Systematic Review: Genetic, Neuroimaging, and Fluids Biomarkers for Frontotemporal Dementia Across Latin America Countries

Gaps in biomedical research in frontotemporal dementia: A call for diversity and disparities focused research

Frontotemporal dementia (FTD) is one of the leading causes of young-onset dementia before age 65, typically manifesting as abnormal behavior (in behavioral variant FTD) or language impairment (in primary progressive aphasia). Although FTD affects all populations across the globe, knowledge regarding the pathophysiology and genetics derives primarily from studies conducted in North America and Western Europe. Globally, biomedical research for FTD is hindered by variable access to diagnosis, discussed in this group's earlier article, and by reduced access to expertise, funding, and infrastructure. This perspective paper was produced by two professional interest areas of the Alzheimer's Association International Society to Advance Alzheimer's Research and Treatment (ISTAART) and discusses the field's current status on the cross-cultural aspects of basic and translational research in FTD (including that focused on epidemiology, genetics, biomarkers, and treatment). It subsequently provides a summary of gaps and needs to address the disparities and advance global FTD biomedical research.

Advancements in dementia research, diagnostics, and care in Latin America: Highlights from the 2023 Alzheimer's Association International conference satellite symposium in Mexico City

INTRODUCTION

While Latin America (LatAm) is facing an increasing burden of dementia due to the rapid aging of the population, it remains underrepresented in dementia research, diagnostics, and care.

METHODS

In 2023, the Alzheimer's Association hosted its eighth satellite symposium in Mexico, highlighting emerging dementia research, priorities, and challenges within LatAm.

RESULTS

Significant initiatives in the region, including intracountry support, showcased their efforts in fostering national and international collaborations; genetic studies unveiled the unique genetic admixture in LatAm; researchers conducting emerging clinical trials discussed ongoing culturally specific interventions; and the urgent need to harmonize practices and studies, improve diagnosis and care, and use affordable biomarkers in the region was highlighted.

DISCUSSION

The myriad of topics discussed at the 2023 AAIC satellite symposium highlighted the growing research efforts in LatAm, providing valuable insights into dementia biology, genetics, epidemiology, treatment, and care.

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.

A synergetic turn in cognitive neuroscience of brain diseases

Despite significant improvements in our understanding of brain diseases, many barriers remain. Cognitive neuroscience faces four major challenges: complex structure-function associations; disease phenotype heterogeneity; the lack of transdiagnostic models; and oversimplified cognitive approaches restricted to the laboratory. Here, we propose a synergetics framework that can help to perform the necessary dimensionality reduction of complex interactions between the brain, body, and environment. The key solutions include low-dimensional spatiotemporal hierarchies for brain-structure associations, whole-brain modeling to handle phenotype diversity, model integration of shared transdiagnostic pathophysiological pathways, and naturalistic frameworks balancing experimental control and ecological validity. Creating whole-brain models with reduced manifolds combined with ecological measures can improve our understanding of brain disease and help identify novel interventions. Synergetics provides an integrated framework for future progress in clinical and cognitive neuroscience, pushing the boundaries of brain health and disease toward more mature, naturalistic approaches.

Diagnostic utility of transcranial magnetic stimulation for neurodegenerative disease: a critical review

Neurodegenerative diseases pose significant challenges due to their impact on brain structure, function, and cognition. As life expectancy rises, the prevalence of these disorders is rapidly increasing, resulting in substantial personal, familial, and societal burdens. Efforts have been made to optimize the diagnostic and therapeutic processes, primarily focusing on clinical, cognitive, and imaging characterization. However, the emergence of non-invasive brain stimulation techniques, specifically transcranial magnetic stimulation (TMS), offers unique functional insights and diagnostic potential. TMS allows direct evaluation of brain function, providing valuable information inaccessible through other methods. This review aims to summarize the current and potential diagnostic utility of TMS in investigating neurodegenerative diseases, highlighting its relevance to the field of cognitive neuroscience. The findings presented herein contribute to the growing body of research focused on improving our understanding and management of these debilitating conditions, particularly in regions with limited resources and a pressing need for innovative approaches.

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.

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.

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.

A predictive coding framework of allostatic-interoceptive overload in frontotemporal dementia

Recent allostatic-interoceptive explanations using predictive coding models propose that efficient regulation of the body's internal milieu is necessary to correctly anticipate environmental needs. We review this framework applied to understanding behavioral variant frontotemporal dementia (bvFTD) considering both allostatic overload and interoceptive deficits. First, we show how this framework could explain divergent deficits in bvFTD (cognitive impairments, behavioral maladjustment, brain atrophy, fronto-insular-temporal network atypicality, aberrant interoceptive electrophysiological activity, and autonomic disbalance). We develop a set of theory-driven predictions based on levels of allostatic interoception associated with bvFTD phenomenology and related physiopathological mechanisms. This approach may help further understand the disparate behavioral and physiopathological dysregulations of bvFTD, suggesting targeted interventions and strengthening clinical models of neurological and psychiatric disorders.

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.

Potential Protein Blood-Based Biomarkers in Different Types of Dementia: A Therapeutic Overview

Abstract:

Biomarkers capable of identifying and distinguishing types of dementia such as Alzheimer's disease (AD), Parkinson's disease dementia (PDD), Lewy body dementia (LBD), and frontotemporal dementia (FTD) have been become increasingly relentless. Studies of possible biomarker proteins in the blood that can help formulate new diagnostic proposals and therapeutic visions of different types of dementia are needed. However, due to several limitations of these biomarkers, especially in discerning dementia, their clinical applications are still undetermined. Thus, the updating of biomarker blood proteins that can help in the diagnosis and discrimination of these main dementia conditions is essential to enable new pharmacological and clinical management strategies, with specificities for each type of dementia. To review the literature concerning protein blood-based AD and non-AD biomarkers as new pharmacological targets and/or therapeutic strategies. Recent findings for protein-based AD, PDD, LBD, and FTD biomarkers are focused on in this review. Protein biomarkers were classified according to the pathophysiology of the dementia types. The diagnosis and distinction of dementia through protein biomarkers is still a challenge. The lack of exclusive biomarkers for each type of dementia highlights the need for further studies in this field. Only after this, blood biomarkers may have a valid use in clinical practice as they are promising to help in diagnosis and in the differentiation of diseases.