Home > Publications database > Derivation of Fiber Orientations From Oblique Views Through Human Brain Sections in 3D-Polarized Light Imaging > print |
001 | 856602 | ||
005 | 20220930130159.0 | ||
024 | 7 | _ | |a 10.3389/fnana.2018.00075 |2 doi |
024 | 7 | _ | |a 2128/19842 |2 Handle |
024 | 7 | _ | |a pmid:30323745 |2 pmid |
024 | 7 | _ | |a WOS:000445758400001 |2 WOS |
024 | 7 | _ | |a altmetric:49541463 |2 altmetric |
037 | _ | _ | |a FZJ-2018-05974 |
082 | _ | _ | |a 610 |
100 | 1 | _ | |a Schmitz, Daniel |0 P:(DE-Juel1)164129 |b 0 |e Corresponding author |u fzj |
245 | _ | _ | |a Derivation of Fiber Orientations From Oblique Views Through Human Brain Sections in 3D-Polarized Light Imaging |
260 | _ | _ | |a Lausanne |c 2018 |b Frontiers Research Foundation |
336 | 7 | _ | |a article |2 DRIVER |
336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1563786876_20751 |2 PUB:(DE-HGF) |
336 | 7 | _ | |a ARTICLE |2 BibTeX |
336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
520 | _ | _ | |a 3D-Polarized Light Imaging (3D-PLI) enables high-resolution three-dimensional mapping of the nerve fiber architecture in unstained histological brain sections based on the intrinsic birefringence of myelinated nerve fibers. The interpretation of the measured birefringent signals comes with conjointly measured information about the local fiber birefringence strength and the fiber orientation. In this study, we present a novel approach to disentangle both parameters from each other based on a weighted least squares routine (ROFL) applied to oblique polarimetric 3D-PLI measurements. This approach was compared to a previously described analytical method on simulated and experimental data obtained from a post mortem human brain. Analysis of the simulations revealed in case of ROFL a distinctly increased level of confidence to determine steep and flat fiber orientations with respect to the brain sectioning plane. Based on analysis of histological sections of a human brain dataset, it was demonstrated that ROFL provides a coherent characterization of cortical, subcortical, and white matter regions in terms of fiber orientation and birefringence strength, within and across sections. Oblique measurements combined with ROFL analysis opens up new ways to determine physical brain tissue properties by means of 3D-PLI microscopy |
536 | _ | _ | |a 574 - Theory, modelling and simulation (POF3-574) |0 G:(DE-HGF)POF3-574 |c POF3-574 |f POF III |x 0 |
536 | _ | _ | |a 511 - Computational Science and Mathematical Methods (POF3-511) |0 G:(DE-HGF)POF3-511 |c POF3-511 |f POF III |x 1 |
536 | _ | _ | |a HBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907) |0 G:(EU-Grant)785907 |c 785907 |f H2020-SGA-FETFLAG-HBP-2017 |x 2 |
536 | _ | _ | |a HBP SGA1 - Human Brain Project Specific Grant Agreement 1 (720270) |0 G:(EU-Grant)720270 |c 720270 |f H2020-Adhoc-2014-20 |x 3 |
536 | _ | _ | |a 3D Reconstruction of Nerve Fibers in the Human, the Monkey, the Rodent, and the Pigeon Brain (jinm11_20181101) |0 G:(DE-Juel1)jinm11_20181101 |c jinm11_20181101 |f 3D Reconstruction of Nerve Fibers in the Human, the Monkey, the Rodent, and the Pigeon Brain |x 4 |
588 | _ | _ | |a Dataset connected to CrossRef |
700 | 1 | _ | |a Münzing, Sascha |0 P:(DE-Juel1)171951 |b 1 |u fzj |
700 | 1 | _ | |a Schober, Martin |0 P:(DE-Juel1)128854 |b 2 |u fzj |
700 | 1 | _ | |a Schubert, Nicole |0 P:(DE-Juel1)159224 |b 3 |u fzj |
700 | 1 | _ | |a Minnerop, Martina |0 P:(DE-Juel1)131622 |b 4 |u fzj |
700 | 1 | _ | |a Lippert, Thomas |0 P:(DE-Juel1)132179 |b 5 |u fzj |
