Yu Meng

The folding of the human cerebral cortex is highly complex and variable across individuals, but certain common major patterns of cortical folding do exist. Mining such common patterns of cortical folding is of great importance in understanding the inter‐individual variability of cortical folding and their relationship with cognitive functions and brain disorders. As primary cortical folds are mainly genetically influenced and are well established at term birth, neonates with minimal exposure to the complicated postnatal environmental influences are ideal candidates for mining the major patterns of cortical folding. In this paper, we propose a sulcal‐pit‐based method to discover the major sulcal patterns of cortical folding. In our method, first, the sulcal pattern is characterized by the spatial distribution of sulcal pits, which are the locally deepest points in cortical sulci. Since deep sulcal pits are genetically related, relatively consistent across individuals, and also stable during brain development, they are well suited for representing and characterizing the sulcal patterns. Then, the similarity between the distributions of sulcal pits is measured from the spatial, geometrical, and topological points of view. Next, a comprehensive similarity matrix is constructed for the whole dataset by adaptively fusing these measurements together, thus capturing both their common and complementary information. Finally, leveraging the similarity matrix, a hierarchical affinity propagation algorithm is used to group similar sulcal folding patterns together. The proposed method has been applied to 677 neonatal brains, and revealed multiple distinct and meaningful sulcal patterns in the central sulcus, superior temporal sulcus, and cingulate sulcus.

Tissue segmentation of infant brain MRIs with risk of autism is critically important for characterizing early brain development and identifying biomarkers. However, it is challenging due to low tissue contrast caused by inherent ongoing myelination and maturation. In particular, at around 6 months of age, the voxel intensities in both gray matter and white matter are within similar ranges, thus leading to the lowest image contrast in the first postnatal year. Previous studies typically employed intensity images and tentatively estimated tissue probabilities to train a sequence of classifiers for tissue segmentation. However, the important prior knowledge of brain anatomy is largely ignored during the segmentation. Consequently, the segmentation accuracy is still limited and topological errors frequently exist, which will significantly degrade the performance of subsequent analyses. Although topological errors could be partially handled by retrospective topological correction methods, their results may still be anatomically incorrect. To address these challenges, in this article, we propose an anatomy‐guided joint tissue segmentation and topological correction framework for isointense infant MRI. Particularly, we adopt a signed distance map with respect to the outer cortical surface as anatomical prior knowledge, and incorporate such prior information into the proposed framework to guide segmentation in ambiguous regions. Experimental results on the subjects acquired from National Database for Autism Research demonstrate the effectiveness to topological errors and also some levels of robustness to motion. Comparisons with the state‐of‐the‐art methods further demonstrate the advantages of the proposed method in terms of both segmentation accuracy and topological correctness.

Understanding the early dynamic development of the human cerebral cortex remains a challenging problem. Cortical thickness, as one of the most important morphological attributes of the cerebral cortex, is a sensitive indicator for both normal neurodevelopment and neuropsychiatric disorders, but its early postnatal development remains largely unexplored. In this study, we investigate a key question in neurodevelopmental science: can we predict the future dynamic development of cortical thickness map in an individual infant based on its available MRI data at birth? If this is possible, we might be able to better model and understand the early brain development and also early detect abnormal brain development during infancy. To this end, we develop a novel learning‐based method, called Dynamically‐Assembled Regression Forest (DARF), to predict the development of the cortical thickness map during the first postnatal year, based on neonatal MRI features. We applied our method to 15 healthy infants and predicted their cortical thickness maps at 3, 6, 9, and 12 months of age, with respectively mean absolute errors of 0.209 mm, 0.332 mm, 0.340 mm, and 0.321 mm. Moreover, we found that the prediction precision is region‐specific, with high precision in the unimodal cortex and relatively low precision in the high‐order association cortex, which may be associated with their differential developmental patterns. Additional experiments also suggest that using more early time points for prediction can further significantly improve the prediction accuracy. Hum Brain Mapp 38:2865–2874, 2017. © 2017 Wiley Periodicals, Inc.

To accurately chart the dynamic brain developmental trajectories in infants, many longitudinal neuroimaging studies prefer having a complete dataset. Unfortunately, missing data at certain time points are unavoidable in longitudinal datasets. To better use incomplete longitudinal data, we propose a novel method to estimate the subject-specific vertex-wise cortical thickness maps at missing time points, by using a customized regression forest, Dynamically-Assembled Regression Forest (DARF). DARF ensures spatial smoothness of the estimated cortical thickness maps and also the computational efficiency. The proposed method can fully exploit the available information from the subjects both with and without missing scans. Our method has been applied to estimate the missing cortical thickness maps in a longitudinal infant dataset, which includes 31 healthy subjects, with each having up to 5 scans. The experimental results indicate that our method can accurately estimate missing cortical thickness maps, with the average vertex-wise error less than 0.23 mm.

This paper studies the fault-tolerant cooperative control problem of agent groups in the context of multi-agent flocking tasks with second order linear dynamics. A distributed flocking algorithm with biconnected topology is proposed which is composed of two parts: motion strategy of biconnectivity and fault-tolerant flocking algorithm with bounded control input. The proposed algorithm handles the movement and reconfiguration of the flock, while maintaining the desired shape. It is proved that the proposed control algorithm can not only achieve the resultant biconnected network which is able to tolerate temporary node failures, but also guarantee the stable flocking motion. Several simulations are presented to demonstrate the efficiency of the theoretical results.

Analysis of anatomical covariance for cortex morphology in individual subjects plays an important role in the study of human brains. However, the approaches for constructing individual structural networks have not been well developed yet. Existing methods based on patch-wise image intensity similarity suffer from several major drawbacks, i.e., 1) violation of cortical topological properties, 2) sensitivity tointensity heterogeneity, and3) influence by patch size heterogeneity. To overcome these limitations, this paper presents a novel cortical surface-based method for constructing individual structural networks. Specifically, our method first maps the cortical surfaces onto a standard spherical surface atlas and then uniformly samples vertices on the spherical surface as the nodes of the networks. The similarity between any two nodes is computed based on the biologically-meaningful cortical attributes (e.g., cortical thickness) in the spherical neighborhood of their sampled vertices. The connection between any two nodes is established only if the similarity is larger than a user-specified threshold. Through leveraging spherical cortical surface patches, our method generates biologically-meaningful individual networks that are comparable across ages and subjects. The proposed method has been applied to construct cortical-thickness networks for 73 healthy infants, with each infant having two MRI scans at 0 and 1 year of age. The constructed networks during the two ages were compared using various network metrics, such as degree, clustering coefficient, shortest path length, small world property, global efficiency, and local efficiency. Experimental results demonstrate that our method can effectively construct individual structural networks and reveal meaningful patterns in early brain development.