Deep Modeling of Growth Trajectories for Longitudinal Prediction of Missing Infant Cortical Surfaces

Charting cortical growth trajectories is of paramount importance for understanding brain development. However, such analysis necessitates the collection of longitudinal data, which can be challenging due to subject dropouts and failed scans. In this paper, we will introduce a method for longitudinal prediction of cortical surfaces using a spatial graph convolutional neural network (GCNN), which extends conventional CNNs from Euclidean to curved manifolds. The proposed method is designed to model the cortical growth trajectories and jointly predict inner and outer cortical surfaces at multiple time points. Adopting a binary flag in loss calculation to deal with missing data, we fully utilize all available cortical surfaces for training our deep learning model, without requiring a complete collection of longitudinal data. Predicting the surfaces directly allows cortical attributes such as cortical thickness, curvature, and convexity to be computed for subsequent analysis. We will demonstrate with experimental results that our method is capable of capturing the nonlinearity of spatiotemporal cortical growth patterns and can predict cortical surfaces with improved accuracy.Comment: Accepted as oral presentation at IPMI 201

Construction of 4D high-definition cortical surface atlases of infants: Methods and applications

In neuroimaging, cortical surface atlases play a fundamental role for spatial normalization, analysis, visualization, and comparison of results across individuals and different studies. However, existing cortical surface atlases created for adults are not suitable for infant brains during the first two years of life, which is the most dynamic period of postnatal structural and functional development of the highly-folded cerebral cortex. Therefore, spatiotemporal cortical surface atlases for infant brains are highly desired yet still lacking for accurate mapping of early dynamic brain development. To bridge this significant gap, leveraging our infant-dedicated computational pipeline for cortical surface-based analysis and the unique longitudinal infant MRI dataset acquired in our research center, in this paper, we construct the first spatiotemporal (4D) high-definition cortical surface atlases for the dynamic developing infant cortical structures at 7 time points, including 1, 3, 6, 9, 12, 18, and 24 months of age, based on 202 serial MRI scans from 35 healthy infants. For this purpose, we develop a novel method to ensure the longitudinal consistency and unbiasedness to any specific subject and age in our 4D infant cortical surface atlases. Specifically, we first compute the within-subject mean cortical folding by unbiased groupwise registration of longitudinal cortical surfaces of each infant. Then we establish longitudinally-consistent and unbiased inter-subject cortical correspondences by groupwise registration of the geometric features of within-subject mean cortical folding across all infants. Our 4D surface atlases capture both longitudinally-consistent dynamic mean shape changes and the individual variability of cortical folding during early brain development. Experimental results on two independent infant MRI datasets show that using our 4D infant cortical surface atlases as templates leads to significantly improved accuracy for spatial normalization of cortical surfaces across infant individuals, in comparison to the infant surface atlases constructed without longitudinal consistency and also the FreeSurfer adult surface atlas. Moreover, based on our 4D infant surface atlases, for the first time, we reveal the spatially-detailed, region-specific correlation patterns of the dynamic cortical developmental trajectories between different cortical regions during early brain development

Infant Cognitive Scores Prediction With Multi-stream Attention-based Temporal Path Signature Features

There is stunning rapid development of human brains in the first year of life. Some studies have revealed the tight connection between cognition skills and cortical morphology in this period. Nonetheless, it is still a great challenge to predict cognitive scores using brain morphological features, given issues like small sample size and missing data in longitudinal studies. In this work, for the first time, we introduce the path signature method to explore hidden analytical and geometric properties of longitudinal cortical morphology features. A novel BrainPSNet is proposed with a differentiable temporal path signature layer to produce informative representations of different time points and various temporal granules. Further, a two-stream neural network is included to combine groups of raw features and path signature features for predicting the cognitive score. More importantly, considering different influences of each brain region on the cognitive function, we design a learning-based attention mask generator to automatically weight regions correspondingly. Experiments are conducted on an in-house longitudinal dataset. By comparing with several recent algorithms, the proposed method achieves the state-of-the-art performance. The relationship between morphological features and cognitive abilities is also analyzed

A novel diffusion tensor imaging-based computer-aided diagnostic system for early diagnosis of autism.

