Academic Journal of Computing & Information Science, 2021, 4(1); doi: 10.25236/AJCIS.2021.040114.
Fei Han, Miao Song*
College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
*Corresponding Author: [email protected]
Brain network based on resting-state functional magnetic resonance (rs-fMRI) is the most popular method for brain disease diagnosis, which is expected to provide accurate and effective biomarkers. The original fully connected resting-state networks (RSNs) are too dense and must be filtered to get the real network model. In this study, orthogonal minimum spanning trees (OMSTs) was used to filter the connection matrix of 49 age-matched healthy controls (HC) and 50 patients with mild cognitive impairment (MCI). At the same time, we also used global cost efficiency (GCE) algorithm to filter brain network for comparison with OMSTs. We calculated the topological metrices of brain network. Fisher score was used to select features, and the optimal feature subset was used to construct SVM classifier. The classification accuracy of OMSTs was 87%, while that of GCE algorithm was 81%. The experimental results show that the classification accuracy is greatly improved by using OMSTs, which is an effective brain network filtering method.
brain network, graph theory, orthogonal minimum spanning tree, support vector machine
Fei Han, Miao Song. Classification of MCI Brain Network Based on Orthogonal Minimum Spanning Tree. Academic Journal of Computing & Information Science (2021), Vol. 4, Issue 1: 87-93. https://doi.org/10.25236/AJCIS.2021.040114.
[1] KHAZAEE A, EBRAHIMZADEH A, BABAJANI-FEREMI A. Application of advanced machine learning methods on resting-state fMRI network for identification of mild cognitive impairment and Alzheimer's disease [J]. Brain Imaging Behav, 2016, 10(3): 799-817.
[2] SOARES J M, MAGALHAES R, MOREIRA P S, et al. A Hitchhiker's Guide to Functional Magnetic Resonance Imaging [J]. Front Neurosci, 2016, 10: 515.
[3] KHAZAEE A, EBRAHIMZADEH A, BABAJANI-FEREMI A. Identifying patients with Alzheimer's disease using resting-state fMRI and graph theory [J]. Clin Neurophysiol, 2015, 126(11): 2132-2141.
[4] DIMITRIADIS S I, SALIS C, TARNANAS I, et al. Topological Filtering of Dynamic Functional Brain Networks Unfolds Informative Chronnectomics: A Novel Data-Driven Thresholding Scheme Based on Orthogonal Minimal Spanning Trees (OMSTs). [J]. Front Neuroinform, 2017, 11: 28.
[5] LV H, WANG Z, TONG E, et al. Resting-State Functional MRI: Everything That Nonexperts Have Always Wanted to Know. [J]. AJNR Am J Neuroradiol, 2018, 39(8): 1390-1399.
[6] ZHANG L, NI H, YU Z, et al. Investigation on the Alteration of Brain Functional Network and Its Role in the Identification of Mild Cognitive Impairment [J]. Front Neurosci, 2020, 14: 558434.
[7] CHAN Y L, UNG W C, LIM L G, et al. Automated Thresholding Method for fNIRS-Based Functional Connectivity Analysis: Validation With a Case Study on Alzheimer's Disease [J]. IEEE Trans Neural Syst Rehabil Eng, 2020, 28(8): 1691-1701.
[8] ZHANG T, ZHAO Z, ZHANG C, et al. Classification of Early and Late Mild Cognitive Impairment Using Functional Brain Network of Resting-State fMRI. [J]. Front Psychiatry, 2019, 10: 572.
[9] BULLMORE E, SPORNS O. Complex brain networks: graph theoretical analysis of structural and functional systems[J]. Nat Rev Neurosci, 2009, 10(3): 186-198.
[10] XU X, LI W, MEI J, et al. Feature Selection and Combination of Information in the Functional Brain Connectome for Discrimination of Mild Cognitive Impairment and Analyses of Altered Brain Patterns. [J]. Front Aging Neurosci, 2020, 12: 28.
[11] MAX A. LITTLE G V, SOHRAB SAEB, LUCA LONINI, ARUN JAYARAMAN, DAVID C. MOHR AND KONRAD P. KORDING. Using and understanding cross-validation strategies. [J]. Gigascience, 2017.