Protocols

Writing the methods section

A good methods section lets someone else run your experiment. Here's a head start.

Reviewers and readers want to know exactly how the test was run. These example paragraphs show the kind of detail to include. Treat them as a starting point: change every value to match what you actually did, and add your own pool, room and animal details.

Morris water maze (video tracking)

A ready-to-edit methods paragraph for a water maze tracked with HVS Image. Replace each [square bracket] with your own values.

Swim paths were recorded with a tracking camera and analysed with HVS Image video tracking software ([version and build]). The pool ([pool diameter] cm) was calibrated by clicking the North, South, West and East edges of the water surface, and every platform position used was calibrated, with the pool centre as position 0; the platform diameter was [target diameter] cm. [Number of trials] trials per day were given for [number of days] days, from [start points], with a maximum duration of [Max Seconds] s; trials ended [on arrival at the platform / after the subject had stayed on the platform for [n] s / manually]. In probe trials ([probe duration] s) the learned platform position was entered so that measures were calculated relative to the learned location. Latency, path length, average and active swim speed, % time in the target quadrant, platform crossings, close encounters, thigmotaxis and Gallagher proximity were calculated in HVS Image, with a thigmotaxis band of [band value] of the pool radius, a close encounter counter of [counter value] and an inactivity threshold of [threshold] m/s. [Distal cue arrangement.] [Water depth, temperature and opacity, from your protocol.] The procedure was based on Morris (1984), and proximity measures on Gallagher et al. (1993).

Notes for authors

Fill in every [square bracket] from your own protocol and your saved files. Analysis settings such as counter size are chosen in ‘Advanced’, and the build is shown on the right of the HVS Image main menu. Report the build and the analysis settings you used, and keep them constant across a study. Probe measures such as platform crossings, close encounters, Gallagher proximity and the Direct Path Measures are interpreted relative to the learned location.

Vorhees and Williams (2006) describe reference memory as being determined by preference for the platform area when the platform is absent, and note that cued trials determine whether performance factors unrelated to place learning are present; consider describing any cued trials you ran.

Barnes maze (video tracking)

A ready-to-edit methods paragraph for a Barnes maze tracked with HVS Image.

Behaviour was recorded with a tracking camera and analysed with the Barnes maze in HVS Image video tracking software ([version and build]). The maze surface ([maze diameter] cm, [number of holes] holes) was calibrated by clicking the top, bottom, left and right extremes of the surface, and each hole position was calibrated. Trials lasted up to [Max Seconds] s and ended when the subject entered the escape hole [manually / automatically with ‘Auto-end on escape hole’]. Hole approaches were counted automatically, and pokes (errors) per hole, latency and path length to the target hole and primary path length were calculated, with a thigmotaxis band of [band value] (set so that the holes fell within it) and a counter size of [counter value]. In probe trials ([probe duration] s) the learned target hole was entered as the target. [Escape box, aversive stimulus and lighting, from your protocol.]

Notes for authors

Fill in every [square bracket] from your own protocol and your saved files. Analysis settings such as counter size are chosen in ‘Advanced’, and the build is shown on the right of the HVS Image main menu. Report the build and the analysis settings you used, and keep them constant across a study. State whether trials ended manually or automatically with ‘Auto-end on escape hole’.

Barnes (1979) tested rats on a circular platform which favoured the use of spatial cues for its solution; cite it if you describe the task's origin.

Open field and novel object recognition (video tracking)

A ready-to-edit methods paragraph for open field or object tests tracked with HVS Image.

Behaviour was recorded with a tracking camera and analysed with HVS Image video tracking software ([version and build]). The tracking area ([height] × [width] cm; [number of arenas] arenas) was calibrated by clicking its top-left and bottom-right corners. For analysis the tracking area was divided into a [grid size] grid, and % time, % path, entries and latency were calculated for each zone, together with total path length, average speed and [time bins, if used: [number of bins]]. Freezing was defined as moving no more than [distance] cm in [time] s. [For object tests: objects were placed in zone centres and time near each object was scored from body-centre tracking; sniffing and contacts were scored manually by [method].] Trials lasted [Max secs] s. [Arena material, lighting and cleaning between subjects, from your protocol.]

Notes for authors

Fill in every [square bracket] from your own protocol and your saved files. Analysis settings such as counter size are chosen in ‘Advanced’, and the build is shown on the right of the HVS Image main menu. Report the build and the analysis settings you used, and keep them constant across a study. Freezing and inactivity criteria are user-set, so state both the distance and the time window.

Open field anxiety-related measures: Simon et al. (1994) considered the degree of thigmotaxis during the first minutes as an index of anxiety in mice, and Prut and Belzung (2003) review the open field as a very popular animal model of anxiety-like behaviour. For object recognition, cite the one-trial object recognition method you followed.

Multi-arm mazes and elevated plus maze (video tracking)

A ready-to-edit methods paragraph for T, Y, plus and radial mazes tracked with HVS Image.

Behaviour was recorded with a tracking camera and analysed with HVS Image video tracking software ([version and build]). The [maze type] ([arm dimensions]) was calibrated by clicking the maze points in the order indicated by the software. An arm entry was defined using a Detection Area of [percentage]% of each arm (at 100% any entry counts). Total arm entries, arms visited, entries per arm, latencies, % path and % time per arm and the arm-entry sequence were calculated[; for the elevated plus maze, open versus closed arm % time and entries were calculated (Walf and Frye, 2007)]. Trials lasted [Max secs] s. [Maze height, wall material, lighting and cleaning, from your protocol.]

