Valid statistical approaches for analyzing sholl data: Mixed effects versus simple linear models.

Area of Science:

• Neuroscience
• Biostatistics
• Computational Biology

Background:

• The Sholl technique is standard for quantifying dendritic morphology in neuroscience.
• Current analyses often use simple linear models, which overlook intra-class correlation in clustered data (multiple neurons per animal).
• This oversight can lead to inaccurate statistical inferences.

Purpose of the Study:

• To highlight the limitations of simple linear models in Sholl analysis.
• To advocate for the adoption of mixed effects models in neuroscience.
• To demonstrate the benefits of mixed effects models for accurate dendritic morphology analysis.

Main Methods:

• Review of existing literature on Sholl data analysis.
• Application of both simple linear and mixed effects models to Sholl data from mouse hippocampal neurons.
• Comparison of statistical outputs (p-values, standard deviations) between the two modeling approaches.

Main Results:

• Simple linear models yield biased downward standard deviations and p-values.
• This bias leads to an erroneous rejection of the null hypothesis in some analyses.
• Mixed effects models provide a more accurate estimation of true data variability.

Conclusions:

• Mixed effects models prevent faulty inferences in Sholl analysis by accounting for intra-class correlation.
• The widespread practice of multiple measurements per subject in neuroscience necessitates wider application of mixed effects models.
• Adopting mixed effects models ensures more accurate and reliable conclusions in dendritic morphology studies.