Abstract
It is widely believed that human brain is a complicated network and many neurological disorders such as Alzheimer’s disease (AD) are related to abnormal changes of the brain network architecture. In this work, we present a kernel-based method to establish a network for each subject using mean cortical thickness, which we refer to hereafter as the individual’s network. We construct individual networks for 83 subjects, including AD patients and normal controls (NC), which are taken from the Open Access Series of Imaging Studies database. The network edge features are used to make prediction of AD/NC through the sophisticated machine learning technology. As the number of edge features is much more than that of samples, feature selection is applied to avoid the adverse impact of high-dimensional data on the performance of classifier. We use a hybrid feature selection that combines filter and wrapper methods, and compare the performance of six different combinations of them. Finally, support vector machines are trained using the selected features. To obtain an unbiased evaluation of our method, we use a nested cross validation framework to choose the optimal hyper-parameters of classifier and evaluate the generalization of the method. We report the best accuracy of 90.4 % using the proposed method in the leave-one-out analysis, outperforming that using the raw cortical thickness data by more than 10 %.
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Brookmeyer, R., Johnson, E., Ziegler-Graham, K., Arrighi, H.: Forecasting the global burden of alzheimer’s disease. Alzheimer’s Dement. 3(3), 186–191 (2007)
Petersen, R., Doody, R., Kurz, A., Mohs, R., Morris, J., Rabins, P., Ritchie, K., Rossor, M., Thal, L., Winblad, B.: Current concepts in mild cognitive impairment. Arch. Neurol. 58(12), 1985–1992 (2001)
Grundman, M., Petersen, R., Ferris, S., Thomas, R., Aisen, P., Bennett, D., Foster, N., Galasko, D., Doody, R., et al.: Mild cognitive impairment can be distinguished from alzheimer disease and normal aging for clinical trials. Arch. Neurol. 61(1), 59–66 (2004)
Jack Jr, C., Shiung, M., Weigand, S., OBrien, P., Gunter, J., Boeve, B., Knopman, D., Smith, G., Ivnik, R., Tangalos, E., et al.: Brain atrophy rates predict subsequent clinical conversion in normal elderly and amnestic mci. Neurology 65(8), 1227–1231 (2005)
de Toledo Morrell, L., Stoub, T., Bulgakova, M., Wilson, R., Bennett, D., Leurgans, S., Wuu, J., Turner, D.: Mri-derived entorhinal volume is a good predictor of conversion from mci to ad. Neurobiol. aging 25(9), 1197–1203 (2004)
Thompson, P., Mega, M., Woods, R., Zoumalan, C., Lindshield, C., Blanton, R., Moussai, J., Holmes, C., Cummings, J., Toga, A.: Cortical change in Alzheimer’s disease detected with a disease-specific population-based brain atlas. Cereb. Cortex 11(1), 1–16 (2001)
Du, A., Schuff, N., Kramer, J., Rosen, H., Gorno-Tempini, M., Rankin, K., Miller, B., Weiner, M.: Different regional patterns of cortical thinning in Alzheimer’s disease and frontotemporal dementia. Brain 130(4), 1159–1166 (2007)
Vapnik, V.: The nature of statistical learning theory. Springer Verlag, Berlin (2000)
Bishop, C.: Pattern recognition and machine learning. springer, New York (2006)
Fan, Y., Shen, D., Gur, R., Gur, R., Davatzikos, C.: Compare: classification of morphological patterns using adaptive regional elements. IEEE Trans. Med. Imaging 26(1), 93–105 (2007)
Fan, Y., Batmanghelich, N., Clark, C., Davatzikos, C., et al.: Spatial patterns of brain atrophy in mci patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline. Neuroimage 39(4), 1731–1743 (2008)
