Abstract
Conventional functional connectivity (FC), referred to as low-order FC, estimates temporal correlation of the resting-state functional magnetic resonance imaging (rs-fMRI) time series between any pair of brain regions, simply ignoring the potentially high-level relationship among these brain regions. A high-order FC based on “correlation’s correlation” has emerged as a new approach for abnormality detection of brain disease. However, separate construction of the low- and high-order FC networks overlooks information exchange between the two FC levels. Such a higher-level relationship could be more important for brain diseases study. In this paper, we propose a novel framework, namely “hybrid high-order FC networks” by exploiting the higher-level dynamic interaction among brain regions for early mild cognitive impairment (eMCI) diagnosis. For each sliding window-based rs-fMRI sub-series, we construct a whole-brain associated high-order network, by estimating the correlations between the topographical information of the high-order FC sub-network from one brain region and that of the low-order FC sub-network from another brain region. With multi-kernel learning, complementary features from multiple time-varying FC networks constructed at different levels are fused for eMCI classification. Compared with other state-of-the-art methods, the proposed framework achieves superior diagnosis accuracy, and hence could be promising for understanding pathological changes of brain connectome.
Highlights
Rs-fMRI is an in vivo brain functional imaging modality, measuring blood oxygen level-dependent (BOLD) signals[22] when subjects are in natural rest
One of our hypotheses is that, with features extracted from our newly developed associated high-order functional connectivity (FC) networks, early mild cognitive impairment (eMCI) classification could be more accurate, compared with those extracted using either the traditional low-order or high-order FC. Another hypothesis is that our computational framework of the hybrid high-order FC networks could effectively conduct multi-kernel fusion of the three types of brain dynamic networks and further boost classification performance
Consistent with our hypotheses, the main results are: 1) The high-order FC networks enhanced the classification performance and our associated high-order FC networks gained the highest one if only single type of FC network was used; 2) Integrating all the three types of networks with multi-kernel learning, eMCI classification yielded better performance compared to that using only single type of networks; and 3) The classifications based on the dynamic FC networks consistently outperformed those based on the static FC networks, indicating the necessity of integrating dynamic FC into classification
Summary
Rs-fMRI is an in vivo brain functional imaging modality, measuring blood oxygen level-dependent (BOLD) signals[22] when subjects are in natural rest. Previous studies on rs-fMRI based AD early diagnosis often utilized calculated FC by measuring inter-regional BOLD signal temporal synchronization with Pearson’s correlation or, more generally, with sparse representation[8, 33,34,35]. This type of networks is low-order by definition because they characterize BOLD signal synchronizations and are insufficient to characterize high-level inter-regional interactions. Experimental results indicate that our method achieves superior performance than those using only the static FC or only the traditional low- and high-order FC networks
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