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Application of advanced machine learning methods on resting-state fMRI network for identification of mild cognitive impairment and Alzheimer’s disease

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Abstract

The study of brain networks by resting-state functional magnetic resonance imaging (rs-fMRI) is a promising method for identifying patients with dementia from healthy controls (HC). Using graph theory, different aspects of the brain network can be efficiently characterized by calculating measures of integration and segregation. In this study, we combined a graph theoretical approach with advanced machine learning methods to study the brain network in 89 patients with mild cognitive impairment (MCI), 34 patients with Alzheimer’s disease (AD), and 45 age-matched HC. The rs-fMRI connectivity matrix was constructed using a brain parcellation based on a 264 putative functional areas. Using the optimal features extracted from the graph measures, we were able to accurately classify three groups (i.e., HC, MCI, and AD) with accuracy of 88.4 %. We also investigated performance of our proposed method for a binary classification of a group (e.g., MCI) from two other groups (e.g., HC and AD). The classification accuracies for identifying HC from AD and MCI, AD from HC and MCI, and MCI from HC and AD, were 87.3, 97.5, and 72.0 %, respectively. In addition, results based on the parcellation of 264 regions were compared to that of the automated anatomical labeling atlas (AAL), consisted of 90 regions. The accuracy of classification of three groups using AAL was degraded to 83.2 %. Our results show that combining the graph measures with the machine learning approach, on the basis of the rs-fMRI connectivity analysis, may assist in diagnosis of AD and MCI.

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References

  • Anderson, A., & Cohen, M.S. (2013). Decreased small-world functional network connectivity and clustering across resting state networks in schizophrenia: an fMRI classification tutorial. Frontiers in Human Neuroscience, 7.

  • Bai, F., Zhang, Z., Yu, H., Shi, Y., Yuan, Y., Zhu, W., & Qian, Y. (2008). Default-mode network activity distinguishes amnestic type mild cognitive impairment from healthy aging: a combined structural and resting-state functional MRI study. Neuroscience Letters, 438(1), 111–115.

    Article  CAS  PubMed  Google Scholar 

  • Bai, F., Liao, W., Watson, D. R., Shi, Y., Wang, Y., Yue, C., & Jia, J. (2011). Abnormal whole-brain functional connection in amnestic mild cognitive impairment patients. Behavioural Brain Research, 216(2), 666–672.

    Article  PubMed  Google Scholar 

  • Bassett, D. S., Bullmore, E., Verchinski, B. A., Mattay, V. S., Weinberger, D. R., & Meyer-Lindenberg, A. (2008). Hierarchical organization of human cortical networks in health and schizophrenia. The Journal of Neuroscience, 28(37), 9239–9248. doi:10.1523/JNEUROSCI.1929-08.2008.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Bassett, D. S., Bullmore, E. T., Meyer-Lindenberg, A., Apud, J. A., Weinberger, D. R., & Coppola, R. (2009). Cognitive fitness of cost-efficient brain functional networks. Proceedings of the National Academy of Sciences of the United States of America, 106(28), 11747–11752. doi:10.1073/pnas.0903641106.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Binnewijzend, M. A., Adriaanse, S. M., Van der Flier, W. M., Teunissen, C. E., de Munck, J. C., Stam, C. J., & Wink, A. M. (2014). Brain network alterations in Alzheimer’s disease measured by eigenvector centrality in fMRI are related to cognition and CSF biomarkers. Human Brain Mapping, 35(5), 2383–2393. doi:10.1002/hbm.22335.

    Article  PubMed  Google Scholar 

  • Boldi, P., Santini, M., & Vigna, S. (2009). PageRank: functional dependencies. ACM Transactions on Information Systems (TOIS), 27(4), 19.

    Article  Google Scholar 

  • Brandes, U. (2001). A faster algorithm for betweenness centrality*. Journal of Mathematical Sociology, 25(2), 163–177.

    Article  Google Scholar 

  • Brier, M. R., Thomas, J. B., Fagan, A. M., Hassenstab, J., Holtzman, D. M., Benzinger, T. L., & Ances, B. M. (2014). Functional connectivity and graph theory in preclinical Alzheimer’s disease. Neurobiology of Aging, 35(4), 757–768.

    Article  PubMed  Google Scholar 

  • Buckner, R. L., Andrews‐Hanna, J. R., & Schacter, D. L. (2008). The brain’s default network. Annals of the New York Academy of Sciences, 1124(1), 1–38.

