|Table of Contents|

[1] Jiao Yun, Wang Xunheng, Tang Tianyu, Zhu Xiqi, et al. Discrimination for minimal hepatic encephalopathybased on Bayesian modeling of default mode network [J]. Journal of Southeast University (English Edition), 2015, 31 (4): 582-587. [doi:10.3969/j.issn.1003-7985.2015.04.026]
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Discrimination for minimal hepatic encephalopathybased on Bayesian modeling of default mode network()
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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
31
Issue:
2015 4
Page:
582-587
Research Field:
Computer Science and Engineering
Publishing date:
2015-12-30

Info

Title:
Discrimination for minimal hepatic encephalopathybased on Bayesian modeling of default mode network
Author(s):
Jiao Yun1;  Wang Xunheng2;  Tang Tianyu1;  Zhu Xiqi3;  Teng Gaojun1
1 Medical School, Southeast University, Nanjing 210009, China
2 College of Life Information Science and Instrument Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
3 Radiology Department, The Second Hospital of Nanjing, Nanjing 210003, China
Keywords:
graphical-model-based multivariate analysis;  Bayesian modeling;  machine learning;  functional integration;  minimal hepatic encephalopathy;  resting-state functional magnetic resonance imaging(fMRI)
PACS:
TP391
DOI:
10.3969/j.issn.1003-7985.2015.04.026
Abstract:
In order to classify the minimal hepatic encephalopathy(MHE)patients from healthy controls, the independent component analysis(ICA)is used to generate the default mode network(DMN)from resting-state functional magnetic resonance imaging(fMRI). Then a Bayesian voxel-wised method, graphical-model-based multivariate analysis(GAMMA), is used to explore the associations between abnormal functional integration within DMN and clinical variable. Without any prior knowledge, five machine learning methods, namely, support vector machines(SVMs), classification and regression trees(CART), logistic regression, the Bayesian network, and C4.5, are applied to the classification. The functional integration patterns were alternative within DMN, which have the power to predict MHE with an accuracy of 98%. The GAMMA method generating functional integration patterns within DMN can become a simple, objective, and common imaging biomarker for detecting MHE and can serve as a supplement to the existing diagnostic methods.

References:

[1] Bajaj J S, Wade J B, Sanyal A J. Spectrum of neurocognitive impairment in cirrhosis: implications for the assessment of hepatic encephalopathy [J]. Hepatology, 2009, 50(6): 2014-2021.
[2] Fox M D, Raichle M E. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging [J]. Nat Rev Neurosci, 2007, 8(9):700-711.
[3] Zhang L J, Qi R, Zhong J, et al. Disrupted functional connectivity of the anterior cingulate cortex in cirrhotic patients without overt hepatic encephalopathy: a resting state fMRI study [J]. PLoS One, 2013, 8(1): e53206.
[4] Zhang D, Raichle M E. Disease and the brain’s dark energy [J]. Nat Rev Neurol, 2010, 6(1): 15-28.
[5] Fox M D, Snyder A Z, Vincent J L, et al. The human brain is intrinsically organized into dynamic, anticorrelated functional networks [J]. Proc Natl Acad Sci USA, 2005, 102(27): 9673-9678.
[6] Fox M D, Corbetta M, Snyder A Z, et al. Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems[J]. Proc Natl Acad Sci USA, 2006, 103(26): 10046-10051.
[7] Qi R, Zhang L, Wu S, et al. Altered resting-state brain activity at functional MR imaging during the progression of hepatic encephalopathy [J]. Radiology, 2012, 264(1): 187-195.
[8] Lin W C, Hsu T W, Chen C L, et al. Connectivity of default-mode network is associated with cerebral edema in hepatic encephalopathy [J]. PLoS One, 2012, 7(5): e36986.
[9] Qi R, Zhang L J, Xu Q, et al. Selective impairments of resting-state networks in minimal hepatic encephalopathy [J]. PLoS One, 2012, 7(5): e37400.
[10] Chen H J, Jiao Y, Zhu X Q, et al. Brain dysfunction primarily related to previous overt hepatic encephalopathy compared with minimal hepatic encephalopathy: resting-state functional MR imaging demonstration [J]. Radiology, 2013, 266(1):261-270.
[11] Chen R, Herskovits E H. Graphical model based multivariate analysis(GAMMA): an open-source, cross-platform neuroimaging data analysis software package [J]. Neuroinformatics, 2012, 10(2):119-127.
[12] Chen R, Herskovits E H. Graphical-model-based morphometric analysis [J]. IEEE Trans Med Imaging, 2005, 24(10): 1237-1248.
[13] Chen R, Herskovits E H. Graphical-model-based multivariate analysis of functional magnetic-resonance data [J]. Neuroimage, 2007, 35(2): 635-647.
[14] Chen R, Pawlak M A, Flynn T B, et al. Brain morphometry and intelligence quotient measurements in children with sickle cell disease [J]. J Dev Behav Pediatr, 2009, 30(6): 509-517.
[15] Chen R, Herskovits E H. Clinical diagnosis based on bayesian classification of functional magnetic-resonance data [J]. Neuroinformatics, 2007, 5(3): 178-188.
[16] Smith S M, Jenkinson M, Woolrich M W, et al. Advances in functional and structural MR image analysis and implementation as FSL [J]. Neuroimage, 2004, 23(Sup 1): 208-219.
[17] Woolrich M W, Jbabdi S, Patenaude B, et al. Bayesian analysis of neuroimaging data in FSL [J]. Neuroimage, 2009, 45(Sup): 173-186.
[18] Jenkinson M, Bannister P, Brady M, et al. Improved optimization for the robust and accurate linear registration and motion correction of brain images [J]. Neuroimage, 2002, 17(2): 825-841.
[19] Jenkinson M, Smith S. A global optimisation method for robust affine registration of brain images [J]. Med Image Anal, 2001, 5(2):143-156.
[20] Calhoun V D, Adali T, Pearlson G D, et al. A method for making group inferences from functional MRI data using independent component analysis [J]. Hum Brain Mapp, 2001, 14(3): 140-151.
[21] Qi Z, Wu X, Wang Z, et al. Impairment and compensation coexist in amnestic MCI default mode network [J]. Neuroimage, 2010, 50(1):48-55.
[22] Zhang L, Qi R, Wu S, et al. Brain default-mode network abnormalities in hepatic encephalopathy: a resting-state functional MRI study [J]. Hum Brain Mapp, 2012, 33(6): 1384-1392.
[23] Greicius M D, Srivastava G, Reiss A L, et al. Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: evidence from functional MRI [J]. Proc Natl Acad Sci USA, 2004, 101(13): 4637-4642.
[24] Mantini D, Perrucci M G, Del Gratta C, et al. Electrophysiological signatures of resting state networks in the human brain [J]. Proc Natl Acad Sci USA, 2007, 104(32): 13170-13175.
[25] Chen H J, Zhu X Q, Shu H, et al. Structural and functional cerebral impairments in cirrhotic patients with a history of overt hepatic encephalopathy [J]. Eur J Radiol, 2012, 81(10): 2463-2469.
[26] Ferenci P, Lockwood A, Mullen K, et al. Hepatic encephalopathy-definition, nomenclature, diagnosis, and quantification: final report of the working party at the 11th World Congresses of Gastroenterology, Vienna, 1998 [J]. Hepatology, 2002, 35(3): 716-721.

Memo

Memo:
Biographies: Jiao Yun(1983—), male, doctor, lecturer, yunjiao@seu.edu.cn; Teng Gaojun(corresponding author), male, professor, gjteng@vip.sina.com.
Foundation items: The National Natural Science Foundation of China(No.81230034, 81271739, 81501453), the Special Program of Medical Science of Jiangsu Province(No.BL2013029), the Natural Science Foundation of Jiangsu Province(No.BK20141342).
Citation: Jiao Yun, Wang Xunheng, Tang Tianyu, et al.Discrimination for minimal hepatic encephalopathy based on Bayesian modeling of default mode network[J].Journal of Southeast University(English Edition), 2015, 31(4):582-587.[doi:10.3969/j.issn.1003-7985.2015.04.026]
Last Update: 2015-12-20