Evidence map›Paper›PMID 41469850›Full record

ArticleBMC gastroenterology2025

Integrated structural and functional brain imaging reveals biomarkers of disease activity in Crohn's disease.

Chunhui Bao, Shuai Xu, Luyi Wu, Xinyi Zhu, Di Wang, Zhenjie Zhang, Changcheng Qu, Yin Shi, Xiaoqing Zeng, Jie Zhong and 7 more

Abstract read
In one paragraph

Article in BMC gastroenterology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

17 authors.

Chunhui Bao *Yueyang Hospital of Integrated Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, 110 Ganhe Road, Shanghai, 200437, China.
Shuai Xu *Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, 220 Handan Road, Shanghai, 200433, China.
Luyi Wu *Yueyang Hospital of Integrated Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, 110 Ganhe Road, Shanghai, 200437, China.
Xinyi ZhuYueyang Hospital of Integrated Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, 110 Ganhe Road, Shanghai, 200437, China.
Di WangKey Laboratory of Acupuncture and Immunological Effects, Shanghai University of Traditional Chinese Medicine, 650 South Wanping Road, Shanghai, 200030, China.
Zhenjie ZhangKey Laboratory of Acupuncture and Immunological Effects, Shanghai University of Traditional Chinese Medicine, 650 South Wanping Road, Shanghai, 200030, China.
Changcheng QuKey Laboratory of Acupuncture and Immunological Effects, Shanghai University of Traditional Chinese Medicine, 650 South Wanping Road, Shanghai, 200030, China.
Yin ShiOutpatient Department, Shanghai Research Institute of Acupuncture and Meridian, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Xiaoqing ZengDepartment of Gastroenterology, Zhongshan Hospital, Fudan University, Shanghai, China.
Jie ZhongDepartment of Gastroenterology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Jianye ZhangDepartment of Radiology, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Song WangDepartment of Radiology, Longhua Hospital Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Rencheng ZhengInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, 220 Handan Road, Shanghai, 200433, China.
Xiaoming JinDepartment of Anatomy and Cell Biology, Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA.
He WangInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, 220 Handan Road, Shanghai, 200433, China. hewang@fudan.edu.cn.
Huirong LiuYueyang Hospital of Integrated Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, 110 Ganhe Road, Shanghai, 200437, China. liuhuirong@shutcm.edu.cn.
Huangan WuYueyang Hospital of Integrated Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, 110 Ganhe Road, Shanghai, 200437, China. wuhuangan@shutcm.edu.cn.

Funding

2024 Shanghai Oriental Talent Plan Youth Project NAExcellent Youth Fund of National Natural Science Foundation of China 82422074National Key Basic Research Program of China 2015CB554500Shanghai Rising-Star Program 19QA1408100Special Clinical Research Project in the Health Industry of Shanghai Municipal Health Commission 202340036
6 · The paper itself

Abstract

backgroundThe brain’s response to intestinal inflammation in Crohn’s disease (CD) remains poorly characterized. We aimed to identify a neuroimaging signature of disease activity by integrating structural and functional brain metrics with machine learning.

methodsWe analyzed data from 235 participants across two cohorts: a primary cohort of 180 individuals (75 healthy controls [HCs], 70 CD in remission [CDR], 35 active CD [CDA]) for model development, and an independent validation cohort of 55 CD patients (16 CDA, 39 CDR). All participants underwent 3D T1-weighted and resting-state functional MRI. We performed voxel-based morphometry and seed-based functional connectivity (FC) analyses. A machine-learning pipeline combining Lasso regression and support vector machine was used to select features and construct a classifier to distinguish CDA from CDR.

resultsPatients with CD exhibited widespread brain alterations compared to HCs. Critically, the gray matter volume (GMV) of the right inferior frontal gyrus (opercular part extending to the triangular part; rIFGoper) was significantly higher in CDA than in CDR and was positively correlated with fecal calprotectin levels. FC of the rIFGoper and bilateral putamen with the default mode and sensorimotor networks was diminished in CD and correlated with clinical disease activity. A classifier built on 10 key imaging features (including rIFGoper GMV) differentiated CDA from CDR with an area under the curve (AUC) of 0.85 in the internal test set and 0.73 in an independent external set.

conclusionsWe identified a distinct neuroimaging signature of active CD, characterized by structural enlargement of the rIFGoper alongside characteristic network dysconnectivity. This signature, which correlates with gut inflammation, demonstrates strong potential as an objective biomarker for stratifying disease activity.

Indexed as

BrainCrohn DiseaseMagnetic Resonance ImagingNeuroimagingAdultBiomarkersCase-Control StudiesFemaleGray MatterHumansLeukocyte L1 Antigen ComplexMachine LearningMaleMiddle AgedYoung AdultBiomarkersLeukocyte L1 Antigen ComplexFunctional connectivityGray matterGut-brain axisInflammatory bowel diseaseMachine learning

Identifiers

PMID41469850
PMCPMC12866283

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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.