Automated analysis of written language in the three variants of primary progressive aphasia
Authors: Josephy-Hernandez Sylvia , Rezaii Neguine , Jones Amelia , Loyer Emmaleigh , Hochberg Daisy , Quimby Megan , Wong Bonnie , Dickerson Bradford C. 
Published: July 2022

Despite the important role of written language in everyday life, abnormalities in functional written communication have been sparsely investigated in Primary Progressive Aphasia (PPA). Prior studies have analyzed written language separately in the three variants of PPA – nonfluent (nfvPPA), logopenic (lvPPA), and semantic (svPPA) – but have rarely compared them to each other or to spoken language. Manual analysis of written language can be a time-consuming process. We developed a program which uses a language parser and quantifies content units (CU) and total units (U) in written language samples. The program was used to analyze written and spoken descriptions of the WAB Picnic scene, based on a pre-defined CU corpus. We then calculated the ratio of CU to U (CU/U Ratio) as a measure of content density. Our cohort included 115 participants (20 control participants for written, 20 control participants for spoken, 28 participants with nfvPPA, 30 with lvPPA, and 17 with svPPA). We compared written language between patients with PPA and control participants and written to spoken language in patients with the three variants of PPA. Finally, we analyzed CU and U in relation to the Progressive Aphasia Severity Scale Sum of Boxes and the Clinical Dementia Rating Sum of Boxes. Our program identified CU with a validity of 99.7% (95%CI 99.5 to 99.8) compared to manual annotation of the samples. All patients with PPA wrote fewer total units than controls (p<0.001). Patients with lvPPA (p=0.013) and svPPA (0.004) wrote fewer CU than controls. The CU/U Ratio was higher in nfvPPA and svPPA than controls (p=0.019 in both cases), but no different between lvPPA patients and controls (p=0.962). Participants with lvPPA (p<0.001) and svPPA (p=0.04) produced fewer CU in written samples compared to spoken. A two-way ANOVA showed all groups produced fewer units in written samples compared to spoken (p<0.001). However, the decrease in written CU compared to spoken was smaller than the decrease in written units compared to spoken in participants with PPA, resulting in a larger written CU/U Ratio when compared to spoken language (p<0.001). nfvPPA patients produced correlated written and spoken CU (R=0.5,p=0.009) and total units (R=0.64,p<0.001), but this was not the case for lvPPA or svPPA. Considering all PPA patients, fewer CU were produced in those with greater aphasia severity (PASS SoB, R=-0.24, p=0.04) and dementia severity (CDR SoB, R=-0.34, p=0.004). In conclusion, we observed reduced written content in patients with PPA compared to controls, with a preference for content over non-content units in patients with nfvPPA and svPPA. When comparing written to spoken language, we observed a similar “telegraphic” style in both modalities in patients with nfvPPA, which was different from patients with svPPA and lvPPA, who use significantly less non-content units in writing than in speech. Lastly, we show how our program provides a time-efficient tool, which could enable feedback and tracking of writing as an important feature of language and cognition.