anxiety and depression, at rates threefold higher than healthy individuals [ 7 ]. Chronic symptoms can foster the development of maladaptive cognition, visceral hypersensitivity, and catastrophizing—factors that perpetuate low productivity, absenteeism, and avoidance of everyday activities believed to trigger symptoms [ 8 ]. IBS is considered a disorder of brain‐gut interactions (DBGI). Across resting state and task‐dependent studies, neuroimaging has provided critical insight into IBS‐related abnormalities in central mechanisms. Compared to healthy controls, patients with IBS have demonstrated functional alterations in regions including the default [ 9 , 10 , 11 ], cognitive control/central executive [ 9 , 12 , 13 , 14 , 15 ], salience/ventral attention [ 13 , 16 , 17 , 18 , 19 , 20 , 21 ], emotional arousal [ 13 , 21 , 22 , 23 , 24 , 25 ], and somatomotor [ 9 , 19 , 21 , 22 , 24 ] brain networks. While these networks are often attributed to specific psychological phenomena, they can also work in tandem with one another and with interoceptive body input to allow the brain to efficiently regulate bodily systems in a process called allostasis [ 26 , 27 ]. When alterations in functional connectivity within and between these networks occur, it can result in both psychological and physiological dysregulation [ 28 , 29 ]. Therefore, alterations in these interconnected networks may manifest in a wide range of symptoms, including altered pain sensitivity and modulation, stress hyperresponsiveness, biased threat appraisal, cognitive inflexibility, autonomic hyperarousal, and symptom focused attention [ 30 ]. As such, targeting central mechanisms involved in the development and maintenance of IBS and IBS‐related symptoms may improve both psychological and physiological outcomes simultaneously. One such intervention that may target brain networks associated with IBS is mindfulness training, which aims to improve well‐being by cultivating awareness and acceptance of the present moment experience with a nonjudgmental attitude. Emerging evidence has demonstrated that mindfulness‐based interventions impact the functioning of brain networks shown to be altered in patients with IBS, such as the default [ 31 , 32 ], central executive [ 33 , 34 , 35 ], salience/ventral attention [ 33 , 36 , 37 ], somatomotor [ 33 , 38 , 39 ], and emotional arousal networks. Most of the existing literature points to the default network as a potential biomarker for monitoring acquisition of mindfulness [ 31 , 40 , 41 ]. The default mode network is a widely distributed brain network active during internally focused, non‐task directed thought. It has been associated with sense of self, rumination, future planning, and introspective thought [ 42 , 43 , 44 ]. The central executive network coordinates with multiple brain networks to adjust behavior in response to changing demands during tasks requiring higher‐order cognition such as attention, working memory, planning, and reasoning [ 45 ]. Maintenance of mindfulness has been suggested to depend on functions of the salience/ventral attention network, particularly its role in interoception and reallocation of attention [ 39 , 46 ]. In other words, these brain networks work in conjunction to influence the magnitude and duration of autonomic modulation of various body functions including the gut [ 45 , 47 ]. These same networks also play an important role in generating pain, depression, and anxiety [ 29 , 48 ]. The current study aims to probe the specific impacts of mindfulness on various brain networks and how they relate to IBS symptom improvement. While the specific neural mechanisms behind mindfulness require further investigation, the overall efficacy of mindfulness‐based interventions for improvements in IBS symptoms as well as IBS‐related conditions has been repeatedly demonstrated [ 8 , 49 , 50 , 51 ]. Among patients with IBS, mindfulness decreases visceral sensitivity, reduces catastrophizing in the moment of symptom onset, reduces symptom severity by 38.2%, and improves quality of life [ 8 , 50 ]. For instance, Naliboff and colleagues (2020) reported that a mindfulness‐based intervention resulted in a high gastrointestinal symptom responder rate (> 70%). These improvements are strongly tied to mindfulness skills, such as increased ability to act with awareness and retain present moment focus [ 51 ]. Furthermore, mindfulness may be more effective in decreasing IBS symptom severity when compared to cognitive‐behavioral treatment [ 52 ]. As the clinical application of mindfulness continues to grow, there is increasing demand for not only evidence of efficacy, but also an understanding of the mechanisms underpinning mindfulness‐based interventions and symptom improvement. Despite robust findings of neural mechanisms impacted by mindfulness training, the specific brain changes underpinning IBS symptom improvement in response to mindfulness‐based interventions are unclear. Existing evidence points to overlap in networks associated with mindfulness training and those impacted by IBS, such as default mode, central executive, and salience/ventral attention networks. Therefore, this study aimed to determine the effects that successful mindfulness training has on resting state brain network connectivity in individuals who have IBS. We hypothesized that when successful, the mindfulness intervention would alter functional connectivity between and within brain networks, particularly default, dorsal attention, salience/ventral attention, cognitive, emotional arousal, and somatomotor networks. Specifically, we predicted that those changes would vary between IBS patients characterized as clinical Responders compared to Nonresponders. 2. Materials and Methods 2.1. Participants Patients with IBS were recruited from collaborating clinics and by advertisement. All participants met the Rome III criteria for IBS, defined by persistent abdominal pain or discomfort for at least 3 days per month in the last 3 months as well as symptom onset 6 months prior to diagnosis. Inclusion criteria also required lack of red flag symptoms such weight loss, bloody stool, and fever. All IBS bowel habit subtypes were included. Exclusion criteria included the presence of other active medical issues that could interfere with study participation or testing of study hypotheses (i.e., active substance use disorder, neurological disorder, obesity with BMI > 30 or psychiatric disorder other than controlled anxiety or depression), use of centrally acting medications that would interfere with the neuroimaging testing (e.g., opiates, stimulants, or sedatives). However, subjects who were taking stable doses of antidepressant medications (TCAs, SSRIs, SNRIs) for at least 3 months prior were still eligible. All subjects were right‐handed. Participants included in this analysis are a subset of the group from our previously published analysis of the MBSR outcomes [ 51 ] who not only participated in the MBSR training but also completed the imaging sessions and had high‐quality imaging data. 2.2. Study Design and Protocol A single‐arm interventional design was utilized to evaluate the central mechanisms of action of MBSR response in IBS. Subjects underwent initial screening and functional MRI, followed by MBSR training and repeat imaging. Informed consent was obtained from all participants and the study was carried out in accordance with guidelines and approval of the Human Research Protection Program at University of California, Los Angeles. A medical history, physical exam, and psychological exam (MINI International Neuropsychiatric Interview) were performed to assure subject diagnosis and health. 2.3. Mindfulness Training All subjects participated in the mindfulness‐based training, conducted by experienced instructors at the University of California, Los Angeles (SRS, JGS). The intervention utilized the format and all components of standard Mindfulness Based Stress Reduction (MBSR) [ 53 ]. The full manual can be reviewed at: https://professional.brown.edu/academics/mindfulness‐education/mindfulness‐resources . It consisted of eight weekly 120‐min group sessions in addition to a half day retreat in the sixth week. In total, the training amounted to 24 class hours. Class sizes varied from 8 to 12 subjects. Centered around the development of mindful awareness of how one responds to stress, each group session included a check‐in, guided meditations, didactic instructions, yoga/mindful movement, and inquiry. Inquiry in this setting includes the process of group discussion and learning through experience in the present moment. Additionally, participants were expected to engage in 30 min of daily home practice 6 days a week until study completion. For assessment of their level of compliance, participants were required to keep a record of their home practice. The instructor recorded attendance at each group session. Two instructors were present at each session (one teacher and one observer) and conferred regularly to maintain adherence to the study protocol. Failure to attend more than two group sessions resulted in exclusion from the final analysis. 2.4. Study Questionnaires IBS symptom severity was measured using the IBS Severity Scoring System (IBS‐SSS) [ 54 ]. IBS‐SSS is a 5‐item instrument which assess abdominal pain intensity and frequency, distention, dissatisfaction with bowel habits, and impact of IBS on quality of life over the past 10 days. The IBS‐SSS questionnaire uses a 500‐point scale ranging from 0 (no symptoms) to 500 (the most severe symptoms) with a 50‐point change between assessment scores considered clinically significant. IBS severity is categorized as mild (75–175), moderate (175–300), or severe (> 300), while a score < 75 indicate absence of IBS or in remission. Mindfulness skills were assessed using the Five Facets of Mindfulness Questionnaire (FFMQ) [ 55 ]. The FFMQ is a 39‐item questionnaire with subscales that individually evaluate aspects of mindfulness—specifically observation of internal experience, description of internal experience, acting with awareness, nonjudgment of experience, and nonreactivity to internal experience. Each item is rated on a 5‐point Likert scale, ranging from 1 (never or very rarely true) to 5 (very often or always true). Higher FFM scores reflect higher levels of mindfulness. Total scores were used in the current study and represent the sum of scores derived from the 39‐item questionnaire. 