Resting-state 7T brain fMRI reveals common neurobiological changes in patients with Crohn’s disease and major depressive disorder

Resting-state 7T brain fMRI reveals common neurobiological changes in patients with Crohn’s disease and major depressive disorder | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Resting-state 7T brain fMRI reveals common neurobiological changes in patients with Crohn’s disease and major depressive disorder Ravichandran Rajkumar, Hanna Hartmann, Marja-Lisa Berthold, Shukti Ramkiran, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5349946/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Sep, 2025 Read the published version in Communications Medicine → Version 1 posted You are reading this latest preprint version Abstract Psychological stress is increasingly recognized as a key factor in Crohn’s disease (CD), yet the neurobiological connections between CD and major depressive disorder (MDD) remain poorly defined. In this study, we utilize advanced neuroimaging techniques to explore these neurobiological changes. Resting-state functional MRI (RS-fMRI) was performed on CD patients, MDD patients, and healthy controls (HCs) using a 7 Tesla scanner. CD patients showed higher depression scores than HCs but lower than those of patients with MDD. There was also a clear link between the severity of digestive symptoms and depression scores in CD patients. RS-fMRI analysis identified both CD and MDD patients had changes in activity in the precuneus region. Additionally, the connectivity between the precuneus and anterior cingulate cortex was also similarly altered in both groups. These findings reveal overlapping neurobiological pathways and underscore the importance of integrated therapeutic strategies to address the comorbidities of CD and MDD. Biological sciences/Psychology Biological sciences/Neuroscience Biological sciences/Biological techniques/Imaging/Functional magnetic resonance imaging Crohn’s disease Major depressive disorder Resting-state fMRI fALFF Seed-based connectivity Ultra-high field BDI-II GSRS Figures Figure 1 Figure 2 Figure 3 Figure 4 Highlights 1. Significant differences in BDI-II scores among CD, MDD, and HC groups. 2. Positive correlation between GSRS and BDI-II scores in CD patients. 3. fMRI revealed significant clusters in the precuneus cortex. 4. Shared connectivity changes in CD and MDD imply integrated treatments. Introduction The central nervous system has a profound impact on complex processes, such as metabolism and immunity, at distant body sites 1 . This effect is particularly pronounced in inflammatory bowel disease (IBD), with several epidemiologic studies showing that stressful life events can trigger IBD flares 2 – 4 . Crohn's disease (CD), one of the two major subtypes of IBD, represents a chronic inflammatory condition of the gastrointestinal tract, impacting millions worldwide, with a notable prevalence in developed nations 5 . Patients with CD exhibit higher incidences of MDD compared to the general population, and stressful life events have been linked to IBD flares, suggesting a bidirectional link 6 . However, the neurobiological correlates of stress-induced IBD exacerbations remain incompletely understood. Neuroimaging technologies – particularly magnetic resonance imaging (MRI) and functional MRI (fMRI) – enable the precise investigation of altered brain structure and functioning underlying the (comorbid) psychiatric disorders. Blood oxygenation level dependent (BOLD) fMRI is a neuroimaging method that measures brain activity by indirectly detecting the associated changes in blood flow and blood oxygenation that follow neuronal activity 7 . Resting-state fMRI is an fMRI technique that measures the low-frequency fluctuations in the BOLD signal while the subject is in the resting condition (not actively performing any task) 8 . Compared to fMRI conducted at standard MRI field strengths, ultra-high field (UHF) 7T fMRI offers significantly improved resolution with enhanced signal-to-noise (SNR) and contrast-to-noise (CNR) ratios, enabling more precise imaging 9 – 11 . This allows for the detection of neurostructural changes, connectivity alterations in smaller brain regions, and the identification of potential biomarkers that are not visible with lower magnetic field strengths. To the best of the authors' knowledge, no research team has previously used 7T fMRI to compare CD and MDD directly. Hence, the aim of this study is to precisely identify brain regions associated with CD and MDD, thereby paving the way for integrated approaches to managing these complex diseases and offering new perspectives on the bidirectional relationship. Prior research has established associations between CD and MDD 2 – 4 , yet, to the best of the authors' understanding, to date, no study has concurrently examined both patient groups to elucidate the shared neurobiological alterations underlying these conditions. The present investigation posits a hypothesis that functional changes manifest in both CD and MDD cohorts, associated with psychological comorbidities and shared neurobiological disruptions when compared with healthy individuals. To explore this hypothesis, resting-state functional magnetic resonance imaging (rs-fMRI) experiments were conducted with an ultra-high field 7 Tesla (7T) scanner across both patient groups alongside age and gender-matched HCs. The use of a 7T scanner significantly improves the outcome of fMRI signals compared to those obtained at lower field strengths (resolution, SNR, and CNR), bringing considerable benefits to the study 10 , 11 . The objective centres on identifying shared neurobiological modifications in cerebral function across CD and MDD groups. To achieve this, we used the fractional amplitude of low-frequency fluctuations (fALFF) as a neurophysiological marker, capturing the intrinsic low-frequency oscillations in cerebral activity during the resting state. fALFF serves as an index to quantify spontaneous brain activity in brain regions, particularly in resting-state fMRI data. In the first step, regions exhibiting commonly altered BOLD signals in both the CD and MDD groups compared to HCs were identified using fALFF maps. In the subsequent step, a data-driven approach was applied using the peak voxel locations of the altered regions in the fALFF maps as seeds for connectivity analysis. This allowed the identification of specific regions showing changed connectivity between different contrasts of the three observed groups (CD, MDD and HC). These regions present a promising area for understanding the impact of CD and MDD on brain structures and their comorbidities origin more comprehensively. Methods Experimental Modal and study participant details: CD patients (n = 18, age = 29,79 ± 6,84; 12 males) along with age matched MDD patients (n = 18, age = 28,97 ± 5,96; 13 males), and HCs (n = 18, age = 28,35 ±, 5,23; 12 males) were included in the study. The CD patients were recruited from the Clinic for Gastroenterology, Metabolic Diseases, and Internal Intensive Care Medicine (Medical Clinic III), University Hospital Aachen, Germany. The CD patients were diagnosed based on results from colonoscopy and histology, which were evaluated by experienced gastroenterologists in the clinic. In addition to the clinical diagnosis, gastrointestinal symptoms in CD patients were assessed via the Gastrointestinal Symptom Rating Scale (GSRS), 17 licenced from AstraZeneca AB, Sweden. The GSRS questionnaire consists of 15 items, divided into five sub-scores. These sub-scores describe the symptom clusters of reflux, abdominal pain, indigestion, diarrhoea and constipation. A 7-point scale reflects the current status of the symptom severity profile, where 1 describes the absence of the symptom, and 7 is the highest symptom severity 18 , 19 . The MDD patients were recruited from the Clinic for Psychiatry, Psychotherapy and Psychosomatics, University Hospital Aachen. The MDD patients were diagnosed based on ICD − 10 and DSM-5 criteria and an absence of psychotic features. The HC group was recruited based on having no history of neurologic or psychiatric disorders, as determined by the German version 6.0.0 of the Mini International Neuropsychiatric Interview (MINI) 20 . The handedness of all the subjects was assessed using the Edinburgh Handedness Inventory. Only right-handed subjects with no contraindication for 7T MRI were included in the study. Additionally, depression symptom severity in all subjects was assessed using the German version of the Beck Depression Inventory-II (BDI-II) 16 . The BDI-II consists of 21 multiple-choice items, with four possible responses each. Statistical analysis All statistical analysis was performed using the MATLAB (R2022a) software package. Differences in BDI-II scores were compared using a rank-based nonparametric Kruskal-Wallis test ( kruskalwallis function in MATLAB). Subsequent post-hoc analyses were conducted using the multcompare function in MATLAB to determine the specific group differences. Within each Kruskal-Wallis test, the corrections for multiple comparisons was performed using the Bonferroni correction method with a significance level of 5%. To explore associations between gastrointestinal symptoms and depression symptom severity in CD patients, a correlation analysis was performed between the GSRS total as well as the sub-scores and BDI-II scores, respectively. Spearman’s correlation coefficients were computed with a significance level of 5%. In all of the correlation analyses, the family-wise error rate (FWER), due to multiple comparisons, was controlled for using a permutation test 15 . 1000 permutations were performed for each comparison (correlation), and the p-value was adjusted using the “max statistics” method 15 . MRI data acquisition MRI data acquisition was performed at Forschungszentrum Juelich using a 7T Magnetom Terra scanner (Siemens Healthineers, Erlangen, Germany) equipped with a 1Tx/32Rx Head Coil 7T Clinical (Nova Medical, Wilmongton, MA, USA). Resting-state fMRI data were acquired using a 2D T2* weighted multiband accelerated echo planar imaging (EPI) sequence developed at the Center for Magnetic Resonance Research (CMRR), Minneapolis, MN, USA ( https://www.cmrr.umn.edu/multiband/ ) 21–23 . The entire brain was covered with a field of view (FOV) of 220 x 220 mm 2 , a matrix size of 168 x 168, and a slice thickness of 1.3mm. In total, 305 volumes with 100 slices each were acquired with a repetition time (TR) of 2000 ms, an echo time (TE) of 25 ms, and a flip angle (FA) of 70° using a multiband factor of 4. Subjects were instructed to close their eyes and not to fall asleep during the resting-state measurement. In addition, the lights in the scanner room were switched off during the entire resting-state measurement. To correct for susceptibility-induced geometric distortions, two additional fMRI volumes were recorded with opposite phase encoding direction (posterior-anterior phase-encoding). Structural images were obtained using a T1-weighted MP2RAGE. The MP2RAGE acquires two gradient echo images with different inversion times (TI) and flip angles (FA) (inversion image 1 (INV1) TI = 840 ms, flip, FA = 4°, INV2 TI = 2370 ms, FA = 5°). The other sequence-related parameters were similar for both gradient echo images: echo time (TE) = 1.99 ms; repetition time (TR) = 4500 ms for signal-to-noise ratio (SNR) optimization. The image matrix was set to 320 x 300, achieving an isotropic resolution of 0.75 mm 3 in 208 sagittal slices. The T1-weighted anatomical images referred to here were produced by combining the two gradient echo images by means of a ratio 24 . fMRI data preprocessing The raw DICOM scans (structural and functional) were 3D converted into the neuroimaging informatics technology initiative (NIfTI) format using the dcm2niix tool 14 . The 3D structural and functional images were visually audited to check for poor scan quality, artefacts and abnormal tissues using FSL View software ( https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FslView ). Due to poor quality fMRI scans and artefacts, data from five CD patients were excluded. This resulted in 13 CD patients (age = 29,43 ± 7,44; 9 males), 13 age-matched MDD patients (age = 29,04 ± 6,10; 9 males) and 13 HCs (age = 28,77 ± 6,65; 9 males) being included in the fMRI analysis. The susceptibility-induced off-resonance field (fieldmap) was initially estimated using a method similar to that described in 25 as implemented in FSL 26 . In the next step, the fMRI images were pre-processed using CONN 13 release 22.a 27 , SPM 12 release 12.7771 and MATLAB (R2022a). Functional and anatomical data were pre-processed using a flexible pre-processing pipeline 28 , which included the creation of voxel-displacement maps, realignment with susceptibility distortion correction using fieldmaps, slice timing correction, outlier detection, direct segmentation and MNI-space normalization, and