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Santamaria-Garcia H, Sainz-Ballesteros A, Hernandez H, Moguilner S, Maito M, Ochoa-Rosales C, Corley M, Valcour V, Miranda JJ, Lawlor B, Ibanez A. Factors associated with healthy aging in Latin American populations. Nat Med 2023; 29:2248-2258. [PMID: 37563242 PMCID: PMC10504086 DOI: 10.1038/s41591-023-02495-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 07/11/2023] [Indexed: 08/12/2023]
Abstract
Latin American populations may present patterns of sociodemographic, ethnic and cultural diversity that can defy current universal models of healthy aging. The potential combination of risk factors that influence aging across populations in Latin American and Caribbean (LAC) countries is unknown. Compared to other regions where classical factors such as age and sex drive healthy aging, higher disparity-related factors and between-country variability could influence healthy aging in LAC countries. We investigated the combined impact of social determinants of health (SDH), lifestyle factors, cardiometabolic factors, mental health symptoms and demographics (age, sex) on healthy aging (cognition and functional ability) across LAC countries with different levels of socioeconomic development using cross-sectional and longitudinal machine learning models (n = 44,394 participants). Risk factors associated with social and health disparities, including SDH (β > 0.3), mental health (β > 0.6) and cardiometabolic risks (β > 0.22), significantly influenced healthy aging more than age and sex (with null or smaller effects: β < 0.2). These heterogeneous patterns were more pronounced in low-income to middle-income LAC countries compared to high-income LAC countries (cross-sectional comparisons), and in an upper-income to middle-income LAC country, Costa Rica, compared to China, a non-upper-income to middle-income LAC country (longitudinal comparisons). These inequity-associated and region-specific patterns inform national risk assessments of healthy aging in LAC countries and regionally tailored public health interventions.
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Affiliation(s)
- Hernando Santamaria-Garcia
- Global Brain Health Institute, University of California San Francisco, San Francisco, CA, USA.
- Center of Memory and Cognition Intellectus, Hospital Universitario San Ignacio Bogotá, San Ignacio, Colombia.
- Pontificia Universidad Javeriana (PhD Program in Neuroscience) Bogotá, San Ignacio, Colombia.
| | | | - Hernán Hernandez
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile
- Faculty of Engineering, University of Concepción, Concepción, Chile
| | - Sebastian Moguilner
- Global Brain Health Institute, University of California San Francisco, San Francisco, CA, USA
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile
- Cognitive Neuroscience Center, Universidad de San Andrés and Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Marcelo Maito
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile
| | - Carolina Ochoa-Rosales
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile
| | - Michael Corley
- Department of Medicine, Division of Infectious Diseases, Weill Cornell Medicine, New York, NY, USA
| | - Victor Valcour
- Global Brain Health Institute, University of California San Francisco, San Francisco, CA, USA
- Memory and Aging Center, University California San Francisco, San Francisco, CA, USA
| | - J Jaime Miranda
- Centro de Excelencia en Enfermedades Crónicas, Universidad Peruana Cayetano Heredia, Lima, Peru
- Department of Medicine, School of Medicine, Universidad Peruana Cayetano Heredia, Lima, Peru
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
- The George Institute for Global Health, University of New South Wales, Sydney, New South Wales, Australia
| | - Brian Lawlor
- Global Brain Health Institute, University of California San Francisco, San Francisco, CA, USA
- Trinity College Dublin, The University of Dublin, Dublin, Ireland
| | - Agustin Ibanez
- Global Brain Health Institute, University of California San Francisco, San Francisco, CA, USA.
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile.
- Cognitive Neuroscience Center, Universidad de San Andrés and Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina.
- Trinity College Dublin, The University of Dublin, Dublin, Ireland.
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Santamaria-Garcia H, Moguilner S, Rodriguez-Villagra OA, Botero-Rodriguez F, Pina-Escudero SD, O'Donovan G, Albala C, Matallana D, Schulte M, Slachevsky A, Yokoyama JS, Possin K, Ndhlovu LC, Al-Rousan T, Corley MJ, Kosik KS, Muniz-Terrera G, Miranda JJ, Ibanez A. The impacts of social determinants of health and cardiometabolic factors on cognitive and functional aging in Colombian underserved populations. GeroScience 2023; 45:2405-2423. [PMID: 36849677 PMCID: PMC10651610 DOI: 10.1007/s11357-023-00755-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 02/14/2023] [Indexed: 03/01/2023] Open
Abstract
Global 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.
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Affiliation(s)
- Hernando Santamaria-Garcia
- Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), San Francisco, CA, USA.
