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Quantitative Template for Subtyping Primary Progressive Aphasia
Marsel Mesulam, MD;
Christina Wieneke, BA;
Emily Rogalski, PhD;
Derin Cobia, PhD;
Cynthia Thompson, PhD;
Sandra Weintraub, PhD
Arch Neurol. 2009;66(12):1545-1551.
Background The syndrome of primary progressive aphasia (PPA) is diagnosed when a gradual failure of word usage or comprehension emerges as the principal feature of a neurodegenerative disease.
Objective To provide a quantitative algorithm for classifying PPA into agrammatic (PPA-G), semantic (PPA-S), and logopenic (PPA-L) variants, each of which is known to have a different probability of association with Alzheimer disease vs frontotemporal lobar degeneration.
Design Prospective study.
Setting University medical center.
Patients Sixteen consecutively enrolled patients with PPA who underwent neuropsychological testing and magnetic resonance imaging recruited nationally in the United States as part of a longitudinal study.
Results A 2-dimensional template that reflects performance on tests of syntax (Northwestern Anagram Test) and lexical semantics (Peabody Picture Vocabulary Test—Fourth Edition) classified all 16 patients in concordance with a clinical diagnosis that had been made before the administration of quantitative tests. All 3 PPA subtypes had distinctly asymmetrical atrophy of the left perisylvian language network. Each subtype also had distinctive peak atrophy sites: PPA-G in the inferior frontal gyrus (Broca area), PPA-S in the anterior temporal lobe, and PPA-L in Brodmann area 37.
Conclusions Once an accurate root diagnosis of PPA is made, subtyping can be quantitatively guided using a 2-dimensional template based on orthogonal tasks of grammatical competence and word comprehension. Although the choice of tasks and the precise cutoff levels may need to be adjusted to fit linguistic and educational backgrounds, these 16 patients demonstrate the feasibility of using a simple algorithm for clinicoanatomical classification in PPA. Prospective studies will show whether this subtyping can improve clinical prediction of the underlying neuropathologic condition.
Author Affiliations: Cognitive Neurology and Alzheimer's Disease Center, Northwestern University, Chicago, Illinois.
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