Systems and methods for monitoring behavior informatics

• US 7,269,516 B2
• Filed: 05/15/2002
• Issued: 09/11/2007
• Est. Priority Date: 05/15/2001
• Status: Expired due to Term

• Current Assignee: Carnegie Mellon University, PsychoGenics Inc.
• Original Assignee: Carnegie Mellon University, PsychoGenics Inc.
• Inventors: Leahy, Emer, Gondhalekar, Vijay, LaRose, David, Brunner, Daniela, Ross, William P.
• Primary Examiner(s): Moran; Marjorie A.
• Application Number:
  US10/147,334
• Publication Number:
  US 20030028327A1
• Time in Patent Office: 1,945 Days
• Field of Search: 702/19
• US Class Current: 702/19
• CPC Class Codes: A01K 1/031 Cages for laboratory animals; Cages for measuring metabolism of animals
• CPC Class Codes: G16B 20/00 ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• CPC Class Codes: G16B 40/00 ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• CPC Class Codes: G16B 40/20 Supervised data analysis
• CPC Class Codes: G16B 40/30 Unsupervised data analysis
• CPC Class Codes: G16B 50/00 ICT programming tools or database systems specially adapted for bioinformatics
• CPC Class Codes: G16B 50/20 Heterogeneous data integration
• CPC Class Codes: G16H 10/20 for electronic clinical trials or questionnaires
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 15/00 ICT specially adapted for medical reports, e.g. generation or transmission thereof
• CPC Class Codes: G16H 20/10 relating to drugs or medications, e.g. for ensuring correct administration to patients
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: G16H 70/40 relating to drugs, e.g. their side effects or intended usage

Abstract

A system and method used to assess animal behavior includes a module having sensors that collects a variety of physical and biological data from a test subject. Interpretation of the data is provided to assess the test subject'"'"'s behavior, neurology, biochemistry and physiology. The module is useful in observing the effects of a drug on the test animal and providing information on the drug'"'"'s signature. Another advantage is the module'"'"'s portability that allows it to be used in standard laboratory cages. This portability allows the animal to be tested in its own habitat, that can reduce any erroneous data due to stressing the animal when removed to a test cage. Additionally, the module'"'"'s design allows for parallel data collection and interpretation from several laboratory animals undergoing different experiments. Multi-dimensional modeling of the test subject based the system'"'"'s interpretation of the data allows pattern recognition of the drug signature, and predictive drug analysis.

Claims

1. A method of predicting whether a test treatment belongs to a class of test treatments having a pattern of animal behavior associated with said class of test treatments, said method comprising:

(a) generating an electronic database of stored class signature information of at least one treatment class signature by (i) collecting behavioral data from animals administered at least one known treatment which is a member of a class of treatments; and

(ii) using a computer to define said signatures for said treatment class by correlating features of said behavioral data with said known treatment class member; and

(iii) storing said signature information in a computer;

(b) administering said test treatment to at least one animal;

(c) using a computer to capture behavioral data of said at least one animal administered said test treatment;

(d) using a computer to compare information derived from an analysis of the captured data from (c) with stored class signature information from (a) to predict a probability that the test treatment is a member of at least one treatment class represented in the stored class signature information; and

(e) outputting a result from (d) that is indicative of whether said test treatment is a member of at least one treatment class.

2. The method of claim 1 wherein the prediction is generated based on matching one or more elements of the data of step (c) to one or more elements of at least one stored class signature.

3. The method of claim 1 wherein the signatures are continuously updated by adding behavioral data of one or more animals affected by at least one known test treatment which is a member of a class of treatments.

4. The method of claim 1 wherein one or more animals affected by a known test treatment is subject to at least one mechanical challenge.

5. The method of claim 1 wherein one or more animals administered a test treatment is subject to at least one mechanical challenge.

6. The method of claim 1, wherein the test treatment comprises:

administering a drug.

7. The method of claim 1, wherein the test treatment comprises:

introducing a genetic mutation.

8. The method of claim 1, wherein the test treatment comprises:

introducing a lesion.

9. The method of claim 1, wherein the test treatment comprises:

performing a surgical procedure.

10. The method of claim 1, wherein the test treatment comprises:

exposure to a chemical.

11. The method of claim 1, wherein the test treatment comprises:

exposure to an environmental change.

12. The method of claim 1, wherein the test treatment is performed shortly before automatic capture of the information.

13. The method of claim 1, wherein the test treatment is performed chronically before automatic capture of the information.

14. The method of claim 1, wherein the test treatment is performed chronically during automatic capture of the information.

15. The method of claim 1, wherein the test treatment is performed during an early developmental stage of the animals.

16. The method of claim 1, wherein the test treatment is performed prior to birth of the animals.

17. The method of claim 1, wherein the stored class signature information further comprises:

physiological states.

18. The method of claim 1, wherein the stored class signature information further comprises:

movement states.

19. The method of claim 1, wherein the stored class signature information further comprises:

biochemical measures.

20. The method of claim 1, wherein the behavioral data comprise:

state transitions.

21. The method of claim 1, wherein the behavioral data comprise:

probability of state occurrences.

22. The method of claim 1, wherein the behavioral data comprise:

duration of state occurrences.

23. The method of claim 1, wherein the behavioral data comprise:

probability of transitions between states.

24. The method of claim 1, wherein the behavioral data comprise:

video data describing body shape.

25. The method of claim 1, wherein the behavioral data comprise:

video data describing body position.

26. The method of claim 1, wherein the behavioral data comprise:

video data describing body part position.

27. The method of claim 1, wherein the behavioral data comprise:

video data describing body velocity.

28. The method of claim 1, wherein the behavioral data comprise:

video data describing animal location.

29. The method of claim 1, wherein the behavioral data comprise:

mechanical data describing animal movement.

30. The method of claim 1, wherein the behavioral data comprise:

derived variables resulting from a transformation of one or more output variables.

31. The method of claim 1, wherein the behavioral data comprise:

Support Vector Machine Algorithms.

32. The method of claim 1, wherein the behavioral data comprise:

fuzzy clustering.

33. The method of claim 1, wherein the behavioral data comprise:

principle component analysis.

34. The method of claim 1, wherein the behavioral data comprise:

Kohonen self organizing maps.