A predictive model using the mesoscopic architecture of the living brain to detect Alzheimer’s disease
The clinical, radiomics and genetic data to reproduce the key findings in "A predictive model using the mesoscopic architecture of the living brain to detect Alzheimer’s disease". Alzheimer’s disease is the most common cause of dementia. It is a neurodegenerative disorder characterized by gradually progressive cognitive and functional deficits, as well as behavioral changes. The diagnosis of Alzheimer’s disease is often challenging leading to suboptimal patient care. In this study, we develop a new unsupervised analytic method based on the extraction of statistical features from multiple brain regions identified through structural magnetic resonance imaging data, which is able to reliably discriminate people with Alzheimer’s disease-related pathologies from those without. We provide a diagnostic tool that is ready to be integrated into the clinical decision support system without the need for additional sampling or patient testing.
Steps to reproduce
The folder contains the code for the feature reduction with the LASSO. The code has been tested on MAtlab 2019b and runs on Windows/Linux and MAC operating systems. The function does not require any installation, if Matlab is already present. The folder needs to be added to the path. Further instructions are reported with the comments in the script files. gLASSO_train is used to obtain the ApV from the training set gLASSO_test is used to obtain the ApV from the testing set Training and testing set are included in the data.mat file (standardised by the mean and standard deviation)