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 ne-onates 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 682 infants, incorporating with data augmentation, to
train the model and fine-tuned the parameters based on 60 infants who had
longitudinal scans. The model was validated on 30 longitudinal scanned infant
data, and remarkable Top1 and Top5 accuracies of 71.37% and 84.10% were
achieved, respectively. The sensorimotor and visual cortices were recognized as
the most contributive regions in individual identification. Moreover, the
folding morphology demonstrated greater discriminative capability than the
cortical thickness, which could serve as the morphological fingerprint in
perinatal brains. 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 in-dividual uniqueness in the brain during early development.

本研究提出了一个深度学习框架，通过准保角映射将三维球面网格投影到二维平面上，利用残差神经网络和对比学习进行个体识别，发现在胎儿脑中存在形态指纹，其中感觉运动皮层和视觉皮层是最具贡献的区域，纹理形态学在识别个体方面的能力优于皮层厚度，这对于理解大脑早期发育中个体独特性的形成具有重要意义。