Next Article in Journal
Leisure Activities and Change in Cognitive Stability: A Multivariate Approach
Previous Article in Journal
Neuronal Stress and Injury Caused by HIV-1, cART and Drug Abuse: Converging Contributions to HAND
Article Menu

Export Article

Open AccessArticle
Brain Sci. 2017, 7(3), 26; doi:10.3390/brainsci7030026

Application of Linear Mixed-Effects Models in Human Neuroscience Research: A Comparison with Pearson Correlation in Two Auditory Electrophysiology Studies

1
and
1,2,3,4,*
1
Department of Speech-Language-Hearing Sciences, University of Minnesota, Minneapolis, MN 55455, USA
2
Center for Neurobehavioral Development, University of Minnesota, Minneapolis, MN 55455, USA
3
Center for Applied and Translational Sensory Science, University of Minnesota, Minneapolis, MN 55455, USA
4
Speech-Language-Hearing Center, School of Foreign Languages, Shanghai Jiao Tong University, Shanghai 200240, China
*
Author to whom correspondence should be addressed.
Academic Editor: Heather Bortfeld
Received: 31 December 2016 / Revised: 15 February 2017 / Accepted: 24 February 2017 / Published: 27 February 2017
View Full-Text   |   Download PDF [356 KB, uploaded 27 February 2017]   |  

Abstract

Neurophysiological studies are often designed to examine relationships between measures from different testing conditions, time points, or analysis techniques within the same group of participants. Appropriate statistical techniques that can take into account repeated measures and multivariate predictor variables are integral and essential to successful data analysis and interpretation. This work implements and compares conventional Pearson correlations and linear mixed-effects (LME) regression models using data from two recently published auditory electrophysiology studies. For the specific research questions in both studies, the Pearson correlation test is inappropriate for determining strengths between the behavioral responses for speech-in-noise recognition and the multiple neurophysiological measures as the neural responses across listening conditions were simply treated as independent measures. In contrast, the LME models allow a systematic approach to incorporate both fixed-effect and random-effect terms to deal with the categorical grouping factor of listening conditions, between-subject baseline differences in the multiple measures, and the correlational structure among the predictor variables. Together, the comparative data demonstrate the advantages as well as the necessity to apply mixed-effects models to properly account for the built-in relationships among the multiple predictor variables, which has important implications for proper statistical modeling and interpretation of human behavior in terms of neural correlates and biomarkers. View Full-Text
Keywords: Pearson correlation; linear mixed-effects regression models; repeated measures; neurophysiology; event-related potential Pearson correlation; linear mixed-effects regression models; repeated measures; neurophysiology; event-related potential
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Koerner, T.K.; Zhang, Y. Application of Linear Mixed-Effects Models in Human Neuroscience Research: A Comparison with Pearson Correlation in Two Auditory Electrophysiology Studies. Brain Sci. 2017, 7, 26.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Brain Sci. EISSN 2076-3425 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top