Related Experiment Video
Updated: Jul 9, 2026
Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms
Published on: March 3, 2023
A novel method for automatic quantification of psychostimulant-evoked route-tracing stereotypy: application to Mus
Stephen J Bonasera1, A Katrin Schenk, Evan J Luxenberg
1Division of Geriatrics, Department of Medicine, University of California, San Francisco, 1550 4th St., Box 2822, San Francisco, CA 94158-2822, USA. Steve.Bonasera@ucsf.edu
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.
