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Dynamic phenotypes

Dynamic phenotypes We consider quantitative measures of behavioral and neuronal dynamics as a means of characterizing phenotypes. Such measures are important from a scientific perspective; because understanding brain function is contingent on understanding the link between the dynamics of the nervous system and behavioral dynamics. They are also important from a biomedical perspective because they provide a contrast to purely psychological characterizations of phenotype or characterizations via static brain images or maps, and are a potential means for differential diagnoses of neuropsychiatric illnesses. After a brief presentation of background work and some current advances, we suggest that more attention needs to be paid to dynamic characterizations of phenotypes. We will discuss some of the relevant time series analysis tools. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Neuroinformatics Springer Journals

Dynamic phenotypes

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Publisher
Springer Journals
Copyright
Copyright © 2006 by Humana Press Inc
Subject
Chemistry; Biotechnology; Engineering, general; Neurology
ISSN
1539-2791
eISSN
1559-0089
DOI
10.1385/NI:4:1:119
pmid
16595862
Publisher site
See Article on Publisher Site

Abstract

We consider quantitative measures of behavioral and neuronal dynamics as a means of characterizing phenotypes. Such measures are important from a scientific perspective; because understanding brain function is contingent on understanding the link between the dynamics of the nervous system and behavioral dynamics. They are also important from a biomedical perspective because they provide a contrast to purely psychological characterizations of phenotype or characterizations via static brain images or maps, and are a potential means for differential diagnoses of neuropsychiatric illnesses. After a brief presentation of background work and some current advances, we suggest that more attention needs to be paid to dynamic characterizations of phenotypes. We will discuss some of the relevant time series analysis tools.

Journal

Neuroinformatics – Springer Journals

Published: Apr 11, 2007

References

  • Computer techniques in correlation and spectral analyses of cerebral slow waves during discriminative behavior
    Adey, W. R.; Walter, D. O.; Hendrix, C. E.
  • Cortical networks generating movement-related EEG rhythms in Alzheimer's disease: an EEG coherence study
    Babiloni, C.; Miniussi, C.; Moretti, D. V.
  • Comparative Methods in Psychology
    Beer, C. G.
  • Cross-correlation and autocorrelation studies of electroencephalographic potentials
    Brazier, M. A.; Casby, J. U.
  • Multiple neural spike train data analysis: state-of-the-art and future challenges
    Brown, E. N.; Kass, R. E.; Mitra, P. P.
  • Quantitative spectral analysis of EEG in psychiatry revisited: drawing signs out of numbers in a clinical setting
    Coutin-Churchman, P.; Anez, Y.; Uzcategui, M.
  • SEE: a tool for the visualization and analysis of rodent exploratory behavior
    Drai, D.; Golani, I.
  • Oscillatory neuronal synchronization in primary visual cortex as a correlate of stimulus selection
    Fries, P.; Schroder, J. H.; Roelfsema, P. R.; Singer, W.; Engel, A. K.
  • Phenotyping stereotypic behaviour: collective variables, range of variation and predictability
    Golani, I.; Kafkafi, N.; Drai, D.
  • Sampling properties of the spectrum and coherency of sequences of action potentials
    Jarvis, M. R.; Mitra, P. P.
  • Linkage and linkage disequilibrium of evoked EEG oscillations with CHRM2 receptor gene polymorphisms: implications for human brain dynamics and cognition
    Jones, K. A.; Porjesz, B.; Almasy, L.
  • Thalamocortical dysrhythmia: a neurological and neuropsychiatric syndrome characterized by magnetoencephalography
    Llinas, R. R.; Ribary, U.; Jeanmonod, D.; Kronberg, E.; Mitra, P. P.
  • Spectral Analysis For Physical Applications: Multitaper and Conventional Univeriate Techniques
    Percival, D. B.; Walden, A. T.
  • Temporal structure in neuronal activity during working memory in macaque parietal cortex
    Pesaran, B.; Pezaris, J. S.; Sahani, M.; Mitra, P. P.; Andersen, R. A.
  • Linkage disequilibrium between the beta frequency of the human EEG and a GABAA receptor gene locus
    Porjesz, B.; Almasy, L.; Edenberg, H. J.
  • The Behavior of Organisms: An Experimental Analysis
    Skinner, B. F.
  • Dynamics of the vocal imitation process: how a zebra finch learns its song
    Tchernichovski, O.; Mitra, P. P.; Lints, T.; Nottebohm, F.
  • A procedure for an automated measurement of song similarity
    Tchernichovski, O.; Nottebohm, F.; Ho, C. E.; Pesaran, B.; Mitra, P. P.
  • Advances in quantitative electroencephalogram analysis methods
    Thakor, N. V.; Tong, S.
  • Spectrum Estimation and Harmonic-Analysis
    Thomson, D. J.
  • Advances in Spectrum Estimation
    Thomson, D. J.; Chave, A. D.
  • The Study of Instinct
    Tinbergen, N.