smoothing. Functional data were realigned using the SPM realign and unwarp procedure 29 with integrated fieldmaps for susceptibility distortion correction. All scans were co-registered to a reference image (first fMRI volume) using a least-squares approach and a 6-parameter (rigid body) transformation and were then resampled using b-spline interpolation 30 to correct for motion, magnetic susceptibility geometric distortions, and their interaction simultaneously. Temporal misalignment between slices of the functional data was corrected using the SPM slice-timing correction (STC) procedure 31 , 32 with sinc temporal interpolation to resample each slice’s BOLD timeseries to a common mid-acquisition time. Potential outliers were identified using ART 33 based on framewise displacement above 0.9 mm or global BOLD signal changes above 5 standard deviations 34 , 35 . A reference BOLD image was computed for each subject by averaging all scans excluding outliers. Both functional and anatomical data were normalized to standard MNI space, segmented into grey matter, white matter, and CSF tissue classes, and resampled to 1 mm isotropic voxels following a direct normalization procedure 35 , 36 using the SPM unified segmentation and normalization algorithm 37 , 38 with the default IXI-549 tissue probability map template. Finally, functional data were smoothed using spatial convolution with a 4 mm full width half maximum (FWHM) Gaussian kernel. Functional data were also denoised using a standard denoising pipeline 28 , which included the regression of potential confounding effects such as white matter timeseries (5 CompCor noise components), CSF timeseries (5 CompCor noise components), motion parameters and their first order derivatives (12 factors) 39 , outlier scans (below 26 factors) 34 , and linear trends (2 factors), within each functional run. This was followed by bandpass frequency filtering of the BOLD timeseries 40 between 0.008 Hz and 0.09 Hz. CompCor 41 , 42 noise components within white matter and CSF were estimated by computing the average BOLD signal as well as the largest principal components orthogonal to the BOLD average, motion parameters, and outlier scans within each subject's eroded segmentation masks. From the number of noise terms included in this denoising strategy, the effective degrees of freedom of the BOLD signal after denoising were estimated to range from 83 to 91.5 (average 90.5) across all subjects 35 . fALFF - first and group-level analysis Following preprocessing, rs-fMRI-fALFF was calculated using the processing pipelines from the CONN toolbox 13 . fALFF maps characterizing low-frequency BOLD signal variability at each voxel were estimated as the ratio between the root mean square (RMS) of the BOLD signal after denoising and band-pass filtering between 0.008 Hz and 0.09 Hz, divided by the same measure computed before band-pass filtering 43 . Finally, fALFF values across voxels were rank sorted and normalized separately for each individual subject using a Gaussian inverse cumulative distribution function with zero mean and unit variance. To test our hypothesis of shared neurobiological changes in CD and MDD patients compared to HCs, a group-level general linear model (GLM) 28 based analysis was performed on normalized fALFF maps. For each individual voxel, a separate GLM was estimated, with the fALFF value at this voxel as the dependent variable and group BDI-II scores, age, and gender were set as the independent variables. The group analysis was performed to test the common differences between CD and MDD patients and HCs. The contrast was designed as [1 -0.5 -0.5 0 0 0] for HCs, CD patients, MDD patients, age, gender, and BDI-II score, respectively, to directly compare the average effect of CD and MDD patients against the HCs. Voxel-level hypotheses were evaluated using multivariate parametric statistics with random effects across subjects. Inferences were performed at the level of individual clusters (groups of contiguous voxels). Cluster-level inferences were based on parametric statistics from Gaussian Random Field theory 28 , 44 . Results were thresholded using a combination of a cluster-forming p < 0.01 voxel-level threshold and a familywise corrected p-FDR < 0.05 cluster-size threshold 45 . The rationale for utilizing a lower threshold (p < 0.01) at the voxel level was to enhance the detection of potential signals of interest given the low sample size while also acknowledging the potential for increased false positives. However, the subsequent application of a stringent correction for multiple comparisons at the cluster level (p-FDR < 0.05) ensured that only those clusters with a high degree of statistical reliability were considered significant. This two-level thresholding method facilitates the discovery of true differences in neural activity patterns while minimizing the risk of Type I errors. The peak cluster positions of significant clusters were considered as seeds for subsequent seed-based connectivity analysis. Seed-based connectivity - first and group-level analysis In order to find functional connectivity differences, whole-brain seed-based connectivity maps (SBC) were estimated using 10 mm spherical seed regions created from the significant peak cluster positions of fALFF. Functional connectivity strength was represented using Fisher-transformed bivariate correlation coefficients from a weighted GLM 28 . Functional connectivity strength was defined separately for each pair of seed and target voxels, modelling the association between their BOLD signal timeseries. In order to compensate for possible transient magnetization effects at the beginning of each run, individual scans were weighted using a step function convolved with an SPM canonical hemodynamic response function and rectified. Group-level analysis was performed on SBC maps using separate GLM models to test differences between the HC group and the MDD group (HC > MDD), the HC group and the CD group (HC > CD), the HC group compared to a combined average of the MDD and CD groups (HC > 0.5MDD + 0.5CD), and regions with altered connectivity between the MDD and CD groups (MDD > CD) 28 . Within every model, a separate GLM was estimated for each individual voxel, with first-level SBC measures at this voxel as the dependent variable and the group BDI-II scores, age, and gender as the independent variables. Results were thresholded using a combination of a cluster-forming p < 0.001 voxel-level threshold and a familywise corrected p-FDR MDD, HC > CD, HC > 0.5MDD + 0.5CD, and MDD > CD) were not performed in this analysis. This decision was based on the rationale that each model addresses a distinct hypothesis and is part of an exploratory investigation. Consequently, the findings should be interpreted as being preliminary and hypothesis-generating rather than confirmatory. Additionally, we placed a strong emphasis on the biological relevance and effect sizes of the findings, rather than relying solely on p-values for interpretation. Results This study hypothesized that functional changes, specifically variations in BOLD signals and functional connectivity, manifest in both CD and MDD cohorts, and are associated with the psychological comorbidities and neurobiological disruptions experienced by these groups when compared with healthy individuals. To explore this hypothesis, rs-fMRI experiments were conducted on CD patients, MDD patients, and age- and gender-matched HCs using a 7T MRI scanner. The use of high-resolution imaging aimed to identify shared neurobiological modifications in cerebral function across these groups by using fALFF as a neurophysiological marker and seed-based connectivity analysis to assess functional connectivity changes. Higher BDI-II scores in CD patients - indicating depressive symptoms The BDI-II scores of the experimental groups are shown in Fig. 1 . The Kruskal-Wallis test was performed to assess the differences in BDI-II scores among the three experimental groups. A statistically significant difference was observed (χ 2 = 37.16, p < 0.0001), indicating that the BDI-II scores varied significantly across the groups. Post-hoc analyses showed significant differences in the BDI-II score between HCs and MDD patients (p = 0.0041) and between CD patients and MDD patients (p < 0.001). Although the CD patients were not diagnosed with MDD, our results show that they are ranked between “minimal or mild depression” in the standardized cut-offs for the BDI-II. These findings are in line with previous studies that also showed a strong comorbidity of IBD and MDD 46 , 47 . This underlines the importance of simultaneous consultation of psychiatric diagnosis and support in CD patients. Association between the severity of digestive symptoms (GSRS) and depression (BDI-II) in CD patients To explore associations between the GSRS and BDI-II in CD patients, a Spearman’s correlation analysis was performed between the total and sub-scores of the GSRS and the BDI-II scores. The Spearman’s correlation coefficients between the GSRS and BDI-II scores are shown in Fig. 2 . The correlation analysis revealed a significant association between gastrointestinal symptoms and depression symptom severity in CD patients. A positive trend between high BDI-II scores and GSRS scores is noticeable in the scatter plot (Fig. 2 ). These results demonstrate the strong comorbidity of CD and MDD. Our findings are consistent with previous research demonstrating the bidirectional influences of both diseases. Evidence from neuroimaging: Decreased fALFF in the precuneus as shared neurobiological changes in MDD and CD Following fMRI data preprocessing, rs-fMRI-fALFF was calculated using processing pipelines implemented in the CONN 13 toolbox. The fALFF maps were computed, and a group-level GLM analysis was performed on fALFF maps to find commonly altered regions in CD and MDD patients compared to HCs. The results showed a significant cluster (446 voxels, p-unc 0.00028, p-FDR 0.030336) in the precuneus cortex region of the brain, with a peak cluster at the MNI coordinate (-02, -69, + 49). The identified cluster is shown in Fig. 3 . Both MDD and CD patients displayed decreased fALFF in the precuneus compared to HCs, suggesting that decreased activity in this cluster is part of the pathophysiology of both MDD and CD. The precuneus is located in the parietal lobe and is an important component of the default mode network (DMN) 48 . The DMN describes a network with high functional connectivity during rest and is linked to non-goal-directed processes 49 , 50 . The DMN is also involved in the consolidation of memory, working memory 50 , the continuous sampling of external and internal environments 51 , cognitive and emotional processing and functioning 52 , and self-referential mental processes 53 – 56 . As the precuneus is associated with critical self-related processing, the observed convergent aberrant activity patterns suggest a significant involvement of the precuneus in the pathophysiology of MDD and its comorbidity with CD. Previous research indicates a correlation between depressive symptoms and both the severity and risk of exacerbation in CD 57 . Hence, these findings align with prior research demonstrating altered activity in the precuneus in patients with depressive symptoms or diagnosed with MDD 58 – 63 . Furthermore, recent functional MRI studies have demonstrated that ALFF values in the precuneus can serve as a neurobiomarker to predict the current activity of CD 64 . By conducting a direct comparison of patients with MDD and CD, our results emphasize the critical role of the precuneus in the comorbidity of these conditions. Seed-based connectivity analysis reveals shared and unique functional links with the Precuneus: Whole SBC maps were estimated using 10 mm spherical seed regions from significant fALFF peak clusters. Group-level analysis was performed on these SBC maps using separate GLM models to test various contrasts: the HC group vs. the MDD group (HC > MDD), the HC group vs. the CD group (HC > CD), the HC group vs. the combined average of the MDD and CD groups (HC > 0.5MDD + 0.5CD), and regions with altered connectivity between the MDD and CD groups (MDD > CD). The results of these individual contrasts showing the position of peak cluster, size and p-values are given in Supplementary Table 1. The identified cluster is shown in Fig. 4 . These analyses showed regions with altered connectivity patterns in CD and MDD patients compared to HCs, and are mainly evident in key areas such as the precuneus cortex, supramarginal gyrus, postcentral gyrus, anterior cingulate gyrus, and planum polare. Distinct patterns of altered functional connectivity in MDD (HC > MDD contrast): Four significant clusters were identified in the HC > MDD contrast. The first cluster, with a peak at MNI coordinate (-2, -62, + 12), primarily covered the precuneus cortex and showed decreased functional connectivity