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. Author manuscript; available in PMC: 2023 Jul 1.
Published in final edited form as: Brain Res Bull. 2022 Apr 25;185:86–90. doi: 10.1016/j.brainresbull.2022.04.009

Neuroscience and Actometry: an example of the benefits of the precise measurement of behavior

Troy J Zarcone 1
PMCID: PMC9167777  NIHMSID: NIHMS1804650  PMID: 35472566

Abstract

Purpose

Assess the impact the force-plate actometer, invented by Stephen C. Fowler, has had on behavioral neuroscience so far and what may be possible for future progress.

Methods

The web service Scopus was queried on April 28, 2021 for articles that cited the Journal of Neuroscience Methods paper titled “A force-plate actometer for quantitating rodent behaviors: illustrative data on locomotion, rotation, spatial patterning, stereotypies, and tremor” resulting in 134 articles. Articles were coded by the author for type (e.g., research, review, book chapter), phenomenon (e.g., stress, addiction), intervention (e.g., pharmacological), and measure (e.g., distance traveled, tremor).

Conclusions

Of the 134 citations, 116 were research articles, 10 were review articles, 7 were book chapters and one was an advertisement. The force-plate actometer has been used to study a variety of phenomena and its measurement capabilities were expanded. While primarily used for rats and mice, other species have been used.

Keywords: behavior, genetics, measurement, mice, rat

Introduction

In 2000, Dr. Fowler, with the assistance of Dr. Zarcone, developed and patented the device called the “force-plate actometer” (US09/586,899). In 2001, Fowler and colleagues published a Journal of Neuroscience Methods paper describing the new measurement device(Fowler S.C. 2001). The purpose of the paper was to describe in detail the principles of the force-plate’s operation, the equipment needed to build it, the measures that could be derived from force-plates, the software needed to record and analyze the data generated, and examples of behaviors that could be examined (e.g., activity, tremor). The Force-Plate Actometer (FPA) is the combination of a ridged lightweight platform supported at each corner by force sensors/amplifiers connected to a computer that allows for continuous multiple physical measurements of a freely moving rat or mouse. The platform and sensors are supported by a heavy base (ballast plate) to isolate the system from other sources of vibration (e.g., HVAC systems). A plexiglass enclosure (e.g., box or cylinder) is suspended above the platform to keep the subject on the platform. The FPA is often enclosed inside an isolation chamber (except for the amplifiers and computer) to control light and sound during measurement sessions. The FPA was developed to address some of the limitations of other systems designed to measure the effects of drugs, lesions, or gene manipulations. The most widely used behavioral methods are based on human observation which can be implemented quickly and inexpensively with “off-the-shelf” technology and access to student labor. Human observations, however, are subject to bias and error and are limited to rudimentary quantification (e.g., rating scale). Other quantitative systems at the time, like the photo-beam chamber could estimate general activity but were limited for quantifying stereotypy and other fine-motor behavior. Fowler et al., 2001 laid out the physical principles that underlie force-plate measurements taking advantage of a known coordinate system within a controlled environment. The individual measure from each sensor could be used to calculate the position of an animal on the platform (position measure) while adding the measures from all the sensors together over time could be used to detect rhythmic patterns like walking, running, tremor, or stereotypy. The purpose of the present article is to describe the impact that the FPA has had on several areas of neuroscience so far and what may be possible for future progress in the digital quantification of behavior.

Methods

A Scopus citation search of Fowler et al 2001 (on 4/28/2021) resulted in 134 articles, 45 in which Dr. Fowler was a co-author, 11 of which he was the first author, and 89 in which he was not a co-author. At the time of the Scopus search those 134 articles were in turn cited 4388 times.

Results

The articles from the Scopus citation search were coded by the author for subjects used (e.g., mice, rats), the intervention studied (e.g., knockout comparison, Amphetamine administration), the phenomenon of interest (e.g., tremor, Parkinson’s disease) and the type of measure derived from the FPA (e.g., distance traveled, tremor, low mobility bouts).

