AUTHOR=Matias-Guiu Jordi A. , Díaz-Álvarez Josefa , Ayala José Luis , Risco-Martín José Luis , Moreno-Ramos Teresa , Pytel Vanesa , Matias-Guiu Jorge , Carreras José Luis , Cabrera-Martín María Nieves
TITLE=Clustering Analysis of FDG-PET Imaging in Primary Progressive Aphasia
JOURNAL=Frontiers in Aging Neuroscience
VOLUME=10
YEAR=2018
URL=https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2018.00230
DOI=10.3389/fnagi.2018.00230
ISSN=1663-4365
ABSTRACT=Background: Primary progressive aphasia (PPA) is a clinical syndrome characterized by the neurodegeneration of language brain systems. Three main clinical forms (non-fluent, semantic, and logopenic PPA) have been recognized, but applicability of the classification and the capacity to predict the underlying pathology is controversial. We aimed to study FDG-PET imaging data in a large consecutive case series of patients with PPA to cluster them into different subtypes according to regional brain metabolism.
Methods: 122 FDG-PET imaging studies belonging to 91 PPA patients and 28 healthy controls were included. We developed a hierarchical agglomerative cluster analysis with Ward's linkage method, an unsupervised clustering algorithm. We conducted voxel-based brain mapping analysis to evaluate the patterns of hypometabolism of each identified cluster.
Results: Cluster analysis confirmed the three current PPA variants, but the optimal number of clusters according to Davies-Bouldin index was 6 subtypes of PPA. This classification resulted from splitting non-fluent variant into three subtypes, while logopenic PPA was split into two subtypes. Voxel-brain mapping analysis displayed different patterns of hypometabolism for each PPA group. New subtypes also showed a different clinical course and were predictive of amyloid imaging results.
Conclusion: Our study found that there are more than the three already recognized subtypes of PPA. These new subtypes were more predictive of clinical course and showed different neuroimaging patterns. Our results support the usefulness of FDG-PET in evaluating PPA, and the applicability of computational methods in the analysis of brain metabolism for improving the classification of neurodegenerative disorders.