Alzheimer's Disease Neuroimaging Initiative Research Articles

Measurement Error and Methodologic Issues in Analyses of the Proportion of Variance Explained in Cognition.

Existing studies examining the predictive ability of biomarkers for cognitive outcomes do not account for variance due to measurement error, which could lead to under-estimates oftheproportion ofvariance explained. We used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) (N = 1084) to estimate the proportion of variance explained by Alzheimer's disease (AD) imaging biomarkers in four cognitive outcomes: memory, executive functioning, language, and visuospatial functioning. We compared estimates from standard models that do not account for measurement error, and multilevel models that do account for measurement error. We also examined estimates across diagnostic subgroups (normal, MCI, AD). Estimates of the proportion of variance explained from multilevel models accounting for measurement error were larger (e.g., for language, 9-47% vs. 7-34% under standard modeling), with relatively greater differences between standard and multilevel measurement models for cognitive outcomes that have larger measurement error variance. Heterogeneity across subgroups also emphasized the importance of sample composition. Future studies should evaluate measurement error adjustments when considerable measurement error in cognitive outcomes is suspected.

Positron emission tomography harmonization in the Alzheimer's Disease Neuroimaging Initiative: A scalable and rigorous approach to multisite amyloid and tau quantification.

A key goal of the Alzheimer's Disease NeuroImaging Initiative (ADNI) positron emission tomography (PET) Core is to harmonize quantification of β-amyloid (Aβ) and tau PET image data across multiple scanners and tracers. We developed an analysis pipeline (Berkeley PET Imaging Pipeline, B-PIP) for ADNI Aβ and tau PET images and applied it to PET data from other multisite studies. Steps include image pre-processing, refacing, magnetic resonance imaging (MRI)/PET co-registration, visual quality control (QC), quantification of tracer uptake, and standardization of Aβ and tau standardized uptake value ratios (SUVrs) across tracers. Measurements from 10,105 cross-sectional and longitudinal Aβ and tau PET scans acquired in several studies between 2010 and 2024 can be processed, harmonized, and directly merged across tracers and cohorts. The B-PIP developed in ADNI is a scalable image harmonization approach used in several observational studies and clinical trials that facilitates rigorous Aβ and tau PET quantification and data sharing. Quantitative results from ADNI Aβ and tau PET data are generated using a rigorous, scalable image processing pipeline This pipeline has been applied to PET data from several other large, multisite studies and trials Quantitative outcomes are harmonizable across studies and are shared with the scientific community.

A Transcriptomics-Based Machine Learning Model Discriminating Mild Cognitive Impairment and the Prediction of Conversion to Alzheimer’s Disease

The clinical spectrum of Alzheimer’s disease (AD) ranges dynamically from asymptomatic and mild cognitive impairment (MCI) to mild, moderate, or severe AD. Although a few disease-modifying treatments, such as lecanemab and donanemab, have been developed, current therapies can only delay disease progression rather than halt it entirely. Therefore, the early detection of MCI and the identification of MCI patients at high risk of progression to AD remain urgent unmet needs in the super-aged era. This study utilized transcriptomics data from cognitively unimpaired (CU) individuals, MCI, and AD patients in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort and leveraged machine learning models to identify biomarkers that differentiate MCI from CU and also distinguish AD from MCI individuals. Furthermore, Cox proportional hazards analysis was conducted to identify biomarkers predictive of the progression from MCI to AD. Our machine learning models identified a unique set of gene expression profiles capable of achieving an area under the curve (AUC) of 0.98 in distinguishing those with MCI from CU individuals. A subset of these biomarkers was also found to be significantly associated with the risk of progression from MCI to AD. A linear mixed model demonstrated that plasma tau phosphorylated at threonine 181 (pTau181) and neurofilament light chain (NFL) exhibit the prognostic value in predicting cognitive decline longitudinally. These findings underscore the potential of integrating machine learning (ML) with transcriptomic profiling in the early detection and prognostication of AD. This integrated approach could facilitate the development of novel diagnostic tools and therapeutic strategies aimed at delaying or preventing the onset of AD in at-risk individuals. Future studies should focus on validating these biomarkers in larger, independent cohorts and further investigating their roles in AD pathogenesis.

