Leading with well-being: Introducing a model for well-being informed leadership.

Higher education brings a catalog of peaks and valleys for students, staff, and faculty. These are heightened by global crises, challenging legislation, and exclusionary practices. These kinds of adversities influence how we show up in higher education spaces and impact both our leadership and well-being. As leadership reciprocally affects, and is affected by, one's well-being, the responsibility to cultivate both within higher education continuously increases. To consistently support and uplift our students and understand the intricate challenges higher education continues to face, we introduce the well-being & leadership transformation (WBLT) model. Informed by leadership and well-being frameworks, the WBLT model integrates leadership and well-being in an intentional and holistic way. This piece establishes the elements of the model and demonstrates the WBLT model in action through various examples.

Amyand's Hernia: Incarcerated Appendicitis in a Recurrent Inguinal Hernia in an Adult.

Inguinal hernias are among the most common cases presented to a surgeon. In spite of extensive research and clinical experience over centuries, inguinal hernias still pose anatomical challenges for operating surgeons, especially with a propensity for recurrence. One such complicated entity is the Amyand's hernia - defined as an inguinal hernia contained within the hernial sac - the vermiform appendix - as the herniated content. It is a rare clinical presentation and carries with it certain complexities with regard to operative decisions and clinical management. We present a case of a 71-year-old male presenting with a recurrent inguinal hernia, with an incarcerated, inflamed appendix as the content; managed surgically with appendicectomy and herniorraphy, without the use of a prosthetic mesh.

Mapping Brain Structure Variability in Chronic Pain: The Role of Widespreadness and Pain Type and Its Mediating Relationship With Suicide Attempt.

BACKGROUND: Chronic pain affects nearly 20% of the U.S. POPULATION: It is a leading cause of disability globally and is associated with a heightened risk for suicide. The role of the central nervous system in the perception and maintenance of chronic pain has recently been accepted, but specific brain circuitries involved have yet to be mapped across pain types in a large-scale study. METHODS: We used data from the UK Biobank (N = 21,968) to investigate brain structural alterations in individuals reporting chronic pain compared with pain-free control participants and their mediating effect on history of suicide attempt. RESULTS: Chronic pain and, more notably, chronic multisite pain was associated with, on average, lower surface area throughout the cortex after adjusting for demographic, clinical, and neuropsychiatric confounds. Only participants with abdominal pain showed lower subcortical volumes, including the amygdala and brainstem, and lower cerebellum volumes. Participants with chronic headaches showed a widespread thicker cortex compared with control participants. Mediation analyses revealed that precuneus thickness mediated the relationship of chronic multisite pain and history of suicide attempt. Mediating effects were also identified specific to localized pain, with the strongest effect being amygdala volume in individuals with chronic abdominal pain. CONCLUSIONS: Results support a widespread effect of chronic pain on brain structure and distinct brain structures underlying chronic musculoskeletal pain, visceral pain, and headaches. Mediation effects of regions in the extended ventromedial prefrontal cortex subsystem suggest that exacerbated negative internal states, negative self-referencing, and impairments in future planning may underlie suicidal behaviors in individuals with chronic pain.

The Influence of Brain MRI Defacing Algorithms on Brain-Age Predictions via 3D Convolutional Neural Networks.

In brain imaging research, it is becoming standard practice to remove the face from the individual's 3D structural MRI scan to ensure data privacy standards are met. Face removal - or 'defacing' - is being advocated for large, multi-site studies where data is transferred across geographically diverse sites. Several methods have been developed to limit the loss of important brain data by accurately and precisely removing non-brain facial tissue. At the same time, deep learning methods such as convolutional neural networks (CNNs) are increasingly being used in medical imaging research for diagnostic classification and prognosis in neurological diseases. These neural networks train predictive models based on patterns in large numbers of images. Because of this, defacing scans could remove informative data. Here, we evaluated 4 popular defacing methods to identify the effects of defacing on 'brain age' prediction - a common benchmarking task of predicting a subject's chronological age from their 3D T1-weighted brain MRI. We compared brain-age calculations using defaced MRIs to those that were directly brain extracted, and those with both brain and face. Significant differences were present when comparing average per-subject error rates between algorithms in both the defaced brain data and the extracted facial tissue. Results also indicated brain age accuracy depends on defacing and the choice of algorithm. In a secondary analysis, we also examined how well comparable CNNs could predict chronological age from the facial region only (the extracted portion of the defaced image), as well as visualize areas of importance in facial tissue for predictive tasks using CNNs. We obtained better performance in age prediction when using the extracted face portion alone than images of the brain, suggesting the need for caution when defacing methods are used in medical image analysis.

