The Latin America and the Caribbean Consortium on Dementia (LAC-CD): From Networking to Research to Implementation Science

Polyphenols and Diets as Current and Potential Nutrition Senotherapeutics in Alzheimer's Disease: Findings from Clinical Trials

Cellular senescence, a hallmark of aging, plays an important role in age-related conditions among older adults. Targeting senescent cells and its phenotype may provide a promising strategy to delay the onset or progression of Alzheimer's disease (AD). In this review article, we investigated efficacy and safety of nutrition senotherapy in AD, with a focus on the role of polyphenols as current and potential nutrition senotherapeutic agents, as well as relevant dietary patterns. Promising results with neuroprotective effects of senotherapeutic agents such as quercetin, resveratrol, Epigallocatechin-gallate, curcumin and fisetin were reported from preclinical studies. However, in-human trials remain limited, and findings were inconclusive. In future, nutrition senotherapeutic agents should be studied both individually and within dietary patterns, through the perspective of cellular senescence and AD. Further studies are warranted to investigate bioavailability, dosing regimen, long term effects of nutrition senotherapy and provide better understanding of the underlying mechanisms. Collaboration between researchers needs to be established, and methodological limitations of current studies should be addressed.

Performance on the Latin American version of the Face-Name Associative Memory Exam (LAS-FNAME) distinguishes individuals with Mild Cognitive Impairment from age-matched controls in a sample from Argentina

INTRODUCTION

The Latin American Spanish version of the Face-Name Associative Memory Exam (LAS-FNAME) has shown promise in identifying cognitive changes in those at risk for Alzheimer's disease (AD). However, its applicability for Mild Cognitive Impairment (MCI) detection in the Latin American population remains unexplored. This study aims to analyze the psychometric properties in terms of validity and reliability and diagnostic performance of the LAS-FNAME for the detection of memory disorders in patients with amnestic MCI (aMCI).

MATERIALS AND METHODS

The study included 31 participants with aMCI, diagnosed by a neurologist according to Petersen's criteria, and 19 healthy controls. Inclusion criteria for the aMCI group were to be 60 years of age or older, report cognitive complaints, have a memory test score (Craft Story 21) below a -1.5 z-score and have preserved functioning in activities of daily living. Participants completed LAS-FNAME and a comprehensive neuropsychological assessment.

RESULTS

LAS-FNAME showed the ability to discriminate against healthy controls from patients with aMCI (AUC= 75) in comparison with a gold-standard memory test (AUC = 69.1). LAS-FNAME also showed evidence of concurrent and divergent validity with a standard memory test (RAVLT) (r = 0.58, p < .001) and with an attention task (Digit Span) (r = -0.37, p = .06). Finally, the reliability index was very high (α = 0.88).

DISCUSSION

LAS-FNAME effectively distinguished aMCI patients from healthy controls, suggesting its potential for detecting early cognitive changes in Alzheimer's prodromal stages among Spanish speakers.

Assessing the lack of diversity in genetics research across neurodegenerative diseases: a systematic review of the GWAS Catalog and literature

Importance

The under-representation of participants with non-European ancestry in genome-wide association studies (GWAS) is a critical issue that has significant implications, including hindering the progress of precision medicine initiatives. This issue is particularly significant in the context of neurodegenerative diseases (NDDs), where current therapeutic approaches have shown limited success. Addressing this under-representation is crucial to harnessing the full potential of genomic medicine in underserved communities and improving outcomes for NDD patients.

Objective

Our primary objective was to assess the representation of non-European ancestry participants in genetic discovery efforts related to NDDs. We aimed to quantify the extent of inclusion of diverse ancestry groups in NDD studies and determine the number of associated loci identified in more inclusive studies. Specifically, we sought to highlight the disparities in research efforts and outcomes between studies predominantly involving European ancestry participants and those deliberately targeting non-European or multi-ancestry populations across NDDs.

Evidence Review

We conducted a systematic review utilizing existing GWAS results and publications to assess the inclusion of diverse ancestry groups in neurodegeneration and neurogenetics studies. Our search encompassed studies published up to the end of 2022, with a focus on identifying research that deliberately included non-European or multi-ancestry cohorts. We employed rigorous methods for the inclusion of identified articles and quality assessment.

Findings

Our review identified a total of 123 NDD GWAS. Strikingly, 82% of these studies predominantly featured participants of European ancestry. Endeavors specifically targeting non-European or multi-ancestry populations across NDDs identified only 52 risk loci. This contrasts with predominantly European studies, which reported over 90 risk loci for a single disease. Encouragingly, over 65% of these discoveries occurred in 2020 or later, indicating a recent increase in studies deliberately including non-European cohorts.

Conclusions and relevance

Our findings underscore the pressing need for increased diversity in neurodegenerative research. The significant under-representation of non-European ancestry participants in NDD GWAS limits our understanding of the genetic underpinnings of these diseases. To advance the field of neurodegenerative research and develop more effective therapies, it is imperative that future investigations prioritize and harness the genomic diversity present within and across global populations.

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.

