Brain network efficiency and topology depend on the fiber tracking method: 11 tractography algorithms compared in 536 subjects
As connectivity analyses become more popular, claims are often made about how the brain's anatomical networks depend on age, sex, or disease. It is unclear how results depend on tractography methods used to compute fiber networks. We applied 11 tractography methods to high angular resolution di...
Saved in:
Published in | 2013 IEEE 10th International Symposium on Biomedical Imaging pp. 1134 - 1137 |
---|---|
Main Authors | , , , , , , , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
01.04.2013
|
Subjects | |
Online Access | Get full text |
ISBN | 1467364568 9781467364560 |
ISSN | 1945-7928 |
DOI | 10.1109/ISBI.2013.6556679 |
Cover
Abstract | As connectivity analyses become more popular, claims are often made about how the brain's anatomical networks depend on age, sex, or disease. It is unclear how results depend on tractography methods used to compute fiber networks. We applied 11 tractography methods to high angular resolution diffusion images of the brain (4-Tesla 105-gradient HARDI) from 536 healthy young adults. We parcellated 70 cortical regions, yielding 70 × 70 connectivity matrices, encoding fiber density. We computed popular graph theory metrics, including network efficiency, and characteristic path lengths. Both metrics were robust to the number of spherical harmonics used to model diffusion (4 th -8 th order). Age effects were detected only for networks computed with the probabilistic Hough transform method, which excludes smaller fibers. Sex and total brain volume affected networks measured with deterministic, tensor-based fiber tracking but not with the Hough method. Each tractography method includes different fibers, which affects inferences made about the reconstructed networks. |
---|---|
AbstractList | As connectivity analyses become more popular, claims are often made about how the brain's anatomical networks depend on age, sex, or disease. It is unclear how results depend on tractography methods used to compute fiber networks. We applied 11 tractography methods to high angular resolution diffusion images of the brain (4-Tesla 105-gradient HARDI) from 536 healthy young adults. We parcellated 70 cortical regions, yielding 70 × 70 connectivity matrices, encoding fiber density. We computed popular graph theory metrics, including network efficiency, and characteristic path lengths. Both metrics were robust to the number of spherical harmonics used to model diffusion (4 th -8 th order). Age effects were detected only for networks computed with the probabilistic Hough transform method, which excludes smaller fibers. Sex and total brain volume affected networks measured with deterministic, tensor-based fiber tracking but not with the Hough method. Each tractography method includes different fibers, which affects inferences made about the reconstructed networks. |
Author | Jahanshad, Neda McMahon, Katie L. Liang Zhan Wright, Margaret J. de Zubicaray, Greig I. Yan Jin Martin, Nicholas G. Thompson, Paul M. Toga, Arthur W. |
Author_xml | – sequence: 1 surname: Liang Zhan fullname: Liang Zhan organization: Sch. of Med., Lab. of Neuro Imaging, UCLA, Los Angeles, CA, USA – sequence: 2 givenname: Neda surname: Jahanshad fullname: Jahanshad, Neda organization: Sch. of Med., Lab. of Neuro Imaging, UCLA, Los Angeles, CA, USA – sequence: 3 surname: Yan Jin fullname: Yan Jin organization: Sch. of Med., Lab. of Neuro Imaging, UCLA, Los Angeles, CA, USA – sequence: 4 givenname: Arthur W. surname: Toga fullname: Toga, Arthur W. organization: Sch. of Med., Lab. of Neuro Imaging, UCLA, Los Angeles, CA, USA – sequence: 5 givenname: Katie L. surname: McMahon fullname: McMahon, Katie L. organization: Center for Adv. Imaging, Univ. of Queensland, Brisbane, QLD, Australia – sequence: 6 givenname: Greig I. surname: de Zubicaray fullname: de Zubicaray, Greig I. organization: Sch. of Psychol., Univ. of Queensland, Brisbane, QLD, Australia – sequence: 7 givenname: Nicholas G. surname: Martin fullname: Martin, Nicholas G. organization: Queensland Inst. of Med. Res., Brisbane, QLD, Australia – sequence: 8 givenname: Margaret J. surname: Wright fullname: Wright, Margaret J. organization: Queensland Inst. of Med. Res., Brisbane, QLD, Australia – sequence: 9 givenname: Paul M. surname: Thompson fullname: Thompson, Paul M. organization: Sch. of Med., Lab. of Neuro Imaging, UCLA, Los Angeles, CA, USA |
