Discriminating nonfluent/agrammatic and logopenic PPA variants with automatically extracted morphosyntactic measures from connected speech
Open accessAbstract
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 (p FWE < .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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Acoustic features in Alzheimer's disease may include slower articulation rate, increased pauses, and reduced prosody, though these are typically more subtle than in disorders with prominent motor speech involvement such as Parkinson's disease or nonfluent variant primary progressive aphasia, which is additionally marked by agrammatism.
“Acoustic features in AD may include slower articulation rate, increased pauses, and reduced prosody,86 though these are typically more subtle than in disorders with prominent motor speech involvement, such as Parkinson’s disease87 or the nonfluent variant of primary progressive aphasia (PPA), which is additionally marked by agrammatism.88”
From Review of Artificial Intelligence for Clinical Use in Alzheimer's Disease and Related Dementias (2026) · cites this paper as [88]
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Neural bases of elements of syntax during speech production in patients with aphasia
Gleichgerrcht, E. L., Roth, R., Fridriksson, J., den Ouden, D., Gaizo, J. D., Stark, B. C., Hickok, G. S., Rorden, C., Wilmskoetter, J., Hillis, A. E., et al. (2021) · Brain and Language · doi:10.1016/j.bandl.2021.105025 · Cited by 22 · see citing works
Automation of the Northwestern Narrative Language Analysis System
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Neural correlates of morphological processing: An activation likelihood estimation meta-analysis.
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Diagnostic Assessment in Primary Progressive Aphasia: An Illustrative Case Example
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Identifying predictive factors of patient dropout in Alzheimer’s disease clinical trials
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Coordination and subordination
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Review of Artificial Intelligence for Clinical Use in Alzheimer's Disease and Related Dementias
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Self-citation
- Authors
- Lukic, S., Fan, Z., García, A. M., Welch, A. E., Ratnasiri, B. M., Wilson, S. M., Henry, M. L., Vonk, J., Deleon, J., Miller, B. L., Miller, Z., Mandelli, M. L., & Gorno-Tempini, M. L.
- Affiliations
- University of California, San Francisco (US)
- Carnegie Mellon University (US)
- Universidad de Santiago de Chile (CL)
- Adelphi University (US)
- The University of Queensland (AU)
- The University of Texas at Austin (US)
- Corresponding author
- Lukic, Sladjana
- ORCID
- Venue
- Cortex
- Publisher
- Elsevier BV
- Published
- 2024-04
- Volume
- 173
- Pages
- 34-48
- ISSN
- 0010-9452 (ISSN-L)
- PMC
- PMC11246552
- OpenAlex
- W4391430792
- Cited by
- 19
- References
- 105
- Citations by year
- 2026: 10 · 2025: 9
- FWCI
- 5.56
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Cortex (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- Alzheimer's Association (SG-20-725707-ReDLat, 20-725707, SG-20- 725707, UK-22-865742, GBHI ALZ UK-22-865742, P50 AG023501, ALZ UK-22-865742)
- Universidad de Santiago de Chile
- Global Brain Health Institute (UK-22-865742)
- Alzheimer's Society
- Agencia Nacional de Investigación y Desarrollo (1210195, 1210176)
- National Institutes of Health (AG023501, NIA P01 AG019724), AG019724, NIA P50AG023501, NS050915)
- National Institute on Aging (1210176, AG023501, 1210195, AG019724, AG052943, NIA U01 AG052943, U01AG052943, R01AG057234, P01AG019724, R01AG075775, P50AG023501)
- National Institute on Deafness and Other Communication Disorders (K23DC018021, NIA U01 AG052943, DC013270, K24DC015544, R01DC013270)
- Fogarty International Center
- National Institute of Neurological Disorders and Stroke (RF1NS050915, NIA U01 AG052943, R01NS050915)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neurobiology of Language and Bilingualism
- Social Sciences › Psychology › Developmental and Educational Psychology › Language Development and Disorders
- Keywords
- primary progressive aphasia, logopenic variant PPA, morphosyntactic analysis, automated speech analysis, part-of-speech tagging, subordinating conjunctions, posterior inferior frontal gyrus, cortical atrophy, connected speech
- MeSH terms
- Atrophy, Humans, Language, Magnetic Resonance Imaging, Speech, Aphasia, Primary Progressive
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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