guest ::
| Home > Publications database > Characterizing the gradients of structural covariance in the human hippocampus > print |
| Home > Publications database > Characterizing the gradients of structural covariance in the human hippocampus > print |
| 001 | 877237 | ||
| 005 | 20210130004938.0 | ||
| 024 | 7 | _ | |a 10.1016/j.neuroimage.2020.116972 |2 doi |
| 024 | 7 | _ | |a 2128/26170 |2 Handle |
| 024 | 7 | _ | |a altmetric:82693897 |2 altmetric |
| 024 | 7 | _ | |a pmid:32454206 |2 pmid |
| 024 | 7 | _ | |a WOS:000555460300011 |2 WOS |
| 037 | _ | _ | |a FZJ-2020-02064 |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Kharabian, Shahrzad |0 P:(DE-Juel1)171719 |b 0 |e Corresponding author |u fzj |
| 245 | _ | _ | |a Characterizing the gradients of structural covariance in the human hippocampus |
| 260 | _ | _ | |a Orlando, Fla. |c 2020 |b Academic Press |
| 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 1605534831_2430 |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 The hippocampus is a plastic brain structure that has been associated with a range ofbehavioral aspects but also shows vulnerability to the most frequent neurocognitivediseases. Different aspects of its organization have been revealed by studies probingits different neurobiological properties. In particular, histological work has shown apattern of differentiation along the proximal-distal dimension, while studies examiningfunctional properties and large-scale functional integration have primarily highlighted apattern of differentiation along the anterior-posterior dimension. To better understandhow these organizational dimensions underlie the pattern of structural covariance (SC)in the human hippocampus, we here applied a non-linear decomposition approach,disentangling the major modes of variation, to the pattern of grey matter volumecorrelation of hippocampus voxels with the rest of the brain in a sample of 377 healthyyoung adults. We additionally investigated the consistency of the derived gradients inan independent sample of life-span adults and also examined the relationshipsbetween these major modes of variations and the patterns derived from microstructureand functional connectivity mapping. Our results showed that similar major modes ofSC-variability are identified across the two independent datasets. The major dimensionof variation found in SC runs along the hippocampal anterior-posterior axis andfollowed closely the principal dimension of functional differentiation, suggesting aninfluence of network level interaction in this major mode of morphological variability.The second main mode of variability in the SC showed a gradient along the dorsalventralaxis, and was moderately related to variability in hippocampal microstructuralproperties. Thus our results depicting relatively reliable patterns of SC-variability withinthe hippocampus show an interplay between the already known organizationalprinciples on the pattern of variability in hippocampus' macrostructural properties. Thisstudy hence provides a first insight on the underlying organizational forces generatingdifferent co-plastic modes within the human hippocampus that may, in turn, help tobetter understand different vulnerability patterns of this crucial structure in differentneurological and psychiatric diseases. |
| 536 | _ | _ | |a 574 - Theory, modelling and simulation (POF3-574) |0 G:(DE-HGF)POF3-574 |c POF3-574 |f POF III |x 0 |
| 700 | 1 | _ | |a Plachti, Anna |0 P:(DE-Juel1)167223 |b 1 |u fzj |
| 700 | 1 | _ | |a Hoffstaedter, Felix |0 P:(DE-Juel1)131684 |b 2 |u fzj |
| 700 | 1 | _ | |a Eickhoff, Simon |0 P:(DE-Juel1)131678 |b 3 |u fzj |
| 700 | 1 | _ | |a GENON, Sarah |0 P:(DE-Juel1)161225 |b 4 |u fzj |
| 773 | _ | _ | |a 10.1016/j.neuroimage.2020.116972 |0 PERI:(DE-600)1471418-8 |p 116972 |t NeuroImage |v 218 |y 2020 |x 1053-8119 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/877237/files/1-s2.0-S1053811920304584-main%20Shahrzad.pdf |y OpenAccess |
| 909 | C | O | |o oai:juser.fz-juelich.de:877237 |p openaire |p open_access |p VDB |p driver |p dnbdelivery |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)171719 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 1 |6 P:(DE-Juel1)167223 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 2 |6 P:(DE-Juel1)131684 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 3 |6 P:(DE-Juel1)131678 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 4 |6 P:(DE-Juel1)161225 |
| 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 |
| 914 | 1 | _ | |y 2020 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |d 2020-08-28 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0160 |2 StatID |b Essential Science Indicators |d 2020-08-28 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1030 |2 StatID |b Current Contents - Life Sciences |d 2020-08-28 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1190 |2 StatID |b Biological Abstracts |d 2020-08-28 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0600 |2 StatID |b Ebsco Academic Search |d 2020-08-28 |
| 915 | _ | _ | |a Creative Commons Attribution-NonCommercial-NoDerivs CC BY-NC-ND 4.0 |0 LIC:(DE-HGF)CCBYNCND4 |2 HGFVOC |
| 915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b NEUROIMAGE : 2018 |d 2020-08-28 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0501 |2 StatID |b DOAJ Seal |d 2020-08-28 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0500 |2 StatID |b DOAJ |d 2020-08-28 |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0113 |2 StatID |b Science Citation Index Expanded |d 2020-08-28 |
| 915 | _ | _ | |a Fees |0 StatID:(DE-HGF)0700 |2 StatID |d 2020-08-28 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |d 2020-08-28 |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b ASC |d 2020-08-28 |
| 915 | _ | _ | |a Article Processing Charges |0 StatID:(DE-HGF)0561 |2 StatID |d 2020-08-28 |
| 915 | _ | _ | |a IF >= 5 |0 StatID:(DE-HGF)9905 |2 StatID |b NEUROIMAGE : 2018 |d 2020-08-28 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1050 |2 StatID |b BIOSIS Previews |d 2020-08-28 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |d 2020-08-28 |
| 915 | _ | _ | |a Nationallizenz |0 StatID:(DE-HGF)0420 |2 StatID |d 2020-08-28 |w ger |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |d 2020-08-28 |
| 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 |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|