Browsing by Author "Sedeno, Lucas"

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    Does culture shape our understanding of others' thoughts and emotions? An investigation across 12 countries
    (2022) Quesque, François; Coutrot, Antoine; Cox, Sharon; Cruz de Souza, Leonardo; Baez, Sandra; Cardona, Juan; Mulet, Hannah; Flanagan, Emma; Neely, Alejandra; Clarens, María; Cassimiro, Luciana; Musa, Gada; Kemp, Jennifer; Botzung, Anne; Philippi, Nathalie; Cosseddu, Maura; Trujillo, Catalina; Grisales, Johan; Fittipaldi, Sol; Magrath, Nahuel; Calandri, Ismael; Crivelli, Lucia; Sedeno, Lucas; Sedeno, Lucas; Garcia, Adolfo; Moreno, Fermin; Indakoetxea, Begoña; Benussi, Alberto; Brandão, Millena; Santamaria, Hernando; Matallana, Diana; Pryanishnikova, Galina; Morozova, Anna; Iakovleva, Olga; Veryugina, Nadezda; Levin, Oleg; Zhao, Lina; Liang, Junhua; Duning, Thomas; Lebouvier, Thibaud; Pasquier, Florence; Huepe, David; Barandiaran, Myriam; Johnen, Andreas; Lyashenko, Elena; Allegri, Ricardo; Borroni, Barbara; Blanc, Frederic; Wang, Fen; Sanches, Monica; Lillo, Patricia; Teixeira, Antonio; Caramelli, Paulo; Hudon, Carol; Andrea Slachevsky; Ibáñez, Agustin; Hornberger, Michael; Bertoux, Maxime
    Measures of social cognition have now become central in neuropsychology, being essential for early and differential diagnoses, follow-up, and rehabilitation in a wide range of conditions. With the scientific world becoming increasingly interconnected, international neuropsychological and medical collaborations are burgeoning to tackle the global challenges that are mental health conditions. These initiatives commonly merge data across a diversity of populations and countries, while ignoring their specificity. Objective: In this context, we aimed to estimate the influence of participants' nationality on social cognition evaluation. This issue is of particular importance as most cognitive tasks are developed in highly specific contexts, not representative of that encountered by the world's population. Method: Through a large international study across 18 sites, neuropsychologists assessed core aspects of social cognition in 587 participants from 12 countries using traditional and widely used tasks. Results: Age, gender, and education were found to impact measures of mentalizing and emotion recognition. After controlling for these factors, differences between countries accounted for more than 20% of the variance on both measures. Importantly, it was possible to isolate participants' nationality from potential translation issues, which classically constitute a major limitation. Conclusions: Overall, these findings highlight the need for important methodological shifts to better represent social cognition in both fundamental research and clinical practice, especially within emerging international networks and consortia. (PsycInfo Database Record (c) 2022 APA, all rights reserved)
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    Evaluating the reliability of neurocognitive biomarkers of neurodegenerative diseases across countries: A machine learning approach
    (2020) Bachli, M. Belen; Sedeno, Lucas; Ochab, Jeremi K.; Piguet, Olivier; Kumfor, Fiona; Reyes, Pablo; Torralva, Teresa; Roca, María; Cardona, Juan Felipe; Gonzalez Campo, Cecilia; Herrera, Eduar; Slachevsky, Andrea; Matallana, Diana; Manes, Facundo; García, Adolfo M.; Ibanez, Agustín; Chialvo, Dante R.
    Accurate early diagnosis of neurodegenerative diseases represents a growing challenge for current clinical practice. Promisingly, current tools can be complemented by computational decision-support methods to objectively analyze multidimensional measures and increase diagnostic confidence. Yet, widespread application of these tools cannot be recommended unless they are proven to perform consistently and reproducibly across samples from different countries. We implemented machine-learning algorithms to evaluate the prediction power of neurocognitive biomarkers (behavioral and imaging measures) for classifying two neurodegenerative conditions –Alzheimer Disease (AD) and behavioral variant frontotemporal dementia (bvFTD)– across three different countries (>200 participants). We use machine-learning tools integrating multimodal measures such as cognitive scores (executive functions and cognitive screening) and brain atrophy volume (voxel based morphometry from fronto-temporo-insular regions in bvFTD, and temporo-parietal regions in AD) to identify the most relevant features in predicting the incidence of the diseases. In the Country-1 cohort, predictions of AD and bvFTD became maximally improved upon inclusion of cognitive screenings outcomes combined with atrophy levels. Multimodal training data from this cohort allowed predicting both AD and bvFTD in the other two novel datasets from other countries with high accuracy (>90%), demonstrating the robustness of the approach as well as the differential specificity and reliability of behavioral and neural markers for each condition. In sum, this is the first study, across centers and countries, to validate the predictive power of cognitive signatures combined with atrophy levels for contrastive neurodegenerative conditions, validating a benchmark for future assessments of reliability and reproducibility

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