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| Hauptseite > Publikationsdatenbank > Sex classification from resting-state functional brain networks > print |
| Hauptseite > Publikationsdatenbank > Sex classification from resting-state functional brain networks > print |
| 001 | 865946 | ||
| 005 | 20210130003222.0 | ||
| 024 | 7 | _ | |a 2128/23359 |2 Handle |
| 037 | _ | _ | |a FZJ-2019-05212 |
| 100 | 1 | _ | |a Wiersch, Lisa |0 P:(DE-Juel1)176497 |b 0 |e Corresponding author |u fzj |
| 245 | _ | _ | |a Sex classification from resting-state functional brain networks |f 2018-08-01 - 2019-07-02 |
| 260 | _ | _ | |c 2019 |
| 300 | _ | _ | |a 61 |
| 336 | 7 | _ | |a Output Types/Supervised Student Publication |2 DataCite |
| 336 | 7 | _ | |a Thesis |0 2 |2 EndNote |
| 336 | 7 | _ | |a MASTERSTHESIS |2 BibTeX |
| 336 | 7 | _ | |a masterThesis |2 DRIVER |
| 336 | 7 | _ | |a Master Thesis |b master |m master |0 PUB:(DE-HGF)19 |s 1573639549_5622 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a SUPERVISED_STUDENT_PUBLICATION |2 ORCID |
| 502 | _ | _ | |a Masterarbeit, Heinrich-Heine Universität Düsseldorf, 2019 |c Heinrich-Heine Universität Düsseldorf |b Masterarbeit |d 2019 |
| 520 | _ | _ | |a Sex differences in the brain have received a wide interest in neuroscientific research. Insteadof task-based group comparisons, the present study employed a machine-learning (ML)-approach to examine whether the resting-state functional connectivity of 12 meta-analyticallydefined networks carries enough information to accurately predict the sex of a person. It washypothesized that especially emotion-related networks should classify well. Sex classificationanalyses were conducted in the datasets of the healthy brain network (HBN, n = 218) theRockland Sample of the enhanced Nathan Kline Institute (eNKI, n = 574), the HumanConnectome Project (HCP, n = 734) and the 1000BRAINS-dataset (n = 995). The MLalgorithmsLASSO, LSVM, Ridge and RVM were used for this classification approach. Theresults showed that the eNKI- and HCP-datasets as well as the algorithms LASSO and Ridgereceived on average higher classification accuracies than the other datasets and algorithms.The networks of autobiographical and semantic memory reached the highest accuracies of allnetworks. Taken together, the results did not support the initial hypothesis. Instead, theresults generally displayed a strong dependency on the datasets and ML-algorithms. |
| 536 | _ | _ | |a 572 - (Dys-)function and Plasticity (POF3-572) |0 G:(DE-HGF)POF3-572 |c POF3-572 |f POF III |x 0 |
| 856 | 4 | _ | |y OpenAccess |u https://juser.fz-juelich.de/record/865946/files/Sex%20classification%20from%20resting-state%20functional%20brain%20networks.pdf |
| 856 | 4 | _ | |y OpenAccess |x pdfa |u https://juser.fz-juelich.de/record/865946/files/Sex%20classification%20from%20resting-state%20functional%20brain%20networks.pdf?subformat=pdfa |
| 909 | C | O | |o oai:juser.fz-juelich.de:865946 |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)176497 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Decoding the Human Brain |1 G:(DE-HGF)POF3-570 |0 G:(DE-HGF)POF3-572 |2 G:(DE-HGF)POF3-500 |v (Dys-)function and Plasticity |x 0 |4 G:(DE-HGF)POF |3 G:(DE-HGF)POF3 |
| 914 | 1 | _ | |y 2019 |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 920 | _ | _ | |l yes |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-7-20090406 |k INM-7 |l Gehirn & Verhalten |x 0 |
| 980 | _ | _ | |a master |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | _ | _ | |a I:(DE-Juel1)INM-7-20090406 |
| 980 | 1 | _ | |a FullTexts |
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