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

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

SUBMITTER: Nelson TD 

PROVIDER: S-EPMC8514662 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature