ArticleBiological psychiatry. Cognitive neuroscience and neuroimaging2019
Predicting Bipolar Disorder Risk Factors in Distressed Young Adults From Patterns of Brain Activation to Reward: A Machine Learning Approach.
Article in Biological psychiatry. Cognitive neuroscience and neuroimaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 23 citations in OpenAlex.
- Neural Markers of Reward Valuation and Impulsive Decision-Making in Mania Risk.JAMA psychiatry · 2026Article
- Predicting suicidal ideation in academic communities using machine learning methods: a cross-sectional study.Lancet regional health. Americas · 2026Article
- Elevated pre-supplementary motor area activity during reward expectancy: An impulsivity-related neural marker of vulnerability to bipolar and externalizing disorders.Molecular psychiatry · 2026Article
- Acoustic and Natural Language Markers for Bipolar Disorder: A Pilot, mHealth Cross-Sectional Study.JMIR formative research · 2025Observational
- Left Ventrolateral Prefrontal Cortical Activity During Reward Expectancy and Mania Risk.JAMA psychiatry · 2025Article
- Patterns of Neural Network Functional Connectivity Associated With Mania/Hypomania and Depression Risk in 3 Independent Young Adult Samples.JAMA psychiatry · 2024Article
- Neurobehavioral Reward and Sleep-Circadian Profiles Predict Present and Next-Year Mania/Hypomania Symptoms.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2023Article
- Machine learning applied to fMRI patterns of brain activation in response to mutilation pictures predicts PTSD symptoms.BMC psychiatry · 2023Article
- Segregation, integration and balance in resting-state brain functional networks associated with bipolar disorder symptoms.Human brain mapping · 2023Article
- Opportunities and challenges in application of artificial intelligence in pharmacology.Pharmacological reports : PR · 2023Review
- Using Acoustic Speech Patterns From Smartphones to Investigate Mood Disorders: Scoping Review.JMIR mHealth and uHealth · 2021Article
- Changes in Intrinsic Brain Connectivity in Family-Focused Therapy Versus Standard Psychoeducation Among Youths at High Risk for Bipolar Disorder.Journal of the American Academy of Child and Adolescent Psychiatry · 2021Article
- Vulnerability and Protective Factors for PTSD and Depression Symptoms Among Healthcare Workers During COVID-19: A Machine Learning Approach.Frontiers in psychiatry · 2021Article
Corrections and comments
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Authors and funding
12 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundThe aim of this study was to apply multivariate pattern recognition to predict the severity of behavioral traits and symptoms associated with risk for bipolar spectrum disorder from patterns of whole-brain activation during reward expectancy to facilitate the identification of individual-level neural biomarkers of bipolar disorder risk.
methodsWe acquired functional neuroimaging data from two independent samples of transdiagnostically recruited adults (18-25 years of age; n = 56, mean age 21.9 ± 2.2 years, 42 women; n = 36, mean age 21.2 ± 2.2 years, 24 women) during reward expectancy task performance. Pattern recognition model performance in each sample was measured using correlation and mean squared error between actual and whole-brain activation-predicted scores on behavioral traits and symptoms.
resultsIn the first sample, the model significantly predicted severity of a specific hypo/mania-related symptom, heightened energy, measured by the energy manic subdomain of the Mood Spectrum Structured Interviews (r = .42, p = .001; mean squared error = 9.93, p = .001). The region with the highest contribution to the model was the left ventrolateral prefrontal cortex. Results were confirmed in the second sample (r = .33, p = .01; mean squared error = 8.61, p = .01), in which the severity of this symptom was predicted using a bilateral ventrolateral prefrontal cortical mask (r = .33, p = .009, mean squared error = 9.37, p = .04).
conclusionsThe severity of a specific hypo/mania-related symptom was predicted from patterns of whole-brain activation in two independent samples. Given that emerging manic symptoms predispose to bipolar disorders, these findings could provide neural biomarkers to aid early identification of individual-level bipolar disorder risk in young adults.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.