The Worldwide Alzheimer's Disease Neuroimaging Initiative: An update

The role of the Alzheimer's Association in the genesis of Alzheimer's Disease Neuroimaging Initiative

Here we highlight the Alzheimer's Association's role since its inception, as a strategic collaborator with National Institutes of Health-National Institute on Aging in the development of the modern era of the Alzheimer's Movement and in making Alzheimer's disease (AD) a national priority in the United States by developing several initiatives to advance knowledge about the cause, diagnosis, and treatment of dementia. Among these collaborative undertakings, the Alzheimer's Disease Neuroimaging Initiative (ADNI) is an exemplary case, launched with groundwork by the Neuroimaging Working Group sponsored by the Association's Ronald and Nancy Reagan Research Institute on AD. The unique contribution of the Association to the development of ADNI includes participation as a member of ADNI's Private Partner Scientific Board and involvement in developing an AD biomarker standardization and validation subproject, which has led to a conceptual shift in the field to define AD based on its underlying biology. Furthermore, the creation of Worldwide ADNI (WW-ADNI) is highlighted, underscoring the global impact of these efforts. HIGHLIGHTS: The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a keystone undertaking in the evolving landscape of Alzheimer's disease (AD) research, and is now in its fourth iteration. The Alzheimer's Association has partnered with ADNI since its inception. ADNI 4 and the Association continue to collaborate, ensuring representation within the study population.

Sharing brain imaging data in the Open Science era: how and why?

The sharing of human neuroimaging data has great potential to accelerate the development of imaging biomarkers in neurological and psychiatric disorders; however, major obstacles remain in terms of how and why to share data in the Open Science context. In this Health Policy by the European Cluster for Imaging Biomarkers, we outline the current main opportunities and challenges based on the results of an online survey disseminated among senior scientists in the field. Although the scientific community fully recognises the importance of data sharing, technical, legal, and motivational aspects often prevent active adoption. Therefore, we provide practical advice on how to overcome the technical barriers. We also call for a harmonised application of the General Data Protection Regulation across EU countries. Finally, we suggest the development of a system that makes data count by recognising the generation and sharing of data as a highly valuable contribution to the community.

Genetic architectures of cerebral ventricles and their overlap with neuropsychiatric traits

The cerebral ventricles are recognized as windows into brain development and disease, yet their genetic architectures, underlying neural mechanisms and utility in maintaining brain health remain elusive. Here we aggregated genetic and neuroimaging data from 61,974 participants (age range, 9 to 98 years) in five cohorts to elucidate the genetic basis of ventricular morphology and examined their overlap with neuropsychiatric traits. Genome-wide association analysis in a discovery sample of 31,880 individuals identified 62 unique loci and 785 candidate genes associated with ventricular morphology. We replicated over 80% of loci in a well-matched cohort of lateral ventricular volume. Gene set analysis revealed enrichment of ventricular-trait-associated genes in biological processes and disease pathogenesis during both early brain development and degeneration. We explored the age-dependent genetic associations in cohorts of different age groups to investigate the possible roles of ventricular-trait-associated loci in neurodevelopmental and neurodegenerative processes. We describe the genetic overlap between ventricular and neuropsychiatric traits through comprehensive integrative approaches under correlative and causal assumptions. We propose the volume of the inferior lateral ventricles as a heritable endophenotype to predict the risk of Alzheimer's disease, which might be a consequence of prodromal Alzheimer's disease. Our study provides an advance in understanding the genetics of the cerebral ventricles and demonstrates the potential utility of ventricular measurements in tracking brain disorders and maintaining brain health across the lifespan.

