High-order resting-state functional connectivity network for MCI classification

Abstract

Brain functional connectivity (FC) network, estimated with resting-state functional magnetic resonance imaging (RS-fMRI) technique, has emerged as a promising approach for accurate diagnosis of neurodegenerative diseases. However, the conventional FC network is essentially low-order in the sense that only the correlations among brain regions (in terms of RS-fMRI time series) are taken into account. The features derived from this type of brain network may fail to serve as an effective disease biomarker. To overcome this drawback, we propose extraction of novel high-order FC correlations that characterize how the low-order correlations between different pairs of brain regions interact with each other. Specifically, for each brain region, a sliding window approach is first performed over the entire RS-fMRI time series to generate multiple short overlapping segments. For each segment, a low-order FC network is constructed, measuring the short-term correlation between brain regions. These low-order networks (obtained from all segments) describe the dynamics of short-term FC along the time, thus also forming the correlation time series for every pair of brain regions. To overcome the curse of dimensionality, we further group the correlation time series into a small number of different clusters according to their intrinsic common patterns. Then, the correlation between the respective mean correlation time series of different clusters is calculated to represent the high-order correlation among different pairs of brain regions. Finally, we design a pattern classifier, by combining features of both low-order and high-order FC networks. Experimental results verify the effectiveness of the high-order FC network on disease diagnosis. Hum Brain Mapp 37:3282-3296, 2016. © 2016 Wiley Periodicals, Inc.

Keywords: brain disease diagnosis; functional connectivity; low-order and high-order networks; mild cognitive impairment.

Figure 1

Framework for construction of high‐order FC network. [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com.]

Figure 2

Calculation of the high‐order correlation from the low‐order correlation layer by layer. [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com.]

Figure 3

High‐order FC network reduction. [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com.]

Figure 4

Averaged low‐order FC networks for all eMCI subjects (a) and NC subjects (b), respectively. [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com.]

Figure 5

Static and temporal low‐order FC networks for one eMCI subject. (a) static network; (b–g) temporal networks generated by sliding window approach. [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com.]

Figure 6

Some selected correlation time series and clustering results for one eMCI subject. (a) Original correlation time series; (b) Three different clusters of correlation time series; (c) the mean correlation time series of each cluster. [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com.]

Figure 7

Averaged high‐order FC networks for all eMCI subjects (a) and NC subjects (b), respectively. [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com.]

Figure 8

The variation of recognition accuracy against different number of clusters. [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com.]

Figure 9

Feature selection results. (a) ROI selection from the low‐order FC networks; (b) cluster selection from the high‐order FC networks. [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com.]

Figure 10

Importance of each cluster (same color), selected from the high‐order FC network, in eMCI classification. [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com.]