Journal
EUROPEAN CHILD & ADOLESCENT PSYCHIATRY
Volume 30, Issue 1, Pages 55-64Publisher
SPRINGER
DOI: 10.1007/s00787-020-01483-x
Keywords
Bipolar disorder; Offspring; Magnetic resonance spectroscopy; Support vector machine; Mood episode
Categories
Funding
- National Institute of Mental Health (NIMH) [P50 MH077138, 5R01MH080973]
- National Natural Science Foundation of China [81671664, 81621003]
- Postdoctoral Interdisciplinary Research Project of Sichuan University [0040204153248]
- Miaozi Project in Science and Technology Innovation Program of Sichuan Province
- 1.3.5 project for disciplines of excellence, West China Hospital, Sichuan University [ZYYC08001, ZYJC18020]
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This study utilized support vector machine (SVM) to predict the first mood episode in youth bipolar offspring based on proton magnetic resonance spectroscopy (H-1-MRS) data. The findings suggest that a combination of mI, PCr + Cr, and Cho from left VLPFC may be useful for predicting the development of first mood episodes in high-risk individuals. Further prospective studies are needed to validate these metabolites as predictors of mood episodes.
Children of individuals with bipolar disorder (bipolar offspring) are at increased risk for developing mood disorders, but strategies to predict mood episodes are unavailable. In this study, we used support vector machine (SVM) to characterize the potential of proton magnetic resonance spectroscopy (H-1-MRS) in predicting the first mood episode in youth bipolar offspring. From a longitudinal neuroimaging study, 19 at-risk youth who developed their first mood episode (converters), and 19 without mood episodes during follow-up (non-converters) were selected and matched for age, sex and follow-up time. Baseline H-1-MRS data were obtained from anterior cingulate cortex (ACC) and bilateral ventrolateral prefrontal cortex (VLPFC). Glutamate (Glu), myo-inositol (mI), choline (Cho), N-acetyl aspartate (NAA), and phosphocreatine plus creatine (PCr + Cr) levels were calculated. SVM with a linear kernel was adopted to classify converters and non-converters based on their baseline metabolites. SVM allowed the significant classification of converters and non-converters across all regions for Cho (accuracy = 76.0%), but not for other metabolites. Considering all metabolites within each region, SVM allowed the significant classification of converters and non-converters for left VLPFC (accuracy = 76.5%), but not for right VLPFC or ACC. The combined mI, PCr + Cr, and Cho from left VLPFC achieved the highest accuracy differentiating converters from non-converters (79.0%). Our findings from this exploratory study suggested that H-1-MRS levels of mI, Cho, and PCr + Cr from left VLPFC might be useful to predict the development of first mood episode in youth bipolar offspring using machine learning. Future studies that prospectively examine and validate these metabolites as predictors of mood episodes in high-risk individuals are necessary.
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