Neuronautix ← All presentations

Tecniplast DVC® · Metric glossary · 2026-06-18

What the DVC measures — one signal, many metrics

A metric-by-metric guide to Digital Ventilated Cage analytics: what each readout is, how it is derived from the raw capacitance signal, and what it measures.

Damien Huzard, PhD · Neuronautix

The sensor — where every metric comes from

One capacitance grid under each cage

A board with a grid of electrodes (12×4 or similar) sits under each standard IVC, sensing the animal's position through dielectric (capacitance) change — no cameras, tethers, or handling. Sampling is sub-second, then aggregated into the metrics that follow. DVC-derived activity is comparable to video tracking [1], and the diurnal rhythm reproduces across three sites — CNR Rome, The Jackson Laboratory, Karolinska [2].

Iannello, Heliyon 2019 · Pernold et al., PLoS One 2019

Metric · Locomotion

Activation Density & Distance Walked

Activation Density How many electrodes activate over a time window — a measure of how much of the floor the animal disturbs.
Distance Walked How the centroid of electrode activation moves over time — translated into distance travelled.
Validation Both are derived continuously from the capacitance signal and were benchmarked against video tracking [1].

Iannello, Heliyon 2019 — foundational technology paper [1]

Metric · Activity budget & rest/activity bouts

Decomposing the day into bouts

Distance / day

~330 m

Travelled by singly housed C57BL/6J, mostly during the dark (active) phase [3].

Discrete bouts / day

~7,100

The day resolves into long rest, short rest, local movement, and locomotion bouts [3].

Time in long rest

~67%

Long rest (≥40 s); ~16% in physical activity (6% local movement + 10% locomotion) [3].

Pernold et al., PLoS One 2023 — the baseline activity budget [3]

Metric · Rest Disturbance Index (RDI)

RDI is transdiagnostic

RDI quantifies the fragmentation and interruption of rest — derived from the same activity stream, without EEG/EMG. The same construct recurs across unrelated disease models:

ALS (SOD1G93A) Non-invasive detection of rest disturbance — early proof RDI maps onto neurodegeneration [4].
Myotonic dystrophy type 1 (DMSXL) Reliably increased rest during the active phase — a hypersomnia correlate [5].
Narcolepsy type I (HCRT-KO, DTA) Fragmented sleep/wake and disease progression captured without instrumentation [6].
Aging Activity/RDI shift with age and are perturbed by husbandry [7].

Golini 2020 · Golini 2023 · Piilgaard 2023 · Moore 2024 [4–7]

Metric · Circadian / regularity

Day/night ratio, period, amplitude

What is measured

Day/night activity ratio

Circadian period

Amplitude, phase, entrainment

How — per-cage LED

In-built LED makes each cage an independent light-controlled chamber

Recovers period/phase even in cryptochrome-deficient mice

Benchmarked against wheel-running, the classical readout

Tir et al., Sci Rep 2025 — circadian phenotyping at rack scale [8]

Metric · Spatial preference

Frontality & wall activity are strain-specific

Beyond "how much," the grid records "where": time spent near the cage walls ("wall activity") and toward the front ("frontality"). These differ by strain — C57BL/6NCrl, BALB/c, and CD1 each show distinct spatial signatures — and were derived entirely from previously recorded data, with no new animals: a worked example of data repurposing for the Reduction principle [9].

Fuochi, Rigamonti et al., Sci Rep 2023 [9]

Environmental channel #1 · Bedding Status Index (BSI)

Bedding "wetness" from the same sensors

The electrode grid also senses soiled-bedding moisture. An ML model trained on human annotations predicts the Bedding Status Index ("wetness") at >90% accuracy at higher densities (5/cage). It supports data-driven cage-change intervals of 3–6 weeks (vs the standard 2-week schedule) and cut cage changes by 65–70% — with no effect on intracage ammonia, CO₂, growth, or circadian metrics [10].

Collins et al., JAALAS 2025 [10]

Environmental channel #2 · Urination Index

From bedding wetness to blood glucose

The same bedding-moisture signal becomes a metabolic biomarker. The Urination Index algorithm detects polyuria — a hallmark of diabetes — and correlates highly with blood glucose during hyperglycemia, enabling non-invasive, continuous, in-cage assessment of onset, progression, severity, and drug response, without stressful blood sampling [11].

