The dynamics of spatial behavior: how can robust smoothing techniques help?

Abstract

A variety of setups and paradigms are used in the neurosciences for automatically tracking the location of an animal in an experiment and for extracting features of interest out of it. Many of these features, however, are critically sensitive to the unavoidable noise and artifacts of tracking. Here, we examine the relevant properties of several smoothing methods and suggest a combination of methods for retrieving locations and velocities and recognizing arrests from time series of coordinates of an animal’s center of gravity. We accomplish these by using robust nonparametric methods, such as Running Median (RM) and locally weighted regression methods. The smoothed data may, subsequently, be segmented to obtain discrete behavioral units with proven ethological relevance. New parameters such as the length, duration, maximal speed, and acceleration of these units provide a wealth of measures for, e.g., mouse behavioral phenotyping, studies on spatial orientation in vertebrates and invertebrates, and studies on rodent hippocampal function. This methodology may have implications for many tests of spatial behavior.

Introduction

In the neurosciences, data on locomotor behavior, spatial orientation, navigation, spatial memory, and even social behavior often consist of a time series of coordinates representing the organism’s location. Common experimental setups collecting such data include the Open Field Test, the Photobeam Cage, the Morris Swim Task, the Elevated Plus Maze, the Holeboard, and a variety of other spatial mazes. Most of the studies performed in these setups focus on the animal’s location, ignoring velocity and acceleration (see, however, Kafkafi et al., 2001, Pierce-Shimomura et al., 1999, Tchernichovski and Golani, 1995; Tchernichovski et al., 1998, Wallace et al., 2002, Whishaw et al., 2001).

The benefits of moment-to-moment record of velocity and acceleration cannot, however, be overestimated. Within a dynamic framework, the acceleration and velocity of the animal are the outcome of all the concurrent endogenous and exogenous “forces” acting upon it. Conversely, the attraction or repulsion exerted by a wall, a cliff, a familiar place, a partner or a chemical gradient is revealed by the momentary values of these parameters. In rodent open field studies, for example, the forces exerted by the animal’s home base (Eilam and Golani, 1989), or any other familiar place (Tchernichovski et al., 1996), are reflected in the animal’s velocity and acceleration. The momentary velocity of an animal can tell us whether it “thinks” it is running away or toward its home base, or how familiar the immediate environment is (Tchernichovski and Golani, 1995, Tchernichovski et al., 1998), or what method of navigation it uses (Wallace et al., 2002, Whishaw et al., 2001).

One ethologically-relevant point regarding velocities is that involving zero or close-to-zero velocities, i.e., stops. An organism’s locomotor behavior often consists of an alternation between progression and stopping, be it a nematode (Pierce-Shimomura et al., 1999), an insect (Collins et al., 1994, Collins et al., 1995, Miller, 1979), a fish (Nilsson et al., 1993, O’Brien et al., 1989, Winberg et al., 1993), a lizard (Pietruska, 1986), a bird (Pienkowski, 1983), or a mammal (Golani et al., 1993, Kenagy, 1974). The movements it performs during a stop, be it foraging movements, scanning or movements related to any other form of information gathering, are reflected indirectly in the spatiotemporal properties of the stop, (e.g., Drai et al., 2000). Mouse inbred strains, for example, may differ substantially in the rate, type, rhythm, and number of scans they perform during a stop. These differences in the manner and intensity of information gathering are indirectly reflected in the duration, spatial spread, and average velocity of movement during stopping behavior (Drai and Golani, 2001). Characterizing the stop-and-go behavior should therefore be both ethologically meaningful and results-wise fruitful.

As elaborated in this study, however, the data acquired by the above listed mazes and setups suffer from noise and artifact problems, which are inherent to all tracking systems and critically affect the results. Even a straightforward measure such as the overall distance traveled by the animal is highly sensitive to these problems, but they have a particularly devastating effect on the derivation of velocities and accelerations. Smoothing the raw data is required to obtain a smooth path, correct computation of velocities when the animal is moving, and an isolation of arrests (zero velocity) when stopping. As we show, however, the sometimes-erratic nature of animal movement requires the correct application of the appropriate smoothing methods. Furthermore, those methods appropriate when the animal is on the go become inappropriate when it stops. Therefore, a combination of methods must be used. An automated high-throughput analysis of moment-to-moment velocities becomes proper only after the data have been carefully smoothed by such combination of methods.