ORIGINAL RESEARCH ARTICLE
published: 09 March 2009
doi: 10.3389/neuro.07.004.2009

INTEGRATIVE NEUROSCIENCE

What is the most sensitive measure of water maze probe test
performance?
Hamid R. Maei 1,2,3,†, Kirill Zaslavsky1,2,3, Cátia M. Teixeira1,2,3,‡ and Paul W. Frankland 1,2,3*
1

Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada
Department of Physiology, University of Toronto, Toronto, ON, Canada
3
Institute of Medical Science, University of Toronto, Toronto, ON, Canada
2

Edited by:
Rui M. Costa, National Institutes of
Health, USA
Reviewed by:
Tsuyoshi Miyakawa, Kyoto University,
Japan; Fujita Health University, Japan;
Bong-Kiun Kaang, National University
Seoul, Korea (South)*Correspondence:
Paul W. Frankland, Program in
Neurosciences and Mental Health, The
Hospital for Sick Children, 555
University Avenue, Toronto, ON,
Canada M5G 1X8.
e-mail: paul.frankland@sickkids.ca†
Current address: Department ofComputing Science, University ofAlberta, Edmonton, AB, Canada T6G
2E8‡
Current address: Institute for Researchin Biomedicine, Department of Cell
Biology, and Centro de Investigación
Biomédica en Red para Enfermedades
Neurodegenerativas, Instituto de Salud
Carlos III, University of Barcelona,
08028 Barcelona, Spain.

The water maze is commonly used to assay spatial cognition, or, more generally, learning andmemory in experimental rodent models. In the water maze, mice or rats are trained to navigate
to a platform located below the water’s surface. Spatial learning is then typically assessed ina probe test, where the platform is removed from the pool and the mouse or rat is allowed tosearch for it. Performance in the probe test may then be evaluated using either occupancybased (percent time in a virtual quadrant [Q] or zone [Z] centered on former platform location),
error-based (mean proximity to former platform location [P]) or counting-based (platform
crossings [X]) measures. While these measures differ in their popularity, whether they differ
in their ability to detect group differences is not known. To address this question we compiled
five separate databases, containing more than 1600 mouse probe tests. Random selectionof individual trials from respective databases then allowed us to simulate experiments withvarying sample and effect sizes. Using this Monte Carlo-based method, we found that the
P measure consistently outperformed the Q, Z and X measures in its ability to detect groupdifferences. This was the case regardless of sample or effect size, and using both parametricand non-parametric statistical analyses. The relative superiority of P over other commonly usedmeasures suggests that it is the most appropriate measure to employ in both low- and highthroughput water maze screens.
Keywords: water maze, hippocampus, parametric, non-parametric, Monte Carlo simulation

INTRODUCTION
Developed in the 1980s by Richard Morris (Morris, 1981, 1984;
Morris et al., 1982), the water maze has become one of the mostcommonly used tasks to measure spatial learning in rodents, including normal and genetically modified mice. The popularity of thewater maze is due, in part, to its extensive validation as an assay
for hippocampus-dependent learning and memory. First, lesioning or inactivating the hippocampus prevents water maze learning(Logue et al., 1997; Morris et al., 1982; Moser et al., 1993; Riedel
et al., 1999; Teixeira et al., 2006). Second, genetic or pharmacological manipulations that disrupt activity-dependent plasticity inthe hippocampus also prevent water maze learning (Morris et al.,
1986; Silva et al., 1992; Tsien et al., 1996). Third, mouse models ofhuman disease associated with hippocampal dysfunction exhibit
impairments in water maze learning (Chapman et al., 1999; Chenet al., 2000). Moreover, each of these major findings has been reliably replicated across multiple labs and species.
In the water maze task, training typically takes place over several
days in a large, circular tank filled with opaque water (Kee et al.,2007a; Morris, 1984; Vorhees and Williams, 2006; Wolfer et al., 1998).
In each training trial a mouse is given the opportunity to navigate
to a platform submerged below the water’s surface, and, because

