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IC‐P‐037: Morphological differences in the striatum in early‐ and late‐onset Alzheimer's disease
Author(s) -
Pievani Michela,
Bocchetta Martina,
Boccardi Marina,
Galluzzi Samantha,
Bonetti Matteo,
Thompson Paul,
Frisoni Giovanni
Publication year - 2012
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2012.05.069
Subject(s) - putamen , striatum , atrophy , nucleus accumbens , basal ganglia , early onset alzheimer's disease , neuropathology , caudate nucleus , neuroscience , psychology , anatomy , medicine , dementia , pathology , central nervous system , disease , dopamine
subjects from ten scanners located at nine sites. Objective: To assess the accuracy ofML classifiers for the detection of AD based on a large multicenter DTI data set using different approaches to reduce inter-site variability. Methods: After strict quality control we pooled the remaining 280 DTI and MRI scans derived from 137 patients with clinically probable AD and 143 healthy elderly controls. For classification we used fractional anisotropy (FA) maps and mean diffusivity (MD) maps and performed a tenfold cross validation.We selected discriminative voxels using the information gain criterion and classified the data with a Support Vector Machine. In a second step, we eliminated variance attributable to center and other covariates including age, education, gender, using principal component analysis (PCA) before repeating the classification procedure. Results: For FA and MD the feature selection identified areas in the medial temporal lobe and corpus callosum that had the strongest contribution to the group separation.We achieved an accuracy of 80% for FA and 83% for MD. For the tissue density maps we obtained 83% for WM and 89% for gray matter. The reduction of variance components arising from center, gender, age and education effects did not significantly change the classification results for FA andMD.Conclusions:Multicenter acquisition of DTI data in combination with multivariate ML approaches show promising results which can be compared to earlier monocenter DTI studies. Variance introduced by different scanners can be detected by PCA, but it seems not to affect the performance of the classifier.