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During the first 2 postnatal years, cortical thickness of the human brain develops dynamically and spatially heterogeneously and likely peaks between 1 and 2 y of age. The striking development renders this period critical for later cognitive outcomes and vulnerable to early neurodevelopmental disorders. However, due to the difficulties in longitudinal infant brain MRI acquisition and processing, our knowledge still remains limited on the dynamic changes, peak age, and spatial heterogeneities of cortical thickness during infancy. To fill this knowledge gap, in this study, we discover the developmental regionalization of cortical thickness, i.e., developmentally distinct regions, each of which is composed of a set of codeveloping cortical vertices, for better understanding of the spatiotemporal heterogeneities of cortical thickness development. We leverage an infant-dedicated computational pipeline, an advanced multivariate analysis method (i.e., nonnegative matrix factorization), and a densely sampled longitudinal dataset with 210 serial MRI scans from 43 healthy infants, with each infant being scheduled to have 7 longitudinal scans at around 1, 3, 6, 9, 12, 18, and 24 mo of age. Our results suggest that, during the first 2 y, the whole-brain average cortical thickness increases rapidly and reaches a plateau at about 14 mo of age and then decreases at a slow pace thereafter. More importantly, each discovered region is structurally and functionally meaningful and exhibits a distinctive developmental pattern, with several regions peaking at varied ages while others keep increasing in the first 2 postnatal years. Our findings provide valuable references and insights for early brain development. [ABSTRACT FROM AUTHOR]

A conventional matrix-based visual secret sharing scheme has the drawbacks of pixel expansion and the requirement of predetermined sophisticated codebooks. A random grid-based visual secret sharing (RGVSS) scheme is an effective approach to solving these two problems. However, up to now, most of the existing publications about the RGVSS scheme deal with the threshold access structures, while there is hardly any appropriate method to construct the RGVSS scheme for general access structures (GASs). In this paper, a novel method to construct a generalized RGVSS (GRGVSS) scheme for GASs is proposed. The construction algorithm consists of two parts. In the first part, a more general (n, n)-GRGVSS scheme is proposed; in addition, the visual quality of the reconstructed secret image for this GRGVSS scheme is formally analyzed. In the second part, we utilize this (n, n)- GRGVSS to construct the GRGVSS scheme for given GASs by treating the procedure as a nonlinear 0-1 programming model. In the experimental phase, by changing the given preconditions, we analyze the security of the proposed scheme and assess the visual quality of the recovered secret images for the proposed scheme under different situations. The simulation results show that the proposed GRGVSS scheme for GASs is feasible and efficient. [ABSTRACT FROM AUTHOR]