Brachs et al., Lab Anim (NY) 2025 [11]

Optional metric · GYM500 wheel running

When bulk activity is not enough

An optional in-cage GYM500 running wheel logs running distance alongside DVC activity. In a cancer-induced bone pain model, wheel-running distance fell and tracked limb-use/weight-bearing scores, while general home-cage activity did not — making wheel running the more sensitive spontaneous pain-like readout, and showing exactly when to add the wheel [12].

Hopkins et al., In Vivo 2025 [12]

Synthesis · Reading the metric stack

Three patterns hold across the corpus

The dark phase holds the signal Disease- and drug-related deviations show up predominantly in dark-phase activity — the window daytime testing misses [3, 13].
Environmental channels are biomarkers The same bedding-moisture signal drives husbandry [10] and a metabolic readout [11] — welfare and science from one channel.
Data repurposing operationalises the 3Rs Re-mining archived DVC data yields new biology with zero additional animals [9, 14].

Pernold 2023 · Tomanelli 2024 · Collins 2025 · Brachs 2025 · Fuochi 2023 · Fuochi 2024 [3, 9–11, 13, 14]

References — full citations with DOIs

Sources

[1]Iannello F. Non-intrusive high-throughput automated data collection from the home cage. Heliyon 2019;5(4):e01454. doi.org/10.1016/j.heliyon.2019.e01454

[2]Pernold K. et al. Towards large-scale automated cage monitoring (24/7 capacitive). PLoS One 2019;14(2):e0211063. doi.org/10.1371/journal.pone.0211063

[3]Pernold K. et al. Bouts of rest and physical activity in C57BL/6J mice. PLoS One 2023;18(1):e0280416. doi.org/10.1371/journal.pone.0280416

[4]Golini E. et al. A non-invasive digital biomarker for rest disturbances in the SOD1G93A ALS model. Front Neurosci 2020;14:896. doi.org/10.3389/fnins.2020.00896

[5]Golini E. et al. Excessive rest time during active phase in a DM1 (DMSXL) model. Front Behav Neurosci 2023;17:1130055. doi.org/10.3389/fnbeh.2023.1130055

[6]Piilgaard L. et al. Non-invasive detection of narcolepsy type I (HCRT-KO, DTA). Sleep 2023;46(11):zsad144. doi.org/10.1093/sleep/zsad144

[7]Moore J. et al. Automated home-cage monitoring of an aging colony of mice. Front Neurosci 2024;18:1489308. doi.org/10.3389/fnins.2024.1489308

[8]Tir S. et al. Evaluation of the DVC® system for circadian phenotyping. Sci Rep 2025;15:s41598-025-87530-6. doi.org/10.1038/s41598-025-87530-6

[9]Fuochi S., Rigamonti M. et al. Data repurposing from digital home-cage monitoring. Sci Rep 2023;13:10851. doi.org/10.1038/s41598-023-37464-8

[10]Collins J.M. et al. ML/AI to determine cage-change frequency (Bedding Status Index). JAALAS 2025;64(4):1–14. doi.org/10.30802/AALAS-JAALAS-24-151

[11]Brachs S. et al. Robust non-invasive detection of hyperglycemia using the Urination Index. Lab Anim (NY) 2025;54(12):379–389. doi.org/10.1038/s41684-025-01648-8

[12]Hopkins C. et al. Wheel running in DVCs is impaired in cancer-induced bone pain. In Vivo 2025;39(6):3205–3215. doi.org/10.21873/invivo.14120

[13]Tomanelli M. et al. Aberrant locomotor activity in a lung cancer model. Front Oncol 2024;14:1504938. doi.org/10.3389/fonc.2024.1504938

[14]Fuochi S. et al. Big data and its impact on the 3Rs: a home-cage monitoring review. Front Big Data 2024;7:1390467. doi.org/10.3389/fdata.2024.1390467

Thanks.

Treat DVC output as a structured, FAIR-ready metric stack — know what each number means before you analyse it, and consider data repurposing for the 3Rs.

Damien Huzard, PhD · Neuronautix · 2026-06-18
neuronautix.com/contact