Identification of mild cognitive impairment using multimodal 3D imaging data and graph convolutional networks

Objective.Mild cognitive impairment (MCI) is a precursor stage of dementia characterized by mild cognitive decline in one or more cognitive domains, without meeting the criteria for dementia. MCI is considered a prodromal form of Alzheimer's disease (AD). Early identification of MCI is crucial for both intervention and prevention of AD. To accurately identify MCI, a novel multimodal 3D imaging data integration graph convolutional network (GCN) model is designed in this paper.Approach.The proposed model utilizes 3D-VGGNet to extract three-dimensional features from multimodal imaging data (such as structural magnetic resonance imaging and fluorodeoxyglucose positron emission tomography), which are then fused into feature vectors as the node features of a population graph. Non-imaging features of participants are combined with the multimodal imaging data to construct a population sparse graph. Additionally, in order to optimize the connectivity of the graph, we employed the pairwise attribute estimation （PAE） method to compute the edge weights based on non-imaging data, thereby enhancing the effectiveness of the graph structure. Subsequently, a population-based GCN integrates the structural and functional features of different modal images into the features of each participant for MCI classification.Main results.Experiments on the AD Neuroimaging Initiative demonstrated accuracies of 98.57%, 96.03%, and 96.83% for the normal controls (NC)-early MCI (EMCI), NC-late MCI (LMCI), and EMCI-LMCI classification tasks, respectively. The AUC, specificity, sensitivity, and F1-score are also superior to state-of-the-art models, demonstrating the effectiveness of the proposed model. Furthermore, the proposed model is applied to the ABIDE dataset for autism diagnosis, achieving an accuracy of 91.43% and outperforming the state-of-the-art models, indicating excellent generalization capabilities of the proposed model.Significance.This study demonstratesthe proposed model's ability to integrate multimodal imaging data and its excellent ability to recognize MCI. This will help achieve early warning for AD and intelligent diagnosis of other brain neurodegenerative diseases.

An exploration of testing genetic associations using goodness-of-fit statistics based on deep ReLU neural networks

As a driving force of the fourth industrial revolution, deep neural networks are now widely used in various areas of science and technology. Despite the success of deep neural networks in making accurate predictions, their interpretability remains a mystery to researchers. From a statistical point of view, how to conduct statistical inference (e.g., hypothesis testing) based on deep neural networks is still unknown. In this paper, goodness-of-fit statistics are proposed based on commonly used ReLU neural networks, and their potential to test significant input features is explored. A simulation study demonstrates that the proposed test statistic has higher power compared to the commonly used t-test in linear regression when the underlying signal is nonlinear, while controlling the type I error at the desired level. The testing procedure is also applied to gene expression data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI).

Constructing and exploring neuroimaging projects: a survey from clinical practice to scientific research.

Over the past decades, numerous large-scale neuroimaging projects that involved the collection and release of multimodal data have been conducted globally. Distinguished initiatives such as the Human Connectome Project, UK Biobank, and Alzheimer's Disease Neuroimaging Initiative, among others, stand as remarkable international collaborations that have significantly advanced our understanding of the brain. With the advancement of big data technology, changes in healthcare models, and continuous development in biomedical research, various types of large-scale projects are being established and promoted worldwide. For project leaders, there is a need to refer to common principles in project construction and management. Users must also adhere strictly to rules and guidelines, ensuring data safety and privacy protection. Organizations must maintain data integrity, protect individual privacy, and foster stakeholders' trust. Regular updates to legislation and policies are necessary to keep pace with evolving technologies and emerging data-related challenges. CRITICAL RELEVANCE STATEMENT: By reviewing global large-scale neuroimaging projects, we have summarized the standards and norms for establishing and utilizing their data, and provided suggestions and opinions on some ethical issues, aiming to promote higher-quality neuroimaging data development. KEY POINTS: Global neuroimaging projects are increasingly advancing but still face challenges. Constructing and utilizing neuroimaging projects should follow set rules and guidelines. Effective data management and governance should be developed to support neuroimaging projects.

An intelligent magnetic resonance imagining-based multistage Alzheimer's disease classification using swish-convolutional neural networks.