Source space connectomics of neurodegeneration: One-metric approach does not fit all

Brain functional connectivity in dementia has been assessed with dissimilar EEG connectivity metrics and estimation procedures, thereby increasing results' heterogeneity. In this scenario, joint analyses integrating information from different metrics may allow for a more comprehensive characterization of brain functional interactions in different dementia subtypes. To test this hypothesis, resting-state electroencephalogram (rsEEG) was recorded in individuals with Alzheimer's Disease (AD), behavioral variant frontotemporal dementia (bvFTD), and healthy controls (HCs). Whole-brain functional connectivity was estimated in the EEG source space using 101 different types of functional connectivity, capturing linear and nonlinear interactions in both time and frequency-domains. Multivariate machine learning and progressive feature elimination was run to discriminate AD from HCs, and bvFTD from HCs, based on joint analyses of i) EEG frequency bands, ii) complementary frequency-domain metrics (e.g., instantaneous, lagged, and total connectivity), and iii) time-domain metrics with different linearity assumption (e.g., Pearson correlation coefficient and mutual information). <10% of all possible connections were responsible for the differences between patients and controls, and atypical connectivity was never captured by >1/4 of all possible connectivity measures. Joint analyses revealed patterns of hypoconnectivity (patientsHCs) in both groups was mainly identified in frontotemporal regions. These atypicalities were differently captured by frequency- and time-domain connectivity metrics, in a bandwidth-specific fashion. The multi-metric representation of source space whole-brain functional connectivity evidenced the inadequacy of single-metric approaches, and resulted in a valid alternative for the selection problem in EEG connectivity. These joint analyses reveal patterns of brain functional interdependence that are overlooked with single metrics approaches, contributing to a more reliable and interpretable description of atypical functional connectivity in neurodegeneration.

Visual deep learning of unprocessed neuroimaging characterises dementia subtypes and generalises across non-stereotypic samples

BACKGROUND

Dementia's diagnostic protocols are mostly based on standardised neuroimaging data collected in the Global North from homogeneous samples. In other non-stereotypical samples (participants with diverse admixture, genetics, demographics, MRI signals, or cultural origins), classifications of disease are difficult due to demographic and region-specific sample heterogeneities, lower quality scanners, and non-harmonised pipelines.

METHODS

We implemented a fully automatic computer-vision classifier using deep learning neural networks. A DenseNet was applied on raw (unpreprocessed) data from 3000 participants (behavioural variant frontotemporal dementia-bvFTD, Alzheimer's disease-AD, and healthy controls; both male and female as self-reported by participants). We tested our results in demographically matched and unmatched samples to discard possible biases and performed multiple out-of-sample validations.

FINDINGS

Robust classification results across all groups were achieved from standardised 3T neuroimaging data from the Global North, which also generalised to standardised 3T neuroimaging data from Latin America. Moreover, DenseNet also generalised to non-standardised, routine 1.5T clinical images from Latin America. These generalisations were robust in samples with heterogenous MRI recordings and were not confounded by demographics (i.e., were robust in both matched and unmatched samples, and when incorporating demographic variables in a multifeatured model). Model interpretability analysis using occlusion sensitivity evidenced core pathophysiological regions for each disease (mainly the hippocampus in AD, and the insula in bvFTD) demonstrating biological specificity and plausibility.

INTERPRETATION

The generalisable approach outlined here could be used in the future to aid clinician decision-making in diverse samples.

FUNDING

The specific funding of this article is provided in the acknowledgements section.

Model-based whole-brain perturbational landscape of neurodegenerative diseases

The treatment of neurodegenerative diseases is hindered by lack of interventions capable of steering multimodal whole-brain dynamics towards patterns indicative of preserved brain health. To address this problem, we combined deep learning with a model capable of reproducing whole-brain functional connectivity in patients diagnosed with Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD). These models included disease-specific atrophy maps as priors to modulate local parameters, revealing increased stability of hippocampal and insular dynamics as signatures of brain atrophy in AD and bvFTD, respectively. Using variational autoencoders, we visualized different pathologies and their severity as the evolution of trajectories in a low-dimensional latent space. Finally, we perturbed the model to reveal key AD- and bvFTD-specific regions to induce transitions from pathological to healthy brain states. Overall, we obtained novel insights on disease progression and control by means of external stimulation, while identifying dynamical mechanisms that underlie functional alterations in neurodegeneration.