in MDD patients compared to HCs. The second cluster, peaking at (-66, -42, + 26), was mainly located in the supramarginal gyrus (posterior division) and the parietal operculum cortex and exhibited increased connectivity in MDD patients compared to HCs. The third cluster, centred at (2, -34, + 69), and the fourth cluster, centred at (-10, + 41, +4), predominantly cover the precentral and postcentral gyri and the anterior cingulate gyrus, and show decreased connectivity in MDD patients compared to HCs. Previous research comparing MDD and HC has identified a similar pattern of altered connectivity, particularly in the precuneus, which has been associated with symptom severity in MDD. This hypoconnectivity in MDD patients may indicate a failure in processing negative memories and emotions 61 , 65 – 67 . Additionally, earlier studies have shown an increased amplitude of low-frequency fluctuation (ALFF) and functional connectivity in the supramarginal gyrus and parietal operculum among individuals with MDD 68 . Regions such as the postcentral gyrus, supramarginal gyrus, and cingulate gyrus have also been implicated in the onset of MDD symptoms 69 . Notably, the anterior cingulate gyrus has been observed to exhibit both altered and decreased connectivity 70 – 72 . Our findings reveal decreased connectivity in these regions, suggesting the existence of distinct MDD subtypes characterized by differential interactions between brain regions. Decreased precuneus-ACC connectivity in CD patients as a neurobiological link to MDD (HC > CD contrast) : One significant cluster was identified, centred at (-6, + 40, +3), primarily covering the anterior cingulate gyrus. This cluster exhibited decreased connectivity between the precuneus and the anterior cingulate gyrus (ACC) in CD patients compared to HCs, mirroring the connectivity pattern observed in MDD patients. This cluster has been reported in previous studies comparing CD and HC. The anterior cingulate cortex (ACC) has been recognized as a neurobiological correlate of both CD and MDD, with connectivity alterations that may differentiate irritable bowel syndrome (IBS) patients with and without MDD 73 . These findings support our hypothesis that the ACC is involved in the neurobiological mechanisms underlying depression-related comorbidity in CD patients. The observed hypoconnectivity between the precuneus and ACC may reflect deficits in emotional regulation in MDD, with a smaller effect size in CD, which corresponds to the milder depressive symptoms typically seen in CD patients. Shared functional connectivity changes in CD and MDD (HC > 0.5MDD + 0.5CD contrast) Three significant clusters emerged in the SBC group-level analysis using the precuneus as the seed region to identify common functional connectivity changes in the MDD and CD groups compared to the HC group. The first cluster, at (-66, -42, + 25), exhibited increased connectivity between the precuneus and the left supramarginal gyrus in MDD and CD patients compared to HCs. The second cluster, centred at (-2, -62, + 12), and the third cluster, centred at (-10, + 41, +4), showed decreased connectivity within the precuneus and between the precuneus and the anterior cingulate gyrus in both MDD and CD patients compared to HCs. These results are particularly intriguing, as the patterns of beta values (connectivity strength) shown in Fig. 4 (C) reveal a trend whereby the connectivity strength in the CD group lies between that of the MDD and HC groups across all clusters. This observation suggests a similar pattern of altered connectivity between the MDD and CD groups, indicating shared neurobiological disruptions. The regions identified in this analysis also emerged in the HC > MDD contrast, aligning with previous studies that highlight common connectivity changes in both conditions. Consistent with prior findings, these results suggest that both MDD and CD are characterized by increased functional connectivity in regions associated with emotional regulation, sensory processing, and attention. These shared neurobiological changes may help explain the increased vulnerability of CD patients with depressive symptoms to more frequent relapses and a greater need for medication compared to CD patients without depressive symptoms 57 . Functional connectivity changes in MDD patients compared to CD patients, suggesting distinct alterations in sensory and self-referential processing (MDD > CD contrast) : Five significant clusters were observed. The first cluster, peaking at (-63, -42, + 23), was mainly located in the supramarginal gyrus (posterior division) and showed decreased connectivity in the CD group compared to the MDD group. The other clusters, primarily covering the planum polare, precuneous cortex, and planum temporale, showed increased connectivity in the CD group compared to the MDD group. The first cluster, located In the posterior division of the supramarginal gyrus, exhibited increased connectivity in the MDD group compared to the CD group, similar to patterns observed in the contrast HC > MDD. This increased connectivity in the supramarginal gyrus is likely attributable to its critical role in overcoming emotional egocentricity in social judgment and self-referential processing, distinguishing individuals with MDD from those with CD and HCs 74 . Other significant clusters were observed in regions including the planum polare, precuneus cortex, and planum temporale. Notably, these regions displayed increased connectivity in the CD group compared to the MDD group. The planum polare and planum temporale are associated with auditory processing and higher-order cognitive functions, while the precuneus is linked to self-referential thinking and consciousness 61 , 65 – 67 . The increased connectivity in these regions in CD patients suggests that CD may involve distinct alterations in sensory processing and self-referential cognition, potentially differentiating it from the more emotion-focused disruptions observed in MDD patients. Our findings suggest that the shared neurobiological changes in the precuneus, the left supramarginal gyrus, and the cingulate gyrus observed in both MDD and CD patients may be key mechanisms underlying the comorbidity of these conditions. These results underscore the critical role of these brain regions in the pathophysiology of both MDD and CD. Despite significant progress, the structural and functional alterations in these brain regions are not yet fully understood, necessitating further research to elucidate these complex interactions. The bidirectional relationship between CD and MDD highlighted in our study aligns with previous research, emphasizing the interconnected nature of these diseases. Our data indicates that aberrant functional connectivity in specific brain regions may contribute to the occurrence and severity of both MDD and CD. Understanding these shared neural mechanisms is crucial for developing integrated therapeutic strategies that address both conditions simultaneously. Discussion This study aimed to uncover the shared neurobiological alterations in CD and MDD patients by examining functional changes using rs-fMRI at a 7T MRI scanner. Although prior research has established associations between CD and MDD, concurrent examination of both patient groups to explore shared neurobiological disruptions remains unexplored. We hypothesized that functional changes, specifically variations in BOLD signals and functional connectivity, would manifest in both CD and MDD cohorts compared to healthy individuals. To test this hypothesis, rs-fMRI experiments were conducted with ultra-high field 7T MRI scanners using fALFF as a neurophysiological marker. Our analysis included group-level GLM to assess fALFF maps and seed-based connectivity (SBC) maps. The results revealed significant clusters with altered common connectivity patterns in both CD and MDD patients, confirming our hypothesis and highlighting potential shared neurobiological mechanisms underlying these conditions. When comparing HCs with MDD and CD patients, aberrant functional connectivity has been identified in the precuneus cortex, left supramarginal gyrus, and cingulate gyrus. Numerous studies have investigated brain changes in MDD and CD independently, with the main effects in each condition being replicated many times, aligning with our findings. Notably, the precuneus region has consistently emerged in previous studies focusing on MDD 58 – 63 . Given that MDD is a frequently observed comorbidity in IBD, where the pathophysiology remains unclear, our research sheds light on critical brain regions, offering significant potential to identify key mechanisms that may explain this comorbidity. 57 The effects of various antidepressants on the gut have been investigated in animal studies, demonstrating anti-inflammatory properties comparable to the effects of dexamethasone 75 – 78 . Clinically, similar observations have been made in humans, where IBD patients treated concurrently with antidepressants experienced lower relapse rates, reduced steroid usage, fewer endoscopies in the years following study enrolment, and even a selectively protective effect for CD 57 , 79 . A recent fMRI study identified altered brain activation patterns across different CD stages, suggesting that brain activity could possibly serve as a neurobiological biomarker for the disease 64 . We propose that the key brain regions identified in our study act as neurobiological correlates of these observations, helping to bridge the gap between animal model research and clinical application in humans, ultimately contributing to the improvement of treatment strategies. One fMRI study has shown that the midcingulate cortex plays a role in integrating afferent sensory information from the gut, as stress-evoked hyperactivity could be seen in this region in CD patients compared to controls 80 . Recent findings also suggest that the activation of certain neurons in the insular cortex can even induce inflammation via memory traces 81 . Considering this, the brain regions highlighted in our data should be examined further to determine whether the shared neural changes in CD and MDD patients originate from a common underlying mechanism or if one condition precipitates the onset of the other. Our study underscores the need for incorporating psychological diagnostics and support in the treatment of IBD patients, given the significant overlap in the neural substrates of CD and MDD. Utilizing multimodal data, including multi-omics and clinical assessment data, future research should aim to clarify the precise mechanisms behind these shared changes and their contributions to the pathophysiology of both disorders. Additionally, further investigation into the effects of antidepressants on CD could provide valuable insights for more effective, holistic treatment approaches. These efforts will ultimately improve patient outcomes by addressing both the psychological and physiological aspects of these interconnected diseases. Limitations: Several limitations of this study must be considered. First, its cross-sectional design precludes establishing causality between brain abnormalities and the origins of the diseases; we can only infer associations. Additionally, the relatively small sample size of 13 subjects per group limits the generalizability and robustness of our findings. Future studies with larger sample sizes are needed to enhance the reliability of the results, while longitudinal designs would allow for better assessment of causal relationships. Another consideration is the potential influence of medication on brain connectivity and structure, which was not addressed in this study, but should be considered in future research. Furthermore, distinguishing between active and inactive phases of CD in future studies could provide more nuanced insights into the brain's role in the disease's progression and symptomatology. Addressing these factors in future investigations will provide a more comprehensive understanding of the neurobiological underpinnings of CD and its comorbidity with MDD. Declarations Declaration of Interests The authors declare no competing interests. Ethics Statement All participants signed informed consent prior to the study, and the study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Faculty of Medicine of the RWTH Aachen University. Author Contributions Hartmann HA: conceptualization, methodology, investigation, formal analysis, writing -original draft. Berthold M L: Investigation, manuscript review & correction. Ramkiran S: Investigation, manuscript review & correction. Bündgens L: Investigation, manuscript review & correction Jaeger JW: Investigation, manuscript review & correction. Hagen J: Investigation, validation, manuscript review & correction. Colee M: Investigation, manuscript review & correction. Backhaus M: Investigation, manuscript review & correction. Schnellbächer GJ: Investigation, validation, manuscript review & correction. Veselinovic T: Supervision, validation, manuscript review & correction. Shah NJ: Resources, funding acquisition, manuscript review & correction. Schneider M: Investigation, validation, manuscript review & correction. Neuner I: Supervision, funding acquisition, resources, manuscript review & correction. Rajkumar R: conceptualization, investigation, methodology, software, formal analysis, visualization, supervision, funding acquisition, manuscript review & correction. Acknowledgement This study is part of the doctoral thesis (Dr. med.) of Hanna Antonia Hartmann at the Medical Faculty of the RWTH Aachen University, Germany. This project was partly Funded by the Federal Ministry of Education and Research (BMBF) and the Ministry of Culture and Science of the German State of North Rhine-Westphalia (MKW) under the Excellence Strategy of the Federal Government and the Länder (Project-ID G:(DE-82) EXS-SF-OPSF845). The authors gratefully thank all participants in the study. The authors would also like to thank Petra Engels, Anita Köth, Elke Bechholz, Andrea Muren, and Silke Frensch for their technical assistance. Finally, the authors would like to acknowledge their gratitude to Claire Rick for proofreading the manuscript. Data Availability Statement Data will be made available upon request to the corresponding authors. 