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Optimized data preprocessing for multivariate analysis applied to 99mTc-ECD SPECT data sets of Alzheimer's patients and asymptomatic controls

Optimized data preprocessing for multivariate analysis applied to 99mTc-ECD SPECT data sets of Alzheimer's patients and asymptomatic controls

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Prüfsumme: MD5:b4152e142e4d3474606af00cde9ba41a

MERHOF, Dorit, Pawel J. MARKIEWICZ, Günther PLATSCH, Jerome DECLERCK, Markus WEIH, Johannes KORNHUBER, Torsten KUWERT, Julian C. MATTHEWS, Karl HERHOLZ, 2011. Optimized data preprocessing for multivariate analysis applied to 99mTc-ECD SPECT data sets of Alzheimer's patients and asymptomatic controls. In: Journal of Cerebral Blood Flow & Metabolism. 31(1), pp. 371-383. ISSN 0271-678X. eISSN 1559-7016. Available under: doi: 10.1038/jcbfm.2010.112

@article{Merhof2011-01Optim-14441, title={Optimized data preprocessing for multivariate analysis applied to 99mTc-ECD SPECT data sets of Alzheimer's patients and asymptomatic controls}, year={2011}, doi={10.1038/jcbfm.2010.112}, number={1}, volume={31}, issn={0271-678X}, journal={Journal of Cerebral Blood Flow & Metabolism}, pages={371--383}, author={Merhof, Dorit and Markiewicz, Pawel J. and Platsch, Günther and Declerck, Jerome and Weih, Markus and Kornhuber, Johannes and Kuwert, Torsten and Matthews, Julian C. and Herholz, Karl} }

Kuwert, Torsten Declerck, Jerome eng Platsch, Günther Matthews, Julian C. 2011-11-08T09:51:47Z Multivariate image analysis has shown potential for classification between Alzheimer's disease (AD) patients and healthy controls with a high-diagnostic performance. As image analysis of positron emission tomography (PET) and single photon emission computed tomography (SPECT) data critically depends on appropriate data preprocessing, the focus of this work is to investigate the impact of data preprocessing on the outcome of the analysis, and to identify an optimal data preprocessing method. In this work, technetium-99methylcysteinatedimer (99mTc-ECD) SPECT data sets of 28 AD patients and 28 asymptomatic controls were used for the analysis. For a series of different data preprocessing methods, which includes methods for spatial normalization, smoothing, and intensity normalization, multivariate image analysis based on principal component analysis (PCA) and Fisher discriminant analysis (FDA) was applied. Bootstrap resampling was used to investigate the robustness of the analysis and the classification accuracy, depending on the data preprocessing method. Depending on the combination of preprocessing methods, significant differences regarding the classification accuracy were observed. For 99mTc-ECD SPECT data, the optimal data preprocessing method in terms of robustness and classification accuracy is based on affine registration, smoothing with a Gaussian of 12 mm full width half maximum, and intensity normalization based on the 25% brightest voxels within the whole-brain region. Weih, Markus 2011-11-08T09:51:47Z Declerck, Jerome Herholz, Karl Kornhuber, Johannes Kuwert, Torsten Platsch, Günther 2011-01 Herholz, Karl deposit-license Merhof, Dorit Markiewicz, Pawel J. Markiewicz, Pawel J. Matthews, Julian C. Weih, Markus Kornhuber, Johannes Merhof, Dorit First publ. in: Journal of Cerebral Blood Flow & Metabolism ; 31 (2011), 1. - S. 371-383 Optimized data preprocessing for multivariate analysis applied to 99mTc-ECD SPECT data sets of Alzheimer's patients and asymptomatic controls

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