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    Item type:Publication,
    Impact of cardiometabolic factors and AD plasma biomarkers on white matter hyperintensities volume in individuals with cognitive complaints from the global south
    (Elsevier BV, 2026-02)
    Patricio Riquelme-Contreras
    ;
    Fernando Henriquez
    ;
    Cecilia Gonzalez-Campo
    ;
    Florencia Altschuler
    ;
    Matías Fraile-Vazquez
      2
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    Heterogeneous factors influence social cognition across diverse settings in brain health and age-related diseases
    (Springer Science and Business Media LLC, 2024-01-02)
    Sol Fittipaldi
    ;
    Agustina Legaz
    ;
    Marcelo Maito
    ;
    Hernan Hernandez
    ;
    Florencia Altschuler
    Aging diminishes social cognition, and changes in this capacity can indicate brain diseases. However, the relative contribution of age, diagnosis and brain reserve to social cognition, especially among older adults and in global settings, remains unclear when considering other factors. Here, using a computational approach, we combined predictors of social cognition from a diverse sample of 1,063 older adults across nine countries. Emotion recognition, mentalizing and overall social cognition were predicted via support vector regressions from various factors, including diagnosis (subjective cognitive complaints, mild cognitive impairment, Alzheimer’s disease and behavioral variant frontotemporal dementia), demographics, cognition/executive function, brain reserve and motion artifacts from functional magnetic resonance imaging recordings. Higher cognitive/executive functions and education ranked among the top predictors, outweighing age, diagnosis and brain reserve. Network connectivity did not show predictive values. The results challenge traditional interpretations of age-related decline, patient–control differences and brain associations of social cognition, emphasizing the importance of heterogeneous factors.
    Scopus© Citations 31  1
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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