TY  - JOUR
AU  - Kalc, Polona
AU  - Dahnke, Robert
AU  - Hoffstaedter, Felix
AU  - Gaser, Christian
TI  - BrainAGE : Revisited and reframed machine learning workflow
JO  - Human brain mapping
VL  - 45
IS  - 3
SN  - 1065-9471
CY  - New York, NY
PB  - Wiley-Liss
M1  - FZJ-2024-02509
SP  - e26632
PY  - 2024
AB  - Since the introduction of the BrainAGE method, novel machine learning methods for brain age prediction have continued to emerge. The idea of estimating the chronological age from magnetic resonance images proved to be an interesting field of research due to the relative simplicity of its interpretation and its potential use as a biomarker of brain health. We revised our previous BrainAGE approach, originally utilising relevance vector regression (RVR), and substituted it with Gaussian process regression (GPR), which enables more stable processing of larger datasets, such as the UK Biobank (UKB). In addition, we extended the global BrainAGE approach to regional BrainAGE, providing spatially specific scores for five brain lobes per hemisphere. We tested the performance of the new algorithms under several different conditions and investigated their validity on the ADNI and schizophrenia samples, as well as on a synthetic dataset of neocortical thinning. The results show an improved performance of the reframed global model on the UKB sample with a mean absolute error (MAE) of less than 2 years and a significant difference in BrainAGE between healthy participants and patients with Alzheimer's disease and schizophrenia. Moreover, the workings of the algorithm show meaningful effects for a simulated neocortical atrophy dataset. The regional BrainAGE model performed well on two clinical samples, showing disease-specific patterns for different levels of impairment. The results demonstrate that the new improved algorithms provide reliable and valid brain age estimations.
LB  - PUB:(DE-HGF)16
C6  - 38379519
UR  - <Go to ISI:>//WOS:001173400200001
DO  - DOI:10.1002/hbm.26632
UR  - https://juser.fz-juelich.de/record/1024843
ER  -