2.5. Secondary Outcomes Quality of life (QOL) was evaluated using the total score from the IBS‐QOL, a 34‐item questionnaire which assesses the interference of IBS symptoms on various aspects of daily activities [ 56 ]. Gastrointestinal (GI) symptom‐specific fear and anxiety was assessed using the Visceral Sensitivity Index (VSI), a 15‐item measurement of fear and anxiety related to IBS symptoms [ 57 ]. Symptoms of anxiety and depression were measured using the Hospital Anxiety and Depression Scale (HAD), which assesses severity of anxiety (HAD‐A) and depression (HAD‐D) over the past two‐weeks in non‐psychiatric patients [ 58 ]. Somatic symptoms were evaluated using the Personal Health Questionnaire‐12 (PHQ), which measures the severity of 15 somatic symptoms (excluding three GI‐related items) over the past 2 weeks [ 59 ]. Using the Pain Catastrophizing Scale (PCS), the 13‐item scale assessed pain‐related catastrophic thinking on the basis of helpless, rumination and magnification [ 60 ]. The Pennebaker Inventory of Limbic Languidness (PILL) measure individuals tendency's to report physical symptoms and sensations [ 61 ]. The pain vigilance and awareness questionnaire PVAQ used to assess attention to pain [ 62 , 63 ]. 2.6. Imaging Acquisition Each subject underwent brain imaging pre and post‐mindfulness training. During each scanning session, subjects underwent rs‐fMRI followed by structural MRI. Imaging was performed on a Siemens 3 Tesla Trio scanner with a 12‐channel head coil. During the 10‐min rs‐fMRI, subjects wore noise‐canceling headphones and were instructed to lie quietly with their eyes closed. Resting state scans utilized the following parameters: echo time (TE) = 28 ms, repetition time (TR) = 2000 ms, flip angle = 77 degrees, field of view (FOV) = 220 × 220 mm, acquisition matrix = 64 × 64, slice thickness = 4.0 mm with 0.48‐mm skip, and voxel size = 3.4 × 3.4 × 4 mm. For structural imaging, a standard high‐resolution T1‐weighted magnetization‐prepared rapid acquisition gradient echo (MP‐RAGE) scan was obtained (TE = 2.98 ms, TR = 2300 ms, flip angle = 9 degrees, FOV = 256 × 240 mm, percent phase FOV = 93.75, slice thickness = 1.0 mm, voxel size = 1 mm 3 , and 240 slices). 2.7. Image Pre‐Processing and Quality Control Preprocessing for quality control was performed at the Bioinformatics and Neuroimaging core of the G. Oppenheimer Center for Neurobiology of Stress and Resilience at UCLA. Inclusion criteria for structural scans included compliance with acquisition protocol, full brain coverage, minimal motion, Gibbs ringing, absence of flow/zipper and minor atrophy/vascular degeneration. For resting state scans, robust measures of motion mean frame‐wise displacement (FD) was calculated using publicly available MATLAB code from GitHub. Images with excessive motion that is, average framewise displacement (FD) greater than 0.35 [ 64 ], signal dropout, altered fields of view, or other significant anomalies were removed from further analysis. Furthermore, male subjects were also excluded due their disproportionately smaller sample size when compared to that of female subjects. Of the initial 96 subjects who met the Rome III criteria for IBS, the final analysis included 48 female subjects. We defined cortical, subcortical, and brain stem regions of interest using the Schaeffer functional atlas (Schaefer et al., 2018), Harvard‐Oxford subcortical (Dale et al., 2018), and the Harvard Ascending Arousal Network atlases (Edlow et al., 2012), respectively. The Schaeffer atlas was derived by identifying parcels with homogeneous (similar) rs‐fMRI signals (Schaefer et al., 2018). The Schaeffer atlas includes 17 networks and 400 functional parcellations. We utilized the following labeled networks from the atlas: Somatomotor (A,B), Dorsal Attention (A,B), Salience/Ventral Attention (A,B), Control (A,B,C), Default Mode (A,B,C), Visual (Central, Peripheral), TempPar, and Limbic (A,B). The temporal parietal network consists of only temporal and parietal regions. The Limbic network is comprised of areas in the orbital frontal cortex (A) and the ventral temporal (B) pole. From the Harvard Oxford subcortical atlas, we included the bilateral thalamus, amygdala, hippocampus, and basal ganglia regions (caudate nucleus, pallidum (global pallidus), putamen, and the nucleus accumbens). The Schaeffer atlas maximizes within‐parcel functional connectivity homogeneity and has been shown highly replicable functional connectivity patterns across studies and clinical diagnoses [ 65 ]. Finally, from the Harvard Ascending Arousal Network atlas, we included the locus coeruleus, mesencephalic reticular formation, parabrachial complex, nucleus reticularis pontis oralis, and the pedunculopontine nucleus bilaterally and the dorsal raphe, median raphe, periaqueductal gray, and the ventral tegmental area. 2.8. Resting State Seed‐Based Functional Connectivity Analysis Resting‐state functional images were processed and analyzed using the CONN 17 toolbox in MATLAB (Whitfield‐Gabrieli and Nieto‐Castanon, 2012) specifying the default pipeline which included functional realignment and unwarping, slice‐timing correction, structural segmentation & normalization, functional normalization (resampling to a 2 mm isotropic voxel size), outlier detection/scrubbing (i.e., combined threshold of 1.5 for DVARS (temporal signal change) and 0.5 mm for FD), and 8 mm smoothing. The output of this step is a skull‐stripped normalized structural volume, gray/white. CSF normalized masks, realigned slice time corrected normalized smoothed functional volumes, subject realignment and scrubbing first level covariates. Next, linear regression was then applied to covary out the confounding effects of white matter and cerebral spinal fluid (5 dimensions each), scrubbing, realignment and motion (6 motion and 6 first order temporal derivatives) parameters. The effect of motion was regressed out using 12 dimensions. RS images were then bandpass filtered between 0.008 and 0.08 Hz in the CONN toolbox. Bandpass filtering and regression were performed simultaneously. Linear trends were removed. For each subject, functional pairwise connectivity was computed between all brain regions using bivariate correlations and reflects the association between average BOLD time series signals across all voxels in each ROI. Fisher's Z‐transformation was then applied to the correlation coefficients for downstream analyses. 2.9. Statistical Analyses The goal of our analysis was to determine the effects of mindfulness‐based stress reduction (MBSR) on brain resting state functional connectivity (RSFC) in IBS. First, responder status was defined by a decrease of ≥ 50 point in IBS‐SSS score post MBSR intervention. An independent t ‐test was applied to evaluate differences between MBSR Responders versus Nonresponders in change in IBS‐SSS and FFM as well at each time point. The general linear model, that is, 2 × 2 mixed ANOVA interaction, was applied to test for differences Responders and Nonresponders in functional connectivity pre to post MBSR in the CONN toolbox. Significance was considered at a false discovery rate of 5% corrected at the seed‐level. This multiple‐comparison approach corrects for multiple target ROIs, but separately for each seed ROI. Next, we applied supervised learning to determine which of the significant pairwise RS functional connections had the greatest impact on differentiating Responders and Nonresponders. Finally, we examined the associations between the observed changes in resting state functional connectivity and clinical symptoms using correlational analysis. Post hoc power analysis, based on the single degree of freedom test for group differences (16 NR, 32 Responders) in brain connectivity changes, indicated that the study had 80% power to detect an effect size as small as Cohen's d = 0.88, if it existed ( t [46] = 2.12, noncentrality parameter = 2.86 at alpha = 0.05). 2.9.1. Elastic‐Net Regularized Generalized Logistic Regression After identifying the significant group differences in pairwise connections after MBSR, a feature selection algorithm was employed to determine which of the 64 individual features, that is, changes in Fisher's Z‐transformed functional connectivity coefficients after MBSR, were most informative for the group discrimination. Specifically, an elastic‐net regularized generalized logistic regression model with stability selection [ 66 , 67 , 68 ] was applied with the ‘glmnet’ [ 68 ] and ‘stabs’ [ 67 ] libraries in R. First, 4‐fold cross validation was applied across a range of alpha, α∈[0, 1] to determine the best model (i., lowest predictive error) based on the largest lambda (λ)within 1 standard error of the minimum mean square error. Next, to assess the stability of selected variables we applied a resampling approach, that is, regressions calculated on 100 subsamples based on randomly splitting the sample 50 times [ 69 ], that provides per‐family‐wise error rate (PFER) control and provides an assessment of stability, how often a feature is selected during modeling [ 67 , 68 ]. Since the PFER is dependent the number of selected features per regression model, q, and the threshold for stability, the user must specify two of these parameters. It is recommended that users specific q and PFER to determine the threshold for stability. We specified q = 9 and PFER = 0.05. The upper bound of α < 0.05 is considered extremely conservative compared to other approaches (i.e., family‐wise error rate, false discovery rate). As an alternative, a less conservative PFER = 2 was also examined that still provides some level of family‐wise error rate adjustment and reflects a tolerable number of false positive selected features [ 66 ] (see Tables S1 and S2 ). As a final step, we performed logistic regression using the Bayesian generalized linear model to describe the effects of the selection variables, that is, regression coefficients (i.e., log odds ratios), McFadden's Pseudo R [ 2 , 70 ]. 