Neuroimaging Model Distinguishes Functional Constipation Subtypes with 89% Accuracy

Functional constipation remains a prevalent gastrointestinal challenge in clinical practice, particularly among aging populations where the pooled prevalence reaches 8.5% and the odds ratio for patients aged 70 and older stands at 3.38 [1, 2]. While clinicians often manage the condition as a localized bowel dysfunction, it is frequently complicated by psychiatric comorbidities; specifically, anxiety and depression are associated with significantly higher prevalence (OR 3.16 and 2.74, respectively) and increased symptom severity [1, 3]. These comorbidities often render standard pharmacological interventions, such as 5-HT4 receptor agonists, less effective, prompting a shift toward therapies that address the microbiota-gut-brain axis (the bidirectional communication network between the enteric nervous system and the central nervous system) [4, 3, 5]. Despite the known interplay between emotional processing and gut motility, identifying which patients possess a distinct neuropsychiatric phenotype (a set of observable characteristics resulting from the interaction of neurological and psychiatric factors) remains difficult [6, 5]. A recent study of 187 patients utilized machine learning and structural magnetic resonance imaging to classify patients with and without comorbid anxiety and depression with 89.23% accuracy, identifying key structural features in the temporal pole, amygdala, and insula [7].