小鼠疾病模型的集成行为车站行为表型（INBEST）

The overall goal of this methodology is to improve the reproducibility and standardization of behavioral phenotyping and experimental mice by continuous monitoring of basic behavioral measures in a familiar environment. This modular system is comprised of a digitized climbing mesh computerized running wheel, an automated food dispenser, two photo cell controlled TERs, a computer controlled light stimulus, and a shelter, third party components and infrared MPEG four technology integrated with video tracking and event recording Software packages are interfaced to create eight arenas, allowing for the concurrent monitoring of four experimental and four control animals. Latencies frequencies and durations of specific behaviors are analyzed using MED PC four software.

While locomotor activity and topography of movement paths are assessed with ETH ovision XT 8.5 software using comprehensive statistical analysis. The data was often processed to reveal behavioral changes induced by disease progression in diverse mouse models. The complex and sensitive nature of behavior often makes reliable measurements in chronically sick mice, very difficult.

Using new technologies that allow for accurate, unbiased behavioral analysis. We describe a custom made monitoring system called Inves or Integrated behavioral station. This apparatus is designed to provide prolonged measurements of basic functional outputs such as spontaneous activity, food and water intake, emotional reactivity, motivated behavior, all in a low stress home-based like environment.

The main advantages of invest over single behavioral tests include continuous monitoring, reduce transportation stress, and rapid collection of multiple sets of data that are instantly fed for unbiased computerized analysis. Although continuous monitoring addresses confounds common in traditional studies, it is important to note that some domains of behavior, such as neurological function and spatial learning, cannot be studied in the home cage environment, thus necessitating additional tests To complete the assessment of the behavioral profile, One to two weeks before the start of the experiment, start habituating the mice to a 12 to 12 light to dark cycle. The mice are most active at night.

Set the dark cycle timeframe to parallel the experimenter's working hours. All other environmental factors such as temperature and ventilation should remain constant during this period. For permanent numerical identification, tail tattoo the mice for five to seven days prior to testing.

Regularly handle the mice for up to two hours a day, thus habituating them to the stress measure and record the body weight rectal temperature and take notes of their general appearance. Exclude mice from the study that display gross abnormalities such as low food or water consumption, body weight loss, a hunched posture ruffled or stained fur hydrocephalus, or showing porphyrin discharge from around the eyes. In addition, a wide variety of behavioral tests such as the open field can be performed during this timeframe permitting a more comprehensive understanding of performance deficits.

For a prolonged behavioral study, the food dispenser should have a full supply of mouse chow pellets. Each weighing 20 milligrams. One bottle should always be filled with fresh tap water.

In a preference test, a second bottle can provide an alternate solution of interest, such as one containing sugar contain. Weigh the bottles to estimate the volume consumed between sessions. To measure latency and frequency of drinking bouts, the bottle spouts must be attached to TERs.

An infrared sensor detects licking, so be certain the sensor is not blocked by the spout nozzle. If so, change the spout length so it fits within the cage. A shelter should be placed in a corner distal from the running wheel.

For video tracking, it is necessary that the room is illuminated with a dim diffuse light that does not reflect off the in invests floor or walls. Once the lighting is set correctly, the testing arena can be digitalized for the testing software. For this demonstration, the ETH Ovision XT 8.5 software package is used to set up the arena.

Begin by opening a default tracking project and entering the experimental settings. Then choose the video source in this case the piccolo diligent grabber. Set the number of tracking arenas to four.

Choose center nose and tail for the tracking points, and select the measurement units Next from the trial list. Under the setup menu, define the number of trials by clicking the add trials button. Then toggle the add variable option to specify independent variables like the mouse id, gender group, assignment, and strain.

Now capture a background image of the arena by clicking the arena settings tab and defining the perimeter of the arena using the available draw tools after the arena is defined, use the add zone option to outline zones of interest such as the floor, the TERs, the food dispenser, and so forth. Then add zones where the mouse cannot be seen by using the add hidden zone group option. This would include the shelter and the running wheel.

Each hidden zone should also have a specified entry exit point. After defining the perimeter and zones of each arena, perform calibration tests using the calibration option in the software. Use the scale access tool to determine each arena's width and length.

Then validate each arena's settings using the validate button and proceed with setting up the trial parameters and collecting data. To acquire data acquisition parameters must be first set in the program. Begin by accessing the trial control settings tab and specifying the timeframes for the trial set.

The start condition to begin when the duration of the center point exceeds one second in the arena, the duration of the trial is set by expanding the stop condition box and setting the trial to terminate after a preset delay such as 10 hours, now select an option from the detection method from under the detection settings tab. Next, grab the reference image of the empty arena by using the settings button and selecting Grab current while still in the detection module, adjust the range of contrast for the center nose and tail detection points. Set the range to be continuous.

Then indicate that the mouse is brighter or darker than the background. Now adjust the subject size setting, which is based on the camera's field of view. Then, if needed, adjust the frames per second of analysis based on what the computer is capable of.

Processing 14.9 frames. Second is usually sufficient. When all the changes are made to the detection module options, be sure to save them before proceeding to measure latency, frequency, and duration of behavioral acts.

A custom made routine in MED PC four is used for step-by-step input of the session parameters such as mouse id, trial duration, running wheel bouts, et cetera. When these parameters are all input and all the testing equipment has been turned on, proceed by placing the mouse into the arena. Then synchronize the video and event tracking packages by simultaneously pressing the record buttons and quietly leave the room later when the recording period expires, return the mice to their home cages, measure the bottle weights, and save all the recorded data to digital media.

The raw data can be output to a spreadsheet for analysis, and the raw video files can be scored for infrequent behaviors such as stereotyping or seizures. A prolonged study with CD one mice using the in best was performed several days of pre-surgery. Baseline data was collected followed by post-surgery data that includes sustained intra cerebro ventricular administration of brain reactive antibodies.

Starting from day five, experimental groups showed various impairments including reduced licking of water bottles. They also showed an increased latency to approach bottles with sucralose solution, and they showed decreased food consumption. Coinciding with these changes, the mice administered brain reactive antibodies also showed diminished running wheel activity in comparison to control mice ambulation as measured by the video tracking software was also diminished.

In the experimental group, the treated mice preferred to spend time in the shelters. Using this data, a simple rogram was constructed to illustrate these behavioral differences. After watching this video, you should have a good understanding of how to set up a custom made monitoring apparatus for unbiased behavioral analysis.

In a home-based like setting, Automated home cage phenotyping provides a wealth of information permitting, more accurate assessment of the onset kinetics and severity of disease induced behavioral deficits related to spontaneous locomotion motivated behavior and emotional reactivity by minimizing issues relating to inconsistent environmental settings, transportation, stress, and repeated handling apparatus like inves can significantly enhance consistency and precision of behavioral results across separate studies.