Alzheimer's disease: The role of proteins in formation, mechanisms, and new therapeutic approaches

Alzheimer's disease (AD) is a progressive neurological disorder that affects the central nervous system (CNS), leading to memory and cognitive decline. In AD, the brain experiences three main structural changes: a significant decrease in the quantity of neurons, the development of neurofibrillary tangles (NFT) composed of hyperphosphorylated tau protein, and the formation of amyloid beta (Aβ) or senile plaques, which are protein deposits found outside cells and surrounded by dystrophic neurites. Genetic studies have identified four genes associated with autosomal dominant or familial early-onset AD (FAD): amyloid precursor protein (APP), presenilin 1 (PS1), presenilin 2 (PS2), and apolipoprotein E (ApoE). The formation of plaques primarily involves the accumulation of Aβ, which can be influenced by mutations in APP, PS1, PS2, or ApoE genes. Mutations in the APP and presenilin (PS) proteins can cause an increased amyloid β peptides production, especially the further form of amyloidogenic known as Aβ42. Apart from genetic factors, environmental factors such as cytokines and neurotoxins may also have a significant impact on the development and progression of AD by influencing the formation of amyloid plaques and intracellular tangles. Exploring the causes and implications of protein aggregation in the brain could lead to innovative therapeutic approaches. Some promising therapy strategies that have reached the clinical stage include using acetylcholinesterase inhibitors, estrogen, nonsteroidal anti-inflammatory drugs (NSAIDs), antioxidants, and antiapoptotic agents. The most hopeful therapeutic strategies involve inhibiting activity of secretase and preventing the β-amyloid oligomers and fibrils formation, which are associated with the β-amyloid fibrils accumulation in AD. Additionally, immunotherapy development holds promise as a progressive therapeutic approach for treatment of AD. Recently, the two primary categories of brain stimulation techniques that have been studied for the treatment of AD are invasive brain stimulation (IBS) and non-invasive brain stimulation (NIBS). In this article, the amyloid proteins that play a significant role in the AD formation, the mechanism of disease formation as well as new drugs utilized to treat of AD will be reviewed.

CCDB: A database for exploring inter-chemical correlations in metabolomics and exposomics datasets

Inter-chemical correlations in metabolomics and exposomics datasets provide valuable information for studying relationships among chemicals reported for human specimens. With an increase in the number of compounds for these datasets, a network graph analysis and visualization of the correlation structure is difficult to interpret. We have developed the Chemical Correlation Database (CCDB), as a systematic catalogue of inter-chemical correlation in publicly available metabolomics and exposomics studies. The database has been provided via an online interface to create single compound-centric views. We have demonstrated various applications of the database to explore: 1) the chemicals from a chemical class such as Per- and Polyfluoroalkyl Substances (PFAS), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), phthalates and tobacco smoke related metabolites; 2) xenobiotic metabolites such as caffeine and acetaminophen; 3) endogenous metabolites (acyl-carnitines); and 4) unannotated peaks for PFAS. The database has a rich collection of 35 human studies, including the National Health and Nutrition Examination Survey (NHANES) and high-quality untargeted metabolomics datasets. CCDB is supported by a simple, interactive and user-friendly web-interface to retrieve and visualize the inter-chemical correlation data. The CCDB has the potential to be a key computational resource in metabolomics and exposomics facilitating the expansion of our understanding about biological and chemical relationships among metabolites and chemical exposures in the human body. The database is available at www.ccdb.idsl.me site.

Neuroproteomics Chip-Based Mass Spectrometry and Other Techniques for Alzheimer´S Disease Biomarkers – Update

Background:

Alzheimer's disease (AD) is a progressive neurodegenerative disease of growing interest given that there is cognitive damage and symptom onset acceleration. Therefore, it is important to find AD biomarkers for early diagnosis, disease progression, and discrimination of AD and other diseases.

Objective:

To update the relevance of mass spectrometry for the identification of peptides and proteins involved in AD useful as discriminating biomarkers.

Methods:

Proteomics and peptidomics technologies that show the highest possible specificity and selectivity for AD biomarkers are analyzed, together with the biological fluids used. In addition to positron emission tomography and magnetic resonance imaging, MALDI-TOF mass spectrometry is widely used to identify proteins and peptides involved in AD. The use of protein chips in SELDI technology and electroblotting chips for peptides makes feasible small amounts (L) of samples for analysis.

Results:

Suitable biomarkers are related to AD pathology, such as intracellular neurofibrillary tangles; extraneuronal senile plaques; neuronal and axonal degeneration; inflammation and oxidative stress. Recently, peptides were added to the candidate list, which are not amyloid-b or tau fragments, but are related to coagulation, brain plasticity, and complement/neuroinflammation systems involving the neurovascular unit.

