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× Geir Selbæk
BMC Psychiatry,
Geir Selbæk, Jūratė Šaltytė Benth, Sverre Bergh, Knut Engedal, Tom Borza
英文
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] BMC Public Health,2020年
Martin Knapp, Catherine Henderson, Martha Therese Gjestsen, Martine Marie Kajander, Ingelin Testad, Ingvild Dalen, Guro Hanevold Bjørkløf, Janne Røsvik, Geir Selbæk, Linda Clare, Jessica Amos, Kaarin Anstey, Shelley Rhodes, Jessica Bollen, Lynne Quinn
LicenseType:CC BY |
BMC Geriatrics,2022年
Geir Selbæk, Janne Myhre, Corinna Vossius, Sverre Bergh, Bjørn Lichtwarck, Eivind Aakhus, Jūratė Šaltytė Benth
LicenseType:Unknown |
Nature Communications,2020年
Magda Tsolaki, Karolinska Schizophrenia Project (KaSP) consortium, Simon Cervenka, Helena Fatouros-Bergman, Lena Flyckt, Annette Conzelmann, Dirk Heslenfeld, Jaap Oosterlaan, Marco Papalino, Pasquale Di Carlo, Giulio Pergola, Pieter J. Hoekstra, Peter Kirsch, Jan Buitelaar, Daan van Rooij, Karin Persson, Srdjan Djurovic, Einar A. Høgestøl, Hanne Flinstad Harbo, Elisabeth Gulowsen Celius, Hilkka Soininen, Asta Håberg, Iwona Kłoszewska, Nils Inge Landrø, Ingrid Agartz, André Schmidt, Stefan Borgwardt, Sarah Eisenacher, Andreas Meyer-Lindenberg, Emanuel Schwarz, Mathias Zink, Andreas Reif, Catharina A. Hartman, Ramona Baur-Streubel, Paul Pauli, Rune Jonassen, Thomas Espeseth, Barbara Franke, Deanna M. Barch, Lars Nyberg, Klaus-Peter Lesch, Georg C. Ziegler, Piotr Sowa, Lei Wang, Bruno Vellas, Geir Selbæk, Mona K. Beyer, Ulrik F. Malt, Patrizia Mecocci, Eric Westman, Barbara Gelao, Giuseppe Blasi, Alessandro Bertolino, Stephanie Le Hellard, Vidar M. Steen, Torbjørn Elvsåshagen, Trine Vik Lagerberg, Ole A. Andreassen, Alexey A. Shadrin, Oleksandr Frei, Jaroslav Rokicki, Ingrid Melle, Luigi A. Maglanoc, Torgeir Moberget, Tobias Kaufmann, Dennis van der Meer, Shahram Bahrami, Kevin S. O’ Connell, Dag Alnæs, Lars T. Westlye, Erik G. Jönsson, Erlend Bøen, Birgitte Boye, Jan Egil Nordvik, Andreas Papassotiropoulos, David Coynel, Dominique de Quervain
LicenseType:Unknown |
5 Deep neural networks learn general and clinically relevant representations of the ageing brain [期刊论文]
NeuroImage,,2562022年
Didac Vidal-Piñeiro, Andre F. Marquand, Hanne F. Harbo, Yunpeng Wang, Han Peng, Øystein Sørensen, Thomas Espeseth, Ingrid Agartz, Lars T. Westlye, Ann-Marie de Lange, Tobias Kaufmann, James M. Roe, Esten H. Leonardsen, Stephen M. Smith, Thomas Wolfers, Einar A. Høgestøl, Elisabeth Gulowsen Celius, Geir Selbæk, Ole A. Andreassen
LicenseType:Unknown |
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.
European Review of Aging and Physical Activity,2022年
Geir Selbæk, Arnhild J. Nygård, Jorunn L. Helbostad, Randi Granbo, Kristin Taraldsen
LicenseType:CC BY |
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