Clinical and MRI models predicting amyloid deposition in progressive aphasia and apraxia of speech

Beta-amyloid (A beta) deposition can be observed in primary progressive aphasia (PPA) and progressive apraxia of speech (PAOS). While it is typically associated with logopenic PPA, there are exceptions that make predicting A beta status challenging based on clinical diagnosis alone. We aimed to determine whether MRI regional volumes or clinical data could help predict A beta deposition. One hundred and thirty-nine PPA (n = 97; 15 agrammatic, 53 logopenic, 13 semantic and 16 unclassified) and PAOS (n = 42) subjects were prospectively recruited into a cross-sectional study and underwent speech/language assessments, 3.0 T MRI and C11-Pittsburgh Compound B PET. The presence of A beta was determined using a 1.5 SUVR cut-point. Atlas-based parcellation was used to calculate gray matter volumes of 42 regions-of-interest across the brain. Penalized binary logistic regression was utilized to determine what combination of MRI regions, and what combination of speech and language tests, best predicts A beta (+) status. The optimal MRI model and optimal clinical model both performed comparably in their ability to accurately classify subjects according to A beta status. MRI accurately classified 81% of subjects using 14 regions. Small left superior temporal and inferior parietal volumes and large left Broca's area volumes were particularly predictive of A beta (+) status. Clinical scores accurately classified 83% of subjects using 12 tests. Phonological errors and repetition deficits, and absence of agrammatism and motor speech deficits were particularly predictive of A beta (+) status. In comparison, clinical diagnosis was able to accurately classify 89% of subjects. However, the MRI model performed well in predicting A beta deposition in unclassified PPA. Clinical diagnosis provides optimum prediction of A beta status at the group level, although regional MRI measurements and speech and language testing also performed well and could have advantages in predicting A beta status in unclassified PPA subjects. (C) 2016 The Authors. Published by Elsevier Inc.