Morris Water Maze

So you've got a bunch of data from Morris Water Maze? Time to parse through your files!

Let's pretend you took all possible variables from the computer. Below you will find a list of the variables and what they mean.

Animal is deemed to be floating if its swim speed is below
a user-setable threshold - default is 0.05 meters/sec (5 cms/sec).
A typical rat swims at 0.25 meters/sec (25 cms/sec).
Please take note that when a rat turns sharply it briefly slows-down.
Usually rats do not float - this is something mice specialize in !

THE VALUES OF TARGET-PLATFORM 

The HVS convention has been, that if an 'area' eg platform, is not reached,
the corrsponding measure is set to -1.
So then, in probe-trials, the target-platform does not exist, so it
cannot be reached (!), so now we set those vlues to -1.
Some "target-platform' measures seem to have no behavioral significance,
and are now omitted. We continue to give you values for all platform-positions.

PARAMETERS
__________
TRIALIS$=trial name
RUN_VER$=version # of Run module
ANAZVER$=version # of analyze module
CALDATE$=calibration date
CALUSER$=calibrated by
POOLDIcm=pool diam in cm
PLATDIcm=plat diam in cm
TRACKER$=made using tracker model
ISI_msec=inter-sample interval in msec (default 100) of track-data.
RUNDATE$=trial run date
RUNTIME$=at time of day (this line may not exist)
SUBJECT$=subject ident
DESCRIPT=experiment descriptor, anything you want to write.
FILTERIS=path smoothing filter (default is 'on')
TREATMT$=subject treatment
TRLTYPE$=trial type
PLAT_NUM=platform # (number)
STARTPT$=start location (any string)
MAXTIMsc=max time set for trial, sec
TIMOUTsc=inter-trial timeout, sec
CTR_SIZE=counter diam / plat diam

MEASURES
__________
LATENTsc=latency
PATHISmt=path length (m)
SPEEDmts=ave speed m/sec
FLOATmps=(meters per second). Speeds less than this are 'Floating'  
%TIMEFLT=% time Floating.                                         
CORRECTD=(Corrected speed) Ave speed excluding Floating            
PATHrato=actual path / direct path (was Janus ratio)
HEADGdeg=heading angle relative to ideal, deg.
HDG/DIAM=distance to which hdg measured / pool diam
THGTIME%=thigmotaxis time %
THGPATH%=thigmotaxis path %
THG/POOL=diam of thigmotaxis detection / pool diam (default 0.9)
KELLY_mt=Kelly radius specified in meters, default is 1m. New 5/08
KELLY_sc=Kelly time (to move into pool) measured in secs. New 5/08
SLICEIS$=analyze slice of path?
SLC_ONsc=slice starts secs into swim?
SLC_GOsc=slice duration, secs?
SLICELAT=Latency during a slice = duration of slice
ZONEA%sc=% time in zone A etc
QUAD_%sc=% time in target quad
QUAD1%sc=% time in quad 1 etc
QUAD_%mt=% path in target quad
QUAD1%mt=% path in quad 1 etc
Circling=total pool-circlings
Clockwise=clockwise pool-circlings
Anti=Anticlockwise  pool-circlings
PASSTHRU=passes thru target counterm
PASSTH00=passes thru counter 0 etc
%TIMECTR=% time in target counter
TIMCT%00=% time in counter 0 etc
PROXIMmt=Gallager global measure (meters) for target platform
PROXmt00=Gallagher global measure (meters) for plat 0 etc
GALCUMms=GALLAGHER cumulative (meters x secs) for target platform
CUMUms00=Gallagher cumulative (meters x secs) for plat 0 etc
LATCsc00=Latency to counter 0 (sec) etc (9/2003)
COL01%sc=% time in user defined colored area 1 (thru 14)
COL01%mt=% path in user defined colored area 1 (thru 14)
REMARKIS=Remark (optional)
BLOKLND1=(Arjan Blokland) % time in quad 1 etc, excluding thigmotaxis band
Corridor measures CORR_ and Cone measures CONE_

In order to merge the Atlantis software, the codes for 
end of trial are changed to: ENDEDBY$ = 
"remote"  means  "Ended by Remote left-click"
"auto"    means "Ended by auto": Rem auto-detect
"lost"    means "Tracking lost during swim"
"maxtime" means "Ended by trial max time" 
"risetime"means "Ended by defined time before rise":Rem atlantis
"dwell"   means "Ended by dwell": Rem atlantis

Analyzing your MWM

So, now there are a couple of ways to analyze your MWM. Let's say you have two genotypes and want to compare how they learned. First, make a .csv file concatenating all of the stc files you want to use in your analysis.

For my structure, I used this loop in Unix to make the file: 
for file in /data/chamal/projects/gulebru/JTW/cohort*/watermaze2/stc/* ; do cat $file >> /data/chamal/projects/lani/J20/analysis/MWM/mwm2_all.csv ; done

Then, load your csv into R to run your stats!

You can do a variety of tests, such as using an ANOVA to look at seconds or meters spent in the target quadrant (where the platform was) on the probe days as I did below. If you take the proportion (time in target quadrant/time in all quadrants) you could also use Chi^2.

# set working directory
setwd("/data/chamal/projects/lani/J20/analysis/MWM")

# load libraries
library(ggplot2)
# library(dplyr)
# library(ggfortify)


#load the csv with the mwm data
#data = read.csv(file="mwm1_all.csv",head=TRUE,sep=",")
data = read.csv(file="mwm2_all.csv",head=TRUE,sep=",")

# Filter out everything that is not a probe day
filtered.data = subset(data, data$day == "p")

# use an anova to examine seconds in the target quadrant compared by genotype
geno_probesc <- aov(QUAD1xsc ~ genotype, data=filtered.data) 
summary(geno_probesc)

# use anova to examine distance in the target quadrant
geno_probemt <- aov(filtered.data$QUAD1xmt ~ genotype, data=filtered.data)
summary(geno_probemt)

# visualize with a box plot

figure = qplot(x=genotype, y = QUAD1xmt, fill = genotype, geom="boxplot", data=filtered.data, xlab="Genotype", ylab="Path in Target Quadrant (mt)", main='Path in Quadrant/genotype') + theme_grey(base_size=7)
figure

figure2 = qplot(x=genotype, y = QUAD1xsc, fill = genotype, geom="boxplot", data=filtered.data, xlab="Genotype", ylab="Time in Target Quadrant (sec)", main='Time in Quadrant/genotype') + theme_grey(base_size=7)
figure2

** Please note this wiki is a WIP, kindly contribute with additional methods/edits.