A Framework for Inter-Subject Prediction of Functional Connectivity From Structural Networks
Functional connections between brain regions are supported by structural connectivity. Both functional and structural connectivity are estimated from in vivo magnetic resonance imaging and offer complementary information on brain organization and function. However, imaging only provides noisy measur...
Saved in:
| Published in | IEEE transactions on medical imaging Vol. 32; no. 12; pp. 2200 - 2214 |
|---|---|
| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
IEEE
01.12.2013
Institute of Electrical and Electronics Engineers |
| Series | epub ahead of print |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0278-0062 1558-254X 1558-254X |
| DOI | 10.1109/TMI.2013.2276916 |
Cover
Loading…
| Abstract | Functional connections between brain regions are supported by structural connectivity. Both functional and structural connectivity are estimated from in vivo magnetic resonance imaging and offer complementary information on brain organization and function. However, imaging only provides noisy measures, and we lack a good neuroscientific understanding of the links between structure and function. Therefore, inter-subject joint modeling of structural and functional connectivity, the key to multimodal biomarkers, is an open challenge. We present a probabilistic framework to learn across subjects a mapping from structural to functional brain connectivity. Expanding on our previous work [1], our approach is based on a predictive framework with multiple sparse linear regression. We rely on the randomized LASSO to identify relevant anatomo-functional links with some confidence interval. In addition, we describe resting-state functional magnetic resonance imaging in the setting of Gaussian graphical models, on the one hand imposing conditional independences from structural connectivity and on the other hand parameterizing the problem in terms of multivariate autoregressive models. We introduce an intrinsic measure of prediction error for functional connectivity that is independent of the parameterization chosen and provides the means for robust model selection. We demonstrate our methodology with regions within the default mode and the salience network as well as, atlas-based cortical parcellation. |
|---|---|
| AbstractList | Functional connections between brain regions are supported by structural connectivity. Both functional and structural connectivity are estimated from in vivo magnetic resonance imaging and offer complementary information on brain organization and function. However, imaging only provides noisy measures, and we lack a good neuroscientific understanding of the links between structure and function. Therefore, inter-subject joint modeling of structural and functional connectivity, the key to multimodal biomarkers, is an open challenge. We present a probabilistic framework to learn across subjects a mapping from structural to functional brain connectivity. Expanding on our previous work [1], our approach is based on a predictive framework with multiple sparse linear regression. We rely on the randomized LASSO to identify relevant anatomo-functional links with some confidence interval. In addition, we describe resting-state functional magnetic resonance imaging in the setting of Gaussian graphical models, on the one hand imposing conditional independences from structural connectivity and on the other hand parameterizing the problem in terms of multivariate autoregressive models. We introduce an intrinsic measure of prediction error for functional connectivity that is independent of the parameterization chosen and provides the means for robust model selection. We demonstrate our methodology with regions within the default mode and the salience network as well as, atlas-based cortical parcellation. Functional connections between brain regions are supported by structural connectivity. Both functional and structural connectivity are estimated from in vivo magnetic resonance imaging and offer complementary information on brain organization and function. However, imaging only provides noisy measures, and we lack a good neuroscientific understanding of the links between structure and function. Therefore, inter-subject joint modeling of structural and functional connectivity, the key to multimodal biomarkers, is an open challenge. We present a probabilistic framework to learn across subjects a mapping from structural to functional brain connectivity. Expanding on our previous work [1], our approach is based on a predictive framework with multiple sparse linear regression. We rely on the randomized LASSO to identify relevant anatomo-functional links with some confidence interval. In addition, we describe resting-state functional magnetic resonance imaging in the setting of Gaussian graphical models, on the one hand imposing conditional independences from structural connectivity and on the other hand parameterizing the problem in terms of multivariate autoregressive models. We introduce an intrinsic measure of prediction error for functional connectivity that is independent of the parameterization chosen and provides the means for robust model selection. We demonstrate our methodology with regions within the default mode and the salience network as well as, atlas-based cortical parcellation.Functional connections between brain regions are supported by structural connectivity. Both functional and structural connectivity are estimated from in vivo magnetic resonance imaging and offer complementary information on brain organization and function. However, imaging only provides noisy measures, and we lack a good neuroscientific understanding of the links between structure and function. Therefore, inter-subject joint modeling of structural and functional connectivity, the key to multimodal biomarkers, is an open challenge. We present a probabilistic framework to learn across subjects a mapping from structural to functional brain connectivity. Expanding on our previous work [1], our approach is based on a predictive framework with multiple sparse linear regression. We rely on the randomized LASSO to identify relevant anatomo-functional links with some confidence interval. In addition, we describe resting-state functional magnetic resonance imaging in the setting of Gaussian graphical models, on the one hand imposing conditional independences from structural connectivity and on the other hand parameterizing the problem in terms of multivariate autoregressive models. We introduce an intrinsic measure of prediction error for functional connectivity that is independent of the parameterization chosen and provides the means for robust model selection. We demonstrate our methodology with regions within the default mode and the salience network as well as, atlas-based cortical parcellation. Functional connections between brain regions are supported by structural connectivity. Both functional and structural connectivity are estimated from in-vivo MRI and offer complementary information on brain organisation and