A Comprehensive Corpus Callosum Segmentation Tool for Detecting Callosal Abnormalities and Genetic Associations from Multi Contrast MRIs.

Structural alterations of the midsagittal corpus callosum (midCC) have been associated with a wide range of brain disorders. The midCC is visible on most MRI contrasts and in many acquisitions with a limited field-of-view. Here, we present an automated tool for segmenting and assessing the shape of the midCC from T1w, T2w, and FLAIR images. We train a UNet on images from multiple public datasets to obtain midCC segmentations. A quality control algorithm is also built-in, trained on the midCC shape features. We calculate intraclass correlations (ICC) and average Dice scores in a test-retest dataset to assess segmentation reliability. We test our segmentation on poor quality and partial brain scans. We highlight the biological significance of our extracted features using data from over 40,000 individuals from the UK Biobank; we classify clinically defined shape abnormalities and perform genetic analyses.

Primary Ewing's Sarcoma of Body of Mandible, Multimodal Treatment with Excellent Spontaneous Bone Regeneration: a Case Report.

Ewing's sarcoma is an invariable manifestation in facial bones. Primary lesions in head and neck region had come up with better prognosis compared to other primary sites; hence, management of such jaw lesions is a challenge particularly in pediatric patients during first decade of life as functional impairment and facial disfigurement may affect the quality of life. Here, we are discussing a unique case of primary lesion of horizontal region of mandible with special focus on use of radiation therapy, radiation dose-related effects and spontaneous bone regeneration.

Improved psychosocial measures associated with physical activity may be explained by alterations in brain-gut microbiome signatures.

Obesity contributes to physical comorbidities and mental health consequences. We explored whether physical activity could influence more than metabolic regulation and result in psychological benefits through the brain-gut microbiome (BGM) system in a population with high BMI. Fecal samples were obtained for 16 s rRNA profiling and fecal metabolomics, along with psychological and physical activity questionnaires. Whole brain resting-state functional MRI was acquired, and brain connectivity metrics were calculated. Higher physical activity was significantly associated with increased connectivity in inhibitory appetite control brain regions, while lower physical activity was associated with increased emotional regulation network connections. Higher physical activity was also associated with microbiome and metabolite signatures protective towards mental health and metabolic derangements. The greater resilience and coping, and lower levels of food addiction seen with higher physical activity, may be explained by BGM system differences. These novel findings provide an emphasis on the psychological and resilience benefits of physical activity, beyond metabolic regulation and these influences seem to be related to BGM interactions.

A Comprehensive Corpus Callosum Segmentation Tool for Detecting Callosal Abnormalities and Genetic Associations from Multi Contrast MRIs.

Structural alterations of the midsagittal corpus callosum (midCC) have been associated with a wide range of brain disorders. The midCC is visible on most MRI contrasts and in many acquisitions with a limited field-of-view. Here, we present an automated tool for segmenting and assessing the shape of the midCC from T1w, T2w, and FLAIR images. We train a UNet on images from multiple public datasets to obtain midCC segmentations. A quality control algorithm is also built-in, trained on the midCC shape features. We calculate intraclass correlations (ICC) and average Dice scores in a test-retest dataset to assess segmentation reliability. We test our segmentation on poor quality and partial brain scans. We highlight the biological significance of our extracted features using data from over 40,000 individuals from the UK Biobank; we classify clinically defined shape abnormalities and perform genetic analyses.

The Influence of Brain MRI Defacing Algorithms on Brain-Age Predictions via 3D Convolutional Neural Networks.

