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Author: search.filters.author.Agustina BirbaSubject: search.filters.subject.machine learning

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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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    Multi-feature computational framework for combined signatures of dementia in underrepresented settings
    (2022)
    Sebastian Moguilner
    ;
    Agustina Birba
    ;
    Sol Fittipaldi
    ;
    Cecilia Gonzalez-Campo
    ;
    Enzo Tagliazucchi
    <jats:title>Abstract</jats:title> <jats:p> <jats:italic>Objective.</jats:italic> The differential diagnosis of behavioral variant frontotemporal dementia (bvFTD) and Alzheimer’s disease (AD) remains challenging in underrepresented, underdiagnosed groups, including Latinos, as advanced biomarkers are rarely available. Recent guidelines for the study of dementia highlight the critical role of biomarkers. Thus, novel cost-effective complementary approaches are required in clinical settings. <jats:italic>Approach</jats:italic>. We developed a novel framework based on a gradient boosting machine learning classifier, tuned by Bayesian optimization, on a multi-feature multimodal approach (combining demographic, neuropsychological, magnetic resonance imaging (MRI), and electroencephalography/functional MRI connectivity data) to characterize neurodegeneration using site harmonization and sequential feature selection. We assessed 54 bvFTD and 76 AD patients and 152 healthy controls (HCs) from a Latin American consortium (ReDLat). <jats:italic>Main results</jats:italic>. The multimodal model yielded high area under the curve classification values (bvFTD patients vs HCs: 0.93 (±0.01); AD patients vs HCs: 0.95 (±0.01); bvFTD vs AD patients: 0.92 (±0.01)). The feature selection approach successfully filtered non-informative multimodal markers (from thousands to dozens). <jats:italic>Results</jats:italic>. Proved robust against multimodal heterogeneity, sociodemographic variability, and missing data. <jats:italic>Significance</jats:italic>. The model accurately identified dementia subtypes using measures readily available in underrepresented settings, with a similar performance than advanced biomarkers. This approach, if confirmed and replicated, may potentially complement clinical assessments in developing countries.</jats:p>
    Scopus© Citations 19  1  3
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    Allostatic-Interoceptive Overload in Frontotemporal Dementia
    (2022)
    Agustina Birba
    ;
    Hernando Santamaría-García
    ;
    Pavel Prado
    ;
    Josefina Cruzat
    ;
    Agustín Sainz Ballesteros
    Scopus© Citations 54  1