2.10. Brain‐Symptom Association Network To elucidate the direct and indirect relationships between the observed changes in resting state functional connectivity and clinical symptoms after MBSR, we performed integrative analysis. Specifically, across all participants, Pearson's correlation between the changes in pairwise brain network connections after MBSR was computed, retaining associations at a threshold of p < 0.01. Next, correlations were computed between the changes in functional connectivity showing group differences with changes in FFM total score, Pain Catastrophizing scale total score, Pain attention and Vigilance Questionnaire total score (PVAQ total), somatic awareness (PILL), visceral‐specific anxiety (VSI), mood (HAD)and widespread pain (PHQ‐12 no GI) using a p < 0.05 as a threshold for reporting. The thresholded correlation matrix was then visualized as an association network where the circles of nodes of the networks represent change in pairwise brain connectivity or change in the clinical variables of interest after MBSR. Lines or edges connecting the nodes represent a significant correlation. A compound spring embedding algorithm [ 71 ] was utilized to format the layout in Cytoscape v3.9. This algorithm aids interpretation by organizing the nodes of the network (i.e., brain connectivities, clinical variables) based on similar patterns of correlations. 3. Results 3.1. Demographics and Clinical Variables The final analysis included 48 female subjects, 32 Responders, 16 Nonresponders. Table 1 shows the demographic and clinical symptom data. Prior to treatment, no group differences were found between Responders and Nonresponders in terms of age, IBS symptom severity as measured by the IBS‐SSS, or mindfulness (FFM total score). As shown in Table 2 , following treatment, Responders showed significantly greater effect size decreases in IBS symptom severity (Cohen's d = −3.17) and increased mindfulness ( d = 0.89). The majority of MBSR Responders shifted to less severe IBS‐SSS symptom categories, that is, 31.25% (10/32 subjects) moved from Severe to Moderate, 28.12% (9/32) Moderate to Mild and 21.88% (7/32) Moderate to Remission, 3.13% (1/32) Mild to Remission with the remaining 15.63% (5/32) staying within the same severity category but decreasing by > 50 points. In general, Nonresponders remained within their presenting symptom category, with a few transitioning to worse symptom categories, that is, 6.25% (1/16) Mild to Moderate and 12.5% (2/16) Moderate to Severe. See Figure S1 for graphic depiction of IBS symptom changes. There were small but not statistically significant effect size decrease in Responders compared to Nonresponders in anxiety ( d = −0.20), depression ( d = −0.27), pain vigilance (−0.25), and tendency to report physical symptoms and sensations ( d = −0.39). TABLE 1. Characteristics of the sample. Responder ( n = 32) Non‐responder ( n = 16) 95% confidence intervals Mean (SD) Mean (SD) t df p Cohen's D Lower Upper Age 30.41 (9.51) 31.56 (9.47) 0.40 46 0.70 −0.12 −0.72 0.48 IBS Symptom Severity (IBS‐SSS) 270.50 (76.75) 258.06 (76.44) 0.53 46 0.60 0.16 −0.44 0.76 Mindfulness (FFM) 126.16 (16.08) 130.50 (17.89) −0.85 46 0.40 −0.26 −0.86 0.34 Anxiety (HAD) 8.44 (4.44) 7.63 (3.40) 0.64 46 0.52 0.20 −0.41 0.80 Depression (HAD) 4.00 (3.65) 2.88 (1.54) 1.18 46 0.25 0.36 −0.25 0.96 Pain Catastrophizing (PCS) 23.17 (12.01) 17.75 (8.98) 1.59 46 0.12 0.49 −0.12 1.09 Pain Vigilance (PVAQ) 50.40 (14.70) 45.70 (9.69) 1.15 46 0.25 0.35 −0.25 0.96 Tendency to report physical symptoms and sensations (PILL) 18.64 (9.38) 14.50 (5.79) 1.61 46 0.11 0.49 −0.12 1.10 Somatic Symptoms (PHQ‐GI) 7.77 (4.51) 7.69 (3.03) 0.07 46 0.95 0.02 −0.58 0.62 Gastrointestinal‐specific anxiety (VSI) 48.88 (15.00) 38.75 (15.55) 2.18 46 0.04 0.67 0.05 1.28 Bowel Habit Type n (%) n (%) Constipation 18 (56%) 4 (25%) Diarrhea 8 (25%) 5 (31%) Unspecified 1 (3%) 2 (13%) Mixed 5 (16%) 5 (31%) Open in a new tab Abbreviations: df, degrees of freedom; FFM, Five Facets of Mindfulness Questionnaire total score; HAD, Hospital Anxiety and Depression Scale; IBS‐SSS, IBS Severity Scoring System; n , number of participants; p, probability; PCS, Pain Catastrophizing Scale; PILL, Pennebaker Inventory of Limbic Languidness; SD, standard deviation; t, independent t ‐test statistic; VSI, Visceral Sensitivity Index. TABLE 2. Change in symptom and psychosocial assessment post MBSR. Responder ( n = 32) Non‐responder ( n = 16) 95% confidence intervals Mean (SD) Mean (SD) t df p Cohen's D Lower Upper IBS Symptom Severity (IBS‐SSS) −142.78 (61.77) 36.63 (43.89) −10.36 46 2.08E‐14 −3.17 −4.05 −2.28 Mindfulness (FFM) 15.84 (18.08) 0.56 (15.35) 2.90 46 0.006 0.89 0.26 1.51 Anxiety (HAD) −0.63 (4.14) 0.13 (3.01) −0.65 45 0.52 −0.20 −0.80 0.41 Depression (HAD) −0.48 (3.83) 0.44 (2.53) −0.87 45 0.39 −0.27 −0.87 0.34 Pain Catastrophizing (PCS) −5.62 (11.16) −4.06 (8.83) −0.49 45 0.63 −0.15 −0.75 0.46 Pain Vigilance (PVAQ) −4.23 (11.91) −1.17 (11.89) −3.05 45 0.41 −0.25 −0.86 0.25 Tendency to report physical symptoms and sensations (PILL) 0.21 (4.98) 2.02 (3.84) −1.28 45 0.21 −0.39 −1.00 0.22 Somatic Symptoms (PHQ‐GI) 0.03 (2.46) 0.28 (2.37) −0.34 45 0.74 −0.10 −0.71 0.50 Gastrointestinal‐specific anxiety (VSI) −5.19 (13.75) −5.56 (10.88) 0.09 45 0.93 0.03 −0.58 0.63 Open in a new tab Abbreviations: df, degrees of freedom; FFM, Five Facets of Mindfulness Questionnaire total score; HAD, Hospital Anxiety and Depression Scale; IBS‐SSS, IBS Severity Scoring System; MBSR, mindfulness‐based stress reduction; n , number of participants; p, probability; PCS, Pain Catastrophizing Scale; PILL, Pennebaker Inventory of Limbic Languidness; SD, standard deviation; t , independent t ‐test statistic; VSI, Visceral Sensitivity Index. 3.2. Functional Connectivity Following treatment, Responders compared to Nonresponders primarily showed increased connectivity between default mode, somatomotor, salience, control, and dorsal attention networks (See Figure 1 , Table 3 ). Additionally, functional connectivity within‐networks increased in the default and somatomotor networks in Responders. FIGURE 1. Open in a new tab Change in pairwise resting state connectivity between Responders and Nonresponders. (A) The circle diagram shows the 64 pairwise connections organized by networks that changed post MBSR. Responder > Nonresponders (orange). Responder<Nonresponders (blue). (B) The bar graph highlights the percentage of times each network was represented in the 64 pairwise connectivities. The number on top of the bar graph shows the number of unique regions that were identified in each network. (C) The Table delineates the number of pairwise connections between and within networks. Darker hues in the boxes reflect greater number of connections. Default, default mode; SalVentAttn, salience/ventral attention; Temporal parietal, TempPar. TABLE 3. Changes in network connectivity post MBSR. ID Network_A H ROI_A x y z Network_B H ROI_B x y z t p q 1 SomMot B L S2_5 −48 −24 18 Cont B R Temp_2 63 −42 −11 5.04 < 0.0001 0.0032 2 Default B L PFCd_1 −4 51 28 SomMot A L 16 −14 −11 73 5.01 < 0.0001 0.0036 3 Default A R IPL_1 53 −53 26 SalVentAttn A L ParOper_3 −61 −36 33 4.68 < 0.0001 0.0109 4 Default B R PFCd_1 6 58 29 Default A R pCunPCC_1 6 −52 23 −4.67 < 0.0001 0.0113 5 Default B R Temp_1 63 −23 −7 SalVentAttn A L ParOper_3 −61 −36 33 4.34 0.0001 0.0125 6 Default B R Temp_1 63 −23 −7 DorsAttn B R TempOcc_1 59 −55 −2 4.46 0.0001 0.0125 7 Default B R Temp_1 63 −23 −7 SalVentAttn A R FrOper_3 54 12 12 4.3 0.0001 0.0125 8 Subcortical L Amygdala −24 −4 −18 SalVentAttn A L ParOper_3 −61 −36 33 4.08 0.0002 0.0148 9 Cont B R IPL_2 54 −53 44 SalVentAttn A L ParOper_3 −61 −36 33 4.03 0.0002 0.0148 10 TempPar R 9 55 −46 19 SalVentAttn A L ParOper_3 −61 −36 33 4.13 0.0002 0.0148 11 SomMot B R S2_3 41 −13 18 Cont C R pCun_4 7 −50 45 −4.58 < 0.0001 0.0152 12 Cont B R IPL_1 55 −45 33 SalVentAttn A L ParOper_3 −61 −36 33 3.9 0.0003 0.018 13 TempPar R 6 59 −46 7 SalVentAttn A L ParOper_3 −61 −36 33 3.87 0.0003 0.018 14 Default B L PFCd_2 −14 58 31 SomMot A L 16 −14 −11 73 4.15 0.0001 0.0182 15 Default B L PFCd_4 −8 43 51 SomMot A L 16 −14 −11 73 4.01 0.0002 0.0182 16 SomMot A L 16 −14 −11 73 Cont A R PFCl_5 39 11 34 3.96 0.0003 0.0182 17 SomMot A L 16 −14 −11 73 TempPar R 5 50 −33 2 4.03 0.0002 0.0182 18 Limbic A R TempPole_4 39 −15 −31 Limbic A L TempPole_7 −44 5 −17 4.52 < 0.0001 0.0184 19 Limbic A L TempPole_1 −37 −5 −42 DorsAttn B R PostC_8 16 −47 74 −4.46 0.0001 0.0225 20 Cont B L IPL_2 −53 −50 45 SalVentAttn A L ParOper_3 −61 −36 33 3.72 0.0005 0.0257 21 Default B R PFCd_3 5 44 40 SomMot A L 16 −14 −11 73 3.79 0.0004 0.0267 22 SomMot A L 5 −39 −25 53 DorsAttn B L PostC_5 −39 −37 49 4.33 0.0001 0.0268 23 DorsAttn B L PostC_5 −39 −37 49 SalVentAttn B L PFCmp_1 −6 22 31 −4.19 0.0001 0.0268 24 Default B L IPL_1 −45 −58 21 Cont A L IPS_5 −33 −46 41 4.32 0.0001 0.0272 25 Default B L IPL_1 −45 −58 21 SalVentAttn A L ParOper_3 −61 −36 33 4.18 0.0001 0.0272 26 Limbic A L TempPole_6 −32 12 −29 Brainstem Median Raphe (MR) 0 −32 15 −4.4 0.0001 0.0275 27 Default B L IPL_2 −57 −55 30 SalVentAttn A L ParOper_3 −61 −36 33 3.63 0.0007 0.0279 28 Default B R Temp_2 63 −38 0 SalVentAttn A L ParOper_3 −61 −36 33 3.64 0.0007 0.0279 29 Default B L PFCv_1 −36 22 −16 SalVentAttn B R Ins_2 37 23 5 4.38 0.0001 0.0295 30 Limbic B L OFC_1 −12 25 −21 DorsAttn A R TempOcc_1 34 −37 −23 4.38 0.0001 0.0296 31 TempPar R 7 51 −41 13 SalVentAttn A L ParOper_3 −61 −36 33 3.56 0.0009 0.0313 32 Cont C R pCun_4 7 −50 45 SalVentAttn A R PrC_1 51 3 41 3.91 0.0003 0.0343 33 SomMot A R 15 22 −29 68 Cont C R pCun_4 7 −50 45 −3.89 0.0003 0.0343 34 SomMot B R S2_2 35 −21 14 Cont C R pCun_4 7 −50 45 −3.96 0.0003 0.0343 35 Default A R IP1 53 −53 26 DorsAttn B R TempOcc_1 59 −55 −2 3.7 0.0006 0.0348 36 Default A L IPL_1 −47 −64 31 DorsAttn B R TempOcc_1 59 −55 −2 3.73 0.0005 0.0348 37 Default B L PFCd_6 −6 10 65 DorsAttn B R TempOcc_1 59 −55 −2 3.8 0.0004 0.0348 38 Default B L Temp_3 −62 −18 −21 DorsAttn B R TempOcc_1 59 −55 −2 3.7 0.0006 0.0348 39 TempPar R 1 47 16 −20 DorsAttn B R TempOcc_1 59 −55 −2 3.83 0.0004 0.0348 40 TempPar R 4 62 −19 0 DorsAttn B R TempOcc_1 59 −55 −2 3.94 0.0003 0.0348 41 SomMot B R S2_1 37 −8 14 SomMot A L 10 −32 −29 63 4.32 0.0001 0.0354 42 SomMot B R S2_2 35 −21 14 SomMot B R S2_1 37 −8 14 4.1 0.0002 0.0359 43 Cont C R pCun_3 5 −64 44 DorsAttn A L SPL_6 −15 −71 57 4.13 0.0002 0.0361 44 Default B R pCunPCC_2 5 −63 31 DorsAttn A L SPL_6 −15 −71 57 4.1 0.0002 0.0361 45 Default B R PFCd_1 6 58 29 SomMot A L 16 −14 −11 73 4.09 0.0002 0.0365 46 SomMot B R S2_1 37 −8 14 SomMot B L S2_2 −36 −26 19 3.93 0.0003 0.0403 47 SomMot B L Aud_2 −56 −22 8 Cont B R Temp_2 63 −42 −11 4.04 0.0002 0.0429 48 Cont A L Temp_1 −55 −62 −1 TempPar R 7 51 −41 13 3.99 0.0002 0.0433 49 SomMot A L 6 −9 −38 54 TempPar R 7 51 −41 13 3.74 0.0005 0.0433 50 Cont A R PFC4 49 8 25 TempPar