Alzheimer's disease (AD) refers to a neurological disorder that causes damage to brain cells and results in decreasing cognitive abilities and memory. In brain scans, this degeneration can be seen in different ways. The disease can be classified into four stages: Non-demented (ND), moderate demented (MoD), mild demented (MiD), and very mild demented (VMD). To prepare the raw dataset for analysis, the collected magnetic resonance imaging (MRI) images are subjected to several pre-processing techniques in order to improve the performance accuracy of the proposed model. Medical images generally have poor contrast and get affected by noise, which ends up with inaccurate diagnosis. For the different phases of AD to be detected, a clear image is necessary. To address this issue, the influence of the artefacts must be reduced, enhance the contrast, and reduce the loss of information. A novel framework for image enhancement is suggested to increase the accuracy in the detection and identification of AD. In this study, the raw MRI dataset from the Alzheimer's disease neuroimaging initiative (ADNI) database is subjected to skull stripping, contrast enhancement, and image filtering followed by data augmentation to balance the dataset with four types of Alzheimer's classes. The pre-processed data are subjected to five different pre-trained models such as AlexNet, ResNet, VGG 16, EfficientNet, and Inceptionv3 achieving a testing accuracy rate of 91.2%, 88.21%, 92.34%, 93.45%, and 85.12%, respectively. These pre-trained models are compared with the proposed CNN (convolutional neural network) model designed with Adam optimizer and Flatten Swish activation function which reaches the highest accuracy of 96.5% with a learning rate of 0.000001. The five pre-trained CNNmodels along with the proposed swish-based AD-CNN were tested using various performance metrics to evaluate the model efficiency in classifying and identifying the AD classes. From the result analysis, it is evident that the proposed AD-CNN model outperforms all the other models.

Affliction class moderates the dementing impact of adipokines.

Biomarker-specific interventions (e.g., for dementia) will necessitate an individualized approach to treatment. We have constructed a psychometric classifier to identify persons adversely impacted by plasma adipokines. The subjects (N = 1,737) of the Alzheimer's Disease Neuroimaging Initiative were assigned to groups "afflicted" by versus "resilient" against the unique effect of plasma adipokines using a classifier derived by confirmatory factor analysis in a structural equation model framework. The impact of affliction class above and beyond observed biomarker levels and covariates was tested by multivariate regression using CDR "Sum of Boxes" as the dependent variable. The affliction class' moderation of adipokines' effect was tested by chi-square difference. The effect of affliction class on prospective conversion risk was tested by Cox's proportional hazards models. Seven hundred four out of the 1,737 subjects (40.53%) were assigned to the afflicted class. The afflicted subjects had greater dementia severity, lower (adverse) Adipokines factor composite scores (by analysis of variance, F(1, 1,735) 2619.68, p < .001) and higher observed levels of plasma adipokines (by Tukey's honestly significant difference test, all p < .001). Adipokines' association with dementia severity was moderated by affliction class. The effect persisted at 48 months. Afflicted cases were more likely to convert to Alzheimer's disease in that timeframe, by Cox's F: F(234, 286) = 3.89, p < .001. Our approach could guide precision interventions against specific biomarkers. This classifier could be administered by telephone, making class assignment feasible without direct patient contact or biomarker assessment. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Robust double machine learning model with application to omics data.

Recently, there has been a growing interest in combining causal inference with machine learning algorithms. Double machine learning model (DML), as an implementation of this combination, has received widespread attention for their expertise in estimating causal effects within high-dimensional complex data. However, the DML model is sensitive to the presence of outliers and heavy-tailed noise in the outcome variable. In this paper, we propose the robust double machine learning (RDML) model to achieve a robust estimation of causal effects when the distribution of the outcome is contaminated by outliers or exhibits symmetrically heavy-tailed characteristics. In the modelling of RDML model, we employed median machine learning algorithms to achieve robust predictions for the treatment and outcome variables. Subsequently, we established a median regression model for the prediction residuals. These two steps ensure robust causal effect estimation. Simulation study show that the RDML model is comparable to the existing DML model when the data follow normal distribution, while the RDML model has obvious superiority when the data follow mixed normal distribution and t-distribution, which is manifested by having a smaller RMSE. Meanwhile, we also apply the RDML model to the deoxyribonucleic acid methylation dataset from the Alzheimer's disease (AD) neuroimaging initiative database with the aim of investigating the impact of Cerebrospinal Fluid Amyloid 42 (CSF A 42) on AD severity. These findings illustrate that the RDML model is capable of robustly estimating causal effect, even when the outcome distribution is affected by outliers or displays symmetrically heavy-tailed properties.

