Healthy aging meta-analyses and scoping review of risk factors across Latin America reveal large heterogeneity and weak predictive models.

Models of healthy aging are typically based on the United States and Europe and may not apply to diverse and heterogeneous populations. In this study, our objectives were to conduct a meta-analysis to assess risk factors of cognition and functional ability across aging populations in Latin America and a scoping review focusing on methodological procedures. Our study design included randomized controlled trials and cohort, case-control and cross-sectional studies using multiple databases, including MEDLINE, the Virtual Health Library and Web of Science. From an initial pool of 455 studies, our meta-analysis included 38 final studies (28 assessing cognition and 10 assessing functional ability, n = 146,000 participants). Our results revealed significant but heterogeneous effects for cognition (odds ratio (OR) = 1.20, P = 0.03, confidence interval (CI) = (1.0127, 1.42); heterogeneity: I2 = 92.1%, CI = (89.8%, 94%)) and functional ability (OR = 1.20, P = 0.01, CI = (1.04, 1.39); I2 = 93.1%, CI = (89.3%, 95.5%)). Specific risk factors had limited effects, especially on functional ability, with moderate impacts for demographics and mental health and marginal effects for health status and social determinants of health. Methodological issues, such as outliers, inter-country differences and publication bias, influenced the results. Overall, we highlight the specific profile of risk factors associated with healthy aging in Latin America. The heterogeneity in results and methodological approaches in studying healthy aging call for greater harmonization and further regional research to understand healthy aging in Latin America.

Brain health in diverse settings: How age, demographics and cognition shape brain function.

Diversity in brain health is influenced by individual differences in demographics and cognition. However, most studies on brain health and diseases have typically controlled for these factors rather than explored their potential to predict brain signals. Here, we assessed the role of individual differences in demographics (age, sex, and education; n = 1298) and cognition (n = 725) as predictors of different metrics usually used in case-control studies. These included power spectrum and aperiodic (1/f slope, knee, offset) metrics, as well as complexity (fractal dimension estimation, permutation entropy, Wiener entropy, spectral structure variability) and connectivity (graph-theoretic mutual information, conditional mutual information, organizational information) from the source space resting-state EEG activity in a diverse sample from the global south and north populations. Brain-phenotype models were computed using EEG metrics reflecting local activity (power spectrum and aperiodic components) and brain dynamics and interactions (complexity and graph-theoretic measures). Electrophysiological brain dynamics were modulated by individual differences despite the varied methods of data acquisition and assessments across multiple centers, indicating that results were unlikely to be accounted for by methodological discrepancies. Variations in brain signals were mainly influenced by age and cognition, while education and sex exhibited less importance. Power spectrum activity and graph-theoretic measures were the most sensitive in capturing individual differences. Older age, poorer cognition, and being male were associated with reduced alpha power, whereas older age and less education were associated with reduced network integration and segregation. Findings suggest that basic individual differences impact core metrics of brain function that are used in standard case-control studies. Considering individual variability and diversity in global settings would contribute to a more tailored understanding of brain function.

Allostasis, health, and development in Latin America.

The lifespan is influenced by adverse childhood experiences that create predispositions to poor health outcomes. Here we propose an allostatic framework of childhood experiences and their impact on health across the lifespan, focusing on Latin American and Caribbean countries. This region is marked by significant social and health inequalities nested in environmental and social stressors, such as exposure to pollution, violence, and nutritional deficiencies, which critically influence current and later-life health outcomes. We review several manifestations across cognition, behavior, and the body, observed at the psychological (e.g., cognitive, socioemotional, and behavioral dysfunctions), brain (e.g., alteration of the development, structure, and function of the brain), and physiological levels (e.g., dysregulation of the body systems and damage to organs). To address the complexity of the interactions between environmental and health-related factors, we present an allostatic framework regarding the cumulative burden of environmental stressors on physiological systems (e.g., cardiovascular, metabolic, immune, and neuroendocrine) related to health across the life course. Lastly, we explore the relevance of this allostatic integrative approach in informing regional interventions and public policy recommendations. We also propose a research agenda, potentially providing detailed profiling and personalized care by assessing the social and environmental conditions. This framework could facilitate the delivery of evidence-based interventions and informed childhood-centered policy-making.

A Multidimensional, Person-Centered Framework for Functional Assessment in Dementia: Insights from the 'What', 'How', 'To Whom', and 'How Much' Questions.

