Integration of Network Topological and Connectivity Properties for Neuroimaging Classification

Abstract

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 Alzheimer’s 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.

A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages

“…Overall, the functional connectivity-based methods demonstrated good classification results (97.00% for AD/MCI (Challis et al, 2015) and 91.90% for MCI/CN (Jie et al, 2014)).…”

“…The preliminary evidence of disrupted functional connectivity (Li et al, 2002; Wang et al, 2007a; Wang et al, 2006), and its association with AD have led researchers to hypothesize that proper quantification of the functional connectivity across different brain regions can capture the global distribution of the abnormalities present in AD, and can further aid in AD classification (Chen et al, 2011; Jie et al, 2014). Such quantification involves spatial parcellation of fMRI data according to a structural brain template, and calculation of pair-wise connectivity between the activation in all pairs of regions.…”

“…For example, Jie et al, proposed to extract global topology and local connectivity based features from the graph. The least absolute shrinkage and selection operator was used for feature selection, while multi-kernel SVM was used for MCI classification (Jie et al, 2014). Similarly, Khazaee et al computed integration and segregation measures from the graph, and used Fisher score for feature selection and SVM for AD classification (Khazaee et al, 2015).…”

Hyper-connectivity of functional networks for brain disease diagnosis

“…For example, based on the blood oxygen level-dependent (BOLD) signals (that can reflect the endogenous or spontaneous brain activity with both high spatial and temporal resolutions) can be extracted from resting -state functional magnetic resonance imaging (R-fMRI) images. Then, the inter-regional interactions of brain activities at rest can be characterized via functional connectivity networks derived from BOLD signals and used for classification of AD and MCI (Chen et al, 2011; Jie et al, 2014b; Richiardi et al, 2012; Wee et al, 2013a). …”

Manifold regularized multitask feature learning for multimodality disease classification

“…As AD-specific brain changes begin years before the patient becomes symptomatic, early clinical diagnosis of AD becomes a challenging task. Therefore, many studies focus on possible identification of such changes at the early stage, that is, mild cognitive impairment (MCI), by leveraging neuroimaging data [Jie et al, 2014a; Sui et al, 2012; Ye et al, 2011]. …”