Journal Article

DZNE-2022-01695

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Artificial neural networks for non-linear age correction of diffusion metrics in the brain.

Kocar, T. D. ; Behler, A. ; Leinert, C. ; Denkinger, M. ; Ludolph, A. C.DZNE* ; Müller, H.-P. ; Kassubek, J. (Last author)DZNE*

2022
Frontiers Research Foundation Lausanne

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Please use a persistent id in citations: doi:10.3389/fnagi.2022.999787

Abstract: Human aging is characterized by progressive loss of physiological functions. To assess changes in the brain that occur with increasing age, the concept of brain aging has gained momentum in neuroimaging with recent advancements in statistical regression and machine learning (ML). A common technique to assess the brain age of a person is, first, fitting a regression model to neuroimaging data from a group of healthy subjects, and then, using the resulting model for age prediction. Although multiparametric MRI-based models generally perform best, models solely based on diffusion tensor imaging have achieved similar results, with the benefits of faster data acquisition and better replicability across scanners and field strengths. In the present study, we developed an artificial neural network (ANN) for brain age prediction based upon tract-based fractional anisotropy (FA). Consequently, we investigated if this age-prediction model could also be used for non-linear age correction of white matter diffusion metrics in healthy adults. The brain age prediction accuracy of the ANN (R 2 = 0.47) was similar to established multimodal models. The comparison of the ANN-based age-corrected FA with the tract-wise linear age-corrected FA resulted in an R 2 value of 0.90 [0.82; 0.93] and a mean difference of 0.00 [-0.04; 0.05] for all tract systems combined. In conclusion, this study demonstrated the applicability of complex ANN models to non-linear age correction of tract-based diffusion metrics as a proof of concept.

Keyword(s): age dependence ; diffusion tensor imaging ; diffusivity ; fractional anisotropy ; machine learning ; magnetic resonance imaging ; neural network

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Contributing Institute(s):

1. Clinical Study Center Ulm (Clinical Study Center Ulm)

Research Program(s):

1. 353 - Clinical and Health Care Research (POF4-353) (POF4-353)

Appears in the scientific report 2022

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