Prediction of Alzheimer’s disease in mild cognitive impairment using sulcal morphology and cortical thickness

Abstract

Mild cognitive impairment (MCI) is an intermediate condition between healthy ageing and dementia. The amnestic MCI is often a high risk factor for subsequent Alzheimer’s disease (AD) conversion. Some MCI patients never develop AD (MCI non-converters, or MCInc), but some do progress to AD (MCI converters, or MCIc). The purpose of this study was to predict future AD-conversion in patients with MCI using machine learning with sulcal morphology and cortical thickness measures as classification features. 32 sulci per subject were extracted from 1.5T T1-weighted ADNI database MRI scans of 90 MCIc and 104 MCInc subjects. We computed sulcal morphology features and cortical thickness measurements for support vector machine classification to identify structural patterns distinguishing future AD conversions. The linear kernel classifier trained with these features was able to predict 87.0% of MCI subjects as future converters, (89.7% sensitivity, 84.4% specificity, 0.94 AUC), using 10-fold cross-validation. These results using sulcal and cortical features are superior to the state-of-the-art methods. The most discriminating predictive features were observed in the temporal and frontal lobes in the left hemispheres, and in the entorhinal cortices, which is consistent with literature. However, we also observed structural changes in the cingulate and calcarine cortices, suggesting that the limbic and occipital lobe atrophy may be linked to AD conversion.