Notes for authors

Fill in every [square bracket] from your own protocol and your saved files. Analysis settings such as counter size are chosen in ‘Advanced’, and the build is shown on the right of the HVS Image main menu. Report the build and the analysis settings you used, and keep them constant across a study. You can re-analyse with other Detection Area percentages at any time, so state the one used in each analysis.

Further sources: Pellow et al. (1985) validated open : closed arm entries as a measure of anxiety in the rat; Olton and Samuelson (1976) tested rats on an eight-arm maze; Deacon and Rawlins (2006) give a T-maze alternation protocol.

Forced swim test (video tracking)

A ready-to-edit methods paragraph for the forced swim test tracked with HVS Image.

Behaviour was recorded with a tracking camera and analysed with HVS Image video tracking software ([version and build]). [Number of containers] containers were tracked; the tracking area was calibrated as a band around the water line ([band height] × [band width] cm), sized so that the subject's highest point lay in the top third when climbing, the middle third when swimming or floating and the bottom third when diving. HVS Image classified climbing, swimming/floating and diving from the highest part of the subject within the tracking area and separated swimming from floating by speed. % time climbing, swimming, floating and diving, total path length and average speed were calculated. Trials lasted [Max secs] s. [Container dimensions, water depth and temperature, and pre-test session, from your protocol.]

Notes for authors

Fill in every [square bracket] from your own protocol and your saved files. Analysis settings such as counter size are chosen in ‘Advanced’, and the build is shown on the right of the HVS Image main menu. Report the build and the analysis settings you used, and keep them constant across a study. Describe the band carefully, since the classification depends on where the animal's highest point falls within it.

No forced swim paper is yet in the verified publications database (a classic reference has been proposed and awaits approval), so add your own primary reference for the procedure.

VR water maze (HVS 6D VR)

A ready-to-edit methods paragraph for a human VR water maze run in HVS 6D VR and analysed in HVS Image.

The task was run in HVS 6D VR ([version]) with a Quest 3 headset connected by Link cable; participants navigated using [the Omnidrive treadmill / treadplate / arrow keys / head tilts]. Each experiment was defined in a vrScript file (HVS Image vrScript technical reference, doi:10.5281/zenodo.22303968), with a tracking diameter of [TRACKING_DIAMETER], a target radius of [TARGET_RADIUS] m, a maximum trial duration of [MAXIMUM_TIME] and [cue arrangement and scene]. Start positions and orientation were defined per trial in the vrScript. HVS 6D VR recorded time-stamped positions, which HVS Image ([version and build]) smoothed to one position per 100 ms (10 Hz) for all measures. Trials were analysed in HVS Image as for the water maze, with an inactivity threshold of [threshold] m/s (HVS Image Inactivity technical note, doi:10.5281/zenodo.21797205). [Participant recruitment, consent, safety procedures and practice, from your protocol.]

Notes for authors

Fill in every [square bracket] from your vrScript files and .BOND headers, which are the best configuration record. A TRACKING_DIAMETER of 200 is recommended for comparability with the rodent maze. Rodent and human VR values should not be compared directly. HVS 6D VR does not record experimenter assistance (scripted audio is possible), so describe any you gave.

Astur et al. (1998) used a computerised version of the Morris water task to assess whether it generalises to the human domain; cite it if you discuss human virtual water maze precedent.

Inactivity (floating) measure

A sentence to add when you report inactivity or active speed from HVS Image.

Inactivity was defined as speed below [threshold] m/s. Positions [sampled at up to 60 Hz (video tracking) / recorded at a variable rate (HVS 6D VR)] were smoothed to one position per 100 ms; speed per interval was the Euclidean distance between successive smoothed positions divided by the elapsed time, intervals below the threshold were classed as inactive, and % inactivity was the summed inactive duration divided by the total trial duration (HVS Image Inactivity technical note, doi:10.5281/zenodo.21797205).

Notes for authors

The default threshold is 0.05 m/s (5 cm/s) and can be changed for the species, protocol or virtual environment, so state the value you used.

Active speed uses only intervals at or above the inactivity threshold (exactly 0.05 counts as active at the default), so report it alongside inactivity when interpreting latency and path length.

Direct Path Measures

A sentence to add when you report Direct Path Measures from HVS Image.

Initial approaches were classified with the HVS Image Direct Path Measures (HVS Image Direct Path Measures technical note, doi:10.5281/zenodo.21870806; HVS Image [version and build]), using a close encounter counter of [Counter diam x value], a poolside zone (ThigRatio) of [value] and a heading distance of [value, as exported in Heading range]. A path was classed as direct when actual path / shortest path was no more than 1.2; classifications considered only the first arrival and were evaluated only when the trial started outside the destination.

Notes for authors

Keep these three settings constant across a study and report them; check the exported Heading range rather than assuming a value. Report the build with re-analysed data.

The automated rules are recent and have no peer-reviewed validation papers yet, so describe them as defined in the technical note. Direct Path classifications are not produced for time slices.

Gallagher proximity, raw and corrected

A sentence to add when you report Gallagher proximity from HVS Image.

Search proximity was quantified as Gallagher global proximity (average distance from the target over the [trial / time slice]) and cumulative proximity (sum of one-second average distances) (Gallagher et al., 1993), calculated in HVS Image ([version and build]). Corrected values excluded the time needed to swim the straight-line start-to-target distance at the subject's mean active swimming speed; raw and corrected values are reported together.

Notes for authors

Cumulative proximity should be read with latency and path length, because escape time sets the summing window. Corrected values are 'not applicable' when there is no valid target, too little path data, no active speed or no samples left after correction.

Gallagher et al. (1993) found this proximity measure to be highly sensitive to age-related impairment. Very short remaining records after correction may limit interpretation even when a value is produced.

If you used HVS Image, see the citations for HVS Image analyses and methods.

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