Background: Machine learning can predict an individual's "brain age" from the brain MRI. Differences of ML-predicted and actual chronological ages show the accelearated or delayed aging, and can be associated with diseases, lifestyle, socioeconomics, genetic, and other environment influences.
Algorithm: We recently developed deep learning algorithms to predict children's brain ages [He2020], and lifespan ages in 0-100 years [He2021]. The lifespan age predictor is based on (a) explicitly splitting a T1-weighted MRI into contrast and morphometry channels [Ou2011; Ou2014]; (b) feature-level multi-channel fusion, with (c) a proposed multi-channel fusion-with-attention convolutional neural network (FiA-Net); and (d) powered by >16,000 brain MRIs acquired during 0-100 years [Pereira2021].
Accuracy: The figure below shows that each component in our algorithm played their expected role and reduced the prediction errors.
Overall, our FiA-Net achieved promising accuracy compared to the state-of-the-art multi-channel fusion convolutional neural networks -- lower mean absolute error (MAE) and higher correlation between predicted and actual chronological ages.
The above compared different algorithms on the same dataset. Below is the comparison on different recent studies each reporting their best performance in the data they chose. Our work is unique in that we covered 0-100 years of age (long purple bar), and the error is promising compared to other studies with similar age ranges (red dots).
Software: https://github.com/shengfly/FiAnet
Applications: We are applying this algorithm and software to study how different diseases and environmental factors deviate human brain from normal aging.
References:
[He2020] S He, RL Gollub, SN Murphy, JD Perez, S Prabhu, R Pienaar, RL Robertson, PE Grant, Y Ou. “Brain Age Estimation Using LSTM on Children's Brain MRI", IEEE International Symposium on Biomedical Imaging (ISBI), 420-423. (2020).
[He2021] S He, D Pereira, JD Perez, RL Gollub, SN Murphy, S Prabhu, R Pienaar, RL Robertson, PE Grant, Y Ou. "Multi-channel Attention-Fusion Neural Network for Brain Age Estimation: Accuracy, Generality, and Interpretation with 16,705 Healthy MRIs across Lifespan", Medical Image Analysis, 2021
[Ou2011] Y Ou, A Sotiras, N Paragios, C Davatzikos, "DRAMMS: Deformable registration via attribute matching and mutual-saliency weighting", Medical Image Analysis, 15(4): 622-639, (2011).
[Ou2014] Y Ou, H Akbari, M Bilello, X Da, C Davatzikos, "Comparative Evaluation of Registration Algorithms in Different Brain Databases with Varying Difficulty: Results and Insights". IEEE Transactions on Medical Imaging, 33(10): 2039-2065, (2014).
[Pereira2021] DN Pereira*, S He*, JD Perez, T Ge, SU Morton, R Pienaar, NM Benson, RL Gollub, PE Grant, Y Ou. "Over 71,000 Healthy Brain MRIs are Publicly-Available across the Lifespan for Big-Data Studies", under review, 2021.
[He2021b] S He, PE Grant, Y Ou, "Global-Local Transformer for Brain Age Estimation", IEEE Transactions on Medical Imaging, doi: 10.1109/TMI.2021.3108910, 2021