Literature DB >> 33826702

From 'loose fitting' to high-performance, uncertainty-aware brain-age modelling.

Tim Hahn1, Lukas Fisch1, Jan Ernsting1,2, Nils R Winter1, Ramona Leenings1, Kelvin Sarink1, Daniel Emden1, Tilo Kircher3, Klaus Berger4, Udo Dannlowski1.   

Abstract

Year:  2021        PMID: 33826702     DOI: 10.1093/brain/awaa454

Source DB:  PubMed          Journal:  Brain        ISSN: 0006-8950            Impact factor:   13.501


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  3 in total

1.  Cardiometabolic risk factors associated with brain age and accelerate brain ageing.

Authors:  Dani Beck; Ann-Marie G de Lange; Mads L Pedersen; Dag Alnaes; Ivan I Maximov; Irene Voldsbekk; Geneviève Richard; Anne-Marthe Sanders; Kristine M Ulrichsen; Erlend S Dørum; Knut K Kolskår; Einar A Høgestøl; Nils Eiel Steen; Srdjan Djurovic; Ole A Andreassen; Jan E Nordvik; Tobias Kaufmann; Lars T Westlye
Journal:  Hum Brain Mapp       Date:  2021-10-09       Impact factor: 5.038

2.  Editorial: Predicting Chronological Age From Structural Neuroimaging: The Predictive Analytics Competition 2019.

Authors:  Lukas Fisch; Ramona Leenings; Nils R Winter; Udo Dannlowski; Christian Gaser; James H Cole; Tim Hahn
Journal:  Front Psychiatry       Date:  2021-08-05       Impact factor: 4.157

3.  Mind the gap: Performance metric evaluation in brain-age prediction.

Authors:  Ann-Marie G de Lange; Melis Anatürk; Jaroslav Rokicki; Laura K M Han; Katja Franke; Dag Alnaes; Klaus P Ebmeier; Bogdan Draganski; Tobias Kaufmann; Lars T Westlye; Tim Hahn; James H Cole
Journal:  Hum Brain Mapp       Date:  2022-03-21       Impact factor: 5.399

  3 in total

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