In brain imaging research, it is becoming standard practice to remove the face from the individual's 3D structural MRI scan to ensure data privacy standards are met. Face removal - or 'defacing' - is being advocated for large, multi-site studies where data is transferred across geographically diverse sites. Several methods have been developed to limit the loss of important brain data by accurately and precisely removing non-brain facial tissue. At the same time, deep learning methods such as convolutional neural networks (CNNs) are increasingly being used in medical imaging research for diagnostic classification and prognosis in neurological diseases. These neural networks train predictive models based on patterns in large numbers of images. Because of this, defacing scans could remove informative data. Here, we evaluated 4 popular defacing methods to identify the effects of defacing on 'brain age' prediction - a common benchmarking task of predicting a subject's chronological age from their 3D T1-weighted brain MRI. We compared brain-age calculations using defaced MRIs to those that were directly brain extracted, and those with both brain and face. Significant differences were present when comparing average per-subject error rates between algorithms in both the defaced brain data and the extracted facial tissue. Results also indicated brain age accuracy depends on defacing and the choice of algorithm. In a secondary analysis, we also examined how well comparable CNNs could predict chronological age from the facial region only (the extracted portion of the defaced image), as well as visualize areas of importance in facial tissue for predictive tasks using CNNs. We obtained better performance in age prediction when using the extracted face portion alone than images of the brain, suggesting the need for caution when defacing methods are used in medical image analysis.

Effectiveness of yoga on Ewing's battery autonomic function test: cross-sectional study.

INTRODUCTION: The cardiovascular autonomic functions can be tested by a Battery of five tests developed by Ewing and Clark in 1981 in Edinburgh. Yogic practices are immensely useful for physical, mental and spiritual development required for better autonomic function. AIM AND OBJECTIVES: To assess the ANS (Autonomic function system) function with help of Ewing's Battery tests in yoga participants and healthy participants not practicing yoga. MATERIALS AND METHODS: A cross-sectional study was conducted on 270 participants which were divided into two groups viz: 135 in healthy control (Group I) and 135 in yoga group (Group II). Subjects with informed consent between 40-50 years, were included in control (Group I) and those practicing yoga for past minimum 3 months were included in Group II. Anthropometric measurements were done and parasympathetic tests like Heart rate (HR) response to standing from the supine posture, to Valsalva maneuvers and to slow deep breathing were done. Also, sympathetic tests, Blood Pressure (BP) response to cold in cold pressor test (CPT), to sustained handgrip test and to standing from lying posture were carried out. RESULTS: P value was found to be statically significant among yoga group as compared with healthy control group in all the sympathetic and parasympathetic tests except in CPT. As per the Ewing's criteria, normal, early, diseased and severe CAN (Cardiac autonomic neuropathy) in healthy controls findings were 11.11%, 58.51%, 37.03%, 17.77% and in yoga participants findings were 37.7%, 34.8%, 6.66% and 8.88% respectively. According to Bellavere's classification, maximum diseased CAN were found in healthy control as compared to yoga group. As per AIIMS (All India Institute of Medical Sciences) criteria, parasympathetic neuropathy was observed in 11.85% of the healthy controls and in 6.66% of the yoga group, and that maximum sympathetic neuropathy was observed in 11.11% of the healthy patients and only 3.7% of the yoga group. CONCLUSION: More emphasis should be given on implementation of yoga from early ages at the institutional levels, hospital levels. Yoga practices will suffice and lead to improvement of unhealthy ANS condition. Overall, Yoga showed better ANS function than healthy control group.

Integrated multi-modal brain signatures predict sex-specific obesity status.

