4.1 Infant identification based on cortical folding features

The first main contribution of this study resides in the finding that the cortical folding morphology fingerprints the dynamic developing infant brain and is reliable for individual identification during the first postnatal years. Despite the dramatic global change in cortical size, shape and folding features during birth and 2 years of age (Li, Nie, Wang, Shi, Lin, et al., 2013a; Li, Wang, Shi, Lyall et al., 2014; Lyall et al., 2014; Meng et al., 2014), as also shown in Figure 1, we achieved promising accuracies in identifying 1‐ and 2‐year‐old brains from neonatal cortices using the combinations of cortical folding features (Table 2 ). More importantly, we can thus anticipate that the evidenced fingerprinting power of the neonatal brain of a specific subject can be carried out across the whole lifespan. The reasons for this assumption are in two aspects. First, all major cortical folds and individual variability patterns of the human brain are established at term birth (Duan et al., 2018; Hill et al., 2010; Li, Wang, Shi, Lin, & Shen, 2014a). Second, the most dynamic phase of postnatal brain development is the first 2 years of life, and the folding patterns only undergo minor changes during later childhood and adulthood, thus the 2‐year‐olds' brains largely resemble the adult brains in cortical folding (Gilmore et al., 2007; Li, Nie, Wang, Shi, Lin, et al., 2013a; Li, Nie, Wang, Shi, Gilmore, et al., 2014). Hence, once the 2‐year‐olds can be correctly identified, the possibility of identifying the adult brains based on their neonatal cortical folding patterns would be very high. However, further investigations are required to validate this assumption using a larger longitudinal dataset covering both developing and adult periods.

Table 2 provides us further insights into the infant identification tasks from neonatal cortical folding. Specifically, first, the combination of three kinds of cortical folding features can slightly improve identification accuracies compared to any single feature. Though the improvement is not significant, we prefer to adopt the combinations of three features into the proposed individual identification framework because of two reasons: (a) the mean curvature, average convexity, and sulcal depth provide complementary morphological information of cortical folding from different aspects, as described at the beginning of Section 2.3; (b) the accuracies are all 100% in all tasks, outperforming any single feature and other feature combinations. Second, the identification accuracies in the first two tasks using neonatal brain to identify 1‐ and 2‐year‐olds are lower than that in the third task using 1‐year‐olds to identify 2‐year‐olds. Compared to the first two tasks, the third task is more similar to the adult individual identification, due to the moderate change of cortical folding from Year 1 to Year 2. Thus, these results indirectly validate that the infant individual identification involving neonates is much more challenging than the adult individual identification. Furthermore, the identification accuracies in the first task (i.e., using the dataset with scans at Year 0 to predict the identities of scans at Year 1) are sometimes lower than that of the second task (i.e., using the dataset with scans at Year 0 to predict the identities of scans at Year 2). It might seem less reasonable, since the first task should be easier than the second one, because of the smaller brain development in the first year, in comparison to the first 2 years. To analyze whether this result is caused by the imbalanced datasets in the first two tasks, we repeated experiments with balanced datasets based on both ROI‐based and global‐based methods, as shown in Table S3. Here, to obtain the balanced Year 1 and Year 2 datasets, we randomly selected 200 subjects for 10 times from their original datasets, respectively. Table S3 shows the averaged accuracies of 10 times experiments. As we can see, the accuracies in Task 1 are still lower than Task 2 in some experiments. Excluding the reason of imbalanced datasets, we speculate that the different surface quality in Year 1 and Year 2 datasets might be responsible for this unexpected result in Table 2. Specifically, in the image processing pipeline, the surfaces in Year 0 dataset are reconstructed based on the segmentation results obtained from T2‐weighted images, which show better tissue contrast than the T1‐weighted images of neonatal brains, while the surfaces in Year 1 and Year 2 datasets are reconstructed from T1‐weighted images. Due to the poorer contrast of T1‐weighted images at Year 1 compared with those at Year 2, the surface quality of images in Year 1 dataset in the first task is poorer than that in Year 2 dataset in the second task, which thus might lead to the unexpected slightly lower identification accuracies in the first task.

To handle the case where the subject to be identified has no corresponding scan in the dataset, we set a threshold of the ratio between the frequencies of the first ranked potential identity and the second ranked potential identity. We recorded the ratios during all subjects' identification procedures, and found that the minimum ratios in the first two tasks are 2.0 and 2.2, respectively. The distributions of the ratios are displayed in histograms in Figure 7. Of note, choosing a proper threshold of the ratios is important for the individual identification method. If the threshold is too large, the FPR would be 0, but the FNR would be large; if it is too small, the FPR would be large, and the FNR would be 0. In both situations with improper thresholds, the identification accuracies would be low. The accuracies, FPRs and FNRs based on different thresholds in inverse tasks are displayed in Table S1. Here, we set the threshold to 2.0 according to the above minimum ratio in the first two tasks. Based on thresholding, if a new coming scan has no corresponding scan in the early dataset, we would reject it, thus controlling the false discovery rate.