A multivariate statistical analysis of the developing human brain in preterm infants

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Thomaz C.E.
Boardman J.P.
Counsell S.
Hill D.L.G.
Hajnal J.V.
Edwards A.D.
Rutherford M.A.
Gillies D.F.
Rueckert D.
Image and Vision Computing
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THOMAZ, C. E.;Thomaz, Carlos E;Thomaz, Carlos Eduardo;THOMAZ, C.E.;THOMAZ, CARLOS E.;THOMAZ, C.;THOMAZ, CARLOS; BOARDMAN, James; COUNSELL, Serena; HILL, Derek L. G.; HAJNAL, Jo V.; EDWARDS, David; RUTHERFORD, Mary A.; GILLIES, D. F.; RUECKERT, Daniel. A Multivariate Statistical Analysis of the Developing Human Brain in Preterm Infants. Image and Vision Computing, v. 25, n. 6, p. 981-994, 2007.
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Preterm delivery accounts for 5% of all deliveries and its consequences contribute to significant individual, medical, and social problems. The neuroanatomical substrates of these disorders are not known, but are essential for understanding mechanisms of causation, and developing strategies for intervention. In the recent years, multivariate pattern recognition methods that analyse all voxels simultaneously have been proposed to characterise the neuroanatomical differences between a reference group of magnetic resonance (MR) images and the population under investigation. Most of these techniques have overcome the difficulty of dealing with the inherent high dimensionality of 3D MR brain image data by using pre-processed segmented images or a small number of specific features. However, an intuitive way of mapping the classification results back into the original image domain for further interpretation remains challenging. In this paper, we propose the idea of using Principal Components Analysis (PCA) plus the maximum uncertainty Linear Discriminant Analysis (MLDA) approach to classify and analyse MR brain images that have been aligned with either affine or non-rigid registration techniques. This approach avoids the computation costs intrinsic to commonly used covariance-based optimisation processes for solving small sample size problems, resulting in a simple and efficient implementation for the maximisation and interpretation of the Fisher's classification results. In order to demonstrate the effectiveness of the approach, we have used a neonatal MR brain data set that contains images of 93 preterm infants at term equivalent age and 20 term controls. Our results indicate that the two-stage linear framework makes clear the statistical differences between the control and preterm samples, showing a classification accuracy of 95.0% and 97.8% for the controls and preterms samples, respectively, using the leave-one-out method. Moreover, it provides a simple and intuitive method of visually analysing the differences between preterm infants at term equivalent age and the control group, such as differences in cerebrospinal fluid spaces, structure of the corpus callosum, and subtle differences in myelination. © 2006 Elsevier B.V. All rights reserved.