Primary progressive aphasia (PPA) is a clinically and pathologically heterogeneous condition in which language impairment is the predominant cause of functional impairment during the initial phases of disease.¹ In 2011, an international consortium of investigators established a classification scheme for the 3 most common variants: the semantic (svPPA), nonfluent/agrammatic (nfvPPA), and logopenic (lvPPA) variants of PPA.² Classification may occur at 1 of 3 levels: clinical, imaging-supported, or definite pathologic diagnosis. These guidelines reflected the accumulated knowledge of the patterns of speech and language dysfunction, brain atrophy, and underlying pathology typically associated with each clinical variant and represent a collective effort to increase comparability between studies and eventually improve the ability to predict the underlying pathology.

This prospective study developed an MRI-based method for identification of individual motor neuron disease (MND) patients and test its accuracy at the individual patient level in an independent sample compared with mimic disorders. 123 patients with amyotrophic lateral sclerosis (ALS), 44 patients with predominantly upper motor neuron disease (PUMN), 20 patients with ALS-mimic disorders, and 78 healthy controls were studied. The diagnostic accuracy of precentral cortical thickness and diffusion tensor (DT) MRI metrics of corticospinal and motor callosal tracts were assessed in a training cohort and externally proved in a validation cohort using a random forest analysis. In the training set, precentral cortical thickness showed 0.86 and 0.89 accuracy in differentiating ALS and PUMN patients from controls, while DT MRI distinguished the two groups from controls with 0.78 and 0.92 accuracy. In ALS vs controls, the combination of cortical thickness and DT MRI metrics (combined model) improved the classification pattern (0.91 accuracy). In the validation cohort, the best accuracy was reached by DT MRI (0.87 and 0.95 accuracy in ALS and PUMN vs mimic disorders). The combined model distinguished ALS and PUMN patients from mimic syndromes with 0.87 and 0.94 accuracy. A multimodal MRI approach that incorporates motor cortical and white matter alterations yields statistically significant improvement in accuracy over using each modality separately in the individual MND patient classification. DT MRI represents the most powerful tool to distinguish MND from mimic disorders.