guest ::
| Home > Publications database > Individual-Specific Areal-Level Parcellations Improve Functional Connectivity Prediction of Behavior > print |
| Home > Publications database > Individual-Specific Areal-Level Parcellations Improve Functional Connectivity Prediction of Behavior > print |
| 001 | 904409 | ||
| 005 | 20220224125201.0 | ||
| 024 | 7 | _ | |a 10.1093/cercor/bhab101 |2 doi |
| 024 | 7 | _ | |a 1047-3211 |2 ISSN |
| 024 | 7 | _ | |a 1460-2199 |2 ISSN |
| 024 | 7 | _ | |a altmetric:105289771 |2 altmetric |
| 024 | 7 | _ | |a pmid:33942058 |2 pmid |
| 024 | 7 | _ | |a WOS:000695807700007 |2 WOS |
| 037 | _ | _ | |a FZJ-2021-05979 |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Kong, Ru |b 0 |
| 245 | _ | _ | |a Individual-Specific Areal-Level Parcellations Improve Functional Connectivity Prediction of Behavior |
| 260 | _ | _ | |a Oxford |c 2021 |b Oxford Univ. 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 1642776064_3620 |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 Resting-state functional magnetic resonance imaging (rs-fMRI) allows estimation of individual-specific cortical parcellations. We have previously developed a multi-session hierarchical Bayesian model (MS-HBM) for estimating high-quality individual-specific network-level parcellations. Here, we extend the model to estimate individual-specific areal-level parcellations. While network-level parcellations comprise spatially distributed networks spanning the cortex, the consensus is that areal-level parcels should be spatially localized, that is, should not span multiple lobes. There is disagreement about whether areal-level parcels should be strictly contiguous or comprise multiple noncontiguous components; therefore, we considered three areal-level MS-HBM variants spanning these range of possibilities. Individual-specific MS-HBM parcellations estimated using 10 min of data generalized better than other approaches using 150 min of data to out-of-sample rs-fMRI and task-fMRI from the same individuals. Resting-state functional connectivity derived from MS-HBM parcellations also achieved the best behavioral prediction performance. Among the three MS-HBM variants, the strictly contiguous MS-HBM exhibited the best resting-state homogeneity and most uniform within-parcel task activation. In terms of behavioral prediction, the gradient-infused MS-HBM was numerically the best, but differences among MS-HBM variants were not statistically significant. Overall, these results suggest that areal-level MS-HBMs can capture behaviorally meaningful individual-specific parcellation features beyond group-level parcellations. |
| 536 | _ | _ | |a 5252 - Brain Dysfunction and Plasticity (POF4-525) |0 G:(DE-HGF)POF4-5252 |c POF4-525 |f POF IV |x 0 |
| 588 | _ | _ | |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de |
| 700 | 1 | _ | |a Yang, Qing |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Gordon, Evan |b 2 |
| 700 | 1 | _ | |a Xue, Aihuiping |b 3 |
| 700 | 1 | _ | |a Yan, Xiaoxuan |b 4 |
| 700 | 1 | _ | |a Orban, Csaba |b 5 |
| 700 | 1 | _ | |a Zuo, Xi-Nian |0 0000-0001-9110-585X |b 6 |
| 700 | 1 | _ | |a Spreng, Nathan |b 7 |
| 700 | 1 | _ | |a Ge, Tian |b 8 |
| 700 | 1 | _ | |a Holmes, Avram |b 9 |
| 700 | 1 | _ | |a Eickhoff, Simon |0 P:(DE-Juel1)131678 |b 10 |u fzj |
| 700 | 1 | _ | |a Yeo, B T Thomas |0 P:(DE-HGF)0 |b 11 |e Corresponding author |
| 773 | _ | _ | |a 10.1093/cercor/bhab101 |g Vol. 31, no. 10, p. 4477 - 4500 |0 PERI:(DE-600)1483485-6 |n 10 |p 4477 - 4500 |t Cerebral cortex |v 31 |y 2021 |x 1047-3211 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/904409/files/bhab101.pdf |y Restricted |
| 909 | C | O | |o oai:juser.fz-juelich.de:904409 |p VDB |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 10 |6 P:(DE-Juel1)131678 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Natural, Artificial and Cognitive Information Processing |1 G:(DE-HGF)POF4-520 |0 G:(DE-HGF)POF4-525 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Decoding Brain Organization and Dysfunction |9 G:(DE-HGF)POF4-5252 |x 0 |
| 914 | 1 | _ | |y 2021 |
| 915 | _ | _ | |a Nationallizenz |0 StatID:(DE-HGF)0420 |2 StatID |d 2021-01-26 |w ger |
| 915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b CEREB CORTEX : 2019 |d 2021-01-26 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |d 2021-01-26 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |d 2021-01-26 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0320 |2 StatID |b PubMed Central |d 2021-01-26 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |d 2021-01-26 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1030 |2 StatID |b Current Contents - Life Sciences |d 2021-01-26 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0160 |2 StatID |b Essential Science Indicators |d 2021-01-26 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1050 |2 StatID |b BIOSIS Previews |d 2021-01-26 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1190 |2 StatID |b Biological Abstracts |d 2021-01-26 |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0113 |2 StatID |b Science Citation Index Expanded |d 2021-01-26 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |d 2021-01-26 |
| 915 | _ | _ | |a IF >= 5 |0 StatID:(DE-HGF)9905 |2 StatID |b CEREB CORTEX : 2019 |d 2021-01-26 |
| 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 I:(DE-Juel1)INM-7-20090406 |
| 980 | _ | _ | |a UNRESTRICTED |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|