Information-Theoretic Clustering of Neuroimaging Metrics Related to Cognitive Decline in the Elderly
- Cite this paper as:
- Daianu M. et al. (2016) Information-Theoretic Clustering of Neuroimaging Metrics Related to Cognitive Decline in the Elderly. In: Menze B. et al. (eds) Medical Computer Vision: Algorithms for Big Data. MCV 2015. Lecture Notes in Computer Science, vol 9601. Springer, Cham
As Alzheimer’s disease progresses, there are changes in metrics of brain atrophy and network breakdown derived from anatomical or diffusion MRI. Neuroimaging biomarkers of cognitive decline are crucial to identify, but few studies have investigated how sets of biomarkers cluster in terms of the information they provide. Here, we evaluated more than 700 frequently studied diffusion and anatomical measures in 247 elderly participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We used a novel unsupervised machine learning technique - CorEx - to identify groups of measures with high multivariate mutual information; we computed latent factors to explain correlations among them. We visualized groups of measures discovered by CorEx in a hierarchical structure and determined how well they predict cognitive decline. Clusters of variables significantly predicted cognitive decline, including measures of cortical gray matter, and correlated measures of brain networks derived from graph theory and spectral graph theory.