The ADNI Administrative Core: Ensuring ADNI's success and informing future AD clinical trials.

The Alzheimer's Disease Neuroimaging Initiative (ADNI) Administrative Core oversees and coordinates all ADNI activities, to ensure the success and maximize the impact of ADNI in advancing Alzheimer's disease (AD) research and clinical trials. It manages finances and develops policies for data sharing, publications using ADNI data, and access to ADNI biospecimens. The Core develops and executes pilot projects to guide future ADNI activities and identifies key innovative methods for inclusion in ADNI. For ADNI4, the Administrative Core collaborates with the Engagement, Clinical, and Biomarker Cores to develop and evaluate novel, digital methods and infrastructure for participant recruitment, screening, and assessment of participants. The goal of these efforts is to enroll 500 participants, including>50% from underrepresented populations, 40% with mild cognitive impairment, and 80% with elevated AD biomarkers. This new approach also provides a unique opportunity to validate novel methods. HIGHLIGHTS: The Alzheimer's Disease Neuroimaging Initiative (ADNI) Administrative Core oversees and coordinates all ADNI activities. The overall goal is to ensure ADNI's success and help design future Alzheimer's disease (AD) clinical trials. A key innovation is data sharing without embargo to maximize scientific impact. For ADNI4, novel, digital methods for recruitment and assessment were developed. New methods are designed to improve the participation of underrepresented populations.

Cerebrovascular markers of WMH and infarcts in ADNI: A historical perspective and future directions.

White matter hyperintensities (WMH) and infarcts found on magnetic resonance imaging (MR infarcts) are common biomarkers of cerebrovascular disease. In this review, we summarize the methods, publications, and conclusions stemming from the Alzheimer's Disease Neuroimaging Initiative (ADNI) related to these measures. We combine analysis of WMH and MR infarct data from across the three main ADNI cohorts with a review of existing literature discussing new methodologies and scientific findings derived from these data. Although ADNI inclusion criteria were designed to minimize vascular risk factors and disease, data across all the ADNI cohorts found consistent trends of increasing WMH volumes associated with advancing age, female sex, and cognitive impairment. ADNI, initially proposed as a study to investigate biomarkers of AD pathology, has also helped elucidate the impact of asymptomatic cerebrovascular brain injury on cognition within a cohort relatively free of vascular disease. Future ADNI work will emphasize additional vascular biomarkers. HIGHLIGHTS: White matter hyperintensities (WMHs) are common to advancing age and likely reflect brain vascular injury among older individuals. WMH and to a lesser extent, magnetic resonance (MR) infarcts, affect risk for transition to cognitive impairment. WMHs and MR infarcts are present, even among Alzheimer's Disease Neuroimaging Initiative (ADNI) participants highly selected to have Alzheimer's disease (AD) as the primary pathology. WMH burden in ADNI is greater among individuals with cognitive impairment and has been associated with AD neurodegenerative markers and cerebral amyloidosis. The negative additive effects of cerebrovascular disease appear present, even in select populations, and future biomarker work needs to further explore this relationship.

Integrated cerebellar radiomic-network model for predicting mild cognitive impairment in Alzheimer's disease.

