Linked Life Data

18347739

Source:http://linkedlifedata.com/resource/pubmed/id/18347739

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2008-3-18
pubmed:abstractText
Functional MRI resting state and connectivity studies of brain focus on neural fluctuations at low frequencies which share power with physiological fluctuations originating from lung and heart. Due to the lack of automated software to process physiological signals collected at high magnetic fields, a gap exists in the processing pathway between the acquisition of physiological data and its use in fMRI software for both physiological noise correction and functional analyses of brain activation and connectivity. To fill this gap, we developed an open source, physiological signal processing program, called PhysioNoise, in the python language. We tested its automated processing algorithms and dynamic signal visualization on resting monkey cardiac and respiratory waveforms. PhysioNoise consistently identifies physiological fluctuations for fMRI noise correction and also generates covariates for subsequent analyses of brain activation and connectivity.
pubmed:grant
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1932-6203
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
3
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
e1751
pubmed:dateRevised
2009-11-18
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
Automatic physiological waveform processing for FMRI noise correction and analysis.
pubmed:affiliation
Waisman Laboratory for Brain Imaging and Behavior, Waisman Center, University of Wisconsin, Madison, Wisconsin, United States of America. djkelley@wisc.edu
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural