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001024843 245__ $$aBrainAGE : Revisited and reframed machine learning workflow
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001024843 520__ $$aSince 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.
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001024843 7001_ $$0P:(DE-HGF)0$$aDahnke, Robert$$b1
001024843 7001_ $$0P:(DE-Juel1)131684$$aHoffstaedter, Felix$$b2$$ufzj
001024843 7001_ $$0P:(DE-HGF)0$$aGaser, Christian$$b3$$eCorresponding author
001024843 773__ $$0PERI:(DE-600)1492703-2$$a10.1002/hbm.26632$$gVol. 45, no. 3, p. e26632$$n3$$pe26632$$tHuman brain mapping$$v45$$x1065-9471$$y2024
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001024843 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Christian Gaser, Structural Brain Mapping Group, Department of Neurology, Jena University Hospital, Am Klinikum 1, 07747 Jena, DE, Germany.  Email: christian.gaser@uni-jena.de$$b3
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