Computer Science ›› 2016, Vol. 43 ›› Issue (7): 265-267.doi: 10.11896/j.issn.1002-137X.2016.07.048

Previous Articles     Next Articles

Classification of Multi-scale Functional Brain Network in Depression

CHENG Chen, GUO Hao and CHEN Jun-jie   

  • Online:2018-12-01 Published:2018-12-01

Abstract: As a complex network analysis method,brain network has been widely accepted in the field of neuroimaging.According to the research,the scale of nodes in the brain has a major impact on the network topological properties.This paper used the resting state functional imaging data to construct brain networks for patients and normal controls respectively under five different node scales and compared variances of the network topological properties,and then selected four different algorithms to do the classification.The results show that the node scale can not only affect the topological properties,but also has a direct effect on the construction of classification model.Support vector machine (RBF kernel function) model shows the best classification results when the node scale is 250,the average accuracy is 83.18%.The research results have an important application value in the clinical diagnosis of depression,and provide a significant reference basis on the network nodes’ selection based on machine learning of brain network.

Key words: Brain network,Topological properties,Node scale,Machine learning,Depression

[1] ERDdS P,Wi A.On random graphs[J].I.Publ.Math.Debre-cen,1959,6:290-297
[2] Albert R,Barabási A L.Statistical mechanics of complex net-works[J].Reviews of Modern Physics,2002,26(1)
[3] Lynall M E,Bassett D S,Kerwin R,et al.Functional connectivity and brain networks in schizophrenia[J].The Journal of Neuroscience,2010,30(28):9477-9487
[4] Stam C.Use of magnetoencephalography (MEG) to study functional brain networks in neurodegenerative disorders[J].Journal of the Neurological Sciences,2010,289(1):128-134
[5] Horstmann M T,Bialonski S,Noenning N,et al.State dependent properties of epileptic brain networks:Comparative graph-theoretical analyses of simultaneously recorded EEG and MEG[J].Clinical Neurophysiology,2010,121(2):172-185
[6] Liang Wang,Zhu Chao-zhe,He Yong,et al.Altered small-world brain functional networks in children with attention-deficit/hyperactivity disorder[J].Human Brain Mapping,2009,30(2):638-649
[7] De Vico Fallani F,Laura A,Febo C,et al.Evaluation of thebrain network organization from EEG signals:a preliminary evidence in stroke patient[J].The Anatomical Record,2009,292(12):2023-2031
[8] Guo H,C C ,Cao Xiao-hua,et al.Resting-state functional connectivity abnormalities in first-onset unmedicated depression[J].Neural Regen Res.,2014,9(2):153-163
[9] Guo H,Cao X H,Liu Z F,et al.Machine learning classifier using abnormal brain network topological metrics in major depressive disorder[J].Neuroreport,2012,23(17):1006-1011
[10] Tzourio-Mazoyer N,Landeaub B,Papathanassioua D,et al.Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain[J].Neuroimage,2002,15(1):273-289
[11] Collins D L,Holmes C J,Deters T M,et al.Automatic 3-D model-based neuroanatomical segmentation[J].Humanbrain Mapping,1995,3(3):190-208
[12] Salvador R,Martinez A,Pomaral-Clotet E,et al.A simple view of the brain through a frequency-specific functional connectivity measure[J].Neuroimage,2008,39(1):279-289
[13] Williams J B.A structured interview guide for the Hamilton Depression Rating Scale[J].Archives of General Psychiatry,1988,45(8):742-747
[14] Rubinov M,Sporns O.Complex network measures of brain connectivity:uses and interpretations[J].Neuroimage,2010,52(3):1059-1069
[15] Latora V,Marchiori M.Efficient behavior of small-world net-works[J].Physical Review Letters,2001,87(19):198701
[16] Watts D J,Strogatz S H.Collective dynamics of ‘small-world’networks[J].Nature,1998,393(6684):440-442
[17] Humphries M D,Gurney K,Prescott T J.The brainstem reticular formation is a small-world,not scale-free,network[J].Proceedings of the Royal Society B:Biological Sciences,2006,273(1585):503-511
[18] Guo Li-li,Ding Shi-fei.Rearch Progress on Deep Learning[J].Computer Science,2015,2(5):28-33(in Chinese) 郭丽丽,丁世飞.深度学习研究进展[J].计算机科学,2015,2(5):28-33
[19] Pereira F,Mitchell T,Botvinick M.Machine learning classifiers and fMRI:a tutorial overview[J].Neuroimage,2008,45(1 Suppl):S199-209
[20] Bullmore E,Sporns O.Complex brain networks:graph theoretical analysis of structural and functional systems[J].Nature Reviews Neuroscience,2009,10(3):186-198
[21] Cox D D,Savoy R L.Functional magnetic resonance imaging (fMRI)“brain reading”:detecting and classifying distributed patterns of fMRI activity in human visual cortex[J].Neuroi-mage,2003,19(2):261-270

No related articles found!
Full text



No Suggested Reading articles found!