Behaviors measured in original paper:

While the examples provided in the original paper were with rats and mice, the purpose was to describe the potential for adapting similar measures and environments for use with other species and behavior. The following is a brief overview of the measures described in Fowler et al., 2001. A measure of general activity, the “distance traveled” which tracks the displacement of center of force, technically described as the “line integral of movement of the center of force during a period of time” was a starting point for this approach. The time period could be chosen by the experimenter but was typically summed for the length of a recording session (e.g., 30 min). The recording session could be divided into smaller periods (e.g., 5 min) to measure changes within a session. Comparisons of total distance across successive periods, either within a session or across sessions, could be used to assess habituation or sensitization. The FPA also measured changes in direction (i.e., turning left or right) and was expanded to the concept of “rotations” (i.e., circling) used in amphetamine-induced stereotypy or 6-hydroxydopamine lesion of the nigrostriatal system. The same FPA raw data was used to estimate non-locomotor behavior by redefining the calculation. Adding criteria to the calculation that the displacement of center of force does not exceed a specified distance within a period of time was used to define the measure of “Low Mobility Bouts” (LMB). The FPA raw data could also be used independently of the position information via Discrete Fourier Transform (i.e., Fast Fourier Transform) to assess rhythmic behavior like walking, jumping, stereotypy, whole body tremor. Combining human observation with the FPA was used to identify incidents of tremor which was then quantified with power spectra analysis. The measures described above were used and modified in subsequently published research.

Behaviors measured in citing papers:

The following are basic descriptions of how the FPA data were used to measure behavior.

Definitions

Distance Traveled, at the most basic level, is a measure of the change in location of an organism. Smoothing the raw data (moving average of 5 samples) was used to obtain a measure in millimeters. Simply measuring the change in xy-coordinates could be considered a measure of general activity. Looking at distance traveled across time, with or across sessions, can provide estimates of habituation to a new environment or characterize basic drug effects (e.g., sedation, hyperactivity, sensitization).

Rotations within a FPA can be estimated by using two xy-coordinates from a subject in relation to the center of the platform (origin of the coordinate system) to define a triangle (Heron’s formula) from which the angle and direction (left or right) of a subject can be calculated. This was done by modifying the chamber from a cube to a cylinder to constrain a rat to the center of the platform floor.

Location Time uses the xy coordinates compared to an investigator defined mathematical grid or other pre-defined area (e.g., distance from the edge of a chamber wall). The time spent in each coordinated is tallied. These tallies can be aggregated for the entire session or subdivided into periods across the session (e.g., 5 min bins across a 30 min session).

Low Mobility Bouts (LMB) can be defined as periods where the movement of the center of force does not exceed a specified distance and time. For example, an animal does not travel outside a 30 mm diameter circle for at least 10 s. These events can be tallied for specified intervals (e.g., 2 min). Movement within these defined bouts could be analyzed further (e.g., a stereotype index/score, see below).

Rearing or Jumping can be detected when the total measures of the four sensors decreases by a predefined limit for a given time. When an animal jumps, the total force recorded by the four sensors increases as an animal pushes downward as it propels itself upward, the sensor values approach zero as the animal leaves contact with the floor and then the sensor values increase to a value above an animal’s bodyweight when it returns to the floor. A similar series of events occurs when an animal lifts its forepaws from the floor and rests one or both paws on the side wall of a chamber (displacing some of its bodyweight to the wall) which can be combined with the xy-coordinates to confirm the animal is close enough to a wall to contact the wall.

Ataxia indices can be estimated by using the xy-coordinates to calculate the area covered during a time-interval divided by the net distance traveled during that interval. Ataxia was also measured with a modified grid-actometer. Suspending a grid floor above the solid platform floor of the actometer allowed for recording the total force (total of all four sensors) on the lower solid floor when an animal’s paw slipped from the upper grid floor and contacted the solid floor below.

Gait can be estimated by using the xy-coordinates to track an animal’s center of force, the position. The walking trajectory can be calculated from the linear connection of the temporally consecutive xy-coordinate positions which when aggregated results in the total distance traveled measure. An area measure is concurrently calculated by summing all the triangle areas defined by three consecutive center of force measures. Dividing the area measure by the distance measure for defined time periods can provide an index of lateral displacement from the path trajectory.

Tremor (whole body tremor) can be measured by applying a Fast-Fourier Transform (with Hanning data window) to the total force for defined time periods (e.g., 40.96 s). The resulting power spectra are observed for peaks in specific frequency regions (e.g., 12 – 18 Hz for Harmaline in mice).

Stereotypy can take on many forms and requires considerable thought to create an automated method for quantifying. In the case of amphetamine induced stereotypy, the basic idea was to define the event as the intensity of behavior while the animal was stationary (i.e., low mobility bouts, see above). Behavior occurring under these conditions was compared after saline or amphetamine injections. In addition to counting the number of low mobility bouts, the distance travelled (see above) was calculated during these bouts. The low mobility bouts were tallied for each 2 min bin of a session (e.g., 45 min) and multiplied by the distance traveled during those low mobility bouts. These quantities were summed followed by taking the square root of that sum to normalize the data. This calculation resulted in a stereotype score for each animal.