Conclusion:

The progress made in the application of mass spectrometry and recent chip techniques is promising for discriminating between AD, mild cognitive impairment, and matched healthy controls. The application of this technique to blood samples from patients with AD has shown to be less invasive and fast enough to determine the diagnosis, stage of the disease, prognosis, and follow-up of the therapeutic response.

Measurement invariance testing of longitudinal neuropsychiatric test scores distinguishes pathological from normative cognitive decline and highlights its potential in early detection research

OBJECTIVE

Alzheimer's disease (AD) is a growing challenge worldwide, which is why the search for early-onset predictors must be focused as soon as possible. Longitudinal studies that investigate courses of neuropsychological and other variables screen for such predictors correlated to mild cognitive impairment (MCI). However, one often neglected issue in analyses of such studies is measurement invariance (MI), which is often assumed but not tested for. This study uses the absence of MI (non-MI) and latent factor scores instead of composite variables to assess properties of cognitive domains, compensation mechanisms, and their predictability to establish a method for a more comprehensive understanding of pathological cognitive decline.

METHODS

An exploratory factor analysis (EFA) and a set of increasingly restricted confirmatory factor analyses (CFAs) were conducted to find latent factors, compared them with the composite approach, and to test for longitudinal (partial-)MI in a neuropsychiatric test battery, consisting of 14 test variables. A total of 330 elderly (mean age: 73.78 ± 1.52 years at baseline) were analyzed two times (3 years apart).

RESULTS

EFA revealed a four-factor model representing declarative memory, attention, working memory, and visual-spatial processing. Based on CFA, an accurate model was estimated across both measurement timepoints. Partial non-MI was found for parameters such as loadings, test- and latent factor intercepts as well as latent factor variances. The latent factor approach was preferable to the composite approach.

CONCLUSION

The overall assessment of non-MI latent factors may pose a possible target for this field of research. Hence, the non-MI of variances indicated variables that are especially suited for the prediction of pathological cognitive decline, while non-MI of intercepts indicated general aging-related decline. As a result, the sole assessment of MI may help distinguish pathological from normative aging processes and additionally may reveal compensatory neuropsychological mechanisms.

BMI1 is associated with CS8F amyloid-β and rates of cognitive decline in Alzheimer's disease

Background

Accumulating evidence suggests that BMI1 confers protective effects against Alzheimer’s disease (AD). However, the mechanism remains elusive. Based on recent pathophysiological evidence, we sought for the first time to identify genetic variants in BMI1 as associated with AD biomarkers, including amyloid-β.

Methods

We used genetic, longitudinal cognition, and cerebrospinal fluid (CSF) biomarker data from participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort (N = 1565). First, we performed a gene-based association analysis of common single nucleotide polymorphisms (SNPs) (minor allele frequency (MAF) > 5%) located within ± 20 kb of the gene boundary of BMI1, an optimal width for including potential regulatory SNPs in the 5′ and 3′ untranslated regions (UTR) of BMI1, with CSF Aβ_1-42 levels. Second, we performed cross-sectional and longitudinal association analyses of SNPs in BMI1 with cognitive performance using linear and mixed-effects models. We replicated association of SNPs in BMI1 with cognitive performance in an independent cohort (N=1084), Religious Orders Study and the Rush Memory and Aging Project (ROS/MAP).

Results

Gene-based genetic association analysis showed that BMI1 was significantly associated with CSF Aβ_1-42 levels after adjusting for multiple testing using permutation (permutation-corrected p value=0.005). rs17415557 in BMI1 showed the most significant association with CSF Aβ_1-42 levels. Participants with minor alleles of rs17415557 have increased CSF Aβ_1-42 levels compared to those with no minor alleles. Further analysis identified and replicated the minor allele of rs17415557 as being significantly associated with slower cognitive decline rates in AD.

Conclusions

Our findings provide fundamental evidence that BMI1 rs17415557 may serve as a protective mechanism related to AD pathogenesis, which supports the results of previous studies linking BMI1 to protection against AD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13195-021-00906-4.