Temporal Irreversibility of Large-Scale Brain Dynamics in Alzheimer's Disease

Healthy brain dynamics can be understood as the emergence of a complex system far from thermodynamic equilibrium. Brain dynamics are temporally irreversible and thus establish a preferred direction in time (i.e., arrow of time). However, little is known about how the time-reversal symmetry of spontaneous brain activity is affected by Alzheimer's disease (AD). We hypothesized that the level of irreversibility would be compromised in AD, signaling a fundamental shift in the collective properties of brain activity toward equilibrium dynamics. We investigated the irreversibility from resting-state fMRI and EEG data in male and female human patients with AD and elderly healthy control subjects (HCs). We quantified the level of irreversibility and, thus, proximity to nonequilibrium dynamics by comparing forward and backward time series through time-shifted correlations. AD was associated with a breakdown of temporal irreversibility at the global, local, and network levels, and at multiple oscillatory frequency bands. At the local level, temporoparietal and frontal regions were affected by AD. The limbic, frontoparietal, default mode, and salience networks were the most compromised at the network level. The temporal reversibility was associated with cognitive decline in AD and gray matter volume in HCs. The irreversibility of brain dynamics provided higher accuracy and more distinctive information than classical neurocognitive measures when differentiating AD from control subjects. Findings were validated using an out-of-sample cohort. Present results offer new evidence regarding pathophysiological links between the entropy generation rate of brain dynamics and the clinical presentation of AD, opening new avenues for dementia characterization at different levels.SIGNIFICANCE STATEMENT By assessing the irreversibility of large-scale dynamics across multiple brain signals, we provide a precise signature capable of distinguishing Alzheimer's disease (AD) at the global, local, and network levels and different oscillatory regimes. Irreversibility of limbic, frontoparietal, default-mode, and salience networks was the most compromised by AD compared with more sensory-motor networks. Moreover, the time-irreversibility properties associated with cognitive decline and atrophy outperformed and complemented classical neurocognitive markers of AD in predictive classification performance. Findings were generalized and replicated with an out-of-sample validation procedure. We provide novel multilevel evidence of reduced irreversibility in AD brain dynamics that has the potential to open new avenues for understating neurodegeneration in terms of the temporal asymmetry of brain dynamics.

Social concepts and the cerebellum: behavioural and functional connectivity signatures in cerebellar ataxic patients

Neurocognitive research on social concepts underscores their reliance on fronto-temporo-limbic regions mediating broad socio-cognitive skills. Yet, the field has neglected another structure increasingly implicated in social cognition: the cerebellum. The present exploratory study examines this link combining a novel naturalistic text paradigm, a relevant atrophy model and functional magnetic resonance imaging. Fifteen cerebellar ataxia (CA) patients with focal cerebellar atrophy and 29 matched controls listened to a social text (highlighting interpersonal events) as well as a non-social text (focused on a single person's actions), and answered comprehension questionnaires. We compared behavioural outcomes between groups and examined their association with cerebellar connectivity. CA patients showed deficits in social text comprehension and normal scores in the non-social text. Also, social text outcomes in controls selectively correlated with connectivity between the cerebellum and key regions subserving multi-modal semantics and social cognition, including the superior and medial temporal gyri, the temporal pole and the insula. Conversely, brain-behaviour associations involving the cerebellum were abolished in the patients. Thus, cerebellar structures and connections seem involved in processing social concepts evoked by naturalistic discourse. Such findings invite new theoretical and translational developments integrating social neuroscience with embodied semantics. This article is part of the theme issue 'Concepts in interaction: social engagement and inner experiences'.

Biomarkers for dementia in Latin American countries: Gaps and opportunities

Limited knowledge on dementia biomarkers in Latin American and Caribbean (LAC) countries remains a serious barrier. Here, we reported a survey to explore the ongoing work, needs, interests, potential barriers, and opportunities for future studies related to biomarkers. The results show that neuroimaging is the most used biomarker (73%), followed by genetic studies (40%), peripheral fluids biomarkers (31%), and cerebrospinal fluid biomarkers (29%). Regarding barriers in LAC, lack of funding appears to undermine the implementation of biomarkers in clinical or research settings, followed by insufficient infrastructure and training. The survey revealed that despite the above barriers, the region holds a great potential to advance dementia biomarkers research. Considering the unique contributions that LAC could make to this growing field, we highlight the urgent need to expand biomarker research. These insights allowed us to propose an action plan that addresses the recommendations for a biomarker framework recently proposed by regional experts.

Harmonizing Ethno-Regionally Diverse Datasets to Advance the Global Epidemiology of Dementia

Understanding dementia and cognitive impairment is a global effort needing data from multiple sources across diverse ethno-regional groups. Methodological heterogeneity means that these data often require harmonization to make them comparable before analysis. We discuss the benefits and challenges of harmonization, both retrospective and prospective, broadly and with a focus on data types that require particular sorts of approaches, including neuropsychological test scores and neuroimaging data. Throughout our discussion, we illustrate general principles and give examples of specific approaches in the context of contemporary research in dementia and cognitive impairment from around the world.

Moral Emotions and Their Brain Structural Correlates Across Neurodegenerative Disorders

BACKGROUND

Although social cognition is compromised in patients with neurodegenerative disorders such as behavioral variant frontotemporal dementia (bvFTD) and Alzheimer's disease (AD), research on moral emotions and their neural correlates in these populations is scarce. No previous study has explored the utility of moral emotions, compared to and in combination with classical general cognitive state tools, to discriminate bvFTD from AD patients.

OBJECTIVE

To examine self-conscious (guilt and embarrassment) and other-oriented (pity and indignation) moral emotions, their subjective experience, and their structural brain underpinnings in bvFTD (n = 31) and AD (n = 30) patients, compared to healthy controls (n = 37). We also explored the potential utility of moral emotions measures to discriminate bvFTD from AD.

METHODS

We used a modified version of the Moral Sentiment Task measuring the participants' accuracy scores and their emotional subjective experiences.

RESULTS

bvFTD patients exhibited greater impairments in self-conscious and other-oriented moral emotions as compared with AD patients and healthy controls. Moral emotions combined with general cognitive state tools emerged as useful measures to discriminate bvFTD from AD patients. In bvFTD patients, lower moral emotions scores were associated with lower gray matter volumes in caudate nucleus and inferior and middle temporal gyri. In AD, these scores were associated with lower gray matter volumes in superior and middle frontal gyri, middle temporal gyrus, inferior parietal lobule and supramarginal gyrus.