Investigating sex as a biological variable is key to determine obesity manifestation and treatment response. Individual neuroimaging modalities have uncovered mechanisms related to obesity and altered ingestive behaviours. However, few, if any, studies have integrated data from multi-modal brain imaging to predict sex-specific brain signatures related to obesity. We used a data-driven approach to investigate how multi-modal MRI and clinical features predict a sex-specific signature of participants with high body mass index (overweight/obese) compared to non-obese body mass index in a sex-specific manner. A total of 78 high body mass index (55 female) and 105 non-obese body mass index (63 female) participants were enrolled in a cross-sectional study. All participants classified as high body mass index had a body mass index greater than 25 kg/m2 and non-obese body mass index had a body mass index between 19 and 20 kg/m2. Multi-modal neuroimaging (morphometry, functional resting-state MRI and diffusion-weighted scan), along with a battery of behavioural and clinical questionnaires were acquired, including measures of mood, early life adversity and altered ingestive behaviours. A Data Integration Analysis for Biomarker discovery using Latent Components was conducted to determine whether clinical features, brain morphometry, functional connectivity and anatomical connectivity could accurately differentiate participants stratified by obesity and sex. The derived models differentiated high body mass index against non-obese body mass index participants, and males with high body mass index against females with high body mass index obtaining balanced accuracies of 77 and 75%, respectively. Sex-specific differences within the cortico-basal-ganglia-thalamic-cortico loop, the choroid plexus-CSF system, salience, sensorimotor and default-mode networks were identified, and were associated with early life adversity, mental health quality and greater somatosensation. Results showed multi-modal brain signatures suggesting sex-specific cortical mechanisms underlying obesity, which fosters clinical implications for tailored obesity interventions based on sex.

Brain-gut microbiome profile of neuroticism predicts food addiction in obesity: A transdiagnostic approach.

Neuroticism is one of the most robust risk factors for addictive behaviors including food addiction (a key contributor to obesity), although the associated mechanisms are not well understood. A transdiagnostic approach was used to identify the neuroticism-related neuropsychological and gut metabolomic patterns associated with food addiction. Predictive modeling of neuroticism was implemented using multimodal features (23 clinical, 13,531 resting-state functional connectivity (rsFC), 336 gut metabolites) in 114 high body mass index (BMI ≥25 kg/m2) (cross-sectional) participants. Gradient boosting machine and logistic regression models were used to evaluate classification performance for food addiction. Neuroticism was significantly associated with food addiction (P < 0.001). Neuroticism-related features predicted food addiction with high performance (89% accuracy). Multimodal models performed better than single-modal models in predicting food addiction. Transdiagnostic alterations corresponded to rsFC involved in the emotion regulation, reward, and cognitive control and self-monitoring networks, and the metabolite 3-(4-hydroxyphenyl) propionate, as well as anxiety symptoms. Neuroticism moderated the relationship between BMI and food addiction. Neuroticism drives neuropsychological and gut microbial signatures implicated in dopamine synthesis and inflammation, anxiety, and food addiction. Such transdiagnostic models are essential in identifying mechanisms underlying food addiction in obesity, as it can help develop multiprong interventions to improve symptoms.

The neurobiology of irritable bowel syndrome.

Irritable bowel syndrome (IBS) is the most prevalent disorder of brain-gut interactions that affects between 5 and 10% of the general population worldwide. The current symptom criteria restrict the diagnosis to recurrent abdominal pain associated with altered bowel habits, but the majority of patients also report non-painful abdominal discomfort, associated psychiatric conditions (anxiety and depression), as well as other visceral and somatic pain-related symptoms. For decades, IBS was considered an intestinal motility disorder, and more recently a gut disorder. However, based on an extensive body of reported information about central, peripheral mechanisms and genetic factors involved in the pathophysiology of IBS symptoms, a comprehensive disease model of brain-gut-microbiome interactions has emerged, which can explain altered bowel habits, chronic abdominal pain, and psychiatric comorbidities. In this review, we will first describe novel insights into several key components of brain-gut microbiome interactions, starting with reported alterations in the gut connectome and enteric nervous system, and a list of distinct functional and structural brain signatures, and comparing them to the proposed brain alterations in anxiety disorders. We will then point out the emerging correlations between the brain networks with the genomic, gastrointestinal, immune, and gut microbiome-related parameters. We will incorporate this new information into a systems-based disease model of IBS. Finally, we will discuss the implications of such a model for the improved understanding of the disorder and the development of more effective treatment approaches in the future.

