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Lukic S, Fan Z, García AM, Welch AE, Ratnasiri BM, Wilson SM, Henry ML, Vonk J, Deleon J, Miller BL, Miller Z, Mandelli ML, Gorno-Tempini ML. Discriminating nonfluent/agrammatic and logopenic PPA variants with automatically extracted morphosyntactic measures from connected speech. Cortex 2024; 173:34-48. [PMID: 38359511 DOI: 10.1016/j.cortex.2023.12.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 10/15/2023] [Accepted: 12/12/2023] [Indexed: 02/17/2024]
Abstract
Morphosyntactic assessments are important for characterizing individuals with nonfluent/agrammatic variant primary progressive aphasia (nfvPPA). Yet, standard tests are subject to examiner bias and often fail to differentiate between nfvPPA and logopenic variant PPA (lvPPA). Moreover, relevant neural signatures remain underexplored. Here, we leverage natural language processing tools to automatically capture morphosyntactic disturbances and their neuroanatomical correlates in 35 individuals with nfvPPA relative to 10 healthy controls (HC) and 26 individuals with lvPPA. Participants described a picture, and ensuing transcripts were analyzed via part-of-speech tagging to extract sentence-related features (e.g., subordinating and coordinating conjunctions), verbal-related features (e.g., tense markers), and nominal-related features (e.g., subjective and possessive pronouns). Gradient boosting machines were used to classify between groups using all features. We identified the most discriminant morphosyntactic marker via a feature importance algorithm and examined its neural correlates via voxel-based morphometry. Individuals with nfvPPA produced fewer morphosyntactic elements than the other two groups. Such features robustly discriminated them from both individuals with lvPPA and HCs with an AUC of .95 and .82, respectively. The most discriminatory feature corresponded to subordinating conjunctions was correlated with cortical atrophy within the left posterior inferior frontal gyrus across groups (pFWE < .05). Automated morphosyntactic analysis can efficiently differentiate nfvPPA from lvPPA. Also, the most sensitive morphosyntactic markers correlate with a core atrophy region of nfvPPA. Our approach, thus, can contribute to a key challenge in PPA diagnosis.
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Affiliation(s)
- Sladjana Lukic
- University of California, San Francisco Memory and Aging Center, CA, USA; Ruth S. Ammon College of Education and Health Sciences, Department of Communication Sciences and Disorders, Adelphi University, Garden City, NY, USA.
| | - Zekai Fan
- Heinz College of Information Systems and Public Policy, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Adolfo M García
- Global Brain Health Institute (GBHI), University of California, San Francisco, CA, USA; Cognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina; Departamento de Lingüística y Literatura, Facultad de Humanidades, Universidad de Santiago de Chile, Santiago, Chile
| | - Ariane E Welch
- Ruth S. Ammon College of Education and Health Sciences, Department of Communication Sciences and Disorders, Adelphi University, Garden City, NY, USA
| | | | - Stephen M Wilson
- School of Health and Rehabilitation Sciences, University of Queensland, Brisbane, QLD, Australia
| | - Maya L Henry
- University of Texas at Austin Moody College of Communication, Austin, TX, USA
| | - Jet Vonk
- University of California, San Francisco Memory and Aging Center, CA, USA
| | - Jessica Deleon
- University of California, San Francisco Memory and Aging Center, CA, USA
| | - Bruce L Miller
- University of California, San Francisco Memory and Aging Center, CA, USA
| | - Zachary Miller
- University of California, San Francisco Memory and Aging Center, CA, USA
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