ArticleCognitive neuropsychiatry2019
The use of component-wise gradient boosting to assess the possible role of cognitive measures as markers of vulnerability to pediatric bipolar disorder.
Article in Cognitive neuropsychiatry, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Implementing Precision Psychiatry: A Systematic Review of Individualized Prediction Models for Clinical Practice.Schizophrenia bulletin · 2021Pooled it
- Predicting Cognitive Outcome Through Nutrition and Health Markers Using Supervised Machine Learning.The Journal of nutrition · 2025Article
- Using machine learning to identify predictors of imminent drinking and create tailored messages for at-risk drinkers experiencing homelessness.Journal of substance abuse treatment · 2021Article
- The Greater Houston Area Bipolar Registry-Clinical and Neurobiological Trajectories of Children and Adolescents With Bipolar Disorders and High-Risk Unaffected Offspring.Frontiers in psychiatry · 2021Article
- Predicting Daily Sheltering Arrangements among Youth Experiencing Homelessness Using Diary Measurements Collected by Ecological Momentary Assessment.International journal of environmental research and public health · 2020Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
BACKGROUND AND
aimsCognitive impairments are primary hallmarks symptoms of bipolar disorder (BD). Whether these deficits are markers of vulnerability or symptoms of the disease is still unclear. This study used a component-wise gradient (CGB) machine learning algorithm to identify cognitive measures that could accurately differentiate pediatric BD, unaffected offspring of BD parents, and healthy controls.
methods59 healthy controls (HC; 11.19 ± 3.15 yo; 30 girls), 119 children and adolescents with BD (13.31 ± 3.02 yo, 52 girls) and 49 unaffected offspring of BD parents (UO; 9.36 ± 3.18 yo; 22 girls) completed the CANTAB cognitive battery.
resultsCGB achieved accuracy of 73.2% and an AUROC of 0.785 in classifying individuals as either BD or non-BD on a dataset held out for validation for testing. The strongest cognitive predictors of BD were measures of processing speed and affective processing. Measures of cognition did not differentiate between UO and HC.
conclusionsAlterations in processing speed and affective processing are markers of BD in pediatric populations. Longitudinal studies should determine whether UO with a cognitive profile similar to that of HC are at less or equal risk for mood disorders. Future studies should include relevant measures for BD such as verbal memory and genetic risk scores.
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