Deep Learning with Neuroimaging and Genomics in Alzheimer's Disease

A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of Alzheimer's disease (AD). In light of the latest advancements in neuroimaging and genomics, numerous deep learning models are being exploited to distinguish AD from normal controls and/or to distinguish AD from mild cognitive impairment in recent research studies. In this review, we focus on the latest developments for AD prediction using deep learning techniques in cooperation with the principles of neuroimaging and genomics. First, we narrate various investigations that make use of deep learning algorithms to establish AD prediction using genomics or neuroimaging data. Particularly, we delineate relevant integrative neuroimaging genomics investigations that leverage deep learning methods to forecast AD on the basis of incorporating both neuroimaging and genomics data. Moreover, we outline the limitations as regards to the recent AD investigations of deep learning with neuroimaging and genomics. Finally, we depict a discussion of challenges and directions for future research. The main novelty of this work is that we summarize the major points of these investigations and scrutinize the similarities and differences among these investigations.

Evolution of the Research Literature and the Scientific Community of Alzheimer's Disease from 1983-2017: A 35-Year Survey

This study surveys the development of Alzheimer's disease (AD) in the research literature, the scientific community, and the journals containing AD papers over a 35-year period. Research papers on AD published from 1983 to 2017 in journals indexed in the Web of Science were analyzed in seven five-year periods. The number of AD papers increased from 1,095 in 1983-1987 to 50,532 by 2013-2017 and in the same time period, the number of participating countries went from 27 to 152. The US was the most prolific country throughout, followed by several European countries, Canada, Australia, and Japan. Asian countries have emerged and by 2013-2017, China surpassed all but the US in productivity. Countries in Latin America and Africa have also contributed to AD research. Additionally, several new non-governmental institutions (e.g., ADNI, ADI) have emerged and now play a key role in the fight against AD. Likewise the AD scientific publishing universe evolved in various aspects: an increase in number of journals containing AD papers (227 journals in 1983-1987 to 3,257 in 2013-2017); appearance of several AD-focused journals, e.g., Alzheimer's & Dementia, Journal of Alzheimer's Disease; and the development of special issues dedicated to AD. Our paper complements the numerous extant papers on theoretical and clinical aspects of AD and provides a description of the research landscape of the countries and journals contributing papers related to AD.

Early Identification of Alzheimer's Disease in Mouse Models: Application of Deep Neural Network Algorithm to Cognitive Behavioral Parameters

Alzheimer's disease (AD) is a worldwide burden. Diagnosis is complicated by the fact that AD is asymptomatic at an early stage. Studies using AD-modeled animals offer important and useful insights. Here, we classified mice with a high risk of AD at a preclinical stage by using only their behaviors. Wild-type and knock-in AD-modeled (App NL-G-F/NL-G-F ) mice were raised, and their cognitive behaviors were assessed in an automated monitoring system. The classification utilized a machine learning method, i.e., a deep neural network, together with optimized stepwise feature selection and cross-validation. The AD risk could be identified on the basis of compulsive and learning behaviors (89.3% ± 9.8% accuracy) shown by AD-modeled mice in the early age (i.e., 8-12 months old) when the AD symptomatic cognitions were relatively underdeveloped. This finding reveals the advantage of machine learning in unveiling the importance of compulsive and learning behaviors for early AD diagnosis in mice.

The complexity of Alzheimer's disease: an evolving puzzle

The history of Alzheimer's disease (AD) started in 1907, but we needed to wait until the end of the century to identify the components of pathological hallmarks and genetic subtypes and to formulate the first pathogenic hypothesis. Thanks to biomarkers and new technologies, the concept of AD then rapidly changed from a static view of an amnestic dementia of the presenium to a biological entity that could be clinically manifested as normal cognition or dementia of different types. What is clearly emerging from studies is that AD is heterogeneous in each aspect, such as amyloid composition, tau distribution, relation between amyloid and tau, clinical symptoms, and genetic background, and thus it is probably impossible to explain AD with a single pathological process. The scientific approach to AD suffers from chronological mismatches between clinical, pathological, and technological data, causing difficulty in conceiving diagnostic gold standards and in creating models for drug discovery and screening. A recent mathematical computer-based approach offers the opportunity to study AD in real life and to provide a new point of view and the final missing pieces of the AD puzzle.

