Publications by authors named "S Fittipaldi"
Structural inequality, the uneven distribution of resources and opportunities, influences health outcomes. However, the biological embedding of structural inequality in aging and dementia, especially among underrepresented populations, is unclear. We examined the association between structural inequality (country-level and state-level Gini indices) and brain volume and connectivity in 2,135 healthy controls, and individuals with Alzheimer's disease and frontotemporal lobe degeneration from Latin America and the United States.
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- Early detection of cognitive impairment, both subjective and objective, is crucial, as subjective complaints can appear before any measurable deficits.
- A study involving 3,327 participants used a smartphone app to examine how 13 dementia risk factors relate to subjective memory and objective cognitive functions.
- Results showed subjective memory issues were more strongly linked to risk factors like depression, socioeconomic status, and loneliness, while smartphone assessments can help identify early cognitive problems across different age groups.
Article Synopsis
- Structural income inequality, defined as the uneven distribution of income across regions, affects brain dynamics and functions more significantly than individual factors like age or education.
- This study used EEG signals from 1,394 healthy participants across 10 countries to explore how structural inequality predicts various brain activity metrics, revealing a connection between socioeconomic conditions and neural functioning.
- Results show that higher structural income inequality is associated with lower brain signal complexity, increased random neural activity, and reduced power in certain brain wave frequencies, suggesting the need for a broader understanding of how social factors influence brain health.
Brain clocks, which quantify discrepancies between brain age and chronological age, hold promise for understanding brain health and disease. However, the impact of diversity (including geographical, socioeconomic, sociodemographic, sex and neurodegeneration) on the brain-age gap is unknown. We analyzed datasets from 5,306 participants across 15 countries (7 Latin American and Caribbean countries (LAC) and 8 non-LAC countries).
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