ArticlePloS one2016
Can Emotional and Behavioral Dysregulation in Youth Be Decoded from Functional Neuroimaging?
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Resilience and Brain Changes in Long-Term Ayahuasca Users: Insights From Psychometric and fMRI Pattern Recognition.Journal of magnetic resonance imaging : JMRI · 2025Article
- Consensus Paper: Cerebellum and Reward.Cerebellum (London, England) · 2024Review
- Migraine aura discrimination using machine learning: an fMRI study during ictal and interictal periods.Medical & biological engineering & computing · 2024Article
- Machine learning applied to fMRI patterns of brain activation in response to mutilation pictures predicts PTSD symptoms.BMC psychiatry · 2023Article
- Vulnerability and Protective Factors for PTSD and Depression Symptoms Among Healthcare Workers During COVID-19: A Machine Learning Approach.Frontiers in psychiatry · 2021Article
- Cingulum-Callosal white-matter microstructure associated with emotional dysregulation in children: A diffusion tensor imaging study.NeuroImage. Clinical · 2020Article
- Predicting Bipolar Disorder Risk Factors in Distressed Young Adults From Patterns of Brain Activation to Reward: A Machine Learning Approach.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2019Article
- Predicting anxiety from wholebrain activity patterns to emotional faces in young adults: a machine learning approach.NeuroImage. Clinical · 2019Article
- Decoding negative affect personality trait from patterns of brain activation to threat stimuli.NeuroImage · 2017Article
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32 authors.
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Abstract
introductionHigh comorbidity among pediatric disorders characterized by behavioral and emotional dysregulation poses problems for diagnosis and treatment, and suggests that these disorders may be better conceptualized as dimensions of abnormal behaviors. Furthermore, identifying neuroimaging biomarkers related to dimensional measures of behavior may provide targets to guide individualized treatment. We aimed to use functional neuroimaging and pattern regression techniques to determine whether patterns of brain activity could accurately decode individual-level severity on a dimensional scale measuring behavioural and emotional dysregulation at two different time points.
methodsA sample of fifty-seven youth (mean age: 14.5 years; 32 males) was selected from a multi-site study of youth with parent-reported behavioral and emotional dysregulation. Participants performed a block-design reward paradigm during functional Magnetic Resonance Imaging (fMRI). Pattern regression analyses consisted of Relevance Vector Regression (RVR) and two cross-validation strategies implemented in the Pattern Recognition for Neuroimaging toolbox (PRoNTo). Medication was treated as a binary confounding variable. Decoded and actual clinical scores were compared using Pearson's correlation coefficient (r) and mean squared error (MSE) to evaluate the models. Permutation test was applied to estimate significance levels.
resultsRelevance Vector Regression identified patterns of neural activity associated with symptoms of behavioral and emotional dysregulation at the initial study screen and close to the fMRI scanning session. The correlation and the mean squared error between actual and decoded symptoms were significant at the initial study screen and close to the fMRI scanning session. However, after controlling for potential medication effects, results remained significant only for decoding symptoms at the initial study screen. Neural regions with the highest contribution to the pattern regression model included cerebellum, sensory-motor and fronto-limbic areas.
conclusionsThe combination of pattern regression models and neuroimaging can help to determine the severity of behavioral and emotional dysregulation in youth at different time points.
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