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Differentiation between Subtypes of Primary Progressive Aphasia by Using Cortical Thickness and Diffusion-Tensor MR
Federica Agosta1, Pilar M Ferraro1, Elisa Canu1
1From the Neuroimaging Research Unit (F.A., P.M.F., E.C., S.G., P.V., A.S., M.F.), Department of Neurology, Institute of Experimental Neurology (G.M., G.C., M.F.), Department of Clinical Neurosciences (A.M.), and Department of Neuroradiology and CERMAC, Division of Neuroscience (A.F.), San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Via Olgettina 60, 20132 Milan, Italy; and Biostatistics Unit, IRCCS-Ospedale Casa Sollievo della Sofferenza, San Giovanni Rotondo, Foggia, Italy (M.C.).
This study developed a multimodal magnetic resonance imaging approach combining gray matter and white matter metrics to accurately differentiate subtypes of primary progressive aphasia (PPA). The novel method shows promise for clinical differential diagnosis of NFVPPA and SVPPA.
Area of Science:
- Neuroimaging
- Neurology
- Radiology
Background:
- Primary progressive aphasia (PPA) comprises distinct clinical variants, including nonfluent/agrammatic (NFVPPA) and semantic (SVPPA) PPA.
- Accurate differentiation of PPA variants is crucial for prognosis and management but can be challenging based on clinical presentation alone.
- Magnetic resonance imaging (MRI) offers structural and diffusion metrics that may aid in distinguishing these subtypes.
Purpose of the Study:
- To evaluate a multimodal MRI approach using cortical thickness and white matter (WM) integrity metrics.
- To discriminate between NFVPPA and SVPPA patients at an individual level.
- To assess the combined diagnostic power of gray matter and WM imaging markers.
Main Methods:
- Acquired T1-weighted and diffusion-tensor (DT) MRI from 13 NFVPPA, 13 SVPPA patients, and 23 controls.
- Quantified cortical thickness and DT MRI indices in associative and interhemispheric WM tracts.
- Employed random forest analysis for feature selection and receiver operator characteristic analysis for classification.
Main Results:
- Key discriminators included WM diffusivity in the left inferior longitudinal and uncinate fasciculi, and cortical thickness in the left temporal pole and inferior frontal gyrus.
- A combined gray matter and WM model achieved high diagnostic performance: AUC 0.91, accuracy 0.89, sensitivity 0.92, specificity 0.85.
- Leave-one-out analysis confirmed the multimodal model's superiority over single-modality approaches (accuracy 0.86 vs. 0.73/0.68).
Conclusions:
- A multimodal MRI approach integrating structural (cortical thickness) and diffusion (WM integrity) metrics provides a quantitative method for differentiating NFVPPA and SVPPA.
- The developed 'gray-matter-and-WM model' demonstrates significant potential for improving differential diagnosis accuracy in clinical practice.
- This quantitative imaging procedure aids in distinguishing PPA variants at the individual patient level.
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