Enhancing the representation of functional connectivity networks by fusing multi-view information for autism spectrum disorder diagnosis

Abstract

Functional connectivity network provides novel insights on how distributed brain regions are functionally integrated, and its deviations from healthy brain have recently been employed to identify biomarkers for neuropsychiatric disorders. However, most of brain network analysis methods utilized features extracted only from one functional connectivity network for brain disease detection and cannot provide a comprehensive representation on the subtle disruptions of brain functional organization induced by neuropsychiatric disorders. Inspired by the principles of multi-view learning which utilizes information from multiple views to enhance object representation, we propose a novel multiple network based framework to enhance the representation of functional connectivity networks by fusing the common and complementary information conveyed in multiple networks. Specifically, four functional connectivity networks corresponding to the four adjacent values of regularization parameter are generated via a sparse regression model with group constraint ( l_2,1 -norm), to enhance the common intrinsic topological structure and limit the error rate caused by different views. To obtain a set of more meaningful and discriminative features, we propose using a modified version of weighted clustering coefficients to quantify the subtle differences of each group-sparse network at local level. We then linearly fuse the selected features from each individual network via a multi-kernel support vector machine for autism spectrum disorder (ASD) diagnosis. The proposed framework achieves an accuracy of 79.35%, outperforming all the compared single network methods for at least 7% improvement. Moreover, compared with other multiple network methods, our method also achieves the best performance, that is, with at least 11% improvement in accuracy.

Keywords: computer-aided diagnosis; functional connectivity network; multi-kernel fusion; multi-view group-sparse network; multi-view learning; resting-state functional magnetic resonance imaging (R-fMRI).

Figure 1

Overview of the proposed classification framework based on multi‐kernel fusion of multiple group‐sparse networks [Color figure can be viewed at http://wileyonlinelibrary.com]

Figure 2

Illustration of Onnela's weighted clustering coefficient (a) and our modified weighted clustering coefficient (b). (a) is the sum of the geometric mean of triangles around a central node, and (b) is the sum of the strengths of red edges between the neighbors around a central node [Color figure can be viewed at http://wileyonlinelibrary.com]

Figure 3

Procedure of the two‐stage feature selection [Color figure can be viewed at http://wileyonlinelibrary.com]

Figure 4

Classification performance of multiple network fusion with 3 (a), 4 (b), and 5 (c) networks in three parameter optimization ways (i.e., non‐adjacent value 1, non‐adjacent value 2, and adjacent value). (d) Performance comparison of 3, 4, and 5 networks when adopting adjacent value method. ACC, accuracy; SEN, sensitivity; SPE, specificity; PPV, positive predictive value; NPV, negative predictive value [Color figure can be viewed at http://wileyonlinelibrary.com]

Figure 5

Classification performance comparison between the proposed clustering coefficient and other local network measures (such as connectivity strength, local efficiency, and betweenness centrality). ACC, accuracy; SEN, sensitivity; SPE, specificity; PPV, positive predictive value; NPV, negative predictive value [Color figure can be viewed at http://wileyonlinelibrary.com]

Figure 6

Contribution (a) and mean clustering coefficient (b) of discriminative ROIs between ASD and TD children in four FCNs. The abbreviations of the ROIs can be found in Table A1. The contributions of ROIs are visualized on a brain surface rendering and are proportional to the sizes of spheres in (a). The ROIs are related with different functional networks represented by different colors (blue: default mode network; yellow: executive attention network; green: visual network; red: sensorimotor network; brown: cingulo‐opercular network; cyan: subcortical regions; violet: regions in the cerebellum). (b) shows the comparison of the normalized mean clustering coefficient between the TD and ASD children [Color figure can be viewed at http://wileyonlinelibrary.com]

Figure 7

Common and network‐specific discriminative ROIs. The abbreviations of the ROIs can be found in Table A1. Common ROIs are in the red rectangle [Color figure can be viewed at http://wileyonlinelibrary.com]

Figure 8

Topological differences between three FCNs [with regularization parameter λ as 0.08 (a), 0.07 (b), and 0.06 (c), respectively] and the FCN (λ = 0.09). Compared with the FCN (λ = 0.09) as benchmark, the number of extra links from three FCNs increases with the decrease of λ. The thickness of links represents the weights of connections from other ROIs to a specific ROI. The abbreviations of the ROIs can be found in Table A1 [Color figure can be viewed at http://wileyonlinelibrary.com]

Figure 9

The network structures with the maximum number of connections for the group of 7–10 years old (a) and the group of 11–15 years old (b). Only the connections with connection strength above 0.1 are shown [Color figure can be viewed at http://wileyonlinelibrary.com]

Figure 10

Classification performance of the entire group and the two subgroups (7–10 years old and 11–15 years old). ACC, accuracy; SEN, sensitivity; SPE, specificity; PPV, positive predictive value; NPV, negative predictive value [Color figure can be viewed at http://wileyonlinelibrary.com]