Literature DB >> 16595862

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

Hemant Bokil1, Ofer Tchernichovsky, Partha P Mitra.   

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.

Entities:  

Mesh:

Year:  2006        PMID: 16595862     DOI: 10.1385/NI:4:1:119

Source DB:  PubMed          Journal:  Neuroinformatics        ISSN: 1539-2791


  15 in total

1.  Sampling properties of the spectrum and coherency of sequences of action potentials.

Authors:  M R Jarvis; P P Mitra
Journal:  Neural Comput       Date:  2001-04       Impact factor: 2.026

2.  Oscillatory neuronal synchronization in primary visual cortex as a correlate of stimulus selection.

Authors:  Pascal Fries; Jan-Hinrich Schröder; Pieter R Roelfsema; Wolf Singer; Andreas K Engel
Journal:  J Neurosci       Date:  2002-05-01       Impact factor: 6.167

Review 3.  SEE: a tool for the visualization and analysis of rodent exploratory behavior.

Authors:  D Drai; I Golani
Journal:  Neurosci Biobehav Rev       Date:  2001-07       Impact factor: 8.989

4.  Computer techniques in correlation and spectral analyses of cerebral slow waves during discriminative behavior.

Authors:  W R ADEY; D O WALTER; C E HENDRIX
Journal:  Exp Neurol       Date:  1961-06       Impact factor: 5.330

5.  Cross-correlation and autocorrelation studies of electroencephalographic potentials.

Authors:  M A B BRAZIER; J U CASBY
Journal:  Electroencephalogr Clin Neurophysiol       Date:  1952-05

Review 6.  Multiple neural spike train data analysis: state-of-the-art and future challenges.

Authors:  Emery N Brown; Robert E Kass; Partha P Mitra
Journal:  Nat Neurosci       Date:  2004-05       Impact factor: 24.884

Review 7.  Advances in quantitative electroencephalogram analysis methods.

Authors:  Nitish V Thakor; Shanbao Tong
Journal:  Annu Rev Biomed Eng       Date:  2004       Impact factor: 9.590

8.  Linkage disequilibrium between the beta frequency of the human EEG and a GABAA receptor gene locus.

Authors:  Bernice Porjesz; Laura Almasy; Howard J Edenberg; Kongming Wang; David B Chorlian; Tatiana Foroud; Alison Goate; John P Rice; Sean J O'Connor; John Rohrbaugh; Samuel Kuperman; Lance O Bauer; Raymond R Crowe; Marc A Schuckit; Victor Hesselbrock; P Michael Conneally; Jay A Tischfield; Ting-Kai Li; Theodore Reich; Henri Begleiter
Journal:  Proc Natl Acad Sci U S A       Date:  2002-03-12       Impact factor: 11.205

9.  Quantitative spectral analysis of EEG in psychiatry revisited: drawing signs out of numbers in a clinical setting.

Authors:  P Coutin-Churchman; Y Añez; M Uzcátegui; L Alvarez; F Vergara; L Mendez; R Fleitas
Journal:  Clin Neurophysiol       Date:  2003-12       Impact factor: 3.708

10.  Cortical networks generating movement-related EEG rhythms in Alzheimer's disease: an EEG coherence study.

Authors:  Claudio Babiloni; Carlo Miniussi; Davide V Moretti; Fabrizio Vecchio; Serenella Salinari; Giovanni Frisoni; Paolo Maria Rossini
Journal:  Behav Neurosci       Date:  2004-08       Impact factor: 1.912

View more
  5 in total

1.  Dendritic sodium channels promote active decorrelation and reduce phase locking to parkinsonian input oscillations in model globus pallidus neurons.

Authors:  Jeremy R Edgerton; Dieter Jaeger
Journal:  J Neurosci       Date:  2011-07-27       Impact factor: 6.167

2.  Development and temporal organization of repetitive behavior in an animal model.

Authors:  Yoko Tanimura; Mark C K Yang; Andrew K Ottens; Mark H Lewis
Journal:  Dev Psychobiol       Date:  2010-12       Impact factor: 3.038

3.  Chronux: a platform for analyzing neural signals.

Authors:  Hemant Bokil; Peter Andrews; Jayant E Kulkarni; Samar Mehta; Partha P Mitra
Journal:  J Neurosci Methods       Date:  2010-07-15       Impact factor: 2.390

4.  Database analysis of simulated and recorded electrophysiological datasets with PANDORA's toolbox.

Authors:  Cengiz Günay; Jeremy R Edgerton; Su Li; Thomas Sangrey; Astrid A Prinz; Dieter Jaeger
Journal:  Neuroinformatics       Date:  2009-05-28

5.  Finding synchrony in the desynchronized EEG: the history and interpretation of gamma rhythms.

Authors:  Omar J Ahmed; Sydney S Cash
Journal:  Front Integr Neurosci       Date:  2013-08-12
  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.