Abstract
Gaussian smoothing of images is an important step in Voxel-based Analysis and Statistical Parametric Mapping (VBA-SPM); it accounts for registration errors and integrates imaging signals from a region around each voxel being analyzed. However, it has also become a limitation of VBA-SPM based methods, since it is often chosen empirically, non-optimally, and lacks spatial adaptivity to the shape and spatial extent of the region of interest. In this paper, we propose a new framework, named Optimally-Discriminative Voxel-Based Analysis (ODVBA), for determining the optimal spatially adaptive smoothing of images, followed by applying voxel-based group analysis. In ODVBA, Nonnegative Discriminative Projection is applied locally to get the direction that best discriminates between two groups, e.g. patients and controls; this direction is equivalent to local filtering by an optimal kernel whose coefficients define the optimally discriminative direction. By considering all the neighborhoods that contain a given voxel, we then compose this information to produce the statistic for each voxel. Permutation tests are finally used to obtain the statistical significance. The experiments on Mild Cognitive Impairment (MCI) study have shown the effectiveness of the framework.
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References
Alzheimer’s Disease Neuroimaging Initiative, http://www.loni.ucla.edu/ADNI
Ashburner, J., Friston, K.J.: Voxel-based morphometry-the methods. Neuroimage 11(6), 805–821 (2000)
Cohen, J.: Statistical Power Analysis for the Behavioral Sciences, 2nd edn. Lawrence Erlbaum Associates, Mahwah (1988)
Davatzikos, C., Genc, A., Xu, D., Resnick, S.M.: Voxel-based morphometry using the RAVENS maps: Methods and validation using simulated longitudinal atrophy. NeuroImage 14(6), 1361–1369 (2001)
Davatzikos, C., Li, H.H., Herskovits, E., Resnick, S.M.: Accuracy and sensitivity of detection of activation foci in the brain via statistical parametric mapping: a study using a PET simulator. NeuroImage 13(1), 176–184 (2001)
Duda, R., Hart, P., Stork, D.: Pattern Classification, 2nd edn. Wiley, Chichester (2000)
Friston, K.J., Holmes, A.P., Worsley, K.J., Poline, J.B., Frith, C.D., Frackowiak, R.S.J.: Statistical parametric maps in functional imaging: a general linear approach. Hum. Brain Map. 2, 189–210 (1995)
Genovese, C.R., Lazar, N.A., Nichols, T.: Thresholding of statistical maps in functional neuroimaging using the false discovery rate. NeuroImage 15, 870–878 (2002)
Lee, D.D., Seung, H.S.: Algorithms for non-negative matrix factorization. In: NIPS, vol. 13, pp. 556–562 (2001)
Nichols, T.E., Holmes, A.P.: Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum. Brain Map. 15(1), 1–25 (2002)
Sha, F., Lin, Y., Saul, L.K., Lee, D.D.: Multiplicative updates for nonnegative quadratic programming. Neural. Comp. 19(8), 2004–2031 (2007)
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Zhang, T., Davatzikos, C. (2010). Optimally-Discriminative Voxel-Based Analysis. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010. MICCAI 2010. Lecture Notes in Computer Science, vol 6362. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15745-5_32
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DOI: https://doi.org/10.1007/978-3-642-15745-5_32
Publisher Name: Springer, Berlin, Heidelberg
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