The above descriptions are basic examples of how data from the FPA can be used to measure different activities in response to experimental conditions (e.g., drug injection, ablation, genetic modifications). The original articles cited below will have more detailed descriptions of the measurement techniques. One useful feature of the FPA is that once these real-time raw data are captured, they can be used to modify parameters for the measures described above or develop new operationally defined measures.

Citations

Distance Traveled was often used (67 of the 89 citing papers that used the FPA) as a control between groups (e.g., mouse strains) or as a control for another index (slips from a grid, see below). “Total Distance Traveled” usually summed behavior across a session (e.g., 30 min). Only one other paper reported within-session habituation(Fowler S.C. 2002). Occasionally the Total Distance Traveled was measured but not reported when no differences were found, a practice that deprives the scientific community of reference data for the consistency of measurement across laboratories and species. Ten articles used rotational measures in either rats or mice for comparisons across groups or for the effects of drugs or stress(Wang G. 2001, Fowler S.C. 2007, Bethel-Brown C.S. 2010, Bethel-Brown, Morris et al. 2011, Russell K.L. 2011, Russell, Berman et al. 2013, Rothwell P.E. 2014, Godar S.C. 2016, Pes R. 2017, Tischfield D.J. 2017). Ten articles used location time in either rats, mice, or voles for comparisons across animal type, drug effects, aging, stress, or diet(Fowler S.C. 2007, Fowler S.C. 2010, Levant B. 2010, Levant B. 2011, Schneider E.H. 2014, Tian, Yang et al. 2017, Godar S.C. 2019, Hövel, Leiter et al. 2019, Nolte, Nolte et al. 2019, Tickerhoof, Hale et al. 2020). Low Mobility Bouts (LMB) were reported in 26 of the citing articles with either mice or rats(McKerchar T.L. 2006, Hurlock, McMahon et al. 2008, Dai Y. 2009, Fowler S.C. 2009, Kaeser, Deng et al. 2009, Fowler S.C. 2010, Miller B.R. 2010, Russell K.L. 2011, Stucky, Gregory et al. 2011, Burré, Sharma et al. 2012, Sharma, Burré et al. 2012, Russell, Berman et al. 2013, Ossowska, Wardas et al. 2014, Rothwell P.E. 2014, Schneider E.H. 2014, Burré, Sharma et al. 2015, Dearborn J.T. 2015, Fowler S.C. 2015, Godar S.C. 2016, Akkhawattanangkul Y. 2017, Pes R. 2017, Polepalli, Wu et al. 2017, Tian, Yang et al. 2017, Tischfield D.J. 2017, Hövel, Leiter et al. 2019). Most of the time LMB was used for comparisons to wild-type or knockout groups or gender and were also used to assess drugs, stress, diet, and aging. Rearing/jumping was reported in 13 articles to compare strains, knockouts, wild-type, stress, drug effects and aging(Fowler S.C. 2002, McKerchar T.L. 2006, Hurlock, McMahon et al. 2008, Fowler S.C. 2009, Miller, Walker et al. 2011, Tague, Clarke et al. 2011, Rothwell P.E. 2014, Schneider E.H. 2014, Fowler S.C. 2015, Godar S.C. 2016, Akkhawattanangkul Y. 2017, Tischfield D.J. 2017, Hövel, Leiter et al. 2019). Six papers measured ataxia (inability to coordinate voluntary movements) in mice, mainly for comparisons to other mice groups(Matsukawa, Wolf et al. 2003, Chandra, Gallardo et al. 2005, Fowler S.C. 2005, Ho, Morishita et al. 2006, Pang, Sun et al. 2006, Aoto, Földy et al. 2015). Twelve papers, 10 mice and 2 rat, used the FPA to measure gait primarily for comparison to wild-type or knockout groups, but also to assess drug or stress effects(Stanford J.A. 2002, Joho, Street et al. 2006, Hurlock, Bose et al. 2009, Joho and Hurlock 2009, Ortiz A.N. 2012, Schneider E.H. 2014, Dearborn J.T. 2015, Fowler S.C. 2015, Stanford J.A. 2015, Akkhawattanangkul Y. 2017, Fowler S.C. 2017, Zhang X. 2018). Twenty papers measured tremor in either mice or rats in the classic harmaline procedure (4 papers), or for other interventions (e.g., comparison to knockout or other strain, brain stimulation, genetic ablation)(Wang G. 2001, Fowler S.C. 2002, McMahon A. 2004, Joho and Hurlock 2009, Iseri, Karson et al. 2011, Reddy A.S. 2011, Dhanushkodi, Akano et al. 2013, Ma, Rudacille et al. 2013, Schneider E.H. 2014, Ossowska, Głowacka et al. 2015, Tao, Shokry et al. 2015, Kosmowska, Wardas et al. 2016, Kosmowska, Ossowska et al. 2017, Tian, Yang et al. 2017, Anderson, Figueroa et al. 2019, Lee and Chang 2019, Li Y. 2019, Mikulka, Dearborn et al. 2020, Zhou, Melin et al. 2020, Ajima, Yoshida et al. 2021). Fifteen papers assessed stereotypy with rats or mice, with one paper looking at human stereotypy(Chen R. 2003, Fowler S.C. 2003, Crosland K.A. 2005, Fowler S.C. 2005, Fowler S.C. 2007, Fowler S.C. 2007, Fowler S.C. 2009, Fulks J.L. 2010, Ortiz A.N. 2012, Rothwell P.E. 2014, Ossowska, Głowacka et al. 2015, Pes R. 2017, Polepalli, Wu et al. 2017, Tian, Yang et al. 2017, Tischfield D.J. 2017). The human study combined the FPA with a Functional Analysis procedure(Crosland K.A. 2005), the rodent studies primarily assessed dopamine agonists or compared wild-type, strain, or knockout groups. Other behavioral measures included pigeon peck peak force(Pinkston J.W. 2008), postural sway(Seigneur and Südhof 2018), number of sectors used(Fowler S.C. 2007), movement trajectories(Fowler S.C. 2007), in-place movement(Fowler S.C. 2015), or focal energy densities(Hövel, Leiter et al. 2019).

