D-37
Cognition, Behavior, and Memory
Socioculturally generalizable speech biomarkers of Alzheimer’s and frontotemporal dementia in Latinos
Franco J. Ferrante1,2,3, Agustina Birba1, Jeremias Inchauspe1,2, Gonzalo Perez1,2,3, Lucas Golombek1, Alejandro Sosa-Welford1,2, Matias Caccia1, Nicolas Pelella1, Lucas Sterpin1,2, Claudio Estienne4, Eugenia Hesse1,5, Lucía Amoruso1,6,7, Agustín Ibañez1,8,9,10,11, Adolfo M. García1,2,8,12,13
1. Cognitive Neuroscience Center, Department of Behavioral and Life Sciences, Universidad de San Andrés, Victoria, Buenos Aires, Argentina.
2. National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina.
3. School of Engineering, University of Buenos Aires, Buenos Aires, Argentina.
4. Instituto de Ingeniería Biomédica, Universidad de Buenos Aires, Buenos Aires, Argentina.
5. Departamento de Matemática y Ciencias, Universidad de San Andrés, Buenos Aires, Argentina.
6. Basque Center on Cognition, Brain, and Language (BCBL), San Sebastian, Spain.
7. Ikerbasque, Basque Foundation for Science, Bilbao, Spain.
8. Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile.
9. Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland.
10. Department of Biophysics, School of Medicine, Istanbul Medipol University, Istanbul, Türkiye.
11. Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain.
12. Global Brain Health Institute, University of California, San Francisco, CA, USA.
13. Departamento de Lingüística y Literatura, Facultad de Humanidades, Universidad de Santiago de Chile, Santiago, Chile.
Presenting Author:
francoferrante95@gmail.com
Neurodegenerative diseases such as Alzheimer’s disease (AD) and frontotemporal dementia (FTD) affect cognition, behavior, and daily functioning. Early diagnosis is critical to reduce their personal, clinical, and social consequences, yet diagnosis and follow-up often rely on costly, time-consuming assessments. Automated speech and language analysis (ASLA) offers a practical window into these disorders, providing fast, objective, and low-cost markers. Previous work from our team has shown that ASLA can distinguish AD, FTD, and related syndromes; support cognitive phenotyping; and reveal language-based alterations linked to specific cognitive domains and neurophysiological markers. Together, these studies suggest that digital language markers capture clinically meaningful signals. However, further validation across sociodemographic heterogeneity is needed. Differences in age, gender, education, dialect, multilingualism, and social determinants of health modulate both language production and dementia presentation. Standard group matching reduces average differences between groups but may miss individual variability, while machine-learning splits can reintroduce imbalance. Our work in 1689 Latin American participants with AD and FTD models this influence using residualization, while differentiating groups and predicting cognitive domains. This approach tests whether ASLA biomarkers remain robust, fair, and clinically informative across heterogeneous population
