Identification of morphological fingerprint in perinatal brains using
  quasi-conformal mapping and contrastive learning
Boyang Wang Weihao Zheng Ying Wang Zhe Zhang Yuchen Sheng Minmin Wang
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.