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Brain clocks, which quantify discrepancies between brain age and chronological age, hold promise for understanding brain health and disease. However, the impact of multimodal diversity (geographical, socioeconomic, sociodemographic, sex, neurodegeneration) on the brain age gap (BAG) is unknown. Here, we analyzed datasets from 5,306 participants across 15 countries (7 Latin American countries -LAC, 8 non-LAC). Based on higher-order interactions in brain signals, we developed a BAG deep learning architecture for functional magnetic resonance imaging (fMRI=2,953) and electroencephalography (EEG=2,353). The datasets comprised healthy controls, and individuals with mild cognitive impairment, Alzheimer's disease, and behavioral variant frontotemporal dementia. LAC models evidenced older brain ages (fMRI: MDE=5.60, RMSE=11.91; EEG: MDE=5.34, RMSE=9.82) compared to non-LAC, associated with frontoposterior networks. Structural socioeconomic inequality and other disparity-related factors (pollution, health disparities) were influential predictors of increased brain age gaps, especially in LAC (R2=0.37, F2=0.59, RMSE=6.9). A gradient of increasing BAG from controls to mild cognitive impairment to Alzheimer's disease was found. In LAC, we observed larger BAGs in females in control and Alzheimer's disease groups compared to respective males. Results were not explained by variations in signal quality, demographics, or acquisition methods. Findings provide a quantitative framework capturing the multimodal diversity of accelerated brain aging.
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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.
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Encéfalo , Cognição , Eletroencefalografia , Humanos , Masculino , Feminino , Adulto , Cognição/fisiologia , Pessoa de Meia-Idade , Encéfalo/fisiologia , Idoso , Adulto Jovem , Individualidade , Adolescente , Fatores Etários , Envelhecimento/fisiologiaRESUMO
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.
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Doença de Alzheimer , Encéfalo , Eletroencefalografia , Demência Frontotemporal , Humanos , Demência Frontotemporal/fisiopatologia , Demência Frontotemporal/patologia , Encéfalo/fisiopatologia , Encéfalo/patologia , Feminino , Doença de Alzheimer/fisiopatologia , Masculino , Idoso , Conectoma , Pessoa de Meia-Idade , Modelos NeurológicosRESUMO
The field of blood-based biomarkers for Alzheimer's disease (AD) has advanced at an incredible pace, especially after the development of sensitive analytic platforms that can facilitate large-scale screening. Such screening will be important when more sophisticated diagnostic methods are scarce and expensive. Thus, blood-based biomarkers can potentially reduce diagnosis inequities among populations from different socioeconomic contexts. This large-scale screening can be performed so that older adults at risk of cognitive decline assessed using these methods can then undergo more complete assessments with classic biomarkers, increasing diagnosis efficiency and reducing costs to the health systems. Blood-based biomarkers can also aid in assessing the effect of new disease-modifying treatments. This paper reviews recent advances in the area, focusing on the following leading candidates for blood-based biomarkers: amyloid-beta (Aß), phosphorylated tau isoforms (p-tau), neurofilament light (NfL), and glial fibrillary acidic (GFAP) proteins, as well as on new candidates, Neuron-Derived Exosomes contents (NDEs) and Transactive response DNA-binding protein-43 (TDP-43), based on data from longitudinal observational cohort studies. The underlying challenges of validating and incorporating these biomarkers into routine clinical practice and primary care settings are also discussed. Importantly, challenges related to the underrepresentation of ethnic minorities and socioeconomically disadvantaged persons must be considered. If these challenges are overcome, a new time of cost-effective blood-based biomarkers for AD could represent the future of clinical procedures in the field and, together with continued prevention strategies, the beginning of an era with a lower incidence of dementia worldwide.
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Doença de Alzheimer , Disfunção Cognitiva , Humanos , Idoso , Doença de Alzheimer/diagnóstico , Doença de Alzheimer/psicologia , Peptídeos beta-Amiloides , Disfunção Cognitiva/diagnóstico , Estudos de Coortes , Biomarcadores , Proteínas tauRESUMO
Brain functional networks have been traditionally studied considering only interactions between pairs of regions, neglecting the richer information encoded in higher orders of interactions. In consequence, most of the connectivity studies in neurodegeneration and dementia use standard pairwise metrics. Here, we developed a genuine high-order functional connectivity (HOFC) approach that captures interactions between 3 or more regions across spatiotemporal scales, delivering a more biologically plausible characterization of the pathophysiology of neurodegeneration. We applied HOFC to multimodal (electroencephalography [EEG], and functional magnetic resonance imaging [fMRI]) data from patients diagnosed with behavioral variant of frontotemporal dementia (bvFTD), Alzheimer's disease (AD), and healthy controls. HOFC revealed large effect sizes, which, in comparison to standard pairwise metrics, provided a more accurate and parsimonious characterization of neurodegeneration. The multimodal characterization of neurodegeneration revealed hypo and hyperconnectivity on medium to large-scale brain networks, with a larger contribution of the former. Regions as the amygdala, the insula, and frontal gyrus were associated with both effects, suggesting potential compensatory processes in hub regions. fMRI revealed hypoconnectivity in AD between regions of the default mode, salience, visual, and auditory networks, while in bvFTD between regions of the default mode, salience, and somatomotor networks. EEG revealed hypoconnectivity in the γ band between frontal, limbic, and sensory regions in AD, and in the δ band between frontal, temporal, parietal and posterior areas in bvFTD, suggesting additional pathophysiological processes that fMRI alone can not capture. Classification accuracy was comparable with standard biomarkers and robust against confounders such as sample size, age, education, and motor artifacts (from fMRI and EEG). We conclude that high-order interactions provide a detailed, EEG- and fMRI compatible, biologically plausible, and psychopathological-specific characterization of different neurodegenerative conditions.
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Doença de Alzheimer , Demência Frontotemporal , Humanos , Encéfalo/patologia , Demência Frontotemporal/patologia , Doença de Alzheimer/patologia , Imageamento por Ressonância Magnética , Mapeamento EncefálicoRESUMO
BACKGROUND: The predictive coding theory of allostatic-interoceptive load states that brain networks mediating autonomic regulation and interoceptive-exteroceptive balance regulate the internal milieu to anticipate future needs and environmental demands. These functions seem to be distinctly compromised in behavioral variant frontotemporal dementia (bvFTD), including alterations of the allostatic-interoceptive network (AIN). Here, we hypothesize that bvFTD is typified by an allostatic-interoceptive overload. METHODS: We assessed resting-state heartbeat evoked potential (rsHEP) modulation as well as its behavioral and multimodal neuroimaging correlates in patients with bvFTD relative to healthy control subjects and patients with Alzheimer's disease (N = 94). We measured 1) resting-state electroencephalography (to assess the rsHEP, prompted by visceral inputs and modulated by internal body sensing), 2) associations between rsHEP and its neural generators (source location), 3) cognitive disturbances (cognitive state, executive functions, facial emotion recognition), 4) brain atrophy, and 5) resting-state functional magnetic resonance imaging functional connectivity (AIN vs. control networks). RESULTS: Relative to healthy control subjects and patients with Alzheimer's disease, patients with bvFTD presented more negative rsHEP amplitudes with sources in critical hubs of the AIN (insula, amygdala, somatosensory cortex, hippocampus, anterior cingulate cortex). This exacerbated rsHEP modulation selectively predicted the patients' cognitive profile (including cognitive decline, executive dysfunction, and emotional impairments). In addition, increased rsHEP modulation in bvFTD was associated with decreased brain volume and connectivity of the AIN. Machine learning results confirmed AIN specificity in predicting the bvFTD group. CONCLUSIONS: Altogether, these results suggest that bvFTD may be characterized by an allostatic-interoceptive overload manifested in ongoing electrophysiological markers, brain atrophy, functional networks, and cognition.
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Doença de Alzheimer , Demência Frontotemporal , Doença de Alzheimer/patologia , Atrofia/patologia , Encéfalo , Mapeamento Encefálico , Demência Frontotemporal/diagnóstico por imagem , Demência Frontotemporal/patologia , Humanos , Imageamento por Ressonância MagnéticaRESUMO
La teoría de los marcos relaciónales (TMR) es una aproximación conductual novedosa al estudio del lenguaje, que hace especial énfasis en la investigación de tipo experimental. En el presente artículo se revisa la evidencia empírica que apoya la validez de esta teoría como programa de trabajo para el análisis experimental del lenguaje y la cognición. Dicha evidencia . se presenta en relación con dos elementos clave de la teoría: la conceptualización operante del comportamiento relacional derivado y la estrecha relación existente entre el lenguaje y las relaciones estimulares derivadas. Los estudios revisados suponen un claro apoyo empírico al concepto de conducta verbal propuesto por la TMR, a la vez que demuestran que dicha aproximación teórica constituye un programa de trabajo muy prometedor que, por el momento, ya ha generado resultados positivos en áreas de investigación que tradicionalmente han resultado problemáticas para el análisis del comportamiento. Dichos estudios también indican que la TMR puede proporcionar una explicación funcional de algunos de los hallazgos típicamente obtenidos por la investigación cognoscitiva acerca del lenguaje