System Based on NI FieldPoint Used to Monitor the Feeding Activity of Laboratory Animals

Author(s):
Matthew R. Williams - Mack Information Systems, Inc. and PV Knowledge, LLC
Edward Ulman - Research Diets, Inc.

Industry:
Research, Life Science

Products:
LabVIEW, FieldPoint

The Challenge:
Monitoring and recording the feeding activity of laboratory animals, including total consumption, meal duration, meal construction, and the effects of different treatment protocols on feeding behavior.

The Solution:
Building a multiple-channel data acquisition and analysis system based on National Instruments FieldPoint hardware with a PC-based user interface. The NI FieldPoint controller operates as a stand-alone device, recording feeding activity according to a user-configurable algorithm. The PC provides further data analysis, report generation, and long-term storage.

Building a multiple-channel data acquisition and analysis system based on NI FieldPoint hardware with a PC-based user interface. The FieldPoint controller operates as a stand-alone device recording feeding activity according to a user-configurable algorithm. The PC provides further data analysis, report generation, and long-term storage.

The feeding activity of laboratory animals, typically mice and rats, can be significantly influenced by treatment protocols. Being able to quantify these influences is critical to understanding the effects of various drugs under investigation. This system provides researchers with data describing exactly when and how much each animal eats almost down to the individual “nibble”.

Adult laboratory mice typically eat three to six grams per day; however, very little is known about meal size, frequency, duration, or timing. While it is easy to observe gross changes in feeding activity on a daily basis, many treatment protocols have a significant impact on feeding behavior without affecting the total consumption over extended periods. Understanding these changes in feeding characteristics is essential to understanding the effects of drugs, nutrients, and other treatment protocols.

The system allows a researcher to observe the feeding behaviors of the animals in much greater detail than was previously possible. Traditional methods involved direct human observation of feeding activity or periodic hand weighing of food, which disturbs the animals and is error prone. Neither method is easy to automate or scale to many animals.

The FieldPoint system permits the simultaneous observation of 32 animals with a single piece of hardware. Furthermore, because FieldPoint is a stand-alone system, multiple FieldPoint units can be monitored by a single PC, providing an easily scaleable automation solution to a previously labor-intensive activity.

System Architecture

The system has three logical components:

• The FieldPoint node
• The monitor application
• The analysis application

The FieldPoint node gathers data from the primary sensors and stores it in its memory. The monitor application retrieves the data from the FieldPoint node, performs some preliminary data processing, and then stores the results on a local or network disk. The analysis application, which can run on the monitor application PC or a separate PC, retrieves the data from the disk, performs analysis as interactively directed by the user, and then presents the data visually or as various output files.

FieldPoint and NI LabVIEW Real-Time Module Are Key

By implementing the primary measurement algorithm in the FieldPoint controller, we were able to gather all of the raw data within the FieldPoint node. The PC may be left connected, but it is not required. This creates a durable solution, because network or PC outages do not impact the actual data collection.

Strain gages are used to measure the amount of food consumed as well as to indicate the animal’s activity level. Significant development effort went into the algorithm itself, with the resulting in a system that can accurately measure feeding activity down to approximately 0.02 grams even in the presence of temperature changes. The accuracy, resolution, and capability of the FieldPoint platform were essential to the development of the system.

Flexibility of LabVIEW Speeds Development Process

Our primary measurement, the strain gage, produces significant noise relative to the size of our desired signal. By creating our application in a modular fashion, we quickly generated and evaluated different measurement algorithms to extract the desired signals. Additionally, because the algorithms were developed directly on the PC before being targeted to the FieldPoint, development proceeded rapidly through a number of algorithm candidates. The algorithm and various aspects of the system are covered by issued and pending patents.

Modular Architecture Results in Flexibility and Reliability

Due to its modular nature, LabVIEW made it easy to segment the application into manageable chunks and perform unit testing. With data flow, in-placeness, and data compartmentalization, we could rapidly and reliably prototype new features. It was not uncommon during our development process for alpha builds to be distributed to selected end users around the world the next day.

With this modularity, we also extracted pieces of the application for use in other tools such as QA fixtures and system evaluation tools. We ensured that the results reported by the various tools accurately reflect the results from the primary application by using a shared library. This also made it easy to develop spin-off products such as a small channel count unit for use as a sales demo. Pop out the FieldPoint I/O calls, replace with USB-6009 equivalents, build for the PC, and we had a portable system for demonstration purposes.

Graphical Representation of Data

The system can generate records of thousands of bouts per day. Presenting the data in a manageable manner is essential to the researcher’s understanding. The graphing tools in LabVIEW are key to making a researcher-friendly user interface.

We created a data viewer tool that the researcher uses to quickly compare, organize, analyze, and extract the data. By initially presenting the data graphically, the user can rapidly identify animals or time periods of interest. Grouping functions display averages or individual animals at the click of a (computer) mouse. The most common calculations are built into the viewer, and the raw data is also available for external processing.

Baseline feeding patterns have been established for various species of laboratory animals.  While the overall consumption rate for mice was well known, we can now show that within that total, the animals will eat 10 to 20 meals of approximately 0.3 to 0.6 grams each.  The individual meals are further divided into 30 to 60 feeding bouts of approximately 0.01grams. Additionally, we can show quite clearly the relationship between external influences and feeding activity; for instance, rats typically eat three distinct meals during the night, while mice munch more.

This system gives the researcher data that was simply unavailable previously. The level of detail about the animals’ feeding combined with the ease of formatting and presenting the data allows the researcher to quickly and accurately assess the effects of treatment protocols.

For more information, contact:

Edward Ulman

 

Research Diets, Inc.

 

10 Jules Lane, New Brunswick, NJ 08901

 

Tel: (732) 247-2390

 

Fax: (732) 247-2340

 

Email: ulman@researchdiets.com

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