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Goodhand, J.R., Greig, F.I.S., Koodun, Y., McDermott, A., Wahed, M., Langmead, L., and Rampton, D.S. (2012). Do Antidepressants Influence the Disease Course in Inflammatory Bowel Disease? A Retrospective Case-Matched Observational Study: Inflamm. Bowel Dis. 18 , 1232–1239. https://doi.org/10.1002/ibd.21846 . Agostini, A., Ballotta, D., Righi, S., Moretti, M., Bertani, A., Scarcelli, A., Sartini, A., Ercolani, M., Nichelli, P., Campieri, M., et al. (2017). Stress and brain functional changes in patients with Crohn’s disease: A functional magnetic resonance imaging study. Neurogastroenterol. Motil. 29 , e13108. https://doi.org/10.1111/nmo.13108 . Koren, T., Yifa, R., Amer, M., Krot, M., Boshnak, N., Ben-Shaanan, T.L., Azulay-Debby, H., Zalayat, I., Avishai, E., Hajjo, H., et al. (2021). Insular cortex neurons encode and retrieve specific immune responses. Cell 184 , 5902–5915.e17. https://doi.org/10.1016/j.cell.2021.10.013 . Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryTableResultsAllContrasts.docx floatimage1.jpeg Graphical Abstract : Created in BioRender. Hartmann, H. (2024) BioRender.com/x03s830 Cite Share Download PDF Status: Published Journal Publication published 17 Sep, 2025 Read the published version in Communications Medicine → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5349946","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":376669501,"identity":"396e4e90-a756-4fd0-afb8-d41b63a2157e","order_by":0,"name":"Ravichandran Rajkumar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYJACwwYQyd7ADKISQIQEcVp4DpCghRGsRSKBSC3msw8/KJzZtk3efOYbY4MfNXZ5/NINjDc+4NEicy7NwHBj223DObdzjBN7jiUXS845wGw5A48WCR4GA8OHbbcZZ0jnGB/gYTuQuOFGAps0D14t7B9AWuxnSJ4xPvjn34HE/SAtf/Bq4QE7LHGGBI9xMm8b0BYJoBZ83gdqKTCcce528gyetGJj2b7kxBk3Epste/A7bJthT9lt2xnshzdLvvlml9g/I/ngjR/4rGFgYDNAE4DEEz7A/ICQilEwCkbBKBjhAACoOk6njd7+tgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-5875-5316","institution":"Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, 52074 Aachen, Germany","correspondingAuthor":true,"prefix":"","firstName":"Ravichandran","middleName":"","lastName":"Rajkumar","suffix":""},{"id":376669502,"identity":"c1829abf-a4b0-4ec7-bb6a-893015806e79","order_by":1,"name":"Hanna Hartmann","email":"","orcid":"","institution":"Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, 52074 Aachen, Germany","correspondingAuthor":false,"prefix":"","firstName":"Hanna","middleName":"","lastName":"Hartmann","suffix":""},{"id":376669503,"identity":"6a01e591-35dc-429c-9008-09e896bc717a","order_by":2,"name":"Marja-Lisa Berthold","email":"","orcid":"","institution":"Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, 52074 Aachen, Germany","correspondingAuthor":false,"prefix":"","firstName":"Marja-Lisa","middleName":"","lastName":"Berthold","suffix":""},{"id":376669504,"identity":"8eed785d-a3a0-47a3-9fca-c1d1cfc3dacf","order_by":3,"name":"Shukti Ramkiran","email":"","orcid":"","institution":"Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, 52074 Aachen, Germany","correspondingAuthor":false,"prefix":"","firstName":"Shukti","middleName":"","lastName":"Ramkiran","suffix":""},{"id":376669505,"identity":"ac56f425-d260-4746-bd8c-843d6af0c39d","order_by":4,"name":"Lukas Bündgens","email":"","orcid":"","institution":"Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, 52074 Aachen, Germany","correspondingAuthor":false,"prefix":"","firstName":"Lukas","middleName":"","lastName":"Bündgens","suffix":""},{"id":376669506,"identity":"3f6b174c-a99c-4737-ba51-39db25f60b26","order_by":5,"name":"Julius Jaeger","email":"","orcid":"","institution":"Department of Gastroenterology, Metabolic Diseases and Internal Intensive Care Medicine, Uniklinik RWTH Aachen, Germany","correspondingAuthor":false,"prefix":"","firstName":"Julius","middleName":"","lastName":"Jaeger","suffix":""},{"id":376669507,"identity":"69ce3712-1dc5-43b3-93ef-5bcdc30bf1c5","order_by":6,"name":"Jana Hagen","email":"","orcid":"","institution":"Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, 52074 Aachen, Germany","correspondingAuthor":false,"prefix":"","firstName":"Jana","middleName":"","lastName":"Hagen","suffix":""},{"id":376669508,"identity":"9065917d-a610-4a57-90ee-8708aea7289a","order_by":7,"name":"Maria Backhaus","email":"","orcid":"","institution":"Department of Gastroenterology, Metabolic Diseases and Internal Intensive Care Medicine, Uniklinik RWTH Aachen, Germany","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"","lastName":"Backhaus","suffix":""},{"id":376669509,"identity":"f60626cc-3edd-4472-a9e5-0693934c7a58","order_by":8,"name":"Maria Collée","email":"","orcid":"","institution":"Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, 52074 Aachen, Germany","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"","lastName":"Collée","suffix":""},{"id":376669510,"identity":"cde4b0e8-dc46-432a-a916-4dbbc323056c","order_by":9,"name":"Gereon Schnellbächer","email":"","orcid":"","institution":"Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, 52074 Aachen, Germany","correspondingAuthor":false,"prefix":"","firstName":"Gereon","middleName":"","lastName":"Schnellbächer","suffix":""},{"id":376669511,"identity":"8c853971-9bab-44f2-b78b-6f1a07b7eabb","order_by":10,"name":"Tanja Veselinović","email":"","orcid":"","institution":"Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, 52074 Aachen, Germany","correspondingAuthor":false,"prefix":"","firstName":"Tanja","middleName":"","lastName":"Veselinović","suffix":""},{"id":376669512,"identity":"3d5c5787-ecd0-4f94-925a-dc88a76ad1d9","order_by":11,"name":"Jon Shah","email":"","orcid":"","institution":"Institute of Neuroscience and Medicine, INM-4, Forschungszentrum Jülich GmbH","correspondingAuthor":false,"prefix":"","firstName":"Jon","middleName":"","lastName":"Shah","suffix":""},{"id":376669513,"identity":"cf4640e0-0f95-4ee9-98af-927194c89d5c","order_by":12,"name":"Kai Schneider","email":"","orcid":"","institution":"Department of Gastroenterology, Metabolic Diseases and Internal Intensive Care Medicine, Uniklinik RWTH Aachen, Germany","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Schneider","suffix":""},{"id":376669514,"identity":"56ff3297-0cfa-40a2-a48b-f5dab80c189b","order_by":13,"name":"Irene Neuner","email":"","orcid":"","institution":"Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, 52074 Aachen, Germany","correspondingAuthor":false,"prefix":"","firstName":"Irene","middleName":"","lastName":"Neuner","suffix":""}],"badges":[],"createdAt":"2024-10-28 23:30:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5349946/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5349946/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s43856-025-01117-w","type":"published","date":"2025-09-17T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79099261,"identity":"8e9f4d3c-e693-40f0-9f5b-153c8d2c379b","added_by":"auto","created_at":"2025-03-24 11:51:29","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":21098,"visible":true,"origin":"","legend":"\u003cp\u003eBox plot representation of the BDI-II scores of the experimental groups. The Kruskal-Wallis test was performed to assess the differences in BDI-II scores among the three experimental groups. A statistically significant difference was observed (χ² = 37.16, p \u0026lt; 0.0001), indicating that the BDI-II scores varied significantly across the groups. The central line within each box represents the median BDI-II score, while the box encompasses the interquartile range (IQR). Whiskers extend to the minimum and maximum values within 1.5 times the IQR.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5349946/v1/0d25afdcf76421e8716e05fe.jpg"},{"id":79099263,"identity":"cec5dd1b-581b-4569-a5f2-42cec0df320f","added_by":"auto","created_at":"2025-03-24 11:51:29","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":45476,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot depicting the relationship between GSRS and BDI-II scores. The association between GSRS and BDI-II score in CD patients was accessed via Spearman’s correlation analysis. Spearman’s correlation coefficients were computed with a significance level of 5% and multiple comparisons were controlled for using a permutation test. Each blue colour point represents the scores from an individual Crohn’s disease patient on both scales. The Spearman’s correlation coefficients of the observation (r) and p values, adjusted for multiple comparison, are shown inside the boxes.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5349946/v1/137cbb998f86fb21d188d1bf.jpg"},{"id":79099262,"identity":"9799a831-2497-4f3c-9f17-75721d4a0b67","added_by":"auto","created_at":"2025-03-24 11:51:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":5713,"visible":true,"origin":"","legend":"\u003cp\u003efALFF group-level analysis results showing a 3D glass-brain visualization of the cluster identified via GLM analysis (top row) and the effect sizes (beta values) of each group within the identified cluster (bottom row). A group-level GLM analysis was conducted on normalized fALFF maps. The contrasts were set as [1 -0.5 -0.5 0 0 0] for HCs, CD patients, MDD patients, age, gender, and BDI-II. Voxel-level hypotheses were evaluated using multivariate parametric statistics with random-effects. Cluster-level inferences were based on Gaussian Random Field theory\u003csup\u003e28,44\u003c/sup\u003e. Clusters are shown in the left and right medial view of the glass brain.\u003c/p\u003e","description":"","filename":"placeholderimage.png","url":"https://assets-eu.researchsquare.com/files/rs-5349946/v1/81988156c107d6030534f004.png"},{"id":79100101,"identity":"2cbfabe4-204f-4a75-806b-eb573f2fd491","added_by":"auto","created_at":"2025-03-24 11:59:29","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":170217,"visible":true,"origin":"","legend":"\u003cp\u003eSBC group-level analysis results showing a 3D glass-brain visualization of clusters identified via GLM analysis (top row within subplots) and the effect sizes (beta values) of each group within the identified clusters (bottom row within subplots). The whole-brain SBC maps estimated 10 mm spherical seed regions from a significant fALFF peak cluster in the precuneus. Differences in connectivity are shown for (A) the HC group versus the MDD group (HC \u0026gt; MDD), (B) the HC group versus the CD group (HC \u0026gt; CD), (C) the HC group compared to a combined average of the MDD and CD groups (HC \u0026gt; 0.5MDD + 0.5CD), and (D) regions with altered connectivity between the MDD and CD groups (MDD \u0026gt; CD). Clusters are shown in the left and right medial view of the glass brain.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5349946/v1/0070197d6532196a5d58cc34.jpg"},{"id":91817801,"identity":"6c051042-5897-4028-82b0-88e76a54d106","added_by":"auto","created_at":"2025-09-22 07:00:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1334155,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5349946/v1/1582eae6-ddb3-4681-bbc8-c5fb2add7a02.pdf"},{"id":79099266,"identity":"e3c265b8-bb39-420b-8e07-89edbbc338ba","added_by":"auto","created_at":"2025-03-24 11:51:29","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":28437,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableResultsAllContrasts.docx","url":"https://assets-eu.researchsquare.com/files/rs-5349946/v1/3e686027d7699098dbc41f4a.docx"},{"id":79099265,"identity":"5ff123d1-6b19-47c8-b943-f24d41ce2f33","added_by":"auto","created_at":"2025-03-24 11:51:29","extension":"jpeg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":507410,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical Abstract :\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCreated in BioRender. Hartmann, H. (2024) BioRender.com/x03s830\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5349946/v1/568b984f345474a7e79c73f6.jpeg"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Resting-state 7T brain fMRI reveals common neurobiological changes in patients with Crohn’s disease and major depressive disorder","fulltext":[{"header":"Highlights","content":"\u003cp\u003e1. Significant differences in BDI-II scores among CD, MDD, and HC groups.