scholarly journals Quantitative classification of primary progressive aphasia at early and mild impairment stages

Brain ◽  
2012 ◽  
Vol 135 (5) ◽  
pp. 1537-1553 ◽  
Author(s):  
M.- M. Mesulam ◽  
C. Wieneke ◽  
C. Thompson ◽  
E. Rogalski ◽  
S. Weintraub
Keyword(s):  
Primary Progressive Aphasia ◽  
Progressive Aphasia ◽  
Quantitative Classification ◽  
Primary Progressive ◽  
Mild Impairment

scholarly journals Primary progressive aphasia: Classification of variants in 100 consecutive Brazilian cases

2013 ◽  
Vol 7 (1) ◽  
pp. 110-121 ◽  
Author(s):  
Mirna Lie Hosogi Senaha ◽  
Paulo Caramelli ◽  
Sonia M.D. Brucki ◽  
Jerusa Smid ◽  
Leonel T. Takada ◽  
...  
Keyword(s):  
Diagnostic Criteria ◽  
Language Impairment ◽  
Demographic Data ◽  
Primary Progressive Aphasia ◽  
Clinical Syndrome ◽  
Progressive Aphasia ◽  
Primary Progressive ◽  
Diagnosis And Classification ◽  
Consensus Classification

ABSTRACT Primary progressive aphasia (PPA) is a neurodegenerative clinical syndrome characterized primarily by progressive language impairment. Recently, consensus diagnostic criteria were published for the diagnosis and classification of variants of PPA. The currently recognized variants are nonfluent/agrammatic (PPA-G), logopenic (PPA-L) and semantic (PPA-S). Objective: To analyze the demographic data and the clinical classification of 100 PPA cases. Methods: Data from 100 PPA patients who were consecutively evaluated between 1999 and 2012 were analyzed. The patients underwent neurological, cognitive and language evaluation. The cases were classified according to the proposed variants, using predominantly the guidelines proposed in the consensus diagnostic criteria from 2011. Results: The sample consisted of 57 women and 43 men, aged at onset 67.2±8.1 years (range of between 53 and 83 years). Thirty-five patients presented PPA-S, 29 PPA-G and 16 PPA-L. It was not possible to classify 20% of the cases into any one of the proposed variants. Conclusion: It was possible to classify 80% of the sample into one of the three PPA variants proposed. Perhaps the consensus classification requires some adjustments to accommodate cases that do not fit into any of the variants and to avoid overlap where cases fit more than one variant. Nonetheless, the established current guidelines are a useful tool to address the classification and diagnosis of PPA and are also of great value in standardizing terminologies to improve consistency across studies from different research centers.

scholarly journals Automatic subtyping of individuals with Primary Progressive Aphasia

2020 ◽  
Author(s):  
Charalambos Themistocleous ◽  
Bronte Ficek ◽  
Kimberly Webster ◽  
Dirk-Bart den Ouden ◽  
Argye E. Hillis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Classification Accuracy ◽  
Primary Progressive Aphasia ◽  
Support Vector ◽  
Progressive Aphasia ◽  
Primary Progressive ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Automated Machine Learning

AbstractBackgroundThe classification of patients with Primary Progressive Aphasia (PPA) into variants is time-consuming, costly, and requires combined expertise by clinical neurologists, neuropsychologists, speech pathologists, and radiologists.ObjectiveThe aim of the present study is to determine whether acoustic and linguistic variables provide accurate classification of PPA patients into one of three variants: nonfluent PPA, semantic PPA, and logopenic PPA.MethodsIn this paper, we present a machine learning model based on Deep Neural Networks (DNN) for the subtyping of patients with PPA into three main variants, using combined acoustic and linguistic information elicited automatically via acoustic and linguistic analysis. The performance of the DNN was compared to the classification accuracy of Random Forests, Support Vector Machines, and Decision Trees, as well as expert clinicians’ classifications.ResultsThe DNN model outperformed the other machine learning models with 80% classification accuracy, providing reliable subtyping of patients with PPA into variants and it even outperformed auditory classification of patients into variants by clinicians.ConclusionsWe show that the combined speech and language markers from connected speech productions provide information about symptoms and variant subtyping in PPA. The end-to-end automated machine learning approach we present can enable clinicians and researchers to provide an easy, quick and inexpensive classification of patients with PPA.