    Article  PubMed  Google Scholar 

  • Buckner, R. L., Sepulcre, J., Talukdar, T., Krienen, F. M., Liu, H., Hedden, T., & Johnson, K. A. (2009). Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer’s disease. The Journal of Neuroscience, 29(6), 1860–1873.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Bullmore, E., & Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10(3), 186–198.

    Article  CAS  PubMed  Google Scholar 

  • Celone, K. A., Calhoun, V. D., Dickerson, B. C., Atri, A., Chua, E. F., Miller, S. L., & Blacker, D. (2006). Alterations in memory networks in mild cognitive impairment and Alzheimer’s disease: an independent component analysis. The Journal of Neuroscience, 26(40), 10222–10231.

    Article  CAS  PubMed  Google Scholar 

  • Challis, E., Hurley, P., Serra, L., Bozzali, M., Oliver, S., & Cercignani, M. (2015). Gaussian process classification of Alzheimer’s disease and mild cognitive impairment from resting-state fMRI. NeuroImage, 112, 232–243.

    Article  PubMed  Google Scholar 

  • Chao-Gan, Y., & Yu-Feng, Z. (2010). DPARSF: a MATLAB toolbox for “pipeline” data analysis of resting-state fMRI. Frontiers in Systems Neuroscience, 4, 13. doi:10.3389/fnsys.2010.00013.

    PubMed  PubMed Central  Google Scholar 

  • Cheng, B., Liu, M., Zhang, D., Munsell, B. C., & Shen, D. (2015). Domain transfer learning for MCI conversion prediction. Biomedical Engineering, IEEE Transactions on, 62(7), 1805–1817. doi:10.1109/TBME.2015.2404809.

    Article  Google Scholar 

  • Cohen, A. L., Fair, D. A., Dosenbach, N. U., Miezin, F. M., Dierker, D., Van Essen, D. C., & Petersen, S. E. (2008). Defining functional areas in individual human brains using resting functional connectivity MRI. NeuroImage, 41(1), 45–57.

    Article  PubMed  PubMed Central  Google Scholar 

  • Daselaar, S., Prince, S., & Cabeza, R. (2004). When less means more: deactivations during encoding that predict subsequent memory. NeuroImage, 23(3), 921–927.

    Article  CAS  PubMed  Google Scholar 

  • Davatzikos, C., Bhatt, P., Shaw, L. M., Batmanghelich, K. N., & Trojanowski, J. Q. (2011). Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification. Neurobiology of Aging, 32(12), 2322 e2319–2327. doi:10.1016/j.neurobiolaging.2010.05.023.

    Article  Google Scholar 

  • Dey, S., Rao, A.R., & Shah, M. (2012). Exploiting the brain’s network structure in identifying ADHD subjects. Frontiers in Systems Neuroscience, 6.

  • dos Santos Siqueira, A., Biazoli Junior, C.E., Comfort, W.E., Rohde, L.A., & Sato, J.R. (2014). Abnormal functional resting-state networks in ADHD: graph theory and pattern recognition analysis of fMRI Data. BioMed Research International, 2014.

  • Drzezga, A., Becker, J. A., Van Dijk, K. R., Sreenivasan, A., Talukdar, T., Sullivan, C., & Greve, D. (2011). Neuronal dysfunction and disconnection of cortical hubs in non-demented subjects with elevated amyloid burden. Brain, 134(6), 1635–1646.

    Article  PubMed  PubMed Central  Google Scholar 

  • Duda, R.O., Hart, P.E., & Stork, D.G. (2012). Pattern classification. Wiley.

  • Estrada, E., & Higham, D. J. (2010). Network properties revealed through matrix functions. SIAM Review, 52(4), 696–714.

    Article  Google Scholar 

  • Fekete, T., Wilf, M., Rubin, D., Edelman, S., Malach, R., & Mujica-Parodi, L. R. (2013). Combining classification with fMRI-derived complex network measures for potential neurodiagnostics. PloS One, 8(5), e62867.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Foster, J. G., Foster, D. V., Grassberger, P., & Paczuski, M. (2010). Edge direction and the structure of networks. Proceedings of the National Academy of Sciences, 107(24), 10815–10820.