- Pontificia Universidad Javeriana (Ph.D. Program in Neuroscience, Department of Psychiatry), Bogotá, Colombia.
- Center of Memory and Cognition Intellectus, Hospital Universitario San Ignacio, Bogotá, Colombia.
| | - Sebastian Moguilner
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago de Chile, Chile
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, and CONICET, Buenos Aires, Argentina
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Felipe Botero-Rodriguez
- Pontificia Universidad Javeriana (Ph.D. Program in Neuroscience, Department of Psychiatry), Bogotá, Colombia
| | - Stefanie Danielle Pina-Escudero
- Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), San Francisco, CA, USA
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Gary O'Donovan
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago de Chile, Chile
- Facultad de Medicina, Universidad de los Andes, Bogotá, Colombia
| | - Cecilia Albala
- Instituto de Nutrición Y Tecnología de los Alimentos, Universidad de Chile, Avenida El Líbano 5524, Macul, Santiago, Chile
| | - Diana Matallana
- Pontificia Universidad Javeriana (Ph.D. Program in Neuroscience, Department of Psychiatry), Bogotá, Colombia
- Center of Memory and Cognition Intellectus, Hospital Universitario San Ignacio, Bogotá, Colombia
- Mental Health Department, Hospital Universitario Fundación Santa Fe de Bogotá, Memory Clinic, Bogotá, Colombia
| | - Michael Schulte
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago de Chile, Chile
| | - Andrea Slachevsky
- Neuropsychology and Clinical Neuroscience Laboratory (LANNEC), Physiopathology Department - Institute of Biomedical Sciences (ICBM), Neurocience and East Neuroscience Departments, Faculty of Medicine, University of Chile, Santiago de Chile, Chile
- Geroscience Center for Brain Health and Metabolism, (GERO), Santiago de Chile, Chile
- Memory and Neuropsychiatric Center (CMYN), Memory Unit - Neurology Department, Hospital del Salvador and Faculty of Medicine, University of Chile, Santiago de Chile, Chile
- Servicio de Neurología, Departamento de Medicina, Clínica Alemana-Universidad del Desarrollo, Santiago de Chile, Chile
| | - Jennifer S Yokoyama
- Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), San Francisco, CA, USA
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Katherine Possin
- Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), San Francisco, CA, USA
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Lishomwa C Ndhlovu
- Department of Medicine, Division of Infectious Diseases, Weill Cornell Medicine, New York, NY, USA
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - Tala Al-Rousan
- Herbert Wertheim School of Public Health, University of California San Diego, La Jolla, CA, USA
| | - Michael J Corley
- Department of Medicine, Division of Infectious Diseases, Weill Cornell Medicine, New York, NY, USA
| | - Kenneth S Kosik
- Neuroscience Research Institute. Department of Molecular Cellular and Developmental Biology, University of California Santa Barbara, Santa Barbara, CA, USA
| | - Graciela Muniz-Terrera
- Edinburgh Dementia Prevention, University of Edinburgh, Edinburgh, UK
- Department of Primary Care, Ohio University, Athens, USA
| | - J Jaime Miranda
- CRONICAS Center of Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia, Lima, Peru
- Department of Medicine, School of Medicine, Universidad Peruana Cayetano Heredia, Lima, Peru
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
- The George Institute for Global Health, UNSW, Sydney, Australia
| | - Agustin Ibanez
- Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), San Francisco, CA, USA.
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago de Chile, Chile.
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, and CONICET, Buenos Aires, Argentina.
- Trinity College Dublin (TCD), Dublin, Ireland.
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Prado P, Mejía JA, Sainz‐Ballesteros A, Birba A, Moguilner S, Herzog R, Otero M, Cuadros J, Z‐Rivera L, O'Byrne DF, Parra M, Ibáñez A. Harmonized multi-metric and multi-centric assessment of EEG source space connectivity for dementia characterization. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2023; 15:e12455. [PMID: 37424962 PMCID: PMC10329259 DOI: 10.1002/dad2.12455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 06/05/2023] [Accepted: 06/05/2023] [Indexed: 07/11/2023]
Abstract
Introduction Harmonization protocols that address batch effects and cross-site methodological differences in multi-center studies are critical for strengthening electroencephalography (EEG) signatures of functional connectivity (FC) as potential dementia biomarkers. Methods We implemented an automatic processing pipeline incorporating electrode layout integrations, patient-control normalizations, and multi-metric EEG source space connectomics analyses. Results Spline interpolations of EEG signals onto a head mesh model with 6067 virtual electrodes resulted in an effective method for integrating electrode layouts. Z-score transformations of EEG time series resulted in source space connectivity matrices with high bilateral symmetry, reinforced long-range connections, and diminished short-range functional interactions. A composite FC metric allowed for accurate multicentric classifications of Alzheimer's disease and behavioral variant frontotemporal dementia. Discussion Harmonized multi-metric analysis of EEG source space connectivity can address data heterogeneities in multi-centric studies, representing a powerful tool for accurately characterizing dementia.
