Analysis of Hippocampus Evolution Patterns and Prediction of Conversion in Mild Cognitive Impairment Using Multivariate Morphometry Statistics

Background: Mild cognitive impairment (MCI), which is generally regarded as the prodromal stage of Alzheimer's disease (AD), is associated with morphological changes in brain structures, particularly the hippocampus. However, the indicators for characterizing the deformation of hippocampus in conventional methods are not precise enough and ignore the evolution information with the course of disease. Objective: The purpose of this study was to investigate the temporal evolution pattern of MCI and predict the conversion of MCI to AD by using the multivariate morphometry statistics (MMS) as fine features. Methods: First, we extracted MMS features from MRI scans of 64 MCI converters (MCIc), 81 MCI patients who remained stable (MCIs), and 90 healthy controls (HC). To make full use of the time information, the dynamic MMS (DMMS) features were defined. Then, the areas with significant differences between pairs of the three groups were analyzed using statistical methods and the atrophy/expansion were identified by comparing the metrics. In parallel, patch selection, sparse coding, dictionary learning and maximum pooling were used for the dimensionality reduction and the ensemble classifier GentleBoost was used to classify MCIc and MCIs. Results: The longitudinal analysis revealed that the atrophy of both MCIc and MCIs mainly distributed in dorsal CA1, then spread to subiculum and other regions gradually, while the atrophy area of MCIc was larger and more significant. And the introduction of longitudinal information promoted the accuracy to 91.76% for conversion prediction. Conclusion: The dynamic information of hippocampus holds a huge potential for understanding the pathology of MCI.

Automated summary

This study investigated the temporal evolution pattern of MCI and the conversion to AD using multivariate morphometry statistics as features. The atrophy of hippocampus in MCI patients showed a specific distribution pattern, with the introduction of longitudinal information significantly improving the accuracy of conversion prediction.