Much Ado About Missingness: A Demonstration of Full Information Maximum Likelihood Estimation to Address Missingness in Functional Magnetic Resonance Imaging Data

Timothy D. Nelson, Rebecca L. Brock, Sonja Yokum, Cara C. Tomaso, Cary R. Savage, Eric Stice

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

The current paper leveraged a large multi-study functional magnetic resonance imaging (fMRI) dataset (N = 363) and a generated missingness paradigm to demonstrate different approaches for handling missing fMRI data under a variety of conditions. The performance of full information maximum likelihood (FIML) estimation, both with and without auxiliary variables, and listwise deletion were compared under different conditions of generated missing data volumes (i.e., 20, 35, and 50%). FIML generally performed better than listwise deletion in replicating results from the full dataset, but differences were small in the absence of auxiliary variables that correlated strongly with fMRI task data. However, when an auxiliary variable created to correlate r = 0.5 with fMRI task data was included, the performance of the FIML model improved, suggesting the potential value of FIML-based approaches for missing fMRI data when a strong auxiliary variable is available. In addition to primary methodological insights, the current study also makes an important contribution to the literature on neural vulnerability factors for obesity. Specifically, results from the full data model show that greater activation in regions implicated in reward processing (caudate and putamen) in response to tastes of milkshake significantly predicted weight gain over the following year. Implications of both methodological and substantive findings are discussed.

Original languageEnglish (US)
Article number746424
JournalFrontiers in Neuroscience
Volume15
DOIs
StatePublished - Sep 30 2021

Keywords

  • auxiliary variables
  • full information maximum likelihood estimation
  • functional magnetic resonance imaging
  • missing data
  • neural vulnerability factors
  • obesity

ASJC Scopus subject areas

  • General Neuroscience

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