Brain clocks capture diversity and disparity in aging and dementia.

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.

The Mediterranean diet is not associated with neuroimaging or cognition in middle-aged adults: a cross-sectional analysis of the PREVENT dementia programme.

BACKGROUND AND PURPOSE: The Mediterranean diet (MedDiet) has been associated with reduced dementia incidence in several studies. It is important to understand if diet is associated with brain health in midlife, when Alzheimer's disease and related dementias are known to begin. METHODS: This study used data from the PREVENT dementia programme. Three MedDiet scores were created (the Pyramid, Mediterranean Diet Adherence Screener [MEDAS] and MEDAS continuous) from a self-reported food frequency questionnaire. Primary outcomes were hippocampal volume and cube-transformed white matter hyperintensity volume. Secondary outcomes included cornu ammonis 1 and subiculum hippocampal subfield volumes, cortical thickness and measures of cognition. Sex-stratified analyses were run to explore differential associations between diet and brain health by sex. An exploratory path analysis was conducted to study if any associations between diet and brain health were mediated by cardiovascular risk factors for dementia. RESULTS: In all, 504 participants were included in this analysis, with a mean Pyramid score of 8.10 (SD 1.56). There were no significant associations between any MedDiet scoring method and any of the primary or secondary outcomes. There were no differences by sex in any analyses and no significant mediation between the Pyramid score and global cognition by cardiovascular risk factors. CONCLUSIONS: Overall, this study did not find evidence for an association between the MedDiet and either neuroimaging or cognition in a midlife population study. Future work should investigate associations between the MedDiet and Alzheimer's disease and related dementias biomarkers as well as functional neuroimaging in a midlife population.

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.

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.

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.

FAST functional connectivity implicates P300 connectivity in working memory deficits in Alzheimer's disease.

Measuring transient functional connectivity is an important challenge in Electroencephalogram (EEG) research. Here, the rich potential for insightful, discriminative information of brain activity offered by high temporal resolution is confounded by the inherent noise of the medium and the spurious nature of correlations computed over short temporal windows. We propose a novel methodology to overcome these problems called Filter Average Short-Term (FAST) functional connectivity. First, long-term, stable, functional connectivity is averaged across an entire study cohort for a given pair of Visual Short Term Memory (VSTM) tasks. The resulting average connectivity matrix, containing information on the strongest general connections for the tasks, is used as a filter to analyse the transient high temporal resolution functional connectivity of individual subjects. In simulations, we show that this method accurately discriminates differences in noisy Event-Related Potentials (ERPs) between two conditions where standard connectivity and other comparable methods fail. We then apply this to analyse activity related to visual short-term memory binding deficits in two cohorts of familial and sporadic Alzheimer's disease. Reproducible significant differences were found in the binding task with no significant difference in the shape task in the P300 ERP range. This allows new sensitive measurements of transient functional connectivity, which can be implemented to obtain results of clinical significance.

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.

Age-related hearing loss associated with differences in the neural correlates of feature binding in visual working memory.

The underlying neural mechanisms underpinning the association between age-related hearing loss (ARHL) and dementia remain unclear. A limitation has been the lack of functional neuroimaging studies in ARHL cohorts to help clarify this relationship. In the present study, we investigated the neural correlates of feature binding in visual working memory with ARHL (controls = 14, mild HL = 21, and moderate or greater HL = 23). Participants completed a visual change detection task assessing feature binding while their neural activity was synchronously recorded via high-density electroencephalography. There was no difference in accuracy scores for ARHL groups compared to controls. There was increased electrophysiological activity in those with ARHL, particularly in components indexing the earlier stages of visual cognitive processing. This activity was more pronounced with more severe ARHL and was associated with maintained feature binding. Source space (sLORETA) analyses indicated greater activity in networks modulated by frontoparietal and temporal regions. Our results demonstrate there may be increased involvement of neurocognitive control networks to maintain lower-order neurocognitive processing disrupted by ARHL.

The "when" matters: Evidence from memory markers in the clinical continuum of Alzheimer's disease.