CONCLUSION

These findings contribute to a better understanding of moral emotion deficits across neurodegenerative disorders, highlighting the potential benefits of integrating this domain into the clinical assessment.

Classification of Alzheimer's disease and frontotemporal dementia using routine clinical and cognitive measures across multicentric underrepresented samples: A cross sectional observational study

Background

Global brain health initiatives call for improving methods for the diagnosis of Alzheimer's disease (AD) and frontotemporal dementia (FTD) in underrepresented populations. However, diagnostic procedures in upper-middle-income countries (UMICs) and lower-middle income countries (LMICs), such as Latin American countries (LAC), face multiple challenges. These include the heterogeneity in diagnostic methods, lack of clinical harmonisation, and limited access to biomarkers.

Methods

This cross-sectional observational study aimed to identify the best combination of predictors to discriminate between AD and FTD using demographic, clinical and cognitive data among 1794 participants [904 diagnosed with AD, 282 diagnosed with FTD, and 606 healthy controls (HCs)] collected in 11 clinical centres across five LAC (ReDLat cohort).

Findings

A fully automated computational approach included classical statistical methods, support vector machine procedures, and machine learning techniques (random forest and sequential feature selection procedures). Results demonstrated an accurate classification of patients with AD and FTD and HCs. A machine learning model produced the best values to differentiate AD from FTD patients with an accuracy = 0.91. The top features included social cognition, neuropsychiatric symptoms, executive functioning performance, and cognitive screening; with secondary contributions from age, educational attainment, and sex.

Interpretation

Results demonstrate that data-driven techniques applied in archival clinical datasets could enhance diagnostic procedures in regions with limited resources. These results also suggest specific fine-grained cognitive and behavioural measures may aid in the diagnosis of AD and FTD in LAC. Moreover, our results highlight an opportunity for harmonisation of clinical tools for dementia diagnosis in the region.

Funding

This work was supported by the Multi-Partner Consortium to Expand Dementia Research in Latin America (ReDLat), funded by NIA/NIH (R01AG057234), Alzheimer's Association (SG-20-725707-ReDLat), Rainwater Foundation, Takeda (CW2680521), Global Brain Health Institute; as well as CONICET; FONCYT-PICT (2017-1818, 2017-1820); PIIECC, Facultad de Humanidades, Usach; Sistema General de Regalías de Colombia (BPIN2018000100059), Universidad del Valle (CI 5316); ANID/FONDECYT Regular (1210195, 1210176, 1210176); ANID/FONDAP (15150012); ANID/PIA/ANILLOS ACT210096; and Alzheimer's Association GBHI ALZ UK-22-865742.

Multidimensional inhibitory signatures of sentential negation in behavioral variant frontotemporal dementia

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.

Genuine high-order interactions in brain networks and neurodegeneration

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.

Validation of the Norma Latina Neuropsychological Assessment Battery in Patients with Alzheimer's Disease in Mexico

To our knowledge, this is the first study reported in the literature that has validated the Norma Latina Battery in a population of people with Alzheimer's disease (AD) in Mexico. The objective of the study was to determine the discriminant validity of the Norma Latina Battery in a group of Mexican individuals with AD and a group of heathy controls (HC). The Norma Latina Battery was administered to 234 Mexican participants (117 HC and 117 individuals with AD). Results show that: (1) the Norma Latina Battery has high discriminative capacity between groups in all domains; (2) participants with AD presented worse scores in each of the cognitive domains compared to the HC and a greater number of low scores in each of the established thresholds or cut-off points; and finally, (3) the Norma Latina Battery had optimal sensitivity and specificity, especially when a set was observed ≥5 scores below the 10th percentile or ≥4 scores below the 5th percentile. In conclusion, it is recommended that both clinicians and researchers use this battery in the evaluation of Mexican people with AD to better understand the prognosis of the disease and its subsequent treatment.

The Latin American Brain Health Institute, a regional initiative to reduce the scale and impact of dementia

Latin American and Caribbean countries face complex challenges to improve brain health and reduce the impact of dementia. Regional hubs devoted to research, capacity building, implementation science, and education are critically needed. The Latin American Brain Health Institute represent an important step to address many of these needs.

Multi-feature computational framework for combined signatures of dementia in underrepresented settings

Objective.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 countries.

