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A novel method for automatic quantification of psychostimulant-evoked route-tracing stereotypy: application to Mus musculus

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Abstract

Rationale

Route-tracing stereotypy is a powerful behavioral correlate of striatal function that is difficult to quantify. Measurements of route-tracing stereotypy in an automated, high throughput, easily quantified, and replicable manner would facilitate functional studies of this central nervous system region.

Objective

We examined how t-pattern sequential analysis (Magnusson Behav Res Meth Instrum Comput 32:93–110, 2000) can be used to quantify mouse route-tracing stereotypies. This method reveals patterns by testing whether particular sequences of defined states occur within a specific time interval at a probability greater than chance.

Results

Mouse home-cage locomotor patterns were recorded after psychostimulant administration (GBR 12909, 0, 3, 10, and 30 mg/kg; d-amphetamine, 0, 2.5, 5, and 10 mg/kg). After treatment with GBR 12909, dose-dependent increases in the number of found patterns and overall pattern length and depth were observed. Similar findings were seen after treatment with d-amphetamine up to the dosage where focused stereotypies dominated behavioral response. For both psychostimulants, detected patterns displayed similar morphological features. Pattern sets containing a few frequently repeated patterns of greater length/depth accounted for a greater percentage of overall trial duration in a dose-dependant manner. This finding led to the development of a t-pattern-derived route-tracing stereotypy score. Compared to scores derived by manual observation, these t-pattern-derived route-tracing stereotypy scores yielded similar results with less within-group variability. These findings remained similar after reanalysis with removal of patterns unmatched after human scoring and after normalization of locomotor speeds at low and high ranges.

Conclusions

T-pattern analysis is a versatile and robust pattern detection and quantification algorithm that complements currently available observational phenotyping methods.

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Acknowledgements

The authors would like to thank Daniel Healy for computer and CVS support, Albert Willemsen and Wilant Van Giessen for their technical advice, Scott Carra and Kevin Yee for their technical assistance, Magnus Magnusson, Ph.D. for fruitful discussions regarding pattern detection algorithms, Charles McCulloch, Ph.D. for discussions regarding biostatistical issues, and Noldus for their generous Theme trial license. This study was supported by grants from the Stephen D. Bechtel Jr. Foundation (SJB, AKS), the Brookdale National Fellowship Program (SJB), NIA-AG026043 (SJB, AKS), NIMH-MH065983 (SJB), and NIMH-MH077128 (LHT).

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Authors and Affiliations

  1. Division of Geriatrics, Department of Medicine, University of California, San Francisco, 1550 4th St., Box 2822, San Francisco, CA, 94158-2822, USA

    Stephen J. Bonasera

  2. Department of Psychiatry, University of California, San Francisco, San Francisco, CA, 94158-2822, USA

    Laurence H. Tecott

  3. Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA, 94134-1207, USA

    Stephen J. Bonasera & A. Katrin Schenk

  4. Redwood High School, Larkspur, CA, 94939, USA

    Evan J. Luxenberg

Authors
  1. Stephen J. Bonasera
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  2. A. Katrin Schenk
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  3. Evan J. Luxenberg
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  4. Laurence H. Tecott
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Corresponding author

Correspondence to Stephen J. Bonasera.

Additional information

Stephen J. Bonasera and A. Katrin Schenk contributed equally to this work.

Electronic supplementary material

Supplementary methodsThe pattern-detection algorithm implemented in Theme allows adjustment of a number of parameters that subtly affect searching. These parameters are:

  1. 1.

    Minimum number of times that a pattern must occur to be counted (minimum occurrences, MO)

  2. 2.

    A significance level (α)

  3. 3.

    A search-level parameter that stops looking for patterns once they reach a certain depth (maximum search levels, MSL)

  4. 4.

    A lumping (LF) factor that joins adjacent patterns if their conditional probability of occurring together is greater than a set percentage

  5. 5.

    A redundancy factor that drops newly detected patterns if they start and stop at the same location as previously detected patterns (fast approximate redundancy reduction, FARR)

  6. 6.

    A fast-free limit (FFL) factor that rejects patterns that occur over longer periods of time, while favoring patterns that occur over shorter periods of time

  7. 7.