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the platform is not visible, the mouse must locate it using an arrayof distal, visual cues surrounding the pool. As training progresses,
the latency to find the platform typically decreases. Such decreased
escape latencies would most commonly reflect the adoption of a focal
search strategy (i.e., a search strategy centered on the former platform
location with little variance). However, reduced escape latencies may
also reflect the adoption of non-spatial strategies (e.g., mice mightlearn to swim in concentric circles a fixed distance from the wall)(Clapcote and Roder, 2004; Gallagher et al., 1993; Lipp and Wolfer,
1998; Wolfer et al., 1998). Therefore, to discriminate spatial and nonspatial strategies mice are usually given a probe test, where the platform is removed from the pool and the mouse is allowed to search forit, typically over a 60-s period. Mice having adopted a spatial strategy
will search focally near the former location of platform.
Tracking software is routinely used to precisely record the position of the mouse throughout the probe test. From this detailed positional information, several measures of spatial bias are commonly
extracted (Figure 1). These include, for example, calculation of the
proportion of time that mice spend in either a virtual quadrant(Q) or zone (Z) centered on the former location of the platform.
Alternatively, the number of times the mouse crosses (X) the platform location or the mouse’s average proximity (P) to this target

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FIGURE 1 | Examples of five individual probe tests. Mice were trained in thewater maze with six trials per day for 5 days. Shown are representative swim
paths in a probe test conducted following the completion of training.

Corresponding quantification of probe test performance is shown below for thefour widely used probe test measures: quadrant (Q), zone (Z20, Z15, Z10),
crossings (X) and proximity (P).

(Kee et al., 2007a,b; Teixeira et al., 2006). The pool was filled to adepth of 40 cm with water made opaque by adding white non-toxic
paint. Water temperature, monitored by a thermometer located
20 cm below the water surface, was maintained at 28 ± 1°C by aheating pad located beneath the pool. A circular escape platform(5 cm radius) was submerged 0.5 cm below the water surface andlocated in the south-east quadrant. The pool was surrounded bycurtains, at least 1 m from the perimeter of the pool. The curtains
were white, and had distinct cues painted on them.
Training procedures

FIGURE 2 | Popularity of different probe test measures. To assess therelative frequency with which different probe test measures are used, a
PubMed search (http://www.ncbi.nlm.nih.gov/sites/entrez) using the terms[(rat OR mouse) AND water maze] was conducted for the period 2004–2006.
Out of a total of 205 papers surveyed, 135 assessed spatial learning/memory
using a probe test. The pie chart shows the relative frequency that different
measures (or combinations of measures) were used to quantify probe test
performance in these studies.

may be calculated. These measures differ in terms of their popularity,with the percent quadrant measure far and away the most preferred(Figure 2). However, whether these differences in popularity reflect
differences in the ability of these measures to detect experimental
effects is unknown. Accordingly, using databases containing morethan 1600 individual probe tests we conducted a series of simulated experiments to compare the relative sensitivity of these fourmeasures (Q, Z, P, X) in detecting group differences. By examining
the impact of both sample and effect size on detection rates, theseanalyses revealed that proximity outperformed each of the measures
under the majority of experimental conditions.

MATERIALS AND METHODSAPPARATUS AND BEHAVIORAL METHODS

Apparatus

All water maze experiments were conducted in a circular tank(120 cm in diameter, 50 cm deep), located in a dimly-lit room

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Prior to the commencement of training, mice were individuallyhandled for 2 min each day for 1 week. On each training day, micereceived six training trials (presented in two blocks of three trials;
inter-block interval of ∼1 h, inter-trial interval was ∼15 s). On eachtrial they were placed into the pool, facing the wall, in one of four
start locations (north, south, east, west). The order of these startlocations was pseudo-randomly varied throughout training. The
trial was complete once the mouse found the platform or 60 s had
elapsed. If the mouse failed to find the platform on a given trial,
the experimenter guided the mouse onto the platform.
Probe test procedures

During the probe test, mice were placed into the pool facing thewall, in the north location. The probe test was 60 s in duration.
Quantification of probe test performance