Pathological and neuroimaging alterations in the cerebellum of Alzheimer's disease (AD) patients have been documented. However, the role of cerebellum-derived radiomic and structural connectome modeling in the prediction of AD progression remains unclear. Radiomic features were extracted from magnetic resonance imaging (MRI) in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (n=1319) and an in-house dataset (n=308). Integrated machine learning models were developed to predict the conversion risk of normal cognition (NC) to mild cognitive impairment (MCI) over a 6-year follow-up. The cerebellar models outperformed hippocampal models in distinguishing MCI from NC and in predicting transitions from NC to MCI across both cohorts. Key predictors included textural features in the right III and left I and II lobules, and network properties in Vermis I and II, which were associated with cognitive decline in AD. Cerebellum-derived radiomic-network modeling shows promise as a tool for early identification and prediction of disease progression during the preclinical stage of AD. Altered cerebellar radiomic features and topological networks were identified in the subjects with mild cognitive impairment (MCI). The cerebellar radiomic-network integrated models outperformed hippocampal models in distinguishing MCI from normal cognition. The cerebellar radiomic model effectively predicts MCI risk and can stratify individuals into distinct risk categories. Specific cerebellar radiomic features are associated with cognitive impairment across various stages of amyloid beta and tau pathology.

Sex differences in brain MRI using deep learning toward fairer healthcare outcomes

This study leverages deep learning to analyze sex differences in brain MRI data, aiming to further advance fairness in medical imaging. We employed 3D T1-weighted Magnetic Resonance images from four diverse datasets: Calgary-Campinas-359, OASIS-3, Alzheimer's Disease Neuroimaging Initiative, and Cambridge Center for Aging and Neuroscience, ensuring a balanced representation of sexes and a broad demographic scope. Our methodology focused on minimal preprocessing to preserve the integrity of brain structures, utilizing a Convolutional Neural Network model for sex classification. The model achieved an accuracy of 87% on the test set without employing total intracranial volume (TIV) adjustment techniques. We observed that while the model exhibited biases at extreme brain sizes, it performed with less bias when the TIV distributions overlapped more. Saliency maps were used to identify brain regions significant in sex differentiation, revealing that certain supratentorial and infratentorial regions were important for predictions. Furthermore, our interdisciplinary team, comprising machine learning specialists and a radiologist, ensured diverse perspectives in validating the results. The detailed investigation of sex differences in brain MRI in this study, highlighted by the sex differences map, offers valuable insights into sex-specific aspects of medical imaging and could aid in developing sex-based bias mitigation strategies, contributing to the future development of fair AI algorithms. Awareness of the brain's differences between sexes enables more equitable AI predictions, promoting fairness in healthcare outcomes. Our code and saliency maps are available at https://github.com/mahsadibaji/sex-differences-brain-dl.

Explainable early detection of Alzheimer's disease using ROIs and an ensemble of 138 3D vision transformers.

Early detection and accurate diagnosis of brain morphological abnormalities are essential for the effective management and treatment of Alzheimer's disease (AD) and mild cognitive impairment (MCI). Structural magnetic resonance imaging (MRI) is a powerful support tool to aid in disease diagnosis and prediction. In this research study, we present an innovative approach to predict Alzheimer's disease (AD) and mild cognitive impairment (MCI) using MRI data, which integrates regional interest (ROI)-based methodology and deep learning within a comprehensible framework. The proposed method involves dividing the brain into 138 predetermined sections based on anatomical information. Next, we apply three-dimensional vision transformers (3D-ViTs) to each ROI individually, harnessing the power of deep learning. To improve prediction accuracy, we employ a deep belief network (DBN) as an ensemble learning model. Evaluating our approach on the baseline structural MRI dataset obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, and comparing it against five other competing models, we demonstrate its performance across four binary classification tasks and a three-class classification test (AD vs MCI vs CN (Cognitively Normal)). The proposed system outperforms existing models and provides interpretable insights into the brain regions that significantly contribute to solving each classification problem. Our findings align with the existing body of literature and hold promise for guiding future research directions in this domain.

Exploring the relationship among Alzheimer’s disease, aging and cognitive scores through neuroimaging-based approach

