Alzheimer’s Disease Markers Found in Speech Patterns

Alzheimer’s Disease Markers Found in Speech Patterns
Scientists with the University Health Network (UHN) in Toronto, Canada, have discovered a method of diagnosing Alzheimer's disease with more than 80 percent accuracy. The innovative technique evaluates the interplay between four linguistic factors, and the researchers are developing automated technology to detect these impairments. The study, led by Dr. Frank Rudzicz, a scientist at the UHN's Toronto Rehabilitation Institute (TR), is published in the December issue of the Journal of Alzheimer's Disease. The researchers report that the method and automated application of the assessment is more accurate than current Alzheimer's assessment tools used by healthcare professionals, and can also provide an objective diagnostic rating for dementia. In the article, titled "Linguistic Features Identify Alzheimer's Disease in Narrative Speech" (J Alzheimers Dis. 2015 Oct 15;49(2):407-22. doi: 10.3233/JAD-150520), the researchers note that although memory impairment is the main symptom of Alzheimer's disease (AD), language impairment can be an important marker. Relatively few studies of language in AD, however, quantify impairments in connected speech using computational techniques. In their research, the investigators aimed to demonstrate their method's accuracy in identifying Alzheimer's disease from short narrative samples elicited from a picture description task, and to uncover the salient linguistic factors with a statistical factor analysis. Based on their analysis, they determined that four collective dimensions of speech are indicative of dementia: semantic impairment, such as using overly simple words; acoustic impairment, such as speaking more slowly; syntactic impairment, such as using less complex grammar; and information impairment, such as not clea
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