Fan, Y., Resnick, S.M., Wu, X., Davatzikos, C.: Structural and functional biomarkers of prodromal Alzheimer’s disease: a high-dimensional pattern classification study. Neuroimage 41(2), 277–285 (2008)
Vemuri, P., Gunter, J., Senjem, M., Whitwell, J., Kantarci, K., Knopman, D., Boeve, B., Petersen, R., Jack C, Jr.: Alzheimer’s disease diagnosis in individual subjects using structural mr images: validation studies. Neuroimage 39(3), 1186–1197 (2008)
Hinrichs, C., Singh, V., Mukherjee, L., Xu, G., Chung, M., Johnson, S.: Spatially augmented lpboosting for ad classification with evaluations on the adni dataset. Neuroimage 48(1), 138–149 (2009)
Gerardin, E., Chételat, G., Chupin, M., Cuingnet, R., Desgranges, B., Kim, H., Niethammer, M., Dubois, B., Lehéricy, S.: Multidimensional classification of hippocampal shape features discriminates Alzheimer’s disease and mild cognitive impairment from normal aging. Neuroimage 47(4), 1476–1486 (2009)
Magnin, B., Mesrob, L., Kinkingnéhun, S., Pélégrini-Issac, M., Colliot, O., Sarazin, M., Dubois, B., Lehéricy, S., Benali, H.: Support vector machine-based classification of alzheimer’s disease from whole-brain anatomical mri. Neuroradiology 51(2), 73–83 (2009)
Lerch, J., Pruessner, J., Zijdenbos, A., Collins, D., Teipel, S., Hampel, H., Evans, A.: Automated cortical thickness measurements from mri can accurately separate Alzheimer’s patients from normal elderly controls. Neurobiol. Aging 29(1), 23–30 (2008)
Oliveira Jr, P., Nitrini, R., Busatto, G., Buchpiguel, C., Sato, J.: Use of svm methods with surface-based cortical and volumetric subcortical measurements to detect Alzheimer’s disease. J. Alzheimer’s Dis. 19(4), 1263–1272 (2010)
Querbes, O., Aubry, F., Pariente, J., Lotterie, J., Démonet, J., Duret, V., Puel, M., Berry, I., Fort, J., Celsis, P., et al.: Early diagnosis of alzheimer’s disease using cortical thickness: impact of cognitive reserve. Brain 132(8), 2036–2047 (2009)
Wang, Y., Fan, Y., Bhatt, P., Davatzikos, C.: High-dimensional pattern regression using machine learning: From medical images to continuous clinical variables. NeuroImage 50(4), 1519–1535 (2010)
Hinrichs, C., Singh, V., Xu, G., Johnson, S., et al.: Predictive markers for ad in a multi-modality framework: An analysis of mci progression in the adni population. Neuroimage 55(2), 574–589 (2011)
Bassett, D., Bullmore, E.: Small-world brain networks. The Neuroscientist 12(6), 512–523 (2006)
Sporns, O., Tononi, G., Kötter, R.: The human connectome: a structural description of the human brain. PLoS Comput. Biol. 1(4), e42 (2005)
Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: uses and interpretations. NeuroImage 52(3), 1059–1069 (2010)
He, Y., Chen, Z., Evans, A.: Structural insights into aberrant topological patterns of large-scale cortical networks in Alzheimer’s disease. J. Neurosci. 28(18), 4756–4766 (2008)
Yao, Z., Zhang, Y., Lin, L., Zhou, Y., Xu, C., Jiang, T.: Abnormal cortical networks in mild cognitive impairment and Alzheimer’s disease. PLoS Comput. Biol. 6(11), e1001,0066 (2010)
He, Y., Chen, Z., Evans, A.: Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. Cereb. Cortex 17(10), 2407–2419 (2007)
Liu, Y., Liang, M., Zhou, Y., He, Y., Hao, Y., Song, M., Yu, C., Liu, H., Liu, Z., Jiang, T.: Disrupted small-world networks in schizophrenia. Brain 131(4), 945–961 (2008)
He, Y., Dagher, A., Chen, Z., Charil, A., Zijdenbos, A., Worsley, K., Evans, A.: Impaired small-world efficiency in structural cortical networks in multiple sclerosis associated with white matter lesion load. Brain 132(12), 3366–3379 (2009)
Raj, A., Mueller, S., Young, K., Laxer, K., Weiner, M.: Network-level analysis of cortical thickness of the epileptic brain. Neuroimage 52(4), 1302–1313 (2010)
