SLACHEVSKY CHONCHOL, ANDREA MARÍA
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SLACHEVSKY CHONCHOL, ANDREA MARÍA
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aslachevsky@med.uchile.cl
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56394322900

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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 , SLACHEVSKY CHONCHOL, ANDREA MARÍA , Diana Matallana , Ángela Martínez , Eugenia Hesse , Adolfo M. García , Lorenzo Pini

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.

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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 , Eugenia Hesse , Enzo Tagliazucchi , Claudio Estienne , Cecilia Serrano , SLACHEVSKY CHONCHOL, ANDREA MARÍA , Diana Matallana , Pablo Reyes , Agustín Ibáñez , Sol Fittipaldi , Cecilia Gonzalez Campo , Adolfo M. García

AbstractINTRODUCTIONVerbal 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).METHODSPatients 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.RESULTSValid 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.DISCUSSIONWord‐property analysis of fluency can boost AD characterization and diagnosis.Highlights We report novel word‐property analyses of verbal fluency in AD and bvFTD. Standard valid response counts captured deficits and brain patterns in both groups. Specific word properties (e.g., frequency, granularity) were altered only in AD. Such properties predicted cognitive and neural (MRI, fMRI, EEG) patterns in AD. Word‐property analysis of fluency can boost AD characterization and diagnosis.

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The power of knowledge about dementia in Latin America across health professionals working on aging

2020 , Agustin Ibanez , Daniel Flichtentrei , Eugenia Hesse , Martin Dottori , Ailin Tomio , Andrea Slachevsky , Cecilia M Serrano , Christian Gonzalez‐Billaut , Nilton Custodio , Claudia Miranda , Julian Bustin , Marcelo Cetckovitch , Fernando Torrente , Loreto Olavarria , Tomas Leon , Barbara Costa Beber , Sonia Bruki , Claudia K. Suemoto , Ricardo Nitrini , Bruce L. Miller , Jennifer S. Yokoyama

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Your perspective and my benefit: multiple lesion models of self-other integration strategies during social bargaining

2016 , Margherita Melloni , BILLEKE BOBADILLA, PABLO ERNESTO , Sandra Baez , Eugenia Hesse , Laura de la Fuente , Gonzalo Forno , Agustina Birba , Indira García-Cordero , Cecilia Serrano , Angelo Plastino , SLACHEVSKY CHONCHOL, ANDREA MARÍA , David Huepe , Mariano Sigman , Facundo Manes , Adolfo M. García , Lucas Sedeño , Agustín Ibáñez

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Towards affordable biomarkers of frontotemporal dementia: A classification study via network's information sharing

2017 , Martin Dottori , Lucas Sedeño , Miguel Martorell Caro , Florencia Alifano , Eugenia Hesse , Ezequiel Mikulan , Adolfo M. García , Amparo Ruiz-Tagle , Patricia Lillo , Andrea Slachevsky , Cecilia Serrano , Daniel Fraiman , Agustin Ibanez

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