Browsing by Author "Gonzalez, Cecilia"
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Publication Educational disparities in brain health and dementia across Latin America and the United States(2024) Gonzalez, Raul; Legaz, Agustina; Moguilner, Sebastián; Cruzat, Josephine; Hernández, Hernán; Baez, Sandra; Cocchi, Rafael; Coronel, Carlos; Medel,Vicente; Tagliazuchi, Enzo; Migeot, Joaquín; Ochoa, Carolina; Maito, Marcelo; Reyes, Pablo; Santamaria, Hernando; Godoy, Maria; Javande, Shireen; García, Adolfo; Matallana, Diana; Avila, José; Slachevsky Chonchol, Andrea; Behrens, María; Custodio, Nilton; Cardona, Juan; Brusco, Ignacio; Bruno, Martín; Sosa, Ana; Pina, Stefanie; Takada, Leonel; França, Elisa; Valcour, Victor; Possin, Katherine; De Oliveira, Maira; Lopera, Francisco; Lawlor, Brian; Hu, Kun; Miller, Bruce; Yokoyama, Jennifer; Gonzalez, Cecilia; Ibañez, AgustinBackground: Education influences brain health and dementia. However, its impact across regions, specifically Latin America (LA) and the United States (US), is unknown. Methods: A total of 1412 participants comprising controls, patients with Alzheimer's disease (AD), and frontotemporal lobar degeneration (FTLD) from LA and the US were included. We studied the association of education with brain volume and functional connectivity while controlling for imaging quality and variability, age, sex, total intracranial volume (TIV), and recording type. Results: Education influenced brain measures, explaining 24%-98% of the geographical differences. The educational disparities between LA and the US were associated with gray matter volume and connectivity variations, especially in LA and AD patients. Education emerged as a critical factor in classifying aging and dementia across regions. Discussion: The results underscore the impact of education on brain structure and function in LA, highlighting the importance of incorporating educational factors into diagnosing, care, and prevention, and emphasizing the need for global diversity in research. Highlights: Lower education was linked to reduced brain volume and connectivity in healthy controls (HCs), Alzheimer's disease (AD), and frontotemporal lobar degeneration (FTLD). Latin American cohorts have lower educational levels compared to the those in the United States. Educational disparities majorly drive brain health differences between regions. Educational differences were significant in both conditions, but more in AD than FTLD. Education stands as a critical factor in classifying aging and dementia across regions.Publication Functional Capacity in Activities of Daily Living in the Alzheimer’s Disease Continuum(2024) Henriquez, Fernando; Domínguez, Carmen; Grandi, Fabrissio; Gonzalez, Cecilia; Riquelme, Patricio; Lillo, Patricia; Martínez, David; Thumala, Daniela; Henriquez, Rodrigo; Aboitiz, Francisco; Slachevsky Chonchol, AndreaBackground The most common and prevalent dementia worldwide is Alzheimer’s disease (AD). AD is a continuum composed of Subjective Cognitive Impairment (SCD), Mild Cognitive Impairment (MCI), and Alzheimer’s Disease dementia (ADD) stage. One of the main clinical variables in patients with dementia is performance in functional capacity since its alterations are associated with poor prognosis and disease progression. Functional capacity is measured through activities of daily living (ADL), which are divided into three domains: i) Basic (BADL), ii) Instrumental (IADL), and iii) Advanced (AADL). The study aimed to characterize the performance of the different stages of the AD continuum in the ADL domains and their association with cognitive abilities. Method A cross-sectional study of subjects at different stages of the AD continuum was conducted: Healthy Controls (CTR) (n = 17), SCD (n = 77), MCI (n = 30), and ADD (n = 23), who were matched for age, sex, and education. ADLs were estimated using The Technology-Activities of Daily Living Questionnaire (T-ADLQ), which assesses the three domains and a total score. T-ADLQ performance was compared across groups and correlated with cognitive ability instruments (ACE-III and IFS). Result The results showed that patients with ADD performed worse on the BADL, IADL, and total ADLs compared to the other three groups. There were no significant differences between the CTR, SCD, and MCI on the BADL, IADL, and total ADLs. However, the AADL, in addition to differentiating the ADD patients from the other three groups, also showed differences between CTR and MCI subjects and between SCD and MCI subjects (Table 1 and Figure 1). The correlation study showed that AADL correlated significantly with global cognitive and executive function assessment (Figure 2). Conclusion AADL shows progressive functional impairment at different stages of the AD continuum, which is further associated with global cognitive and executive function performances. As one progresses to a more advanced stage of the disease continuum, the performance of ADLs, especially AADLs, worsens, which could indicate a marker of disease progression, allowing for better patient follow-up.Publication 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 Multivariate word properties in fluency tasks reveal markers of Alzheimer's dementia(2024) Ferrante, Franco J.; Migeot, Joaquín; Birba, Agustina; Amoruso, Lucía; Pérez, Gonzalo; Hesse, Eugenia; Tagliazucchi, Enzo; Estienne, Claudio; Serrano, Cecilia; Slachevsky, Andrea; Matallana, Diana; Reyes, Pablo; Ibáñez, Agustín; Fittipaldi, Sol; Gonzalez, Cecilia; García, Adolfo M.Introduction: Verbal fluency tasks are common in Alzheimer's disease (AD) assessments. Yet, standard valid response counts fail to reveal disease-specific semantic memory patterns. Here, we leveraged automated word-property analysis to capture neurocognitive markers of AD vis-à-vis behavioral variant frontotemporal dementia (bvFTD). Methods: Patients and healthy controls completed two fluency tasks. We counted valid responses and computed each word's frequency, granularity, neighborhood, length, familiarity, and imageability. These features were used for group-level discrimination, patient-level identification, and correlations with executive and neural (magnetic resonanance imaging [MRI], functional MRI [fMRI], electroencephalography [EEG]) patterns. Results: Valid responses revealed deficits in both disorders. Conversely, frequency, granularity, and neighborhood yielded robust group- and subject-level discrimination only in AD, also predicting executive outcomes. Disease-specific cortical thickness patterns were predicted by frequency in both disorders. Default-mode and salience network hypoconnectivity, and EEG beta hypoconnectivity, were predicted by frequency and granularity only in AD. Discussion: Word-property analysis of fluency can boost AD characterization and diagnosis. Highlights: We report novel word-property analyses of verbal fluency in AD and bvFTD. Standard valid response counts captured deficits and brain patterns in both groups. Specific word properties (e.g., frequency, granularity) were altered only in AD. Such properties predicted cognitive and neural (MRI, fMRI, EEG) patterns in AD. Word-property analysis of fluency can boost AD characterization and diagnosis.