Gyral net: A new representation of cortical folding organization

Chen, Hanbo,Li, Yujie,Ge, Fangfei,Li, Gang,Shen, Dinggang,Liu, Tianming Elsevier 2017 Medical image analysis Vol.42 No.-

<P><B>Abstract</B></P> <P>One distinct feature of the cerebral cortex is its convex (gyri) and concave (sulci) folding patterns. Due to the remarkable complexity and variability of gyral/sulcal shapes, it has been challenging to quantitatively model their organization patterns. Inspired by the observation that the lines of gyral crests can form a connected graph on each brain hemisphere, we propose a new representation of cortical gyri/sulci organization pattern – gyral net, which models cortical architecture from a graph perspective, starting with nodes and edges obtained from the reconstructed cortical surfaces. A novel computational framework is developed to efficiently and automatically construct gyral nets from surface meshes, and four measurements are devised to quantify the folding patterns. Using an MRI dataset for autism study as a test bed, we identified reduced local connectivity cost and increased curviness of gyral net bilaterally on the parietal lobe, occipital lobe, and temporal lobe in autistic patients. Additionally, we found that the cortical thickness and the gyral straightness of gyral joints are higher than the rest of gyral crest regions. The proposed representation offers a new tool for a comprehensive and reliable characterization of the cortical folding organization. This novel computational framework will enable large-scale analyses of cortical folding patterns in the future.</P> <P><B>Highlights</B></P> <P> <UL> <LI> A new representation of cortical gyri/sulci organization pattern. </LI> <LI> A novel framework to efficiently and automatically construct gyral net from mesh surface. </LI> <LI> A new tool for a comprehensive and reliable characterization of the cortical folding organization. </LI> <LI> Enable large-scale cortical folding pattern analyses in the future. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>Illustration of the concept of gyral net and gyral joint. (a) Reconstructed cortical surface color-coded by gyral altitude. (b) Extracted gyral net. (c) Zoom in view of the circled area in (b). In this paper, we propose a new representation of cortical gyri/sulci organization pattern – gyral net, which models cortical architecture from a graph perspective, starting with nodes and edges obtained from the surface reconstructions.</P> <P>[DISPLAY OMISSION]</P>

Scalable joint segmentation and registration framework for infant brain images

Dong, Pei,Wang, Li,Lin, Weili,Shen, Dinggang,Wu, Guorong Elsevier 2017 Neurocomputing Vol.229 No.-

<P><B>Abstract</B></P> <P>The first year of life is the most dynamic and perhaps the most critical phase of postnatal brain development. The ability to accurately measure structure changes is critical in early brain development study, which highly relies on the performances of image segmentation and registration techniques. However, either infant image segmentation or registration, if deployed independently, encounters much more challenges than segmentation/registration of adult brains due to dynamic appearance change with rapid brain development. In fact, image segmentation and registration of infant images can assists each other to overcome the above challenges by using the growth trajectories (i.e., temporal correspondences) learned from a large set of training subjects with complete longitudinal data. Specifically, a one-year-old image with ground-truth tissue segmentation can be first set as the reference domain. Then, to register the infant image of a new subject at earlier age, we can estimate its tissue probability maps, i.e., with sparse patch-based multi-atlas label fusion technique, where only the training images at the respective age are considered as atlases since they have similar image appearance. Next, these probability maps can be fused as a good initialization to guide the level set segmentation. Thus, image registration between the new infant image and the reference image is free of difficulty of appearance changes, by establishing correspondences upon the reasonably segmented images. Importantly, the segmentation of new infant image can be further enhanced by propagating the much more reliable label fusion heuristics at the reference domain to the corresponding location of the new infant image via the learned growth trajectories, which brings image segmentation and registration to assist each other. It is worth noting that our joint segmentation and registration framework is also flexible to handle the registration of any two infant images even with significant age gap in the first year of life, by linking their joint segmentation and registration through the reference domain. Thus, our proposed joint segmentation and registration method is scalable to various registration tasks in early brain development studies. Promising segmentation and registration results have been achieved for infant brain MR images aged from 2-week-old to 1-year-old, indicating the applicability of our method in early brain development study.</P> <P><B>Highlights</B></P> <P> <UL> <LI> We developed an efficient approach to deal with the tissue segmentation and registration for the infant brain MR images. </LI> <LI> Our proposed framework is scalable to various registration tasks in early brain development studies. </LI> <LI> Promising results have been achieved for infant brain MR images aged from 2-week-old to 1-year-old. </LI> <LI> The proposed technique can be very useful for many ongoing early brain development studies. </LI> </UL> </P>

A novel relational regularization feature selection method for joint regression and classification in AD diagnosis

Zhu, Xiaofeng,Suk, Heung-Il,Wang, Li,Lee, Seong-Whan,Shen, Dinggang Elsevier 2017 Medical image analysis Vol.38 No.-

<P><B>Abstract</B></P> <P>In this paper, we focus on joint regression and classification for Alzheimer’s disease diagnosis and propose a new feature selection method by embedding the relational information inherent in the observations into a sparse multi-task learning framework. Specifically, the relational information includes three kinds of relationships (such as feature-feature relation, response–response relation, and sample-sample relation), for preserving three kinds of the similarity, such as for the features, the response variables, and the samples, respectively. To conduct feature selection, we first formulate the objective function by imposing these three relational characteristics along with an ℓ<SUB>2,1</SUB>-norm regularization term, and further propose a computationally efficient algorithm to optimize the proposed objective function. With the dimension-reduced data, we train two support vector regression models to predict the clinical scores of ADAS-Cog and MMSE, respectively, and also a support vector classification model to determine the clinical label. We conducted extensive experiments on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset to validate the effectiveness of the proposed method. Our experimental results showed the efficacy of the proposed method in enhancing the performances of both clinical scores prediction and disease status identification, compared to the state-of-the-art methods.</P> <P><B>Highlights</B></P> <P> <UL> <LI> A novel graph feature selection method for the AD/MCI diagnosis. </LI> <LI> A novel regularization exploiting the relational information inherent in the observations. </LI> <LI> First work considering three relationships for joint classification and regression. </LI> <LI> High accuracy of 95.7% for AD classification and 79.9% for MCI classification. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>

Sparse Multi-Response Tensor Regression for Alzheimer's Disease Study With Multivariate Clinical Assessments

Li, Zhou,Suk, Heung-Il,Shen, Dinggang,Li, Lexin IEEE 2016 IEEE transactions on medical imaging Vol.35 No.8

<P>Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disorder that has recently seen serious increase in the number of affected subjects. In the last decade, neuroimaging has been shown to be a useful tool to understand AD and its prodromal stage, amnestic mild cognitive impairment (MCI). The majority of AD/MCI studies have focused on disease diagnosis, by formulating the problem as classification with a binary outcome of AD/MCI or healthy controls. There have recently emerged studies that associate image scans with continuous clinical scores that are expected to contain richer information than a binary outcome. However, very few studies aim at modeling multiple clinical scores simultaneously, even though it is commonly conceived that multivariate outcomes provide correlated and complementary information about the disease pathology. In this article, we propose a sparse multi-response tensor regression method to model multiple outcomes jointly as well as to model multiple voxels of an image jointly. The proposed method is particularly useful to both infer clinical scores and thus disease diagnosis, and to identify brain subregions that are highly relevant to the disease outcomes. We conducted experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and showed that the proposed method enhances the performance and clearly outperforms the competing solutions.</P>

Mapping Longitudinal Development of Local Cortical Gyrification in Infants from Birth to 2 Years of Age

Li, Gang,Wang, Li,Shi, Feng,Lyall, Amanda E.,Lin, Weili,Gilmore, John H.,Shen, Dinggang Society for Neuroscience 2014 The Journal of neuroscience Vol.34 No.12

<P>Human cortical folding is believed to correlate with cognitive functions. This likely correlation may have something to do with why abnormalities of cortical folding have been found in many neurodevelopmental disorders. However, little is known about how cortical gyrification, the cortical folding process, develops in the first 2 years of life, a period of dynamic and regionally heterogeneous cortex growth. In this article, we show how we developed a novel infant-specific method for mapping longitudinal development of local cortical gyrification in infants. By using this method, via 219 longitudinal 3T magnetic resonance imaging scans from 73 healthy infants, we systemically and quantitatively characterized for the first time the longitudinal cortical global gyrification index (GI) and local GI (LGI) development in the first 2 years of life. We found that the cortical GI had age-related and marked development, with 16.1% increase in the first year and 6.6% increase in the second year. We also found marked and regionally heterogeneous cortical LGI development in the first 2 years of life, with the high-growth regions located in the association cortex, whereas the low-growth regions located in sensorimotor, auditory, and visual cortices. Meanwhile, we also showed that LGI growth in most cortical regions was positively correlated with the brain volume growth, which is particularly significant in the prefrontal cortex in the first year. In addition, we observed gender differences in both cortical GIs and LGIs in the first 2 years, with the males having larger GIs than females at 2 years of age. This study provides valuable information on normal cortical folding development in infancy and early childhood.</P>

Estimating patient-specific and anatomically correct reference model for craniomaxillofacial deformity via sparse representation.