    A parameter that excludes highly frequent events (frequent-event exclusion, FEE)

Except where mentioned in the following analysis, these parameters were set as follows. MO = 3; by definition, a given series of events has to occur at least twice before even being considered a pattern. We chose MO to reflect the most agnostic assumptions regarding how many times a given pattern may occur. α = 0.001; we focused on only highly significant patterns. MSL = 99; this value would search patterns of maximum possible depth. LF = 0.90; we chose this value to slightly decrease the total number of patterns identified and thus reduce computational time required for complex data set composition. Supplemental Figure 1 shows the effect of varying this parameter along its entire range on final pattern composition. FARR = 0.90; we also set this parameter to slightly reduce total number of identified patterns and reduce composition time. Supplemental Figure 1 shows the minor effect of varying this parameter along its entire range. FFL = 99 (off value); we turned this parameter off to keep the most agnostic assumptions regarding pattern timing. FEE = 2.5; this parameter was designed to reject event types that are known a priori to be noise-related. In our experiments, this condition was not met. We set this parameter to ensure that no event types were excluded from our data set. These are also the same parameter values recommended by Magnusson (2004) in technical discussions of the software package.

Supplemental Figure 1
figure 6

Varying pattern-detection parameters across their operating range evokes small and consistent changes in overall t-pattern-derived route-tracing stereotypy score. Scores were calculated for a randomly selected trial (mouse received d-amphetamine 2.5 mg/kg) while varying the lumping factor, fast approximate redundancy reduction, and frequent-event exclusion (FEE) parameters across the scope of their recommended ranges. Note a consistent small increase in route-tracing stereotypy score when the lumping factor is set to 1.0 (at this value, no pattern lumping will occur); otherwise, route-tracing stereotypy scores are quite consistent across a variety of FARR and FEE values. The parameter set used for all calculations in this study is highlighted in red (GIF 57.3 kb).

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Supplemental Figure 2
figure 7figure 7figure 7

Stereotypic locomotor paths detected through t-pattern analysis have appropriate face validity. Representative pattern compositions for mice treated with vehicle (a) and GBR 12909 30 mg/kg (b). For both a and b, the major pattern types detected in the analysis are color coded within the dendrogram vs time plot and the arena outlines for the individual locomotor activity plots. Within each individual locomotor activity plot, the green asterisk and red square depict the path start and stop points, respectively. Blue arrowheads superimposed on the path trace depict movement direction; the width of these arrowheads are proportional to movement speed (longer arrowheads indicate faster locomotor speeds). The number in each individual locomotor activity plot indicates pattern occurrence within the dendrogram. For instance, in b, the group of paths in the red boxes are all instances of one identified pattern occurring in one trial. These paths all have similar shape except for the ‘offshape’ paths marked with a grey × and a grey dot in the dendrogram vs time plot. To find these ‘offshape’ paths for each trial, a consensus strategy was employed. Individual patterns were kept in the analysis if two or more of the investigators agreed that this pattern occurred at least twice within the trial. All patterns not meeting this criterion were deemed ‘offshape’ and excluded from the analysis. The pie chart represents total trial percentage spent in route-tracing stereotypy. Time in seconds. a Representative analysis for mouse receiving vehicle administration. Route-tracing stereotypy accounts for 13% of total trial duration. By consensus, only locomotor pattern 15 was removed from analysis (GIF 42.2 KB). b Representative analysis for mouse receiving GBR 12909 30 mg/kg administration. Composition detected three major patterns shown in three different colors in the dendrogram vs time plot. For dendrograms in black, the 24 instances where that pattern occurred are shown in the first four rows of locomotor plots. For dendrograms in red, the four instances where that pattern occurred are shown in the fifth row. For dendrograms in blue, the 11 instances where that pattern occurred are shown in the last two rows of locomotor plots. Patterns 31, 32, and 42 (all from red “family”) rejected by consensus. Route-tracing stereotypy accounts for 53% of total trial duration (pie chart inset; GIF 104 KB). c Worst-case pattern composition. In this example, a majority of the detected patterns were not included in the final consensus set. Of the 28 trials analyzed by this method, this trial demonstrated the worst performance (in terms of discarded algorithm-identified patterns). d Comparison of t-pattern route-tracing stereotypy scores before (unfilled bars) and after (filled bars) rejection of patterns not identified by consensus (GIF 82.9 kb).

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Supplemental Figure 3
figure 8

a GBR 12909 increases home-cage locomotion in a dose-dependent manner. Primary effect of GBR 12909 dosage significant by one-way ANOVA (F3,25 = 19.3165, p < 0.001); individual group comparisons significant by Duncan’s multiple range test except for the vehicle vs GBR 12909 3 mg/kg comparison and the GBR 12909 10 mg/kg vs GBR 12909 30 mg/kg comparison. b Home-cage locomotion after d-amphetamine administration increases in a dose-dependent manner until focused stereotypy dominated the behavioral response. Primary effect of d-amphetamine dosage significant by one-way ANOVA (F3,28 = 20.02, p < 0.001); individual group comparisons significant by Duncan’s multiple range test except for the d-amphetamine 2.5 mg/kg vs d-amphetamine 10 mg/kg comparison and the vehicle vs d-amphetamine 10 mg/kg comparison. Error bars are ±1 standard error (GIF 51.8 kb).