A reusable benchmark of brain-age prediction from M/EEG resting-state signals

Population-level modeling can define quantitative measures of individual aging by applying machine learning to large volumes of brain images. These measures of brain age, obtained from the general population, helped characterize disease severity in neurological populations, improving estimates of diagnosis or prognosis. Magnetoencephalography (MEG) and Electroencephalography (EEG) have the potential to further generalize this approach towards prevention and public health by enabling assessments of brain health at large scales in socioeconomically diverse environments. However, more research is needed to define methods that can handle the complexity and diversity of M/EEG signals across diverse real-world contexts. To catalyse this effort, here we propose reusable benchmarks of competing machine learning approaches for brain age modeling. We benchmarked popular classical machine learning pipelines and deep learning architectures previously used for pathology decoding or brain age estimation in 4 international M/EEG cohorts from diverse countries and cultural contexts, including recordings from more than 2500 participants. Our benchmarks were built on top of the M/EEG adaptations of the BIDS standard, providing tools that can be applied with minimal modification on any M/EEG dataset provided in the BIDS format. Our results suggest that, regardless of whether classical machine learning or deep learning was used, the highest performance was reached by pipelines and architectures involving spatially aware representations of the M/EEG signals, leading to R^2 scores between 0.60-0.71. Hand-crafted features paired with random forest regression provided robust benchmarks even in situations in which other approaches failed. Taken together, this set of benchmarks, accompanied by open-source software and high-level Python scripts, can serve as a starting point and quantitative reference for future efforts at developing M/EEG-based measures of brain aging. The generality of the approach renders this benchmark reusable for other related objectives such as modeling specific cognitive variables or clinical endpoints.

Allostatic-Interoceptive Overload in Frontotemporal Dementia

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.

Barriers to Effective Memory Assessments for Alzheimer's Disease

Recently, Alzheimer's Disease International (ADI) stressed that around 75% of people living with dementia globally are still not receiving a diagnosis. In this commentary, I reflect on how efforts towards better cognitive assessments, particularly of memory, can be aligned and harmonized to contribute to such needs. I highlight some barriers that ongoing collaborations and trials are facing and their potential drivers. I suggest some strategies that can help overcome them and in so doing, integrate research agendas. We need to ignite the debate towards strategies that can help level the playfield to tackle Alzheimer's disease with true global solutions.

Dementia ConnEEGtome: Towards multicentric harmonization of EEG connectivity in neurodegeneration

The proposal to use brain connectivity as a biomarker for dementia phenotyping can be potentiated by conducting large-scale multicentric studies using high-density electroencephalography (hd- EEG). Nevertheless, several barriers preclude the development of a systematic "ConnEEGtome" in dementia research. Here we review critical sources of variability in EEG connectivity studies, and provide general guidelines for multicentric protocol harmonization. We describe how results can be impacted by the choice for data acquisition, and signal processing workflows. The implementation of a particular processing pipeline is conditional upon assumptions made by researchers about the nature of EEG. Due to these assumptions, EEG connectivity metrics are typically applicable to restricted scenarios, e.g., to a particular neurocognitive disorder. "Ground truths" for the choice of processing workflow and connectivity analysis are impractical. Consequently, efforts should be directed to harmonizing experimental procedures, data acquisition, and the first steps of the preprocessing pipeline. Conducting multiple analyses of the same data and a proper integration of the results need to be considered in additional processing steps. Furthermore, instead of using a single connectivity measure, using a composite metric combining different connectivity measures brings a powerful strategy to scale up the replicability of multicentric EEG connectivity studies. These composite metrics can boost the predictive strength of diagnostic tools for dementia. Moreover, the implementation of multi-feature machine learning classification systems that include EEG-based connectivity analyses may help to exploit the potential of multicentric studies combining clinical-cognitive, molecular, genetics, and neuroimaging data towards a multi-dimensional characterization of the dementia.

Dementia caregiving across Latin America and the Caribbean and brain health diplomacy

The prevalence of dementia in Latin America and the Caribbean is growing rapidly, increasing the burden placed on caregivers. Exacerbated by fragile health-care systems, unstable economies, and extensive inequalities, caregiver burden in this region is among the highest in the world. We reviewed the major challenges to caregiving in Latin America and the Caribbean, and we propose regional and coordinated actions to drive future change. Current challenges include the scarcity of formal long-term care, socioeconomic and social determinants of health disparities, gender-biased burdens, growing dementia prevalence, and the effect of the current COVID-19 pandemic on families affected by dementia. Firstly, we propose local and regional short-term strategic recommendations, including systematic identification of specific caregiver needs, testing of evidence-based local interventions, contextual adaptation of strategies to different settings and cultures, countering gender bias, strengthening community support, provision of basic technology, and better use of available information and communications technology. Additionally, we propose brain health diplomacy (ie, global actions aimed to overcome the systemic challenges to brain health by bridging disciplines and sectors) and convergence science as frameworks for long-term coordinated responses, integrating tools, knowledge, and strategies to expand access to digital technology and develop collaborative models of care. Addressing the vast inequalities in dementia caregiving across Latin America and the Caribbean requires innovative, evidence-based solutions coordinated with the strengthening of public policies.