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Affiliation(s)
- Pavel Prado
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo IbáñezSantiago de ChileChile
- Escuela de FonoaudiologíaFacultad de Odontología y Ciencias de la RehabilitaciónUniversidad San SebastiánSantiagoChile
| | - Jhony A. Mejía
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo IbáñezSantiago de ChileChile
- Departamento de Ingeniería BiomédicaUniversidad de Los AndesBogotáColombia
- Memory and Aging ClinicUniversity of CaliforniaSan FranciscoUnited States
| | - Agustín Sainz‐Ballesteros
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo IbáñezSantiago de ChileChile
- Cognitive Neuroscience Center (CNC)Universidad de San AndrésBuenos AiresArgentina
| | - Agustina Birba
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo IbáñezSantiago de ChileChile
- Cognitive Neuroscience Center (CNC)Universidad de San AndrésBuenos AiresArgentina
- Instituto Universitario de NeurocienciaUniversidad de La LagunaTenerifeSpain
- Facultad de PsicologíaUniversidad de La LagunaTenerifeSpain
| | - Sebastian Moguilner
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo IbáñezSantiago de ChileChile
- Cognitive Neuroscience Center (CNC)Universidad de San AndrésBuenos AiresArgentina
- Department of NeurologyMassachusetts General Hospital and Harvard Medical SchoolBostonUnited States
| | - Rubén Herzog
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo IbáñezSantiago de ChileChile
- Fundación para el Estudio de la Conciencia Humana (EcoH)Santiago de ChileChile
| | - Mónica Otero
- Facultad de Ingeniería, Arquitectura y DiseñoUniversidad San SebastiánSantiagoChile
- Centro BASAL Ciencia & Vida; Facultad de Ingeniería y TecnologíaUniversidad San SebastiánSantiago de ChileChile
| | - Jhosmary Cuadros
- Advanced Center for Electrical and Electronic Engineering (AC3E)Universidad Técnica Federico Santa MaríaValparaísoChile
| | - Lucía Z‐Rivera
- Advanced Center for Electrical and Electronic Engineering (AC3E)Universidad Técnica Federico Santa MaríaValparaísoChile
| | - Daniel Franco O'Byrne
- Center for Social and Cognitive Neuroscience (CSCN)School of PsychologyUniversidad Adolfo IbáñezSantiagoChile
| | - Mario Parra
- School of Psychological Sciences and HealthUniversity of StrathclydeGlasgowUK
| | - Agustín Ibáñez
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo IbáñezSantiago de ChileChile
- Cognitive Neuroscience Center (CNC)Universidad de San AndrésBuenos AiresArgentina
- Center for Social and Cognitive Neuroscience (CSCN)School of PsychologyUniversidad Adolfo IbáñezSantiagoChile
- National Scientific and Technical Research Council (CONICET)Buenos AiresArgentina
- Global Brain Health Institute (GBHI)University of California San FranciscoCalifornia and Trinity College DublinDublinIreland
- Trinity College Dublin (TCD)DublinIreland
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4
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Prado P, Moguilner S, Mejía JA, Sainz-Ballesteros A, Otero M, Birba A, Santamaria-Garcia H, Legaz A, Fittipaldi S, Cruzat J, Tagliazucchi E, Parra M, Herzog R, Ibáñez A. Source space connectomics of neurodegeneration: One-metric approach does not fit all. Neurobiol Dis 2023; 179:106047. [PMID: 36841423 PMCID: PMC11170467 DOI: 10.1016/j.nbd.2023.106047] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 02/05/2023] [Accepted: 02/15/2023] [Indexed: 02/25/2023] Open
Abstract
Brain functional connectivity in dementia has been assessed with dissimilar EEG connectivity metrics and estimation procedures, thereby increasing results' heterogeneity. In this scenario, joint analyses integrating information from different metrics may allow for a more comprehensive characterization of brain functional interactions in different dementia subtypes. To test this hypothesis, resting-state electroencephalogram (rsEEG) was recorded in individuals with Alzheimer's Disease (AD), behavioral variant frontotemporal dementia (bvFTD), and healthy controls (HCs). Whole-brain functional connectivity was estimated in the EEG source space using 101 different types of functional connectivity, capturing linear and nonlinear interactions in both time and frequency-domains. Multivariate machine learning and progressive feature elimination was run to discriminate AD from HCs, and bvFTD from HCs, based on joint analyses of i) EEG frequency bands, ii) complementary frequency-domain metrics (e.g., instantaneous, lagged, and total connectivity), and iii) time-domain metrics with different linearity assumption (e.g., Pearson correlation coefficient and mutual information). <10% of all possible connections were responsible for the differences between patients and controls, and atypical connectivity was never captured by >1/4 of all possible connectivity measures. Joint analyses revealed patterns of hypoconnectivity (patientsHCs) in both groups was mainly identified in frontotemporal regions. These atypicalities were differently captured by frequency- and time-domain connectivity metrics, in a bandwidth-specific fashion. The multi-metric representation of source space whole-brain functional connectivity evidenced the inadequacy of single-metric approaches, and resulted in a valid alternative for the selection problem in EEG connectivity. These joint analyses reveal patterns of brain functional interdependence that are overlooked with single metrics approaches, contributing to a more reliable and interpretable description of atypical functional connectivity in neurodegeneration.