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Supplemental Figure 4
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Total number of detected patterns and overall pattern length and depth increase in a dose-dependent fashion after treatment with GBR 12909. All detected patterns, including those with only two states, included. a Overall trend significant by one way ANOVA (F 3,26 = 14.75, p < 0.001), all pairwise comparison significance values are p < 0.05 or better by Duncan’s multiple range test except comparison between GBR 12909 10 mg/kg and GBR 12909 30 mg/kg. b Increasing dosage of GBR 12909 increased overall pattern length (F 7,223 = 11.65, p < 0.001) and overall pattern depth (F 6,224 = 25.60, p < 0.001). Furthermore, as demonstrated by the color map, for any pattern length or depth, higher GBR doses were associated with more patterns found at that length/depth compared to lower GBR 12909 doses. c Total number of detected patterns increases after d-amphetamine treatment until response dominated by focused stereotypies (F 3,28 = 5.14, p < 0.0059), all pairwise comparison significance values are p < 0.05 or better by Duncan’s multiple range test except between d-amphetamine 2.5 mg/kg and d-amphetamine 5 mg/kg, and d-amphetamine 2.5 mg/kg and d-amphetamine 10 mg/kg. d Increasing dosage of d-amphetamine increased overall pattern length (F 7,224 = 5.66, p < 0.001) and overall pattern depth (F 5,225 = 13.58, p < 0.001) until response dominated by focused stereotypies. Note semilog axes (GIF 46.8 kb).

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Supplemental Figure 5
figure 10

The number of unique patterns in the t-pattern maximal composition decreases with increased psychostimulant dosage. a GBR 12909; one-way ANOVA on dosage effect is F 3,24 = 5.33 (p < 0.005, pairwise comparisons between vehicle and 30 mg/kg, and 3 and 30 mg/kg groups significant by Duncan’s multiple range test). b d-Amphetamine; one-way ANOVA on dosage effect is F 3,28 = 6.83 (p < 0.0013, pairwise comparisons between vehicle and 10 mg/kg, and vehicle and 30 mg/kg groups significant by Duncan’s multiple range test; GIF 31.2 kb).

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Supplemental Figure 6
figure 11figure 11

Stereotypic locomotor paths evoked by d-amphetamine treatment and detected through t-pattern analysis have appropriate face validity. Representative pattern compositions for mice treated with vehicle (a) and d-amphetamine 10 mg/kg (b). Figure annotations analogous to those described in Supplemental Figure 2. a Representative paths for mouse receiving vehicle administration. Route-tracing stereotypy accounts for 12% of total trial duration. Locomotor patterns 3, 7, 9, and 14 were removed from analysis. Note similarities in overall pattern morphology and route-tracing stereotypy score when compared with vehicle trial from Supplemental Figure 2 (GIF 44.6 KB). b Representative paths for mouse receiving d-amphetamine 10 mg/kg. Composition detected a single major pattern repeated 39 times. Patterns 2 and 38 were removed from analysis. Route-tracing stereotypy accounts for 50% of total trial duration (pie chart inset). By observation, the mouse was observed to enter a phase of continual focused-grooming stereotypies immediately after the completion of the final detected pattern (GIF 144 kb).

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Supplemental Figure 7
figure 12

Route-tracing stereotypy scores are insensitive to locomotor distance. White bars depict route-tracing stereotypy scores calculated for mouse locomotion normalized to that seen in vehicle and GBR 12909 3 mg/kg doses; dark grey bars similarly depict route-tracing stereotypy scores calculated for mouse locomotion normalized to that seen in GBR 12909 10 mg/kg and GBR 12909 30 mg/kg doses. Light grey bars depict unnormalized route-tracing stereotypy scores and are identical to those shown in Fig. 4. Only the primary effect of GBR 12909 dosage was significant (F 3,79 = 78.57, p < 0.0001); the primary effect of locomotor normalization and the interaction between normalization and GBR 12909 dosage did not achieve significance (F 2,79 = 1.6; F6,79 = 1.14, respectively; GIF 27.9 kb).

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Bonasera, S.J., Schenk, A.K., Luxenberg, E.J. et al. A novel method for automatic quantification of psychostimulant-evoked route-tracing stereotypy: application to Mus musculus . Psychopharmacology 196, 591–602 (2008). https://doi.org/10.1007/s00213-007-0994-6

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