Combining multiple anatomical MRI measures improves Alzheimer's disease classification. Issue 5 (25th February 2016)

• Record Type: Journal Article
• Authors: de Vos, Frank; Schouten, Tijn M.; Hafkemeijer, Anne; Dopper, Elise G. P.; van Swieten, John C.; de Rooij, Mark; van der Grond, Jeroen; Rombouts, Serge A. R. B.
• Is Part Of: Human brain mapping. Volume 37:Issue 5(2016:May)
• Page Start: 1920
• Page End: 1929
• Publication Date: 2016-02-25
• Subjects: Alzheimer's disease -- anatomical MRI -- cortical thickness -- cortical area -- cortical curvature -- grey matter density -- subcortical volumes -- hippocampal shape -- classification; Brain mapping -- Periodicals; 611.81
• DOI: 10.1002/hbm.23147
• Languages: English
• ISSNs: 1065-9471

Abstract

Abstract: Several anatomical MRI markers for Alzheimer's disease (AD) have been identified. Hippocampal volume, cortical thickness, and grey matter density have been used successfully to discriminate AD patients from controls. These anatomical MRI measures have so far mainly been used separately. The full potential of anatomical MRI scans for AD diagnosis might thus not yet have been used optimally. In this study, we therefore combined multiple anatomical MRI measures to improve diagnostic classification of AD. For 21 clinically diagnosed AD patients and 21 cognitively normal controls, we calculated (i) cortical thickness, (ii) cortical area, (iii) cortical curvature, (iv) grey matter density, (v) subcortical volumes, and (vi) hippocampal shape. These six measures were used separately and combined as predictors in an elastic net logistic regression. We made receiver operating curve plots and calculated the area under the curve (AUC) to determine classification performance. AUC values for the single measures ranged from 0.67 (cortical thickness) to 0.94 (grey matter density). The combination of all six measures resulted in an AUC of 0.98. Our results demonstrate that the different anatomical MRI measures contain complementary information. A combination of these measures may therefore improve accuracy of AD diagnosis in clinical practice. Hum Brain Mapp 37:1920–1929, 2016 . ©2016 Wiley Periodicals, Inc .