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132 Chapter 5 Quantitative shape analysis of hippocampus in AD Figure 5.13: Three-dimensional representation of hippocampal subfields by La Joie et al. (2010): CA1 (blue), subiculum (green) and other (pink) subfields obtained by manual delineation of the three hippocampal subfields onto the DARTEL whole-brain group template, using landmarks adapted from Harding et al. (1998). The current results demonstrate the potentiality in improving discrimination ability of SSM by localizing the model to hippocampal subregions. The optimal thresh- old selecting the landmarks on the training set does not give the optimal classification performance on the testing set, which calls for more targeted localization of the atrophy than simple thresholding. In addition to the statistical difference in the landmark position, histological and anatomical knowledge of hippocampus may be incorporated into the identification of regions in order capture the shape variation that is more closely linked to the AD pathology. 5.6.4 Correlation analysis Since the accuracy of classification could possibly be the results of overfitting of the given data, the correlation analysis connects the link between shape components explaining most shape variations on the selected subregions and the memory decline in the disease, confirming that the subregional shape components display- ing higher discrimination ability are related to the development of the disease.
Chapter 5 Quantitative shape analysis of hippocampus in AD 133 The correlation between subregional shape components and the memory scores are comparable to the between mean difference. The variation modes extracted on the hippocampal subregions (Figure 5.12) are also consistent with the pattern along the mean difference direction in Figure 5.14. In the correlation analysis, the MS models were able to identify the shape components with the maximum correlation higher than other shape or size-and-shape models, despite of the superiority of MR to MS in the disease classification. It is because more principal components from MR models are found to correlate with the memory decline, while to a less degree comparing to the fewer but higher cor- relations of MS components. Therefore using the features from MR though less correlated with the memory decline may contribute to the higher classification accuracy. The mean difference direction in the MS models are also more correlated with the memory scores than rigidly aligned models. The correlation analysis was not designed to specify the involvement of one partic- ular subfield in episodic memory processes, for instance in encoding and retrieval processes that are thought to preferentially engage different hippocampal subfields (Henson, 2005; Eldridge et al., 2005). Our findings however confirm the preferen- tial implication of both CA1 and subiculum changes in episodic memory deficits in AD. The regions with variation of most significant correlation with the episodic memory index also mainly matches to the CA1 and subiculum subfields. 5.7 Summary The shape of the hippocampus can provide valuable information for the diagnosis of AD. The principal components of the hippocampus among the population as modeled by the SSM can be used to classify AD against NC. The conventional PCA in SSM is performed on all the landmark points on the surface, which rep- resents the original shape data in a lower dimensional subspace, but may be not discriminative between two groups. The whole-surface description of variation in- corporating both hippocampal shape among the population may be not sensitive
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UNIVERSITE DE BOURGOGNE U.F.R. SCIE
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Résumé L’objectif de cette thè
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Abstract The aim of this thesis is
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TABLE DES MATIÈRES Acknowledgement
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Page 164 and 165: 146 BIBLIOGRAPHY Alzheimer, A. (191
Page 166 and 167: 148 BIBLIOGRAPHY Baillard, C., Hell
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Page 170 and 171: 152 BIBLIOGRAPHY B. (2008). Three-d
Page 172 and 173: 154 BIBLIOGRAPHY Cootes, T. F., Tay
Page 174 and 175: 156 BIBLIOGRAPHY head in MR images
Page 176 and 177: 158 BIBLIOGRAPHY Evans, A. C., Coll
Page 179 and 180: BIBLIOGRAPHY 161 Ghanei, A., Soltan
Page 181 and 182: BIBLIOGRAPHY 163 Heckemann, R. A.,
Page 183 and 184: BIBLIOGRAPHY 165 R. C. (2003). MRI
Page 185 and 186: BIBLIOGRAPHY 167 Konrad, C., Ukas,
Page 187 and 188: BIBLIOGRAPHY 169 Mahony, R. and Man
Page 189 and 190: BIBLIOGRAPHY 171 disease, mild cogn
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Page 193 and 194: BIBLIOGRAPHY 175 Rorden, C. and Bre
Page 195 and 196: BIBLIOGRAPHY 177 Shen, K., Bourgeat
Page 197 and 198: BIBLIOGRAPHY 179 Barillot, C., Hayn
Page 199 and 200: BIBLIOGRAPHY 181 Tzourio-Mazoyer, N
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BIBLIOGRAPHY 183 Vos, F. M., de Bru
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BIBLIOGRAPHY 185 Woolrich, M. W., J
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