20527815

Source:http://linkedlifedata.com/resource/pubmed/id/20527815

Download in:

Switch to

Custom View

Named Graph Language Inference

Statements in which the resource exists as a subject.
Predicate	Object
rdf:type	pubmed:Citation
lifeskim:mentions	umls-concept:C0026777, umls-concept:C0439596, umls-concept:C0680844, umls-concept:C0681916, umls-concept:C0699794, umls-concept:C0750572, umls-concept:C1511726, umls-concept:C1515655
pubmed:issue	13
pubmed:dateCreated	2010-6-30
pubmed:abstractText	Principal component regression has been used in the past to separate current contributions from different neuromodulators measured with in vivo fast-scan cyclic voltammetry. Traditionally, a percent cumulative variance approach has been used to determine the rank of the training set voltammetric matrix during model development; however, this approach suffers from several disadvantages including the use of arbitrary percentages and the requirement of extreme precision of training sets. Here, we propose that Malinowski's F-test, a method based on a statistical analysis of the variance contained within the training set, can be used to improve factor selection for the analysis of in vivo fast-scan cyclic voltammetric data. These two methods of rank estimation were compared at all steps in the calibration protocol including the number of principal components retained, overall noise levels, model validation as determined using a residual analysis procedure, and predicted concentration information. By analyzing 119 training sets from two different laboratories amassed over several years, we were able to gain insight into the heterogeneity of in vivo fast-scan cyclic voltammetric data and study how differences in factor selection propagate throughout the entire principal component regression analysis procedure. Visualizing cyclic voltammetric representations of the data contained in the retained and discarded principal components showed that using Malinowski's F-test for rank estimation of in vivo training sets allowed for noise to be more accurately removed. Malinowski's F-test also improved the robustness of our criterion for judging multivariate model validity, even though signal-to-noise ratios of the data varied. In addition, pH change was the majority noise carrier of in vivo training sets while dopamine prediction was more sensitive to noise.
pubmed:grant	http://linkedlifedata.com/resource/pubmed/grant/DA 10900, http://linkedlifedata.com/resource/pubmed/grant/DA 17318, http://linkedlifedata.com/resource/pubmed/grant/R01 DA010900-12, http://linkedlifedata.com/resource/pubmed/grant/R01 DA010900-14, http://linkedlifedata.com/resource/pubmed/grant/R01 DA017318-07
pubmed:commentsCorrections	http://linkedlifedata.com/resource/pubmed/commentcorrection/20527815-10424168, http://linkedlifedata.com/resource/pubmed/commentcorrection/20527815-10500480, http://linkedlifedata.com/resource/pubmed/commentcorrection/20527815-12506716, http://linkedlifedata.com/resource/pubmed/commentcorrection/20527815-15456288, http://linkedlifedata.com/resource/pubmed/commentcorrection/20527815-16006505, http://linkedlifedata.com/resource/pubmed/commentcorrection/20527815-16454919, http://linkedlifedata.com/resource/pubmed/commentcorrection/20527815-16686693, http://linkedlifedata.com/resource/pubmed/commentcorrection/20527815-17416375, http://linkedlifedata.com/resource/pubmed/commentcorrection/20527815-17442245, http://linkedlifedata.com/resource/pubmed/commentcorrection/20527815-17603481, http://linkedlifedata.com/resource/pubmed/commentcorrection/20527815-17868375, http://linkedlifedata.com/resource/pubmed/commentcorrection/20527815-18433146, http://linkedlifedata.com/resource/pubmed/commentcorrection/20527815-18484744, http://linkedlifedata.com/resource/pubmed/commentcorrection/20527815-18689678, http://linkedlifedata.com/resource/pubmed/commentcorrection/20527815-19168413, http://linkedlifedata.com/resource/pubmed/commentcorrection/20527815-19834691, http://linkedlifedata.com/resource/pubmed/commentcorrection/20527815-7636874, http://linkedlifedata.com/resource/pubmed/commentcorrection/20527815-8934210
pubmed:language	eng
pubmed:journal	http://linkedlifedata.com/resource/pubmed/journal/0370536
pubmed:citationSubset	IM
pubmed:status	MEDLINE
pubmed:month	Jul
pubmed:issn	1520-6882
pubmed:author	pubmed-author:CarelliRegina MRM, pubmed-author:KeithleyRichard BRB, pubmed-author:WightmanR MarkRM
pubmed:issnType	Electronic
pubmed:day	1
pubmed:volume	82
pubmed:owner	NLM
pubmed:authorsComplete	Y
pubmed:pagination	5541-51
pubmed:dateRevised	2011-8-1
pubmed:meshHeading	pubmed-meshheading:20527815-Animals, pubmed-meshheading:20527815-Dopamine, pubmed-meshheading:20527815-Electrochemical Techniques, pubmed-meshheading:20527815-Male, pubmed-meshheading:20527815-Multivariate Analysis, pubmed-meshheading:20527815-Principal Component Analysis, pubmed-meshheading:20527815-Rats, pubmed-meshheading:20527815-Rats, Sprague-Dawley
pubmed:year	2010
pubmed:articleTitle	Rank estimation and the multivariate analysis of in vivo fast-scan cyclic voltammetric data.
pubmed:affiliation	Department of Chemistry, Neuroscience Center and Neurobiology Curriculum, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA.
pubmed:publicationType	Journal Article, Research Support, U.S. Gov't, Non-P.H.S., Research Support, N.I.H., Extramural