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.