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000908614 005__ 20220721190604.0
000908614 037__ $$aFZJ-2022-02723
000908614 1001_ $$0P:(DE-Juel1)177823$$aMore, Shammi$$b0$$eCorresponding author
000908614 1112_ $$aOrganisation for Human Brain Mapping$$cGlasgow, Scotland$$d2022-06-19 - 2022-06-23$$wUK
000908614 245__ $$aBrain-age prediction: a systematic comparison of machine learning workflows
000908614 260__ $$c2022
000908614 3367_ $$033$$2EndNote$$aConference Paper
000908614 3367_ $$2DataCite$$aOther
000908614 3367_ $$2BibTeX$$aINPROCEEDINGS
000908614 3367_ $$2DRIVER$$aconferenceObject
000908614 3367_ $$2ORCID$$aLECTURE_SPEECH
000908614 3367_ $$0PUB:(DE-HGF)6$$2PUB:(DE-HGF)$$aConference Presentation$$bconf$$mconf$$s1658387834_21083$$xAfter Call
000908614 520__ $$aPrediction of age using anatomical brain MRI, i.e., brain age, is proving valuable in exploring accelerated aging (brain age delta) as a proxy for aging-related diseases and crucial future health outcomes [1]. While various data representations and machine learning (ML) algorithms have been used for brain-age prediction [2,3], the impact of these choices on prediction accuracy remains uncharacterized. Moreover, several methodological challenges remain before a predictive model can be deployed in the real world; (1) robust within-site performance, (2) accurate cross-site prediction and, (3) consistent prediction for the same individual. To fill this gap, we systematically evaluated 70 workflows consisting of ten feature spaces derived from grey matter (GM) images and seven ML algorithms with diverse inductive biases to establish guidelines for designing brain-age prediction workflows.
000908614 536__ $$0G:(DE-HGF)POF4-5251$$a5251 - Multilevel Brain Organization and Variability (POF4-525)$$cPOF4-525$$fPOF IV$$x0
000908614 536__ $$0G:(DE-HGF)POF4-5254$$a5254 - Neuroscientific Data Analytics and AI (POF4-525)$$cPOF4-525$$fPOF IV$$x1
000908614 7001_ $$0P:(DE-HGF)0$$aAntonoupolous, Georgios$$b1
000908614 7001_ $$0P:(DE-Juel1)131684$$aHoffstaedter, Felix$$b2
000908614 7001_ $$0P:(DE-Juel1)144344$$aCaspers, Julian$$b3
000908614 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon$$b4
000908614 7001_ $$0P:(DE-Juel1)172843$$aPatil, Kaustubh$$b5
000908614 909CO $$ooai:juser.fz-juelich.de:908614$$pVDB
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000908614 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)177823$$a HHU Düsseldorf$$b0
000908614 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-HGF)0$$aForschungszentrum Jülich$$b1$$kFZJ
000908614 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a HHU Düsseldorf$$b1
000908614 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131684$$aForschungszentrum Jülich$$b2$$kFZJ
000908614 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)131684$$a HHU Düsseldorf$$b2
000908614 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)144344$$a University Hospital Düsseldorf $$b3
000908614 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131678$$aForschungszentrum Jülich$$b4$$kFZJ
000908614 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)131678$$a HHU Düsseldorf$$b4
000908614 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)172843$$aForschungszentrum Jülich$$b5$$kFZJ
000908614 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)172843$$a HHU Düsseldorf$$b5
000908614 9131_ $$0G:(DE-HGF)POF4-525$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5251$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vDecoding Brain Organization and Dysfunction$$x0
000908614 9131_ $$0G:(DE-HGF)POF4-525$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5254$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vDecoding Brain Organization and Dysfunction$$x1
000908614 9141_ $$y2022
000908614 920__ $$lyes
000908614 9201_ $$0I:(DE-Juel1)INM-7-20090406$$kINM-7$$lGehirn & Verhalten$$x0
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