The Psychiatric Misdiagnosis of Behavioral Variant Frontotemporal Dementia in a Colombian Sample

Objective: To describe the demographic characteristics, initial psychiatric diagnoses, and the time to reach a diagnosis of probable behavioral variant frontotemporal dementia (bvFTD) in a public psychiatric hospital in Cali, Colombia. Methods: We retrospectively reviewed the medical records of 28 patients who were diagnosed with probable bvFTD based on a multidisciplinary evaluation that included a structural MRI, neuropsychological testing, functional assessment, and neurological exam. Prior to this evaluation, all patients were evaluated by a psychiatrist as part of their initial consultation at the hospital. The initial consultation included the Neuropsychiatric Inventory and diagnoses based on the DSM-V. Demographics, clinical features, and initial psychiatric misdiagnoses were extracted from clinical records and summarized in the full sample and by gender. Results: The study sample had a mean education of 10.0 years (SD = 4.9) and 68.0% were female. In the full sample, 28.6% were initially diagnosed with dementia, and 71.4% with a psychiatric disorder. The psychiatric diagnosis at initial consultation differed by gender. Women were most likely to be diagnosed with depression (26.3%) or bipolar disorder (26.3%), while the men were most likely to be diagnosed with anxiety (33.3%) or a psychotic disorder (22.2%). Psychotic symptoms were common (delusions, 60.7% and hallucinations, 39.3%), and the pattern of neuropsychiatric symptoms did not differ by gender. Conclusions: This is one of few case series of bvFTD in a Colombian population, where bvFTD is a recognizable and prevalent disorder. In this psychiatric hospital, the majority of patients with bvFTD were initially diagnosed with a primary psychiatric condition. There was a gender difference in psychiatric diagnosis, but not in neuropsychiatric symptoms. In this sample, the rate of psychiatric misdiagnosis, as well as the psychotic symptoms, were higher compared to rates described in other countries. These results highlight the need for interventions to improve bvFTD diagnosis in under-represented populations.

Dementia with Lewy bodies research consortia: A global perspective from the ISTAART Lewy Body Dementias Professional Interest Area working group

Dementia with Lewy bodies (DLB) research has seen a significant growth in international collaboration over the last three decades. However, researchers face a challenge in identifying large and diverse samples capable of powering longitudinal studies and clinical trials. The DLB research community has begun to focus efforts on supporting the development and harmonization of consortia, while also continuing to forge networks within which data and findings can be shared. This article describes the current state of DLB research collaborations on each continent. We discuss several established DLB cohorts, many of whom have adopted a common framework, and identify emerging collaborative initiatives that hold the potential to expand DLB networks and diversify research cohorts. Our findings identify geographical areas into which the global DLB networks should seek to expand, and we propose strategies, such as the creation of data-sharing platforms and the harmonization of protocols, which may further potentiate international collaboration.

Social Cognition and Behavioral Assessments Improve the Diagnosis of Behavioral Variant of Frontotemporal Dementia in Older Peruvians With Low Educational Levels

Background: The behavioral variant of frontotemporal dementia (bvFTD), characterized by early behavioral abnormalities and late memory impairment, is a neurodegenerative disorder with a detrimental impact on patients and their caregivers. bvFTD is often difficult to distinguish from other neurodegenerative diseases, such as Alzheimer's disease (AD), using brief cognitive tests. Combining brief socio-cognitive and behavioral evaluations with standard cognitive testing could better discriminate bvFTD from AD patients. We sought to evaluate the diagnostic accuracy of brief socio-cognitive tests that may differentiate bvFTD and AD patients with low educational levels. Methods: A prospective study was performed on 51 individuals over the age of 50 with low educational levels, with bvFTD or AD diagnosed using published criteria, and who were receiving neurological care at a multidisciplinary neurology clinic in Lima, Peru, between July 2017 and December 2020. All patients had a comprehensive neurological evaluation, including a full neurocognitive battery and brief tests of cognition (Addenbrooke's Cognitive Examination version III, ACE-III), social cognition (Mini-social Cognition and Emotional Assessment, Mini-SEA), and behavioral assessments (Frontal Behavioral Inventory, FBI; Interpersonal Reactivity Index-Emphatic Concern, IRI-EC; IRI-Perspective Taking, IRI-PT; and Self-Monitoring Scale-revised version, r-SMS). Receiver operating characteristic (ROC) analysis to calculate the area under the curve (AUC) was performed to compare the brief screening tests individually and combined to the gold standard of bvFTD and AD diagnoses. Results: The AD group was significantly older than the bvFTD group (p < 0.001). An analysis of the discriminatory ability of the ACE-III to distinguish between patients with AD and bvFTD (AUC = 0.85) and the INECO Frontal Screening (IFS; AUC = 0.78) shows that the former has greater discriminatory ability. Social and behavioral cognition tasks were able to appropriately discriminate bvFTD from AD. The Mini-SEA had high sensitivity and high moderate specificity (83%) for discriminating bvFTD from AD, which increased when combined with the brief screening tests ACE-III and IFS. The FBI was ideal with high sensitivity (83%), as well as the IRI-EC and IRI-PT that also were adequate for distinguishing bvFTD from AD. Conclusions: Our study supports the integration of socio-behavioral measures to the standard global cognitive and social cognition measures utilized for screening for bvFTD in a population with low levels of education.

Air Pollution: A Neglected Risk Factor for Dementia in Latin America and the Caribbean

The risk of dementia and Alzheimer's disease in Latin America and the Caribbean (LAC) rises with increasing age and polluted air. Currently, at least 172 million people breathe unhealthy levels of air pollution in LAC countries. Several cohort studies have indicated that air pollution increases the risk of developing dementia and neurodegenerative diseases, but the mechanisms underlying the association are still not clear. Air pollution causes and aggravates five established risk factors for dementia (obesity, hypertension, stroke, diabetes mellitus, and heart diseases) and is linked to three other risk factors (physical inactivity, cognitive inactivity, and depression). Some of these risk factors could be mediating the association between air pollution and dementia. Reducing the risks for dementia is crucial and urgently needed in LAC countries. There is room for improving air quality in many urban areas in the LAC region and other low- and middle-income countries (LMICs), a routealready explored by many urban areas in developing regions. Moreover, reducing air pollution has proved to improve health outcomes before. In this article, we propose that despite the ongoing and valid scientific discussion, if air pollution can or cannot directly affect the brain and cause or aggravate dementia, we are ready to consider air pollution as a potentially modifiable risk factor for dementia in LAC and possibly in other LMICs. We suggest that controlling and reducing current air pollution levels in LAC and other LMIC regions now could strongly contribute.