BookMark | eNpFkMtKAzEYhSNWsK19AHGTF5iaNLeJO1u8FAouVHBXMpl_ZtJ2kiETkW58dgctuDp8Z_EdOBM08sEDQteUzCkl-nb9ulzPF4SyuRRCSqXP0IRyqZjkQnyc_4PMR2hMNReZ0ov8Es36fkcIGSSSUjZG38tonMce0leIewxV5awDb4_Y-BKn0IVDqI-4hA4GDh6nBnDlCog4RWP3zte4hdSE8g5T-tulUEfTNYPhUIfoUtP22Ia2MxFKPGwJJnH_WezApv4KXVTm0MPslFP0_vjwtnrONi9P69X9JnNUiZSVghQqz3lOiOEcpJDMskIzJQnnXFW5YUxzS7Tlkg1Iy1yKgsKC86rSWrEpuvnzOgDYdtG1Jh63p-_YD4CMYrU |
ContentType | Conference Proceeding |
DBID | 6IE 6IL CBEJK RIE RIL |
DOI | 10.1109/ISBI.2013.6556679 |
DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Xplore POP ALL IEEE Xplore All Conference Proceedings IEEE Electronic Library (IEL) IEEE Proceedings Order Plans (POP All) 1998-Present |
DatabaseTitleList | |
Database_xml | – sequence: 1 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 | Engineering |
EISBN | 146736455X 9781467364553 9781467364546 1467364541 |
EndPage | 1137 |
ExternalDocumentID | 6556679 |
Genre | orig-research |
GroupedDBID | 6IE 6IF 6IK 6IL 6IN AAJGR AAWTH ADFMO ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ CBEJK IEGSK IERZE OCL RIE RIL |
ID | FETCH-LOGICAL-i175t-d50b7884800a44e6563c3b937604447f8a3394c09c4637f81d865b1e244ff9973 |
IEDL.DBID | RIE |
ISBN | 1467364568 9781467364560 |
ISSN | 1945-7928 |
IngestDate | Wed Aug 27 04:13:32 EDT 2025 |
IsPeerReviewed | false |
IsScholarly | true |
Language | English |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-i175t-d50b7884800a44e6563c3b937604447f8a3394c09c4637f81d865b1e244ff9973 |
PageCount | 4 |
ParticipantIDs | ieee_primary_6556679 |
PublicationCentury | 2000 |
PublicationDate | 2013-April |
PublicationDateYYYYMMDD | 2013-04-01 |
PublicationDate_xml | – month: 04 year: 2013 text: 2013-April |
PublicationDecade | 2010 |
PublicationTitle | 2013 IEEE 10th International Symposium on Biomedical Imaging |
PublicationTitleAbbrev | ISBI |
PublicationYear | 2013 |
Publisher | IEEE |
Publisher_xml | – name: IEEE |
SSID | ssj0001106113 ssj0000744304 |
Score | 1.9346396 |
Snippet | As connectivity analyses become more popular, claims are often made about how the brain's anatomical networks depend on age, sex, or disease. It is unclear how... |
SourceID | ieee |
SourceType | Publisher |
StartPage | 1134 |
SubjectTerms | Anatomical connectivity brain diffusion imaging efficiency Graph theory Magnetic resonance imaging networks Optical fiber networks Optical fiber theory Probabilistic logic random effects analysis tractography Transforms |
Title | Brain network efficiency and topology depend on the fiber tracking method: 11 tractography algorithms compared in 536 subjects |
URI | https://ieeexplore.ieee.org/document/6556679 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV09T8MwELXaTrDw0SK-dQMjaePacWLGIqoWqQgJKnWr7NiGCkhRmwww8NuxnbR8iIEt9mLHOsnvzu_eQ-gsllwwE5nACCoCSmMaSG5wkGIdykRyirFrTh7dsMGYXk-iSQ2dr3thtNaefKbb7tO_5at5WrhSWYdFFnzEvI7qcczLXq11PcVehZRUqYWvr7hcx7sj2zTdqTJ2E9_X5XhMFjQkK7mnarx68cQh7wzvekNH-iLtasEfziv-4ulvodFqyyXf5Kld5LKdvv9Sc_zvP22j1leLH9yuL68dVNPZLtr8pk7YRB89Zx8BWUkUB-3FJlynJohMQV66K7xBaaML8wwslgTjGCiQL0TqavBQGlRfAMZ-Lq8EskE8P8wXs_zxZQkrFjzYtSLCYFlIVxtattC4f3V_OQgqu4ZgZjFIHqgolDahphaCCkq1BYokJZI71g21QWASQQinachTyogdYpWwSGJtAYYxnMdkDzWyeab3ESjFRcS10YI7QXjFmUpUyGPNFLPhxA5Q053k9LVU5JhWh3j49_QR2uh6EwvHtzlGjXxR6BMLJXJ56mPoE2ykwX0 |
linkProvider | IEEE |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV27TsMwFLUKDMDCoyDe3IGRlLh2nJixiKqFtkKildgqO7YBASlq0wEGvh3bSctDDGyxhzixLfnc63PPQegkllwwE5nACCoCSmMaSG5wkGIdykRyirErTu72WGtAr-6iuwo6ndfCaK09-UzX3KO_y1ejdOpSZWcssuAj5gtoKbJvrRfVWvOMij0MKSmDC59hcdGO90e2gbrTZawnvrLLMZksbEhmgk9le3bniUN-1r5ttB3ti9TKIX94r_ijp7mGurOPLhgnT7VpLmvp-y89x__-1Tra-iryg5v58bWBKjrbRKvf9Amr6KPhDCQgK6jioL3chKvVBJEpyAt_hTcojHRhlIFFk2AcBwXysUhdFh4Ki-pzwNj35aVENojn-9H4MX94mcCMBw92rIgwmEylyw5NttCgedm_aAWlYUPwaFFIHqgolDakphaECkq1hYokJZI73g21C2YSQQinachTyohtYpWwSGJtIYYxnMdkGy1mo0zvIFCKi4hrowV3kvCKM5WokMeaKWY3FNtFVTeTw9dCk2NYTuLe393HaLnV73aGnXbveh-t1L2lhWPfHKDFfDzVhxZY5PLI76dPvVnEyA |
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%3Abook&rft.genre=proceeding&rft.title=2013+IEEE+10th+International+Symposium+on+Biomedical+Imaging&rft.atitle=Brain+network+efficiency+and+topology+depend+on+the+fiber+tracking+method%3A+11+tractography+algorithms+compared+in+536+subjects&rft.au=Liang+Zhan&rft.au=Jahanshad%2C+Neda&rft.au=Yan+Jin&rft.au=Toga%2C+Arthur+W.&rft.date=2013-04-01&rft.pub=IEEE&rft.isbn=9781467364560&rft.issn=1945-7928&rft.spage=1134&rft.epage=1137&rft_id=info:doi/10.1109%2FISBI.2013.6556679&rft.externalDocID=6556679 |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1945-7928&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1945-7928&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1945-7928&client=summon |