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Published on: July 6, 2019
Clinical and MRI models predicting amyloid deposition in progressive aphasia and apraxia of speech
Jennifer L Whitwell1, Stephen D Weigand2, Joseph R Duffy3
1Department of Radiology, Mayo Clinic, Rochester, MN, USA.
Abstract:
Beta-amyloid (Aβ) 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 Aβ status challenging based on clinical diagnosis alone. We aimed to determine whether MRI regional volumes or clinical data could help predict Aβ 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 Aβ 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 Aβ (+) status. The optimal MRI model and optimal clinical model both performed comparably in their ability to accurately classify subjects according to Aβ 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 Aβ (+) 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 Aβ (+) status. In comparison, clinical diagnosis was able to accurately classify 89% of subjects. However, the MRI model performed well in predicting Aβ deposition in unclassified PPA. Clinical diagnosis provides optimum prediction of Aβ status at the group level, although regional MRI measurements and speech and language testing also performed well and could have advantages in predicting Aβ status in unclassified PPA subjects.
Insights
Predicting beta-amyloid (Aβ) deposition in primary progressive aphasia (PPA) is challenging. Both MRI regional volumes and clinical tests accurately predict Aβ status, with clinical diagnosis being most effective overall.
Area of Science:
- Neuroimaging
- Neurology
- Speech-Language Pathology
Background:
- Beta-amyloid (Aβ) deposition is a hallmark of neurodegenerative diseases, often associated with primary progressive aphasia (PPA).
- Predicting Aβ status solely on clinical diagnosis can be challenging due to overlapping symptoms and atypical presentations.
- Understanding predictors of Aβ deposition is crucial for accurate diagnosis and management of PPA and progressive apraxia of speech (PAOS).
Purpose of the Study:
- To investigate whether magnetic resonance imaging (MRI) regional volumes or clinical speech and language data can predict beta-amyloid (Aβ) deposition in PPA and PAOS.
- To compare the predictive accuracy of MRI, clinical tests, and clinical diagnosis for Aβ positivity.
Main Methods:
- Prospective cross-sectional study of 139 PPA/PAOS subjects undergoing speech/language assessments, 3.0T MRI, and C11-Pittsburgh Compound B Positron Emission Tomography (PET).
- Atlas-based parcellation to calculate gray matter volumes of 42 brain regions.
- Penalized binary logistic regression to identify predictors of Aβ (+) status from MRI and clinical data.
Main Results:
- The optimal MRI model (14 regions) and clinical model (12 tests) achieved comparable classification accuracy (81% and 83%, respectively) for Aβ status.
- Specific MRI findings (e.g., small superior temporal/inferior parietal volumes, large Broca's area) and clinical features (e.g., phonological errors, repetition deficits, absence of agrammatism/motor speech deficits) were predictive of Aβ positivity.
- Clinical diagnosis achieved the highest accuracy (89%) at the group level, but the MRI model showed promise for unclassified PPA cases.
Conclusions:
- Clinical diagnosis offers the best group-level prediction of Aβ status in PPA/PAOS.
- Regional MRI volumes and speech/language tests provide valuable, comparable predictive information.
- MRI and clinical assessments may offer advantages in predicting Aβ status for PPA subjects with unclassified diagnoses.
