Enhancing Early Diagnosis of Bipolar Disorder in Adolescents through Multimodal Neuroimaging
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".