    Article  CAS  Google Scholar 

  • Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., & Raichle, M. E. (2005). The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proceedings of the National Academy of Sciences of the United States of America, 102(27), 9673–9678.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Fransson, P. (2005). Spontaneous low‐frequency BOLD signal fluctuations: an fMRI investigation of the resting‐state default mode of brain function hypothesis. Human Brain Mapping, 26(1), 15–29.

    Article  PubMed  Google Scholar 

  • Friston, K. J., Frith, C. D., Frackowiak, R. S., & Turner, R. (1995). Characterizing dynamic brain responses with fMRI: a multivariate approach. NeuroImage, 2(2PA), 166–172.

    Article  CAS  PubMed  Google Scholar 

  • Grady, C., Springer, M., Hongwanishkul, D., McIntosh, A., & Winocur, G. (2006). Age-related changes in brain activity across the adult lifespan. Journal of Cognitive Neuroscience, 18(2), 227–241.

    Article  PubMed  Google Scholar 

  • Greicius, M. D., Krasnow, B., Reiss, A. L., & Menon, V. (2003). Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proceedings of the National Academy of Sciences, 100(1), 253–258.

    Article  CAS  Google Scholar 

  • Guimera, R., Sales-Pardo, M., & Amaral, L. A. (2007). Classes of complex networks defined by role-to-role connectivity profiles. Nature Physics, 3(1), 63–69.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C. J., Wedeen, V. J., & Sporns, O. (2008). Mapping the structural core of human cerebral cortex. PLoS Biology, 6(7), e159.

    Article  PubMed  PubMed Central  Google Scholar 

  • He, Y., Chen, Z., & Evans, A. (2008). Structural insights into aberrant topological patterns of large-scale cortical networks in Alzheimer’s disease. The Journal of Neuroscience, 28(18), 4756–4766.

    Article  CAS  PubMed  Google Scholar 

  • Humphries, M. D., & Gurney, K. (2008). Network ‘small-world-ness’: a quantitative method for determining canonical network equivalence. PloS One, 3(4), e0002051.

    Article  PubMed  Google Scholar 

  • Jack, C. R., Bernstein, M. A., Fox, N. C., Thompson, P., Alexander, G., Harvey, D., & Ward, C. (2008). The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. Journal of Magnetic Resonance Imaging, 27(4), 685–691.

    Article  PubMed  PubMed Central  Google Scholar 

  • Jie, B., Zhang, D., Suk, H.-I., Wee, C.-Y., & Shen, D. (2013). Integrating Multiple Network Properties for MCI Identification. In G. Wu, D. Zhang, D. Shen, P. Yan, K. Suzuki & F. Wang (Eds.), Machine Learning in Medical Imaging (Vol. 8184, pp. 9–16). Springer International Publishing.

  • Jie, B., Zhang, D., Wee, C. Y., & Shen, D. (2014). Topological graph kernel on multiple thresholded functional connectivity networks for mild cognitive impairment classification. Human Brain Mapping, 35(7), 2876–2897. doi:10.1002/hbm.22353.

    Article  PubMed  Google Scholar 

  • Kelly, A., Uddin, L. Q., Biswal, B. B., Castellanos, F. X., & Milham, M. P. (2008). Competition between functional brain networks mediates behavioral variability. NeuroImage, 39(1), 527–537.

    Article  PubMed  Google Scholar 

  • Khazaee, A., Ebrahimzadeh, A., & Babajani-Feremi, A. (2015). Identifying patients with Alzheimer’s disease using resting-state fMRI and graph theory. Clinical Neurophysiology. doi:10.1016/j.clinph.2015.02.060.

    PubMed  Google Scholar 

  • Koch, W., Teipel, S., Mueller, S., Benninghoff, J., Wagner, M., Bokde, A. L., & Meindl, T. (2012). Diagnostic power of default mode network resting state fMRI in the detection of Alzheimer’s disease. Neurobiology of Aging, 33(3), 466–478.

    Article  PubMed  Google Scholar 

  • Kohavi, R., & John, G. H. (1997). Wrappers for feature subset selection. Artificial Intelligence, 97(1), 273–324.

    Article  Google Scholar 

  • Koivunen, J., Scheinin, N., Virta, J., Aalto, S., Vahlberg, T., Någren, K., & Rinne, J. (2011). Amyloid PET imaging in patients with mild cognitive impairment A 2-year follow-up study. Neurology, 76(12), 1085–1090.

    Article  CAS  PubMed  Google Scholar 

  • Latora, V., & Marchiori, M. (2001). Efficient behavior of small-world networks. Physical Review Letters, 87(19), 198701.