Alzheimer’s disease (AD) is a fatal neurodegenerative disorder, with the Mini-Mental State Examination (MMSE) and Clinical Dementia Rating (CDR) serving significant roles in monitoring its progression. We hypothesize that while cognitive assessment scores can detect AD-related brain changes, the targeted brain regions may differ. Additionally, given AD’s strong association with aging, we propose that specific brain regions are influenced by both AD pathology and aging, exhibiting strong correlations with both. To test these hypotheses, we developed a 3D convolutional network with a mixed-attention mechanism to recognize AD subjects from structural magnetic resonance imaging (sMRI) data and utilize 3D convolutional methods to pinpoint brain regions significantly correlated with the AD, MMSE, CDR and age. All models were trained and internally validated on 417 samples from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), and the classification model was externally validated on 382 samples from the Australian Imaging and Lifestyle flagship (AIBL). This approach provided robust support for using MMSE and CDR in assessing AD progression and visually illustrated the relationship between aging and AD. The analysis revealed correlations among the four identification tasks (AD, MMSE, CDR and age) and highlighted asymmetric brain lesions in both AD and aging. Notably, we found that AD can accelerate aging to some extent, and a significant correlation exists between the rate of aging and cognitive assessment scores. This offers new insights into the relationship between AD and aging.

Tensor Landmark Analysis With Application to ADNI data

ABSTRACTRecent advancements in data collection have facilitated the use of multidimensional arrays, also known as tensors, in prediction of health outcomes. In this article, we introduce a tensor landmark model for predicting survival outcomes using multiple longitudinal biomarkers as tensor covariates through CANDECOMP/PARAFAC (CP) decomposition. An iteratively reweighted least squares estimation is adopted for components of the tensor coefficients in the landmark model. We also present empirical results of the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) for right‐censored data to select the CP rank. Simulations and Alzheimer's disease neuroimaging initiative (ADNI) data analysis demonstrate that our proposed model accurately estimates survival coefficients and predicts survival probabilities. The implementation code can be found online (https://github.com/sparkqkr/TensorCoxReg).

Valuing the importance of sex differences in prodromal Alzheimer's disease based on structural magnetic resonance imaging.

Alzheimer's disease (AD) can be optimally managed from a healthcare point of view if detected at a prodromal stage. Amnestic mild cognitive impairment (MCI) is known as prodromal AD, has attracted extensive attention and research. To identify the differences in cognitive function and structural magnetic resonance imaging (MRI) features between men and women with MCI on the basis of A/T/N classification system ("A" means amyloid-β biomarker, "T" means tau biomarker, and "N" means neurodegeneration biomarker as determined by clinical imaging (e.g., positron emission tomography, magnetic resonance imaging (MRI)) or by the measurement of total tau protein (T-tau) in the cerebrospinal fluid (CSF)) and to further explore the correlation between them. 406 MCI subjects were selected from the Alzheimer's Disease Neuroimaging Initiative database and divided into male and female MCI groups. Differences in demographic characteristics, biomarkers, cognitive assessment performance, and regions of interest (ROIs) of structural MRI were compared between the two groups. The correlations between brain structural changes quantified by MRI and cognitive abilities were investigated through linear regression models. Compared with male MCI subjects, females had significantly higher T-tau concentration in CSF. There were significant differences in ROIs between the sex groups. In the male MCI group, the average cortical thicknesses of the right posterior cingulate, right anterior cingulate and right supramarginal gyrus were more closely correlated with cognitive function. In the female MCI group, the volume of the right rostral anterior cingulate, and the surface area and average cortical thickness of the right isthmus of the cingulate gyrus were more closely correlated with cognitive function. Based on A/T/N classification system, the structural MRI data analysis was closely correlated with the difference of cognitive function from patients with prodromal AD in a sex-dependent manner.

Dementia risk reduction in the African context: Multi-national implementation of multimodal strategies to promote healthy brain aging in Africa (the Africa-FINGERS project).

