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| 001 | 911751 | ||
| 005 | 20221123131044.0 | ||
| 037 | _ | _ | |a FZJ-2022-05003 |
| 100 | 1 | _ | |a Friedrich, Patrick |0 P:(DE-Juel1)185938 |b 0 |
| 111 | 2 | _ | |a 8th North Sea Laterality Conference on Brain Asymmetry |c Bergen |d 2022-08-24 - 2022-08-27 |w Norway |
| 245 | _ | _ | |a Is it left or is it right? A machine learning framework for studying hemispheric differences |
| 260 | _ | _ | |c 2022 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a Other |2 DataCite |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a LECTURE_SPEECH |2 ORCID |
| 336 | 7 | _ | |a Conference Presentation |b conf |m conf |0 PUB:(DE-HGF)6 |s 1669201820_20076 |2 PUB:(DE-HGF) |x Invited |
| 520 | _ | _ | |a The comparison between regions or tracts in the left and right hemispheres grants insight into localasymmetries as one characteristic feature of brain organization. These pairwise comparisons,however, are reminiscent of a localists’ view and come with several caveats when assessing multipleasymmetries. To overcome some of the limitations set by conventional statistical comparisons, werecently introduced a novel machine learning-based framework for studying hemispheric differences.In a recent proof-of-principle study, we showed the capability of machine learning-based classificationto distinguish the hemispheres based on voxel-wise features. Using a Boruta feature-selectionalgorithm allowed the mapping of voxels that were important for correctly classifying a givenhemisphere. Furthermore, relating these maps of hemisphere-determining voxels with volumetricasymmetries validated our approach for mapping lateralized brain structure. In this talk, I will highlightour framework for studying hemispheric differences and present ongoing work on possibleapplications. |
| 536 | _ | _ | |a 5251 - Multilevel Brain Organization and Variability (POF4-525) |0 G:(DE-HGF)POF4-5251 |c POF4-525 |f POF IV |x 0 |
| 700 | 1 | _ | |a Patil, Kaustubh |0 P:(DE-Juel1)172843 |b 1 |
| 700 | 1 | _ | |a Mochalski, Lisa |0 P:(DE-Juel1)174198 |b 2 |u fzj |
| 700 | 1 | _ | |a Li, Xuan |0 P:(DE-Juel1)184969 |b 3 |u fzj |
| 700 | 1 | _ | |a Camilleri, Julia |0 P:(DE-Juel1)172024 |b 4 |u fzj |
| 700 | 1 | _ | |a Kröll, Jean-Philippe |0 P:(DE-Juel1)176972 |b 5 |u fzj |
| 700 | 1 | _ | |a Wiersch, Lisa |0 P:(DE-Juel1)176497 |b 6 |u fzj |
| 700 | 1 | _ | |a Vickery, Sam |0 P:(DE-Juel1)173931 |b 7 |u fzj |
| 700 | 1 | _ | |a Hopkins, William D. |0 P:(DE-HGF)0 |b 8 |
| 700 | 1 | _ | |a Hoffstaedter, Felix |0 P:(DE-Juel1)131684 |b 9 |u fzj |
| 700 | 1 | _ | |a Eickhoff, Simon |0 P:(DE-Juel1)131678 |b 10 |u fzj |
| 700 | 1 | _ | |a Weis, Susanne |0 P:(DE-Juel1)172811 |b 11 |u fzj |
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| 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)POF4-5251 |x 0 |
| 914 | 1 | _ | |y 2022 |
| 920 | _ | _ | |l yes |
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| 980 | _ | _ | |a UNRESTRICTED |
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