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Automated free speech analysis reveals distinct markers of Alzheimer's and frontotemporal dementia

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dc.contributor.authorLopes da Cunha, Pamela
dc.contributor.authorRuiz, Fabián
dc.contributor.authorFerrante, Franco
dc.contributor.authorSterpin, Lucas
dc.contributor.authorIbáñez, Agustín
dc.contributor.authorSlachevsky Chonchol, Andrea
dc.contributor.authorMatallana, Diana
dc.contributor.authorMartínez, Ángela
dc.contributor.authorHesse, Eugenia
dc.contributor.authorGarcía, Adolfo
dc.date.accessioned2024-12-16T14:24:57Z
dc.date.available2024-12-16T14:24:57Z
dc.date.issued2024
dc.description.abstractDementia can disrupt how people experience and describe events as well as their own role in them. Alzheimer's disease (AD) compromises the processing of entities expressed by nouns, while behavioral variant frontotemporal dementia (bvFTD) entails a depersonalized perspective with increased third-person references. Yet, no study has examined whether these patterns can be captured in connected speech via natural language processing tools. To tackle such gaps, we asked 96 participants (32 AD patients, 32 bvFTD patients, 32 healthy controls) to narrate a typical day of their lives and calculated the proportion of nouns, verbs, and first- or third-person markers (via part-of-speech and morphological tagging). We also extracted objective properties (frequency, phonological neighborhood, length, semantic variability) from each content word. In our main study (with 21 AD patients, 21 bvFTD patients, and 21 healthy controls), we used inferential statistics and machine learning for group-level and subject-level discrimination. The above linguistic features were correlated with patients' scores in tests of general cognitive status and executive functions. We found that, compared with HCs, (i) AD (but not bvFTD) patients produced significantly fewer nouns, (ii) bvFTD (but not AD) patients used significantly more third-person markers, and (iii) both patient groups produced more frequent words. Machine learning analyses showed that these features identified individuals with AD and bvFTD (AUC = 0.71). A generalizability test, with a model trained on the entire main study sample and tested on hold-out samples (11 AD patients, 11 bvFTD patients, 11 healthy controls), showed even better performance, with AUCs of 0.76 and 0.83 for AD and bvFTD, respectively. No linguistic feature was significantly correlated with cognitive test scores in either patient group. These results suggest that specific cognitive traits of each disorder can be captured automatically in connected speech, favoring interpretability for enhanced syndrome characterization, diagnosis, and monitoring.
dc.description.versionVersión Publicada
dc.identifier.citationLopes da Cunha P, Ruiz F, Ferrante F, Sterpin LF, Ibáñez A, Slachevsky A, Matallana D, Martínez Á, Hesse E, García AM. Automated free speech analysis reveals distinct markers of Alzheimer's and frontotemporal dementia. PLoS One. 2024 Jun 6;19(6):e0304272. doi: 10.1371/journal.pone.0304272
dc.identifier.doihttps://doi.org/10.1371/journal.pone.0304272
dc.identifier.urihttps://hdl.handle.net/11447/9477
dc.language.isoen
dc.subjectAlzheimer Disease* / diagnosis
dc.subjectAlzheimer Disease* / psychology
dc.subjectCase-Control Studies
dc.subjectExecutive Function / physiology
dc.subjectFrontotemporal Dementia* / diagnosis
dc.subjectFrontotemporal Dementia* / psychology
dc.subjectMachine Learning
dc.subjectMiddle Aged
dc.subjectNatural Language Processing
dc.subjectNeuropsychological Tests
dc.titleAutomated free speech analysis reveals distinct markers of Alzheimer's and frontotemporal dementia
dc.typeArticle
dcterms.accessRightsAcceso Abierto
dcterms.sourcePloS one
dspace.entity.typePublication
relation.isAuthorOfPublicatione25c3d3e-63b5-4e04-951a-12a4989aa772
relation.isAuthorOfPublication.latestForDiscoverye25c3d3e-63b5-4e04-951a-12a4989aa772

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