Dementia prevention in Africa is critically underexplored, despite the continent's high prevalence of modifiable risk factors. With a predominantly young and middle-aged population, Africa presents a prime opportunity to implement evidence-based strategies that could significantly reduce future dementia cases and mitigate its economic impact. The multinational Africa-FINGERS program offers an innovative solution, pioneering culturally sensitive, multidomain interventions tailored to the unique challenges of the region. Leveraging insights from landmark global studies such as Worldwide-FINGERS and Alzheimer's Disease Neuroimaging Initiative, the program employs a multideterminant precision prevention framework, grounded in community based systems dynamics. Africa-FINGERS further integrates cutting-edge state-of-the-art multimodal biomarker evaluations tailored to regional contexts, with the goal of advancing brain health and establishing a global standard for dementia prevention. This groundbreaking initiative highlights the potential for scalableand sustainable interventions, thus is poised to transform dementia risk reduction efforts across the continent. HIGHLIGHTS: Dementia rates are escalating in Africa, largely due to longer life spans and increased prevalence of modifiable risk factors. Yet, few regional interventions have directly targeted lifestyle factors to reduce dementia risk. The multinational Africa-FINGERS study will address this gap by adapting the successful FINGERS lifestyle intervention to African populations. Africa-FINGERS will pioneer a culturally informed, multidomain dementia risk reduction intervention in the African region through feasibility dementia prevention trials in rural and urban sites across Kenya and Nigeria in the first instance, enrolling 600 at-risk adults (≥ 50 years). The program adoptsparticipatory research methods to develop culturally appropriate interventions and build infrastructure to evaluate dementia biomarkers from ante and post mortem samples. A cost-effectiveness analysis will be conducted to guide the strategic implementation of Africa-FINGERS into regional health systems. The Africa-FINGERS strategy aligns with the Worldwide-FINGERS framework and integrates the global Alzheimer's Disease Neuroimaging Initiative approach, emphasizing multimodal analysis.

A new AI-assisted data standard accelerates interoperability in biomedical research.

In this paper, we leveraged Large Language Models(LLMs) to accelerate data wrangling and automate labor-intensive aspects of data discovery and harmonization. This work promotes interoperability standards and enhances data discovery, facilitating AI-readiness in biomedical science with the generation of Common Data Elements (CDEs) as key to harmonizing multiple datasets. Thirty-one studies, various ontologies, and medical coding systems served as source material to create CDEs from which available metadata and context was sent as an API request to 4th-generation OpenAI GPT models to populate each metadata field. A human-in-the-loop (HITL) approach was used to assess quality and accuracy of the generated CDEs. To regulate CDE generation, we employed ElasticSearch and HITL to avoid duplicate CDEs and instead, added them as potential aliases for existing CDEs. The generated CDEs are foundational to assess the interoperability potential of datasets by determining how many data set column headers can be correctly mapped to CDEs as well as quantifying compliance with permissible values and data types. Subject matter experts reviewed generated CDEs and determined that 94.0% of generated metadata fields did not require manual revisions. Data tables from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Global Parkinson's Genetic Program (GP2) were used as test cases for interoperability assessments. Column headers from all test cases were successfully mapped to generated CDEs at a rate of 32.4% via elastic search.The interoperability score, a metric for dataset compatibility to CDEs and other connected datasets, based on relevant criteria such as data field completeness and compliance with common harmonization standards averaged 53.8 out of 100 for test cases. With this project, we aim to automate the most tedious aspects of data harmonization, enhancing efficiency and scalability in biomedical research while decreasing activation energy for federated research.

Global brain activity and its coupling with cerebrospinal fluid flow is related to tau pathology.

Factors responsible for the deposition of pathological tau in the brain are incompletely understood. This study links macroscale tau deposition in the human brain to cerebrospinal fluid (CSF) flow dynamics using resting-state functional magnetic resonance imaging (rsfMRI). Low-frequency (<0.1Hz) resting-state global brain activity is coupled with CSF flow and potentially reflects CSF dynamics-related clearance. We examined the correlation between rsfMRI measures of CSF inflow and global activity (gBOLD-CSF coupling) as a predictor, interacting with amyloid beta (Aβ), of tau and cortical thickness (dependent variables) across Alzheimer's Disease Neuroimaging Initiative (ADNI) participants from cognitively unimpaired through mild cognitive impairment (MCI) and Alzheimer's disease (AD). Tau deposition in Aβ+ participants, accompanied by cortical thinning and cognitive decline, is associated with decreased gBOLD-CSF coupling. Tau mediates the relationship between coupling and thickness. Findings suggest that resting-state global brain activity and CSF movements comodulate Alzheimer's tau deposition, presumably related to CSF clearance. A non-invasive functional magnetic resonance imaging (fMRI) assessment of a CSF clearance-related process is carried out. Global brain activity is coupled with CSF inflow in human fMRI during resting state. Global fMRI-CSF coupling is correlated with tau in Alzheimer's disease (AD). This coupling measure is also associated with cortical thickness, mediated by tau.