ArticleJournal of the American Statistical Association2022
Elucidating age and sex-dependent association between frontal EEG asymmetry and depression: An application of multiple imputation in functional regression.
Article in Journal of the American Statistical Association, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Beyond the label "major depressive disorder"-detailed characterization of study population matters for EEG-biomarker research.Frontiers in neuroscience · 2025Pooled it
- A feasibility study on inferring connectivity changes in frontal lobes of MDD patients via spectral DCM.Brain informatics · 2026Article
- Missing Data Gap Imputation Methods in Electroencephalogram (EEG) Signals: A Systematic Scoping Review.Sensors (Basel, Switzerland) · 2026Article
- Association Between Organophosphate Flame Retardant Exposure and Trouble Sleeping: Integrating Epidemiological Evidence with Mechanistic Insights.International journal of molecular sciences · 2026Article
- Development and validation of a bedside prognostic model for in-hospital mortality in acute diquat poisoning.BMC emergency medicine · 2026Article
- The neutrophil-to-albumin ratio is associated with in-hospital mortality in patients with cirrhosis and pneumonia: a multicenter retrospective cohort study.BMC infectious diseases · 2026Article
- A 7-cm inflection point: nonlinear association between maximum tumor diameter and capsular invasion risk after primary liver cancer resection.BMC gastroenterology · 2026Article
- Degradation-aware neural imputation: Advancing decoding stability in brain machine interfaces.APL bioengineering · 2025Article
- Frontal Alpha Asymmetry and Its Modulation by Monoaminergic Neurotransmitters in Depression.Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology · 2024Review
- Latent factor model for multivariate functional data.Biometrics · 2023Article
- A novel EEG-based major depressive disorder detection framework with two-stage feature selection.BMC medical informatics and decision making · 2022Article
- EEG-responses to mood induction interact with seasonality and age.Frontiers in psychiatry · 2022Article
- A Causal Approach to Functional Mediation Analysis with Application to a Smoking Cessation Intervention.Multivariate behavioral researchArticle
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 1 country.
Funding
Abstract
Frontal power asymmetry (FA), a measure of brain function derived from electroencephalography, is a potential biomarker for major depressive disorder (MDD). Though FA is functional in nature, it is typically reduced to a scalar value prior to analysis, possibly obscuring its relationship with MDD and leading to a number of studies that have provided contradictory results. To overcome this issue, we sought to fit a functional regression model to characterize the association between FA and MDD status, adjusting for age, sex, cognitive ability, and handedness using data from a large clinical study that included both MDD and healthy control (HC) subjects. Since nearly 40% of the observations are missing data on either FA or cognitive ability, we propose an extension of multiple imputation (MI) by chained equations that allows for the imputation of both scalar and functional data. We also propose an extension of Rubin's Rules for conducting valid inference in this setting. The proposed methods are evaluated in a simulation and applied to our FA data. For our FA data, a pooled analysis from the imputed data sets yielded similar results to those of the complete case analysis. We found that, among young females, HCs tended to have higher FA over the
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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.