    Article  CAS  PubMed  Google Scholar 

  • Li, Y., Qin, Y., Chen, X., & Li, W. (2013). Exploring the functional brain network of Alzheimer’s disease: based on the computational experiment. PloS One, 8(9), e73186. doi:10.1371/journal.pone.0073186.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Lo, C.-Y., Wang, P.-N., Chou, K.-H., Wang, J., He, Y., & Lin, C.-P. (2010). Diffusion tensor tractography reveals abnormal topological organization in structural cortical networks in Alzheimer’s disease. The Journal of Neuroscience, 30(50), 16876–16885.

    Article  CAS  PubMed  Google Scholar 

  • Madsen, S. K., Ho, A. J., Hua, X., Saharan, P. S., Toga, A. W., Jack, C. R., Jr., & Initiative, A. s. D. N. (2010). 3D maps localize caudate nucleus atrophy in 400 Alzheimer’s disease, mild cognitive impairment, and healthy elderly subjects. Neurobiology of Aging, 31(8), 1312–1325.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Mccarthy, P., Benuskova, L., & Franz, E.A. (2014). The age-related posterior-anterior shift as revealed by voxelwise analysis of functional brain networks. Frontiers in Aging Neuroscience, 6. doi: 10.3389/fnagi.2014.00301.

  • Mesrob, L., Magnin, B., Colliot, O., Sarazin, M., Hahn-Barma, V., Dubois, B., & Benali, H. (2008). Identification of atrophy patterns in Alzheimer’s disease based on SVM feature selection and anatomical parcellation. In T. Dohi, I. Sakuma, & H. Liao (Eds.), Medical imaging and augmented reality (Vol. 5128, pp. 124–132). Berlin Heidelberg: Springer.

    Google Scholar 

  • Miller, S. L., Celone, K., DePeau, K., Diamond, E., Dickerson, B. C., Rentz, D., & Sperling, R. A. (2008). Age-related memory impairment associated with loss of parietal deactivation but preserved hippocampal activation. Proceedings of the National Academy of Sciences, 105(6), 2181–2186.

    Article  CAS  Google Scholar 

  • Mintun, M., Larossa, G., Sheline, Y., Dence, C., Lee, S. Y., Mach, R., & Morris, J. (2006). [11C] PIB in a nondemented population potential antecedent marker of Alzheimer disease. Neurology, 67(3), 446–452.

    Article  CAS  PubMed  Google Scholar 

  • Nelson, S. M., Cohen, A. L., Power, J. D., Wig, G. S., Miezin, F. M., Wheeler, M. E., & Schlaggar, B. L. (2010). A parcellation scheme for human left lateral parietal cortex. Neuron, 67(1), 156–170.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Newman, M. E. J. (2008). mathematics of networks. In S. N. Durlauf & L. E. Blume (Eds.), The new palgrave dictionary of economics. Palgrave Macmillan: Basingstoke.

    Google Scholar 

  • Noble, W. S. (2006). What is a support vector machine? Nature Biotechnology, 24(12), 1565–1567.

    Article  CAS  PubMed  Google Scholar 

  • Ota, K., Oishi, N., Ito, K., Fukuyama, H., & Group, S.-J. S. (2014). A comparison of three brain atlases for MCI prediction. Journal of Neuroscience Methods, 221, 139–150.

    Article  PubMed  Google Scholar 

  • Petersen, R. C., Smith, G. E., Waring, S. C., Ivnik, R. J., Tangalos, E. G., & Kokmen, E. (1999). Mild cognitive impairment: clinical characterization and outcome. Archives of Neurology, 56(3), 303–308.

    Article  CAS  PubMed  Google Scholar 

  • Petersen, R. C., Doody, R., Kurz, A., Mohs, R. C., Morris, J. C., Rabins, P. V., & Winblad, B. (2001). Current concepts in mild cognitive impairment. Archives of Neurology, 58(12), 1985–1992.

    Article  CAS  PubMed  Google Scholar 

  • Pihlajamäki, M., DePeau, K. M., Blacker, D., & Sperling, R. A. (2008). Impaired medial temporal repetition suppression is related to failure of parietal deactivation in Alzheimer disease. The American Journal of Geriatric Psychiatry, 16(4), 283–292.