Multisite test-retest reliability and compatibility of brain metrics derived from FreeSurfer versions 7.1, 6.0, and 5.3.

Automatic neuroimaging processing tools provide convenient and systematic methods for extracting features from brain magnetic resonance imaging scans. One tool, FreeSurfer, provides an easy-to-use pipeline to extract cortical and subcortical morphometric measures. There have been over 25 stable releases of FreeSurfer, with different versions used across published works. The reliability and compatibility of regional morphometric metrics derived from the most recent version releases have yet to be empirically assessed. Here, we used test-retest data from three public data sets to determine within-version reliability and between-version compatibility across 42 regional outputs from FreeSurfer versions 7.1, 6.0, and 5.3. Cortical thickness from v7.1 was less compatible with that of older versions, particularly along the cingulate gyrus, where the lowest version compatibility was observed (intraclass correlation coefficient 0.37-0.61). Surface area of the temporal pole, frontal pole, and medial orbitofrontal cortex, also showed low to moderate version compatibility. We confirm low compatibility between v6.0 and v5.3 of pallidum and putamen volumes, while those from v7.1 were compatible with v6.0. Replication in an independent sample showed largely similar results for measures of surface area and subcortical volumes, but had lower overall regional thickness reliability and compatibility. Batch effect correction may adjust for some inter-version effects when most sites are run with one version, but results vary when more sites are run with different versions. Age associations in a quality controlled independent sample (N = 106) revealed version differences in results of downstream statistical analysis. We provide a reference to highlight the regional metrics that may yield recent version-related inconsistencies in published findings. An interactive viewer is provided at http://data.brainescience.org/Freesurfer_Reliability/.

A multi-omic brain gut microbiome signature differs between IBS subjects with different bowel habits.

Alterations of the brain-gut-microbiome system (BGM) have been implicated in the pathophysiology of irritable bowel syndrome (IBS), yet bowel habit-specific alterations have not been elucidated. In this cross-sectional study, we apply a systems biology approach to characterize BGM patterns related to predominant bowel habit. Fecal samples and resting state fMRI were obtained from 102 premenopausal women (36 constipation-predominant IBS (IBS-C), 27 diarrhea-predominant IBS (IBS-D), 39 healthy controls (HCs)). Data integration analysis using latent components (DIABLO) was used to integrate data from the phenome, microbiome, metabolome, and resting-state connectome to predict HCs vs IBS-C vs IBS-D. Bloating and visceral sensitivity, distinguishing IBS from HC, were negatively associated with beneficial microbes and connectivity involving the orbitofrontal cortex. This suggests that gut interactions may generate aberrant central autonomic and descending pain pathways in IBS. The connection between IBS symptom duration, key microbes, and caudate connectivity may provide mechanistic insight to the chronicity of pain in IBS. Compared to IBS-C and HCs, IBS-D had higher levels of many key metabolites including tryptophan and phenylalanine, and increased connectivity between the sensorimotor and default mode networks; thus, suggestingan influence on diarrhea, self-related thoughts, and pain perception in IBS-D ('bottom-up' mechanism). IBS-C's microbiome and metabolome resembled HCs, but IBS-C had increased connectivity in the default mode and salience networks compared to IBS-D, which may indicate importance of visceral signals, suggesting a more 'top-down' BGM pathophysiology. These BGM characteristics highlight possible mechanistic differences for variations in the IBS bowel habit phenome. This article is part of the Special Issue on 'Microbiome & the Brain: Mechanisms & Maladies'.

Cortical profiles of numerous psychiatric disorders and normal development share a common pattern.

