File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Article: A Sparse Reduced Rank Framework for Group Analysis of Functional Neuroimaging Data

TitleA Sparse Reduced Rank Framework for Group Analysis of Functional Neuroimaging Data
Authors
KeywordsFunctional connectivity
Lasso
Low rank representation
Resting-state functional MRI
Singular value decomposition
Issue Date2015
PublisherAcademia Sinica, Institute of Statistical Science. The Journal's web site is located at http://www.stat.sinica.edu.tw/statistica/
Citation
Statistica Sinica, 2015, v. 25 n. 1, p. 295-312 How to Cite?
AbstractIn spatial-temporal neuroimaging studies, there is an evolving literature on the analysis of functional imaging data in order to learn the intrinsic functional connectivity patterns among different brain regions. However, there are only few efficient approaches for integrating functional connectivity pattern across subjects, while accounting for spatial-temporal functional variation across multiple groups of subjects. The objective of this paper is to develop a new sparse reduced rank (SRR) modeling framework for carrying out functional connectivity analysis across multiple groups of subjects in the frequency domain. Our new framework not only can extract both frequency and spatial factors across subjects, but also imposes sparse constraints on the frequency factors. It thus leads to the identification of important frequencies with high power spectra. In addition, we propose two novel adaptive criteria for automatic selection of sparsity level and model rank. Using simulated data, we demonstrate that SRR outperforms several existing methods. Finally, we apply SRR to detect group differences between controls and two subtypes of attention deficit hyperactivity disorder (ADHD) patients, through analyzing the ADHD-200 data.
Persistent Identifierhttp://hdl.handle.net/10722/232097
ISSN
2015 Impact Factor: 0.838
2015 SCImago Journal Rankings: 2.292

 

DC FieldValueLanguage
dc.contributor.authorAhn, M-
dc.contributor.authorShen, H-
dc.contributor.authorLin, W-
dc.contributor.authorZhu, H-
dc.date.accessioned2016-09-20T05:27:42Z-
dc.date.available2016-09-20T05:27:42Z-
dc.date.issued2015-
dc.identifier.citationStatistica Sinica, 2015, v. 25 n. 1, p. 295-312-
dc.identifier.issn1017-0405-
dc.identifier.urihttp://hdl.handle.net/10722/232097-
dc.description.abstractIn spatial-temporal neuroimaging studies, there is an evolving literature on the analysis of functional imaging data in order to learn the intrinsic functional connectivity patterns among different brain regions. However, there are only few efficient approaches for integrating functional connectivity pattern across subjects, while accounting for spatial-temporal functional variation across multiple groups of subjects. The objective of this paper is to develop a new sparse reduced rank (SRR) modeling framework for carrying out functional connectivity analysis across multiple groups of subjects in the frequency domain. Our new framework not only can extract both frequency and spatial factors across subjects, but also imposes sparse constraints on the frequency factors. It thus leads to the identification of important frequencies with high power spectra. In addition, we propose two novel adaptive criteria for automatic selection of sparsity level and model rank. Using simulated data, we demonstrate that SRR outperforms several existing methods. Finally, we apply SRR to detect group differences between controls and two subtypes of attention deficit hyperactivity disorder (ADHD) patients, through analyzing the ADHD-200 data.-
dc.languageeng-
dc.publisherAcademia Sinica, Institute of Statistical Science. The Journal's web site is located at http://www.stat.sinica.edu.tw/statistica/-
dc.relation.ispartofStatistica Sinica-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectFunctional connectivity-
dc.subjectLasso-
dc.subjectLow rank representation-
dc.subjectResting-state functional MRI-
dc.subjectSingular value decomposition-
dc.titleA Sparse Reduced Rank Framework for Group Analysis of Functional Neuroimaging Data-
dc.typeArticle-
dc.identifier.emailShen, H: haipeng@hku.hk-
dc.identifier.authorityShen, H=rp02082-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.5705/ss.2013.232w-
dc.identifier.hkuros263852-
dc.identifier.volume25-
dc.identifier.issue1-
dc.identifier.spage295-
dc.identifier.epage312-
dc.publisher.placeTaiwan, Republic of China-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats