Biao Jie

Topological graph kernel on multiple thresholded functional connectivity networks for mild cognitive impairment classification

Recently, brain connectivity networks have been used for classification of Alzheimers disease and mild cognitive impairment (MCI) from normal controls (NC). In typical connectivity‐networks‐based classification approaches, local measures of connectivity networks are first extracted from each region‐of‐interest as network features, which are then concatenated into a vector for subsequent feature selection and classification. However, some useful structural information of network, especially global topological information, may be lost in this type of approaches. To address this issue, in this article, we propose a connectivity‐networks‐based classification framework to identify accurately the MCI patients from NC. The core of the proposed method involves the use of a new graph‐kernel‐based approach to measure directly the topological similarity between connectivity networks. We evaluate our method on functional connectivity networks of 12 MCI and 25 NC subjects. The experimental results show that our proposed method achieves a classification accuracy of 91.9%, a sensitivity of 100.0%, a balanced accuracy of 94.0%, and an area under receiver operating characteristic curve of 0.94, demonstrating a great potential in MCI classification, based on connectivity networks. Further connectivity analysis indicates that the connectivity of the selected brain regions is different between MCI patients and NC, that is, MCI patients show reduced functional connectivity compared with NC, in line with the findings reported in the existing studies. Hum Brain Mapp 35:2876–2897, 2014.

Integration of Network Topological and Connectivity Properties for Neuroimaging Classification

Rapid advances in neuroimaging techniques have provided an efficient and noninvasive way for exploring the structural and functional connectivity of the human brain. Quantitative measurement of abnormality of brain connectivity in patients with neurodegenerative diseases, such as mild cognitive impairment (MCI) and Alzheimers disease (AD), have also been widely reported, especially at a group level. Recently, machine learning techniques have been applied to the study of AD and MCI, i.e., to identify the individuals with AD/MCI from the healthy controls (HCs). However, most existing methods focus on using only a single property of a connectivity network, although multiple network properties, such as local connectivity and global topological properties, can potentially be used. In this paper, by employing multikernel based approach, we propose a novel connectivity based framework to integrate multiple properties of connectivity network for improving the classification performance. Specifically, two different types of kernels (i.e., vector-based kernel and graph kernel) are used to quantify two different yet complementary properties of the network, i.e., local connectivity and global topological properties. Then, multikernel learning (MKL) technique is adopted to fuse these heterogeneous kernels for neuroimaging classification. We test the performance of our proposed method on two different data sets. First, we test it on the functional connectivity networks of 12 MCI and 25 HC subjects. The results show that our method achieves significant performance improvement over those using only one type of network property. Specifically, our method achieves a classification accuracy of 91.9%, which is 10.8% better than those by single network-property-based methods. Then, we test our method for gender classification on a large set of functional connectivity networks with 133 infants scanned at birth, 1 year, and 2 years, also demonstrating very promising results.

Manifold regularized multitask feature learning for multimodality disease classification

Multimodality based methods have shown great advantages in classification of Alzheimers disease (AD) and its prodromal stage, that is, mild cognitive impairment (MCI). Recently, multitask feature selection methods are typically used for joint selection of common features across multiple modalities. However, one disadvantage of existing multimodality based methods is that they ignore the useful data distribution information in each modality, which is essential for subsequent classification. Accordingly, in this paper we propose a manifold regularized multitask feature learning method to preserve both the intrinsic relatedness among multiple modalities of data and the data distribution information in each modality. Specifically, we denote the feature learning on each modality as a single task, and use group‐sparsity regularizer to capture the intrinsic relatedness among multiple tasks (i.e., modalities) and jointly select the common features from multiple tasks. Furthermore, we introduce a new manifold‐based Laplacian regularizer to preserve the data distribution information from each task. Finally, we use the multikernel support vector machine method to fuse multimodality data for eventual classification. Conversely, we also extend our method to the semisupervised setting, where only partial data are labeled. We evaluate our method using the baseline magnetic resonance imaging (MRI), fluorodeoxyglucose positron emission tomography (FDG‐PET), and cerebrospinal fluid (CSF) data of subjects from AD neuroimaging initiative database. The experimental results demonstrate that our proposed method can not only achieve improved classification performance, but also help to discover the disease‐related brain regions useful for disease diagnosis. Hum Brain Mapp 36:489–507, 2015.

Hyper-connectivity of functional networks for brain disease diagnosis.

Exploring structural and functional interactions among various brain regions enables better understanding of pathological underpinnings of neurological disorders. Brain connectivity network, as a simplified representation of those structural and functional interactions, has been widely used for diagnosis and classification of neurodegenerative diseases, especially for Alzheimers disease (AD) and its early stage - mild cognitive impairment (MCI). However, the conventional functional connectivity network is usually constructed based on the pairwise correlation among different brain regions and thus ignores their higher-order relationships. Such loss of high-order information could be important for disease diagnosis, since neurologically a brain region predominantly interacts with more than one other brain regions. Accordingly, in this paper, we propose a novel framework for estimating the hyper-connectivity network of brain functions and then use this hyper-network for brain disease diagnosis. Here, the functional connectivity hyper-network denotes a network where each of its edges representing the interactions among multiple brain regions (i.e., an edge can connect with more than two brain regions), which can be naturally represented by a hyper-graph. Specifically, we first construct connectivity hyper-networks from the resting-state fMRI (R-fMRI) time series by using sparse representation. Then, we extract three sets of brain-region specific features from the connectivity hyper-networks, and further exploit a manifold regularized multi-task feature selection method to jointly select the most discriminative features. Finally, we use multi-kernel support vector machine (SVM) for classification. The experimental results on both MCI dataset and attention deficit hyperactivity disorder (ADHD) dataset demonstrate that, compared with the conventional connectivity network-based methods, the proposed method can not only improve the classification performance, but also help discover disease-related biomarkers important for disease diagnosis.

Label-aligned multi-task feature learning for multimodal classification of Alzheimer’s disease and mild cognitive impairment

Multimodal classification methods using different modalities of imaging and non-imaging data have recently shown great advantages over traditional single-modality-based ones for diagnosis and prognosis of Alzheimer’s disease (AD), as well as its prodromal stage, i.e., mild cognitive impairment (MCI). However, to the best of our knowledge, most existing methods focus on mining the relationship across multiple modalities of the same subjects, while ignoring the potentially useful relationship across different subjects. Accordingly, in this paper, we propose a novel learning method for multimodal classification of AD/MCI, by fully exploring the relationships across both modalities and subjects. Specifically, our proposed method includes two subsequent components, i.e., label-aligned multi-task feature selection and multimodal classification. In the first step, the feature selection learning from multiple modalities are treated as different learning tasks and a group sparsity regularizer is imposed to jointly select a subset of relevant features. Furthermore, to utilize the discriminative information among labeled subjects, a new label-aligned regularization term is added into the objective function of standard multi-task feature selection, where label-alignment means that all multi-modality subjects with the same class labels should be closer in the new feature-reduced space. In the second step, a multi-kernel support vector machine (SVM) is adopted to fuse the selected features from multi-modality data for final classification. To validate our method, we perform experiments on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database using baseline MRI and FDG-PET imaging data. The experimental results demonstrate that our proposed method achieves better classification performance compared with several state-of-the-art methods for multimodal classification of AD/MCI.

Manifold regularized multi-task feature selection for multi-modality classification in Alzheimer's disease.

Accurate diagnosis of Alzheimers disease (AD), as well as its prodromal stage (i.e., mild cognitive impairment, MCI), is very important for possible delay and early treatment of the disease. Recently, multi-modality methods have been used for fusing information from multiple different and complementary imaging and non-imaging modalities. Although there are a number of existing multi-modality methods, few of them have addressed the problem of joint identification of disease-related brain regions from multi-modality data for classification. In this paper, we proposed a manifold regularized multi-task learning framework to jointly select features from multi-modality data. Specifically, we formulate the multi-modality classification as a multi-task learning framework, where each task focuses on the classification based on each modality. In order to capture the intrinsic relatedness among multiple tasks (i.e., modalities), we adopted a group sparsity regularizer, which ensures only a small number of features to be selected jointly. In addition, we introduced a new manifold based Laplacian regularization term to preserve the geometric distribution of original data from each task, which can lead to the selection of more discriminative features. Furthermore, we extend our method to the semi-supervised setting, which is very important since the acquisition of a large set of labeled data (i.e., diagnosis of disease) is usually expensive and time-consuming, while the collection of unlabeled data is relatively much easier. To validate our method, we have performed extensive evaluations on the baseline Magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) data of Alzheimers Disease Neuroimaging Initiative (ADNI) database. Our experimental results demonstrate the effectiveness of the proposed method.

Frequent and discriminative subnetwork mining for mild cognitive impairment classification.