Serum Calcium Predicts Cognitive Decline and Clinical Progression of Alzheimer's Disease

Relationship between serum calcium and Alzheimer's disease (AD) remains unclear. The aim of this study is to test whether serum calcium is associated with other AD-associated biomarkers and could predict clinical progression in nondemented elders. This was a longitudinal population-based study. The sample was derived from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, which included 1224 nondemented elders: 413 cognitively normal (CN) and 811 mild cognition impairment (MCI). Associations were investigated between serum calcium and longitudinal changes in Aβ/tau pathologic features, brain structure, cognitive function, and disease progression. Serum calcium concentrations increased with disease severity. Serum calcium predicted longitudinal cognitive decline and conversion from nondemented status to AD dementia (adjusted HR = 1.41, 95% CI 1.13-1.76). Furthermore, serum calcium levels were negatively correlated with CSF-Aβ₄₂ (β = - 0.558, P = 0.008), FDG-PET (β = - 0.292, P < 0.001), whole brain volume (β = - 0.148, P = 0.001), and middle temporal volume (β = - 0.216, P = 0.042). Similar results were obtained in CN and MCI groups. Higher serum calcium status (even if not hypercalcemia) may increase the risk of AD in elders. Serum calcium is a useful biomarker in predicting clinical progression in nondemented elders. More researches are needed in the future to explore the underlying mechanism.

The power of knowledge about dementia in Latin America across health professionals working on aging

INTRODUCTION

Expert knowledge is critical to fight dementia in inequitable regions like Latin American and Caribbean countries (LACs). However, the opinions of aging experts on public policies' accessibility and transmission, stigma, diagnostic manuals, data-sharing platforms, and use of behavioral insights (BIs) are not well known.

METHODS

We investigated opinions among health professionals working on aging in LACs (N = 3365) with regression models including expertise-related information (public policies, BI), individual differences (work, age, academic degree), and location.

RESULTS

Experts specified low public policy knowledge (X2  = 41.27, P < .001), high levels of stigma (X2  = 2636.37, P < .001), almost absent BI knowledge (X2  = 56.58, P < .001), and needs for regional diagnostic manuals (X2  = 2893.63, df = 3, P < .001) and data-sharing platforms (X2 = 1267.5, df = 3, P < .001). Lack of dementia knowledge was modulated by different factors. An implemented BI-based treatment for a proposed prevention program improved perception across experts.

DISCUSSION

Our findings help to prioritize future potential actions of governmental agencies and non-governmental organizations (NGOs) to improve LACs' dementia knowledge.

MAPT rs242557 variant is associated with hippocampus tau uptake on 18F-AV-1451 PET in non-demented elders

The microtubule-associated protein tau gene (MAPT) rs242557 variant is associated with multiple tauopathies and dementia. This study investigated whether it was correlated with brain tau-PET uptake in non-demented elders. Ninety non-demented elders were identified from the Alzheimer's Disease Neuroimaging Initiative cohort. We compared standardized uptake value ratios (SUVRs) of tau-PET tracer ¹⁸F-AV-1451 between rs242557 variant carriers and non-carriers in 25 regions of interest (ROIs). The minor allele A was associated with increased hippocampus ¹⁸F-AV-1451 uptake in non-demented elders (left: β = 0.111, Bonferroni corrected p = 0.035; right: β = 0.103, Bonferroni corrected p = 0.031). Aβ-positive participants (left: β = 0.206, Bonferroni corrected p = 0.029; right: β = 0.198, Bonferroni corrected p = 0.035) and APOE ε4 non-carriers (left: β = 0.140, Bonferroni corrected p = 0.006; right: β = 0.134, Bonferroni corrected p = 0.004) exhibited approximately the same findings in hippocampus. Considering no obvious associations in other regions, we confirmed the significant correlation of MAPT rs242557 risk variant with increased hippocampus tau deposition in non-demented elders. With higher magnitude signals in the hippocampus that is more likely to be uniquely affected in AD, the tau PET ligand ¹⁸F-AV-1451 seemed to possess a specific binding property for AD-like tau pathology.