Optimizing Dementia Care for Mexicans and Mexican-Origin U.S. Residents

In this article, we report on the recommendations of a bi-national conference that examined the institutional capacities and future ability of Mexico and the United States to address the need for affordable and sustainable dementia care that results from growing older adult populations. These recommendations reflect the large difference in resources between the two nations and each country's political and institutional capacity. Progress in both countries will require an expansion of programs or generation of new ones, to meet the needs of older adults, including improving access to services and actively managing the dementia care burden. A comprehensive federal health care safety net will be required in both nations, but economic realities will constrain its implementation. Both nations suffer from a persistent shortage of geriatric primary care physicians and geriatricians, especially in rural areas. Advances in diagnosis, treatment and care management require additional knowledge and skills of general and specialized staff in the healthcare workforce to deliver evidence-based, culturally and linguistically appropriate long-term care, and human rights-oriented services. We conclude with a discussion of recommendations for bi-national dementia care policy and practice.

Interoception Primes Emotional Processing: Multimodal Evidence from Neurodegeneration

Recent frameworks in cognitive neuroscience and behavioral neurology underscore interoceptive priors as core modulators of negative emotions. However, the field lacks experimental designs manipulating the priming of emotions via interoception and exploring their multimodal signatures in neurodegenerative models. Here, we designed a novel task that involves interoceptive and control-exteroceptive priming conditions followed by post-interoception and post-exteroception facial emotion recognition (FER). We recruited 114 participants, including healthy controls (HCs) as well as patients with behavioral variant frontotemporal dementia (bvFTD), Parkinson's disease (PD), and Alzheimer's disease (AD). We measured online EEG modulations of the heart-evoked potential (HEP), and associations with both brain structural and resting-state functional connectivity patterns. Behaviorally, post-interoception negative FER was enhanced in HCs but selectively disrupted in bvFTD and PD, with AD presenting generalized disruptions across emotion types. Only bvFTD presented impaired interoceptive accuracy. Increased HEP modulations during post-interoception negative FER was observed in HCs and AD, but not in bvFTD or PD patients. Across all groups, post-interoception negative FER correlated with the volume of the insula and the ACC. Also, negative FER was associated with functional connectivity along the (a) salience network in the post-interoception condition, and along the (b) executive network in the post-exteroception condition. These patterns were selectively disrupted in bvFTD (a) and PD (b), respectively. Our approach underscores the multidimensional impact of interoception on emotion, while revealing a specific pathophysiological marker of bvFTD. These findings inform a promising theoretical and clinical agenda in the fields of nteroception, emotion, allostasis, and neurodegeneration.SIGNIFICANCE STATEMENT We examined whether and how emotions are primed by interoceptive states combining multimodal measures in healthy controls and neurodegenerative models. In controls, negative emotion recognition and ongoing HEP modulations were increased after interoception. These patterns were selectively disrupted in patients with atrophy across key interoceptive-emotional regions (e.g., the insula and the cingulate in frontotemporal dementia, frontostriatal networks in Parkinson's disease), whereas persons with Alzheimer's disease presented generalized emotional processing abnormalities with preserved interoceptive mechanisms. The integration of both domains was associated with the volume and connectivity (salience network) of canonical interoceptive-emotional hubs, critically involving the insula and the anterior cingulate. Our study reveals multimodal markers of interoceptive-emotional priming, laying the groundwork for new agendas in cognitive neuroscience and behavioral neurology.

Short-Term Memory Binding Distinguishing Amnestic Mild Cognitive Impairment from Healthy Aging: A Machine Learning Study

BACKGROUND

Amnestic mild cognitive impairment (aMCI) is the most common preclinical stage of Alzheimer's disease (AD). A strategy to reduce the impact of AD is the early aMCI diagnosis and clinical intervention. Neuroimaging, neurobiological, and genetic markers have proved to be sensitive and specific for the early diagnosis of AD. However, the high cost of these procedures is prohibitive in low-income and middle-income countries (LIMCs). The neuropsychological assessments currently aim to identify cognitive markers that could contribute to the early diagnosis of dementia.

OBJECTIVE

Compare machine learning (ML) architectures classifying and predicting aMCI and asset the contribution of cognitive measures including binding function in distinction and prediction of aMCI.

METHODS

We conducted a two-year follow-up assessment of a sample of 154 subjects with a comprehensive multidomain neuropsychological battery. Statistical analysis was proposed using complete ML architectures to compare subjects' performance to classify and predict aMCI. Additionally, permutation importance and Shapley additive explanations (SHAP) routines were implemented for feature importance selection.

