Comparative evaluation of interpretation methods in surface-based age prediction for neonates

• Published in: NeuroImage (Orlando, Fla.) Fla.), 2024-10, Vol.300, p.120861, Article 120861
• Main Authors: Wu, Xiaotong, Xie, Chenxin, Cheng, Fangxiao, Li, Zhuoshuo, Li, Ruizhuo, Xu, Duan, Kim, Hosung, Zhang, Jianjia, Liu, Hongsheng, Liu, Mengting
• Format: Article
• Language: English
• Subjects: Adaptability; Age; Biomarkers; Brain age prediction; Brain mapping; Brain research; Comparative analysis; Critical period; Decision making; Deep learning; Interpretative techniques; Machine learning; Medical imaging; Medical research; Morphology; Neonates; Neuroimaging; Predictions; Regional brain age; Saliency index
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2024.120861

•We propose interpretative methods to pinpoint brain regions vital for age prediction.•Introduce the saliency index and relative brain age index to assess these methods.•Explore the clinical implications and brain network features of the indices.•Provide insights into which method might be more suitable for age prediction. Significant changes in brain morphology occur during the third trimester of gestation. The capability of deep learning in leveraging these morphological features has enhanced the accuracy of brain age predictions for this critical period. Yet, the opaque nature of deep learning techniques, often described as "black box" approaches, limits their interpretability, posing challenges in clinical applications. Traditional interpretable methods developed for computer vision and natural language processing may not directly translate to the distinct demands of neuroimaging. In response, our research evaluates the effectiveness and adaptability of two interpretative methods—regional age prediction and the perturbation-based saliency map approach—for predicting the brain age of neonates. Analyzing 664 T1 MRI scans with the NEOCIVET pipeline to extract brain surface and cortical features, we assess how these methods illuminate key brain regions for age prediction, focusing on technical analysis with clinical insight. Through a comparative analysis of the saliency index (SI) with relative brain age (RBA) and the examination of structural covariance networks, we uncover the saliency index's enhanced ability to pinpoint regions vital for accurate indication of clinical factors. Our results highlight the advantages of perturbation techniques in addressing the complexities of medical data, steering clinical interventions for premature neonates towards more personalized and interpretable approaches. This study not only reveals the promise of these methods in complex medical scenarios but also offers a blueprint for implementing more interpretable and clinically relevant deep learning models in healthcare settings.