Autism spectrum disorders (ASDs) denote a significant growing public health concern. Currently, one in 68 children has been diagnosed with ASDs in the United States, and most children are diagnosed after the age of four, despite the fact that ASDs can be identified as early as age two. The ultimate goal of this thesis is to develop a computer-aided diagnosis (CAD) system for the accurate and early diagnosis of ASDs using diffusion tensor imaging (DTI). This CAD system consists of three main steps. First, the brain tissues are segmented based on three image descriptors: a visual appearance model that has the ability to model a large dimensional feature space, a shape model that is adapted during the segmentation process using first- and second-order visual appearance features, and a spatially invariant second-order homogeneity descriptor. Secondly, discriminatory features are extracted from the segmented brains. Cortex shape variability is assessed using shape construction methods, and white matter integrity is further examined through connectivity analysis. Finally, the diagnostic capabilities of these extracted features are investigated. The accuracy of the presented CAD system has been tested on 25 infants with a high risk of developing ASDs. The preliminary diagnostic results are promising in identifying autistic from control patients

Learning-based subject-specific estimation of dynamic maps of cortical morphology at missing time points in longitudinal infant studies: Estimation of Cortical Morphological Maps

Longitudinal neuroimaging analysis of the dynamic brain development in infants has received increasing attention recently. Many studies expect a complete longitudinal dataset in order to accurately chart the brain developmental trajectories. However, in practice, a large portion of subjects in longitudinal studies often have missing data at certain time points, due to various reasons such as the absence of scan or poor image quality. To make better use of these incomplete longitudinal data, in this paper, we propose a novel machine learning-based method to estimate the subject-specific, vertex-wise cortical morphological attributes at the missing time points in longitudinal infant studies. Specifically, we develop a customized regression forest, named Dynamically-Assembled Regression Forest (DARF), as the core regression tool. DARF ensures the spatial smoothness of the estimated maps for vertex-wise cortical morphological attributes and also greatly reduces the computational cost. By employing a pairwise estimation followed by a joint refinement, our method is able to fully exploit the available information from both subjects with complete scans and subjects with missing scans for estimation of the missing cortical attribute maps. The proposed method has been applied to estimating the dynamic cortical thickness maps at missing time points in an incomplete longitudinal infant dataset, which includes 31 healthy infant subjects, each having up to 5 time points in the first postnatal year. The experimental results indicate that our proposed framework can accurately estimate the subject-specific vertex-wise cortical thickness maps at missing time points, with the average error less than 0.23 mm

Computational Cortical Surface Analysis for Study of Early Brain Development

The study of morphological attributes of the cerebral cortex and their development is very important in understanding the dynamic and critical early brain development. Comparing with conventional studies in the image space, cortical surface-based analysis provides a better way to display, observe, and quantify the attributes of the cerebral cortex. The goal of this dissertation is to develop novel cortical surface-based methods for better studying the attributes of the cerebral cortex during early brain development. Specifically, this dissertation aims to develop methods for 1) estimating the development of morphological attributes of the cerebral cortex and 2) discovering the major cortical folding patterns. Estimation of the Development of Cortical Attributes. The early development of cortical attributes is highly correlated to the brain cognitive functionality and some neurodevelopmental disorders. Hence, accurately modeling the early development of cortical attributes is crucial for better understanding the mysterious normal and abnormal brain development. This task is very challenging, because infant cortical attributes change dramatically, complicatedly and regionally-heterogeneously during the first year of life. To address these problems, this dissertation proposes a Dynamically-Assembled Regression Forest (DARF). DARF first trains a single decision tree at each vertex on the cortical surface, and then groups nearby decision trees around each vertex as a vertex-specific forest to predict the cortical attribute. Since the vertex-specific forest can better capture regional details than the conventional regression forest trained for the whole brain, the prediction result is more precise. Moreover, because nearby forests share a large portion of decision trees, the prediction result is spatially smooth. On the other hand, missing cortical attribute maps in the longitudinal datasets often lead to insufficient data for unbiased analysis or training of accurate prediction models. To address this issue, a missing data estimation strategy based on DARF is further proposed. Experiments show that DARF outperforms the existing popular regression methods, and the proposed missing data estimation strategy based on DARF can effectively recover the missing cortical attribute maps. Discovery of Major Cortical Folding Patterns. The folding patterns of the cerebral cortex are highly variable across subjects. Exploring major cortical folding patterns in neonates is of great importance in neuroscience. Conventional geometric measurements of the cortex have limited capability in distinguishing major folding patterns. Although the recent sulcal pits-based analysis provides a better way for comparing sulcal patterns across individuals of adults or older children, whether and how sulcal pits are suitable for discovering major sulcal patterns in infants remain unknown. This dissertation adapts a sulcal pits extraction method from adults to infants, and validates the spatial consistency of sulcal pits in infants, so that they can be used as reliable landmarks for exploring major sulcal patterns. This dissertation further proposes a sulcal graph-based method for discovering major sulcal patterns, which is then applied to studying three primary cortical regions in 677 neonatal cortical surfaces. The experiments show that the proposed method is able to identify the previously unreported major sulcal patterns. Finally, this dissertation investigates and verifies that the sulcal pattern information could be utilized to help DARF for better estimating cortical attribute maps.Doctor of Philosoph