Enhancing early diagnosis of bipolar disorder in adolescents through multimodal neuroimaging

Abstract

Background Bipolar Disorder (BD), a severe neuropsychiatric condition, often appears during adolescence. Traditional diagnostic methods, which primarily relying on clinical interviews and single-modal MRI techniques, may have limitations in accuracy. This study aimed to improve adolescent BD diagnosis by integrating behavioral assessments with multimodal MRI. We hypothesized that this combination would enhance diagnostic accuracy for at-risk adolescents.

Methods A retrospective cohort of 309 subjects, including BD patients, offspring of BD patients (with and without subthreshold symptoms), non-BD offspring with subthreshold symptoms, and healthy controls, was analysed. Behavioral attributes were integrated with MRI features from T1, rsfMRI, and DTI. Three diagnostic models were developed using GLMNET multinomial regression: a clinical diagnosis model based on behavioral attributes, an MRI-based model, and a comprehensive model integrating both datasets.

Results The comprehensive model achieved a prediction accuracy of 0.83 (CI: [0.72, 0.92]), significantly higher than the clinical (0.75) and MRI-based (0.65) models. Validation with an external cohort showed high accuracy (0.89, AUC=0.95). Structural equation modelling revealed that Clinical Diagnosis (β=0.487, p<0.0001), Parental BD History (β=-0.380, p<0.0001), and Global Function (β=0.578, p<0.0001) significantly impacted Brain Health, while Psychiatric Symptoms showed only a marginal influence (β=-0.112, p=0.056).

Conclusion This study highlights the value of integrating multimodal MRI with behavioral assessments for early diagnosis in at-risk adolescents. Combining neuroimaging enables more accurate patient subgroup distinctions, facilitating timely interventions and improving health outcomes. Our findings suggest a paradigm shift in BD diagnostics, advocating for incorporating advanced imaging techniques in routine evaluations.