Abstract
Mild cognitive impairment (MCI), often an early stage of Alzheimer’s disease (AD), is difficult to diagnose due to the subtlety of cognitive impairment. Recent emergence of reliable network characterization techniques based on diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI) has made the understanding of neurological disorders at a whole-brain connectivity level possible, providing new avenues for brain classification. Taking a multi-kernel SVM, we attempt to integrate these two imaging modalities for improving classification performance. Our results indicate that the multimodality classification approach performs better than the single modality approach, with statistically significant improvement in accuracy. It was also found that the prefrontal cortex, orbitofrontal cortex, temporal pole, anterior and posterior cingulate gyrus, precuneus, amygdala, thalamus, parahippocampal gyrus and insula regions provided the most discriminant features for classification, in line with the results reported in previous studies. The multimodality classification approach allows more accurate early detection of brain abnormalities with larger sensitivity, and is important for treatment management of potential AD patients.
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References
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001), software, available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
Dai, W., Lopez, O.L., Carmichael, O.T., Becker, J.T., Kuller, L.H., Gach, H.M.: Mild cognitive impairment and Alzheimer disease: Patterns of altered cerebral blood flow at MR imaging. Radiology 250, 856–866 (2009)
Davatzikos, C., Bhatt, P., Shaw, L.M., Batmanghelich, K.N., Trojanowski, J.Q.: Prediction of MCI to AD conversion, via MRI, CSF biomarkers and pattern classification. Neurobiol Aging (2010)
Flavio, N., Dario, S., Silvia, M., Nicola, G., Arnoldo, P., Andrea, B., Barbara, D., Stig, A.L., Guido, R., Marco, P.: Principal component analysis of FDG PET in amnestic MCI. Eur. J. Nucl. Med. Mol. Imaging 35(12), 2191–2202 (2008)
Grady, C.L., McIntosh, A.R., Beig, S., Keightley, M.L., Burian, H., Black, S.E.: Evidence from functional neuroimaging of a compensatory prefrontal network in Alzheimer’s disease. J. Neurosci 23(3), 986–993 (2003)
Haller, S., Nguyen, D., Rodriguez, C., Emch, J., Gold, G., Bartsch, A., Lovblad, K., Giannakopoulos, P.: Individual prediction of cognitive decline in mild cognitive impairment using support vector machine-based analysis of diffusion tensor imaging data. J. Alzheimers Dis. 22(1), 315–527 (2010)
Leemans, A., Jeurissen, B., Sijbers, J., Jones, D.K.: ExploreDTI: A graphical toolbox for processing, analyzing, and visualizing diffusion MR data. In: 17th Annual Meeting of Intl. Soc. Mag. Reson. Med., Hawaii, USA, p. 3537 (2009)
Machulda, M.M., Senjem, M.L., Weigand, S.D., Smith, G.E., Ivnik, R.J., Boeve, B.F., Knopman, D.S., Petersen, R.C., Jack Jr., C.R.: Functional MRI changes in amnestic and non-amnestic MCI during encoding and recognition tasks. J. Int. Neuropsych. Soc. 15(3), 372–382 (2009)
McEvoy, L.K., Fennema-Notestine, C., Roddey, J.C., Hagler Jr., D.J., Holland, D., Karow, D.S., Pung, C.J., Brewer, J.B., Dale, A.M.: Alzheimer disease: Quantitative structural neuroimaging for detection and prediction of clinical and structural changes in mild cognitive impairment. Radiology 251, 195–205 (2009)
Sokolova, M.V., Japkowicz, N., Szpakowicz, S.: Beyond accuracy, F-score and ROC: A family of discriminant measures for performance evaluation. In: Sattar, A., Kang, B.-h. (eds.) AI 2006. LNCS (LNAI), vol. 4304, pp. 1015–1021. Springer, Heidelberg (2006)
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, 273–289 (2002)
Van Dijk, K.R.A., Hedden, T., Venkataraman, A., Evans, K.C., Lazar, S.W., Buckner, R.L.: Intrinsic functional connectivity as a tool for human connectomics: Theory, properties and optimization. J. Neurophysiol 103, 297–321 (2010)
Wang, Z., Jia, X., Liang, P., Qi, Z., Yang, Y., Zhou, W., Li, K.: Changes in thalamus connectivity in mild cognitive impairment: Evidence from resting state fMRI. Eur. J Radiol. (2011)
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Wee, CY., Yap, PT., Zhang, D., Denny, K., Wang, L., Shen, D. (2011). Identification of Individuals with MCI via Multimodality Connectivity Networks. In: Fichtinger, G., Martel, A., Peters, T. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011. MICCAI 2011. Lecture Notes in Computer Science, vol 6892. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23629-7_34
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DOI: https://doi.org/10.1007/978-3-642-23629-7_34
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