Modified FPA Chambers

Fowler and Zarcone (Fowler S.C. 2002) modified the FPA creating a “Grid Actometer” by suspending 0.5 inch hardwire cloth (grid floor) 1 cm above the platform of the FPA. This allowed for the detection of foot slips through the grid floor onto the FPA platform which defined “grid ataxia”. A second version of the Grid Actometer modified FPA by Fowler and Zarcone combined two force-sensitive platforms, a grid sensitive platform suspended 1 cm above a force-sensitive smooth floor (Fowler S.C. 2005) which allowed for a measure of slips as a function of distance travelled. This addressed concerns that slip counts alone might be confounded if locomotion was suppressed by an intervention. Fowler and colleagues (2009) combined the FPA with an operant chamber and lick-o-meter (force-sensitive water delivery disk) to see how rats would position themselves during differential-reinforcement-of-low-rate (DRL) schedules of reinforcement.

Diseases/problems addressed:

Dr. Fowler published 11 more papers as the first author that used the FPA to examine Huntington’s, Parkinson’s, ADHD, Tourette’s, behavioral pharmacology, and genetic differences(Fowler S.C. 2002, Fowler S.C. 2002, Fowler S.C. 2003, Fowler S.C. 2005, Fowler S.C. 2007, Fowler S.C. 2007, Fowler S.C. 2009, Fowler S.C. 2009, Fowler S.C. 2010, Fowler S.C. 2015, Fowler S.C. 2017). All the articles used either rats or mice. Five of the articles examined pharmacological effects methylphenidate, amphetamine, cocaine, or clozapine. Three articles compared rodent strains. The remaining three articles were a demonstration experiment, a longitudinal study, and an effect of stress study. He co-authored 34 other FPA papers (cited throughout this paper) on similar topics and many other phenomena (e.g., tremor, aging, stereotypy, Batten’s, Fragile X, traumatic brain injury). Again, most of the articles used rats (21) or mice (11), but there was one human and one pigeon study as well. Most of the collaborations revolved around rodent wild-type comparisons and pharmacological interventions, but other independent variables included diet, genetic ablation, model testing, and functional analyses. Of the remaining 90 citations that did not include Dr. Fowler as a co-author, 40 did not use the FPA or similar measurement system (e.g., review paper, citing results) (not cited). Only Fifty articles used the FPA or similar device (e.g., BASi Actimeter) and examined an even broader range of phenomena (e.g., Alzheimer’s, Krabbe’s, ataxia, stroke, anxiety, pain) (see below). Most of the studies used mice (32), followed by rats (16), one study used both mice and rats, and one study used voles. The greatest number of studies examined knockout or transgenic comparisons, followed by many behavioral pharmacology experiments. Other independent variables included deep cerebellar stimulation(Lee and Chang 2019), gene therapy(Reddy A.S. 2011), Vitamin D deficiency(Tague, Clarke et al. 2011).

