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Data-driven classification of patients with primary progressive aphasia.
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Abstract:
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.
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Keyword:
Aged; Algorithms; Alzheimer's disease; Alzheimer’s disease; Aphasia; Case-Control Studies; Cluster Analysis; Cognitive Dysfunction; Communication and Culture; Experimental Psychology; Female; Frontotemporal dementia; Humans; Language; Logopenic aphasia; Male; Medical and Health Sciences; Non-fluent aphasia; Primary Progressive; Primary progressive aphasia; Principal Component Analysis; Psychology and Cognitive Sciences; Semantic dementia; Semantics; Speech
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URL: https://escholarship.org/uc/item/5m4842c0
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2 |
Data-driven classification of patients with primary progressive aphasia
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Data-driven classification of patients with primary progressive aphasia
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Abnormalities of connected speech in the non-semantic variants of primary progressive aphasia
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