MRI Images of AD

Classification accuracy that can be achieved by combining features from different structural MRI analysis techniques.

Automatically estimated MR features used are hippocampal volume, tensor-based morphometry, cortical thickness and a novel technique based on manifold learning.

Results for t-tests for statistically significant group differences based on cortical thickness measurements.

(SVMs) and a linear discriminant analys (LDA) to evaluate classification accuracy (CCR), sensitivity (SEN) and specificity (SPE). Brain MR images were acquired at regular intervals after an initial baseline scan from approximately 200 cognitively normal older subjects (HC), 400 subjects with mild cognitive impairment (MCI), and 200 subjects with early AD.

The results for the comparisons HC vs AD, HC vs P-MCI and S-MCI vs P-MCI in the full ADNI database are presented.95% confidence interval for the classification accuracy is estimated based on the multiple classification runs. Statistically significant improvements achieved when combining all features are marked with  (p0.0001). To test for significance, unpaired t-tests were carried out between distribution estimates for the corresponding classification rates based on the multiple runs. All estimated distributions passed a normality test using a Kolmogorov-Smirnov test at .