Behavioral data from the probe tests were acquired and analyzed
using an automated tracking system (Actimetrics, Wilmette, IL,
USA). Using this software, the precise mouse location (in x, y coordinates) was recorded throughout the probe test (capture rate 10
frames/s). From this spatial distribution, the following performancemeasures were calculated automatically:
1. Percent quadrant time (Q). Amount of time mice searched avirtual quadrant (i.e., 25% of total pool surface area), centeredon the location of the platform during training (Morris, 1981,
1984; Morris et al., 1982).
2. Percent zone. Amount of time mice searched virtual target
zones (20 [Z20], 15 [Z15] and 10 [Z10] cm in radius, centered on

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Evaluation of water maze probe test measuresthe location of the platform during training) during the 60-s
test (de Hoz et al., 2004; Moser and Moser, 1998; Moser et al.,1993). These zones represent 1/9th (∼11.1%), 1/16th (∼6.25%)
and 1/36th (∼2.8%) of the total pool surface area, respectively.
3. Crossings (X). Number of times mice cross the exact locationof the platform (5 cm in radius) during the 60-s test (Morris,
1981, 1984; Morris et al., 1982).
4. Proximity (P) measure (Gallagher’s measure) (Gallagher et al.,1993). Average distance in centimeters of mice from center ofthe platform location across the 60-s test.
These measures (or combinations thereof) are used to quantify
probe test performance in more than 98% of published papers(Figure 2).
DATA SETS

Probe test data were pooled from experiments conducted in ourlaboratory between June 2004 and June 2008. All experiments wereconducted using identical apparatus, training and probe test procedures, as described above. Procedures were approved by the Animal
Care Committee at Hospital for Sick Children.
Analysis A

In the first analysis, probe test data were pooled from experiments
where wild-type mice were initially trained for 5 days (six trials perday) and then given a probe test at variable delays following thecompletion of training. These experiments examined the impact
of different genetic, pharmacological and neuroanatomical lesion
manipulations on water maze performance (for details see Kee et al.,2007b; Teixeira et al., 2006; Wang et al., 2009). For these analyses,
probe test data were divided into two data sets. First, a control dataset (n = 370 probe tests) that included data from control mice in thegenetic (i.e., wild-type mice), pharmacological (i.e., mice received
control infusions of phosphate-buffered saline) and neuroanatomical lesion (i.e., sham surgery) experiments. Second, an experimental
data set (n = 388 probe tests) that included data from experimental
mice in the genetic [e.g., α-CaMKIIT286A knockin mice (Giese et al.,
1998; Kee et al., 2007b)], pharmacological [e.g., mice received lidocaine infusion into the dorsal hippocampus prior to testing (Teixeira
et al., 2006)] and neuroanatomical lesion [i.e., NMDA-induced
complete hippocampal lesion (Wang et al., 2009)] experiments. All
mice used in these and subsequent experiments were in a mixed
C57Bl/6NTacfBr [C57B6] and 129Svev [129] background (50:50)(Taconic, Germantown, NY, USA). In the majority of experiments,
these were the F1 generation. In a subset of experiments, the F2 generation was used. The mean and standard deviation for the control
and experimental datasets are shown in Figure 3A.
Analysis B

In the second analysis, probe test data were pooled from experiments where wild-type mice (n = 282) were trained for 5 days with
six trials per day. At variable delays following the completion oftraining, they received a series of three consecutive probe tests.
Performance declined across probe tests, most likely reflecting
extinction of spatial memory (Lattal et al., 2003). The decline inperformance therefore provides three datasets with three distinct
levels of performance (see Figure 5A).

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QUANTITATIVE AND STATISTICAL ANALYSES

Datasets used for analyses A and B were exported to Matlab (http://
www.mathworks.com/products/matlab/) and Q, Z20, Z15, Z10, X andP were computed for each individual trajectory. For each dataset,
descriptive statistics (mean, standard deviation) were computed.
Additionally, for the control and experimental datasets used inanalysis A, between-measure correlations (Pearson’s r) were computed and the Lilliefors [Kolmogorov–Smirnov (K–S)] test wasused to evaluate whether measures were normally distributed.
In order to compare the sensitivity of the different measures at
detecting experimental effects a series of simulated experiments
were conducted. For analysis A, N (range 5–40) probe tests wererandomly selected (without replacement) from the control andexperimental datasets, respectively. Whether the two samples differed was then evaluated using either a parametric (Student’s t-test)
or non-parametric (K–S test) statistic1. For each N, 1000 simulations
were conducted and, to compute the rate of rejection of the null
hypothesis for each N, 10 replications were performed. In order toevaluate the false-positive rate, the above analyses were repeated, butboth samples were drawn from the control dataset (again without
replacement). All analyses were conducted with α set at 0.05, 0.01
and 0.005, respectively. For analysis B, a similar series of simulations
were conducted to compare the probe 1, 2 and 3 datasets.

