Predicting very early stage mild cognitive impairment based on a voxel-wise arterial spin labeling analysis

Abstract

In recent years, medical images have been increasingly used as an objective method for the diagnosis of neurodegenerative diseases. Most previous studies have been based on structural or functional magnetic resonance imaging. However, the results are not yet sufficient to identify early stages of dementia. In this paper, we present an image processing and pattern recognition strategy that allows to predict short-term conversion to Mild Cognitive Impairment (MCI) based on the analysis of Arterial Spin Labeling images. Healthy subjects, categorized as individuals at risk of dementia, were assessed annually in order to identify those that converted to MCI. After 1 − 2 years, 20 subjects were classified as non-converters and 15 as converters according to the Mini–Mental State Examination test and other neuropsychiatric scales. The proposed approach was able to classify converter from non-converter subjects with an accuracy of 0.88 using the leave-one-out cross-validation method.