|
|
A Diagnosis Model for Amnestic Mild Cognitive Impairment Based on Structural Network Characteristics of the Brain#br# |
1 北京工业大学生命科学与生物工程学院, 北京 100124
2 长庚大学资讯工程学系, 桃园 333
3 长庚大学健康老化中心,桃园 333
4 阳明大学生物医学影像暨放射科学系,台北 112
5 阳明大学医学系,台北 112 |
|
|
Abstract The aim of this work is to select topological characteristics of structural network which were higher correlated with cognitive performance and estimate the classification models to classify normal aging and amenstic mild cognitive impairment (aMCI) patients. In two groups of diffusion tensor image (DTI) dataset, a group has 52 normal aging subjects and another group has 39 aMCI patients. We employed the diffusion tensor image to construct the structural network in each group and used the method of graph theory to extract the characteristics of structural network. We selected significant features by the correlation analysis between the characteristics of brain network and subject’s
minimental state examination (MMSE) score. These features were used to establish five kinds of classification model for evaluating the classification efficiency of the models. For normal aging dataset and aMCI dataset, 18 characteristics of the structural network were selected as features, which are significantly associated with cognitive ability and locate in 9 brain areas according to the automated anatomical labeling (AAL) template in each group. However, the features and the related regions are different for the two datasets. Among 5 algorithms, sequential minimal optimization learning algorithm for support vector machine regression model was more accurate due to the higher specificity of 8846%, the higher sensitivity of 8305% and the higher accuracy of 8571%. Brain structural network metrics which were correlated greatly with the cognitive performance could be taken as biological marker pointers to establish the classification model to classify normal aging subjects and aMCI patients. Furthermore, these structural network features can provide the information of connection changes between corresponding brain regions.
|
|
|
|
|
[1]Kukull WA,Bowen JD. Dementia epidemiology[J]. Med Clin North Am, 2002, 86(3): 573-590.
[2]Wimo A, PrinceMJ. World Alzheimer Report 2010: the global economic impact of dementia[M].
London: Alzheimer’s Disease International: 2010.
[3]Prince M, Jackson J. World Alzheimer Report 2009Executive Summary[M]. London: AlzheimerLs Disease International, 2009.
[4]Morris JC,Cummings J. Mild cognitive impairment (MCI) represents earlystage Alzheimer’s disease[J]. J Alzheimers Dis, 2005, 7(3): 235-239, 255-262.[5]Caselli RJ,Dueck AC,Osborne D, et al. Longitudinal modeling of agerelated memory decline and the APOE epsilon4 effect[J]. N Engl J Med, 2009, 361(3): 255-263.
[6]Duchesne S, Caroli A, Geroldi C, et al. Relating oneyear cognitive change in mild cognitive impairment to baseline MRI features[J]. Neuroimage, 2009, 47(4): 1363-1370.[7]Whitwell JL. Voxelbased morphometry: an automated technique for assessing structural changes in the brain[J]. J Neurosci, 2009, 29(31): 9661-9664.
[8]Desikan RS,Cabral HJ,Hess CP, et al. Automated MRI measures identify individuals with mild cognitive impairment and Alzheimer’s disease[J]. Brain, 2009, 132(8): 2048-2057.
[9]Magnin B,Mesrob L,Kinkingnehun S, et al. Support vector machinebased classification of Alzheimer’s disease from wholebrain anatomical MRI[J]. Neuroradiology, 2009, 51(2): 73-83.
[10]O’Dwyer L,Lamberton F,Bokde AL, et al. Using support vector machines with multiple indices of diffusion for automated classification of mild cognitive impairment[J]. PLoS One, 2012, 7(2): e32441.
[11]Park H,Yang JJ, Seo J, et al. Dimensionality reduced cortical features and their use in the classification of Alzheimer’s disease and mild cognitive impairment[J]. Neurosci Lett, 2012, 529(2): 123-127.
