Home > Publications database > Tract-specific statistics based on diffusion-weighted probabilistic tractography > print

001     906378
005     20230123110601.0
024 7 _ |2 doi
|a 10.1038/s42003-022-03073-w
024 7 _ |2 Handle
|a 2128/30806
024 7 _ |2 pmid
|a 35177755
024 7 _ |2 WOS
|a WOS:000757506400006
024 7 _ |a altmetric:123293881
|2 altmetric
037 _ _ |a FZJ-2022-01407
082 _ _ |a 570
100 1 _ |0 P:(DE-Juel1)162109
|a Reid, Andrew T.
|b 0
|e Corresponding author
245 _ _ |a Tract-specific statistics based on diffusion-weighted probabilistic tractography
260 _ _ |a London
|b Springer Nature
|c 2022
336 7 _ |2 DRIVER
|a article
336 7 _ |2 DataCite
|a Output Types/Journal article
336 7 _ |0 PUB:(DE-HGF)16
|2 PUB:(DE-HGF)
|a Journal Article
|b journal
|m journal
|s 1646125147_5297
336 7 _ |2 BibTeX
|a ARTICLE
336 7 _ |2 ORCID
|a JOURNAL_ARTICLE
336 7 _ |0 0
|2 EndNote
|a Journal Article
520 _ _ |a Diffusion-weighted neuroimaging approaches provide rich evidence for estimating the structural integrity of white matter in vivo, but typically do not assess white matter integrity for connections between two specific regions of the brain. Here, we present a method for deriving tract-specific diffusion statistics, based upon predefined regions of interest. Our approach derives a population distribution using probabilistic tractography, based on the Nathan Kline Institute (NKI) Enhanced Rockland sample. We determine the most likely geometry of a path between two regions and express this as a spatial distribution. We then estimate the average orientation of streamlines traversing this path, at discrete distances along its trajectory, and the fraction of diffusion directed along this orientation for each participant. The resulting participant-wise metrics (tract-specific anisotropy; TSA) can then be used for statistical analysis on any comparable population. Based on this method, we report both negative and positive associations between age and TSA for two networks derived from published meta-analytic studies (the "default mode" and "what-where" networks), along with more moderate sex differences and age-by-sex interactions. The proposed method can be applied to any arbitrary set of brain regions, to estimate both the spatial trajectory and DWI-based anisotropy specific to those regions.
536 _ _ |0 G:(DE-HGF)POF4-5251
|a 5251 - Multilevel Brain Organization and Variability (POF4-525)
|c POF4-525
|f POF IV
|x 0
588 _ _ |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de
700 1 _ |0 P:(DE-Juel1)172024
|a Camilleri, Julia
|b 1
|u fzj
700 1 _ |0 P:(DE-Juel1)131684
|a Hoffstaedter, Felix
|b 2
700 1 _ |0 P:(DE-Juel1)131678
|a Eickhoff, Simon B.
|b 3
|u fzj
773 _ _ |0 PERI:(DE-600)2919698-X
|a 10.1038/s42003-022-03073-w
|g Vol. 5, no. 1, p. 138
|n 1
|p 138
|t Communications biology
|v 5
|x 2399-3642
|y 2022
856 4 _ |u https://juser.fz-juelich.de/record/906378/files/s42003-022-03073-w.pdf
|y OpenAccess
909 C O |o oai:juser.fz-juelich.de:906378
|p openaire
|p open_access
|p VDB
|p driver
|p dnbdelivery
910 1 _ |0 I:(DE-588b)5008462-8
|6 P:(DE-Juel1)162109
|a Forschungszentrum Jülich
|b 0
|k FZJ
910 1 _ |0 I:(DE-588b)5008462-8
|6 P:(DE-Juel1)172024
|a Forschungszentrum Jülich
|b 1
|k FZJ
910 1 _ |0 I:(DE-588b)5008462-8
|6 P:(DE-Juel1)131684
|a Forschungszentrum Jülich
|b 2
|k FZJ
910 1 _ |0 I:(DE-588b)5008462-8
|6 P:(DE-Juel1)131678
|a Forschungszentrum Jülich
|b 3
|k FZJ
913 1 _ |0 G:(DE-HGF)POF4-525
|1 G:(DE-HGF)POF4-520
|2 G:(DE-HGF)POF4-500
|3 G:(DE-HGF)POF4
|4 G:(DE-HGF)POF
|9 G:(DE-HGF)POF4-5251
|a DE-HGF
|b Key Technologies
|l Natural, Artificial and Cognitive Information Processing
|v Decoding Brain Organization and Dysfunction
|x 0
914 1 _ |y 2022
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2021-06-15
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1190
|2 StatID
|b Biological Abstracts
|d 2021-06-15
915 _ _ |a Creative Commons Attribution CC BY 4.0
|0 LIC:(DE-HGF)CCBY4
|2 HGFVOC
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2021-06-15
915 _ _ |a Fees
|0 StatID:(DE-HGF)0700
|2 StatID
|d 2021-06-15
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a Article Processing Charges
|0 StatID:(DE-HGF)0561
|2 StatID
|d 2021-06-15
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b COMMUN BIOL : 2021
|d 2022-11-12
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2022-11-12
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2022-11-12
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0501
|2 StatID
|b DOAJ Seal
|d 2021-10-13T14:52:02Z
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0500
|2 StatID
|b DOAJ
|d 2021-10-13T14:52:02Z
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b DOAJ : Blind peer review
|d 2021-10-13T14:52:02Z
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0600
|2 StatID
|b Ebsco Academic Search
|d 2022-11-12
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b ASC
|d 2022-11-12
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2022-11-12
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2022-11-12
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1050
|2 StatID
|b BIOSIS Previews
|d 2022-11-12
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1030
|2 StatID
|b Current Contents - Life Sciences
|d 2022-11-12
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1040
|2 StatID
|b Zoological Record
|d 2022-11-12
915 _ _ |a IF >= 5
|0 StatID:(DE-HGF)9905
|2 StatID
|b COMMUN BIOL : 2021
|d 2022-11-12
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)INM-7-20090406
|k INM-7
|l Gehirn & Verhalten
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-Juel1)INM-7-20090406
980 1 _ |a FullTexts

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21