\u003c/p\u003e\u003cp\u003e2. Positive correlation between GSRS and BDI-II scores in CD patients.\u003c/p\u003e\u003cp\u003e3. fMRI revealed significant clusters in the precuneus cortex.\u003c/p\u003e\u003cp\u003e4. Shared connectivity changes in CD and MDD imply integrated treatments.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eThe central nervous system has a profound impact on complex processes, such as metabolism and immunity, at distant body sites\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. This effect is particularly pronounced in inflammatory bowel disease (IBD), with several epidemiologic studies showing that stressful life events can trigger IBD flares\u003csup\u003e\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Crohn's disease (CD), one of the two major subtypes of IBD, represents a chronic inflammatory condition of the gastrointestinal tract, impacting millions worldwide, with a notable prevalence in developed nations\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Patients with CD exhibit higher incidences of MDD compared to the general population, and stressful life events have been linked to IBD flares, suggesting a bidirectional link\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. However, the neurobiological correlates of stress-induced IBD exacerbations remain incompletely understood.\u003c/p\u003e \u003cp\u003eNeuroimaging technologies \u0026ndash; particularly magnetic resonance imaging (MRI) and functional MRI (fMRI) \u0026ndash; enable the precise investigation of altered brain structure and functioning underlying the (comorbid) psychiatric disorders. Blood oxygenation level dependent (BOLD) fMRI is a neuroimaging method that measures brain activity by indirectly detecting the associated changes in blood flow and blood oxygenation that follow neuronal activity\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Resting-state fMRI is an fMRI technique that measures the low-frequency fluctuations in the BOLD signal while the subject is in the resting condition (not actively performing any task)\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Compared to fMRI conducted at standard MRI field strengths, ultra-high field (UHF) 7T fMRI offers significantly improved resolution with enhanced signal-to-noise (SNR) and contrast-to-noise (CNR) ratios, enabling more precise imaging\u003csup\u003e\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. This allows for the detection of neurostructural changes, connectivity alterations in smaller brain regions, and the identification of potential biomarkers that are not visible with lower magnetic field strengths. To the best of the authors' knowledge, no research team has previously used 7T fMRI to compare CD and MDD directly. Hence, the aim of this study is to precisely identify brain regions associated with CD and MDD, thereby paving the way for integrated approaches to managing these complex diseases and offering new perspectives on the bidirectional relationship.\u003c/p\u003e \u003cp\u003ePrior research has established associations between CD and MDD\u003csup\u003e\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, yet, to the best of the authors' understanding, to date, no study has concurrently examined both patient groups to elucidate the shared neurobiological alterations underlying these conditions. The present investigation posits a hypothesis that functional changes manifest in both CD and MDD cohorts, associated with psychological comorbidities and shared neurobiological disruptions when compared with healthy individuals. To explore this hypothesis, resting-state functional magnetic resonance imaging (rs-fMRI) experiments were conducted with an ultra-high field 7 Tesla (7T) scanner across both patient groups alongside age and gender-matched HCs. The use of a 7T scanner significantly improves the outcome of fMRI signals compared to those obtained at lower field strengths (resolution, SNR, and CNR), bringing considerable benefits to the study\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. The objective centres on identifying shared neurobiological modifications in cerebral function across CD and MDD groups. To achieve this, we used the fractional amplitude of low-frequency fluctuations (fALFF) as a neurophysiological marker, capturing the intrinsic low-frequency oscillations in cerebral activity during the resting state. fALFF serves as an index to quantify spontaneous brain activity in brain regions, particularly in resting-state fMRI data. In the first step, regions exhibiting commonly altered BOLD signals in both the CD and MDD groups compared to HCs were identified using fALFF maps. In the subsequent step, a data-driven approach was applied using the peak voxel locations of the altered regions in the fALFF maps as seeds for connectivity analysis. This allowed the identification of specific regions showing changed connectivity between different contrasts of the three observed groups (CD, MDD and HC). These regions present a promising area for understanding the impact of CD and MDD on brain structures and their comorbidities origin more comprehensively.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eExperimental Modal and study participant details:\u003c/h2\u003e \u003cp\u003eCD patients (n\u0026thinsp;=\u0026thinsp;18, age\u0026thinsp;=\u0026thinsp;29,79\u0026thinsp;\u0026plusmn;\u0026thinsp;6,84; 12 males) along with age matched MDD patients (n\u0026thinsp;=\u0026thinsp;18, age\u0026thinsp;=\u0026thinsp;28,97\u0026thinsp;\u0026plusmn;\u0026thinsp;5,96; 13 males), and HCs (n\u0026thinsp;=\u0026thinsp;18, age\u0026thinsp;=\u0026thinsp;28,35 \u0026plusmn;, 5,23; 12 males) were included in the study. The CD patients were recruited from the Clinic for Gastroenterology, Metabolic Diseases, and Internal Intensive Care Medicine (Medical Clinic III), University Hospital Aachen, Germany. The CD patients were diagnosed based on results from colonoscopy and histology, which were evaluated by experienced gastroenterologists in the clinic. In addition to the clinical diagnosis, gastrointestinal symptoms in CD patients were assessed via the Gastrointestinal Symptom Rating Scale (GSRS), \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e licenced from AstraZeneca AB, Sweden. The GSRS questionnaire consists of 15 items, divided into five sub-scores. These sub-scores describe the symptom clusters of reflux, abdominal pain, indigestion, diarrhoea and constipation. A 7-point scale reflects the current status of the symptom severity profile, where 1 describes the absence of the symptom, and 7 is the highest symptom severity \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe MDD patients were recruited from the Clinic for Psychiatry, Psychotherapy and Psychosomatics, University Hospital Aachen. The MDD patients were diagnosed based on ICD \u0026minus;\u0026thinsp;10 and DSM-5 criteria and an absence of psychotic features. The HC group was recruited based on having no history of neurologic or psychiatric disorders, as determined by the German version 6.0.0 of the Mini International Neuropsychiatric Interview (MINI) \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. The handedness of all the subjects was assessed using the Edinburgh Handedness Inventory. Only right-handed subjects with no contraindication for 7T MRI were included in the study. Additionally, depression symptom severity in all subjects was assessed using the German version of the Beck Depression Inventory-II (BDI-II)\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. The BDI-II consists of 21 multiple-choice items, with four possible responses each.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analysis was performed using the MATLAB (R2022a) software package. Differences in BDI-II scores were compared using a rank-based nonparametric Kruskal-Wallis test (\u003cem\u003ekruskalwallis\u003c/em\u003e function in MATLAB). Subsequent post-hoc analyses were conducted using the \u003cem\u003emultcompare\u003c/em\u003e function in MATLAB to determine the specific group differences. Within each Kruskal-Wallis test, the corrections for multiple comparisons was performed using the Bonferroni correction method with a significance level of 5%.\u003c/p\u003e \u003cp\u003eTo explore associations between gastrointestinal symptoms and depression symptom severity in CD patients, a correlation analysis was performed between the GSRS total as well as the sub-scores and BDI-II scores, respectively. Spearman\u0026rsquo;s correlation coefficients were computed with a significance level of 5%. In all of the correlation analyses, the family-wise error rate (FWER), due to multiple comparisons, was controlled for using a permutation test\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. 1000 permutations were performed for each comparison (correlation), and the p-value was adjusted using the \u0026ldquo;max statistics\u0026rdquo; method \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMRI data acquisition\u003c/h2\u003e \u003cp\u003eMRI data acquisition was performed at Forschungszentrum Juelich using a 7T Magnetom Terra scanner (Siemens Healthineers, Erlangen, Germany) equipped with a 1Tx/32Rx Head Coil 7T Clinical (Nova Medical, Wilmongton, MA, USA). Resting-state fMRI data were acquired using a 2D T2* weighted multiband accelerated echo planar imaging (EPI) sequence developed at the Center for Magnetic Resonance Research (CMRR), Minneapolis, MN, USA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cmrr.umn.edu/multiband/\u003c/span\u003e\u003cspan address=\"https://www.cmrr.umn.edu/multiband/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e21\u0026ndash;23\u003c/sup\u003e. The entire brain was covered with a field of view (FOV) of 220 x 220 mm\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, a matrix size of 168 x 168, and a slice thickness of 1.3mm. In total, 305 volumes with 100 slices each were acquired with a repetition time (TR) of 2000 ms, an echo time (TE) of 25 ms, and a flip angle (FA) of 70\u0026deg; using a multiband factor of 4. Subjects were instructed to close their eyes and not to fall asleep during the resting-state measurement. In addition, the lights in the scanner room were switched off during the entire resting-state measurement. To correct for susceptibility-induced geometric distortions, two additional fMRI volumes were recorded with opposite phase encoding direction (posterior-anterior phase-encoding).\u003c/p\u003e \u003cp\u003eStructural images were obtained using a T1-weighted MP2RAGE. The MP2RAGE acquires two gradient echo images with different inversion times (TI) and flip angles (FA) (inversion image 1 (INV1) TI\u0026thinsp;=\u0026thinsp;840 ms, flip, FA\u0026thinsp;=\u0026thinsp;4\u0026deg;, INV2 TI\u0026thinsp;=\u0026thinsp;2370 ms, FA\u0026thinsp;=\u0026thinsp;5\u0026deg;). The other sequence-related parameters were similar for both gradient echo images: echo time (TE)\u0026thinsp;=\u0026thinsp;1.99 ms; repetition time (TR)\u0026thinsp;=\u0026thinsp;4500 ms for signal-to-noise ratio (SNR) optimization. The image matrix was set to 320 x 300, achieving an isotropic resolution of 0.75 mm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e in 208 sagittal slices. The T1-weighted anatomical images referred to here were produced by combining the two gradient echo images by means of a ratio \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003efMRI data preprocessing\u003c/h3\u003e\n\u003cp\u003eThe raw DICOM scans (structural and functional) were 3D converted into the neuroimaging informatics technology initiative (NIfTI) format using the dcm2niix tool \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. The 3D structural and functional images were visually audited to check for poor scan quality, artefacts and abnormal tissues using FSL View software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FslView\u003c/span\u003e\u003cspan address=\"https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FslView\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Due to poor quality fMRI scans and artefacts, data from five CD patients were excluded. This resulted in 13 CD patients (age\u0026thinsp;=\u0026thinsp;29,43\u0026thinsp;\u0026plusmn;\u0026thinsp;7,44; 9 males), 13 age-matched MDD patients (age\u0026thinsp;=\u0026thinsp;29,04\u0026thinsp;\u0026plusmn;\u0026thinsp;6,10; 9 males) and 13 HCs (age\u0026thinsp;=\u0026thinsp;28,77\u0026thinsp;\u0026plusmn;\u0026thinsp;6,65; 9 males) being included in the fMRI analysis.