Affiliations: [a] Neuroscience Research Australia, Sydney, Australia | [b] ARC Centre of Excellence in Cognition and its Disorders, NSW, Australia | [c] School of Medical Sciences, The University of New South Wales, Sydney, Australia | [d] Faculty of Health Sciences, The University of Sydney, Sydney, Australia
Correspondence:
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Correspondence to: Olivier Piguet, Neuroscience Research Australia, PO Box 1165, Randwick, NSW 2031, Australia. Tel.: +61 2 9399 1713; E-mail: [email protected].
Abstract: Background:The two non-semantic variants of primary progressive aphasia (PPA), nonfluent/agrammatic PPA (nfv-PPA) and logopenic variant PPA (lv-PPA), share language features despite their different underlying pathology, and may be difficult to distinguish for non-language experts. Objective:To improve diagnostic accuracy of nfv-PPA and lv-PPA using tasks measuring non-language cognition and emotion processing. Methods:Thirty-eight dementia patients meeting diagnostic criteria for PPA (nfv-PPA 20, lv-PPA 18) and 21 matched healthy Controls underwent a comprehensive assessment of cognition and emotion processing, as well as a high-resolution structural MRI and a PiB-PET scan, a putative biomarker of Alzheimer's disease. Task performances were compared between the groups and those found to differ significantly were entered into a logistic regression analysis. Results:Analyses revealed a double dissociation between nfv-PPA and lv-PPA. nfv-PPA exhibited significant emotion processing disturbance compared to lv-PPA and Controls. In contrast, only the lv-PPA group was significantly impaired on tasks of episodic memory. Logistic regression analyses showed that 87% of patients were correctly classified using emotion processing and episodic memory composite scores, together with a measure of visuospatial ability. Conclusions:Non-language presenting features can help differentiate between the two non-semantic PPA syndromes, with a double dissociation observed on tasks of episodic memory and emotion processing. Based on performance on these tasks, we propose a decision tree as a complementary method to differentiate between the two non-semantic variants. These findings have important clinical implications, with identification of patients who may potentially benefit existing therapeutic interventions currently available for Alzheimer's disease.
