dc.date.accessioned | 2022-08-04T16:41:46Z | |
dc.date.available | 2022-08-04T16:41:46Z | |
dc.date.created | 2022-04-27T12:51:56Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Leonardsen, Esten Høyland Peng, Han Kaufmann, Tobias Agartz, Ingrid Andreassen, Ole Celius, Elisabeth Gulowsen Espeseth, Thomas Harbo, Hanne-Cathrin Flinstad Høgestøl, Einar August de Lange, Ann-Marie Glasø Marquand, André F. Vidal-Pineiro, Didac Roe, James Michael Selbæk, Geir Sørensen, Øystein Smith, Stephen M. Westlye, Lars Tjelta Wolfers, Thomas Wang, Yunpeng . Deep neural networks learn general and clinically relevant representations of the ageing brain. NeuroImage. 2022 | |
dc.identifier.uri | http://hdl.handle.net/10852/94764 | |
dc.description.abstract | Abstract The discrepancy between chronological age and the apparent age of the brain based on neuroimaging data - the brain age delta - has emerged as a reliable marker of brain health. With an increasing wealth of data, approaches to tackle heterogeneity in data acquisition are vital. To this end, we compiled raw structural magnetic resonance images into one of the largest and most diverse datasets assembled (n=53542), and trained convolutional neural networks (CNNs) to predict age. We achieved state-of-the-art performance on unseen data from unknown scanners (n=2553), and showed that higher brain age delta is associated with diabetes, alcohol intake and smoking. Using transfer learning, the intermediate representations learned by our model complemented and partly outperformed brain age delta in predicting common brain disorders. Our work shows we can achieve generalizable and biologically plausible brain age predictions using CNNs trained on heterogeneous datasets, and transfer them to clinical use cases. | |
dc.language | EN | |
dc.rights | Attribution 4.0 International | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.title | Deep neural networks learn general and clinically relevant representations of the ageing brain | |
dc.title.alternative | ENEngelskEnglishDeep neural networks learn general and clinically relevant representations of the ageing brain | |
dc.type | Journal article | |
dc.creator.author | Leonardsen, Esten Høyland | |
dc.creator.author | Peng, Han | |
dc.creator.author | Kaufmann, Tobias | |
dc.creator.author | Agartz, Ingrid | |
dc.creator.author | Andreassen, Ole | |
dc.creator.author | Celius, Elisabeth Gulowsen | |
dc.creator.author | Espeseth, Thomas | |
dc.creator.author | Harbo, Hanne-Cathrin Flinstad | |
dc.creator.author | Høgestøl, Einar August | |
dc.creator.author | de Lange, Ann-Marie Glasø | |
dc.creator.author | Marquand, André F. | |
dc.creator.author | Vidal-Pineiro, Didac | |
dc.creator.author | Roe, James Michael | |
dc.creator.author | Selbæk, Geir | |
dc.creator.author | Sørensen, Øystein | |
dc.creator.author | Smith, Stephen M. | |
dc.creator.author | Westlye, Lars Tjelta | |
dc.creator.author | Wolfers, Thomas | |
dc.creator.author | Wang, Yunpeng | |
cristin.unitcode | 185,53,10,70 | |
cristin.unitname | NORMENT part UiO | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 2 | |
dc.identifier.cristin | 2019499 | |
dc.identifier.bibliographiccitation | info:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=NeuroImage&rft.volume=&rft.spage=&rft.date=2022 | |
dc.identifier.jtitle | NeuroImage | |
dc.identifier.volume | 256 | |
dc.identifier.doi | https://doi.org/10.1016/j.neuroimage.2022.119210 | |
dc.identifier.urn | URN:NBN:no-97299 | |
dc.type.document | Tidsskriftartikkel | |
dc.type.peerreviewed | Peer reviewed | |
dc.source.issn | 1053-8119 | |
dc.identifier.fulltext | Fulltext https://www.duo.uio.no/bitstream/handle/10852/94764/1/Deep%2Bneural%2Bnetworks%2Blearn%2Bgeneral%2Band%2Bclinically%2Brelevant%2Brepresentations%2Bof%2Bthe%2Bageing%2Bbrain.pdf | |
dc.type.version | PublishedVersion | |
cristin.articleid | 119210 | |