ArticlePain2015
Multivariate morphological brain signatures predict patients with chronic abdominal pain from healthy control subjects.
Article in Pain, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
45 citing papers in PubMed, 73 citations in OpenAlex.
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- The impact of implicit health symbols on probiotic products on purchase intention among gastrointestinal disease patients: evidence from multiple experiments.Frontiers in nutrition · 2026Article
- Characterization of the Fecal Metabolome in Patients With Early Systemic Sclerosis.ACR open rheumatology · 2025Article
- Mediating Role of Negative Affectivity in the Association Between Lifetime Trauma and Gastrointestinal Symptoms.Healthcare (Basel, Switzerland) · 2025Article
- Interpretable and integrative deep learning for discovering brain-behaviour associations.Scientific reports · 2025Article
- Advances and challenges in neuroimaging-based pain biomarkers.Cell reports. Medicine · 2024Review
- Pathology of pain and its implications for therapeutic interventions.Signal transduction and targeted therapy · 2024Review
- Early life adversity impacts alterations in brain structure and food addiction in individuals with high BMI.Scientific reports · 2024Article
- Distinctive cortical morphological patterns in primary trigeminal neuralgia: a cross-sectional clinical study.Neuroradiology · 2024Observational
- Changes in brain structure and function during early aging in patients with chronic low back pain.Frontiers in aging neuroscience · 2024Article
- Article
- Diagnosis of Chronic Musculoskeletal Pain by Using Functional Near-Infrared Spectroscopy and Machine Learning.Bioengineering (Basel, Switzerland) · 2023Article
- Altered Structural Covariance of Insula, Cerebellum and Prefrontal Cortex Is Associated with Somatic Symptom Levels in Irritable Bowel Syndrome (IBS).Brain sciences · 2021Article
- Chronic Abdominal Pain in IBD Research Initiative: Unraveling Biological Mechanisms and Patient Heterogeneity to Personalize Treatment and Improve Clinical Outcomes.Crohn's & colitis 360 · 2021Article
- Association between pain sensitivity and gray matter properties in the sensorimotor network in women with irritable bowel syndrome.Neurogastroenterology and motility · 2021Article
- Brain Imaging Biomarkers for Chronic Pain.Frontiers in neurology · 2021Review
- Toward a unified framework for interpreting machine-learning models in neuroimaging.Nature protocols · 2020Article
- Multiple Holdouts With Stability: Improving the Generalizability of Machine Learning Analyses of Brain-Behavior Relationships.Biological psychiatry · 2020Article
- Sensor Technologies to Manage the Physiological Traits of Chronic Pain: A Review.Sensors (Basel, Switzerland) · 2020Review
- History of early life adversity is associated with increased food addiction and sex-specific alterations in reward network connectivity in obesity.Obesity science & practice · 2019Article
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Authors and funding
11 authors at 1 institution in 2 countries.
Funding
Abstract
Irritable bowel syndrome (IBS) is the most common chronic visceral pain disorder. The pathophysiology of IBS is incompletely understood; however, evidence strongly suggests dysregulation of the brain-gut axis. The aim of this study was to apply multivariate pattern analysis to identify an IBS-related morphometric brain signature that could serve as a central biological marker and provide new mechanistic insights into the pathophysiology of IBS. Parcellation of 165 cortical and subcortical regions was performed using FreeSurfer and the Destrieux and Harvard-Oxford atlases. Volume, mean curvature, surface area, and cortical thickness were calculated for each region. Sparse partial least squares discriminant analysis was applied to develop a diagnostic model using a training set of 160 females (80 healthy controls and 80 patients with IBS). Predictive accuracy was assessed in an age-matched holdout test set of 52 females (26 healthy controls and 26 patients with IBS). A 2-component classification algorithm comprising the morphometry of (1) primary somatosensory and motor regions and (2) multimodal network regions explained 36% of the variance. Overall predictive accuracy of the classification algorithm was 70%. Small effect size associations were observed between the somatosensory and motor signature and nongastrointestinal somatic symptoms. The findings demonstrate that the predictive accuracy of a classification algorithm based solely on regional brain morphometry is not sufficient, but they do provide support for the utility of multivariate pattern analysis for identifying meaningful neurobiological markers in IBS.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.