Improved Predictive Model for Pre-symptomatic Mild Cognitive Impairment and Alzheimer's Disease

Objective: Delineation of gray matter (GM) structures on brain MRI scans is termed segmentation. Accuracy of segmentation is a key factor in the valid comparison of GM density and volume between individuals and groups. Previously, it was demonstrated that a group of normal subjects who later developed mild cognitive impairment (MCI) had decreased GM volume in the medial temporal lobe compared to other normal subjects who remained normal an average 5.4 years after the scan. The objective of this study was to show whether accuracy of this predictive model was increased using an advanced segmentation technique.

Methods: Structural MRI was performed on 74 longitudinally examined normal aged subjects. All subjects were cognitively normal at the time of their scan, but 18 later developed MCI, and six of these 18 went on from MCI to an AD diagnosis. We independently delineated GM using both a standard segmentation technique and a local Gaussian active contour (LGAC) technique. We compared the contribution of extracted volumes from each technique to a model predicting subjects who will eventually develop MCI.

Results: Accuracy of the standard technique to distinguish pre-MCI from normal using imaging alone was 79% (sensitivity 78% and specificity 73%). Using LGAC, accuracy rose to 84% (sensitivity 78% and specificity 84%).

Discussion: Structural brain changes precede MCI in longitudinally followed normal subjects. The LGAC technique improves the accuracy of a predictive model incorporating these structural changes by improving GM segmentation and the specificity of the model.