R 7 51 −41 13 3.75 0.0005 0.0433 51 TempPar R 7 51 −41 13 SalVentAttn B R PFClv_1 49 40 5 3.82 0.0004 0.0433 52 TempPar R 7 51 −41 13 TempPar R 5 50 −33 2 3.9 0.0003 0.0433 53 SomMot A L 5 −39 −25 53 DorsAttn B L PostC_4 −46 −29 44 4.25 0.0001 0.0441 54 Cont B R PFClv_2 28 55 −14 DorsAttn B L FEF_2 −25 −1 55 4.25 0.0001 0.0445 55 Limbic A L TempPole_7 −44 5 −17 TempPar R 6 59 −46 7 4.25 0.0001 0.0447 56 SomMot A L 10 −32 −29 63 DorsAttn B L PostC_1 −61 −23 33 3.85 0.0004 0.0461 57 SomMot A L 10 −32 −29 63 DorsAttn B L PostC_2 −55 −20 41 3.56 0.0009 0.0461 58 SomMot A L 10 −32 −29 63 DorsAttn B L PostC_4 −46 −29 44 3.57 0.0009 0.0461 59 SomMot A R 10 34 −27 61 SomMot A L 10 −32 −29 63 3.66 0.0007 0.0461 60 SomMot A R 4 49 −26 56 SomMot A L 10 −32 −29 63 3.71 0.0006 0.0461 61 SomMot A R 8 32 −34 63 SomMot A L 10 −32 −29 63 3.61 0.0007 0.0461 62 SomMot A R 9 31 −41 64 SomMot A L 10 −32 −29 63 3.62 0.0007 0.0461 63 Default A L IPL_1 −47 −64 31 Cont B L Temp_2 −60 −49 −10 4 0.0002 0.0482 64 Limbic B R OFC_6 9 63 −14 Cont B L Temp_2 −60 −49 −10 4.19 0.0001 0.0482 Open in a new tab Note: Bidirectional (Network A ↔ Network B) pairwise region to region resting state functional connectivity (RSFC) showing Responder‐NonResponder difference post MBSR. Networks and regions are defined using the nomenclature from the Schaeffer 400 functional cortical parcellation, Schaeffer 400 Atlas with the exception of the Subcortical and Brainstem Networks which reflect regions from the Harvard subcortical and Harvard ascending arousal atlases, respectively. Abbreviations: Cont, control; Default, default mode; DorsAttn, dorsal attention; H, hemisphere; ID, number representing specific connectivity used to label regions in Figure 2 ; L, left; MNI space inferior/superior; p , probability; q , 5% false discovery rate adjusted p value; R, right; ROI, region of interest; SalVentAttn, salience/ventral attention; SomMot, somatomotor; TempPar, temporal parietal; x , x ‐axis MNI space left/right; y , y ‐axis MNI space anterior/posterior; z , z ‐axis. 3.3. Default Network In Responders, compared to Nonresponders, the default network exhibited increased between‐network functional connectivity with the control, dorsal attention, salience/ventral attention, and somatomotor networks. Compared to Nonresponders, Responders demonstrated decreased intrinsic default mode connectivity between the dorsal medial prefrontal cortex and dorsal posterior cingulate cortex. 3.4. Somatomotor Network In Responders, compared to Nonresponders, the somatomotor network showed increased connectivity with the default, dorsal attention, temporal parietal, and control networks. Additionally, Responders showed decreases between the secondary somatosensory cortex and the precuneus of the control network. Responders further demonstrated increased within‐network functional connectivity in the primary motor and secondary somatosensory cortex. 3.5. Dorsal Attention Network In Responders compared to Nonresponders, the dorsal attention network showed increased connectivity with the default, limbic, control, somatomotor, and temporal parietal networks. Responders also demonstrated decreased functional connectivity between the parietal and anterior midcingulate cortex, a hub region of the salience/ventral attention network. 3.6. Salience/Ventral Attention Network In Responders, compared to Nonresponders, the salience network showed decreased functional connectivity with a single region of the dorsal attention network (anterior mid‐cingulate cortex) but several increased connectivities with the default, control networks, and temporal parietal networks as well as the amygdala. 3.7. Limbic Network Responders compared to Nonresponders showed increased limbic network connectivity with the control, dorsal attention, and temporal parietal networks. Additionally, in Responders, the anterior temporal cortex exhibited decreased functional connectivity with the median raphe region of the brainstem and parietal cortex of the dorsal attention network. Increased within‐network connectivity was observed within the inferior temporal cortex. 3.8. Connectivity Features With the Greatest Impact for Discriminating MBSR Responder and Nonresponders Next, we examined which changes in functional connectivity were most predictive for the group discrimination using elastic‐net regularized generalized logistic regression with stability selection. Controlling for the conservative PFER = 0.05 resulted in a cutoff = 1, meaning these features had to be selected in every regression model (see Table 4 ). Under these conditions, the most impactful predictors of response to MBSR were decreased intrinsic connectivity in the default mode network (right dorsal PFC to right precuneus/posterior cingulate cortex) and between the control and somatomotor network (right precuneus to the right secondary somatosensory cortex (S2)). A model with these 2 predictors explained 56% of the predictive variance (see Table 5 ). The results of the feature selection algorithm using a less conservative PFER are in the Tables S1 and S2 . TABLE 4. Estimates of stability of features selected (PFER = 0.05). Region to region connectivity Frequency R_DefaultB_PFCd_1_to_R_DefaultA_pCunPCC_1 1 R_ContC_pCun_4_to_R_SomMotB_S2_3 1 R_DorsAttnB_PostC_8_to_L_LimbicA_TempPole_1 0.97 L_LimbicA_TempPole_6_to_MR 0.92 R_DefaultA_IPL_1_to_L_SalVentAttnA_ParOper_3 0.78 R_DefaultB_Temp_1_to_R_DorsAttnB_TempOcc_1 0.78 R_LimbicA_TempPole_4_to_L_LimbicA_TempPole_7 0.69 L_DefaultB_PFCd_1_to_L_SomMotA_16 0.63 R_SalVentAttnB_Ins_2_to_L_DefaultB_PFCv_1 0.54 R_ContB_Temp_2_to_L_SomMotB_S2_5 0.51 R_DorsAttnA_TempOcc_1_to_L_LimbicB_OFC_1 0.33 L_SalVentAttnB_PFCmp_1_to_L_DorsAttnB_PostC_5 0.23 R_DefaultB_Temp_1_to_L_SalVentAttnA_ParOper_3 0.21 R_ContC_pCun_4_to_R_SomMotA_15 0.14 R_ContC_pCun_4_to_R_SomMotB_S2_2 0.12 L_DefaultB_IPL_1_to_L_ContA_IPS_5 0.07 L_DorsAttnB_PostC_5_to_L_SomMotA_5 0.06 R_SomMotB_S2_1_to_L_SomMotA_10 0.01 R_DefaultB_Temp_1_to_R_SalVentAttnA_FrOper_3 0.01 Open in a new tab Note: This table shows the features selected and their frequency (percentage of times selected). Abbreviation: PFER, per family error rate. TABLE 5. Regression of selected features at PFER = 0.05. B SE Z p (Intercept) 0.91 0.46 1.98 0.048 R_DefaultB_PFCd_1_to_R_DefaultA_pCunPCC_1 −8.83 2.91 −3.04 0.002 R_ContC_pCun_4_to_R_SomMotB_S2_3 −9.46 3.26 −2.90 0.004 Open in a new tab Note: Negative coefficient indicates decreases in this connectivity predict response to MBSR. These two features account for 56% explanatory variance. Abbreviations: B , estimated beta coefficient; p , probability; SE, standard error; Z , z ‐statistic. 3.9. Brain‐Symptom Association Network As shown, in Figure 2 , we computed the association across all participants between the changes in pairwise connections identified as different between Responders and Nonresponders (Figure 1 ) and changes in mindfulness, pain catastrophizing, and vigilance, somatic awareness (PILL), widespread pain (PHQ‐12 no GI), GI‐specific anxiety, and mood. FIGURE 2. Open in a new tab Brain‐Symptom Association Network. The nodes of the network (i.e., circles) are color coded and represent change in functional connectivity in the two regions selected as most important for discriminating Responders and Nonresponders in the first feature selection model (yellow, Table 4 ), change in the functional connectivity, change of four additional connections selected in the second feature selection model (blue, See Supplemental Table S2 ), change in other brain regions identified as significant in the CONN analyses (gray) and change in clinical variables (green). The lines of edges represent positive (red) and negative (blue) correlations. The numbers reflect the specific connections labeled as ID in Table 3 . Inset A. Significant associations between changes in resting state functional connectivity post MBSR. Here we highlight the significant correlations between the two features (4, 11) selected as most important for group discrimination and the other significantly different changes connections between the groups. B. Brain symptom association network. Changes in functional connectivity changes showing differences between group and directly associated with mindfulness and pain/symptom related cognition and depression. FFM Total, Five Facets of Mindfulness Questionnaire total score; PHQ‐No GI, Personal Health Questionnaire excluding Gastrointestinal symptom questions; PILL, Pennebaker Inventory of Limbic Languidness; PVAQ, pain vigilance and awareness questionnaire. As highlighted in Figure 1 , inset A, the decreases in the Default‐Default and Control‐Somatomotor connectivity (See Table 5 ) post‐treatment that contributed the most explanatory variance for IBS symptom severity change (i.e., responder status) served as the hub of the network, that is, these decreases in two connectivities were directly and primarily associated with increases in many other pairwise brain connections that also changed with treatment. However, changes in these two connectivities were not directly correlated with change in mindfulness or changes in the other clinical variables. Instead, as highlighted in Figure 1 , Inset B, the increases in other functional connectivities (associated with the decreases in the two networks hubs) showed direct correlations with increases in mindfulness and decreases in symptom/pain‐related cognitions/anxiety and mood post treatment. Overall, the direct and indirect associations comprising this brain‐symptom association network depict potential functional interactions between the brain regions that may lead to IBS symptom changes with MBSR treatment. 