The neurobiological bases of the association between development and psychopathology remain poorly understood. Here, we identify a shared spatial pattern of cortical thickness (CT) in normative development and several psychiatric and neurological disorders. Principal component analysis (PCA) was applied to CT of 68 regions in the Desikan-Killiany atlas derived from three large-scale datasets comprising a total of 41,075 neurotypical participants. PCA produced a spatially broad first principal component (PC1) that was reproducible across datasets. Then PC1 derived from healthy adult participants was compared to the pattern of CT differences associated with psychiatric and neurological disorders comprising a total of 14,886 cases and 20,962 controls from seven ENIGMA disease-related working groups, normative maturation and aging comprising a total of 17,697 scans from the ABCD Study® and the IMAGEN developmental study, and 17,075 participants from the ENIGMA Lifespan working group, as well as gene expression maps from the Allen Human Brain Atlas. Results revealed substantial spatial correspondences between PC1 and widespread lower CT observed in numerous psychiatric disorders. Moreover, the PC1 pattern was also correlated with the spatial pattern of normative maturation and aging. The transcriptional analysis identified a set of genes including KCNA2, KCNS1 and KCNS2 with expression patterns closely related to the spatial pattern of PC1. The gene category enrichment analysis indicated that the transcriptional correlations of PC1 were enriched to multiple gene ontology categories and were specifically over-represented starting at late childhood, coinciding with the onset of significant cortical maturation and emergence of psychopathology during the prepubertal-to-pubertal transition. Collectively, the present study reports a reproducible latent pattern of CT that captures interregional profiles of cortical changes in both normative brain maturation and a spectrum of psychiatric disorders. The pubertal timing of the expression of PC1-related genes implicates disrupted neurodevelopment in the pathogenesis of the spectrum of psychiatric diseases emerging during adolescence.

Optimum estimator in simple random sampling using two auxiliary attributes with application in agriculture, fisheries and education sectors.

In modern age of information technology, data is available everywhere in huge amount. Every sector generates lot of data every day. The investigation of each unit of data is not feasible due to limited resources like time, labor, and cost. In such situations, survey sampling is recommended to draw the information about the population parameters. Therefore, the main objective of present study is to develop an estimation method for obtaining the information about population parameter. We propose an optimum estimator for enhanced estimation of population mean in simple random sampling by utilizing the information of the two auxiliary attribute. The expression for bias, mean squared error (MSE) and minimum mean squared error of the proposed estimator are derived up to the first order of approximation and it is shown that the proposed estimator under derived conditions perform better than the existing estimators theoretically. Four population are demonstrated to assess the performance as well as applicability of the proposed estimator. The percentage relative efficiency (PRE) of proposed estimator for all the populations is 209.533, 163.852, 210.398 and 340.578, respectively. The numerical illustrations confirm that the proposed estimator dominates over the existing estimators.•The main objective of present study is to propose a new estimator/method for estimation of population mean using two auxiliary attributes under simple random sampling.•The bias and mean square error of the proposed estimator/method is derived and compared with the existing estimators to compare the efficiency theoretically.•Applications of the proposed method/estimator is highlighted using thorough the real data sets of various sectors.

Complex functional brain network properties in anorexia nervosa.

BACKGROUND: Anorexia nervosa (AN) is a disorder characterized by an incapacitating fear of weight gain and by a disturbance in the way the body is experienced, facets that motivate dangerous weight loss behaviors. Multimodal neuroimaging studies highlight atypical neural activity in brain networks involved in interoceptive awareness and reward processing. METHODS: The current study used resting-state neuroimaging to model the architecture of large-scale functional brain networks and characterize network properties of individual brain regions to clinical measures. Resting-state neuroimaging was conducted in 62 adolescents, 22 (21 female) with a history of AN and 40 (39 female) healthy controls (HCs). Sensorimotor and basal ganglia regions, as part of a 165-region whole-brain network, were investigated. Subject-specific functional brain networks were computed to index centrality. A contrast analysis within the general linear model covarying for age was performed. Correlations between network properties and behavioral measures were conducted (significance q < .05). RESULTS: Compared to HCs, AN had lower connectivity from sensorimotor regions, and greater connectivity from the left caudate nucleus to the right postcentral gyrus. AN demonstrated lower sensorimotor centrality, but higher basal ganglia centrality. Sensorimotor connectivity dyads and centrality exhibited negative correlations with body dissatisfaction and drive for thinness, two essential features of AN. CONCLUSIONS: These findings suggest that AN is associated with greater communication from the basal ganglia, and lower information propagation in sensorimotor cortices. This is consistent with the clinical presentation of AN, where individuals exhibit patterns of rigid habitual behavior that is not responsive to bodily needs, and seem "disconnected" from their bodies.

