| RESEARCH ARTICLE |
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| Year : 2022 | Volume
: 1
| Issue : 3 | Page : 128-135 |
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Estimating high-order brain functional network via signed random walk for mild cognitive impairment identification
Li-Mei Zhang1, Xiao Wu2, Hui Su2, Ting-Ting Guo2, Ming-Xia Liu3
1 School of Computer Science and Technology, Shandong Jianzhu University, Jinan; School of Mathematical Sciences, Liaocheng University, Liaocheng, Shandong Province, China., China
2 School of Mathematical Sciences, Liaocheng University, Liaocheng, Shandong Province, China., China
3 Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA., USA
Correspondence Address:
Ming-Xia Liu
USA
 Source of Support: None, Conflict of Interest: None
DOI: 10.4103/2773-2398.356522
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Brain functional network (BFN) has become an increasingly important tool to discover informative biomarkers for diagnosing neurodegenerative diseases, such as Alzheimer’s disease and its prodrome stage, namely mild cognitive impairment. Currently, the most popular BFN estimation methods include Pearson’s correlation and sparse representation. Despite their empirical success in some scenarios, such estimated BFNs only capture the low-order relationship (i.e., the direct connectivity strength between brain regions), ignoring the high-order information in the brain (e.g., the global network structure). Therefore, in this study, we proposed a novel method based on the signed random walk (SRW) to estimate high-order BFNs. Not only can SRW measure the global network structure, but it can also naturally deal with negative brain functional connectivity through the structural balance theory. To the best of our knowledge, this study was the first to use SRW in BFN estimation. Furthermore, considering the complex interaction among different brain regions, we developed a parameterized variant of SRW for improving the flexibility of the high-order BFN estimation model. To illustrate the effectiveness of the proposed method, we identified patients with mild cognitive impairment from normal controls based on the estimated high-order BFNs. Our experimental findings showed that the proposed scheme tended to achieve higher classification performance than baseline methods.
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