Discussion

Limitations of the review

While this limited review of articles citing Fowler et al. 2001 shows the breadth of measures, subjects, and phenomena that the FPA can be used for, it is an underestimate of the influence the FPA has had on behavioral and other sciences. Articles using force-plate measures could have cited other FPA articles instead of Fowler’s 2001 paper. Additional articles using force-plate measures could cite the commercial version of the FPA the BASi Force-Plate Actimeter (Geraets, Langin et al. 2017).

Limitations of the FPA

The FPA has measurement limitations, but at least two factors may be preventing the widespread use of the FPA. The most notably factor is the cost to construct a FPA. Most digital-quantitative systems cannot compete against the most common form of behavioral measurement “human observation”. The early gains of a human observation strategy, low startup cost and flexibility, can be lost when procedures based on human observation are not sensitive to small effect sizes or are not repeatable across observers or laboratories. While reviewers for peer reviewed journals can request inter-observer reliability data from within a laboratory, the standards can be arbitrary and still allow for errors that could affect the interpretation of the outcome. When human-observation results are not replicated across laboratories, measurement error on either or both parties remain a possible explanation. That being said, a transducer and in-line amplifier cost about $1000 for the pair back in 2001 and costs about $2000 in 2021. A do-it-yourself chamber with four transducers would cost about $10,000 today and multiple chambers, typically four, were needed to run a study with enough subjects to meet statistical requirements. However, many technologies decrease in price over time. While the force transducer we used for the FPA remains expensive, alternative force sensing devices are slightly less expensive (e.g., BASi: Force Plate Actometer, Metris: Laboras). There is even more hope for digital measurement of behavior with the growth of technology DIY communities (e.g., 3-D printing, Arduino) which produce products and services of progressively higher quality and lower cost. In addition, these communities educate future graduate students and scientists with the skills needed to develop behavioral models that can precisely measure behavior which addresses the second limitation.

The other factor limiting the use of a FPA can be an investigator’s proficiency in dealing with raw, real-time digital data resulting from living organisms interacting with their physical environment. Many non-behavioral scientists rely on easy-to-understand psychological models with face validity that can be implemented by a graduate student or post-doc to answer simple psychological questions (e.g., did a drug affect memory). The FPA requires expertise in behavior, physics, and computer programming, much like the expertise needed for Magnetic Resonance Imaging (MRI) technology. MRI laboratories assemble a team of scientists and technicians to deal with the multiple domains needed for that type of digital measurement. Most single investigator laboratories do not have the breadth of staff with expertise in these domains. Dr. Fowler was unique in this regard.

A possible future for the digital measurement of behavior

The FPA demonstrates another strategy for measuring behavior. Looking for ways to digitally measure the relation between an organism and its environment causes an investigator to ask different questions from a “face-validity” approach. Typically, the digital approach starts with what technologies can be used to measure a behavior of interest without affecting the performance. Most, if not all, animal models rely on the movement of the animal to assess other psychological processes: perception, learning, cognition, memory, emotion. A motor response (e.g., location in a chamber, lever press, choice between two runways, vocalization) is used to infer an effect of an intervention on one or more psychological process. The goal of an assay is to have a system that can isolate the effect of an intervention on these processes. When operational issues of digitally measuring behavior have been addressed, software algorithms can be developed and modified to analyze and reanalyze the raw data to explore different parameters of measurement. In the case of the FPA, criteria for LMB could be varied to identify an optimal threshold. Additional computational advances in artificial intelligence and machine learning could be used to identify and quantify behavioral events from the raw data. Systems could be developed that integrate several digital data streams, from the environment and the brain, in real-time to create new paradigms.

One factor in scientific progress has been the technology used by a science (e.g., the microscope for biology, chromatography for chemistry). Dr. Fowler embraced this philosophy in his laboratory and looked for ways to use technology to improve behavioral measurement. The FPA was just one example.

Acknowledgements

I would like to thank the editors for their invitation to contribute to the memory of Dr. Stephen C. Fowler. I was fortunate enough to thank Steve personally for his mentorship and friendship and would like to acknowledge his impact on me again here in this tribute to his work.

Footnotes

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