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  3. Multimodal Brain Imaging-based Classification Of Functional Constipation Subtypes Using Machine Learning.
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  3. Multimodal Brain Imaging-based Classification Of Functional Constipation Subtypes Using Machine Learning.

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Multimodal brain imaging-based classification of functional constipation subtypes using machine learning.

Wenchao Zhang1, Yang Hu1, Jiangpeng Wei2

  • 1Center for Brain Imaging, School of Life Science and Technology, Xidian University, Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an, Shaanxi, 710126, China; International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment, Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China.

Journal of Affective Disorders
|April 23, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Functional constipation with anxiety and depression (FCAD) shows distinct brain differences compared to functional constipation without these conditions (FCNAD). Brain imaging can help differentiate these groups, aiding personalized treatment.

Keywords:
AnxietyDepressionFunctional constipationNeuroimagingSubtype

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Area of Science:

  • Neuroscience
  • Gastroenterology
  • Psychiatry

Background:

  • Functional constipation (FC) is prevalent and frequently co-occurs with anxiety and depression (FCAD).
  • FCAD patients exhibit unique responses to treatment compared to those without anxiety and depression (FCNAD).
  • Distinct impacts of FC and mental status on brain structure and function suggest potential for neuroimaging biomarkers.

Purpose of the Study:

  • To investigate if brain structural and diffusion imaging features can differentiate FCAD from FCNAD.
  • To develop and validate a classification model using neuroimaging data for distinguishing FCAD and FCNAD patients.

Main Methods:

  • 187 FC patients underwent structural MRI and diffusion tensor imaging (DTI).
  • Patients completed self-reported anxiety and depression assessments.
  • A classification model was trained using extracted brain imaging features with a stagewise approach.

    • Main Results:

    • The classification model achieved an average accuracy of 89.23% in cross-validation.
    • Predicted FCAD probability correlated significantly with mental health ratings and gastrointestinal symptoms.
    • Key differentiating features were identified in brain regions associated with emotion, sensation, and motor control.

    Conclusions:

    • Brain imaging features can effectively distinguish FCAD from FCNAD.
    • These neuroimaging biomarkers may enhance personalized diagnosis and treatment strategies for functional constipation.
    • The findings underscore the intricate link between gut health and brain function in FCAD.