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Affiliation(s)
- Pavel Prado
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Escuela de Fonoaudiología, Facultad de Odontología y Ciencias de la Rehabilitación, Universidad San Sebastián, Santiago, Chile
| | - Sebastian Moguilner
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Cognitive Neuroscience Center (CNC), Universidad de San Andrés & CONICET, Buenos Aires, Argentina
| | - Jhony A Mejía
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Departamento de Ingeniería Biomédica, Universidad de Los Andes, Bogotá, Colombia
| | | | - Mónica Otero
- Facultad de Ingeniería, Arquitectura y Diseño, Universidad San Sebastián, Santiago, Chile; Centro BASAL Ciencia & Vida, Universidad San Sebastián, Santiago, Chile
| | - Agustina Birba
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Cognitive Neuroscience Center (CNC), Universidad de San Andrés & CONICET, Buenos Aires, Argentina
| | - Hernando Santamaria-Garcia
- PhD Neuroscience Program, Physiology and Psychiatry Departments, Pontificia Universidad Javeriana, Bogotá, Colombia; Memory and Cognition Center Intellectus, Hospital Universitario San Ignacio, Bogotá, Colombia; Global Brain Health Institute, University of California San Francisco, San Francisco, California; Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
| | - Agustina Legaz
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés & CONICET, Buenos Aires, Argentina; National Scientific and Technical Research Council, Buenos Aires, Argentina
| | - Sol Fittipaldi
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Cognitive Neuroscience Center (CNC), Universidad de San Andrés & CONICET, Buenos Aires, Argentina; Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland; National Scientific and Technical Research Council, Buenos Aires, Argentina
| | - Josephine Cruzat
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Enzo Tagliazucchi
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Departamento de Física, Universidad de Buenos Aires and Instituto de Física de Buenos Aires (IFIBA -CONICET), Buenos Aires, Argentina
| | - Mario Parra
- School of Psychological Sciences and Health, University of Strathclyde, Glasgow, United Kingdom
| | - Rubén Herzog
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Fundación para el Estudio de la Conciencia Humana (EcoH), Chile
| | - Agustín Ibáñez
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Cognitive Neuroscience Center (CNC), Universidad de San Andrés & CONICET, Buenos Aires, Argentina; PhD Neuroscience Program, Physiology and Psychiatry Departments, Pontificia Universidad Javeriana, Bogotá, Colombia; Memory and Cognition Center Intellectus, Hospital Universitario San Ignacio, Bogotá, Colombia; Trinity College Dublin (TCD), Dublin, Ireland.
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Castro Martínez JC, Santamaría-García H. Understanding mental health through computers: An introduction to computational psychiatry. Front Psychiatry 2023; 14:1092471. [PMID: 36824671 PMCID: PMC9941647 DOI: 10.3389/fpsyt.2023.1092471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 01/16/2023] [Indexed: 02/10/2023] Open
Abstract
Computational psychiatry recently established itself as a new tool in the study of mental disorders and problems. Integration of different levels of analysis is creating computational phenotypes with clinical and research values, and constructing a way to arrive at precision psychiatry are part of this new branch. It conceptualizes the brain as a computational organ that receives from the environment parameters to respond to challenges through calculations and algorithms in continuous feedback and feedforward loops with a permanent degree of uncertainty. Through this conception, one can seize an understanding of the cerebral and mental processes in the form of theories or hypotheses based on data. Using these approximations, a better understanding of the disorder and its different determinant factors facilitates the diagnostics and treatment by having an individual, ecologic, and holistic approach. It is a tool that can be used to homologate and integrate multiple sources of information given by several theoretical models. In conclusion, it helps psychiatry achieve precision and reproducibility, which can help the mental health field achieve significant advancement. This article is a narrative review of the basis of the functioning of computational psychiatry with a critical analysis of its concepts.
