Machine learning-based Alzheimer’s disease Designation (MAD)
This SPM extension evaluates whether the given brain FDG PET images “look like” an Alzheimer’s disease (AD) patient’s or not (non-AD).
The brain FDG PET images must be already pre-processed (normalized and smoothed with [8 8 8]mm Gaussian filter) using SPM12 (with “old normalize” routine on to a PET template). Please note that the original study was conducted without structural MRI co-registration.
The program computes subject scores and labels (AD vs. non-AD) estimated by 5 different algorithms described in .
1. General Linear Model
2. Scaled Subprofile Modeling - single principal component
3. Scaled Subprofile Modeling - linearly regressed principal components
4. Support Vector Machine – Iterative Single Data Algorithm
5. Support Vector Machine – Sequential Minimal Optimization
The package contains MAT file (~1GB). Please contact us for further instructions on how to download the software package.
The Software and code samples available on this website are provided "as is" without warranty of any kind, either express or implied. Use at your own risk.
 Katako A, Shelton P, Goertzen A, Levin D, Bybel B, Aljuaid M, Yoon HJ, Kang DY, Kim SM, Lee CS, Ko JH, “Machine learning identified an Alzheimer’s disease-related FDG-PET pattern which is also expressed in Lewy body dementia and Parkinson’s disease dementia,” Scientific Reports, vol. 8(1), pp.13236, September 2018.