    Article  PubMed  Google Scholar 

  • Postuma, R. B., & Dagher, A. (2006). Basal ganglia functional connectivity based on a meta-analysis of 126 positron emission tomography and functional magnetic resonance imaging publications. Cerebral Cortex, 16(10), 1508–1521.

    Article  PubMed  Google Scholar 

  • Power, J. D., Cohen, A. L., Nelson, S. M., Wig, G. S., Barnes, K. A., Church, J. A., & Schlaggar, B. L. (2011). Functional network organization of the human brain. Neuron, 72(4), 665–678.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001). A default mode of brain function. Proceedings of the National Academy of Sciences, 98(2), 676–682.

    Article  CAS  Google Scholar 

  • Reitz, C., Brayne, C., & Mayeux, R. (2011). Epidemiology of Alzheimer disease. Nature Reviews Neurology, 7(3), 137–152.

    Article  PubMed  PubMed Central  Google Scholar 

  • Rish, I., Cecchi, G.A., & Heuton, K. (2012). Schizophrenia classification using functional network features. Paper presented at the SPIE Medical Imaging.

  • Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. NeuroImage, 52(3), 1059–1069.

    Article  PubMed  Google Scholar 

  • Rubinov, M., & Sporns, O. (2011). Weight-conserving characterization of complex functional brain networks. NeuroImage, 56(4), 2068–2079.

    Article  PubMed  Google Scholar 

  • Sanz-Arigita, E. J., Schoonheim, M. M., Damoiseaux, J. S., Rombouts, S. A. R. B., Maris, E., Barkhof, F., & Stam, C. J. (2010). Loss of ‘small-world’ networks in Alzheimer's disease: graph analysis of fMRI resting-state functional connectivity. PloS One, 5(11), e13788. doi:10.1371/journal.pone.0013788.

    Article  PubMed  PubMed Central  Google Scholar 

  • Sheline, Y. I., & Raichle, M. E. (2013). Resting state functional connectivity in preclinical Alzheimer’s disease. Biological Psychiatry, 74(5), 340–347.

    Article  PubMed  PubMed Central  Google Scholar 

  • Shirer, W., Ryali, S., Rykhlevskaia, E., Menon, V., & Greicius, M. (2012). Decoding subject-driven cognitive states with whole-brain connectivity patterns. Cerebral Cortex, 22(1), 158–165.

    Article  CAS  PubMed  Google Scholar 

  • Sperling, R. A., LaViolette, P. S., O’Keefe, K., O’Brien, J., Rentz, D. M., Pihlajamaki, M., & Hedden, T. (2009). Amyloid deposition is associated with impaired default network function in older persons without dementia. Neuron, 63(2), 178–188.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Sperling, R. A., Dickerson, B. C., Pihlajamaki, M., Vannini, P., LaViolette, P. S., Vitolo, O. V., & Selkoe, D. J. (2010). Functional alterations in memory networks in early Alzheimer’s disease. Neuromolecular Medicine, 12(1), 27–43.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Stam, C. J., Jones, B. F., Nolte, G., Breakspear, M., & Scheltens, P. (2007). Small-world networks and functional connectivity in Alzheimer’s disease. Cerebral Cortex, 17(1), 92–99. doi:10.1093/cercor/bhj127.

    Article  CAS  PubMed  Google Scholar 

  • Suk, H.-I., Lee, S.-W., & Shen, D. (2015a). Deep sparse multi-task learning for feature selection in Alzheimer’s disease diagnosis. Brain Structure and Function, 1–19. doi: 10.1007/s00429-015-1059-y.

  • Suk, H.-I., Lee, S.-W., & Shen, D. (2015b). Latent feature representation with stacked auto-encoder for AD/MCI diagnosis. Brain Structure and Function, 220(2), 841–859. doi:10.1007/s00429-013-0687-3.

    Article  PubMed  Google Scholar 

  • Supekar, K., Menon, V., Rubin, D., Musen, M., & Greicius, M. D. (2008). Network analysis of intrinsic functional brain connectivity in Alzheimer’s disease. PLoS Computational Biology, 4(6), e1000100. doi:10.1371/journal.pcbi.1000100.

    Article  PubMed  PubMed Central  Google Scholar 

  • Tijms, B. M., Wink, A. M., de Haan, W., van der Flier, W. M., Stam, C. J., Scheltens, P., & Barkhof, F. (2013). Alzheimer’s disease: connecting findings from graph theoretical studies of brain networks. Neurobiology of Aging, 34(8), 2023–2036.

