Enhancing Early Diagnosis of Bipolar Disorder in Adolescents Through Multimodal Neuroimaging

Abstract

BackgroundBipolar disorder (BD), a severe neuropsychiatric condition, often appears during adolescence. Traditional diagnostic methods, which primarily rely on clinical interviews and single-modal magnetic resonance imaging (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. MethodsA retrospective cohort of 309 participants, including patients with BD, offspring of patients with BD (with and without subthreshold symptoms), non-BD offspring with subthreshold symptoms, and healthy control participants, was analyzed. Behavioral attributes were integrated with MRI features from T1-weighted, resting-state functional MRI, and diffusion tensor imaging. 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. ResultsThe comprehensive model achieved a prediction accuracy of 0.83 (95% 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, area under the curve = 0.95). Structural equation modeling revealed that clinical diagnosis (β = 0.487, p < .0001), parental BD history (β = −0.380, p < .0001), and global function (β = 0.578, p < .0001) significantly affected brain health, while psychiatric symptoms showed only a marginal influence (β = −0.112, p = .056). ConclusionsThis 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.