DE eng

Search in the Catalogues and Directories

Page: 1 2 3 4 5...21
Hits 1 – 20 of 402

1
Cortical microstructure in primary progressive aphasia: a multicenter study.
In: Alzheimer's research & therapy, vol 14, iss 1 (2022)
BASE
Show details
2
A Preliminary Report of Network Electroencephalographic Measures in Primary Progressive Apraxia of Speech and Aphasia
In: Brain Sciences; Volume 12; Issue 3; Pages: 378 (2022)
BASE
Show details
3
Primary and Secondary Progressive Aphasia in Posterior Cortical Atrophy
In: Life; Volume 12; Issue 5; Pages: 662 (2022)
BASE
Show details
4
Imaging Clinical Subtypes and Associated Brain Networks in Alzheimer’s Disease
In: Brain Sciences; Volume 12; Issue 2; Pages: 146 (2022)
BASE
Show details
5
A 'Mini Linguistic State Examination' to classify primary progressive aphasia. ...
Patel, Nikil; Peterson, Katie A; Ingram, Ruth U. - : Apollo - University of Cambridge Repository, 2022
BASE
Show details
6
A 'Mini Linguistic State Examination' to classify primary progressive aphasia. ...
Patel, Nikil; Peterson, Katie A; Ingram, Ruth U. - : Apollo - University of Cambridge Repository, 2022
BASE
Show details
7
Improving the diagnostic accuracy of primary progressive aphasia using cognitive tests
Foxe, David Gordon. - : The University of Sydney, 2022. : Faculty of Science, School of Psychology, 2022
BASE
Show details
8
Neural substrates of verbal repetition deficits in primary progressive aphasia.
BASE
Show details
9
Resting functional connectivity in the semantic appraisal network predicts accuracy of emotion identification.
Yang, Winson FZ; Toller, Gianina; Shdo, Suzanne. - : eScholarship, University of California, 2021
BASE
Show details
10
Neural dynamics of semantic categorization in semantic variant of primary progressive aphasia.
Borghesani, V; Dale, CL; Lukic, S. - : eScholarship, University of California, 2021
BASE
Show details
11
Uniform data set language measures for bvFTD and PPA diagnosis and monitoring.
In: Alzheimer's & dementia (Amsterdam, Netherlands), vol 13, iss 1 (2021)
BASE
Show details
12
Resting functional connectivity in the semantic appraisal network predicts accuracy of emotion identification.
Yang, Winson FZ; Toller, Gianina; Shdo, Suzanne. - : eScholarship, University of California, 2021
BASE
Show details
13
What Do We Mean by Behavioral Disinhibition in Frontotemporal Dementia?
Magrath Guimet, Nahuel; Miller, Bruce L; Allegri, Ricardo F. - : eScholarship, University of California, 2021
BASE
Show details
14
Uniform data set language measures for bvFTD and PPA diagnosis and monitoring.
In: Alzheimer's & dementia (Amsterdam, Netherlands), vol 13, iss 1 (2021)
BASE
Show details
15
What Do We Mean by Behavioral Disinhibition in Frontotemporal Dementia?
Magrath Guimet, Nahuel; Miller, Bruce L; Allegri, Ricardo F. - : eScholarship, University of California, 2021
BASE
Show details
16
Primary Progressive Aphasia: Use of Graphical Markers for an Early and Differential Diagnosis
In: Brain Sciences ; Volume 11 ; Issue 9 (2021)
BASE
Show details
17
Survival in the Three Common Variants of Primary Progressive Aphasia: A Retrospective Study in a Tertiary Memory Clinic
In: Brain Sciences ; Volume 11 ; Issue 9 (2021)
BASE
Show details
18
Application of Machine Learning to Electroencephalography for the Diagnosis of Primary Progressive Aphasia: A Pilot Study
In: Brain Sciences ; Volume 11 ; Issue 10 (2021)
Abstract: Background. Primary progressive aphasia (PPA) is a neurodegenerative syndrome in which diagnosis is usually challenging. Biomarkers are needed for diagnosis and monitoring. In this study, we aimed to evaluate Electroencephalography (EEG) as a biomarker for the diagnosis of PPA. Methods. We conducted a cross-sectional study with 40 PPA patients categorized as non-fluent, semantic, and logopenic variants, and 20 controls. Resting-state EEG with 32 channels was acquired and preprocessed using several procedures (quantitative EEG, wavelet transformation, autoencoders, and graph theory analysis). Seven machine learning algorithms were evaluated (Decision Tree, Elastic Net, Support Vector Machines, Random Forest, K-Nearest Neighbors, Gaussian Naive Bayes, and Multinomial Naive Bayes). Results. Diagnostic capacity to distinguish between PPA and controls was high (accuracy 75%, F1-score 83% for kNN algorithm). The most important features in the classification were derived from network analysis based on graph theory. Conversely, discrimination between PPA variants was lower (Accuracy 58% and F1-score 60% for kNN). Conclusions. The application of ML to resting-state EEG may have a role in the diagnosis of PPA, especially in the differentiation from controls. Future studies with high-density EEG should explore the capacity to distinguish between PPA variants.
Keyword: Alzheimer’s disease; biomarkers machine learning; electroencephalography; frontotemporal dementia; graph theory; K-Nearest Neighbors; primary progressive aphasia; resting-state
URL: https://doi.org/10.3390/brainsci11101262
BASE
Hide details
19
Longitudinal Changes in Cognition, Behaviours, and Functional Abilities in the Three Main Variants of Primary Progressive Aphasia: A Literature Review
In: Brain Sciences ; Volume 11 ; Issue 9 (2021)
BASE
Show details
20
Verbal Short-Term Memory Disturbance in the Primary Progressive Aphasias: Challenges and Distinctions in a Clinical Setting
In: Brain Sciences ; Volume 11 ; Issue 8 (2021)
BASE
Show details

Page: 1 2 3 4 5...21

Catalogues
3
0
45
0
0
0
0
Bibliographies
87
0
0
0
0
0
0
0
0
Linked Open Data catalogues
0
Online resources
0
0
0
0
Open access documents
315
0
0
0
0
© 2013 - 2024 Lin|gu|is|tik | Imprint | Privacy Policy | Datenschutzeinstellungen ändern