Data-driven multivariate identification of gyrification patterns in a transdiagnostic patient cohort: A cluster analysis approach
Background: Multivariate data-driven statistical approaches offer the opportunity to study multi-dimensional interdependences between a large set of biological parameters, such as high-dimensional brain imaging data. For gyrification, a putative marker of early neurodevelopment, direct comparisons o...
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Main Authors: | , , , , , , , , , , , , , , , , , , |
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Format: | Article |
Language: | English |
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Philipps-Universität Marburg
2023
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urn:nbn:de:hebis:04-es2024-07620 |
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2024-01-22 |
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Erstveröffentlichung: Pfarr JK, Meller T, Brosch K, Stein F, Thomas-Odenthal F, Evermann U, Wroblewski A, Ringwald KG, Hahn T, Meinert S, Winter A, Thiel K, Flinkenflügel K, Jansen A, Krug A, Dannlowski U, Kircher T, Gaser C, Nenadić I. Data-driven multivariate identification of gyrification patterns in a transdiagnostic patient cohort: A cluster analysis approach. Neuroimage. 2023 Nov 1;281:120349. https://doi.org/10.1016/j.neuroimage.2023.120349 |
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40 (2024) |
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https://creativecommons.org/licenses/by-nc-nd/4.0/ |
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https://archiv.ub.uni-marburg.de/es/2024/0762 https://doi.org/10.1016/j.neuroimage.2023.120349 |