Browsing by Author "Moguilner, Sebastian"

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    Multidimensional inhibitory signatures of sentential negation in behavioral variant frontotemporal dementia
    (2023) Díaz-Rivera, Mariano N.; Birba, Agustina; Fittipaldi, Sol; Mola, Débora; Morera, Yurena; Vega, Manuel de; Moguilner, Sebastian; Lillo, Patricia; Slachevsky Chonchol, Andrea; González Campo, Cecilia; Ibáñez, Agustín; García, Adolfo M.
    Background Processing of linguistic negation has been associated to inhibitory brain mechanisms. However, no study has tapped this link via multimodal measures in patients with core inhibitory alterations, a critical approach to reveal direct neural correlates and potential disease markers. Methods Here we examined oscillatory, neuroanatomical, and functional connectivity signatures of a recently reported Go/No-go negation task in healthy controls and behavioral variant frontotemporal dementia (bvFTD) patients, typified by primary and generalized inhibitory disruptions. To test for specificity, we also recruited persons with Alzheimer's disease (AD), a disease involving frequent but nonprimary inhibitory deficits. Results In controls, negative sentences in the No-go condition distinctly involved frontocentral delta (2–3 Hz) suppression, a canonical inhibitory marker. In bvFTD patients, this modulation was selectively abolished and significantly correlated with the volume and functional connectivity of regions supporting inhibition (e.g. precentral gyrus, caudate nucleus, and cerebellum). Such canonical delta suppression was preserved in the AD group and associated with widespread anatomo-functional patterns across non-inhibitory regions. Discussion These findings suggest that negation hinges on the integrity and interaction of spatiotemporal inhibitory mechanisms. Moreover, our results reveal potential neurocognitive markers of bvFTD, opening a new agenda at the crossing of cognitive neuroscience and behavioral neurology.
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    The BrainLat project, a multimodal neuroimaging dataset of neurodegeneration from underrepresented backgrounds
    (2023) Prado, Pavel; Medel, Vicente; Gonzalez, Raul; Sainz, Agustín; Vidal , Victor; Santamaría, Hernando; Moguilner, Sebastian; Mejia, Jhony; Slachevsky Chonchol, Andrea; Behrens, Maria; Aguillon, David; Lopera, Francisco; Parra, Mario; Matallana,Diana; Maito, Marcelo; Garcia, Adolfo; Custodio, Nilton; Ávila, Alberto; Piña, Stefanie; Birba, Agustina; Fittipaldi, Sol; Legaz, Agustina; Ibañez, Agustín
    The Latin American Brain Health Institute (BrainLat) has released a unique multimodal neuroimaging dataset of 780 participants from Latin American. The dataset includes 530 patients with neurodegenerative diseases such as Alzheimer's disease (AD), behavioral variant frontotemporal dementia (bvFTD), multiple sclerosis (MS), Parkinson's disease (PD), and 250 healthy controls (HCs). This dataset (62.7 ± 9.5 years, age range 21-89 years) was collected through a multicentric effort across five Latin American countries to address the need for affordable, scalable, and available biomarkers in regions with larger inequities. The BrainLat is the first regional collection of clinical and cognitive assessments, anatomical magnetic resonance imaging (MRI), resting-state functional MRI (fMRI), diffusion-weighted MRI (DWI), and high density resting-state electroencephalography (EEG) in dementia patients. In addition, it includes demographic information about harmonized recruitment and assessment protocols. The dataset is publicly available to encourage further research and development of tools and health applications for neurodegeneration based on multimodal neuroimaging, promoting the assessment of regional variability and inclusion of underrepresented participants in research.
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    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, Agustin
    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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