Beyond t-Test and ANOVA: applications of mixed-effects models for more rigorous statistical analysis in neuroscience research

By Zhaoxia Yu, Michele Guindani, S. Grieco, Lujia Chen, Todd C. Holmes, Xiangmin Xu. 2021

Subfields: Statistics and Probability, Neuroscience

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Sigurd L. Alnes
2. Ellen van Maren
3. I. Boccalaro
4. D. Ledergerber
5. Kaspar Anton Schindler
6. Maxime O. Baud
7. Riccardo Cusinato
8. L. Imbach

Citations in the corpus

• Intrinsic neural timescales in the temporal lobe support an auditory processing hierarchy (2022, methodology): “…4.2.0 (R Development Core Team, 2020) using Linear Mixed-Effects models (LMEs) with a random intercept term corresponding to the patient, to account for the fact that electrodes were recorded from different patients (Yu et al., 2022) (implemented with nlme package (Lindstrom & Bates, 1990)).”
• Temporal Interference Stimulation Enhances Neural Regeneration (2026, unknown): “To account for the variability between animals, a linear mixed-effect model was implemented [ 186 ], where the stimulation treatments were considered fixed variables, and the sham control was used as a reference.”