scholarly journals Cortical atrophy signatures and machine learning MR‐based classification of primary progressive aphasia variants

2020 ◽  
Vol 16 (S5) ◽  
Author(s):  
Rania Ezzo ◽  
Claire Cordella ◽  
Brad C. Dickerson ◽  
Jessica A. Collins
Keyword(s):  
Machine Learning ◽  
Primary Progressive Aphasia ◽  
Cortical Atrophy ◽  
Progressive Aphasia ◽  
Primary Progressive

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 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.

Clinical Characteristic in Primary Progressive Aphasia in Relation to Alzheimer’s Disease Biomarkers

2021 ◽  
pp. 1-13
Author(s):  
Sung Hoon Kang ◽  
Hanna Cho ◽  
Jiho Shin ◽  
Hang-Rai Kim ◽  
Young Noh ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Language Impairment ◽  
Amyloid Β ◽  
Primary Progressive Aphasia ◽  
Cortical Atrophy ◽  
Clinical Feature ◽  
Progressive Aphasia ◽  
Disease Biomarkers ◽  
Primary Progressive

Background: Primary progressive aphasia (PPA) is associated with amyloid-β (Aβ) pathology. However, clinical feature of PPA based on Aβ positivity remains unclear. Objective: We aimed to assess the prevalence of Aβ positivity in patients with PPA and compare the clinical characteristics of patients with Aβ-positive (A+) and Aβ-negative (A–) PPA. Further, we applied Aβ and tau classification system (AT system) in patients with PPA for whom additional information of in vivo tau biomarker was available. Methods: We recruited 110 patients with PPA (41 semantic [svPPA], 27 non-fluent [nfvPPA], 32 logopenic [lvPPA], and 10 unclassified [ucPPA]) who underwent Aβ-PET imaging at multi centers. The extent of language impairment and cortical atrophy were compared between the A+ and A–PPA subgroups using general linear models. Results: The prevalence of Aβ positivity was highest in patients with lvPPA (81.3%), followed by ucPPA (60.0%), nfvPPA (18.5%), and svPPA (9.8%). The A+ PPA subgroup manifested cortical atrophy mainly in the left superior temporal/inferior parietal regions and had lower repetition scores compared to the A–PPA subgroup. Further, we observed that more than 90%(13/14) of the patients with A+ PPA had tau deposition. Conclusion: Our findings will help clinicians understand the patterns of language impairment and cortical atrophy in patients with PPA based on Aβ deposition. Considering that most of the A+ PPA patents are tau positive, understanding the influence of Alzheimer’s disease biomarkers on PPA might provide an opportunity for these patients to participate in clinical trials aimed for treating atypical Alzheimer’s disease.

scholarly journals Asymmetric TDP pathology in primary progressive aphasia with right hemisphere language dominance

Neurology ◽  
2018 ◽  
Vol 90 (5) ◽  
pp. e396-e403 ◽  
Author(s):  
Garam Kim ◽  
Shahrooz Vahedi ◽  
Tamar Gefen ◽  
Sandra Weintraub ◽  
Eileen H. Bigio ◽  
...  
Keyword(s):  
Right Hemisphere ◽  
Primary Progressive Aphasia ◽  
Cortical Atrophy ◽  
Progressive Aphasia ◽  
Language Dominance ◽  
Primary Progressive ◽  
Activated Microglia ◽  
Unbiased Stereology ◽  
Cortical Regions ◽  
The Right

ObjectiveTo quantitatively examine the regional densities and hemispheric distribution of the 43-kDa transactive response DNA-binding protein (TDP-43) inclusions, neurons, and activated microglia in a left-handed patient with right hemisphere language dominance and logopenic-variant primary progressive aphasia (PPA).MethodsPhosphorylated TDP-43 inclusions, neurons, and activated microglia were visualized with immunohistochemical and histologic methods. Markers were quantified bilaterally with unbiased stereology in language- and memory-related cortical regions.ResultsClinical MRI indicated cortical atrophy in the right hemisphere, mostly in the temporal lobe. Significantly higher densities of TDP-43 inclusions were present in right language-related temporal regions compared to the left or to other right hemisphere regions. The memory-related entorhinal cortex (ERC) and language regions without significant atrophy showed no asymmetry. Activated microglia displayed extensive asymmetry (R > L). A substantial density of neurons remained in all areas and showed no hemispheric asymmetry. However, perikaryal size was significantly smaller in the right hemisphere across all regions except the ERC. To demonstrate the specificity of this finding, sizes of residual neurons were measured in a right-handed case with PPA and were found to be smaller in the language-dominant left hemisphere.ConclusionsThe distribution of TDP-43 inclusions and microglial activation in right temporal language regions showed concordance with anatomic distribution of cortical atrophy and clinical presentation. The results revealed no direct relationship between density of TDP-43 inclusions and activated microglia. Reduced size of the remaining neurons is likely to contribute to cortical atrophy detected by MRI. These findings support the conclusion that there is no obligatory relationship between logopenic PPA and Alzheimer pathology.

PROMINENT MICROGLIAL ACTIVATION IN CORTICAL WHITE MATTER IS SELECTIVELY ASSOCIATED WITH CORTICAL ATROPHY IN PRIMARY PROGRESSIVE APHASIA

2017 ◽  
Vol 13 (7) ◽  
pp. P1499
Author(s):  
Daniel T. Ohm ◽  
Garam Kim ◽  
Tamar Gefen ◽  
Alfred Rademaker ◽  
Sandra Weintraub ◽  
...  
Keyword(s):  
White Matter ◽  
Microglial Activation ◽  
Primary Progressive Aphasia ◽  
Cortical Atrophy ◽  
Progressive Aphasia ◽  
Primary Progressive

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 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
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