Elucidating Age and Sex-Dependent Association Between Frontal EEG Asymmetry and Depression: An Application of Multiple Imputation in Functional Regression

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 datasets yielded similar results to those of the complete case analysis. We found that, among young females, HCs tended to have higher FA over the θ, α, and β frequency bands, but that the difference between HC and MDD subjects diminishes and ultimately reverses with age. For males, HCs tended to have higher FA in the β frequency band, regardless of age. Young male HCs had higher FA in the θ and α bands, but this difference diminishes with increasing age in the α band and ultimately reverses with increasing age in the θ band. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

KEYWORDS:

• Electroencephalography
• Functional data analysis
• Functional regression
• Major depressive disorder
• Missing data
• Multiple imputation

Supplementary Materials

fregMICE_Appendix.pdf provides additional information and simulation results. The zip file Model_Based_FA_Shiny_App.zip contains the Shiny app described in Section 6. R code for running the simulations and analyses in Section 6 is available in the zip file fregMICE_R_Code_Sim_and_App.zip.

Additional information

Funding

This work is based upon work supported by the National Institutes of Health (grant nos. NIMH K01 MH113850 and NIMH R01 MH099003).