ArticleBrain sciences2017
Application of Linear Mixed-Effects Models in Human Neuroscience Research: A Comparison with Pearson Correlation in Two Auditory Electrophysiology Studies.
Article in Brain sciences, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
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- Combinatorial effects of multi-site stimulation on depression-related brain regions: clinical data analysis and predictive modeling.Frontiers in psychiatry · 2026Article
- Predictive encoding of auditory sequences in the human prefrontal cortex.bioRxiv : the preprint server for biology · 2025Article
- Monte carlo simulation for evaluating spatial dynamics of toxic metals and potential health hazards in sebou basin surface water.Scientific reports · 2025Article
- Geospatial analysis of trace metal pollution and ecological risks in river sediments from agrochemical sources in Morocco's Sebou basin.Scientific reports · 2025Article
- Differential fMRI neural synchrony associated with migraine during naturalistic stimuli with negative emotional valence.The journal of headache and pain · 2025Article
- Impact of repetitive transcranial magnetic stimulation on clinical and cognitive outcomes, and brain-derived neurotrophic factor levels in treatment-resistant depression.Frontiers in psychiatry · 2025Article
- Combination of spatial and temporal de-noising and artifact reduction techniques in multi-channel dry EEG.Frontiers in neuroscience · 2025Article
- Measuring attentional selection of object categories using hierarchical frequency tagging.Journal of vision · 2024Article
- Flexible multi-step hypothesis testing of human ECoG data using cluster-based permutation tests with GLMEs.NeuroImage · 2024Article
- Decentralized Mixed Effects Modeling in COINSTAC.Neuroinformatics · 2024Article
- Oxytocin Modifies the Excitability and the Action Potential Shape of the Hippocampal CA1 GABAergic Interneurons.International journal of molecular sciences · 2024Article
- Exploring the Acquisition of Social Communication Skills in Children with Autism: Preliminary Findings from Applied Behavior Analysis (ABA), Parent Training, and Video Modeling.Brain sciences · 2024Article
- Semantic and episodic processes differently predict false memories in the DRM task.Scientific reports · 2024Article
- Speech Perception in Noise and Medial Olivocochlear Reflex: Effects of Age, Speech Stimulus, and Response-Related Variables.Journal of the Association for Research in Otolaryngology : JARO · 2023Article
- Flower electrodes for comfortable dry electroencephalography.Scientific reports · 2023Article
- Brain Activities Show There Is Nothing Like a Real Friend in Contrast to Influencers and Other Celebrities.Brain sciences · 2023Article
- A localized pallidal physiomarker in Meige syndrome.Frontiers in neurology · 2023Article
- Neurocognitive Dynamics of Prosodic Salience over Semantics during Explicit and Implicit Processing of Basic Emotions in Spoken Words.Brain sciences · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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