RESULTS

AdaBoost, gradient boosting, and XGBoost had the highest performance with over 80%success classifying aMCI, and decision tree and random forest had the highest performance with over 70%success predictive routines. Feature importance points, the auditory verbal learning test, short-term memory binding tasks, and verbal and category fluency tasks were used as variables with the first grade of importance to distinguish healthy cognition and aMCI.

CONCLUSION

Although neuropsychological measures do not replace biomarkers' utility, it is a relatively sensitive and specific diagnostic tool for aMCI. Further studies with ML must identify cognitive performance that differentiates conversion from average MCI to the pathological MCI observed in AD.

Neuroimaging Research on Dementia in Brazil in the Last Decade: Scientometric Analysis, Challenges, and Peculiarities

The last years have evinced a remarkable growth in neuroimaging studies around the world. All these studies have contributed to a better understanding of the cerebral outcomes of dementia, even in the earliest phases. In low- and middle-income countries, studies involving structural and functional neuroimaging are challenging due to low investments and heterogeneous populations. Outstanding the importance of diagnosing mild cognitive impairment and dementia, the purpose of this paper is to offer an overview of neuroimaging dementia research in Brazil. The review includes a brief scientometric analysis of quantitative information about the development of this field over the past 10 years. Besides, discusses some peculiarities and challenges that have limited neuroimaging dementia research in this big and heterogeneous country of Latin America. We systematically reviewed existing neuroimaging literature with Brazilian authors that presented outcomes related to a dementia syndrome, published from 2010 to 2020. Briefly, the main neuroimaging methods used were morphometrics, followed by fMRI, and DTI. The major diseases analyzed were Alzheimer's disease, mild cognitive impairment, and vascular dementia, respectively. Moreover, research activity in Brazil has been restricted almost entirely to a few centers in the Southeast region, and funding could be the main driver for publications. There was relative stability concerning the number of publications per year, the citation impact has historically been below the world average, and the author's gender inequalities are not relevant in this specific field. Neuroimaging research in Brazil is far from being developed and widespread across the country. Fortunately, increasingly collaborations with foreign partnerships contribute to the impact of Brazil's domestic research. Although the challenges, neuroimaging researches performed in the native population regarding regional peculiarities and adversities are of pivotal importance.

The Multi-Partner Consortium to Expand Dementia Research in Latin America (ReDLat): Driving Multicentric Research and Implementation Science

Dementia is becoming increasingly prevalent in Latin America, contrasting with stable or declining rates in North America and Europe. This scenario places unprecedented clinical, social, and economic burden upon patients, families, and health systems. The challenges prove particularly pressing for conditions with highly specific diagnostic and management demands, such as frontotemporal dementia. Here we introduce a research and networking initiative designed to tackle these ensuing hurdles, the Multi-partner consortium to expand dementia research in Latin America (ReDLat). First, we present ReDLat's regional research framework, aimed at identifying the unique genetic, social, and economic factors driving the presentation of frontotemporal dementia and Alzheimer's disease in Latin America relative to the US. We describe ongoing ReDLat studies in various fields and ongoing research extensions. Then, we introduce actions coordinated by ReDLat and the Latin America and Caribbean Consortium on Dementia (LAC-CD) to develop culturally appropriate diagnostic tools, regional visibility and capacity building, diplomatic coordination in local priority areas, and a knowledge-to-action framework toward a regional action plan. Together, these research and networking initiatives will help to establish strong cross-national bonds, support the implementation of regional dementia plans, enhance health systems' infrastructure, and increase translational research collaborations across the continent.

Predicting and Characterizing Neurodegenerative Subtypes with Multimodal Neurocognitive Signatures of Social and Cognitive Processes

BACKGROUND

Social cognition is critically compromised across neurodegenerative diseases, including the behavioral variant frontotemporal dementia (bvFTD), Alzheimer's disease (AD), and Parkinson's disease (PD). However, no previous study has used social cognition and other cognitive tasks to predict diagnoses of these conditions, let alone reporting the brain correlates of prediction outcomes.

OBJECTIVE

We performed a diagnostic classification analysis using social cognition, cognitive screening (CS), and executive function (EF) measures, and explored which anatomical and functional networks were associated with main predictors.

METHODS

Multiple group discriminant function analyses (MDAs) and ROC analyses of social cognition (facial emotional recognition, theory of mind), CS, and EF were implemented in 223 participants (bvFTD, AD, PD, controls). Gray matter volume and functional connectivity correlates of top discriminant scores were investigated.

RESULTS

Although all patient groups revealed deficits in social cognition, CS, and EF, our classification approach provided robust discriminatory characterizations. Regarding controls, probabilistic social cognition outcomes provided the best characterization for bvFTD (together with CS) and PD, but not AD (for which CS alone was the best predictor). Within patient groups, the best MDA probabilities scores yielded high classification rates for bvFTD versus PD (98.3%, social cognition), AD versus PD (98.6%, social cognition + CS), and bvFTD versus AD (71.7%, social cognition + CS). Top MDA scores were associated with specific patterns of atrophy and functional networks across neurodegenerative conditions.

CONCLUSION

Standardized validated measures of social cognition, in combination with CS, can provide a dimensional classification with specific pathophysiological markers of neurodegeneration diagnoses.