Wee, C., Yap, P., Li, W., Denny, K., Browndyke, J., Potter, G., Welsh-Bohmer, K., Wang, L., Shen, D.: Enriched white matter connectivity networks for accurate identification of MCI patients. NeuroImage 54(3), 1812–1822 (2011)
Li, Y., Wang, Y., Wu, G., Shi, F., Zhou, L., Lin, W., Shen, D.: Discriminant analysis of longitudinal cortical thickness changes in Alzheimer’s disease using dynamic and network features. Neurobiol. Aging 33(2), 427.e15–427.e30 (2012)
Zhou, L., Wang, Y., Li, Y., Yap, P.T., Shen, D.: Hierarchical anatomical brain networks for MCI prediction: revisiting volumetric measures. PloSone 6(7), e21935 (2011)
Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Mach. learn. 46(1), 389–422 (2002)
Sled, J., Zijdenbos, A., Evans, A.: A nonparametric method for automatic correction of intensity nonuniformity in mri data. IEEE Trans. Med. Imaging 17(1), 87–97 (1998)
Collins, D., Neelin, P., Peters, T., Evans, A.: Automatic 3d intersubject registration of mr volumetric data in standardized talairach space. J. comput. Assist. tomogr. 18(2), 192–205 (1994)
Zijdenbos, A., Forghani, R., Evans, A.: Automatic quantification of ms lesions in 3d mri brain data sets: validation of insect. In: Medical Image Computing and Computer-Assisted Interventation MICCAI98, pp. 439–448, 1998
Kim, J., Singh, V., Lee, J., Lerch, J., Ad-Dab’bagh, Y., MacDonald, D., Lee, J., Kim, S., Evans, A.: Automated 3-d extraction and evaluation of the inner and outer cortical surfaces using a laplacian map and partial volume effect classification. Neuroimage 27(1), 210–221 (2005)
Chung, M.K., Worsley, K.J., Robbins, S., Paus, T., Taylor, J., Giedd, J.N., Rapoport, J.L., Evans, A.C.: Deformation-based surface morphometry applied to gray matter deformation. Neuroimage 18(2), 198–213 (2003)
Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., Joliot, M.: Automated anatomical labeling of activations in spm using a macroscopic anatomical parcellation of the mni mri single-subject brain. Neuroimage 15(1), 273–289 (2002)
Kohavi, R., John, G.: Wrappers for feature subset selection. Artif. intell. 97(1–2), 273–324 (1997)
Sun, Y., Todorovic, S., Goodison, S.: Local-learning-based feature selection for high-dimensional data analysis. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1610–1626 (2010)
Wilson, S., Ogar, J., Laluz, V., Growdon, M., Jang, J., Glenn, S., Miller, B., Weiner, M., Gorno-Tempini, M.: Automated mri-based classification of primary progressive aphasia variants. Neuroimage 47(4), 1558–1567 (2009)
Prati, R., Batista, G., Monard, M.: A survey on graphical methods for classification predictive performance evaluation. IEEE Trans. Knowl. Data Eng. 23, 1601–1618 (2011)
Chetelat, G., Landeau, B., Eustache, F., Mezenge, F., Viader, F., de La Sayette, V., Desgranges, B., Baron, J.: Using voxel-based morphometry to map the structural changes associated with rapid conversion in mci: a longitudinal mri study. Neuroimage 27(4), 934–946 (2005)
Karas, G., Scheltens, P., Rombouts, S., Visser, P., Van Schijndel, R., Fox, N., Barkhof, F.: Global and local gray matter loss in mild cognitive impairment and Alzheimer’s disease. Neuroimage 23(2), 708–716 (2004)
Acknowledgments
This work was supported by the National Natural Science Foundation of China (61271151, 61228103, 61175076), and the Sci. & Tech. Aiding the Disabled Program of the Chinese Academy of Sciences (Grant #KGCX2-YW-618). We thank Dr. Hai Jiang for proof-reading.
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Dai, D., He, H., Vogelstein, J.T. et al. Accurate prediction of AD patients using cortical thickness networks. Machine Vision and Applications 24, 1445–1457 (2013). https://doi.org/10.1007/s00138-012-0462-0
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DOI: https://doi.org/10.1007/s00138-012-0462-0