Classification of Alzheimer's Disease from structural MRI using sparse logistic regression with optional spatial regularization

Author(s):  
A. Rao ◽  
Ying Lee ◽  
A. Gass ◽  
A. Monsch
2010 ◽  
Vol 16 (5) ◽  
pp. 910-920 ◽  
Author(s):  
MICHAEL M. EHRENSPERGER ◽  
MANFRED BERRES ◽  
KIRSTEN I. TAYLOR ◽  
ANDREAS U. MONSCH

AbstractThe goal of the present study was to evaluate the diagnostic discriminability of three different global scores for the German version of the Consortium to Establish a Registry on Alzheimer’s Disease-Neuropsychological Assessment Battery (CERAD-NAB). The CERAD-NAB was administered to 1100 healthy control participants [NC; Mini-Mental State Examination (MMSE) mean = 28.9] and 352 patients with very mild Alzheimer’s disease (AD; MMSE mean = 26.1) at baseline and subsets of participants at follow-up an average of 2.4 (NC) and 1.2 (AD) years later. We calculated the following global scores: Chandler et al.’s (2005) score (summed raw scores), logistic regression on principal components analysis scores (PCA-LR), and logistic regression on demographically corrected CERAD-NAB variables (LR). Correct classification rates (CCR) were compared with areas under the receiver operating characteristics curves (AUC). The CCR of the LR score (AUC = .976) exceeded that of the PCA-LR, while the PCA-LR (AUC = .968) and Chandler (AUC = .968) scores performed comparably. Retest data improved the CCR of the PCA-LR and Chandler (trend) scores. Thus, for the German CERAD-NAB, Chandler et al.’s total score provided an effective global measure of cognitive functioning, whereby the inclusion of retest data tended to improve correct classification of individual cases. (JINS, 2010, 16, 910–920.)


2017 ◽  
Author(s):  
Kun Zhao ◽  
Yanhui Ding ◽  
Pan Wang ◽  
Xuejiao Dou ◽  
Bo Zhou ◽  
...  

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