Jordi A Matias-Guiu1, Josefa Díaz-Álvarez2, Fernando Cuetos3, María Nieves Cabrera-Martín4, Ignacio Segovia-Ríos2, Vanesa Pytel5, Teresa Moreno-Ramos5, José L Carreras4, Jorge Matías-Guiu5, José L Ayala6. 1. Department of Neurology, Hospital Clínico San Carlos, San Carlos Research Health Institute (IdISSC), Universidad Complutense, Madrid, Spain. Electronic address: jordimatiasguiu@hotmail.com. 2. Department of Computer Architecture and Communications, Centro Universitario de Mérida, Universidad de Extremadura, Mérida, Spain. 3. Department of Psychology, University of Oviedo, Oviedo, Spain; Department of Psychology, University of Malaga, Málaga, Spain. 4. Department of Nuclear Medicine, Hospital Clínico San Carlos, San Carlos Research Health Institute (IdISSC), Universidad Complutense, Madrid, Spain. 5. Department of Neurology, Hospital Clínico San Carlos, San Carlos Research Health Institute (IdISSC), Universidad Complutense, Madrid, Spain. 6. Department of Computer Architecture and Automation, Universidad Complutense. Madrid, Spain.
Abstract
INTRODUCTION: Primary progressive aphasia (PPA) is a clinical syndrome of neurodegenerative origin with 3 main variants: non-fluent, semantic, and logopenic. However, there is some controversy about the existence of additional subtypes. Our aim was to study the language and cognitive features associated with a new proposed classification for PPA. MATERIAL AND METHODS: Sixty-eight patients with PPA in early stages of the disease and 20 healthy controls were assessed with a comprehensive language and cognitive protocol. They were also evaluated with 18F-FDG positron emision tomography (PET). Patients were classified according to FDG PET regional metabolism, using our previously developed algorithm based on a hierarchical agglomerative cluster analysis with Ward's linkage method. Five variants were found, with both the non-fluent and logopenic variants being split into 2 subtypes. Machine learning techniques were used to predict each variant according to language assessment results. RESULTS: Non-fluent type 1 was associated with poorer performance in repetition of sentences and reading of irregular words than non-fluent type 2. Conversely, the second group showed a higher degree of apraxia of speech. Patients with logopenic variant type 1 performed more poorly on action naming than patients with logopenic type 2. Language assessments were predictive of PET-based subtypes in 86%-89% of cases using clustering analysis and principal components analysis. CONCLUSIONS: Our study supports the existence of 5 variants of PPA. These variants show some differences in language and FDG PET imaging characteristics. Machine learning algorithms using language test data were able to predict each of the 5 PPA variants with a relatively high degree of accuracy, and enable the possibility of automated, machine-aided diagnosis of PPA variants.
INTRODUCTION: Primary progressive aphasia (PPA) is a clinical syndrome of neurodegenerative origin with 3 main variants: non-fluent, semantic, and logopenic. However, there is some controversy about the existence of additional subtypes. Our aim was to study the language and cognitive features associated with a new proposed classification for PPA. MATERIAL AND METHODS: Sixty-eight patients with PPA in early stages of the disease and 20 healthy controls were assessed with a comprehensive language and cognitive protocol. They were also evaluated with 18F-FDG positron emision tomography (PET). Patients were classified according to FDG PET regional metabolism, using our previously developed algorithm based on a hierarchical agglomerative cluster analysis with Ward's linkage method. Five variants were found, with both the non-fluent and logopenic variants being split into 2 subtypes. Machine learning techniques were used to predict each variant according to language assessment results. RESULTS: Non-fluent type 1 was associated with poorer performance in repetition of sentences and reading of irregular words than non-fluent type 2. Conversely, the second group showed a higher degree of apraxia of speech. Patients with logopenic variant type 1 performed more poorly on action naming than patients with logopenic type 2. Language assessments were predictive of PET-based subtypes in 86%-89% of cases using clustering analysis and principal components analysis. CONCLUSIONS: Our study supports the existence of 5 variants of PPA. These variants show some differences in language and FDG PET imaging characteristics. Machine learning algorithms using language test data were able to predict each of the 5 PPA variants with a relatively high degree of accuracy, and enable the possibility of automated, machine-aided diagnosis of PPA variants.
Authors: Josefa Díaz-Álvarez; Jordi A Matias-Guiu; María Nieves Cabrera-Martín; Vanesa Pytel; Ignacio Segovia-Ríos; Fernando García-Gutiérrez; Laura Hernández-Lorenzo; Jorge Matias-Guiu; José Luis Carreras; José L Ayala Journal: Front Aging Neurosci Date: 2022-02-03 Impact factor: 5.750