OBJECTIVE: Cognitive assessment able to detect impairments in the early neuropathological stages of Alzheimer's disease (AD) is urgently needed. The visual short-term memory binding task (VSTMBT) and the Free and Cued Selective Reminding Test (FCSRT) have been recommended by the neurodegenerative disease working group as promising tests to aid in the early detection of AD. In this study, we investigated their complementary value across the clinical stages of the AD continuum. METHOD: One hundred and seventeen older adults with subjective cognitive complaint (SCC), 79 with mild cognitive impairment (MCI), 31 patients with AD dementia (ADD), and 37 cognitively unimpaired (CU) subjects, underwent assessment with the VSTMBT and the picture version of the Spanish FCSRT. RESULTS: After controlling for multiple comparisons, significant differences were found across groups. The VSTMBT was the only test that "marginally" differentiated between CU and SCC (d = 0.47, p = .052). Moreover, whereas the FCSRT showed a gradient (CU = SCC) > MCI > ADD, the VSTMBT gradient was CU > SCC > (MCI = ADD) suggesting that conjunctive binding deficits assessed by the latter may be sensitive to the very early stages of the disease. CONCLUSIONS: Our results suggest that the VSTMBT and the FCSRT are sensitive to the clinical continuum of AD. Whereas the former detects changes in the early prodromal stages, the latter is more sensitive to the advanced prodromal stages of AD. These novel tests can aid in the early detection, monitor disease progression and response to treatment, and thus support drug development programs. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The limitations and challenges in the assessment of executive dysfunction associated with real-world functioning: The opportunity of serious games.

Nowadays, there is a broad range of methods for detecting and evaluating executive dysfunction ranging from clinical interview to neuropsychological evaluation. Nevertheless, a critical issue of these assessments is the lack of correspondence of the neuropsychological test's results with real-world functioning. This paper proposes serious games as a new framework to improve the neuropsychological assessment of real-world functioning. We briefly discuss the contribution and limitations of current methods of evaluation of executive dysfunction (paper-and-pencil tests, naturalistic observation methods, and Information and Communications Technologies) to inform on daily life functioning. Then, we analyze what are the limitations of these methods to predict real-world performance: (1) A lack of appropriate instruments to investigate the complexity of real-world functioning, (2) the vast majority of neuropsychological tests assess well-structured tasks, and (3) measurement of behaviors are based on simplistic data collection and statistical analysis. This work shows how serious games offer an opportunity to develop more efficient tools to detect executive dysfunction in everyday life contexts. Serious games provide meaningful narrative stories and virtual or real environments that immerse the user in natural and social environments with social interactions. In those highly interactive game environments, the player needs to adapt his/her behavioral performance to novel and ill-structured tasks which are suited for collecting user interaction evidence. Serious games offer a novel opportunity to develop better tools to improve diagnosis of the executive dysfunction in everyday life contexts. However, more research is still needed to implement serious games in everyday clinical practice.

Short-term memory conjunctive binding in Alzheimer's disease: A systematic review and meta-analysis.

OBJECTIVE: Short-term memory (STM) binding tests assess the ability to temporarily hold conjunctions between surface features, such as objects and their colors (i.e., feature binding condition), relative to the ability to hold the individual features (i.e., single feature condition). Impairments in performance of these tests have been considered cognitive markers of Alzheimer's disease (AD). The objective of the present study was to conduct a meta-analysis of results from STM binding tests used in the assessment of samples mapped along the AD clinical continuum. METHOD: We searched PubMed, Scopus, and Web of Science for articles that assessed patients with AD (from preclinical to dementia) using the STM binding tests and compared their results with those of controls. From each relevant article, we extracted the number of participants, the mean and standard deviations from single feature and of feature binding conditions. Results across studies were combined using standardized mean differences (effect sizes) to produce overall estimates of effect. RESULTS: The feature binding condition of the STM binding showed large effects in all stages of AD. However, small sample sizes across studies, the presence of moderate to high heterogeneity and cross-sectional, case-controls designs decreased our confidence in the current evidence. CONCLUSIONS: To be considered as a cognitive marker for AD, properly powered longitudinal designs and studies that clearly relate conjunctive memory tests with biomarkers (amyloid and tau) are still needed. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

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.

Memory-driven eye movements prospectively predict dementia in people at risk of Alzheimer's disease.

Introduction: Oculomotor behaviors linked to cognitive performance revealed neurocognitive features of Alzheimer's disease (AD) that can enhance the accuracy of its assessment and diagnosis. Methods: A sample of 107 participants (i.e., 65 mild cognitive impairment [MCI] and 42 controls) were recruited and followed up for 40 months. At baseline, they underwent assessment with the ViewMind digital biomarker, which draws cognitive-related patterns of eye movement while people perform the visual short-term memory binding task. Results: Baseline data predicted that 36 patients with MCI would progress to the AD clinical syndrome (ADS Progressing). The remaining 29 MCI patients were predicted to remain as MCI or progress to other forms of dementia. After 40 months of follow-up, 94% of ADS Progressing patients had received a diagnosis of dementia, whereas none of the non-ADS Progressing had. Discussion: The analysis of eye movement behavior combined with cognitive markers for AD can effectively predict progression to ADS among patients with MCI.

