Universidad del Desarrollo

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Automated free speech analysis reveals distinct markers of Alzheimer’s and frontotemporal dementia
(2024)
Pamela Lopes da Cunha
;
Fabián Ruiz
;
Franco Ferrante
;
Lucas Federico Sterpin
;
Agustín Ibáñez
<jats:p>Dementia 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.</jats:p>
2Scopus© Citations 22
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Multivariate word properties in fluency tasks reveal markers of Alzheimer's dementia
(2023)
Franco J. Ferrante
;
Joaquín Migeot
;
Agustina Birba
;
Lucía Amoruso
;
Gonzalo Pérez
<jats:title>Abstract</jats:title><jats:sec><jats:title>INTRODUCTION</jats:title><jats:p>Verbal fluency tasks are common in Alzheimer's disease (AD) assessments. Yet, standard valid response counts fail to reveal disease‐specific semantic memory patterns. Here, we leveraged automated word‐property analysis to capture neurocognitive markers of AD vis‐à‐vis behavioral variant frontotemporal dementia (bvFTD).</jats:p></jats:sec><jats:sec><jats:title>METHODS</jats:title><jats:p>Patients and healthy controls completed two fluency tasks. We counted valid responses and computed each word's frequency, granularity, neighborhood, length, familiarity, and imageability. These features were used for group‐level discrimination, patient‐level identification, and correlations with executive and neural (magnetic resonanance imaging [MRI], functional MRI [fMRI], electroencephalography [EEG]) patterns.</jats:p></jats:sec><jats:sec><jats:title>RESULTS</jats:title><jats:p>Valid responses revealed deficits in both disorders. Conversely, frequency, granularity, and neighborhood yielded robust group‐ and subject‐level discrimination only in AD, also predicting executive outcomes. Disease‐specific cortical thickness patterns were predicted by frequency in both disorders. Default‐mode and salience network hypoconnectivity, and EEG beta hypoconnectivity, were predicted by frequency and granularity only in AD.</jats:p></jats:sec><jats:sec><jats:title>DISCUSSION</jats:title><jats:p>Word‐property analysis of fluency can boost AD characterization and diagnosis.</jats:p></jats:sec><jats:sec><jats:title>Highlights</jats:title><jats:p><jats:list list-type="bullet"> <jats:list-item><jats:p>We report novel word‐property analyses of verbal fluency in AD and bvFTD.</jats:p></jats:list-item> <jats:list-item><jats:p>Standard valid response counts captured deficits and brain patterns in both groups.</jats:p></jats:list-item> <jats:list-item><jats:p>Specific word properties (e.g., frequency, granularity) were altered only in AD.</jats:p></jats:list-item> <jats:list-item><jats:p>Such properties predicted cognitive and neural (MRI, fMRI, EEG) patterns in AD.</jats:p></jats:list-item> <jats:list-item><jats:p>Word‐property analysis of fluency can boost AD characterization and diagnosis.</jats:p></jats:list-item> </jats:list></jats:p></jats:sec>
3Scopus© Citations 10
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Your perspective and my benefit: multiple lesion models of self-other integration strategies during social bargaining
(2016)
Margherita Melloni
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BILLEKE BOBADILLA, PABLO ERNESTO
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Sandra Baez
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Eugenia Hesse
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Laura de la Fuente
1 1Scopus© Citations 103
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The power of knowledge about dementia in Latin America across health professionals working on aging
(2020)
Agustin Ibanez
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Daniel Flichtentrei
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Eugenia Hesse
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Martin Dottori
;
Ailin Tomio
21Scopus© Citations 20
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Towards affordable biomarkers of frontotemporal dementia: A classification study via network's information sharing
(2017)
Martin Dottori
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Lucas Sedeño
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Miguel Martorell Caro
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Florencia Alifano
;
Eugenia Hesse
21Scopus© Citations 50

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