Enhancing Early Diagnosis of Bipolar Disorder in Adolescents Through Multimodal Neuroimaging
- PMID: 39069165
- DOI: 10.1016/j.biopsych.2024.07.018
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 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.
Methods: A 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.
Results: The 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).
Conclusions: 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.
Keywords: Adolescent psychiatric assessment; Behavioral and neuroimaging integration; Bipolar disorder; GLMNET multinomial regression; Multimodal MRI.
Copyright © 2024 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.
Similar articles
-
Multimodal imaging improves brain age prediction and reveals distinct abnormalities in patients with psychiatric and neurological disorders.Hum Brain Mapp. 2021 Apr 15;42(6):1714-1726. doi: 10.1002/hbm.25323. Epub 2020 Dec 19. Hum Brain Mapp. 2021. PMID: 33340180 Free PMC article.
-
A proof of concept machine learning analysis using multimodal neuroimaging and neurocognitive measures as predictive biomarker in bipolar disorder.Asian J Psychiatr. 2020 Apr;50:101984. doi: 10.1016/j.ajp.2020.101984. Epub 2020 Feb 26. Asian J Psychiatr. 2020. PMID: 32143176
-
The impact of psychosis on brain anatomy in bipolar disorder: A structural MRI study.J Affect Disord. 2018 Jun;233:100-109. doi: 10.1016/j.jad.2017.11.092. Epub 2017 Nov 29. J Affect Disord. 2018. PMID: 29223329
-
Will machine learning applied to neuroimaging in bipolar disorder help the clinician? A critical review and methodological suggestions.Bipolar Disord. 2020 Jun;22(4):334-355. doi: 10.1111/bdi.12895. Epub 2020 Mar 20. Bipolar Disord. 2020. PMID: 32108409 Review.
-
Differentiating between bipolar and unipolar depression in functional and structural MRI studies.Prog Neuropsychopharmacol Biol Psychiatry. 2019 Apr 20;91:20-27. doi: 10.1016/j.pnpbp.2018.03.022. Epub 2018 Mar 28. Prog Neuropsychopharmacol Biol Psychiatry. 2019. PMID: 29601896 Review.
Cited by
-
Cognitive and neural abnormalities: working memory deficits in bipolar disorder offspring.Psychol Med. 2025 May 2;55:e130. doi: 10.1017/S0033291725001060. Psychol Med. 2025. PMID: 40314170 Free PMC article.
MeSH terms
LinkOut - more resources
Full Text Sources
Medical