Evidence map›Paper›PMID 28104486›Full record

ReviewJournal of neuroscience methods2017

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

Machelle D Wilson, Sunjay Sethi, Pamela J Lein, Kimberly P Keil

Abstract readReview
In one paragraph

Review in Journal of neuroscience methods, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
53citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

53 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Machelle D WilsonClinical and Translational Science Center, Department of Public Health Sciences, Division of Biostatistics, University of California, Davis, CA, United States.
Sunjay SethiDepartment of Molecular Biosciences, University of California, Davis, CA, United States.
Pamela J LeinDepartment of Molecular Biosciences, University of California, Davis, CA, United States.
Kimberly P KeilDepartment of Molecular Biosciences, University of California, Davis, CA, United States. Electronic address: kpkeil@ucdavis.edu.

Funding

UC Davis Clinical and Translational Science CenterUL1TR001860 · NCATS · UNIVERSITY OF CALIFORNIA AT DAVIS · PI KENYON, NICHOLAS J., LYLES, COURTNEY REES · 2016 to 2025
$47.3M
UC Davis Environmental Health Sciences Core CenterP30ES023513 · NIEHS · UNIVERSITY OF CALIFORNIA AT DAVIS · PI Nicholas J. KENYON · 2015 to 2026
$26.0M
UC Davis Center for Children's Environmental Health (CCEH)P01ES011269 · NIEHS · UNIVERSITY OF CALIFORNIA DAVIS · PI VAN DE WATER, JUDY A. · 2001 to 2018
$12.5M
UC Davis Clinical and Translational Science CenterUL1TR000002 · NCATS · UNIVERSITY OF CALIFORNIA AT DAVIS · PI BERGLUND, LARS F · 2012 to 2015
$12.2M
Molecular and Cellular Basis of PCB Developmental Neurotoxicity: ViCTER supplementR01ES014901 · NIEHS · UNIVERSITY OF CALIFORNIA AT DAVIS · PI HANS-JOACHIM LEHMLER, Pamela J Lein · 2009 to 2026
$9.3M
Rodent Behavior CoreU54HD079125 · NICHD · UNIVERSITY OF CALIFORNIA AT DAVIS · PI ABBEDUTO, LEONARD J. · 2013 to 2019
$8.5M
ENVIRONMENTAL TOXICOLOGYT32ES007059 · NIEHS · UNIVERSITY OF CALIFORNIA DAVIS · PI Laura S Van Winkle · 1985 to 2026
$7.9M
Molecular and Cellular Basis of PCB Developmental NeurotoxicityR56ES014901 · NIEHS · OREGON HEALTH & SCIENCE UNIVERSITY · PI LEIN, PAMELA J · 2007 to 2007
$150k
PCBs and heritable mutations in calcium signaling act via DNA methylation to disrupt dendritic growth andplasticityF32HD088016 · NICHD · UNIVERSITY OF CALIFORNIA AT DAVIS · PI STIETZ, KIMBERLY PRESTON KEIL · 2016 to 2018
$135k
NCATS NIH HHS UL1 TR000002NCATS NIH HHS UL1 TR001860NICHD NIH HHS F32 HD088016NICHD NIH HHS U54 HD079125NIEHS NIH HHS P01 ES011269NIEHS NIH HHS P30 ES023513NIEHS NIH HHS R01 ES014901NIEHS NIH HHS R56 ES014901NIEHS NIH HHS T32 ES007059
6 · The paper itself

Abstract

backgroundThe Sholl technique is widely used to quantify dendritic morphology. Data from such studies, which typically sample multiple neurons per animal, are often analyzed using simple linear models. However, simple linear models fail to account for intra-class correlation that occurs with clustered data, which can lead to faulty inferences. NEW

methodMixed effects models account for intra-class correlation that occurs with clustered data; thus, these models more accurately estimate the standard deviation of the parameter estimate, which produces more accurate p-values. While mixed models are not new, their use in neuroscience has lagged behind their use in other disciplines.

resultsA review of the published literature illustrates common mistakes in analyses of Sholl data. Analysis of Sholl data collected from Golgi-stained pyramidal neurons in the hippocampus of male and female mice using both simple linear and mixed effects models demonstrates that the p-values and standard deviations obtained using the simple linear models are biased downwards and lead to erroneous rejection of the null hypothesis in some analyses. COMPARISON WITH EXISTING

methodsThe mixed effects approach more accurately models the true variability in the data set, which leads to correct inference.

conclusionsMixed effects models avoid faulty inference in Sholl analysis of data sampled from multiple neurons per animal by accounting for intra-class correlation. Given the widespread practice in neuroscience of obtaining multiple measurements per subject, there is a critical need to apply mixed effects models more widely.

Indexed as

Data Interpretation, StatisticalDendritesModels, StatisticalAnimalsFemaleHippocampusImage Processing, Computer-AssistedMaleMice, Inbred C57BLMicroscopyDendritic morphologyFalse discovery rateGolgi stainingMixed modelSholl analysis

Identifiers

PMID28104486
PMCPMC5346342

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.