Metabolically healthy obesity reduces the risk of Alzheimer's disease in elders: a longitudinal study

A subgroup of overweight/obese individuals, who had favorable metabolic profiles, was termed as metabolically healthy overweight/obese (MHO). Several studies suggested that MHO individuals were not at increased risk of cardiovascular disease and all-course mortality. However, whether MHO is associated with excess risk of Alzheimer’s disease (AD) in elders remains unclear. To explore the risk of AD among MHO phenotype and investigate whether MHO associates with neurodegenerative biomarkers of AD, we assessed body mass index-metabolic status phenotypes of 1199 longitudinal elders from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort using the Adult Treatment Panel-III (ATP- III) criteria. MHO subjects were at a significantly decreased risk for AD (adjusted HR=0.73, 95% CI: 0.54-0.97) compared with metabolically healthy normal weight (MHNW) subjects. In multivariable linear regression models, the cross-sectional associations of MHO with cerebrospinal fluid (CSF) biomarkers, brain Aβ load, and cortical structure were explored. MHO was positively correlated with CSF-Aβ (β=0.746, P=0.015), hippocampal volume (β=0.181, P=0.011), and whole brain volume (β=0.133, P=0.004). The MHO phenotype of the elder conferred a decreased risk of AD and its role may be driven by Aβ.

Aβ and tau structure-based biomarkers for a blood- and CSF-based two-step recruitment strategy to identify patients with dementia due to Alzheimer's disease

INTRODUCTION

Alzheimer's disease (AD) diagnosis requires invasive CSF analysis or expensive brain imaging. Therefore, a minimal-invasive reliable and cost-effective blood test is requested to power large clinical AD trials at reduced screening failure.

METHODS

We applied an immuno-infrared sensor to measure the amyloid-β (Aβ) and tau secondary structure distribution in plasma and CSF as structure-based biomarkers for AD (61 disease controls, 39 AD cases).

RESULTS

Within a first diagnostic screening step, the structure-based Aβ blood biomarker supports AD identification with a sensitivity of 90%. In a second diagnostic validation step, the combined use of the structure-based CSF biomarkers Aβ and tau excluded false-positive cases which offers an overall specificity of 97%.

DISCUSSION

The primary Aβ-based blood biomarker funnels individuals with suspected AD for subsequent validation of the diagnosis by structure-based combined analysis of the CSF biomarkers Aβ and tau. Our novel two-step recruitment strategy substantiates the diagnosis of AD with a likelihood of 29.

The Influence of Genetic Factors and Cognitive Reserve on Structural and Functional Resting-State Brain Networks in Aging and Alzheimer's Disease

Magnetic resonance imaging (MRI) offers significant insight into the complex organization of neural networks within the human brain. Using resting-state functional MRI data, topological maps can be created to visualize changes in brain activity, as well as to represent and assess the structural and functional connections between different brain regions. Crucially, Alzheimer's disease (AD) is associated with progressive loss in this connectivity, which is particularly evident within the default mode network. In this paper, we review the recent literature on how factors that are associated with risk of dementia may influence the organization of the brain network structures. In particular, we focus on cognitive reserve and the common genetic polymorphisms of APOE and BDNF Val66Met.

European Prevention of Alzheimer's Dementia Longitudinal Cohort Study (EPAD LCS): study protocol

INTRODUCTION

The European Prevention of Alzheimer's Dementia (EPAD) project is funded initially by the Innovative Medicines Initiative and has been established to overcome the major hurdles hampering drug development for secondary prevention of Alzheimer's dementia, by conducting the EPAD Longitudinal Cohort Study (LCS) in alignment with the Bayesian adaptive designed EPAD Proof-of-Concept (PoC) trial.

METHODS AND ANALYSIS

EPAD LCS is an ongoing prospective, multicentre, pan-European longitudinal cohort study. Participants are recruited mainly from existing parent cohorts across Europe to form a 'probability-spectrum' population covering the entire continuum of anticipated probability for Alzheimer's dementia development. The primary objective of the EPAD LCS is to be a readiness cohort for the EPAD PoC trial though a second major objective is to generate a comprehensive and large data set for disease modelling of preclinical and prodromal Alzheimer's disease. This characterisation of cognitive, biomarker and risk factor (genetic and environmental) status of research participants over time will provide the necessary well-phenotyped population for developing accurate longitudinal models for Alzheimer's disease covering the entire disease course and concurrently create a pool of highly characterised individuals for the EPAD PoC trial.

