Wang Ying, Wang Boyang, Zhu Dalin, Zheng Weihao, Sheng Yucen
School of Information Science and Engineering, Lanzhou University, Lanzhou, China.
Department of Medical Imaging Center, Gansu Maternity and Child-Care Hospital (Gansu Provincial Central Hospital), Lanzhou, China.
Brain Imaging Behav. 2025 Mar 27. doi: 10.1007/s11682-025-00998-8.
The morphological fingerprint in the brain is capable of identifying the uniqueness of an individual. However, whether such individual patterns are present in perinatal brains, and which morphological attributes or cortical regions better characterize the individual differences of neonates remain unclear. In this study, we proposed a deep learning framework that projected three-dimensional spherical meshes of three morphological features (i.e., cortical thickness, mean curvature, and sulcal depth) onto two-dimensional planes through quasi-conformal mapping, and employed the ResNet18 and contrastive learning for individual identification. We used the cross-sectional structural MRI data of 461 infants, incorporating with data augmentation, to train the model and fine-tuned the parameters based on 41 infants who had longitudinal scans. The model was validated on a fold of 20 longitudinal scanned infant data, and remarkable Top1 and Top5 accuracies of 85.90% and 92.20% were achieved, respectively. The sensorimotor and visual cortices were recognized as the most contributive regions in individual identification. Moreover, morphological fingerprints successfully predicted the long-term development of cognition and behavior. Furthermore, the folding morphology demonstrated greater discriminative capability than the cortical thickness. These findings provided evidence for the emergence of morphological fingerprints in the brain at the beginning of the third trimester, which may hold promising implications for understanding the formation of individual uniqueness, and predicting long-term neurodevelopmental risks in the brain during early development.
大脑中的形态学指纹能够识别个体的独特性。然而，围产期大脑中是否存在这种个体模式，以及哪些形态学属性或皮质区域能更好地表征新生儿的个体差异仍不清楚。在本研究中，我们提出了一个深度学习框架，该框架通过拟共形映射将三种形态学特征（即皮质厚度、平均曲率和脑沟深度）的三维球形网格投影到二维平面上，并采用ResNet18和对比学习进行个体识别。我们使用了461名婴儿的横断面结构MRI数据，并结合数据增强来训练模型，并基于41名进行了纵向扫描的婴儿对参数进行微调。该模型在20份纵向扫描婴儿数据的一个折上进行了验证，分别取得了85.90%和92.20%的显著Top1和Top5准确率。感觉运动皮质和视觉皮质被认为是个体识别中最具贡献的区域。此外，形态学指纹成功预测了认知和行为的长期发展。此外，折叠形态显示出比皮质厚度更强的辨别能力。这些发现为妊娠晚期开始时大脑中形态学指纹的出现提供了证据，这可能对理解个体独特性的形成以及预测早期发育过程中大脑的长期神经发育风险具有重要意义。
