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].

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.

 

Requirement: SPM12 must be installed running on MATLAB (with statistics toolbox).

 

Disclaimer:

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.

 

Citation:

[1] 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.