Enhancing Early Diagnosis of Bipolar Disorder in Adolescents through Multimodal Neuroimaging

Abstract Background Bipolar Disorder (BD), a severe neuropsychiatric condition, often manifests during adolescence. Traditional diagnostic methods, relying predominantly on clinical interviews and symptom assessments, may fall short in accuracy, especially when based solely on single-modal MRI techniques. Objective This study aims to bridge the diagnostic gap in adolescent BD by integrating behavioral assessments with a multimodal MRI approach. We hypothesize that this combination will enhance the accuracy of BD diagnosis in adolescents at risk. 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 encompassing psychiatric familial history and assessments were integrated with MRI morphological and network features derived from T1, fMRI, 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 outperformed the clinical and MRI-based models in diagnostic accuracy, achieving a prediction accuracy of 0.83 (CI: [0.72, 0.92]), significantly higher than the clinical diagnosis approach (accuracy of 0.75) and the MRI-based approach (accuracy of 0.65). These findings were further validated with an external cohort, demonstrating a high accuracy of 0.89 (AUC = 0.95). Notably, structural equation modelling revealed that factors like Clinical Diagnosis, Parental BD History, and Global Function significantly impacted Brain Health, with Psychiatric Symptoms having a marginal influence. Conclusion This study underscores the substantial value of integrating multimodal MRI with behavioral assessments for early BD diagnosis in at-risk adolescents. The fusion of phenomenology with neuroimaging promises more accurate patient subgroup distinctions, enabling timely interventions and potentially improving overall health outcomes. Our findings suggest a paradigm shift in the diagnostic approach for BD, highlighting the necessity of incorporating advanced imaging techniques in routine clinical evaluations.