Dynamic phenotypes: Time series analysis techniques for characterizing neuronal and behavioral dynamics

Neuroinformatics

Volumn 4, Issue 1, 2006, Pages 119-128

  Bokil, Hemant ^a   Tchernichovsky, Ofer ^b   Mitra, Partha P ^a  

^a Simons Center for Quantitative Biology   (United States)

^b CITY COLLEGE OF NEW YORK   (United States)

Author keywords

Electrophysiology; Parkinson's disease; Songbird; Spectral estimation; Statistics; Time series analysis

Indexed keywords

BEHAVIOR; BRAIN FUNCTION; DYNAMICS; MEDICAL RESEARCH; NERVOUS SYSTEM; NEUROIMAGING; NEUROPSYCHIATRY; PARKINSON DISEASE; PHENOTYPE; PRIORITY JOURNAL; QUANTITATIVE ANALYSIS; REVIEW; STATISTICAL ANALYSIS; STATISTICAL SIGNIFICANCE; TIME SERIES ANALYSIS; ACTION POTENTIAL; ANIMAL; ANIMAL BEHAVIOR; BRAIN; ELECTROENCEPHALOGRAPHY; ELECTROPHYSIOLOGY; HUMAN; MAGNETOENCEPHALOGRAPHY; METHODOLOGY; NERVE CELL; PHYSIOLOGY; SIGNAL PROCESSING; TIME;

ACTION POTENTIALS; ANIMALS; BEHAVIOR, ANIMAL; BRAIN; ELECTROENCEPHALOGRAPHY; ELECTROPHYSIOLOGY; HUMANS; MAGNETOENCEPHALOGRAPHY; NEURONS; PHENOTYPE; SIGNAL PROCESSING, COMPUTER-ASSISTED; TIME FACTORS;

EID: 33646689573     PISSN: 15392791     EISSN: None     Source Type: Journal    
DOI: 10.1385/NI:4:1:119     Document Type: Review

References (23)

1. Adey, W. R., Walter, D. O., and Hendrix, C. E. (1961) Computer techniques in correlation and spectral analyses of cerebral slow waves during discriminative behavior. Exp. Neurol. 3, 501-524.
2. Babiloni, C., Miniussi, C., Moretti, D. V., et al. (2004) Cortical networks generating movement-related EEG rhythms in Alzheimer's disease: an EEG coherence study. Behav. Neurosci. 118, 698-706.
3. Beer, C. G. (1980) Perspectives on animal behavior comparisons. In: Comparative Methods in Psychology. Bornstein, M. H. (ed.) L. Erlbaum Associates, Hillsdale, NJ.
4. Brazier, M. A. and Casby, J. U. (1952) Cross-correlation and autocorrelation studies of electroencephalographic potentials. Electroencephalogr. Clin. Neurophysiol. Suppl. 4, 201-211.
5. Brown, E. N., Kass, R. E., and Mitra, P. P. (2004) Multiple neural spike train data analysis: state-of-the-art and future challenges. Nat. Neurosci. 7, 456-461.
6. Coutin-Churchman, P., Anez, Y., Uzcategui, M., et al. (2003) Quantitative spectral analysis of EEG in psychiatry revisited: drawing signs out of numbers in a clinical setting. Clin. Neurophysiol. 114, 2294-2306.
7. Drai, D. and Golani, I. (2001) SEE: a tool for the visualization and analysis of rodent exploratory behavior. Neurosci. Biobehav. Rev. 25, 409-426.
8. Fries, P., Schroder, J. H., Roelf sema, P. R., Singer, W., and Engel, A. K. (2002) Oscillatory neuronal synchronization in primary visual cortex as a correlate of stimulus selection. J. Neurosci. 22, 3739-3754.
9. Gallistel, C. R. (1980) The Organization of Action: A New Synthesis. L. Erlbaum Associates, Distributed by Halsted Press, Hillsdale, NJ, New York.
10. Golani, I., Kafkafi, N., and Drai, D. (1999) Phenotyping stereotypic behaviour: collective variables, range of variation and predictability. Appl. Anim. Behav. Sci. 65, 191-220.
11. Jarvis, M. R. and Mitra, P. P. (2001) Sampling properties of the spectrum and coherency of sequences of action potentials. Neural Comput. 13, 717-749.
12. Jones, K. A., Porjesz, B., Almasy, L., et al. (2004) Linkage and linkage disequilibrium of evoked EEG oscillations with CHRM2 receptor gene polymorphisms: implications for human brain dynamics and cognition. Int. J. Psychophysiol. 53, 75-90.
13. Llinas, R. R., Ribary, U., Jeanmonod, D., Kronberg, E., and Mitra, P. P. (1999) Thalamocortical dysrhythmia: a neurological and neuropsychiatric syndrome characterized by magneto-encephalography Proc. Natl. Acad. Sci. USA 96, 15,222-15,227.
14. Percival, D. B. and Walden, A. T. (1993) Spectral Analysis For Physical Applications: Multitaper and Conventional Univeriate Techniques. Cambridge University Press, Cambridge; New York, NY.
15. Pesaran, B., Pezaris, J. S., Sahani, M., Mitra, P. P., and Andersen, R. A. (2002) Temporal structure in neuronal activity during working memory in macaque parietal cortex. Nat. Neurosci. 5, 805-811.
16. Porjesz, B., Almasy, L., Edenberg, H. J., et al. (2002) Linkage disequilibrium between the beta frequency of the human EEG and a GABAA receptor gene locus. Proc. Natl. Acad. Sci. USA 99, 3729-3733.
17. Skinner, B. F. (1991) The Behavior of Organisms: An Experimental Analysis. Copley Pub. Group, Acton, MA.
18. Tchernichovski, O., Mitra, P. P., Lints, T., and Nottebohm, F. (2001) Dynamics of the vocal imitation process: how a zebra finch learns its song. Science 291, 2564-2569.
19. Tchernichovski, O., Nottebohm, F., Ho, C. E., Pesaran, B., and Mitra, P. P. (2000) A procedure for an automated measurement of song similarity. Anim. Behav. 59, 1167-1176.
20. Thakor, N. V. and Tong, S. (2004) Advances in quantitative electroencephalogram analysis methods. Ann. Rev. Biomed. Eng. 6, 453.
21. Thomson, D. J. (1982) Spectrum Estimation and Harmonic-Analysis. Proc. IEEE 70, 1055-1096.
22. Thomson, D. J. and Chave, A. D. (1991) Jackknifed Error Estimates for Spectra, Coherences, and Transfer Functions. In: Advances in Spectrum Estimation. Haykin, S. (ed.) Prentice-Hall, Englewood Cliffs, NJ, pp. 58-113.
23. Tinbergen, N. (1989) The Study of Instinct. Oxford University Press, Oxford.