    Article  PubMed  Google Scholar 

  • Toussaint, P.-J., Maiz, S., Coynel, D., Doyon, J., Messé, A., de Souza, L. C., & Benali, H. (2014). Characteristics of the default mode functional connectivity in normal ageing and Alzheimer’s disease using resting state fMRI with a combined approach of entropy-based and graph theoretical measurements. NeuroImage, 101, 778–786. doi:10.1016/j.neuroimage.2014.08.003.

    Article  PubMed  Google Scholar 

  • van den Heuvel, M. P., & Sporns, O. (2013). Network hubs in the human brain. Trends in Cognitive Sciences, 17(12), 683–696. doi:10.1016/j.tics.2013.09.012.

    Article  PubMed  Google Scholar 

  • Vapnik, V. (1998). Statistical learning theory. New York: Wiley.

    Google Scholar 

  • Wang, Z., Jia, X., Liang, P., Qi, Z., Yang, Y., Zhou, W., & Li, K. (2012). Changes in thalamus connectivity in mild cognitive impairment: evidence from resting state fMRI. European Journal of Radiology, 81(2), 277–285.

    Article  PubMed  Google Scholar 

  • Wang, J., Zuo, X., Dai, Z., Xia, M., Zhao, Z., Zhao, X., & He, Y. (2013). Disrupted functional brain connectome in individuals at risk for Alzheimer’s disease. Biological Psychiatry, 73(5), 472–481.

    Article  CAS  PubMed  Google Scholar 

  • Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’networks. Nature, 393(6684), 440–442.

    Article  CAS  PubMed  Google Scholar 

  • Wee, C.-Y., Yap, P.-T., Denny, K., Browndyke, J. N., Potter, G. G., Welsh-Bohmer, K. A., & Shen, D. (2012a). Resting-state multi-spectrum functional connectivity networks for identification of MCI patients. PloS One, 7(5), e37828.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wee, C.-Y., Yap, P.-T., Zhang, D., Denny, K., Browndyke, J. N., Potter, G. G., & Shen, D. (2012b). Identification of MCI individuals using structural and functional connectivity networks. NeuroImage, 59(3), 2045–2056.

    Article  PubMed  Google Scholar 

  • Zalesky, A., Fornito, A., & Bullmore, E. T. (2010). Network-based statistic: identifying differences in brain networks. NeuroImage, 53(4), 1197–1207.

    Article  PubMed  Google Scholar 

  • Zhang, Z., Liu, Y., Jiang, T., Zhou, B., An, N., Dai, H., & Zhang, X. (2012). Altered spontaneous activity in Alzheimer’s disease and mild cognitive impairment revealed by Regional Homogeneity. NeuroImage, 59(2), 1429–1440. doi:10.1016/j.neuroimage.2011.08.049.

    Article  PubMed  Google Scholar 

  • Zhao, X., Liu, Y., Wang, X., Liu, B., Xi, Q., Guo, Q., & Wang, P. (2012). Disrupted small-world brain networks in moderate Alzheimer’s disease: a resting-state fMRI study. PloS One, 7(3), e33540. doi:10.1371/journal.pone.0033540.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgments

Data used in this paper were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://ADNI.loni. usc.edu). The investigators within the ADNI, who can be found at http://ADNI.loni.usc.edu/study-design/ongoing-investigations, contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this article. This study was supported by the Children’s Foundation Research Institute, Le Bonheur Children’s Hospital, Memphis, TN.

Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

We thank Dr. Amanda Preston for her assistance with manuscript preparation.

Compliance with Ethical Standards

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Funding

This study was funded by the Children’s Foundation Research Institute, Le Bonheur Children’s Hospital, Memphis, TN.

Conflict of Interest

The authors declared that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Babol University of Technology, Babol, Iran

    Ali Khazaee & Ata Ebrahimzadeh

  2. Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, USA

    Abbas Babajani-Feremi

  3. Department of Anatomy and Neurobiology, University of Tennessee Health Science Center, Memphis, TN, USA

    Abbas Babajani-Feremi

  4. Neuroscience Institute, Le Bonheur Children’s Hospital, Memphis, TN, USA

    Abbas Babajani-Feremi

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  2. Ata Ebrahimzadeh
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  3. Abbas Babajani-Feremi
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Correspondence to Ali Khazaee.

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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. Brain Imaging and Behavior 10, 799–817 (2016). https://doi.org/10.1007/s11682-015-9448-7

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