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Record W4410382211 · doi:10.1016/j.ghres.2025.100007

A comparison of microglial morphological complexity in adult mouse brain samples using 2-dimensional and 3-dimensional image analysis tools

2025· article· en· W4410382211 on OpenAlexafffund
Colin J. Murray, Eva D. Tunderman, Haley A. Vecchiarelli, Fernando Gonzàlez Ibáñez, Marie‐Ève Tremblay

Bibliographic record

VenueGlial health research. · 2025
Typearticle
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsUniversité LavalUniversity of Victoria
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaBranch Out Neurological FoundationNational Institutes of HealthConsejo Nacional de Ciencia y TecnologíaCanada Research ChairsCanada Foundation for InnovationMichael Smith Health Research BCUniversity of Victoria
KeywordsImage (mathematics)Artificial intelligencePattern recognition (psychology)NeuroscienceComputer scienceBiologyComputer vision

Abstract

fetched live from OpenAlex

Characterizing cell morphology has been an important aspect of neuroscience for over a century to provide essential insights into cellular function and dysfunction. Microglia, the resident innate immune cells of the central nervous system, undergo drastic changes in morphology in response to various stimuli, with many classifications proposed in recent years. Increased availability of advanced analysis software to study microglial morphology represents a step forward in the field. However, whether the use of advanced analysis tools provides equivalent or varied outcomes remains undetermined. This work re-analyzed raw data—previously processed using a standard 2D microglial morphology analysis method—using 3D analysis methods. Our previously published article observed significant changes in microglial morphology using the 2D analysis method in the mouse ventral hippocampus after administration of a ketogenic diet and exposure to repeated social defeat stress in young adult male mice. Overall, we observed different statistical outcomes in the 3D dataset compared to the previously published 2D results, with both maintained and new findings. However, overall conclusions on microglial morphology changes remain consistent between methods. Lastly, we highlight the difference between a nested statistical design, which considers between animal variability and the dependency of within animal measurements, and a non-nested design. When a nested design is employed, many of the statistically significant post hoc comparisons are lost. Overall, we highlight and discuss differences between 2D and 3D microglial morphology analysis and explore the contribution of individual cell and animal variability to statistical outcomes. • A 3D analysis method generates similar and novel results compared to a 2D method. • Overall microglial morphological changes to stimuli are comparable between methods. • A nested statistical design produces distinct significant differences.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.443
GPT teacher head0.501
Teacher spread0.058 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2025
Admission routes2
Has abstractyes

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