SYSTEMS AND METHODS FOR BEHAVIORAL MONITORING AND CALIBRATION

• US 20100324861A1
• Filed: 01/21/2009
• Published: 12/23/2010
• Est. Priority Date: 01/23/2008
• Status: Active Grant

• Current Assignee: Regents of the University of California (University of California)
• Original Assignee: Regents of the University of California (University of California)
• Inventors: Goulding, Evan, Schenk, A. Katrin, Tecott, Laurence
• Granted Patent: US 9,152,851 B2
• Time in Patent Office: Days
• US Class Current: 702/150
• CPC Class Codes: A01K 1/031 Cages for laboratory animals; Cages for measuring metabolism of animals
• CPC Class Codes: A01K 29/005 Monitoring or measuring activity, e.g. detecting heat or mating
• CPC Class Codes: G06F 18/00 Pattern recognition
• CPC Class Codes: G06F 2218/00 Aspects of pattern recognition specially adapted for signal processing
• CPC Class Codes: G06V 40/20 Movements or behaviour, e.g. gesture recognition recognition of facial expressions G06V40/16

Abstract

Systems and methods for the analysis of the diverse behaviors of animal subjects in defined areas are provided, including tools for filtering and analysis of high-resolution behavioral data. These systems and methods provide an opportunity to examine behavioral patterns with levels of precision and quantization that have not been previously achieved. Methods and systems for managing and analyzing the very large and unique datasets produced by behavioral monitoring systems, including quality assessment and control, archiving, data query, data reduction, analytical procedures and visualization techniques are provided. Such detailed analyses of spontaneous behavior provide fundamental insights into the neural organization of behavior and enable detection of genetic, pharmacological and environmental influences on brain function with high sensitivity.

Claims

1. A method of filtering a set of animal behavioral data collected over a measurement period using a measurement system, wherein said data comprises event information regarding spatial position of an animal subject in a defined measurement area, device event information regarding behavior of the animal subject at or with a plurality of devices at known locations in the defined area, and temporal information associated with the position and device event information;

said method comprising;

receiving the collected behavioral data;

analyzing the behavioral data to detect

     1) position information inconsistent with device event information, wherein said detection is based on the known location of the devices, and/or

     2) device event information for one or more devices inconsistent with device event information for any other device, wherein said detection is based on temporal information associated with the device events; and

updating the data based on at least some of the detected inconsistencies.

2-43. -43. (canceled)

43. An automated method of analyzing animal behavioral data collected using a measurement system, said behavioral data comprising spatial and temporal information regarding the position of the animal in a defined measurement area, said method comprising:

identifying transitions between active states and inactive states of the animal subject using the spatial and temporal information.

44. The automated of claim 43 further comprising characterizing behavioral states within identified active states using both spatial information and temporal information.

45. A method of analyzing animal behavioral data collected using a measurement system, said behavioral data comprising spatial and temporal information regarding the position of the animal in a defined measurement area, said method comprising

using the spatial information to identify transitions between active and inactive states comprises determining the location of the longest duration between animal subject movements during a time period.

46. The method of claim 45 wherein using the spatial information to identify transitions between active and inactive states further comprises determining the relative distance of other positions of the animal subject during the time period from the longest duration location.

47. The method of claim 45, wherein identifying transitions between active states and inactive states comprises determining an inactive position duration threshold.

48. The method of claim 45 further comprising characterizing behavioral states within identified active states using both spatial information and temporal information.

89. The method of claim 48 wherein characterizing behavioral states within identified active states using both spatial information and temporal information comprises receiving position tracking information for the animal subject in a defined area during the measurement period and information about temporal patterns of one or more behaviors during the measurement period;

and using the position tracking information and the temporal information to identify bouts of the one or more behaviors.

49. A method of identifying inactive states of an animal subject during a time window from animal behavioral data collected over a measurement period using a measurement system, the animal behavioral data comprising event information regarding the spatial position of the animal subject in a measurement area;

the method comprising;

receiving event information regarding the spatial position of the animal subject during the time window;

analyzing said event information to determine information regarding the duration of positions in the time window;

determining the longest duration position in the time window;

determining information about the spatial distance of every position from the longest duration position; and

using the distance and duration information to identify inactive states of the animal subject in the time window.

50. The method of claim 49, wherein the animal behavioral data further comprises device event information regarding behavior of the animal subject at or with one or more types of devices at known locations in the measurement area, and further comprising identifying any device events that occur during the identified inactive states.

51. The method of claim 50, further comprising updating the classification based on an inactive state error rate.

52. The method of claim 50 wherein the inactive state error rate is on the occurrence of device events during the identified inactive states.

53. The method of claim 50, further comprising reclassifying inactive states having a device event as active states.

54. The method of claim 53, further comprising calculating an active state error rate.

55. The method of claim 54, wherein calculating an active state error rate comprises comparing the area occupied by the animal subject during each active state to an area occupied by the animal subject during an inactive state.

56. The method of claim 49, further comprising identifying inactive states of the animal subject for multiple time windows in the measurement period and selecting a time window.

57. The method of claim 49, further comprising identifying inactive states of the animal subject for multiple movement thresholds, wherein the movement threshold defines the minimum distance between two consecutive positions in determining the duration of positions, and selecting a movement threshold.

58. An automated method of analyzing a set of animal subject behavioral data collected over a measurement period using a measurement system, comprising:

receiving position tracking information for the animal subject in a defined area during the measurement period and information about temporal patterns of one or more behaviors during the measurement period; and

using the position tracking information and the temporal information to identify bouts of the one or more behaviors.

59. The automated method of claim 58 further comprising receiving device event information regarding behavior of the animal subject at or with one or more devices at known locations in the defined measurement area, wherein each device is associated with a particular behavior.

60-88. -88. (canceled)