Browsing by Author "Matallana, Diana"
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Publication 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, MaximeMeasures 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)Item 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 reproducibilityPublication 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, AgustínObjective.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 countriesPublication The impacts of social determinants of health and cardiometabolic factors on cognitive and functional aging in Colombian underserved populations(2023) Santamaria, Hernando; Moguilner, Sebastian; Rodriguez, Odir; Botero, Felipe; Pina, Stefanie; O’Donovan, Gary; Albala, Cecilia; Matallana, Diana; Schulte, Michael; Slachevsky Chonchol, Andrea; Yokoyama, Jennifer; Possin, Katherine; Ndhlovu, Lishomwa; Al‑Rousan, Tala; Corley, Michael; Kosik, Kenneth; Muniz, Graciela; Miranda, J. Jaime; Ibanez, AgustinGlobal initiatives call for further understanding of the impact of inequity on aging across underserved populations. Previous research in low- and middle-income countries (LMICs) presents limitations in assessing combined sources of inequity and outcomes (i.e., cognition and functionality). In this study, we assessed how social determinants of health (SDH), cardiometabolic factors (CMFs), and other medical/social factors predict cognition and functionality in an aging Colombian population. We ran a cross-sectional study that combined theory- (structural equation models) and data-driven (machine learning) approaches in a population-based study (N = 23,694; M = 69.8 years) to assess the best predictors of cognition and functionality. We found that a combination of SDH and CMF accurately predicted cognition and functionality, although SDH was the stronger predictor. Cognition was predicted with the highest accuracy by SDH, followed by demographics, CMF, and other factors. A combination of SDH, age, CMF, and additional physical/psychological factors were the best predictors of functional status. Results highlight the role of inequity in predicting brain health and advancing solutions to reduce the cognitive and functional decline in LMICs.Item The Multi-Partner Consortium to Expand Dementia Research in Latin America (ReDLat): Driving Multicentric Research and Implementation Science(2021) Ibáñez, Agustín; Yokoyama, Jennifer S.; Possin, Katherine L.; Matallana, Diana; Lopera, Francisco; Nitrini, Ricardo; Takada, Leonel T.; Custodio, Nilton; Sosa Ortiz, Ana Luisa; Avila-Funes, José Alberto; Behrens, María Isabel; Slachevsky, Andrea; Myers, Richard M.; Cochran, J. Nicholas; Brusco, Luis Ignacio; Bruno, Martin A.; Brucki, Sonia M. D.; Pina-Escudero, Stefanie Danielle; Oliveira, Maira Okada de; Donnelly Kehoe, Patricio; Santamaria-Garcia, Hernando; Moguilner, Sebastián; Tagliazucchi, Enzo; Maito, Marcelo; Longoria Ibarrola, Erika Mariana; Pintado-Caipa, Maritza; Godoy, Maria Eugenia; Bakman, Vera; Javandel, Shireen; Kosik, Kenneth S.; Valcour, Victor; Miller, Bruce L.; The Latin America the Caribbean Consortium on Dementia (LAC-CD)Dementia is becoming increasingly prevalent in Latin America, contrasting with stable or declining rates in North America and Europe. This scenario places unprecedented clinical, social, and economic burden upon patients, families, and health systems. The challenges prove particularly pressing for conditions with highly specific diagnostic and management demands, such as frontotemporal dementia. Here we introduce a research and networking initiative designed to tackle these ensuing hurdles, the Multi-partner consortium to expand dementia research in Latin America (ReDLat). First, we present ReDLat’s regional research framework, aimed at identifying the unique genetic, social, and economic factors driving the presentation of frontotemporal dementia and Alzheimer’s disease in Latin America relative to the US. We describe ongoing ReDLat studies in various fields and ongoing research extensions. Then, we introduce actions coordinated by ReDLat and the Latin America and Caribbean Consortium on Dementia (LAC-CD) to develop culturally appropriate diagnostic tools, regional visibility and capacity building, diplomatic coordination in local priority areas, and a knowledge-to-action framework toward a regional action plan. Together, these research and networking initiatives will help to establish strong cross-national bonds, support the implementation of regional dementia plans, enhance health systems’ infrastructure, and increase translational research collaborations across the continent.