function. However, imaging only provides noisy measures, and we lack a good neuroscientific understanding of the links between structure and function. Therefore, inter-subject joint modeling of structural and functional connectivity, the key to multimodal biomarkers, is an open challenge. We present a probabilistic framework to learn across subjects a mapping from structural to functional brain connectivity. Expanding on our previous work, our approach is based on a predictive framework with multiple sparse linear regression. We rely on the randomized LASSO to identify relevant anatomo-functional links with some confidence interval. In addition, we describe resting-state (rs)-fMRI in the setting of Gaussian graphical models, on the one hand imposing conditional independences from structural connectivity and on the other hand parameterizing the problem in terms of multivariate autoregressive models. We introduce an intrinsic measure of prediction error for functional connectivity that is independent of the parameterization chosen and provides the means for robust model selection. We demonstrate our methodology with regions within the default mode and the salience network as well as, atlas-based cortical parcellation. |
| Author | Varoquaux, Gael Thirion, Bertrand Deligianni, Fani Sharp, David J. Leech, Robert Ledig, Christian Rueckert, Daniel |
| Author_xml | – sequence: 1 givenname: Fani surname: Deligianni fullname: Deligianni, Fani email: f.deligianni@ucl.ac.uk organization: Inst. of Child Health, Univ. Coll. London, London, UK – sequence: 2 givenname: Gael surname: Varoquaux fullname: Varoquaux, Gael organization: Parietal team, INRIA-Saclay, Saclay, France – sequence: 3 givenname: Bertrand surname: Thirion fullname: Thirion, Bertrand organization: Parietal team, INRIA-Saclay, Saclay, France – sequence: 4 givenname: David J. surname: Sharp fullname: Sharp, David J. organization: Dept. of Med., Imperial Coll. London, London, UK – sequence: 5 givenname: Christian surname: Ledig fullname: Ledig, Christian organization: Dept. of Comput., BioMedia Group, Imperial Coll. London, London, UK – sequence: 6 givenname: Robert surname: Leech fullname: Leech, Robert organization: Dept. of Med., Imperial Coll. London, London, UK – sequence: 7 givenname: Daniel surname: Rueckert fullname: Rueckert, Daniel organization: Dept. of Comput., BioMedia Group, Imperial Coll. London, London, UK |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/23934663$$D View this record in MEDLINE/PubMed https://inria.hal.science/hal-00852072$$DView record in HAL |
| BookMark | eNp9kc1v1DAQxS3Uim4LdyQklCMcsvgjduzjasXSlbaA1CL1UMlynLFwSeLWcYr63-Owyx44cJrR-PfeyPPO0ckQBkDoDcFLQrD6eHO1XVJM2JLSWigiXqAF4VyWlFe3J2iBaS1LjAU9Q-fjeI8xqThWL9EZZYpVQrAFulsVm2h6-BXiz8KFWGyHBLG8npp7sKn4FqH1NvkwFMEVm2n405uuWIdhyIB_8uk5O4S-uE5xsmmK-fELpNlvfIVOnelGeH2oF-j75tPN-rLcff28Xa92pWWKp9JhaKwxDkBJwFRK51RrXdtUiqlG1VLkIRM1ZrRWhFBDCcmflawGY1rH2QX6sPf9YTr9EH1v4rMOxuvL1U7PM4wlp7imTySz7_fsQwyPE4xJ93600HVmgDCNmkhcSV4JPtu-O6BT00N7dP57vQzgPWBjGMcI7ogQrOeAdA5IzwHpQ0BZIv6RWJ_MfNQUje_-J3y7F3oAOO4RvOZEUfYbPfCcOw |
| CODEN | ITMID4 |
| CitedBy_id | crossref_primary_10_1016_j_jneumeth_2020_108626 crossref_primary_10_3389_fnins_2014_00258 crossref_primary_10_3390_brainsci13091297 crossref_primary_10_1016_j_media_2020_101799 crossref_primary_10_1016_j_neuroimage_2022_119037 crossref_primary_10_1016_j_neuroimage_2023_119975 crossref_primary_10_3389_fnimg_2022_850266 crossref_primary_10_1016_j_neuroimage_2018_07_047 crossref_primary_10_1371_journal_pcbi_1005325 crossref_primary_10_1038_s41598_018_23051_9 crossref_primary_10_1038_s41598_018_37300_4 crossref_primary_10_1371_journal_pone_0153404 crossref_primary_10_1016_j_neuroimage_2021_118288 crossref_primary_10_1002_hbm_25447 crossref_primary_10_1016_j_conbuildmat_2022_126408 crossref_primary_10_1109_TMI_2014_2341732 |