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Affiliation(s)
- Juan Camilo Castro Martínez
- Departamento de Psiquiatría y Salud Mental, Facultad de Medicina, Pontificia Universidad Javeriana, Bogotá, Colombia
| | - Hernando Santamaría-García
- Ph.D. Programa de Neurociencias, Departamento de Psiquiatría y Salud Mental, Pontificia Universidad Javeriana, Bogotá, Colombia
- Centro de Memoria y Cognición Intellectus, Hospital Universitario San Ignacio, Bogotá, Colombia
- Global Brain Health Institute, University of California, San Francisco – Trinity College Dublin, San Francisco, CA, United States
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Ibañez A, Fittipaldi S, Trujillo C, Jaramillo T, Torres A, Cardona JF, Rivera R, Slachevsky A, García A, Bertoux M, Baez S. Predicting and Characterizing Neurodegenerative Subtypes with Multimodal Neurocognitive Signatures of Social and Cognitive Processes. J Alzheimers Dis 2021; 83:227-248. [PMID: 34275897 PMCID: PMC8461708 DOI: 10.3233/jad-210163] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/11/2021] [Indexed: 12/14/2022]
Abstract
BACKGROUND Social cognition is critically compromised across neurodegenerative diseases, including the behavioral variant frontotemporal dementia (bvFTD), Alzheimer's disease (AD), and Parkinson's disease (PD). However, no previous study has used social cognition and other cognitive tasks to predict diagnoses of these conditions, let alone reporting the brain correlates of prediction outcomes. OBJECTIVE We performed a diagnostic classification analysis using social cognition, cognitive screening (CS), and executive function (EF) measures, and explored which anatomical and functional networks were associated with main predictors. METHODS Multiple group discriminant function analyses (MDAs) and ROC analyses of social cognition (facial emotional recognition, theory of mind), CS, and EF were implemented in 223 participants (bvFTD, AD, PD, controls). Gray matter volume and functional connectivity correlates of top discriminant scores were investigated. RESULTS Although all patient groups revealed deficits in social cognition, CS, and EF, our classification approach provided robust discriminatory characterizations. Regarding controls, probabilistic social cognition outcomes provided the best characterization for bvFTD (together with CS) and PD, but not AD (for which CS alone was the best predictor). Within patient groups, the best MDA probabilities scores yielded high classification rates for bvFTD versus PD (98.3%, social cognition), AD versus PD (98.6%, social cognition + CS), and bvFTD versus AD (71.7%, social cognition + CS). Top MDA scores were associated with specific patterns of atrophy and functional networks across neurodegenerative conditions. CONCLUSION Standardized validated measures of social cognition, in combination with CS, can provide a dimensional classification with specific pathophysiological markers of neurodegeneration diagnoses.
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Affiliation(s)
- Agustín Ibañez
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
- Global Brain Health Institute, University of California, San Francisco, CA, USA
- Global Brain Health Institute, Trinity College Dublin (TCD), Dublin, Ireland
| | - Sol Fittipaldi
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
- Facultad de Psicología, Universidad Nacional de Córdoba, Córdoba, Argentina
| | | | - Tania Jaramillo
- Instituto de Psicología, Universidad del Valle, Cali, Colombia
| | | | - Juan F. Cardona
- Instituto de Psicología, Universidad del Valle, Cali, Colombia
| | - Rodrigo Rivera
- Neuroradiology Department, Instituto de Neurocirugia, Universidad de Chile, Santiago, Chile
| | - Andrea Slachevsky
- Geroscience Center for Brain Health and Metabolism (GERO), Faculty of Medicine, University of Chile, Santiago, Chile
- Neuropsychology and Clinical Neuroscience Laboratory (LANNEC), Physiopathology Department - ICBM, Neuroscience and East Neuroscience Departments, Faculty of Medicine, University of Chile, Santiago, Chile
| | - Adolfo García
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
- Global Brain Health Institute, University of California, San Francisco, CA, USA
- Departamento de Lingüística y Literatura, Facultad de Humanidades, Universidad de Santiago de Chile, Santiago, Chile
| | - Maxime Bertoux
- Lille Center of Excellence for Neurodegenerative Disorders (LICEND), CHU Lille, U1172 - Lille Neurosciences & Cognition, Université de Lille, Inserm, Lille, France
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