Dementia is a syndrome characterized by cognitive and neuropsychiatric symptoms associated with progressive functional decline (FD). FD is a core diagnostic criterion for dementia, setting the threshold between its prodromal stages and the full-blown disease. The operationalization of FD continues to generate a great deal of controversy. For instance, the threshold of FD for the diagnosis of dementia varies across diagnostic criteria, supporting the need for standardization of this construct. Moreover, there is a need to reconsider how we are measuring FD to set boundaries between normal aging, mild cognitive impairment, and dementia. In this paper, we propose a multidimensional framework that addresses outstanding issues in the assessment of FD: i) What activities of daily living (ADLs) are necessary to sustain an independent living in aging? ii) How to assess FD in individuals with suspected neurocognitive disorders? iii) To whom is the assessment directed? and iv) How much does FD differentiate healthy aging from mild and major neurocognitive disorders? Importantly, the To Whom Question introduces a person-centered approach that regards patients and caregivers as active agents in the assessment process of FD. Thus, once impaired ADLs have been identified, patients can indicate how significant such impairments are for them in daily life. We envisage that this new framework will guide future strategies to enhance functional assessment and treatment of patients with dementia and their caregivers.

Electrophysiological biomarkers in dual pathology.

INTRODUCTION: The co-occurrence of substance use disorder with at least one other mental disorder is called dual pathology, which in turn is characterised by heterogeneous symptoms that are difficult to diagnose and have a poor response to treatment. For this reason, the identification and validation of biomarkers is necessary. Within this group, possible electroencephalographic biomarkers have been reported to be useful in diagnosis, treatment and follow-up, both in neuropsychiatric conditions and in substance use disorders. This article aims to review the existing literature on electroencephalographic biomarkers in dual pathology. METHODS: A narrative review of the literature. A bibliographic search was performed on the PubMed, Science Direct, OVID, BIREME and Scielo databases, with the keywords: electrophysiological biomarker and substance use disorder, electrophysiological biomarker and mental disorders, biomarker and dual pathology, biomarker and substance use disorder, electroencephalography, and substance use disorder or comorbid mental disorder. RESULTS: Given the greater amount of literature found in relation to electroencephalography as a biomarker of mental illness and substance use disorders, and the few articles found on dual pathology, the evidence is organised as a biomarker in psychiatry for the diagnosis and prediction of risk and as a biomarker for dual pathology. CONCLUSIONS: Although the evidence is not conclusive, it suggests the existence of a subset of sites and mechanisms where the effects of psychoactive substances and the neurobiology of some mental disorders could overlap or interact.

Emerging Theories of Allostatic-Interoceptive Overload in Neurodegeneration.

Recent integrative multilevel models offer novel insights into the etiology and course of neurodegenerative conditions. The predictive coding of allostatic-interoception theory posits that the brain adapts to environmental demands by modulating internal bodily signals through the allostatic-interoceptive system. Specifically, a domain-general allostatic-interoceptive network exerts adaptive physiological control by fine-tuning initial top-down predictions and bottom-up peripheral signaling. In this context, adequate adaptation implies the minimization of prediction errors thereby optimizing energy expenditure. Abnormalities in top-down interoceptive predictions or peripheral signaling can trigger allostatic overload states, ultimately leading to dysregulated interoceptive and bodily systems (endocrine, immunological, circulatory, etc.). In this context, environmental stress, social determinants of health, and harmful exposomes (i.e., the cumulative life-course exposition to different environmental stressors) may interact with physiological and genetic factors, dysregulating allostatic interoception and precipitating neurodegenerative processes. We review the allostatic-interoceptive overload framework across different neurodegenerative diseases, particularly in the behavioral variant frontotemporal dementia (bvFTD). We describe how concepts of allostasis and interoception could be integrated with principles of predictive coding to explain how the brain optimizes adaptive responses, while maintaining physiological stability through feedback loops with multiple organismic systems. Then, we introduce the model of allostatic-interoceptive overload of bvFTD and discuss its implications for the understanding of pathophysiological and neurocognitive abnormalities in multiple neurodegenerative conditions.

Viscous dynamics associated with hypoexcitation and structural disintegration in neurodegeneration via generative whole-brain modeling.

