ArticleFrontiers in neuroscience2016
Bootstrap Enhanced Penalized Regression for Variable Selection with Neuroimaging Data.
Article in Frontiers in neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
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- Oxytocin Enhances an Amygdala Circuit Associated With Negative Symptoms in Schizophrenia: A Single-Dose, Placebo-Controlled, Crossover, Randomized Control Trial.Schizophrenia bulletin · 2020Trial
- ADHD medications and preadolescent brain structure: patterns of cortical attenuation from the ABCD study.Scientific reports · 2026Article
- Blood parameters and whole-blood transcriptomics associated with immune-related adverse events in metastatic renal cell carcinoma during nivolumab plus ipilimumab.Scientific reports · 2026Article
- Brain-immune correlates of quality of life in adolescents with chronic musculoskeletal pain.Brain, behavior, and immunity · 2026Article
- Network-constrained Random Lasso for biologically interpretable gene network inference across unequal sample sizes.PloS one · 2026Article
- Functional connectivity of subsystems of the default-mode network in patients with early psychotic symptoms.Neuroimage. Reports · 2025Article
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- Prevalence, trajectory over time, and risk factor of post-COVID-19 fatigue.Journal of psychiatric research · 2022Article
- Blood cytokines differentiate bipolar disorder and major depressive disorder during a major depressive episode: Initial discovery and independent sample replication.Brain, behavior, & immunity - health · 2021Article
- Prediction of type 2 diabetes mellitus based on nutrition data.Journal of nutritional science · 2021Article
- Extrahippocampal Radiomics Analysis Can Potentially Identify Laterality in Patients With MRI-Negative Temporal Lobe Epilepsy.Frontiers in neurology · 2021Article
- Tumor response prediction inEJNMMI physics · 2020Article
- Immune activity at birth and later psychopathology in childhood.Brain, behavior, & immunity - health · 2020Article
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- Bootstrapping promotes the RSFC-behavior associations: An application of individual cognitive traits prediction.Human brain mapping · 2020Article
- Systemic inflammation is negatively associated with early post discharge growth following acute illness among severely malnourished children - a pilot study.Wellcome open research · 2020Article
- Proinflammatory Cytokines Predict Brain Metabolite Concentrations in the Anterior Cingulate Cortex of Patients With Bipolar Disorder.Frontiers in psychiatry · 2020Article
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6 authors.
Funding
Abstract
Recent advances in fMRI research highlight the use of multivariate methods for examining whole-brain connectivity. Complementary data-driven methods are needed for determining the subset of predictors related to individual differences. Although commonly used for this purpose, ordinary least squares (OLS) regression may not be ideal due to multi-collinearity and over-fitting issues. Penalized regression is a promising and underutilized alternative to OLS regression. In this paper, we propose a nonparametric bootstrap quantile (QNT) approach for variable selection with neuroimaging data. We use real and simulated data, as well as annotated R code, to demonstrate the benefits of our proposed method. Our results illustrate the practical potential of our proposed bootstrap QNT approach. Our real data example demonstrates how our method can be used to relate individual differences in neural network connectivity with an externalizing personality measure. Also, our simulation results reveal that the QNT method is effective under a variety of data conditions. Penalized regression yields more stable estimates and sparser models than OLS regression in situations with large numbers of highly correlated neural predictors. Our results demonstrate that penalized regression is a promising method for examining associations between neural predictors and clinically relevant traits or behaviors. These findings have important implications for the growing field of functional connectivity research, where multivariate methods produce numerous, highly correlated brain networks.
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