Memory markers in the continuum of the Alzheimer's clinical syndrome.

BACKGROUND: The individual and complementary value of the Visual Short-Term Memory Binding Test (VSTMBT) and the Free and Cued Selective Reminding Test (FCSRT) as markers to trace the AD continuum was investigated. It was hypothesised that the VSTMBT would be an early indicator while the FCSRT would inform on imminent progression. METHODS: Healthy older adults (n=70) and patients with mild cognitive impairment (MCI) (n=80) were recruited and followed up between 2012 and 2017. Participants with at least two assessment points entered the study. Using baseline and follow-up assessments four groups were defined: Older adults who were healthy (HOA), with very mild cognitive but not functional impairment (eMCI), and with MCI who did and did not convert to dementia (MCI converters and non-converters). RESULTS: Only the VSTMBT predicted group membership in the very early stages (HOA vs eMCI). As the disease progressed, the FCSRT became a strong predictor excluding the VSTMB from the models. Their complementary value was high during the mid-prodromal stages and decreased in stages closer to dementia. DISCUSSION: The study supports the notion that neuropsychological assessment for AD needs to abandon the notion of one-size-fits-all. A memory toolkit for AD needs to consider tools that are early indicators and tools that suggest imminent progression. The VSTMBT and the FSCRT are such tools.

A novel deep learning approach using AlexNet for the classification of electroencephalograms in Alzheimer's Disease and Mild Cognitive Impairment.

Alzheimer's Disease (AD) is the most common form of dementia. Mild Cognitive Impairment (MCI) is the term given to the stage describing prodromal AD and represents a 'risk factor' in early-stage AD diagnosis from normal cognitive decline due to ageing. The electroencephalogram (EEG) has been studied extensively for AD characterization, but reliable early-stage diagnosis continues to present a challenge. The aim of this study was to introduce a novel way of classifying between AD patients, MCI subjects, and age-matched healthy control (HC) subjects using EEG-derived feature images and deep learning techniques. The EEG recordings of 141 age-matched subjects (52 AD, 37 MCI, 52 HC) were converted into 2D greyscale images representing the Pearson correlation coefficients and the distance Lempel-Ziv Complexity (dLZC) between the 21 EEG channels. Each feature type was computed from EEG epochs of 1s, 2s, 5s and 10s segmented from the original recording. The CNN architecture AlexNet was modified and employed for this three-way classification task and a 70/30 split was used for training and validation with each of the different epoch lengths and EEG-derived images. Whilst a maximum classification accuracy of 73.49% was obtained using dLZC-derived images from 10s epochs as input to the model, the classification accuracy reached 98.13% using the images obtained from Pearson correlation coefficients and 5s epochs. Clinical Relevance- The preliminary findings from this study show that deep learning applied to the analysis of the EEG can classify subjects with accuracies close to 100.

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.

Treatment effects on event-related EEG potentials and oscillations in Alzheimer's disease.

Alzheimer's disease dementia (ADD) is the most diffuse neurodegenerative disorder belonging to mild cognitive impairment (MCI) and dementia in old persons. This disease is provoked by an abnormal accumulation of amyloid-beta and tauopathy proteins in the brain. Very recently, the first disease-modifying drug has been licensed with reserve (i.e., Aducanumab). Therefore, there is a need to identify and use biomarkers probing the neurophysiological underpinnings of human cognitive functions to test the clinical efficacy of that drug. In this regard, event-related electroencephalographic potentials (ERPs) and oscillations (EROs) are promising candidates. Here, an Expert Panel from the Electrophysiology Professional Interest Area of the Alzheimer's Association and Global Brain Consortium reviewed the field literature on the effects of the most used symptomatic drug against ADD (i.e., Acetylcholinesterase inhibitors) on ERPs and EROs in ADD patients with MCI and dementia at the group level. The most convincing results were found in ADD patients. In those patients, Acetylcholinesterase inhibitors partially normalized ERP P300 peak latency and amplitude in oddball paradigms using visual stimuli. In these same paradigms, those drugs partially normalize ERO phase-locking at the theta band (4-7 Hz) and spectral coherence between electrode pairs at the gamma (around 40 Hz) band. These results are of great interest and may motivate multicentric, double-blind, randomized, and placebo-controlled clinical trials in MCI and ADD patients for final cross-validation.

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.