Differentiation between Subtypes of Primary Progressive Aphasia by Using Cortical Thickness and Diffusion-Tensor MR Imaging Measures

Abstract

Purpose: To test a multimodal magnetic resonance (MR) imaging-based approach composed of cortical thickness and white matter (WM) damage metrics to discriminate between variants of primary progressive aphasia (PPA) that are nonfluent and/or agrammatic (NFVPPA) and semantic (SVPPA).

Materials and methods: This study was approved by the local ethics committees on human studies, and written informed consent from all patients was obtained before their enrollment. T1-weighted and diffusion-tensor (DT) MR images were obtained from 13 NFVPPA patients, 13 SVPPA patients, and 23 healthy control participants. Cortical thickness and DT MR imaging indices from the long-associative and interhemispheric WM tracts were obtained. A random forest (RF) analysis was used to identify the image features associated with each clinical syndrome. Individual patient classification was performed by using receiver operator characteristic curve analysis with cortical thickness, DT MR imaging, and a combination of the two modalities. RESULTS RF analysis showed that the best markers to differentiate the two PPA variants at an individual patient level among cortical thickness and DT MR imaging metrics were diffusivity abnormalities of the left inferior longitudinal and uncinate fasciculi and cortical thickness measures of the left temporal pole and inferior frontal gyrus. A combination of cortical thickness and DT MR imaging measures (the so-called gray-matter-and-WM model) was able to distinguish patients with NFVPPA and SVPPA with the following classification pattern: area under the curve, 0.91; accuracy, 0.89; sensitivity, 0.92; specificity, 0.85. Leave-one-out analysis demonstrated that the gray matter and WM model is more robust than the single MR modality models to distinguish PPA variants (accuracy was 0.86, 0.73, and 0.68 for the gray matter and WM model, the gray matter-only model, and the WM-only model, respectively).

Conclusion: A combination of structural and DT MR imaging metrics may provide a quantitative procedure to distinguish NFVPPA and SVPPA patients at an individual patient level. The discrimination accuracies obtained suggest that the gray matter and WM model is potentially relevant for the differential diagnosis of the PPA variants in clinical practice.