[12]Sporns O,Tononi G,Kotter R. The human connectome: A structural description of the human brain[J]. PLoS Comput Biol, 2005, 1(4): e42.
[13]Wu Kai,Taki Y,Sato K, et al. Agerelated changes in topological organization of structural brain networks in healthy individuals[J]. Hum Brain Mapp, 2012, 33(3): 552-568.
[14]Xie Teng,He Yong.Mapping the Alzheimer’s brain with connectomics[J]. Front Psychiatry, 2011, 2: 77.
[15]LoCY,Wang PN,ChouKH, et al. Diffusion tensor tractography reveals abnormal topological organization in structural cortical networks in Alzheimer’s disease[J]. J Neurosci, 2010, 30(50): 16876-16885.
[16]Folstein MF, Folstein SE, McHugh PR. “Minimental state”. A practical method for grading the cognitive state of patients for the clinician[J]. J Psychiatr Res, 1975, 12(3): 189-198.
[17]Hughes CP,Berg L,Danziger WL, et al. A new clinical scale for the staging of dementia[J]. Br J Psychiatry, 1982, 2: 566-572.
[18]Liu CY,Wang SJ,Teng EL, et al. Depressive disorders among older residents in a Chinese rural community[J]. Psychological Medicine, 1997, 27(4): 943-949.
[19]Chang YL,Bondi MW,McEvoy LK, et al. Global clinical dementia rating of 05 in MCI masks variability related to level of function[J]. Neurology, 2011, 76(7): 652-659.
[20]Tzourio-MazoyerN,Landeau B,Papathanassiou D, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI singlesubject brain[J]. Neuroimage, 2002, 15(1): 273-289.
[21]Gong Gaolang,He Yong,Concha L, et al. Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography[J]. Cereb Cortex, 2009, 19(3): 524-536.
[22]Cui Zaixu,Zhong Suyu,Xu Pengfei, et al. PANDA: a pipeline toolbox for analyzing brain diffusion images[J]. Front Hum Neurosci, 2013, 7: 42.
[23]Li Yonghui,Liu Yong,Li Jun, et al. Brain anatomical network and intelligence[J]. PLoS Comput Biol, 2009, 5(5):1000395.
[24]Kaiser M. A tutorial in connectome analysis: topological and spatial features of brain networks[J]. Neuroimage, 2011, 57(3): 892-907.
[25]Rubinov M,Sporns O. Complex network measures of brain connectivity: uses and interpretations[J]. Neuroimage, 2010, 52(3): 1059-1069.
[26]Sporns O. The human connectome: a complex network[J]. Ann N Y Acad Sci, 2011, 1224: 109-125.
[27]Shevade SK,Keerthi SS,Bhattacharyya C, et al. Improvements to the SMO algorithm for SVM regression[J]. IEEE Trans Neural Netw, 2000, 11(5): 1188-1193.
[28]志军. 轻度认知障碍和阿尔兹海默病脑形态异常的磁共振影像研究[D]. 兰州: 兰州大学,2011.
[29]李亚迪. 遗忘型轻度认知障碍和轻度阿尔茨海默病的MRI结构与功能研究[D]. 上海: 复旦大学,2009.
[30]McKhann G,Drachman D,Folstein M, et al. Clinical diagnosis of Alzheimer’s disease: report of the NINCDSADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease[J]. Neurology, 1984, 34(7): 939-944.
[31]Knopman DS,DeKosky ST,Cummings JL, et al. Practice parameter: diagnosis of dementia (an evidencebased review). Report of the Quality Standards Subcommittee of the American Academy of Neurology[J]. Neurology, 2001, 56(9): 1143-1153.
[32]Cui Yue,Wen Wei,Lipnicki DM, et al. Automated detection of amnestic mild cognitive impairment in communitydwelling elderly adults: a combined spatial atrophy and white matter alteration approach[J]. Neuroimage, 2012, 59 |
|
|
|