Characterization of cerebral cortex folding in humans through MRI: quality control and dementia prediction

Magnetic resonance imaging (MRI) is a valuable tool for non-invasively investigating human brain anatomy and functions. The features extracted from MRI data can be used as biomarkers for neurodegenerative diseases, like Dementia. To deeply understand the mechanisms driving the brain changes it is crucial to extract reliable measures from the brain MRI scans and to increase the statistical power by harmonizing different datasets, such as in the ENIGMA studies. Here we applied the ENIGMA-SULCI pipeline to estimate the reliability of the sulcal descriptors extracted across the whole brain and to investigate their correlation with CDR (Clinical Dementia Rating) in the open access dataset OASIS. The OASIS dataset includes T1-weighted acquired from 416 right-handed subjects, for 227 of whose we know CDR. The measurement reliability has been estimated through technical replicates of a subgroup of patients MRI scans. The correlation of each sulcal shape descriptor with the degree of Dementia has been tested through linear regressions between each feature and the CDR series. We have trained linear (regression) and nonlinear (Neural Networks) Machine Learning models in order to classify the subjects in two classes (Dementia and healthy subjects). We got models able to correctly classify more than the 70% of the dataset, starting from sulcal measures.