Whole Brain Atrophy and Sample Size Estimate via Iterative Principal Component Analysis for Twelve-month Alzheimer's Disease Trials

2013 ◽  
Vol 1 (1) ◽  
pp. 40-47 ◽  
Author(s):  
Napatkamon Ayutyanont ◽  
Kewei Chen ◽  
Adam S. Fleisher ◽  
Jessica B.S. Langbaum ◽  
Cole Reschke ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Principal Component Analysis ◽  
Sample Size ◽  
Brain Atrophy ◽  
Principal Component ◽  
Component Analysis ◽  
Whole Brain ◽  
Size Estimate ◽  
Sample Size Estimate

Principal component analysis of synaptic density measured with [11C]UCB-J PET in Alzheimer's disease

2021 ◽  
Vol 29 (4) ◽  
pp. S47-S48
Author(s):  
Ryan O'Dell ◽  
Albert Higgins-Chen ◽  
Dhruva Gupta ◽  
Ming-Kai Chen ◽  
Mika Naganawa ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Synaptic Density

Classification for Alzheimer’s Disease from Structural MRI by General N-Dimensional Principal Component Analysis

2013 ◽  
Vol 336-338 ◽  
pp. 2316-2319
Author(s):  
Song Yuan Tang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Structural Mri ◽  
Component Analysis ◽  
Support Vector ◽  
Linear Discriminant ◽  
Vector Machines ◽  
Normal Controls

In this paper, we propose a classification method for Alzheimer’s disease from structural MRI. In the method, a specific template is firstly constructed. Then all data are registered to the template and the corresponding Jacobians are calculated. And then, a general n-dimensional principal component analysis (GND-PCA) based method is adopted to extract features from the Jacobians and the features are enhanced by the linear discriminant analysis (LDA) . Finally, the enhanced features are used for the support vector machines (SVMs) classifiers. The proposed method classifies AD and normal controls (NC) well.

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