INTRODUCTION: Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) lack mechanistic biophysical modeling in diverse, underrepresented populations. Electroencephalography (EEG) is a high temporal resolution, cost-effective technique for studying dementia globally, but lacks mechanistic models and produces non-replicable results. METHODS: We developed a generative whole-brain model that combines EEG source-level metaconnectivity, anatomical priors, and a perturbational approach. This model was applied to Global South participants (AD, bvFTD, and healthy controls). RESULTS: Metaconnectivity outperformed pairwise connectivity and revealed more viscous dynamics in patients, with altered metaconnectivity patterns associated with multimodal disease presentation. The biophysical model showed that connectome disintegration and hypoexcitability triggered altered metaconnectivity dynamics and identified critical regions for brain stimulation. We replicated the main results in a second subset of participants for validation with unharmonized, heterogeneous recording settings. DISCUSSION: The results provide a novel agenda for developing mechanistic model-inspired characterization and therapies in clinical, translational, and computational neuroscience settings.

The BrainLat project, a multimodal neuroimaging dataset of neurodegeneration from underrepresented backgrounds.

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.

Factors associated with healthy aging in Latin American populations.

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 (ß > 0.3), mental health (ß > 0.6) and cardiometabolic risks (ß > 0.22), significantly influenced healthy aging more than age and sex (with null or smaller effects: ß < 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.

Fear of Falling as a Behavioral Symptom in Neurocognitive Impaired Patients: Evidence from an Underrepresented Population.

BACKGROUND: Fear of falling (FoF) is a condition associated with falls, multi-morbidity, and functional impairment. To date it remains unknow which clinical, somatic, socio-demographic, behavioral, and emotional factors are associated with FoF and how these factors interact in people with Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD). OBJECTIVE: Identify the association of FoF with clinical, socio-demographic, and neuropsychiatric factors in patients with AD and bvFTD. METHODS: We evaluated 98 participants, 58 with AD and 40 with bvFTD at mild or moderate stages and assess FoF using the Falls Efficacy Scale-International. Additionally, we analyzed cognitive, physical performance variables, functional impairment, and affective and behavioral symptoms associated with FoF using standardized scales and a regression model analysis. RESULTS: The prevalence of FoF in AD and bvFTD was 51% and 40%, respectively. In the AD group, physical performance [F (3, 53) = 4.318, p = 0.009], the behavioral symptoms model [F (19, 38) = 3.314, p = 0.001], and the anxiety model [F (1, 56) = 13.4, p≤0.01] showed statistically significant values. In addition, the presence of hallucinations assessed with the Neuropsychiatric Inventory and social behavior assessed with the Mild Behavioral Impairment Checklist were significant. In contrast, in the bvFTD group, a homologous group of models was evaluated but we did not find any significant results. CONCLUSION: FoF in people with AD was related to physical performance, neuropsychiatric symptoms such as apathy and hallucinations, and affective symptoms such as anxiety. However, this pattern was not seen in the bvFTD group, and therefore further studies are required.

Heterogeneous factors influence social cognition across diverse settings in brain health and age-related diseases.

Aging may diminish social cognition, which is crucial for interaction with others, and significant changes in this capacity can indicate pathological processes like dementia. However, the extent to which non-specific factors explain variability in social cognition performance, especially among older adults and in global settings, remains unknown. A computational approach assessed combined heterogeneous contributors to social cognition in a diverse sample of 1063 older adults from 9 countries. Support vector regressions predicted the performance in emotion recognition, mentalizing, and a total social cognition score from a combination of disparate factors, including clinical diagnosis (healthy controls, subjective cognitive complaints, mild cognitive impairment, Alzheimer's disease, behavioral variant frontotemporal dementia), demographics (sex, age, education, and country income as a proxy of socioeconomic status), cognition (cognitive and executive functions), structural brain reserve, and in-scanner motion artifacts. Cognitive and executive functions and educational level consistently emerged among the top predictors of social cognition across models. Such non-specific factors showed more substantial influence than diagnosis (dementia or cognitive decline) and brain reserve. Notably, age did not make a significant contribution when considering all predictors. While fMRI brain networks did not show predictive value, head movements significantly contributed to emotion recognition. Models explained between 28-44% of the variance in social cognition performance. Results challenge traditional interpretations of age-related decline, patient-control differences, and brain signatures of social cognition, emphasizing the role of heterogeneous factors. Findings advance our understanding of social cognition in brain health and disease, with implications for predictive models, assessments, and interventions.