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This chapter provides a practical, science-driven guidance for establishing and scaling HCM as a core facility service. We outline design choices that raise rigor and power: longitudinal baselines aligned to light–dark cycles, pre-planned event tagging for husbandry and interventions, and synchronization of brief arena assays to circadian peaks revealed by continuous cage readouts. We show how to translate raw streams into robust endpoints through standard metadata, FAIR-aligned storage, automated quality control, and analysis templates for rhythms, rare events, and effect-size estimation. For operations, we define an intake and governance framework covering protocol suitability, welfare approvals, authorship and data rights, and a transparent fee model that balances access and sustainability. A competency pathway for staff and users is presented, with tiered training, skills checks, and reusable notebooks that lower the barrier to high-quality analyses. Performance is tracked with a concise metric set: utilization, repeat users, turnaround, data-loss rate, and scholarly output. Use cases from cognition, neurodegeneration, and pharmacology illustrate how HCM exposes early sleep and activity shifts, optimizes assay timing, and detects treatment effects that short tests miss, improving reproducibility and supporting 3Rs. We close with a forward look at closed-loop paradigms, multimodal integrations, and voluntary test add-ons, positioning core directors to deliver scalable, high-value behavioral phenotyping across disciplines.

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Home Cage Monitoring in Practice in Core Labs: Training, Infrastructure, and Research Workflow

  • 章节

  • 出版模式：

    开放获取

  • 发表日期：

    2026年

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  • Philipp Villiger,
  • Michael Tsoory,
  • Silvia Mandillo,
  • Stefano Gaburro

摘要

This chapter provides a practical, science-driven guidance for establishing and scaling HCM as a core facility service. We outline design choices that raise rigor and power: longitudinal baselines aligned to light–dark cycles, pre-planned event tagging for husbandry and interventions, and synchronization of brief arena assays to circadian peaks revealed by continuous cage readouts. We show how to translate raw streams into robust endpoints through standard metadata, FAIR-aligned storage, automated quality control, and analysis templates for rhythms, rare events, and effect-size estimation. For operations, we define an intake and governance framework covering protocol suitability, welfare approvals, authorship and data rights, and a transparent fee model that balances access and sustainability. A competency pathway for staff and users is presented, with tiered training, skills checks, and reusable notebooks that lower the barrier to high-quality analyses. Performance is tracked with a concise metric set: utilization, repeat users, turnaround, data-loss rate, and scholarly output. Use cases from cognition, neurodegeneration, and pharmacology illustrate how HCM exposes early sleep and activity shifts, optimizes assay timing, and detects treatment effects that short tests miss, improving reproducibility and supporting 3Rs. We close with a forward look at closed-loop paradigms, multimodal integrations, and voluntary test add-ons, positioning core directors to deliver scalable, high-value behavioral phenotyping across disciplines.