Multimodal brain imaging-based classification of functional constipation subtypes using machine learning.
Authors
Affiliations (7)
Affiliations (7)
- Center 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.
- State Key Laboratory of Cancer Biology, National Clinical Research Center for Digestive Diseases, Xijing Hospital of Digestive Diseases, The Air Force Medical University, Xi'an, Shaanxi, 710032, China.
- Department of Radiology, Tangdu Hospital, The Air Force Medical University, Xi'an, Shaanxi, 710038, China.
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, School of Medicine, Northwest University, 229 Taibai North Road, Xi'an, China.
- Westa College, Southwest University, Chongqing, 400715, China.
- Laboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD, 20892, USA.
- Center 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. Electronic address: [email protected].
Abstract
Functional constipation (FC) is a common gastrointestinal condition often accompanied by anxiety and depression status (FCAD). Gastrointestinal symptoms in FCAD patients are not fully resolved with medication, and the responses to treatment can differ significantly from those of patients without anxiety and depression (FCNAD). Given the distinct effects of FC and mental status on brain function and structure, we hypothesize that these brain differences could serve as imaging features to differentiate FCAD and FCNAD. Patients with FC (N = 187) underwent structural magnetic resonance imaging, diffusion tensor imaging scans, and completed self-reported assessments of depression and anxiety. The current study first identified FCNAD and FCAD patients with high- and low-confidence labels based on self-reported ratings. Brain structural imaging features were subsequently extracted to train and refine the classification model using a stagewise training approach. The classification model achieved an average accuracy of 89.23% during the cross-validation, and the predicted probability of FCAD was significantly correlated with mental ratings and gastrointestinal symptoms. The top 30 imaging features contributing to classification were primarily located in brain regions involved in emotional processing (temporal pole, amygdala, orbitofrontal cortex), somatosensory (insula), and motor control (corticospinal tract, inferior cerebellar peduncle). These findings highlight potential brain imaging features distinguishing FCAD and FCNAD, which may provide the incremental value over standard behavioral assessments for future personalized diagnosis and treatment strategies.