\u003c/p\u003e \u003cp\u003eThe susceptibility-induced off-resonance field (fieldmap) was initially estimated using a method similar to that described in \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e as implemented in FSL\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. In the next step, the fMRI images were pre-processed using CONN\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e release 22.a\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, SPM\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e release 12.7771 and MATLAB (R2022a).\u003c/p\u003e \u003cp\u003eFunctional and anatomical data were pre-processed using a flexible pre-processing pipeline\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, which included the creation of voxel-displacement maps, realignment with susceptibility distortion correction using fieldmaps, slice timing correction, outlier detection, direct segmentation and MNI-space normalization, and smoothing. Functional data were realigned using the SPM realign and unwarp procedure\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e with integrated fieldmaps for susceptibility distortion correction. All scans were co-registered to a reference image (first fMRI volume) using a least-squares approach and a 6-parameter (rigid body) transformation and were then resampled using b-spline interpolation\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e to correct for motion, magnetic susceptibility geometric distortions, and their interaction simultaneously. Temporal misalignment between slices of the functional data was corrected using the SPM slice-timing correction (STC) procedure\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e with sinc temporal interpolation to resample each slice\u0026rsquo;s BOLD timeseries to a common mid-acquisition time. Potential outliers were identified using ART\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e based on framewise displacement above 0.9 mm or global BOLD signal changes above 5 standard deviations\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. A reference BOLD image was computed for each subject by averaging all scans excluding outliers. Both functional and anatomical data were normalized to standard MNI space, segmented into grey matter, white matter, and CSF tissue classes, and resampled to 1 mm isotropic voxels following a direct normalization procedure\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e using the SPM unified segmentation and normalization algorithm\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e with the default IXI-549 tissue probability map template. Finally, functional data were smoothed using spatial convolution with a 4 mm full width half maximum (FWHM) Gaussian kernel.\u003c/p\u003e \u003cp\u003eFunctional data were also denoised using a standard denoising pipeline\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, which included the regression of potential confounding effects such as white matter timeseries (5 CompCor noise components), CSF timeseries (5 CompCor noise components), motion parameters and their first order derivatives (12 factors)\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, outlier scans (below 26 factors)\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, and linear trends (2 factors), within each functional run. This was followed by bandpass frequency filtering of the BOLD timeseries\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e between 0.008 Hz and 0.09 Hz. CompCor \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e noise components within white matter and CSF were estimated by computing the average BOLD signal as well as the largest principal components orthogonal to the BOLD average, motion parameters, and outlier scans within each subject's eroded segmentation masks. From the number of noise terms included in this denoising strategy, the effective degrees of freedom of the BOLD signal after denoising were estimated to range from 83 to 91.5 (average 90.5) across all subjects\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003efALFF - first and group-level analysis\u003c/h2\u003e \u003cp\u003eFollowing preprocessing, rs-fMRI-fALFF was calculated using the processing pipelines from the CONN toolbox\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. fALFF maps characterizing low-frequency BOLD signal variability at each voxel were estimated as the ratio between the root mean square (RMS) of the BOLD signal after denoising and band-pass filtering between 0.008 Hz and 0.09 Hz, divided by the same measure computed before band-pass filtering\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Finally, fALFF values across voxels were rank sorted and normalized separately for each individual subject using a Gaussian inverse cumulative distribution function with zero mean and unit variance.\u003c/p\u003e \u003cp\u003eTo test our hypothesis of shared neurobiological changes in CD and MDD patients compared to HCs, a group-level general linear model (GLM)\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e based analysis was performed on normalized fALFF maps. For each individual voxel, a separate GLM was estimated, with the fALFF value at this voxel as the dependent variable and group BDI-II scores, age, and gender were set as the independent variables. The group analysis was performed to test the common differences between CD and MDD patients and HCs. The contrast was designed as [1 -0.5 -0.5 0 0 0] for HCs, CD patients, MDD patients, age, gender, and BDI-II score, respectively, to directly compare the average effect of CD and MDD patients against the HCs. Voxel-level hypotheses were evaluated using multivariate parametric statistics with random effects across subjects. Inferences were performed at the level of individual clusters (groups of contiguous voxels). Cluster-level inferences were based on parametric statistics from Gaussian Random Field theory \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Results were thresholded using a combination of a cluster-forming p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 voxel-level threshold and a familywise corrected p-FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 cluster-size threshold\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. The rationale for utilizing a lower threshold (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) at the voxel level was to enhance the detection of potential signals of interest given the low sample size while also acknowledging the potential for increased false positives. However, the subsequent application of a stringent correction for multiple comparisons at the cluster level (p-FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) ensured that only those clusters with a high degree of statistical reliability were considered significant. This two-level thresholding method facilitates the discovery of true differences in neural activity patterns while minimizing the risk of Type I errors. The peak cluster positions of significant clusters were considered as seeds for subsequent seed-based connectivity analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSeed-based connectivity - first and group-level analysis\u003c/h2\u003e \u003cp\u003eIn order to find functional connectivity differences, whole-brain seed-based connectivity maps (SBC) were estimated using 10 mm spherical seed regions created from the significant peak cluster positions of fALFF. Functional connectivity strength was represented using Fisher-transformed bivariate correlation coefficients from a weighted GLM \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Functional connectivity strength was defined separately for each pair of seed and target voxels, modelling the association between their BOLD signal timeseries. In order to compensate for possible transient magnetization effects at the beginning of each run, individual scans were weighted using a step function convolved with an SPM canonical hemodynamic response function and rectified.\u003c/p\u003e \u003cp\u003eGroup-level analysis was performed on SBC maps using separate GLM models to test differences between the HC group and the MDD group (HC\u0026thinsp;\u0026gt;\u0026thinsp;MDD), the HC group and the CD group (HC\u0026thinsp;\u0026gt;\u0026thinsp;CD), the HC group compared to a combined average of the MDD and CD groups (HC\u0026thinsp;\u0026gt;\u0026thinsp;0.5MDD\u0026thinsp;+\u0026thinsp;0.5CD), and regions with altered connectivity between the MDD and CD groups (MDD\u0026thinsp;\u0026gt;\u0026thinsp;CD) \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Within every model, a separate GLM was estimated for each individual voxel, with first-level SBC measures at this voxel as the dependent variable and the group BDI-II scores, age, and gender as the independent variables. Results were thresholded using a combination of a cluster-forming p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 voxel-level threshold and a familywise corrected p-FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 cluster-size threshold\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Corrections for multiple comparisons across the four separate GLM models (HC\u0026thinsp;\u0026gt;\u0026thinsp;MDD, HC\u0026thinsp;\u0026gt;\u0026thinsp;CD, HC\u0026thinsp;\u0026gt;\u0026thinsp;0.5MDD\u0026thinsp;+\u0026thinsp;0.5CD, and MDD\u0026thinsp;\u0026gt;\u0026thinsp;CD) were not performed in this analysis. This decision was based on the rationale that each model addresses a distinct hypothesis and is part of an exploratory investigation. Consequently, the findings should be interpreted as being preliminary and hypothesis-generating rather than confirmatory. Additionally, we placed a strong emphasis on the biological relevance and effect sizes of the findings, rather than relying solely on p-values for interpretation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThis study hypothesized that functional changes, specifically variations in BOLD signals and functional connectivity, manifest in both CD and MDD cohorts, and are associated with the psychological comorbidities and neurobiological disruptions experienced by these groups when compared with healthy individuals. To explore this hypothesis, rs-fMRI experiments were conducted on CD patients, MDD patients, and age- and gender-matched HCs using a 7T MRI scanner. The use of high-resolution imaging aimed to identify shared neurobiological modifications in cerebral function across these groups by using fALFF as a neurophysiological marker and seed-based connectivity analysis to assess functional connectivity changes.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eHigher BDI-II scores in CD patients - indicating depressive symptoms\u003c/h2\u003e \u003cp\u003eThe BDI-II scores of the experimental groups are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The Kruskal-Wallis test was performed to assess the differences in BDI-II scores among the three experimental groups. A statistically significant difference was observed (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;37.16, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), indicating that the BDI-II scores varied significantly across the groups. Post-hoc analyses showed significant differences in the BDI-II score between HCs and MDD patients (p\u0026thinsp;=\u0026thinsp;0.0041) and between CD patients and MDD patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Although the CD patients were not diagnosed with MDD, our results show that they are ranked between \u0026ldquo;minimal or mild depression\u0026rdquo; in the standardized cut-offs for the BDI-II. These findings are in line with previous studies that also showed a strong comorbidity of IBD and MDD \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. This underlines the importance of simultaneous consultation of psychiatric diagnosis and support in CD patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between the severity of digestive symptoms (GSRS) and depression (BDI-II) in CD patients\u003c/h2\u003e \u003cp\u003eTo explore associations between the GSRS and BDI-II in CD patients, a Spearman\u0026rsquo;s correlation analysis was performed between the total and sub-scores of the GSRS and the BDI-II scores. The Spearman\u0026rsquo;s correlation coefficients between the GSRS and BDI-II scores are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The correlation analysis revealed a significant association between gastrointestinal symptoms and depression symptom severity in CD patients. A positive trend between high BDI-II scores and GSRS scores is noticeable in the scatter plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These results demonstrate the strong comorbidity of CD and MDD. Our findings are consistent with previous research demonstrating the bidirectional influences of both diseases.