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This is a Quantitative Study study (also classified as: Experimental Study).

September 17, 2025

Imputing Longitudinal Infant Brain MRI Features Using a Self-Supervised Transformer Model

CNC. C. NingTibet University RZRuoke ZhaoZhejiang University YCYiwei ChenNational Taiwan University

Key Points

The model achieves a 55.7% to 55.8% reduction in mean absolute error for various cortical features.
It enhances longitudinal predictions by 19.1% in mean squared error compared to traditional benchmarks.
An innovative self-supervised approach and time-level imputation loss were utilized for enhanced performance.
This method could significantly enrich longitudinal analyses of infant brain development and disease.

Abstract

Motivation: Morphological changes of infant brain provide critical information of brain development and disease progress. However, inevitable data loss of follow-up visit hinders the longitudinal study of neural development. Goal(s): To develop a robust deep learning method which can impute missing MRI in longitudinal studies. Approach: A transformer-based model was proposed with self-supervised learning framework and a time-level imputation loss. Results: The proposed model demonstrated superior performance with reduced MAE by 55.7%, 55.8%, 28.1%, 12.9%, and 14.6% for four cortical features compared to the benchmarks of BRITS, USGAN, TIMESNET, SAITS, and original Transformer. It also enhanced downstream longitudinal prediction task by 19.1% in MSE. Impact: The proposed model is able to impute the missing data in longitudinal studies of infants, which may enrich the information along development trajectory and downstream analyses.

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