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

Psychopharmacology (Berl). 2008 Mar;196(4):591-602. doi: 10.1007/s00213-007-0994-6. Epub 2007 Dec 21.

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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Animals
  • Behavior, Animal / drug effects
  • Central Nervous System Stimulants / pharmacology*
  • Dextroamphetamine / pharmacology*
  • Dose-Response Relationship, Drug
  • Male
  • Mice
  • Mice, Inbred C57BL
  • Motor Activity / drug effects
  • Pattern Recognition, Automated
  • Piperazines / pharmacology*
  • Stereotyped Behavior / drug effects*

Substances

  • Central Nervous System Stimulants
  • Piperazines
  • vanoxerine
  • Dextroamphetamine