Individuals with anorexia nervosa (AN) usually report a fear of gaining weight. They often develop a dislike and distrust of their bodies, feeling that their bodies had somehow let them down. These fears can in turn lead to dangerous weight loss behaviors. Magnetic resonance imaging of the brain is a tool that helps highlight the underlying biological processes associated with AN. In the current study we aim to investigate how the connections in key regions of the brain are related to clinical and behavioral factors associated with AN. We found regions of two main networks were associated with body dissatisfaction and drive for thinness, which are key features of AN. The brain regions involved help explain why patients with AN have characteristics of feeling disconnected from their bodies, having difficulty labeling and regulating emotions, responding to biological needs such as hunger and fatigue, and differentiating experiences that will be rewarding. These results can help guide interventions that will be directed towards helping individuals with AN to better sense, decipher, and act on the various signals being communicated by their body.

Functional brain rewiring and altered cortical stability in ulcerative colitis.

Despite recent advances, there is still a major need to better understand the interactions between brain function and chronic gut inflammation and its clinical implications. Alterations in executive function have previously been identified in several chronic inflammatory conditions, including inflammatory bowel diseases. Inflammation-associated brain alterations can be captured by connectome analysis. Here, we used the resting-state fMRI data from 222 participants comprising three groups (ulcerative colitis (UC), irritable bowel syndrome (IBS), and healthy controls (HC), N = 74 each) to investigate the alterations in functional brain wiring and cortical stability in UC compared to the two control groups and identify possible correlations of these alterations with clinical parameters. Globally, UC participants showed increased functional connectivity and decreased modularity compared to IBS and HC groups. Regionally, UC showed decreased eigenvector centrality in the executive control network (UC < IBS < HC) and increased eigenvector centrality in the visual network (UC > IBS > HC). UC also showed increased connectivity in dorsal attention, somatomotor network, and visual networks, and these enhanced subnetwork connectivities were able to distinguish UC participants from HCs and IBS with high accuracy. Dynamic functional connectome analysis revealed that UC showed enhanced cortical stability in the medial prefrontal cortex (mPFC), which correlated with severe depression and anxiety-related measures. None of the observed brain changes were correlated with disease duration. Together, these findings are consistent with compromised functioning of networks involved in executive function and sensory integration in UC.

A neuropsychosocial signature predicts longitudinal symptom changes in women with irritable bowel syndrome.

Irritable bowel syndrome (IBS) is a common disorder of brain-gut interactions characterized by chronic abdominal pain, altered bowel movements, often accompanied by somatic and psychiatric comorbidities. We aimed to test the hypothesis that a baseline phenotype composed of multi-modal neuroimaging and clinical features predicts clinical improvement on the IBS Symptom Severity Scale (IBS-SSS) at 3 and 12 months without any targeted intervention. Female participants (N = 60) were identified as "improvers" (50-point decrease on IBS-SSS from baseline) or "non-improvers." Data integration analysis using latent components (DIABLO) was applied to a training and test dataset to determine whether a limited number of sets of multiple correlated baseline'omics data types, including brain morphometry, anatomical connectivity, resting-state functional connectivity, and clinical features could accurately predict improver status. The derived predictive models predicted improvement status at 3-months and 12-months with 91% and 83% accuracy, respectively. Across both time points, non-improvers were classified as having greater correlated morphometry, anatomical connectivity and resting-state functional connectivity characteristics within salience and sensorimotor networks associated with greater pain unpleasantness, but lower default mode network integrity and connectivity. This suggests that non-improvers have a greater engagement of attentional systems to perseverate on painful visceral stimuli, predicting IBS exacerbation. The ability of baseline multimodal brain-clinical signatures to predict symptom trajectories may have implications in guiding integrative treatment in the age of precision medicine, such as treatments targeted at changing attentional systems such as mindfulness or cognitive behavioral therapy.