| Cites_doi | 10.1073/pnas.0811168106 10.1016/j.neuroimage.2010.06.041 10.1007/s00429-009-0208-6 10.2307/2528966 10.1016/S0896-6273(02)00679-7 10.1073/pnas.0913008107 10.1007/s11263-005-3222-z 10.1007/s10444-004-7634-z 10.1093/cercor/10.2.127 10.1111/j.1467-9868.2010.00740.x 10.1002/mrm.10609 10.1007/978-3-642-22092-0_25 10.1109/72.788640 10.1002/hbm.21058 10.1176/appi.ajp.164.3.450 10.1093/med/9780195369779.001.0001 10.1093/cercor/bhn059 10.1109/ISBI.2010.5490188 10.1016/j.neuroimage.2010.02.010 10.1109/TMI.2011.2166083 10.1007/s11682-008-9038-z 10.1098/rstb.2005.1634 10.1109/TMI.2009.2014372 10.1093/brain/awr175 10.1073/pnas.0403743101 10.1002/cpa.20132 10.1152/jn.00338.2011 10.1371/journal.pbio.0060159 10.1016/j.jphysparis.2012.01.001 10.1017/S0140525X04000196 10.1523/JNEUROSCI.2964-08.2008 10.1016/j.neuroimage.2004.07.051 10.1016/j.neuroimage.2008.05.059 10.1002/hbm.460020104 10.1109/42.796284 10.1016/j.neuroimage.2008.08.044 10.1137/S0895479894278952 10.1371/journal.pcbi.0010042 10.1016/j.neuroimage.2011.04.010 10.1109/ISBI.2011.5872537 10.1002/hbm.460020107 10.1109/ISBI.2012.6235693 10.1016/j.neuroimage.2010.08.063 10.1111/j.1467-9868.2011.00771.x 10.1007/s004400050210 10.1186/1471-2202-5-42 10.1016/j.neuroimage.2006.02.024 10.1016/j.neuroimage.2007.02.012 10.1038/nrneurol.2009.198 10.1073/pnas.1113455109 10.1109/TMI.2010.2046908 10.1093/oso/9780198522195.001.0001 10.2217/iim.10.21 10.1109/42.906424 10.1016/j.neubiorev.2009.06.002 10.1016/j.neuroimage.2010.05.081 10.1038/nrn2575 10.1007/s10851-006-6897-z 10.1016/j.tins.2011.05.005 10.1073/pnas.1121329109 10.1016/j.neuroimage.2006.05.061 10.1109/MCSE.2011.35 10.1038/nrn893 10.1016/j.neuroimage.2010.01.019 10.1016/S0047-259X(03)00096-4 |
| ContentType | Journal Article |
| Copyright | Distributed under a Creative Commons Attribution 4.0 International License |
| Copyright_xml | – notice: Distributed under a Creative Commons Attribution 4.0 International License |
| DBID | 97E RIA RIE AAYXX CITATION NPM 7X8 1XC VOOES |
| DOI | 10.1109/TMI.2013.2276916 |
| DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef PubMed MEDLINE - Academic Hyper Article en Ligne (HAL) Hyper Article en Ligne (HAL) (Open Access) |
| DatabaseTitle | CrossRef PubMed MEDLINE - Academic |
| DatabaseTitleList | PubMed MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: RIE name: IEEE Electronic Library (IEL) url: https://proxy.k.utb.cz/login?url=https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine Engineering Computer Science |
| EISSN | 1558-254X |
| EndPage | 2214 |
| ExternalDocumentID | oai_HAL_hal_00852072v1 23934663 10_1109_TMI_2013_2276916 6575192 |
| Genre | orig-research Journal Article |
| GrantInformation_xml | – fundername: Medical Research Council grantid: G0701951 – fundername: Medical Research Council grantid: G0701782 – fundername: Department of Health grantid: NIHR-RP-011-048 |
| GroupedDBID | --- -DZ -~X .GJ 0R~ 29I 4.4 53G 5GY 5RE 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACGFS ACIWK ACNCT ACPRK AENEX AETIX AFRAH AGQYO AGSQL AHBIQ AI. AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ASUFR ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD F5P HZ~ H~9 IBMZZ ICLAB IFIPE IFJZH IPLJI JAVBF LAI M43 MS~ O9- OCL P2P PQQKQ RIA RIE RNS RXW TAE TN5 VH1 AAYOK AAYXX CITATION RIG NPM 7X8 1XC VOOES |