4. Discussion It has been clear for over a decade that mindfulness training has clinical benefits for individuals with IBS but here we show for the first time how shifts in connectivity between widespread brain regions play a role in symptom management. MBSR Responders had clinically significant decreases in IBS severity with the majority shifting into IBS‐SSS categories of mild symptoms or remission. Interestingly, the two most impactful changes in functional brain connectivity contributing to improved IBS symptom severity in response to MBSR were reduced intrinsic (within network) connectivity of the default mode network and decreased connectivity between the cognitive control and somatomotor networks, potentially reflecting normalization of disease‐related hyperconnectivity. These differences in connectivity were then linked to extensive increases in resting state functional connectivity between salience, default mode, somatomotor, control, and dorsal attention networks for Responders after treatment. Both the results of the current study and recent methodological neuroimaging work [ 26 ] suggest that we interpret findings within the broader framework of whole‐brain functioning, thereby enhancing our understanding of the intricate interplay between neural networks and clinical outcomes in IBS management. A useful model of this brain–body link is that of allostasis‐interoception in which the brain acts to prepare the body for predicted states, then uses the body's interoceptive input as feedback to adjust the model [ 72 , 73 , 74 ]. IBS can be considered a disorder of allostasis‐interoception, as it is hypothesized that in IBS, prediction error occurs but model correction using interoceptive feedback fails. There is experimental support for this, demonstrating enhanced anticipation of threat and impaired processing of body signals [ 13 , 15 , 47 , 75 , 76 , 77 ]. We suggest that MBSR training improves the accuracy of both predictions and interoceptive feedback. Prediction error would be reduced by enhancing non‐reactivity and non‐judgmental interpretation of internal and external cues. Similarly, MBSR's focus on the body scan and present moment awareness is hypothesized to enhance interoception, thus providing more accurate feedback from the gut [ 72 ]. The default mode network plays an important role in allostasis, integrating internal and external input to anticipate the body's future state. Important in IBS as well as pain and mental health disorders, excessive reliance on the default mode network is thought to bias the organism towards ruminative perceptions of self rather than present moment experience [ 78 ]. Our finding that reductions in intrinsic default mode network connectivity was associated with improved IBS symptoms is consistent with a shift away from this narrow, often negatively valenced focus. It also maps on to prior literature which points to default network changes as a potential biomarker for monitoring acquisition of mindfulness [ 79 , 80 , 81 ]. In healthy participants undergoing MBSR training and in experienced mindfulness meditators, decreased RSFC within the default mode network has correlated with increased mindfulness [ 31 ]. Higher levels of trait mindfulness have also been associated with reduced intra‐network connectivity of the default mode network [ 42 ]. Given these other findings, it is not surprising that reduced intrinsic connectivity of the default mode network was one of the best predictors of treatment response in the current study. The second key predictor of symptom change was reduced connectivity between a somatomotor and control region, that is, the secondary somatosensory cortex (S2) and precuneus respectively. Adaptive changes in the neuroplasticity of the control‐somatomotor regions provides a mechanistic substrate for specific MBSR practices like the body scan and their proposed role in fostering a different, non‐catastrophic relationship with bodily sensations [ 44 , 82 , 83 ]. The precuneus is thought to be crucial for cognitive control by integrating self‐awareness and memory and links to sensory areas like S2 to process bodily sensations and guide attention. Providing support for this interpretation, in Responders, associations were observed between the Five Facet Mindfulness scores and the connectivity changes involving somatomotor, salience, and attention regions. Maintenance of mindfulness has been suggested to depend on functions of the salience/ventral attention network, particularly its role in interoception and reallocation of attention [ 46 ]. In line with the allostasis‐interoception model, one interpretation of our findings is that participants became less reliant on future predictions of pain/IBS symptoms (i.e., less intra‐network connectivity of default mode network) due to mindfulness training and more accurate in determining the salience of present moment experience, thus, experienced fewer IBS symptoms. Both this study and our previously published clinical data showed that the MBSR therapy resulted in robust increases in mindfulness measures [ 51 ]. Specific mindfulness practices such as meta‐awareness and acceptance likely promote the shift of attention away from habitual, maladaptive predictions of pain by increasing inter‐network connectivity needed to interrupt the pain‐expectation cycle. Indeed, increased activity of sensory processing in the brain has been associated with reductions in pain in previous mindfulness studies [ 42 , 84 ]. Together this may suggest that more coordinated brain networks, particularly those associated with signaling salience, may be particularly beneficial for the management or reduction of pain. In conjunction with the intra‐network decreases observed within the default mode network and between the cognitive control and somatomotor networks, widespread inter‐network increases in connectivity were also observed, some showing associations with changes in additional clinical measures, including increases in functional connectivity between salience, default mode, somatomotor, control, and dorsal attention regions. This finding supports evidence showing that bodily regulation is not accomplished through a specific neural circuit, but instead is the product of a wide‐spread, coordinated communication network throughout the brain [ 27 , 85 , 86 ]. Depression and anxiety scores were low in the study participants and decreases seen in responders had a small effect size, potentially due to a floor effect for detecting change. Despite this, changes depression scores showed associations with brain regions linked to attention, sensation, and executive control. While fascinating to speculate that the adaptations seen in brain connectivity led to improvements in these self‐report measures, the study design does not allow us to determine the directionality or temporal sequence of the events. Furthermore, based on effect size estimation, Nonresponders compared to Responders tended to report more pain catastrophizing ( d = 0.0.49), greater tendency to report physical and symptoms and sensations ( d = 0.49), and higher gastrointestinal‐specific anxiety ( d = 0.67) suggesting these constructs may predict who may respond best to treatment and may map on to specific maladaptive neuroplastic changes. The findings of this study are line with previous research indicating that “brain‐directed” therapies like CBT [ 87 , 88 ], MBSR [ 51 ], and Hypnotherapy [ 89 ] improve IBS by essentially rewiring how the brain communicates with the gut. Similar decreases in default mode connectivity were observed in some of these studies despite the varied clinical interventions and differences in study populations. While specific mind–body interventions target different aspects of the IBS experience (e.g., CBT focuses on cognitive/affective processes whereas MBSR focuses on present moment awareness, interoception, and acceptance [ 51 ]), they may improve maladaptive neuroplasticity in brain networks responsible for pain, emotion, and self‐awareness through both shared and unique central mechanisms. While this study helps elucidate the mechanisms by which mindfulness improves symptoms in IBS, it also is helpful in highlighting the lack of change in those individuals who do not respond. It is fairly common for behavioral treatments in IBS to perform well in 52%–75% of individuals [ 51 , 90 , 91 ], without clarity as to why these treatments fail in a persistent minority. Identification of Nonresponders earlier in treatment may allow for targeted pharmacologic or other interventions to enhance mindfulness response and improve outcomes for this group. Several pharmacological agents including psychostimulants and psychedelics have been shown in other clinical populations to impact the connectivity of the default mode and widely distributed networks [ 92 , 93 , 94 ] and some of these same agents have been shown to augment mindfulness in experimental settings [ 95 , 96 ]. Interestingly, both psychedelics [ 92 ] and mindfulness practices [ 97 ] modulate activity within the DMN potentially decreasing self‐referential thought and enhancing perceptions of one's own psychological and physiological sensations. This opens the possibility that these chemical agents may serve as a catalyst or booster, enhancing the brain's receptivity to mindfulness training, with mindfulness providing the framework for integration and sustained wellbeing [ 95 , 96 ]. Indeed, emerging research suggests that psychedelics may also be a promising therapy for those with IBS [ 98 ]. While intriguing, this research is still in its early stages, and more rigorous studies are needed to confirm synergistic effects. The findings of this study should be considered in the context of their limitations. First, our study included an all‐female sample, thus can't be generalized to males. Also, the median raphe nucleus region comprised only 3.6 voxels at the acquisition resolution (3.4 × 3.4 × 4 mm) and is insufficient to support robust structure‐specific inference. While preprocessing resampled data to 2 × 2 × 2 mm isotropic voxels, this interpolation does not create additional independent spatial information. Signal from this region is dominated by partial volume effects from surrounding brainstem structures and measurement noise. Findings attributed to the median region should therefore be interpreted as reflecting broader midbrain or raphe cluster activity rather than the median raphe nucleus. High‐resolution imaging (≤ 2 mm isotropic acquisition) is required to make valid inferences about the median raphe specifically. Furthermore, the study only examined the brain at two points in time, which makes it more difficult to confirm whether the changes in brain function preceded symptom change or if the change in symptoms led to secondary neuroplasticity. Existing physiologic models favor the former hypothesis, and we believe the most likely sequence of events is that of acquired mindfulness stimulating brain changes followed by continued symptom improvement. We believe that the tendency of individuals undergoing mindfulness training in IBS and other pain conditions to have continued symptom improvement after the study ends supports this and suggests ongoing experience‐based improvement in the prediction models based on real‐time feedback. The current results in the context of the existing limitations provide goals for additional investigation to improve outcomes. Clarity on the temporal sequence of events would require clinical assessments and neuroimaging at more frequent intervals during the intervention. A study design with serial fMRI and symptom measurement to identify early signals of non‐response could help guide development of an IBS‐specific mindfulness program with even more widespread clinical benefit. In prior studies of IBS with cognitive behavioral therapy, rapid responders could be identified by week 4 of treatment [ 91 ]. A similar careful assessment of the timeline of symptom response in IBS would be useful in determining optimal timing of neuroimaging and potential augmenting pharmacological interventions. We know from other pain disorders that the reductions in symptoms after MBSR can be durable even a year after the cessation of treatment [ 99 , 100 , 101 ] therefore, imaging at time points at least 6–12 months after the intervention to assess for durability of both brain and symptomatic response would be helpful in the design of maintenance interventions. In summary, these results further confirm the importance of the gut‐brain axis in IBS symptom severity and expand understanding of the role of mindfulness in treatment of individuals with IBS. Specifically, it provides evidence for mindfulness‐based intervention related changes in neural activity that were predictive of improved IBS symptom severity. These findings highlight the widespread central impact of mindfulness, providing additional evidence for the distributed coordination of functional neural networks involved in allostasis and interoception. The current study contributes to the building of future rigorous studies by identifying the brain signatures of Responders and Nonresponders and helping to identify neural markers that can be used to better discover, personalize, or identify effective treatments for people with IBS. Author Contributions B.N., K.T., J.S.L. acquired funding and designed the research study; S.R.S., J.G.S. designed and led the MBSR groups; B.N., K.T., S.R.S., J.G.S. performed the research study; C.L. managed activities to produce metadata and maintain the research database; All authors were involved in data interpretation, writing the original draft, critical manuscript review and editing. All authors are qualified for authorship, have approved the final version of this manuscript, and agree to the accountability of its accuracy and integrity. Funding This work was supported by the National Institutes of Health (NIH) under the National Center for Complementary and Integrative Health (NCCIH), Grant Number R01 AT007137 (K.T./B.N.), and the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), Grant Number R01HD076756 (J.S.L.). Conflicts of Interest The authors declare no conflicts of interest. Supporting information Figure S1: Shift in IBS Symptom Severity Classification from Visit 1 to Visit 2. Alluvial plot visualizes how IBS Symptom Severity Classification changes over time in MBSR Responders and NonResponders. The colored ribbons show the flow from the original classification at Visit 1 to Visit 2. The numbers on the ribbons reflect the number of participants by responder status represented in each ribbon. NMO-38-e70315-s003.pdf (47.4KB, pdf) Data S1: Figure legends. NMO-38-e70315-s005.docx (15KB, docx) Table S1: Estimates of stability of features selected (PFER = 2, cutoff = 0.67). NMO-38-e70315-s002.docx (15.8KB, docx) Table S2: Regression of selected features at PFER = 2. NMO-38-e70315-s001.docx (15.7KB, docx) Data S2: nmo70315‐sup‐0005‐DataS2.docx. NMO-38-e70315-s004.docx (16.7KB, docx) Data Availability Statement The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions. References 1. Schaefer A., Kong R., Gordon E. M., et al., “Local‐Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI,” Cerebral Cortex 28, no. 9 (2018): 3095–3114, 10.1093/cercor/bhx179. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 2. Desikan R. S., Ségonne F., Fischl B., et al., “An Automated Labeling System for Subdividing the Human Cerebral Cortex on MRI Scans Into Gyral Based Regions of Interest,” NeuroImage 31, no. 3 (2006): 968–980. [ DOI ] [ PubMed ] [ Google Scholar ] 3. Whitfield‐Gabrieli S. and Nieto‐Castanon A., “Conn: A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks,” Brain Connectivity 2, no. 3 (2012): 125–141, Epub 2012/05/31, 10.1089/brain.2012.0073. [ DOI ] [ PubMed ] [ Google Scholar ] 4. Edlow B. L., Takahashi E., Wu O., et al., “Neuroanatomic Connectivity of the Human Ascending Arousal System Critical to Consciousness and its Disorders,” Journal of Neuropathology and Experimental Neurology 71, no. 6 (2012): 531–546, 10.1097/NEN.0b013e3182588293. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 5. Hulisz D., “The Burden of Illness of Irritable Bowel Syndrome: Current Challenges and Hope for the Future,” Journal of Managed Care & Specialty Pharmacy 10 (2004): 299–309, 10.18553/jmcp.2004.10.4.299. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 6. Shiha M. G. and Aziz I., “Review Article: Physical and Psychological Comorbidities Associated With Irritable Bowel Syndrome,” Alimentary Pharmacology & Therapeutics 54, no. Suppl 1 (2021): S12–s23, 10.1111/apt.16589. [ DOI ] [ PubMed ] [ Google Scholar ] 7. Zamani M., Alizadeh‐Tabari S., and Zamani V., “Systematic Review With Meta‐Analysis: The Prevalence of Anxiety and Depression in Patients With Irritable Bowel Syndrome,” Alimentary Pharmacology & Therapeutics 50 (2019): 132–143. [ DOI ] [ PubMed ] [ Google Scholar ] 8. Garland E. L., Gaylord S. A., Palsson O., Faurot K., Douglas Mann J., and Whitehead W. E., “Therapeutic Mechanisms of a Mindfulness‐Based Treatment for IBS: Effects on Visceral Sensitivity, Catastrophizing, and Affective Processing of Pain Sensations,” Journal of Behavioral Medicine 35 (2012): 591–602, 10.1007/s10865-011-9391-z. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 9. Icenhour A., Witt S. T., Elsenbruch S., et al., “Brain Functional Connectivity Is Associated With Visceral Sensitivity in Women With Irritable Bowel Syndrome,” NeuroImage: Clinical 15 (2017): 449–457, 10.1016/j.nicl.2017.06.001. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 10. Longarzo M., Quarantelli M., Aiello M., et al., “The Influence of Interoceptive Awareness on Functional Connectivity in Patients With Irritable Bowel Syndrome,” Brain Imaging and Behavior 11 (2017): 1117–1128, 10.1007/s11682-016-9595-5. [ DOI ] [ PubMed ] [ Google Scholar ] 11. Qi R., Liu C., Ke J., et al., “Intrinsic Brain Abnormalities in Irritable Bowel Syndrome and Effect of Anxiety and Depression,” Brain Imaging and Behavior 10 (2016): 1127–1134, 10.1007/s11682-015-9478-1. [ DOI ] [ PubMed ] [ Google Scholar ] 12. Aizawa E., Sato Y., Kochiyama T., et al., “Altered Cognitive Function of Prefrontal Cortex During Error Feedback in Patients With Irritable Bowel Syndrome, Based on FMRI and Dynamic Causal Modeling,” Gastroenterology 143 (2012): 1188–1198, 10.1053/j.gastro.2012.07.104. [ DOI ] [ PubMed ] [ Google Scholar ] 13. Hong J. Y., Naliboff B., Labus J. S., et al., “Altered Brain Responses in Subjects With Irritable Bowel Syndrome During Cued and Uncued Pain Expectation,” Neurogastroenterology and Motility 28 (2016): 127–138, 10.1111/nmo.12710. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 14. Hubbard C. S., Hong J., Jiang Z., et al., “Increased Attentional Network Functioning Related to Symptom Severity Measures in Females With Irritable Bowel Syndrome,” Neurogastroenterology and Motility 27 (2015): 1282–1294, 10.1111/nmo.12622. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 15. Labus J. S., Naliboff B. D., Berman S. M., et al., “Brain Networks Underlying Perceptual Habituation to Repeated Aversive Visceral Stimuli in Patients With Irritable Bowel Syndrome,” NeuroImage 47 (2009): 952–960, 10.1016/j.neuroimage.2009.05.078. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 16. Elsenbruch S., Rosenberger C., Bingel U., Forsting M., Schedlowski M., and Gizewski E. R., “Patients With Irritable Bowel Syndrome Have Altered Emotional Modulation of Neural Responses to Visceral Stimuli,” Gastroenterology 139 (2010): 1310–1319, 10.1053/j.gastro.2010.06.054. [ DOI ] [ PubMed ] [ Google Scholar ] 17. Elsenbruch S., Rosenberger C., Enck P., Forsting M., Schedlowski M., and Gizewski E. R., “Affective Disturbances Modulate the Neural Processing of Visceral Pain Stimuli in Irritable Bowel Syndrome: An fMRI Study,” Gut 59 (2010): 489–495, 10.1136/gut.2008.175000. [ DOI ] [ PubMed ] [ Google Scholar ] 18. Hall G. B., Kamath M. V., Collins S., et al., “Heightened Central Affective Response to Visceral Sensations of Pain and Discomfort in IBS,” Neurogastroenterology and Motility 22 (2010): 276–e280, 10.1111/j.1365-2982.2009.01436.x. [ DOI ] [ PubMed ] [ Google Scholar ] 19. Hong J. Y., Kilpatrick L. A., Labus J., et al., “Patients With Chronic Visceral Pain Show Sex‐Related Alterations in Intrinsic Oscillations of the Resting Brain,” Journal of Neuroscience 33 (2013): 11994–12002, 10.1523/JNEUROSCI.5733-12.2013. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 20. Hong J. Y., Kilpatrick L. A., Labus J. S., et al., “Sex and Disease‐Related Alterations of Anterior Insula Functional Connectivity in Chronic Abdominal Pain,” Journal of Neuroscience 34 (2014): 14252–14259, 10.1523/JNEUROSCI.1683-14.2014. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 21. Weng Y., Qi R., Liu C., et al., “Disrupted Functional Connectivity Density in Irritable Bowel Syndrome Patients,” Brain Imaging and Behavior 11 (2017): 1812–1822, 10.1007/s11682-016-9653-z. [ DOI ] [ PubMed ] [ Google Scholar ] 22. Ke J., Qi R., Liu C., et al., “Abnormal Regional Homogeneity in Patients With Irritable Bowel Syndrome: A Resting‐State Functional MRI Study,” Neurogastroenterology and Motility 27 (2015): 1796–1803, 10.1111/nmo.12692. [ DOI ] [ PubMed ] [ Google Scholar ] 23. Labus J. S., Hamadani K., Gupta A., et al., “Functional Network Properties of Brain Regions in Irritable Bowel Syndrome,” Gastroenterology 150 (2016): S529. [ Google Scholar ] 24. Ma X., Li S., Tian J., et al., “Altered Brain Spontaneous Activity and Connectivity Network in Irritable Bowel Syndrome Patients: A Resting‐State fMRI Study,” Clinical Neurophysiology 126 (2015): 1190–1197, 10.1016/j.clinph.2014.10.004. [ DOI ] [ PubMed ] [ Google Scholar ] 25. Qi R., Liu C., Ke J., et al., “Abnormal Amygdala Resting‐State Functional Connectivity in Irritable Bowel Syndrome,” AJNR. American Journal of Neuroradiology 37 (2016): 1139–1145, 10.3174/ajnr.A4655. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 26. Westlin C., Theriault J. E., Katsumi Y., et al., “Improving the Study of Brain‐Behavior Relationships by Revisiting Basic Assumptions,” Trends in Cognitive Sciences 27 (2023): 246–257. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 27. Katsumi Y., Theriault J. E., Quigley K. S., and Barrett L. F., “Allostasis as a Core Feature of Hierarchical Gradients in the Human Brain,” Network Neuroscience 6 (2022): 1010–1031. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 28. Barrett L. F. and Simmons W. K., “Interoceptive Predictions in the Brain,” Nature Reviews Neuroscience 16 (2015): 419–429. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 29. Shaffer C., Westlin C., Quigley K. S., Whitfield‐Gabrieli S., and Barrett L. F., “Allostasis, Action, and Affect in Depression: Insights From the Theory of Constructed Emotion,” Annual Review of Clinical Psychology 18 (2022): 553–580, 10.1146/annurev-clinpsy-081219-115627. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 30. Mayer E. A., Ryu H. J., and Bhatt R. R., “The Neurobiology of Irritable Bowel Syndrome,” Molecular Psychiatry 28 (2023): 1451–1465, 10.1038/s41380-023-01972-w. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 31. Doll A., Holzel B. K., Boucard C. C., Wohlschlager A. M., and Sorg C., “Mindfulness Is Associated With Intrinsic Functional Connectivity Between Default Mode and Salience Networks,” Frontiers in Human Neuroscience 9 (2015): 461, 10.3389/fnhum.2015.00461. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 32. Taylor V. A., Daneault V., Grant J., et al., “Impact of Meditation Training on the Default Mode Network During a Restful State,” Social Cognitive and Affective Neuroscience 8 (2013): 4–14, 10.1093/scan/nsr087. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 33. Gard T., Holzel B. K., Sack A. T., et al., “Pain Attenuation Through Mindfulness Is Associated With Decreased Cognitive Control and Increased Sensory Processing in the Brain,” Cerebral Cortex 22 (2012): 2692–2702, 10.1093/cercor/bhr352. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 34. Kral T. R. A., Imhoff‐Smith T., D. C. Dean, III , et al., “Mindfulness‐Based Stress Reduction‐Related Changes in Posterior Cingulate Resting Brain Connectivity,” Social Cognitive and Affective Neuroscience 14 (2019): 777–787, 10.1093/scan/nsz050. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 35. Lorenz J., Minoshima S., and Casey K. L., “Keeping Pain Out of Mind: The Role of the Dorsolateral Prefrontal Cortex in Pain Modulation,” Brain 126 (2003): 1079–1091, 10.1093/brain/awg102. [ DOI ] [ PubMed ] [ Google Scholar ] 36. Su I. W., Wu F. W., Liang K. C., et al., “Pain Perception Can be Modulated by Mindfulness Training: A Resting‐State fMRI Study,” Frontiers in Human Neuroscience 10 (2016): 570, 10.3389/fnhum.2016.00570. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 37. Jabbi M., Bastiaansen J., and Keysers C., “A Common Anterior Insula Representation of Disgust Observation, Experience and Imagination Shows Divergent Functional Connectivity Pathways,” PLoS One 3 (2008): e2939, 10.1371/journal.pone.0002939. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 38. Xiao Q., Zhao X., Bi G., et al., “Alterations of Regional Homogeneity and Functional Connectivity Following Short‐Term Mindfulness Meditation in Healthy Volunteers,” Frontiers in Human Neuroscience 13 (2019): 376, 10.3389/fnhum.2019.00376. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 39. Farb N. A., Segal Z. V., and Anderson A. K., “Mindfulness Meditation Training Alters Cortical Representations of Interoceptive Attention,” Social Cognitive and Affective Neuroscience 8 (2013): 15–26, 10.1093/scan/nss066. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 40. Vishnubhotla R. V., Radhakrishnan R., Kveraga K., et al., “Advanced Meditation Alters Resting‐State Brain Network Connectivity Correlating With Improved Mindfulness,” Frontiers in Psychology 12 (2021): 745344. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 41. Hasenkamp W. and Barsalou L. W., “Effects of Meditation Experience on Functional Connectivity of Distributed Brain Networks,” Frontiers in Human Neuroscience 6 (2012): 38. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 42. Harrison R., Zeidan F., Kitsaras G., Ozcelik D., and Salomons T. V., “Trait Mindfulness Is Associated With Lower Pain Reactivity and Connectivity of the Default Mode Network,” Journal of Pain 20 (2019): 645–654, 10.1016/j.jpain.2018.10.011. [ DOI ] [ PubMed ] [ Google Scholar ] 43. Simon R. and Engstrom M., “The Default Mode Network as a Biomarker for Monitoring the Therapeutic Effects of Meditation,” Frontiers in Psychology 6 (2015): 776, 10.3389/fpsyg.2015.00776. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 44. Alshelh Z., Marciszewski K. K., Akhter R., et al., “Disruption of Default Mode Network Dynamics in Acute and Chronic Pain States,” NeuroImage: Clinical 17 (2018): 222–231, 10.1016/j.nicl.2017.10.019. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 45. Mayer E. A., Labus J., Aziz Q., et al., “Role of Brain Imaging in Disorders of Brain‐Gut Interaction: A Rome Working Team Report,” Gut 68 (2019): 1701–1715, 10.1136/gutjnl-2019-318308. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 46. Mooneyham B. W., Mrazek M. D., Mrazek A. J., and Schooler J. W., “Signal or Noise: Brain Network Interactions Underlying the Experience and Training of Mindfulness,” Annals of the New York Academy of Sciences 1369 (2016): 240–256, 10.1111/nyas.13044. [ DOI ] [ PubMed ] [ Google Scholar ] 47. Mayer E. A., Labus J. S., Tillisch K., Cole S. W., and Baldi P., “Towards a Systems View of IBS,” Nature Reviews. Gastroenterology & Hepatology 12 (2015): 592–605, 10.1038/nrgastro.2015.121. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 48. Barrett L. F., Quigley K. S., and Hamilton P., “An Active Inference Theory of Allostasis and Interoception in Depression,” Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 371 (2016): 20160011, 10.1098/rstb.2016.0011. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 49. Zernicke K. A., Campbell T. S., Blustein P. K., et al., “Mindfulness‐Based Stress Reduction for the Treatment of Irritable Bowel Syndrome Symptoms: A Randomized Wait‐List Controlled Trial,” International Journal of Behavioral Medicine 20 (2013): 385–396, 10.1007/s12529-012-9241-6. [ DOI ] [ PubMed ] [ Google Scholar ] 50. Gaylord S. A., Palsson O. S., Garland E. L., et al., “Mindfulness Training Reduces the Severity of Irritable Bowel Syndrome in Women: Results of a Randomized Controlled Trial,” American Journal of Gastroenterology 106 (2011): 1678–1688, 10.1038/ajg.2011.184. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 51. Naliboff B. D., Smith S. R., Serpa J. G., et al., “Mindfulness‐Based Stress Reduction Improves Irritable Bowel Syndrome (IBS) Symptoms via Specific Aspects of Mindfulness,” Neurogastroenterology and Motility 32 (2020): e13828, 10.1111/nmo.13828. [ DOI ] [ PubMed ] [ Google Scholar ] 52. Zomorodi S., Abdi S., and Tabatabaee S. K., “Comparison of Long‐Term Effects of Cognitive‐Behavioral Therapy Versus Mindfulness‐Based Therapy on Reduction of Symptoms Among Patients Suffering From Irritable Bowel Syndrome,” Gastroenterology and Hepatology From Bed to Bench 7 (2014): 118–124. [ PMC free article ] [ PubMed ] [ Google Scholar ] 53. Baer R. A., “Mindfulness Training as a Clinical Intervention: A Conceptual and Empirical Review,” Clnical Psychology Science and Practice 10 (2006): 125–143. [ Google Scholar ] 54. Francis C. Y., Morris J., and Whorwell P. J., “The Irritable Bowel Severity Scoring System: A Simple Method of Monitoring Irritable Bowel Syndrome and Its Progress,” Alimentary Pharmacology & Therapeutics 11 (1997): 395–402, 10.1046/j.1365-2036.1997.142318000.x. [ DOI ] [ PubMed ] [ Google Scholar ] 55. Baer R. A., Smith G. T., Lykins E., et al., “Construct Validity of the Five Facet Mindfulness Questionnaire in Meditating and Nonmeditating Samples,” Assessment 15 (2008): 329–342, 10.1177/1073191107313003. [ DOI ] [ PubMed ] [ Google Scholar ] 56. Patrick D. L., Drossman D. A., Frederick I. O., DiCesare J., and Puder K. L., “Quality of Life in Persons With Irritable Bowel Syndrome: Development and Validation of a New Measure,” Digestive Diseases and Sciences 43 (1998): 400–411, 10.1023/a:1018831127942. [ DOI ] [ PubMed ] [ Google Scholar ] 57. Labus J. S., Bolus R., Chang L., et al., “The Visceral Sensitivity Index: Development and Validation of a Gastrointestinal Symptom‐Specific Anxiety Scale,” Alimentary Pharmacology & Therapeutics 20 (2004): 89–97, 10.1111/j.1365-2036.2004.02007.x. [ DOI ] [ PubMed ] [ Google Scholar ] 58. Zigmond A. S. and Snaith R. P., “The Hospital Anxiety and Depression Scale,” Acta Psychiatrica Scandinavica 67 (1983): 361–370, 10.1111/j.1600-0447.1983.tb09716.x. [ DOI ] [ PubMed ] [ Google Scholar ] 59. Kroenke K., Spitzer R. L., and Williams J. B., “The PHQ‐15: Validity of a New Measure for Evaluating the Severity of Somatic Symptoms,” Psychosomatic Medicine 64 (2002): 258–266, 10.1097/00006842-200203000-00008. [ DOI ] [ PubMed ] [ Google Scholar ] 60. Sullivan M. J. L., Bishop S. R., and Pivik J., “The Pain Catastrophizing Scale: Development and Validation,” Psychological Assessment 7 (1995): 524–532, 10.1037/1040-3590.7.4.524. [ DOI ] [ Google Scholar ] 61. Pennebaker J. W., The Psychology of Physical Symptoms (Springer, 1982). [ Google Scholar ] 62. Roelofs J., Peters M. L., McCracken L., and Vlaeyen J. W. S., “The Pain Vigilance and Awareness Questionnaire (PVAQ): Further Psychometric Evaluation in Fibromyalgia and Other Chronic Pain Syndromes,” Pain 101 (2003): 299–306. [ DOI ] [ PubMed ] [ Google Scholar ] 63. McCracken L. M., ““Attention” to Pain in Persons With Chronic Pain: A Behavioral Approach,” Behavior Therapy 28 (1997): 271–284. [ Google Scholar ] 64. Power J. D., Barnes K. A., Snyder A. Z., Schlaggar B. L., and Petersen S. E., “Spurious but Systematic Correlations in Functional Connectivity MRI Networks Arise From Subject Motion,” NeuroImage 59 (2012): 2142–2154, 10.1016/j.neuroimage.2011.10.018. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 65. Yan X., Kong R., Xue A., et al., “Homotopic Local‐Global Parcellation of the Human Cerebral Cortex From Resting‐State Functional Connectivity,” NeuroImage 273 (2023): 120010, 10.1016/j.neuroimage.2023.120010. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 66. Friedman J., Hastie T., and Tibshirani R., “Regularization Paths for Generalized Linear Models via Coordinate Descent,” Journal of Statistical Software 33 (2010): 1–22. [ PMC free article ] [ PubMed ] [ Google Scholar ] 67. Hofner B., Boccuto L., and Göker M., “Controlling False Discoveries in High‐Dimensional Situations: Boosting With Stability Selection,” BMC Bioinformatics 16 (2015): 1–17. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 68. Hofner B. and Hothorn T., “Stabs: Stability Selection With Error Control, R Package Version 0.6–3,” (2017), https://CRAN.R‐project.org/package=stabs . 69. Shah R. D. and Samworth R. J., “Variable Selection With Error Control: Another Look at Stability Selection,” Journal of the Royal Statistical Society. Series B, Statistical Methodology 75 (2013): 55–80, 10.1111/j.1467-9868.2011.01034.x. [ DOI ] [ Google Scholar ] 70. McFadden D., Conditional Logit Analysis of Qualitative Choice Behavior (University of California, 1972). [ Google Scholar ] 71. Dogrusoz U., Giral E., Cetintas A., Civril A., and Demir E., “A Layout Algorithm for Undirected Compound Graphs,” Information Sciences 179 (2009): 980–994. [ Google Scholar ] 72. Gibson J., “Mindfulness, Interoception, and the Body: A Contemporary Perspective,” Frontiers in Psychology 10 (2019): 2012, 10.3389/fpsyg.2019.02012. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 73. Santamaría‐García H., Migeot J., Medel V., et al., “Allostatic Interoceptive Overload Across Psychiatric and Neurological Conditions,” Biological Psychiatry 97 (2025): 28–40, 10.1016/j.biopsych.2024.06.024. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 74. Theriault J. E., Katsumi Y., Reimann H. M., et al., “It's Not the Thought That Counts: Allostasis at the Core of Brain Function,” Neuron 113 (2025): 4107–4133, 10.1016/j.neuron.2025.09.028. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 75. Labus J. S., Hubbard C. S., Bueller J., et al., “Impaired Emotional Learning and Involvement of the Corticotropin‐Releasing Factor Signaling System in Patients With Irritable Bowel Syndrome,” Gastroenterology 145 (2013): 1253–1261.e1251–1253, 10.1053/j.gastro.2013.08.016. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 76. Kano M., Muratsubaki T., Morishita J., et al., “Influence of Uncertain Anticipation on Brain Responses to Aversive Rectal Distension in Patients With Irritable Bowel Syndrome,” Psychosomatic Medicine 79 (2017): 988–999, 10.1097/psy.0000000000000484. [ DOI ] [ PubMed ] [ Google Scholar ] 77. Schröder L., Regnath F., Glasauer S., et al., “Altered Sensorimotor Processing in Irritable Bowel Syndrome: Evidence for a Transdiagnostic Pathomechanism in Functional Somatic Disorders,” Frontiers in Neuroscience 16 (2022): 1029126, 10.3389/fnins.2022.1029126. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 78. Chou T., Deckersbach T., Dougherty D. D., and Hooley J. M., “The Default Mode Network and Rumination in Individuals at Risk for Depression,” Social Cognitive and Affective Neuroscience 18 (2023): nsad032, 10.1093/scan/nsad032. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 79. Shaurya Prakash R., De Leon A. A., Klatt M., Malarkey W., and Patterson B., “Mindfulness Disposition and Default‐Mode Network Connectivity in Older Adults,” Social Cognitive and Affective Neuroscience 8 (2013): 112–117. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 80. Rahrig H., Vago D. R., Passarelli M. A., Auten A., Lynn N. A., and Brown K. W., “Meta‐Analytic Evidence That Mindfulness Training Alters Resting State Default Mode Network Connectivity,” Scientific Reports 12 (2022): 12260. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 81. Parkinson T. D., Kornelsen J., and Smith S. D., “Trait Mindfulness and Functional Connectivity in Cognitive and Attentional Resting State Networks,” Frontiers in Human Neuroscience 13 (2019): 112. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 82. Cavanna A. E. and Trimble M. R., “The Precuneus: A Review of Its Functional Anatomy and Behavioural Correlates,” Brain 129 (2006): 564–583, 10.1093/brain/awl004. [ DOI ] [ PubMed ] [ Google Scholar ] 83. Herbet G., Lemaitre A. L., Moritz‐Gasser S., Cochereau J., and Duffau H., “The Antero‐Dorsal Precuneal Cortex Supports Specific Aspects of Bodily Awareness,” Brain 142 (2019): 2207–2214, 10.1093/brain/awz179. [ DOI ] [ PubMed ] [ Google Scholar ] 84. Boly M., Balteau E., Schnakers C., et al., “Baseline Brain Activity Fluctuations Predict Somatosensory Perception in Humans,” Proceedings of the National Academy of Sciences 104 (2007): 12187–12192. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 85. Zhang J., Chen D., Deming P., et al., “Cortical and Subcortical Mapping of the Allostatic‐Interoceptive System in the Human Brain: Replication and Extension With 7 Tesla fMRI,” preprint, bioRxiv, January 13, 2023, 10.1101/2023.07.20.548178. [ DOI ] 86. Kleckner I. R., Zhang J., Touroutoglou A., et al., “Evidence for a Large‐Scale Brain System Supporting Allostasis and Interoception in Humans,” Nature Human Behaviour 1 (2017): e0069, 10.1038/s41562-017-0069. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 87. Jacobs J. P., Gupta A., Bhatt R. R., et al., “Cognitive Behavioral Therapy for Irritable Bowel Syndrome Induces Bidirectional Alterations in the Brain‐Gut‐Microbiome Axis Associated With Gastrointestinal Symptom Improvement,” Microbiome 9 (2021): 236, 10.1186/s40168-021-01188-6. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 88. Lackner J. M., Lou Coad M., Mertz H. R., et al., “Cognitive Therapy for Irritable Bowel Syndrome Is Associated With Reduced Limbic Activity, GI Symptoms, and Anxiety,” Behaviour Research and Therapy 44 (2006): 621–638, 10.1016/j.brat.2005.05.002. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 89. Lowén M. B., Mayer E. A., Sjöberg M., et al., “Effect of Hypnotherapy and Educational Intervention on Brain Response to Visceral Stimulus in the Irritable Bowel Syndrome,” Alimentary Pharmacology & Therapeutics 37 (2013): 1184–1197, 10.1111/apt.12319. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 90. Häuser W., “Gut‐Directed Hypnosis and Hypnotherapy for Irritable Bowel Syndrome: A Mini‐Review,” Frontiers in Psychology 15 (2024): 1389911. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 91. Lackner J. M., Gudleski G. D., Keefer L., Krasner S. S., Powell C., and Katz L. A., “Rapid Response to Cognitive Behavior Therapy Predicts Treatment Outcome in Patients With Irritable Bowel Syndrome,” Clinical Gastroenterology and Hepatology 8 (2010): 426–432. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 92. Gattuso J. J., Perkins D., Ruffell S., et al., “Default Mode Network Modulation by Psychedelics: A Systematic Review,” International Journal of Neuropsychopharmacology 26 (2023): 155–188. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 93. Pereira V. and de Castro‐Manglano P., “The Effects of Medication on Default Mode Network (DMN) Connectivity in Attention Deficit/Hyperactivity Disorder (ADHD): Bibliographic Review,” European Psychiatry 41 (2017): S629, 10.1016/j.eurpsy.2017.01.1022. [ DOI ] [ Google Scholar ] 94. Siegel J. S., Subramanian S., Perry D., et al., “Psilocybin Desynchronizes the Human Brain,” Nature 632 (2024): 131–138, 10.1038/s41586-024-07624-5. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 95. Holas P. and Kamińska J., “Mindfulness Meditation and Psychedelics: Potential Synergies and Commonalities,” Pharmacological Reports 75 (2023): 1398–1409. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 96. Thomas E. M., Freeman T. P., Poplutz P., et al., “Stimulating Meditation: A Pre‐Registered Randomised Controlled Experiment Combining a Single Dose of the Cognitive Enhancer, Modafinil, With Brief Mindfulness Training,” Journal of Psychopharmacology 35 (2021): 621–630. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 97. Brewer J. A., Worhunsky P. D., Gray J. R., Tang Y. Y., Weber J., and Kober H., “Meditation Experience Is Associated With Differences in Default Mode Network Activity and Connectivity,” Proceedings of the National Academy of Sciences 108 (2011): 20254–20259. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 98. Mauney E., F. King, Iv , Burton‐Murray H., and Kuo B., “Psychedelic‐Assisted Therapy as a Promising Treatment for Irritable Bowel Syndrome,” Journal of Clinical Gastroenterology 59 (2025): 385–392. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 99. Cherkin D. C., Anderson M. L., Sherman K. J., et al., “Two‐Year Follow‐Up of a Randomized Clinical Trial of Mindfulness‐Based Stress Reduction vs Cognitive Behavioral Therapy or Usual Care for Chronic Low Back Pain,” JAMA 317 (2017): 642–644. [ DOI ] [ PubMed ] [ Google Scholar ] 100. Damico V., Milani M., and Murano L., “Effect of Mindfulness‐Based Stress Reduction on Patients With Chronic Pain: A Randomized Controlled Trial,” Pain Management Nursing 26 (2025): 510–522. [ DOI ] [ PubMed ] [ Google Scholar ] 101. Zeidan F. and Vago D. R., “Mindfulness Meditation–Based Pain Relief: A Mechanistic Account,” Annals of the New York Academy of Sciences 1373 (2016): 114–127. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials Figure S1: Shift in IBS Symptom Severity Classification from Visit 1 to Visit 2. Alluvial plot visualizes how IBS Symptom Severity Classification changes over time in MBSR Responders and NonResponders. The colored ribbons show the flow from the original classification at Visit 1 to Visit 2. The numbers on the ribbons reflect the number of participants by responder status represented in each ribbon. NMO-38-e70315-s003.pdf (47.4KB, pdf) Data S1: Figure legends. NMO-38-e70315-s005.docx (15KB, docx) Table S1: Estimates of stability of features selected (PFER = 2, cutoff = 0.67). NMO-38-e70315-s002.docx (15.8KB, docx) Table S2: Regression of selected features at PFER = 2. NMO-38-e70315-s001.docx (15.7KB, docx) Data S2: nmo70315‐sup‐0005‐DataS2.docx. NMO-38-e70315-s004.docx (16.7KB, docx) Data Availability Statement The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions. 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