Zhengwang Wu’s research while affiliated with University of North Carolina at Chapel Hill and other places

The surface morphology of the developing mammalian brain is crucial for understanding brain function and dysfunction. Computational modeling offers valuable insights into the underlying mechanisms for early brain folding. While previous studies generally assume uniform growth, recent findings indicate significant regional variations in brain tissue growth. However, the role of these variations in cortical development remains unclear. In this study, we explored how regional cortical growth affects brain folding patterns. We first developed growth models for typical cortical regions using ML-assisted symbolic regression, based on longitudinal data from over 1,000 infant MRI scans that captured cortical surface area and thickness during perinatal and postnatal brains development. These models were subsequently integrated into computational software to simulate cortical development with anatomically realistic geometric models. We quantified the resulting folding patterns using metrics such as mean curvature, sulcal depth, and gyrification index. Our results demonstrate that regional growth models generate complex brain folding patterns that more closely match actual brains structures, both quantitatively and qualitatively, compared to uniform growth models. Growth magnitude plays a dominant role in shaping folding patterns, while growth trajectory has a minor influence. Moreover, multi-region models better capture the intricacies of brain folding than single-region models. Our results underscore the necessity and importance of incorporating regional growth heterogeneity into brain folding simulations, which could enhance early diagnosis and treatment of cortical malformations and neurodevelopmental disorders such as epilepsy and autism.

Autism spectrum disorder has been emerging as a growing public health threat. Early diagnosis of autism spectrum disorder is crucial for timely, effective intervention and treatment. However, conventional diagnosis methods based on communications and behavioral patterns are unreliable for children younger than 2 years of age. Given evidences of neurodevelopmental abnormalities in autism spectrum disorder infants, we resort to a novel deep learning-based method to extract key features from the inherently scarce, class-imbalanced, and heterogeneous structural MR images for early autism diagnosis. Specifically, we propose a Siamese verification framework to extend the scarce data, and an unsupervised compressor to alleviate data imbalance by extracting key features. We also proposed weight constraints to cope with sample heterogeneity by giving different samples different voting weights during validation, and used Path Signature to unravel meaningful developmental features from the two-time point data longitudinally. We further extracted machine learning focused brain regions for autism diagnosis. Extensive experiments have shown that our method performed well under practical scenarios, transcending existing machine learning methods and providing anatomical insights for autism early diagnosis.

Fetal Magnetic Resonance Imaging (MRI) is challenged by fetal movements and maternal breathing. Although fast MRI sequences allow artifact free acquisition of individual 2D slices, motion frequently occurs in the acquisition of spatially adjacent slices. Motion correction for each slice is thus critical for the reconstruction of 3D fetal brain MRI. In this paper, we propose a novel multi-task learning framework that adopts a coarse-to-fine strategy to jointly learn the pose estimation parameters for motion correction and tissue segmentation map of each slice in fetal MRI. Particularly, we design a regression-based segmentation loss as a deep supervision to learn anatomically more meaningful features for pose estimation and segmentation. In the coarse stage, a U-Net-like network learns the features shared for both tasks. In the refinement stage, to fully utilize the anatomical information, signed distance maps constructed from the coarse segmentation are introduced to guide the feature learning for both tasks. Finally, iterative incorporation of the signed distance maps further improves the performance of both regression and segmentation progressively. Experimental results of cross-validation across two different fetal datasets acquired with different scanners and imaging protocols demonstrate the effectiveness of the proposed method in reducing the pose estimation error and obtaining superior tissue segmentation results simultaneously, compared with state-of-the-art methods.

Modern multi-site neuroimaging studies are known to be biased by significant site effects observed in imaging data and their derived structural and functional features. Although many statistical models and deep learning methods have been proposed to eliminate the site effects while maintaining biological characteristics, they have two major drawbacks. First, statistical models are applicable for harmonizing regional-level data but are inherently not suitable to represent the complex non-linear mapping of vertex-wise cortical property maps. Second, existing deep learning methods can only harmonize data between two sites, which are practically less useful in multi-site data harmonization scenario and also ignore the rich information in the whole dataset. To address these issues, we develop a novel, flexible deep learning method to harmonize multi-site cortical surface property maps. Specifically, to detect and remove site effects, we employ a surface-based autoencoder and decompose the encoded cortical features into site-related and site-unrelated components and use an adversarial strategy to encourage the disentanglement. Then decoding the site-unrelated features with other site features can generate mappings across different sites. To learn more controllable and meaningful mappings, we enforce the cycle consistency between forward and backward mappings. Our method can thus efficiently learn rich information from the whole dataset and generate realistic harmonized surface maps at the target site. Experiments on harmonizing infant cortical thickness maps of 2,342 scans from four sites with different scanners and imaging protocols validate the superior performance of our method on both site effects removal and biological variability preservation compared to other methods. To the best of our knowledge, this is the largest validation of different methods on infant cortical data harmonization.

Exploring the relationship between the cognitive ability and infant cortical structural and functional development is critically important to advance our understanding of early brain development, which, however, is very challenging due to the complex and dynamic brain development in early postnatal stages. Conventional approaches typically use either the structural MRI or resting-state functional MRI and rely on the region-level features or inter-region connectivity features after cortical parcellation for predicting cognitive scores. However, these methods have two major issues: 1) spatial information loss, which discards the critical fine-grained spatial patterns containing rich information related to cognitive development; 2) modality information loss, which ignores the complementary information and the interaction between the structural and functional images. To address these issues, we unprecedentedly invent a novel framework, namely cortical surface-based multimodal learning framework (CSML), to leverage fine-grained multimodal features for cognition development prediction. First, we introduce the fine-grained surface-based data representation to capture spatially detailed structural and functional information. Then, a dual-branch network is proposed to extract the discriminative features for each modality respectively and further captures the modality-shared and complementary information with a disentanglement strategy. Finally, an age-guided cognition prediction module is developed based on the prior that the cognition develops along with age. We validate our method on an infant multimodal MRI dataset with 318 scans. Compared to state-of-the-art methods, our method consistently achieves superior performances, and for the first time suggests crucial regions and features for cognition development hidden in the fine-grained spatial details of cortical structure and function.

The cerebellum (i.e., little brain) plays an important role in motion and balances control abilities, despite its much smaller size and deeper sulci compared to the cerebrum. Previous cerebellum studies mainly relied on and focused on conventional volumetric analysis, which ignores the extremely deep and highly convoluted nature of the cerebellar cortex. To better reveal localized functional and structural changes, we propose cortical surface-based analysis of the cerebellar cortex. Specifically, we first reconstruct the cerebellar cortical surfaces to represent and characterize the highly folded cerebellar cortex in a geometrically accurate and topologically correct manner. Then, we propose a novel method to automatically parcellate the cerebellar cortical surface into anatomically meaningful regions by a weakly supervised graph convolutional neural network. Instead of relying on registration or requiring mapping the cerebellar surface to a sphere, which are either inaccurate or have large geometric distortions due to the deep cerebellar sulci, our learning-based model directly deals with the original cerebellar cortical surface by decomposing this challenging task into two steps. First, we learn the effective representation of the cerebellar cortical surface patches with a contrastive self-learning framework. Then, we map the learned representations to parcellation labels. We have validated our method using data from the Baby Connectome Project and the experimental results demonstrate its superior effectiveness and accuracy, compared to existing methods.