Generalization of diffusion magnetic resonance imaging–based brain age prediction model through transfer learning

NeuroImage

Yung-Chin Hsu¹  Yu-Hung Tung²  Wen-Bin Luo²  Chih-Min Liu³  Hai-Gwo Hwu³  Tzung-Jeng Hwang³  Li-Ying Yang⁴  Chang-Le Chen⁴  Wen-Yih Isaac Tseng⁴ 

[1] AcroViz Technology Inc., Taipei, Taiwan;Department of Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan;Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwan;Institute of Medical Device and Imaging, College of Medicine, National Taiwan University, Taipei, Taiwan;

关键词: Brain age;    Diffusion harmonization;    Diffusion MRI;    Neural network;    Transfer learning;    White matter;   

DOI  :  

来源: DOAJ

【 摘 要 】

Brain age prediction models using diffusion magnetic resonance imaging (dMRI) and machine learning techniques enable individual assessment of brain aging status in healthy people and patients with brain disorders. However, dMRI data are notorious for high intersite variability, prohibiting direct application of a model to the datasets obtained from other sites. In this study, we generalized the dMRI-based brain age model to different dMRI datasets acquired under different imaging conditions. Specifically, we adopted a transfer learning approach to achieve domain adaptation. To evaluate the performance of transferred models, brain age prediction models were constructed using a large dMRI dataset as the source domain, and the models were transferred to three target domains with distinct acquisition scenarios. The experiments were performed to investigate (1) the tuning data size needed to achieve satisfactory performance for brain age prediction, (2) the feature types suitable for different dMRI acquisition scenarios, and (3) performance of the transfer learning approach compared with the statistical covariate approach. By tuning the models with relatively small data size and certain feature types, optimal transferred models were obtained with significantly improved prediction performance in all three target cohorts (p ​< ​0.001). The mean absolute error of the predicted age was reduced from 13.89 to 4.78 years in Cohort 1, 8.34 to 5.35 years in Cohort 2, and 8.74 to 5.64 years in Cohort 3. The test–retest reliability of the transferred model was verified using dMRI data acquired at two timepoints (intraclass correlation coefficient ​= ​0.950). Clinical sensitivity of the brain age prediction model was investigated by estimating the brain age in patients with schizophrenia. The prediction made by the transferred model was not significantly different from that made by the reference model. Both models predicted significant brain aging in patients with schizophrenia as compared with healthy controls (p ​< ​0.001); the predicted age difference of the transferred model was 4.63 and 0.26 years for patients and controls, respectively, and that of the reference model was 4.39 and −0.09 years, respectively. In conclusion, transfer learning approach is an efficient way to generalize the dMRI-based brain age prediction model. Appropriate transfer learning approach and suitable tuning data size should be chosen according to different dMRI acquisition scenarios.

【 授权许可】

Unknown