700 | 1 | _ | |a Amunts, Katrin |0 P:(DE-Juel1)131631 |b 6 |u fzj |
700 | 1 | _ | |a Axer, Markus |0 P:(DE-Juel1)131632 |b 7 |u fzj |
773 | _ | _ | |a 10.3389/fnana.2018.00075 |g Vol. 12, p. 75 |0 PERI:(DE-600)2452969-2 |p 75 |t Frontiers in neuroanatomy |v 12 |y 2018 |x 1662-5129 |
856 | 4 | _ | |u https://juser.fz-juelich.de/record/856602/files/2018-0128850-4.pdf |
856 | 4 | _ | |u https://juser.fz-juelich.de/record/856602/files/2018-0128850-4.pdf?subformat=pdfa |x pdfa |
856 | 4 | _ | |u https://juser.fz-juelich.de/record/856602/files/Schmitz_etal_fnana-12-00075.pdf |y OpenAccess |
856 | 4 | _ | |u https://juser.fz-juelich.de/record/856602/files/Schmitz_etal_fnana-12-00075.pdf?subformat=pdfa |x pdfa |y OpenAccess |
909 | C | O | |o oai:juser.fz-juelich.de:856602 |p openaire |p open_access |p OpenAPC |p driver |p VDB |p ec_fundedresources |p openCost |p dnbdelivery |
910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)164129 |
910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 1 |6 P:(DE-Juel1)171951 |
910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 2 |6 P:(DE-Juel1)128854 |
910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 3 |6 P:(DE-Juel1)159224 |
910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 4 |6 P:(DE-Juel1)131622 |
910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 5 |6 P:(DE-Juel1)132179 |
910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 6 |6 P:(DE-Juel1)131631 |
910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 7 |6 P:(DE-Juel1)131632 |
913 | 1 | _ | |a DE-HGF |b Key Technologies |l Decoding the Human Brain |1 G:(DE-HGF)POF3-570 |0 G:(DE-HGF)POF3-574 |2 G:(DE-HGF)POF3-500 |v Theory, modelling and simulation |x 0 |4 G:(DE-HGF)POF |3 G:(DE-HGF)POF3 |
913 | 1 | _ | |a DE-HGF |b Key Technologies |1 G:(DE-HGF)POF3-510 |0 G:(DE-HGF)POF3-511 |2 G:(DE-HGF)POF3-500 |v Computational Science and Mathematical Methods |x 1 |4 G:(DE-HGF)POF |3 G:(DE-HGF)POF3 |l Supercomputing & Big Data |
914 | 1 | _ | |y 2018 |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1050 |2 StatID |b BIOSIS Previews |
915 | _ | _ | |a Creative Commons Attribution CC BY 4.0 |0 LIC:(DE-HGF)CCBY4 |2 HGFVOC |
915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b FRONT NEUROANAT : 2017 |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0501 |2 StatID |b DOAJ Seal |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0500 |2 StatID |b DOAJ |
915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0111 |2 StatID |b Science Citation Index Expanded |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |
915 | _ | _ | |a IF < 5 |0 StatID:(DE-HGF)9900 |2 StatID |
915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b DOAJ : Blind peer review |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0310 |2 StatID |b NCBI Molecular Biology Database |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0320 |2 StatID |b PubMed Central |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |
920 | 1 | _ | |0 I:(DE-Juel1)INM-1-20090406 |k INM-1 |l Strukturelle und funktionelle Organisation des Gehirns |x 0 |
920 | 1 | _ | |0 I:(DE-Juel1)JSC-20090406 |k JSC |l Jülich Supercomputing Center |x 1 |
920 | 1 | _ | |0 I:(DE-82)080012_20140620 |k JARA-HPC |l JARA - HPC |x 2 |
980 | _ | _ | |a journal |
980 | _ | _ | |a VDB |
980 | _ | _ | |a I:(DE-Juel1)INM-1-20090406 |
980 | _ | _ | |a I:(DE-Juel1)JSC-20090406 |
980 | _ | _ | |a I:(DE-82)080012_20140620 |
980 | _ | _ | |a APC |
980 | _ | _ | |a UNRESTRICTED |
980 | 1 | _ | |a APC |
980 | 1 | _ | |a FullTexts |
Library | Collection | CLSMajor | CLSMinor | Language | Author |
---|