| ID | FETCH-LOGICAL-c395t-f0ebcaafee98e0288ff9dcfdb4939b978602836703279112a211916837eaadf53 |
| IEDL.DBID | RIE |
| ISSN | 0278-0062 1558-254X |
| IngestDate | Fri May 09 12:14:37 EDT 2025 Sun Aug 24 03:53:46 EDT 2025 Mon Jul 21 06:05:13 EDT 2025 Tue Jul 01 03:15:53 EDT 2025 Thu Apr 24 22:54:42 EDT 2025 Tue Aug 26 16:42:50 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | false |
| IsScholarly | true |
| Issue | 12 |
| Keywords | functional brain connectivity predictive modeling statistical associations structural brain connectivity |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html Distributed under a Creative Commons Attribution 4.0 International License: http://creativecommons.org/licenses/by/4.0 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c395t-f0ebcaafee98e0288ff9dcfdb4939b978602836703279112a211916837eaadf53 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ORCID | 0000-0002-5801-6318 0000-0003-1076-5122 |
| OpenAccessLink | https://inria.hal.science/hal-00852072 |
| PMID | 23934663 |
| PQID | 1804854655 |
| PQPubID | 23479 |
| PageCount | 15 |
| ParticipantIDs | pubmed_primary_23934663 crossref_primary_10_1109_TMI_2013_2276916 proquest_miscellaneous_1804854655 ieee_primary_6575192 hal_primary_oai_HAL_hal_00852072v1 crossref_citationtrail_10_1109_TMI_2013_2276916 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2013-12-01 |
| PublicationDateYYYYMMDD | 2013-12-01 |
| PublicationDate_xml | – month: 12 year: 2013 text: 2013-12-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States |
| PublicationSeriesTitle | epub ahead of print |
| PublicationTitle | IEEE transactions on medical imaging |
| PublicationTitleAbbrev | TMI |
| PublicationTitleAlternate | IEEE Trans Med Imaging |
| PublicationYear | 2013 |
| Publisher | IEEE Institute of Electrical and Electronics Engineers |
| Publisher_xml | – name: IEEE – name: Institute of Electrical and Electronics Engineers |
| References | ref57 ref13 ref56 ref12 ref59 ref15 ref58 ref53 ref52 ref55 ref11 ref54 ref10 ref17 ref16 ref19 ref18 robinson (ref25) 2008; 5241 ref51 bach (ref37) 2011 ref50 wang (ref14) 2006; 4191 yeo (ref71) 2011; 106 ref45 ref48 ref42 ref41 ref43 ref49 donoho (ref46) 2006; 59 ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref40 ref35 ref34 ref36 ref31 ref30 ref33 f rstner (ref47) 1999 ref2 ref1 ref39 ref38 lauritzen (ref44) 1996 varoquaux (ref32) 2010 varoquaux (ref72) 2011 ref70 pedregosa (ref61) 2011; 12 ref73 ref68 ref24 ref67 ref23 ref26 ref69 ref64 ref20 ref63 ref66 ref22 ref65 ref21 ref28 ref27 ref29 ref60 ref62 |
| References_xml | – ident: ref30 doi: 10.1073/pnas.0811168106 – ident: ref67 doi: 10.1016/j.neuroimage.2010.06.041 – ident: ref29 doi: 10.1007/s00429-009-0208-6 – ident: ref43 doi: 10.2307/2528966 – ident: ref65 doi: 10.1016/S0896-6273(02)00679-7 – ident: ref7 doi: 10.1073/pnas.0913008107 – ident: ref48 doi: 10.1007/s11263-005-3222-z – ident: ref36 doi: 10.1007/s10444-004-7634-z – ident: ref6 doi: 10.1093/cercor/10.2.127 – ident: ref50 doi: 10.1111/j.1467-9868.2010.00740.x – ident: ref59 doi: 10.1002/mrm.10609 – ident: ref1 doi: 10.1007/978-3-642-22092-0_25 – ident: ref33 doi: 10.1109/72.788640 – ident: ref12 doi: 10.1002/hbm.21058 – volume: 4191 start-page: 340 year: 2006 ident: ref14 article-title: Discriminative analysis of early alzheimer's disease based on two intrinsically anti-correlated networks with resting-state fMRI publication-title: Proc MICCAI – ident: ref11 doi: 10.1176/appi.ajp.164.3.450 – ident: ref17 doi: 10.1093/med/9780195369779.001.0001 – ident: ref27 doi: 10.1093/cercor/bhn059 – ident: ref64 doi: 10.1109/ISBI.2010.5490188 – ident: ref58 doi: 10.1016/j.neuroimage.2010.02.010 – ident: ref31 doi: 10.1109/TMI.2011.2166083 – ident: ref62 doi: 10.1007/s11682-008-9038-z – ident: ref21 doi: 10.1098/rstb.2005.1634 – ident: ref56 doi: 10.1109/TMI.2009.2014372 – ident: ref15 doi: 10.1093/brain/awr175 – ident: ref18 doi: 10.1073/pnas.0403743101 – volume: 59 start-page: 797 year: 2006 ident: ref46 article-title: For most large underdetermined systems of linear equations the minimal <tex Notation="TeX">$\ell_{1}$</tex> -norm solution is also the sparsest solution publication-title: Comm Pure Appl Math doi: 10.1002/cpa.20132 – volume: 106 start-page: 1125 year: 2011 ident: ref71 article-title: The organization of the human cerebral cortex estimated by intrinsic functional connectivity publication-title: J Neurophys doi: 10.1152/jn.00338.2011 – ident: ref22 doi: 10.1371/journal.pbio.0060159 – ident: ref42 doi: 10.1016/j.jphysparis.2012.01.001 – ident: ref10 doi: 10.1017/S0140525X04000196 – ident: ref19 doi: 10.1002/mrm.10609 – ident: ref28 doi: 10.1523/JNEUROSCI.2964-08.2008 – ident: ref51 doi: 10.1016/j.neuroimage.2004.07.051 – ident: ref40 doi: 10.1016/j.neuroimage.2008.05.059 – ident: ref66 doi: 10.1002/hbm.460020104 – ident: ref53 doi: 10.1109/42.796284 – start-page: 562 year: 2011 ident: ref72 article-title: Multi-subject dictionary learning to segment an atlas of brain spontaneous activity publication-title: Proc IPMI – ident: ref69 doi: 10.1016/j.neuroimage.2008.08.044 – ident: ref45 doi: 10.1137/S0895479894278952 – ident: ref5 doi: 10.1371/journal.pcbi.0010042 – ident: ref3 doi: 10.1016/j.neuroimage.2011.04.010 – ident: ref70 doi: 10.1109/ISBI.2011.5872537 – ident: ref2 doi: 10.1002/hbm.460020107 – ident: ref57 doi: 10.1109/ISBI.2012.6235693 – ident: ref41 doi: 10.1016/j.neuroimage.2010.08.063 – ident: ref35 doi: 10.1111/j.1467-9868.2011.00771.x – ident: ref34 doi: 10.1007/s004400050210 – ident: ref8 doi: 10.1186/1471-2202-5-42 – ident: ref38 doi: 10.1016/j.neuroimage.2006.02.024 – ident: ref23 doi: 10.1016/j.neuroimage.2007.02.012 – volume: 12 start-page: 2825 year: 2011 ident: ref61 article-title: Scikit-learn: Machine learning in Python publication-title: J Mach Learn Res – start-page: 451 year: 2011 ident: ref37 article-title: Non-asymptotic analysis of stochastic approximation algorithms for machine learning publication-title: Proc Conf Neural Inf Process Syst – ident: ref26 doi: 10.1038/nrneurol.2009.198 – ident: ref16 doi: 10.1073/pnas.1113455109 – ident: ref52 doi: 10.1109/TMI.2010.2046908 – year: 1996 ident: ref44 publication-title: Graphical Models doi: 10.1093/oso/9780198522195.001.0001 – ident: ref39 doi: 10.2217/iim.10.21 – ident: ref55 doi: 10.1109/42.906424 – ident: ref13 doi: 10.1016/j.neubiorev.2009.06.002 – ident: ref9 doi: 10.1016/j.neuroimage.2010.05.081 – ident: ref4 doi: 10.1038/nrn2575 – volume: 5241 start-page: 486 year: 2008 ident: ref25 article-title: Multivariate statistical analysis of whole brain structural networks obtained using probabilistic tractography publication-title: Proc MICCAI – ident: ref49 doi: 10.1007/s10851-006-6897-z – ident: ref63 doi: 10.1016/j.tins.2011.05.005 – start-page: 2334 year: 2010 ident: ref32 article-title: Brain covariance selection: Better individual functional connectivity models using population prior publication-title: Proc Conf Neural Inf Process Syst – ident: ref20 doi: 10.1073/pnas.1121329109 – ident: ref54 doi: 10.1016/j.neuroimage.2006.05.061 – ident: ref73 doi: 10.1109/MCSE.2011.35 – start-page: 113 year: 1999 ident: ref47 article-title: A metric for covariance matrices publication-title: Qua Vadis Geodesia – ident: ref68 doi: 10.1038/nrn893 – ident: ref24 doi: 10.1016/j.neuroimage.2010.01.019 – ident: ref60 doi: 10.1016/S0047-259X(03)00096-4 |
