Applications of multivariate pattern classification analyses in developmental neuroimaging of healthy and clinical populations
Analyses of functional and structural imaging data typically involve testing hypotheses at each voxel in the brain. However, it is often the case that distributed spatial patterns may be a more appropriate metric for discriminating between conditions or groups. Multivariate pattern analysis has been...
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2009-10-01
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doaj-632a0f82e9144674a01e61939ce8398c2020-11-25T02:39:21ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612009-10-01310.3389/neuro.09.032.2009898Applications of multivariate pattern classification analyses in developmental neuroimaging of healthy and clinical populationsSigne L Bray0Catie Chang1Catie Chang2Fumiko Hoeft3Stanford University School of MedicineStanford UniversityStanford University School of MedicineStanford University School of MedicineAnalyses of functional and structural imaging data typically involve testing hypotheses at each voxel in the brain. However, it is often the case that distributed spatial patterns may be a more appropriate metric for discriminating between conditions or groups. Multivariate pattern analysis has been gaining traction in neuroimaging of adult healthy and clinical populations; studies have shown that information present in neuroimaging data can be used to decode intentions and perceptual states, as well as discriminate between healthy and diseased brains. While few studies to date have applied these methods in pediatric populations, in this review we discuss exciting potential applications for studying both healthy, and aberrant, brain development. We include an overview of methods and discussion of challenges and limitations.http://journal.frontiersin.org/Journal/10.3389/neuro.09.032.2009/fulldevelopmentfMRIMRIclinicalmultivariate pattern classification |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Signe L Bray Catie Chang Catie Chang Fumiko Hoeft |
spellingShingle |
Signe L Bray Catie Chang Catie Chang Fumiko Hoeft Applications of multivariate pattern classification analyses in developmental neuroimaging of healthy and clinical populations Frontiers in Human Neuroscience development fMRI MRI clinical multivariate pattern classification |
author_facet |
Signe L Bray Catie Chang Catie Chang Fumiko Hoeft |
author_sort |
Signe L Bray |
title |
Applications of multivariate pattern classification analyses in developmental neuroimaging of healthy and clinical populations |
title_short |
Applications of multivariate pattern classification analyses in developmental neuroimaging of healthy and clinical populations |
title_full |
Applications of multivariate pattern classification analyses in developmental neuroimaging of healthy and clinical populations |
title_fullStr |
Applications of multivariate pattern classification analyses in developmental neuroimaging of healthy and clinical populations |
title_full_unstemmed |
Applications of multivariate pattern classification analyses in developmental neuroimaging of healthy and clinical populations |
title_sort |
applications of multivariate pattern classification analyses in developmental neuroimaging of healthy and clinical populations |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Human Neuroscience |
issn |
1662-5161 |
publishDate |
2009-10-01 |
description |
Analyses of functional and structural imaging data typically involve testing hypotheses at each voxel in the brain. However, it is often the case that distributed spatial patterns may be a more appropriate metric for discriminating between conditions or groups. Multivariate pattern analysis has been gaining traction in neuroimaging of adult healthy and clinical populations; studies have shown that information present in neuroimaging data can be used to decode intentions and perceptual states, as well as discriminate between healthy and diseased brains. While few studies to date have applied these methods in pediatric populations, in this review we discuss exciting potential applications for studying both healthy, and aberrant, brain development. We include an overview of methods and discussion of challenges and limitations. |
topic |
development fMRI MRI clinical multivariate pattern classification |
url |
http://journal.frontiersin.org/Journal/10.3389/neuro.09.032.2009/full |
work_keys_str_mv |
AT signelbray applicationsofmultivariatepatternclassificationanalysesindevelopmentalneuroimagingofhealthyandclinicalpopulations AT catiechang applicationsofmultivariatepatternclassificationanalysesindevelopmentalneuroimagingofhealthyandclinicalpopulations AT catiechang applicationsofmultivariatepatternclassificationanalysesindevelopmentalneuroimagingofhealthyandclinicalpopulations AT fumikohoeft applicationsofmultivariatepatternclassificationanalysesindevelopmentalneuroimagingofhealthyandclinicalpopulations |
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1724786617074319360 |