RESULTSCONTROL VS. EXPERIMENTAL (ANALYSIS A, DESCRIPTIVE STATISTICS)

Pooled probe test data for control (n = 370) and experimental(n = 388) mice are shown in Figure 3. These probe tests wereconducted using identical experimental procedures in the same
apparatus in our behavioral laboratory at The Hospital for Sick
Children, Toronto, between 2004 and 2008. The analyses in thispaper are focused on comparing bias for the target location between
groups. The heat maps indicate that control mice (compared toexperimental mice) searched more extensively around the target
location (i.e., the former platform location) (Figure 3A), and thissuperior performance is captured by all measures (Table to right of
Figure 3A). It is also possible to contrast bias for the target location(e.g., south-east) with other equivalent locations in the pool (e.g.,
north-east, north-west and south-west), and this within-subjects
comparison is shown in Figure 3B.
To examine how the precision of spatial searches changes overthe course of the probe trial, we divided the probe test into 5 s bins.
According to the Q, Z20, Z15, Z10 and P measures, search precision initially rose sharply, peaked between 10–15 s, and then declined thereafter (Figure 3C). Mice began each probe test from a start position
that was opposite to the target location, and so this likely accounts forthe rapid rise in search precision. The subsequent decline in searchprecision likely reflects within-test extinction (Lattal et al., 2003;
Suzuki et al., 2004). The temporal profile of the X measure differedfrom other measures: Crossing probability exhibited the same initial
sharp increase, but was then relatively stable thereafter.
1
For example, for the control dataset, 10 probe tests (out of 370) were randomly
selected. These were then compared to 10 (out of 388) randomly selected probe
tests from the experimental database (the control and experimental databases arecomposed of multiple actual experiments conduced in the lab between 2004 and2008).

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FIGURE 3 | Pooled probe test data for control and experimental mice foranalysis A. (A) Left, density plots for grouped data showing where control andexperimental mice concentrated their searches in the probe test. The color scale
represents the mean number of visits per animal per 5 cm × 5 cm area. Right,
summary of descriptive statistics (mean values, standard deviations) for thetarget quadrant (Q), zone (Z20, Z15 and Z10), crossing (X) and proximity (P)measures for control and experimental datasets. (B) Comparison of target (T) vs.
other pseudo-platform locations (right, R; left, L; opposite, O) for each measure(upper graphs show control data, lower graphs experimental data). (C) Temporal

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profile of spatial bias across 60 s probe test for quadrant (Q), zone (Z20, Z15 andZ10), crossing (X) and proximity (P) measures. Averaged data are shown in 5 s bins
for control (green) and experimental (red) datasets. (D) Scatterplots illustrating
how respective water maze measures correlate with one another for all 758
probe tests included in the control and experimental datasets. Measures tended
to be highly correlated, with r-values range from 0.67 to 0.98 (all P-values <0.01).
(E) Distribution of probe test scores for control (upper; green) and experimental(lower; red) datasets for each measure. According to the Lilliefors (Kolmogorov–
Smirnov) test, many distributions are positively skewed (P-values <0.05).

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We next examined how well the measures correlated with one
another (Figure 3D). As would be expected, the measures weresignificantly correlated with one another (all P-values <0.01),
with Pearson’s r ranging from 0.67 (X vs. P) to 0.98 (Z20 vs. Z15).
Correlation coefficients were generally highest between the various
occupancy-based measures (Q, Z20, Z15 and Z10; 0.84–0.98), andlowest for contrasts that included X (0.67–0.88).
Parametric tests (such as the Student’s t-test or ANOVA) arebased on the assumption that samples are drawn from populations
that are normally distributed2. We therefore next evaluated whether
the measures were normally distributed using the Lilliefors (K–S)
test. These analyses revealed that the measures were not normally
distributed, in the majority of cases, tending to be positively skewed(Figure 3E). This was most pronounced in the experimental condition, most likely because many of these mice are performing at, or
near, floor levels (i.e., mode for Q ≈ 20.4–27.2%, X = 0).
ANALYSIS A, HYPOTHESIS TESTING

We next conducted a series of simulated experiments to compare
the sensitivity of the different measures at detecting differences
between the control and experimental groups. Experiments were
simulated by randomly selecting N probe tests (without replacement) from the control and experimental groups respectively, andtesting for group differences for each of the six measures using theStudent’s t-test. For each N, 1000 simulations were conducted and,to compute the rate of rejection of the null hypothesis for each N,
10 replications were performed (Figure 4A, left). As group size
increased, the detection rates increased for all measures. For Ns
up to around 40, we found that detection rates were highest for P
compared to Q, Z and X, respectively. For example, with α set at
0.05 and N = 15, group differences were more frequently detectedusing P (∼86%) compared to Q (∼70%) for Z20 (∼70%), Z15 (∼63%),
Z10 (∼57%) and X (∼39%). The relative advantage of P over Q, Z
and X held with α set at 0.01 and 0.005.
With α set at 0.05 in the above simulations we would expect a
false-positive rate of ∼5%. To verify that false-positive rates were as
expected we performed the same analyses as above, but randomly
selected two groups of N probe tests from the same control population (Figure 4A, right). For low Ns, false-positive rates were at
expected levels when α was set at 0.05, 0.01 and 0.005, respectively.
As Ns increased false-positive rates tended to decline, however. Thisdecline is most likely because our control database contains a finite
number of probe tests (i.e., 370). Therefore, as N increases (andapproaches this finite value), so does the likelihood that some ofthe same data-points will be selected in both the first and second
samples and such duplication would naturally reduce the likelihoodthat the two groups differ.
An assumption of parametric statistics such as the Student’s
t-test is that the two samples are drawn from normally distributed
populations. Our analyses presented in Figure 3D suggest that thismay not always be the case in water probe test data, regardless ofwhich of the four measures are being used. Therefore, to address this2

Violations of this normality assumption will lead to a modest increase in the Type I
error rate (i.e., incorrectly rejecting the null hypothesis). Such effects would be mostpronounced for smaller sample sizes (i.e., ns < 40) and when sample distributions
are differently shaped (Sawilowsky and Hillman, 1992).

Frontiers in Integrative Neuroscienceissue we next performed an identical series of simulations but useda non-parametric statistic (K–S test) that makes no assumptions
about the underlying distributions of the two samples (Figure 4B,left). As would be expected using this more conservative statistical
approach, overall detection rates were lower. Importantly, however,P maintained its advantage over other measures: Again, with α set
at 0.05 and N = 15, P was considerably more successful at detecting
group differences (∼72%) compared to Q (∼49%) for Z20 (∼52%), Z15(∼49%), Z10 (∼45%) and X (∼13%). False-positive rates were similar
across measures and close to expected values (Figure 4B, right).
ANALYSIS B, HYPOTHESIS TESTING FOR VARYING EFFECT SIZESThe probability of rejecting the null hypothesis (and detecting a
difference) depends upon the effect size (i.e., difference between
means), as well as the sample size (N) and the variance of the samples. As we sampled from two populations in the above analyses,
the effect size was fixed (i.e., QC − QE ≈ 14%, XC − XE ≈ 1.45). In
order to examine the sensitivity of different measures at detecting
intermediate effect sizes we compiled three additional databases,
each containing ≥282 probe tests. These databases were compiled
from mice that had all been trained identically (5 days, six trials
per day) and then given a series of three probe tests. Performancediffered in each of the probe trials (declining from probe 1 → 3,
likely reflecting within session extinction). Therefore, comparisonof different combinations of probe tests provides an opportunity
to evaluate the ability of the different measures to detect differences over a range of intermediate effect sizes (Figure 5A).
Accordingly, we next performed a series of simulated experiments(as above) and tested for differences using both parametric (t-test)
and non-parametric (K–S test) statistics (Figures 5B,C). As in ourprevious analyses, as N increased, detection rates increased for all
measures. In two of the three comparisons, P outperformed Q,
Z and X (probe 1 vs. probe 3 and probe 1 vs. probe 2). However,for the probe 2 vs. probe 3 comparison, Z20, Z15 and Z10 weremost sensitive. This suggests that the advantage of P over othermeasures may not be universal: In situations where both groups
are performing poorly, Z-based measures may be superior. One
possible reason for the poor performance of P for the probe 2
vs. probe 3 comparison is that when mice are performing close
to chance levels (e.g., swimming around the perimeter of thepool), variance for P would be especially high, thereby reducing
the likelihood of detecting group differences. False-positive rates
were similar across measures and close to expected values for botht-tests and K–S tests.

DISCUSSION
In assessing probe test performance in the water maze, four measures are routinely used to assess search accuracy (quadrant [Q],zone [Z], crossings [X] and proximity [P]). Using databases containing more than 1600 individual probe tests we conducted a seriesof Monte Carlo simulations to compare the relative sensitivity ofthese four measures in detecting group differences. Our primaryfinding is that P outperformed Z, Q and X, respectively. This was
the case across a range of sample sizes and for most effect sizes, andwhether parametric or non-parametric analyses were used. While
the water maze has been extensively validated, and all major findings reliably replicated across labs, the sensitivity of measures used

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FIGURE 4 | Monte Carlo simulations (analysis A). (A) t-tests. Left, the likelihoodof detecting a difference between control and experimental groups is shown forvarying sample (N) size, with significance levels α = 0.05, 0.01 and 0.005. Right,
false-positive rates when both samples are drawn from the control dataset.

(B) K–S tests. Left, the likelihood of detecting a difference between control andexperimental groups is shown for varying sample (N) size, with significance levels
α = 0.05, 0.01 and 0.005. Right, false-positive rate when both samples are drawn
from the control dataset. Q (green), Z (red; Z20, Z15 and Z10), X (black) and P (blue).

to assess performance have received less attention. Here, our formal
evaluation of sensitivity suggests the use of the P measure may
facilitate more efficient detection of spatial learning phenotypes inmice by reducing mouse numbers and increasing throughput.
The four measures that we focused on have been used in morethan 98% of water maze studies (Figure 1) and fall into three subcategories. First, occupancy-based measures assess the amount oftime animals spend in a virtual area (quadrant or zone) that iscentered on the former platform location. The crossing measure
is a counting-based measure where the number of times an animal
crosses the exact former location of the platform is recorded. Finally,
proximity is an error-based measure where the animal’s average
distance from the former platform location is recorded. Common

to each of these measures is that bias for the target location (e.g.,south-east) may be contrasted with other equivalent locationsin the pool (e.g., north-east, north-west and south-west). Sucha within-subjects comparison makes it possible to assess whether
a particular cohort of mice search selectively (e.g., whether they
search more in the south-east quadrant relative to the north-east,
north-west and south-west quadrants). However, as both control
and experimental groups may both search selectively, the criticalcomparison is whether one group searches more selectively thanthe other. For this between subjects comparison, relative bias forthe target (Q, Z, X, P) must be contrasted between control andexperimental groups, and this is the comparison that we focusedon in this study.

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FIGURE 5 | Monte Carlo simulations (analysis B). Mice were trained in
the water maze for 5 days (six trials per day) and then given a series of threeprobe tests. (A) Density plots for grouped data showing probes 1, 2 and 3.
The color scale represents the number of visits per animal per 5 cm × 5 cm
area. The table below indicates that performance declined across probe
tests, according to all measures. (B) t-tests. Left, the likelihood of detecting
a difference between the probe 1, 2 and 3 datasets is shown for varying

Frontiers in Integrative Neurosciencesample (N) size, with significance levels α = 0.05. Right, false-positive rates
when both samples are drawn from the same dataset. (C) K–S tests.
Left, the likelihood of detecting a difference between the probe 1, 2 and 3
datasets is shown for varying sample (N) size, with significancelevels α = 0.05. Right, false-positive rates when both samples are
drawn from the same dataset. Q (green), Z (red; Z20, Z15 and Z10), X (black)and P (blue).

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Our surprising finding was that the least popular of the fourmeasures – proximity (Gallagher et al., 1993) – was consistently
more sensitive at detecting group differences. What might accountfor increased sensitivity of proximity measure? The two mostpopular measures – quadrant and crossings – were introduced in
the original water maze studies (Morris, 1981, 1984; Morris et al.,1982) at a time when more sophisticated tracking analysis was
not available. While offering considerable intuitive appeal – forexample, it is readily apparent that an animal searching non-selectively would be expected to spend around 25% of its time in eachquadrant – nonetheless these two measures make use of only very
impoverished spatial information. That is, quadrant (along withzone) simply calculates the proportion of time an animal spends
in one location (or crosses that location), discarding all other spatial information. Contemporary tracking systems contain precise,
moment-by-moment spatial information and much of this detailis retained in the proximity computation. The future developmentof more sensitive measures to assess search accuracy in water mazeprobe tests will likely further exploit the richness of this spatial
distribution and therefore offer greater sensitivity (e.g., Dvorkin
et al., 2008; Valente et al., 2007).
The analysis of a large number of probe trials allowed us to
examine the temporal pattern of searching in some detail. The
most interesting observation is that search accuracy in control
mice peaked between 10 and 15 s, and declined thereafter (asmeasured by Q, Z and P, but not X). This within-test extinction
suggests that relatively early on in the probe test mice learn thatthe platform is absent and shift strategy to search elsewhere. The
exact timing of this peak likely depends on several factors, including the amount of training and the type of escape platform used

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during training [standard vs. Atlantis (de Hoz et al., 2004)] andmight in itself provide an informative index of cognitive function(or ‘certainty’).
Finally, our databases were composed of probe test data that weredrawn from experiments using identical apparatus, training andprobe test procedures. An advantage of this approach, therefore,
is that our simulated experiments closely mimic real experimental
situations, as for any given experiment such factors would typically not vary. However, one disadvantage is also worth noting. The
drawback of using identical procedures is that it is unclear whether
the relative ranking of P, Z, Q and X would necessarily hold acrossa variety of experimental settings. For example, many factors commonly differ across laboratories. These include pool size, size andtype of platform, amount of training, external cues, strain andspecies, all of which impact performance. While we believe it isreasonable to assume that the general ranking of measures would
generalize across experimental settings, nonetheless it be would be
important establish this in future analyses.

ACKNOWLEDGEMENTS
This work was supported by a grant from the Natural Sciencesand Engineering Council Canada [RGPIN 312434-05] (PWF).
Hamid R. Maei received support from the Research Training
Centre at The Hospital for Sick Children. Cátia M. Teixeira
received support from the Graduate Program in Areas of Basicand Applied Biology (GABBA) and the Portuguese Foundationfor Science and Technology (FCT). We thank Adrienne Yeung
for compiling the PubMed analysis, and Sheena Josselyn, Noam
Miller, Steven Kushner and Ilya Zaslavsky for comments on earlier drafts of this manuscript.

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Conflict of Interest Statement: The
authors declare that the research was conducted in the absence of any commercial or
financial relationships that could be construed as a potential conflict of interest.
Received: 31 January 2009; paper pending
published: 12 February 2009; accepted: 25
February 2009; published online: 09 March
2009.

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Citation: Maei HR, Zaslavsky K,
Teixeira CM and Frankland PW (2009)
What is the most sensitive measure ofwater maze probe test performance?
Front. Integr. Neurosci. (2009) 3:4. doi:
10.3389/neuro.07.004.2009
Copyright © 2009 Maei, Zaslavsky, Teixeira
and Frankland. This is an open-access article subject to an exclusive license agreement
between the authors and the Frontiers
Research Foundation, which permits unrestricted use, distribution and reproduction
in any medium, provided the original
authors and source are credited.

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