| SSID | ssj0014509 |
| Score | 2.2423532 |
| Snippet | Functional connections between brain regions are supported by structural connectivity. Both functional and structural connectivity are estimated from in vivo... Functional connections between brain regions are supported by structural connectivity. Both functional and structural connectivity are estimated from in-vivo... |
| SourceID | hal proquest pubmed crossref ieee |
| SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 2200 |
| SubjectTerms | Bioengineering Brain models Cognitive science Computer Science Correlation Covariance matrices Functional brain connectivity Life Sciences Matrix decomposition Medical Imaging Neuroscience predictive modeling Predictive models statistical associations structural brain connectivity Symmetric matrices |
| Title | A Framework for Inter-Subject Prediction of Functional Connectivity From Structural Networks |
| URI | https://ieeexplore.ieee.org/document/6575192 https://www.ncbi.nlm.nih.gov/pubmed/23934663 https://www.proquest.com/docview/1804854655 https://inria.hal.science/hal-00852072 |
| Volume | 32 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1Na9wwEB2SHEpzyGfTOEmDWnIp1Lu2bMur4xK6bEM2FJpADgEjyRKBJuvS7PbQX58ZyTZJaUpvxsjG5o1HT5rnNwAnpaIll6HmLlrFeVrqWOc2ja3LdSpqS7Sc1BYXYnqVn10X1yvwqf8XxlrrxWd2QIe-ll83ZklbZUNfJJCYcFcxzMK_Wn3FIC-CnIOTY2wieFeSTOTwcvaFNFzZgPNSIB0iA-BMZrkQ2bPZaPWWtJC-ycrLfNPPO5NNmHVPHOQm3wfLhR6Y33-YOf7vK23BRktA2ThEzDas2PkOrD-xJdyBV7O24L4LN2M26eRbDPkt8zuIMaYb2r9hX3_SSMKWNY5NcI4MW4vM62dM6EyBd2ju2TdvVEsmH-wiSM8f3sDV5PPl6TRuGzLEJpPFInYJSaeUs1aOLBKTkXOyNq7WucykxvWoILYiMInwEpMoV94-TuAa2CpVuyLbg7V5M7f7wJRTHNlRarmsMYfU2qQGydeIpyZBhsQjGHbAVKZ1K6emGXeVX7UkskJUK0K1alGN4GN_xY_g1PGPsR8Q634YWWxPx-cVnSMOypOS_0oj2CW8-lEtVBG870Kjwk-R6itqbpvlQ5WOMB1Sc_kigrchZvqLu4A7-PtND-E1PV_QyRzBGkJi3yHbWehjH-aPPsD3Hw |
| linkProvider | IEEE |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1di9QwFL2sK_jx4MeuuvUzii-CnWnTNp08DuIwqzOD4CzsgxCSNEFQp7I748P--r03aYuKim-lpKXl3N6c5J6eC_Cy1rTkstTcxei0zGuTmtLlqfOlyUXjiJaT2mIl5iflu9PqdA9eD__COOeC-MyN6DDU8pvW7mirbByKBBIT7lWc98sq_q011AzKKgo6OHnGZoL3RclMjtfLY1JxFSPOa4GEiCyAC1mUQhS_zEdXPpMaMrRZ-TvjDDPP7DYs-2eOgpMvo93WjOzFb3aO__tSd-BWR0HZNMbMXdhzmwO4-ZMx4QFcW3Yl90P4NGWzXsDFkOGysIeYYsKhHRz24YxGErqs9WyGs2TcXGRBQWNjbwq8Q_uNfQxWtWTzwVZRfH5-D05mb9dv5mnXkiG1hay2qc9IPKW9c3LikJpMvJeN9Y0pZSENrkgF8RWBaYTXmEa5DgZyAlfBTuvGV8V92N-0G3cETHvNkR_ljssGs0hjbG6Rfk14bjPkSDyBcQ-Msp1fObXN-KrCuiWTClFVhKrqUE3g1XDF9-jV8Y-xLxDrYRiZbM-nC0XniIXyrOY_8gQOCa9hVAdVAs_70FD4MVKFRW9cuztX-QQTIrWXrxJ4EGNmuLgPuId_vukzuD5fLxdqcbx6_whu0LNG1cxj2Ed43BPkPlvzNIT8JVZV-mw |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+Framework+for+Inter-Subject+Prediction+of+Functional+Connectivity+From+Structural+Networks&rft.jtitle=IEEE+transactions+on+medical+imaging&rft.au=Deligianni%2C+Fani&rft.au=Varoquaux%2C+Gael&rft.au=Thirion%2C+Bertrand&rft.au=Sharp%2C+David+J&rft.date=2013-12-01&rft.eissn=1558-254X&rft.volume=32&rft.issue=12&rft.spage=2200&rft_id=info:doi/10.1109%2FTMI.2013.2276916&rft_id=info%3Apmid%2F23934663&rft.externalDocID=23934663 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0278-0062&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0278-0062&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0278-0062&client=summon |