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eEvidence from neuroimaging: Decreased fALFF in the precuneus as shared neurobiological changes in MDD and CD\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFollowing fMRI data preprocessing, rs-fMRI-fALFF was calculated using processing pipelines implemented in the CONN\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e toolbox. The fALFF maps were computed, and a group-level GLM analysis was performed on fALFF maps to find commonly altered regions in CD and MDD patients compared to HCs. The results showed a significant cluster (446 voxels, p-unc 0.00028, p-FDR 0.030336) in the precuneus cortex region of the brain, with a peak cluster at the MNI coordinate (-02, -69, +\u0026thinsp;49). The identified cluster is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Both MDD and CD patients displayed decreased fALFF in the precuneus compared to HCs, suggesting that decreased activity in this cluster is part of the pathophysiology of both MDD and CD. The precuneus is located in the parietal lobe and is an important component of the default mode network (DMN) \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. The DMN describes a network with high functional connectivity during rest and is linked to non-goal-directed processes\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. The DMN is also involved in the consolidation of memory, working memory\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, the continuous sampling of external and internal environments\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e, cognitive and emotional processing and functioning\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e, and self-referential mental processes \u003csup\u003e\u003cspan additionalcitationids=\"CR54 CR55\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. As the precuneus is associated with critical self-related processing, the observed convergent aberrant activity patterns suggest a significant involvement of the precuneus in the pathophysiology of MDD and its comorbidity with CD. Previous research indicates a correlation between depressive symptoms and both the severity and risk of exacerbation in CD \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Hence, these findings align with prior research demonstrating altered activity in the precuneus in patients with depressive symptoms or diagnosed with MDD \u003csup\u003e\u003cspan additionalcitationids=\"CR59 CR60 CR61 CR62\" citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. Furthermore, recent functional MRI studies have demonstrated that ALFF values in the precuneus can serve as a neurobiomarker to predict the current activity of CD\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. By conducting a direct comparison of patients with MDD and CD, our results emphasize the critical role of the precuneus in the comorbidity of these conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSeed-based connectivity analysis reveals shared and unique functional links with the Precuneus:\u003c/h2\u003e \u003cp\u003eWhole SBC maps were estimated using 10 mm spherical seed regions from significant fALFF peak clusters. Group-level analysis was performed on these SBC maps using separate GLM models to test various contrasts: the HC group vs. the MDD group (HC\u0026thinsp;\u0026gt;\u0026thinsp;MDD), the HC group vs. the CD group (HC\u0026thinsp;\u0026gt;\u0026thinsp;CD), the HC group vs. the combined average of the MDD and CD groups (HC\u0026thinsp;\u0026gt;\u0026thinsp;0.5MDD\u0026thinsp;+\u0026thinsp;0.5CD), and regions with altered connectivity between the MDD and CD groups (MDD\u0026thinsp;\u0026gt;\u0026thinsp;CD). The results of these individual contrasts showing the position of peak cluster, size and p-values are given in Supplementary Table\u0026nbsp;1. The identified cluster is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. These analyses showed regions with altered connectivity patterns in CD and MDD patients compared to HCs, and are mainly evident in key areas such as the precuneus cortex, supramarginal gyrus, postcentral gyrus, anterior cingulate gyrus, and planum polare.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDistinct patterns of altered functional connectivity in MDD (HC\u0026thinsp;\u0026gt;\u0026thinsp;MDD contrast):\u003c/h2\u003e \u003cp\u003eFour significant clusters were identified in the HC\u0026thinsp;\u0026gt;\u0026thinsp;MDD contrast. The first cluster, with a peak at MNI coordinate (-2, -62, +\u0026thinsp;12), primarily covered the precuneus cortex and showed decreased functional connectivity in MDD patients compared to HCs. The second cluster, peaking at (-66, -42, +\u0026thinsp;26), was mainly located in the supramarginal gyrus (posterior division) and the parietal operculum cortex and exhibited increased connectivity in MDD patients compared to HCs. The third cluster, centred at (2, -34, +\u0026thinsp;69), and the fourth cluster, centred at (-10, +\u0026thinsp;41, +4), predominantly cover the precentral and postcentral gyri and the anterior cingulate gyrus, and show decreased connectivity in MDD patients compared to HCs.\u003c/p\u003e \u003cp\u003ePrevious research comparing MDD and HC has identified a similar pattern of altered connectivity, particularly in the precuneus, which has been associated with symptom severity in MDD. This hypoconnectivity in MDD patients may indicate a failure in processing negative memories and emotions\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e,\u003cspan additionalcitationids=\"CR66\" citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. Additionally, earlier studies have shown an increased amplitude of low-frequency fluctuation (ALFF) and functional connectivity in the supramarginal gyrus and parietal operculum among individuals with MDD\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. Regions such as the postcentral gyrus, supramarginal gyrus, and cingulate gyrus have also been implicated in the onset of MDD symptoms\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. Notably, the anterior cingulate gyrus has been observed to exhibit both altered and decreased connectivity\u003csup\u003e\u003cspan additionalcitationids=\"CR71\" citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. Our findings reveal decreased connectivity in these regions, suggesting the existence of distinct MDD subtypes characterized by differential interactions between brain regions.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDecreased precuneus-ACC connectivity in CD patients as a neurobiological link to MDD (HC\u0026thinsp;\u0026gt;\u0026thinsp;CD contrast)\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eOne significant cluster was identified, centred at (-6, +\u0026thinsp;40, +3), primarily covering the anterior cingulate gyrus. This cluster exhibited decreased connectivity between the precuneus and the anterior cingulate gyrus (ACC) in CD patients compared to HCs, mirroring the connectivity pattern observed in MDD patients.\u003c/p\u003e \u003cp\u003eThis cluster has been reported in previous studies comparing CD and HC. The anterior cingulate cortex (ACC) has been recognized as a neurobiological correlate of both CD and MDD, with connectivity alterations that may differentiate irritable bowel syndrome (IBS) patients with and without MDD\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. These findings support our hypothesis that the ACC is involved in the neurobiological mechanisms underlying depression-related comorbidity in CD patients. The observed hypoconnectivity between the precuneus and ACC may reflect deficits in emotional regulation in MDD, with a smaller effect size in CD, which corresponds to the milder depressive symptoms typically seen in CD patients.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eShared functional connectivity changes in CD and MDD (HC\u0026thinsp;\u0026gt;\u0026thinsp;0.5MDD\u0026thinsp;+\u0026thinsp;0.5CD contrast)\u003c/strong\u003e \u003cp\u003eThree significant clusters emerged in the SBC group-level analysis using the precuneus as the seed region to identify common functional connectivity changes in the MDD and CD groups compared to the HC group. The first cluster, at (-66, -42, +\u0026thinsp;25), exhibited increased connectivity between the precuneus and the left supramarginal gyrus in MDD and CD patients compared to HCs. The second cluster, centred at (-2, -62, +\u0026thinsp;12), and the third cluster, centred at (-10, +\u0026thinsp;41, +4), showed decreased connectivity within the precuneus and between the precuneus and the anterior cingulate gyrus in both MDD and CD patients compared to HCs.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThese results are particularly intriguing, as the patterns of beta values (connectivity strength) shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (C) reveal a trend whereby the connectivity strength in the CD group lies between that of the MDD and HC groups across all clusters. This observation suggests a similar pattern of altered connectivity between the MDD and CD groups, indicating shared neurobiological disruptions. The regions identified in this analysis also emerged in the HC\u0026thinsp;\u0026gt;\u0026thinsp;MDD contrast, aligning with previous studies that highlight common connectivity changes in both conditions. Consistent with prior findings, these results suggest that both MDD and CD are characterized by increased functional connectivity in regions associated with emotional regulation, sensory processing, and attention. These shared neurobiological changes may help explain the increased vulnerability of CD patients with depressive symptoms to more frequent relapses and a greater need for medication compared to CD patients without depressive symptoms\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFunctional connectivity changes in MDD patients compared to CD patients, suggesting distinct alterations in sensory and self-referential processing (MDD\u0026thinsp;\u0026gt;\u0026thinsp;CD contrast)\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eFive significant clusters were observed. The first cluster, peaking at (-63, -42, +\u0026thinsp;23), was mainly located in the supramarginal gyrus (posterior division) and showed decreased connectivity in the CD group compared to the MDD group. The other clusters, primarily covering the planum polare, precuneous cortex, and planum temporale, showed increased connectivity in the CD group compared to the MDD group.\u003c/p\u003e \u003cp\u003eThe first cluster, located In the posterior division of the supramarginal gyrus, exhibited increased connectivity in the MDD group compared to the CD group, similar to patterns observed in the contrast HC\u0026thinsp;\u0026gt;\u0026thinsp;MDD. This increased connectivity in the supramarginal gyrus is likely attributable to its critical role in overcoming emotional egocentricity in social judgment and self-referential processing, distinguishing individuals with MDD from those with CD and HCs \u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e .\u003c/p\u003e \u003cp\u003eOther significant clusters were observed in regions including the planum polare, precuneus cortex, and planum temporale. Notably, these regions displayed increased connectivity in the CD group compared to the MDD group. The planum polare and planum temporale are associated with auditory processing and higher-order cognitive functions, while the precuneus is linked to self-referential thinking and consciousness\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e,\u003cspan additionalcitationids=\"CR66\" citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. The increased connectivity in these regions in CD patients suggests that CD may involve distinct alterations in sensory processing and self-referential cognition, potentially differentiating it from the more emotion-focused disruptions observed in MDD patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOur findings suggest that the shared neurobiological changes in the precuneus, the left supramarginal gyrus, and the cingulate gyrus observed in both MDD and CD patients may be key mechanisms underlying the comorbidity of these conditions. These results underscore the critical role of these brain regions in the pathophysiology of both MDD and CD. Despite significant progress, the structural and functional alterations in these brain regions are not yet fully understood, necessitating further research to elucidate these complex interactions. The bidirectional relationship between CD and MDD highlighted in our study aligns with previous research, emphasizing the interconnected nature of these diseases. Our data indicates that aberrant functional connectivity in specific brain regions may contribute to the occurrence and severity of both MDD and CD. Understanding these shared neural mechanisms is crucial for developing integrated therapeutic strategies that address both conditions simultaneously.