From Brain Imaging to Graph Analysis: a study on ADNI's patient cohort
This work addresses Alzheimer's disease diagnosis for medical researchers, but it is incremental as it applies existing graph and classification methods to a specific dataset.
The study tackled the problem of predicting Alzheimer's disease by converting brain volume changes into graphs and extracting substructures, achieving an area under the ROC curve of 0.72 for discriminating between AD patients and healthy controls.
In this paper, we studied the association between the change of structural brain volumes to the potential development of Alzheimer's disease (AD). Using a simple abstraction technique, we converted regional cortical and subcortical volume differences over two time points for each study subject into a graph. We then obtained substructures of interest using a graph decomposition algorithm in order to extract pivotal nodes via multi-view feature selection. Intensive experiments using robust classification frameworks were conducted to evaluate the performance of using the brain substructures obtained under different thresholds. The results indicated that compact substructures acquired by examining the differences between patient groups were sufficient to discriminate between AD and healthy controls with an area under the receiver operating curve of 0.72.