scholarly journals Classification of primary progressive aphasia and its variants

Neurology ◽  
2011 ◽  
Vol 76 (11) ◽  
pp. 1006-1014 ◽  
Author(s):  
M. L. Gorno-Tempini ◽  
A. E. Hillis ◽  
S. Weintraub ◽  
A. Kertesz ◽  
M. Mendez ◽  
...  
Keyword(s):  
Primary Progressive Aphasia ◽  
Progressive Aphasia ◽  
Primary Progressive

scholarly journals Automatic Subtyping of Individuals with Primary Progressive Aphasia

2021 ◽  
pp. 1-10
Author(s):  
Charalambos Themistocleous ◽  
Bronte Ficek ◽  
Kimberly Webster ◽  
Dirk-Bart den Ouden ◽  
Argye E. Hillis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Classification Accuracy ◽  
Primary Progressive Aphasia ◽  
Support Vector ◽  
Progressive Aphasia ◽  
Primary Progressive ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Automated Machine Learning

Background: The classification of patients with primary progressive aphasia (PPA) into variants is time-consuming, costly, and requires combined expertise by clinical neurologists, neuropsychologists, speech pathologists, and radiologists. Objective: The aim of the present study is to determine whether acoustic and linguistic variables provide accurate classification of PPA patients into one of three variants: nonfluent PPA, semantic PPA, and logopenic PPA. Methods: In this paper, we present a machine learning model based on deep neural networks (DNN) for the subtyping of patients with PPA into three main variants, using combined acoustic and linguistic information elicited automatically via acoustic and linguistic analysis. The performance of the DNN was compared to the classification accuracy of Random Forests, Support Vector Machines, and Decision Trees, as well as to expert clinicians’ classifications. Results: The DNN model outperformed the other machine learning models as well as expert clinicians’ classifications with 80% classification accuracy. Importantly, 90% of patients with nfvPPA and 95% of patients with lvPPA was identified correctly, providing reliable subtyping of these patients into their corresponding PPA variants. Conclusion: We show that the combined speech and language markers from connected speech productions can inform variant subtyping in patients with PPA. The end-to-end automated machine learning approach we present can enable clinicians and researchers to provide an easy, quick, and inexpensive classification of patients with PPA.

scholarly journals Primary Progressive Aphasia: Use of Graphical Markers for an Early and Differential Diagnosis

2021 ◽  
Vol 11 (9) ◽  
pp. 1198
Author(s):  
Alexandra Plonka ◽  
Aurélie Mouton ◽  
Joël Macoir ◽  
Thi-Mai Tran ◽  
Alexandre Derremaux ◽  
...  
Keyword(s):  
Differential Diagnosis ◽  
Healthy Subjects ◽  
Language Disorder ◽  
Primary Progressive Aphasia ◽  
Clinical Settings ◽  
Healthy Controls ◽  
Progressive Aphasia ◽  
Primary Progressive ◽  
Digital Tablet

Primary progressive aphasia (PPA) brings together neurodegenerative pathologies whose main characteristic is to start with a progressive language disorder. PPA diagnosis is often delayed in non-specialised clinical settings. With the technologies’ development, new writing parameters can be extracted, such as the writing pressure on a touch pad. Despite some studies having highlighted differences between patients with typical Alzheimer’s disease (AD) and healthy controls, writing parameters in PPAs are understudied. The objective was to verify if the writing pressure in different linguistic and non-linguistic tasks can differentiate patients with PPA from patients with AD and healthy subjects. Patients with PPA (n = 32), patients with AD (n = 22) and healthy controls (n = 26) were included in this study. They performed a set of handwriting tasks on an iPad® digital tablet, including linguistic, cognitive non-linguistic, and non-cognitive non-linguistic tasks. Average and maximum writing pressures were extracted for each task. We found significant differences in writing pressure, between healthy controls and patients with PPA, and between patients with PPA and AD. However, the classification of performances was dependent on the nature of the tasks. These results suggest that measuring writing pressure in graphical tasks may improve the early diagnosis of PPA, and the differential diagnosis between PPA and AD.

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