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to uncover the shared neurobiological alterations in CD and MDD patients by examining functional changes using rs-fMRI at a 7T MRI scanner. Although prior research has established associations between CD and MDD, concurrent examination of both patient groups to explore shared neurobiological disruptions remains unexplored. We hypothesized that functional changes, specifically variations in BOLD signals and functional connectivity, would manifest in both CD and MDD cohorts compared to healthy individuals. To test this hypothesis, rs-fMRI experiments were conducted with ultra-high field 7T MRI scanners using fALFF as a neurophysiological marker. Our analysis included group-level GLM to assess fALFF maps and seed-based connectivity (SBC) maps. The results revealed significant clusters with altered common connectivity patterns in both CD and MDD patients, confirming our hypothesis and highlighting potential shared neurobiological mechanisms underlying these conditions.\u003c/p\u003e \u003cp\u003eWhen comparing HCs with MDD and CD patients, aberrant functional connectivity has been identified in the precuneus cortex, left supramarginal gyrus, and cingulate gyrus. Numerous studies have investigated brain changes in MDD and CD independently, with the main effects in each condition being replicated many times, aligning with our findings. Notably, the precuneus region has consistently emerged in previous studies focusing on MDD \u003csup\u003e\u003cspan additionalcitationids=\"CR59 CR60 CR61 CR62\" citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. Given that MDD is a frequently observed comorbidity in IBD, where the pathophysiology remains unclear, our research sheds light on critical brain regions, offering significant potential to identify key mechanisms that may explain this comorbidity.\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe effects of various antidepressants on the gut have been investigated in animal studies, demonstrating anti-inflammatory properties comparable to the effects of dexamethasone\u003csup\u003e\u003cspan additionalcitationids=\"CR76 CR77\" citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e. Clinically, similar observations have been made in humans, where IBD patients treated concurrently with antidepressants experienced lower relapse rates, reduced steroid usage, fewer endoscopies in the years following study enrolment, and even a selectively protective effect for CD \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e,\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e. A recent fMRI study identified altered brain activation patterns across different CD stages, suggesting that brain activity could possibly serve as a neurobiological biomarker for the disease\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. We propose that the key brain regions identified in our study act as neurobiological correlates of these observations, helping to bridge the gap between animal model research and clinical application in humans, ultimately contributing to the improvement of treatment strategies.\u003c/p\u003e \u003cp\u003eOne fMRI study has shown that the midcingulate cortex plays a role in integrating afferent sensory information from the gut, as stress-evoked hyperactivity could be seen in this region in CD patients compared to controls\u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e. Recent findings also suggest that the activation of certain neurons in the insular cortex can even induce inflammation via memory traces\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e. Considering this, the brain regions highlighted in our data should be examined further to determine whether the shared neural changes in CD and MDD patients originate from a common underlying mechanism or if one condition precipitates the onset of the other.\u003c/p\u003e \u003cp\u003eOur study underscores the need for incorporating psychological diagnostics and support in the treatment of IBD patients, given the significant overlap in the neural substrates of CD and MDD. Utilizing multimodal data, including multi-omics and clinical assessment data, future research should aim to clarify the precise mechanisms behind these shared changes and their contributions to the pathophysiology of both disorders. Additionally, further investigation into the effects of antidepressants on CD could provide valuable insights for more effective, holistic treatment approaches. These efforts will ultimately improve patient outcomes by addressing both the psychological and physiological aspects of these interconnected diseases.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLimitations:\u003c/h2\u003e \u003cp\u003eSeveral limitations of this study must be considered. First, its cross-sectional design precludes establishing causality between brain abnormalities and the origins of the diseases; we can only infer associations. Additionally, the relatively small sample size of 13 subjects per group limits the generalizability and robustness of our findings. Future studies with larger sample sizes are needed to enhance the reliability of the results, while longitudinal designs would allow for better assessment of causal relationships. Another consideration is the potential influence of medication on brain connectivity and structure, which was not addressed in this study, but should be considered in future research. Furthermore, distinguishing between active and inactive phases of CD in future studies could provide more nuanced insights into the brain's role in the disease's progression and symptomatology. Addressing these factors in future investigations will provide a more comprehensive understanding of the neurobiological underpinnings of CD and its comorbidity with MDD.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eDeclaration of Interests\u003c/h2\u003e\n\u003cp\u003e\u003cem\u003eThe authors declare no competing interests.\u003c/em\u003e\u003c/p\u003e\n\u003ch2\u003eEthics Statement\u003c/h2\u003e\n\u003cp\u003eAll participants signed informed consent prior to the study, and the study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Faculty of Medicine of the RWTH Aachen University.\u003c/p\u003e\n\u003ch2\u003eAuthor Contributions\u003c/h2\u003e\n\u003cp\u003eHartmann HA: conceptualization, methodology, investigation, formal analysis, writing -original draft.\u003c/p\u003e\n\u003cp\u003eBerthold M L: Investigation, manuscript review \u0026amp; correction.\u003c/p\u003e\n\u003cp\u003eRamkiran S: Investigation, manuscript review \u0026amp; correction.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eB\u0026uuml;ndgens L: Investigation, manuscript review \u0026amp; correction\u003c/p\u003e\n\u003cp\u003eJaeger JW: Investigation, manuscript review \u0026amp; correction.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHagen J: Investigation, validation, manuscript review \u0026amp; correction.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eColee M: Investigation, manuscript review \u0026amp; correction. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBackhaus M: Investigation, manuscript review \u0026amp; correction.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSchnellb\u0026auml;cher GJ: Investigation, validation, manuscript review \u0026amp; correction.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eVeselinovic T: Supervision, validation, manuscript review \u0026amp; correction.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eShah NJ: Resources, funding acquisition, manuscript review \u0026amp; correction.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSchneider\u0026nbsp;M: Investigation, validation, manuscript review \u0026amp; correction.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNeuner I: Supervision, funding acquisition, resources, manuscript review \u0026amp; correction.\u003c/p\u003e\n\u003cp\u003eRajkumar R: conceptualization, investigation, methodology, software, formal analysis, visualization, supervision, funding acquisition, manuscript review \u0026amp; correction.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eThis study is part of the doctoral thesis (Dr. med.) of Hanna Antonia Hartmann at the Medical Faculty of the RWTH Aachen University, Germany. This project was partly Funded by the Federal Ministry of Education and Research (BMBF) and the Ministry of Culture and Science of the German State of North Rhine-Westphalia (MKW) under the Excellence Strategy of the Federal Government and the L\u0026auml;nder (Project-ID G:(DE-82) EXS-SF-OPSF845). The authors gratefully thank all participants in the study. The authors would also like to thank Petra Engels, Anita K\u0026ouml;th, Elke Bechholz, Andrea Muren, and Silke Frensch for their technical assistance. Finally, the authors would like to acknowledge their gratitude to Claire Rick for proofreading the manuscript.\u003c/p\u003e\n\u003ch2\u003eData Availability Statement\u003c/h2\u003e\n\u003cp\u003eData will be made available upon request to the corresponding authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eS\u0026aelig;ther, L.S., Ueland, T., Haatveit, B., Maglanoc, L.A., Szabo, A., Djurovic, S., Aukrust, P., Roelfs, D., Mohn, C., Ormerod, M.B.E.G., et al. (2023). Inflammation and cognition in severe mental illness: patterns of covariation and subgroups. Mol. 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Cell \u003cem\u003e184\u003c/em\u003e, 5902\u0026ndash;5915.e17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cell.2021.10.013\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2021.10.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Crohn’s disease, Major depressive disorder, Resting-state, fMRI, fALFF, Seed-based connectivity, Ultra-high field, BDI-II, GSRS","lastPublishedDoi":"10.21203/rs.3.rs-5349946/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5349946/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePsychological stress is increasingly recognized as a key factor in Crohn’s disease (CD), yet the neurobiological connections between CD and major depressive disorder (MDD) remain poorly defined. In this study, we utilize advanced neuroimaging techniques to explore these neurobiological changes. Resting-state functional MRI (RS-fMRI) was performed on CD patients, MDD patients, and healthy controls (HCs) using a 7 Tesla scanner. CD patients showed higher depression scores than HCs but lower than those of patients with MDD. There was also a clear link between the severity of digestive symptoms and depression scores in CD patients. RS-fMRI analysis identified both CD and MDD patients had changes in activity in the precuneus region. Additionally, the connectivity between the precuneus and anterior cingulate cortex was also similarly altered in both groups. These findings reveal overlapping neurobiological pathways and underscore the importance of integrated therapeutic strategies to address the comorbidities of CD and MDD.\u003c/p\u003e","manuscriptTitle":"Resting-state 7T brain fMRI reveals common neurobiological changes in patients with Crohn’s disease and major depressive disorder","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-24 11:51:25","doi":"10.21203/rs.3.rs-5349946/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-medicine","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsmed","sideBox":"Learn more about [Communications Medicine](http://www.nature.com/commsmed)","snPcode":"43856","submissionUrl":"https://mts-commsmed.nature.com/cgi-bin/main.plex","title":"Communications Medicine","twitterHandle":"@commsmedicine","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5e807d7e-bc0c-4195-8f34-30d2636f1049","owner":[],"postedDate":"March 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":40095103,"name":"Biological sciences/Psychology"},{"id":40095104,"name":"Biological sciences/Neuroscience"},{"id":40095105,"name":"Biological sciences/Biological techniques/Imaging/Functional magnetic resonance imaging"}],"tags":[],"updatedAt":"2025-09-22T06:53:03+00:00","versionOfRecord":{"articleIdentity":"rs-5349946","link":"https://doi.org/10.1038/s43856-025-01117-w","journal":{"identity":"communications-medicine","isVorOnly":false,"title":"Communications Medicine"},"publishedOn":"2025-09-17 04:00:00","publishedOnDateReadable":"September 17th, 2025"},"versionCreatedAt":"2025-03-24 11:51:25","video":"","vorDoi":"10.1038/s43856-025-01117-w","vorDoiUrl":"https://doi.org/10.1038/s43856-025-01117-w","workflowStages":[]},"version":"v1","identity":"rs-5349946","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5349946","identity":"rs-5349946","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}