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Updated: Jul 12, 2026

High-Throughput Behavioral Aging and Lifespan Assays Using the Lifespan Machine

High-Throughput Behavioral Aging and Lifespan Assays Using the Lifespan Machine

Published on: January 26, 2024

Behavioral phenotypes in aging: structured exploratory computational analysis of multi-assay behavioral data.

Gaurav Singhal¹, Bernhard T Baune²

¹Department of Otolaryngology, University of Wisconsin-Madison, Madison, WI, United States.

Frontiers in Behavioral Neuroscience

|July 11, 2026

Summary

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Aging impacts behavior, but domain-level analysis obscures specific changes. Key age-related effects are clearest in individual assay measures, particularly Barnes Maze performance, highlighting the importance of assay-specific analysis for understanding behavioral alterations.

Area of Science:

Neuroscience
Behavioral Science
Aging Research

Background:

Aging causes progressive behavioral changes, often studied assay-by-assay.
Previous work reported assay-specific behavioral effects in mice.
This study investigates if aging signals are assay-localized or form coordinated domain patterns.

Purpose of the Study:

To determine if aging-related behavioral signals are assay-specific or domain-level.
To analyze behavioral data using a structured workflow for feature engineering and domain construction.
To assess the validity of domain-level composites versus assay-level measures in reflecting aging effects.

Main Methods:

Implemented a rule-based workflow for feature engineering, harmonization, standardization, and domain construction (locomotion, anxiety, depression, cognition, memory, sociability).

Keywords:

C57BL/6 mice aging behavioral phenotyping cognition computational analysis locomotion memory multivariate analysis

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Last Updated: Jul 12, 2026

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Analyzed outcomes using parametric/non-parametric models with effect sizes.

Examined domain-level composites and refined feature-level analyses.

Main Results:

Domain-level composites showed no significant age- or sex-related effects.
Refined analysis revealed modest locomotor differences and significant age-related decline in Barnes Maze efficiency.
Older animals exhibited reduced Barnes Maze performance, with training improvements noted across days.

Conclusions:

Aging-related behavioral changes are concentrated in specific assay measures, not broadly across domains.
Domain-level aggregation obscures age-related effects, suggesting composites are heuristic summaries.
The strongest aging signals are evident in selected measures, especially Barnes Maze outcomes.