TY  - CONF
AU  - More, Shammi
AU  - Antonoupolous, Georgios
AU  - Hoffstaedter, Felix
AU  - Caspers, Julian
AU  - Eickhoff, Simon
AU  - Patil, Kaustubh
TI  - Brain-age prediction: a systematic comparison of machine learning workflows
M1  - FZJ-2022-02636
PY  - 2022
AB  - Prediction 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.
T2  - Organization for Human Brain Mapping
CY  - 19 Jun 2022 - 23 Jun 2022, Glasgow (Scotland)
Y2  - 19 Jun 2022 - 23 Jun 2022
M2  - Glasgow, Scotland
LB  - PUB:(DE-HGF)24
UR  - https://juser.fz-juelich.de/record/908493
ER  -