Data-driven classification of patients with primary progressive aphasia

Autor: Hoffman, P, Sajjadi, SA, Patterson, K, Nestor, PJ
Jazyk: angličtina
Rok vydání: 2017
Předmět:
Male
Aging
physiopathology [Cognitive Dysfunction]
Neurodegenerative
Medical and Health Sciences
frontotemporal dementia
ddc:150
physiopathology [Aphasia
Primary Progressive]
Non-fluent aphasia
2.1 Biological and endogenous factors
Cluster Analysis
Aetiology
Alzheimer's Disease Related Dementias (ADRD)
Language
Principal Component Analysis
Semantic dementia
Experimental Psychology
Alzheimer's disease
Semantics
Frontotemporal Dementia (FTD)
logopenic aphasia
Female
Primary progressive aphasia
complications [Cognitive Dysfunction]
Alzheimer’s disease
Frontotemporal dementia
Algorithms
Primary Progressive
Article
classification [Aphasia
Primary Progressive]
Clinical Research
Behavioral and Social Science
Acquired Cognitive Impairment
Aphasia
Humans
Speech
Cognitive Dysfunction
Logopenic aphasia
Communication and Culture
Aged
Psychology and Cognitive Sciences
complications [Aphasia
Primary Progressive]
Neurosciences
Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD)
Brain Disorders
non-fluent aphasia
Aphasia
Primary Progressive
semantic dementia
Case-Control Studies
Dementia
primary progressive aphasia
diagnosis [Aphasia
Primary Progressive]
Zdroj: Brain and language 174, 86-93 (2017). doi:10.1016/j.bandl.2017.08.001
Hoffman, P, Sajjadi, S A, Patterson, K & Nestor, P J 2017, ' Data-driven classification of patients with primary progressive aphasia ', Brain and Language, vol. 174, pp. 86-93 . https://doi.org/10.1016/j.bandl.2017.08.001
Brain and Language
Hoffman, P; Sajjadi, SA; Patterson, K; & Nestor, PJ. (2017). Data-driven classification of patients with primary progressive aphasia. BRAIN AND LANGUAGE, 174, 86-93. doi: 10.1016/j.bandl.2017.08.001. UC Irvine: Retrieved from: http://www.escholarship.org/uc/item/5m4842c0
DOI: 10.1016/j.bandl.2017.08.001
Popis: Highlights • There is current controversy over how to classify PPA variants. • We used a k-means clustering algorithm, blind to diagnosis, to divide patients. • Patients grouped based on similarities in linguistic and neuropsychological profile. • One cluster of patients with selective semantic impairment. • Two clusters with non-semantic profile, differentiated by overall level of language/cognitive impairment.
Current diagnostic criteria classify primary progressive aphasia into three variants–semantic (sv), nonfluent (nfv) and logopenic (lv) PPA–though the adequacy of this scheme is debated. This study took a data-driven approach, applying k-means clustering to data from 43 PPA patients. The algorithm grouped patients based on similarities in language, semantic and non-linguistic cognitive scores. The optimum solution consisted of three groups. One group, almost exclusively those diagnosed as svPPA, displayed a selective semantic impairment. A second cluster, with impairments to speech production, repetition and syntactic processing, contained a majority of patients with nfvPPA but also some lvPPA patients. The final group exhibited more severe deficits to speech, repetition and syntax as well as semantic and other cognitive deficits. These results suggest that, amongst cases of non-semantic PPA, differentiation mainly reflects overall degree of language/cognitive impairment. The observed patterns were scarcely affected by inclusion/exclusion of non-linguistic cognitive scores.
Databáze: OpenAIRE
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