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Background <p>The 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.</p> Methods <p>We 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.</p> Results <p>Patients 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.</p> Conclusions <p>We 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月30日

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  • Chunhui Bao,
  • Shuai Xu,
  • Luyi Wu,
  • Xinyi Zhu,
  • Di Wang,
  • Zhenjie Zhang,
  • Changcheng Qu,
  • Yin Shi,
  • Xiaoqing Zeng,
  • Jie Zhong,
  • Jianye Zhang,
  • Song Wang,
  • Rencheng Zheng,
  • Xiaoming Jin,
  • He Wang,
  • Huirong Liu,
  • Huangan Wu

摘要

Background

The 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.

Methods

We 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.

Results

Patients 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.

Conclusions

We 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.