Recent studies on brain networks have suggested that many brain diseases, such as Alzheimers disease and mild cognitive impairment (MCI), are related to a large-scale brain network, rather than individual brain regions. However, it is challenging to find such a network from the whole brain network due to the complexity of brain networks. In this article, the authors propose a novel method to mine the discriminative subnetworks for classifying MCI patients from healthy controls (HC). Specifically, the authors first extract a set of frequent subnetworks from each of the two groups (i.e., MCI and HC), respectively. Then, measure the discriminative ability of those frequent subnetworks using the graph kernel-based classification method and select the most discriminative subnetworks for subsequent classification. The results on the functional connectivity networks of 12 MCI and 25 HC show that this method can obtain competitive results compared with state-of-the-art methods on MCI classification.

Temporally Constrained Group Sparse Learning for Longitudinal Data Analysis in Alzheimer's Disease

Sparse learning has been widely investigated for analysis of brain images to assist the diagnosis of Alzheimers disease and its prodromal stage, i.e., mild cognitive impairment. However, most existing sparse learning-based studies only adopt cross-sectional analysis methods, where the sparse model is learned using data from a single time-point. Actually, multiple time-points of data are often available in brain imaging applications, which can be used in some longitudinal analysis methods to better uncover the disease progression patterns. Accordingly, in this paper, we propose a novel temporallyconstrained group sparse learning method aiming for longitudinal analysis with multiple time-points of data. Specifically, we learn a sparse linear regression model by using the imaging data from multiple time-points, where a group regularization term is first employed to group the weights for the same brain region across different time-points together. Furthermore, to reflect the smooth changes between data derived from adjacent time-points, we incorporate two smoothness regularization terms into the objective function, i.e., one fused smoothness term thatrequires that the differences between two successive weight vectors from adjacent time-points should be small, and another output smoothness term thatrequires the differences between outputs of two successive models from adjacent time-points should also be small. We develop an efficient optimization algorithm to solve the proposed objective function. Experimental results on ADNI database demonstrate that, compared with conventional sparse learning-based methods, our proposed method can achieve improved regression performance and also help in discovering disease-related biomarkers.

Network-based classification of ADHD patients using discriminative subnetwork selection and graph kernel PCA

BACKGROUND Attention Deficit Hyperactivity Disorder (ADHD) is one of the most prevalent behavioral disorders in childhood and adolescence. Recently, network-based diagnosis of ADHD has attracted great attentions due to the fact that ADHD disease is related to not only individual brain regions but also the connections among them, while existing methods are hard to discover disorder patterns related with several brain regions. NEW METHOD To overcome this drawback, a discriminative subnetwork selection method is proposed to directly mine those frequent and discriminative subnetworks from the whole brain networks of ADHD and normal control (NC) groups. Then, the graph kernel principal component (PCA) is applied to extract features from those discriminative subnetworks. Finally, support vector machine (SVM) is adopted for classification of ADHD and NC subjects. RESULTS We evaluate the performances of our proposed method using the ADHD200 dataset, which contains 118 ADHD patients and 98 normal controls. The experimental results show that our proposed method can achieve a very high accuracy of 94.91% for ADHD vs. NC classification. Moreover, our proposed method can also discover the discriminative subnetworks as well as the discriminative brain regions, which are helpful for enhancing our understanding of ADHD disease. COMPARISON WITH EXISTING METHOD(S) The accuracy of our proposed method is 9.20% higher than those of the state-of-the-art methods. CONCLUSIONS A lot of experiments in ADHD200 dataset show that, our proposed method can improve the performance significantly comparing to the state-of-the-art methods.

Brain Connectivity Hyper-Network for MCI Classification

Brain connectivity network has been used for diagnosis and classification of neurodegenerative diseases, such as Alzheimers disease (AD) as well as its early stage, i.e., mild cognitive impairment (MCI). However, conventional connectivity network is usually constructed based on the pairwise correlation among brain regions and thus ignores the higher-order relationship among them. Such information loss is unexpected because the brain itself is a complex network and the higher-order interaction may contain useful information for classification. Accordingly, in this paper, we propose a new brain connectivity hyper-network based method for MCI classification. Here, the connectivity hyper-network denotes a network where an edge can connect more than two brain regions, which can be naturally represented with a hyper-graph. Specifically, we first construct connectivity hyper-networks from the resting-state fMRI time series using sparse representation modeling. Then, we extract three sets of the brain-region specific features from the connectivity hyper-networks, and exploit a manifold regularized multi-task feature selection method to jointly select the most discriminative features. Finally, we use multi-kernel support vector machine (SVM) for classification. The experimental results demonstrate the efficacy of our proposed method for MCI classification with comparison to the conventional connectivity network based methods.