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Slachevsky Chonchol, Andrea

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Slachevsky Chonchol

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Andrea

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Andrea María Slachevsky Conchol

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2022 - 20252
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Author: Matallana, Diana × Subject: Neurodegeneration ×

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Now showing 1 - 2 of 2
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    Multi-feature computational framework for combined signatures of dementia in underrepresented settings
    (2022) Moguilner, Sebastián; Birba, Agustina; Fittipaldi, Sol; Gonzalez, Cecilia; Tagliazucchi, Enzo; Reyes, Pablo; Matallana, Diana; Parra, Mario; Slachevsky Chonchol, Andrea; Farías, Gonzalo; Cruzat, Josefina; García, Adolfo; Eyre, Harris; La Joie, Renaud; Rabinovici, Gil; Whelan, Robert; Ibáñez, Agustín
    Objective.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.Approach. 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).Main results. 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).Results. Proved robust against multimodal heterogeneity, sociodemographic variability, and missing data.Significance. 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
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    Cardiovascular risk factors and the allostatic interoceptive network in dementia
    (2025) Hazelton, Jessica; Migeot, Joaquín; Gonzalez, Raul; Altschuler, Florencia; Duran, Claudia; Wen, Olivia; Galván, Dante; Barttfeld, Pablo; Medel , Vicente; González, Cecilia; Castro, Ana; Hernández, Hernán; Gonzalez, Carolina; Castaner, Olga; Hu, Kun; Li, Peng; Maria Isabel Behrens; Behrens, Maria Isabel; Bruno, Martin; Cardona, Juan; Custodio, Nilton; Santamaria, Hernando; Garcia, Adolfo; Godoy, Maria; Avila, José; Maito, Marce; Matallana, Diana; Miller, Bruce; Lopera, Francisco; Okada , Maira; Pina, Stefanie; Possin, Katherine; France, Elisa de Paula; Reyes, Pablo; Slachevsky Chonchol, Andrea; Sosa, Ana; Takada, Leonel; Yokoyama, Jennifer; Ibanez, Agustin
    Aims: Cardiovascular risk factors, such diabetes, hypertension, blood pressure, obesity, and smoking, are linked with allostatic-interoception-the continuous monitoring of internal bodily states in anticipation of environmental demands. These risk factors are associated with dementia risk. How these factors affect brain networks vulnerable to neurodegeneration and involved in allostatic-interoception, such as the Allostatic-Interoceptive Network (AIN), is unknown. We investigated the relationship between cardiovascular risk and AIN structure and function in frontotemporal lobar degeneration (FTLD) and Alzheimer's disease (AD). Methods and results: We recruited 1501 participants (304 with FTLD, 512 with AD, and 685 healthy controls) from the Multi-Partner Consortium to Expand Dementia Research in Latin America (ReDLat). A cardiovascular risk score was calculated based on: age, sex, diabetes, hypertension, systolic blood pressure, body mass index, and smoking status. Cardiovascular risk was associated with grey matter integrity and functional connectivity in age- and sex-matched patient-control groups focusing on predefined regions of interest within the AIN. Higher cardiovascular risk was associated with reduced structural integrity and functional connectivity within the AIN in both FTLD and AD. FTLD patients showed more extensive structural and functional connectivity disruptions throughout the AIN. In AD patients, structural reductions in the AIN were prominent, with functional connectivity restricted to the hippocampus, parahippocampal gyrus, and orbitofrontal regions. Conclusion: Cardiovascular risk factors appear to adversely impact the AIN structure and function, with disease-specific patterns of vulnerability. Results underscore the importance of integrating cardiovascular health into models of neurodegenerative disease and managing cardiovascular health to support brain integrity in dementia.

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