Wang, Li,Ren, Yi,Gao, Yaozong,Tang, Zhen,Chen, Ken-Chung,Li, Jianfu,Shen, Steve G F,Yan, Jin,Lee, Philip K M,Chow, Ben,Xia, James J,Shen, Dinggang Published for the American Association of Physicis 2015 Medical physics Vol.42 No.10

<P>A significant number of patients suffer from craniomaxillofacial (CMF) deformity and require CMF surgery in the United States. The success of CMF surgery depends on not only the surgical techniques but also an accurate surgical planning. However, surgical planning for CMF surgery is challenging due to the absence of a patient-specific reference model. Currently, the outcome of the surgery is often subjective and highly dependent on surgeon's experience. In this paper, the authors present an automatic method to estimate an anatomically correct reference shape of jaws for orthognathic surgery, a common type of CMF surgery.</P>

Automated bone segmentation from dental CBCT images using patch-based sparse representation and convex optimization : Segmentation of CBCT image

Wang, Li,Chen, Ken Chung,Gao, Yaozong,Shi, Feng,Liao, Shu,Li, Gang,Shen, Steve G. F.,Yan, Jin,Lee, Philip K. M.,Chow, Ben,Liu, Nancy X.,Xia, James J.,Shen, Dinggang Published for the American Association of Physicis 2014 Medical physics Vol.41 No.4

<P>Cone-beam computed tomography (CBCT) is an increasingly utilized imaging modality for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. Accurate segmentation of CBCT image is an essential step to generate three-dimensional (3D) models for the diagnosis and treatment planning of the patients with CMF deformities. However, due to the poor image quality, including very low signal-to-noise ratio and the widespread image artifacts such as noise, beam hardening, and inhomogeneity, it is challenging to segment the CBCT images. In this paper, the authors present a new automatic segmentation method to address these problems.</P>

Automated segmentation of dental CBCT image with prior-guided sequential random forests : Automated segmentation of dental CBCT image

Wang, Li,Gao, Yaozong,Shi, Feng,Li, Gang,Chen, Ken-Chung,Tang, Zhen,Xia, James J.,Shen, Dinggang Published for the American Association of Physicis 2016 Medical physics Vol. No.

Sparse multivariate autoregressive modeling for mild cognitive impairment classification.

Li, Yang,Wee, Chong-Yaw,Jie, Biao,Peng, Ziwen,Shen, Dinggang Humana Press, Inc 2014 Neuroinformatics Vol.12 No.3

<P>Brain connectivity network derived from functional magnetic resonance imaging (fMRI) is becoming increasingly prevalent in the researches related to cognitive and perceptual processes. The capability to detect causal or effective connectivity is highly desirable for understanding the cooperative nature of brain network, particularly when the ultimate goal is to obtain good performance of control-patient classification with biological meaningful interpretations. Understanding directed functional interactions between brain regions via brain connectivity network is a challenging task. Since many genetic and biomedical networks are intrinsically sparse, incorporating sparsity property into connectivity modeling can make the derived models more biologically plausible. Accordingly, we propose an effective connectivity modeling of resting-state fMRI data based on the multivariate autoregressive (MAR) modeling technique, which is widely used to characterize temporal information of dynamic systems. This MAR modeling technique allows for the identification of effective connectivity using the Granger causality concept and reducing the spurious causality connectivity in assessment of directed functional interaction from fMRI data. A forward orthogonal least squares (OLS) regression algorithm is further used to construct a sparse MAR model. By applying the proposed modeling to mild cognitive impairment (MCI) classification, we identify several most discriminative regions, including middle cingulate gyrus, posterior cingulate gyrus, lingual gyrus and caudate regions, in line with results reported in previous findings. A relatively high classification accuracy of 91.89 % is also achieved, with an increment of 5.4 % compared to the fully-connected, non-directional Pearson-correlation-based functional connectivity approach.</P>

Remodeling Pearson's Correlation for Functional Brain Network Estimation and Autism Spectrum Disorder Identification

Li, Weikai,Wang, Zhengxia,Zhang, Limei,Qiao, Lishan,Shen, Dinggang Frontiers Media S.A. 2017 Frontiers in neuroinformatics Vol.11 No.-

<P>Functional brain network (FBN) has been becoming an increasingly important way to model the statistical dependence among neural time courses of brain, and provides effective imaging biomarkers for diagnosis of some neurological or psychological disorders. Currently, Pearson's Correlation (PC) is the simplest and most widely-used method in constructing FBNs. Despite its advantages in statistical meaning and calculated performance, the PC tends to result in a FBN with dense connections. Therefore, in practice, the PC-based FBN needs to be sparsified by removing weak (potential noisy) connections. However, such a scheme depends on a hard-threshold without enough flexibility. Different from this traditional strategy, in this paper, we propose a new approach for estimating FBNs by remodeling PC as an optimization problem, which provides a way to incorporate biological/physical priors into the FBNs. In particular, we introduce an L<SUB>1</SUB>-norm regularizer into the optimization model for obtaining a sparse solution. Compared with the hard-threshold scheme, the proposed framework gives an elegant mathematical formulation for sparsifying PC-based networks. More importantly, it provides a platform to encode other biological/physical priors into the PC-based FBNs. To further illustrate the flexibility of the proposed method, we extend the model to a weighted counterpart for learning both sparse and scale-free networks, and then conduct experiments to identify autism spectrum disorders (ASD) from normal controls (NC) based on the constructed FBNs. Consequently, we achieved an 81.52% classification accuracy which outperforms the baseline and state-of-the-art methods.</P>