ETHICS AND DISSEMINATION

The study has received the relevant approvals from numerous Institutional Review Boards across Europe. Findings will be disseminated to several target audiences, including the scientific community, research participants, patient community, general public, industry, regulatory authorities and policy-makers. Regular and coordinated releases of EPAD LCS data will be made available for analysis to help researchers improve their understanding of early Alzheimer's disease stages and facilitate collaborations.

TRIAL REGISTRATION NUMBER

NCT02804789.

Progress of Brain Amyloid Deposition in Familial Alzheimer's Disease with Taiwan D678H APP Mutation

BACKGROUND

The amyloid AV-45 (florbetapir) positron emission tomography (PET) has been used in the study of the familial Alzheimer's disease (FAD) with the D678H amyloid precursor protein (APP) mutation. In addition, the progress of the disease remains unknown.

OBJECTIVE

We aim to investigate the progression rate of amyloid accumulation in FAD patients with this mutation by neuroimages analysis.

METHODS

The clinical course, changes in cognitive function, brain magnetic resonance imaging (MRI) and 18F-AV-45 PET scan were investigated in FAD patients and sporadic AD (sAD) patients. We compared the amyloid deposition pattern in serial brain 18F-AV-45 PET scan among the FAD, familial mild cognitive impairment (FMCI), and sMCI and sAD patients.

RESULTS

Seven familial patients received a follow-up survey. The follow up duration for brain AV-45 PET was from 1.54 to 3.61 years. In 4 FMCI patients, an increased regional SUVR was noted, and the annual change rates were increased from 1.03% to 18.82%. However, a decreased regional SUVR was noted in 3 FAD patients and the annual change rates were from -2.62% to -16.03%. As compared with the sAD and sMCI patients, the annual change rate is statistically significant in FAD and FMCI patients respectively.

CONCLUSIONS

The data indicate a biphasic course with an initial increase and then a decrease of SUVR in brain amyloid PET scan in familial APP mutation patients. The data also reveal that the novel Taiwan APP (D678H) mutation has a more amyloid burden than the sAD patients, particularly in an MCI stage.

Decreased hemodynamic response in inferior frontotemporal regions in elderly with mild cognitive impairment

The verbal fluency task (VFT) is a well-established cognitive marker for mild cognitive impairment (MCI) in the prodromal stage of Alzheimer´s dementia (AD). The behavioral VFT performance of patients allows the prediction of dementia two years later. But effective compensatory mechanism might cover or reduce the predictive value of the VFT. Therefore the aim of this study is to measure the hemodynamic response during VFT in patients with mild cognitive impairment (MCI) to establish the hemodynamic response during the VFT as a screening instrument for the prediction of dementia. One method which allows measuring the hemodynamic response during speech production without severe problems with moving artifacts like in functional magnetic resonance imaging (fMRI) is the functional near-infrared spectroscopy (fNIRS). It is optimal as a screening instrument, as it is easy to apply and without any contraindications. In this study we assessed the hemodynamic response in prefrontal and temporal regions in patients with MCI as well as matched healthy controls with fNIRS. We found a decreased hemodynamic response in the inferior frontotemporal cortex for the MCI group. Our results indicate that a frontotemporal decreased hemodynamic response could serve as a diagnostic biomarker for dementia.

Does Functional Connectivity Provide a Marker for Cognitive Rehabilitation Effects in Alzheimer's Disease? An Interventional Study

BACKGROUND

Cognitive rehabilitation (CR) is a cognitive intervention for patients with Alzheimer's disease (AD) that aims to maintain everyday competences. The analysis of functional connectivity (FC) in resting-state functional MRI has been used to investigate the effects of cognitive interventions.

OBJECTIVES

We evaluated the effect of CR on the default mode network FC in a group of patients with mild AD, compared to an active control group.

METHODS

We performed a three-month interventional study including 16 patients with a diagnosis of AD. The intervention group (IG) consisted of eight patients, performing twelve sessions of CR. The active control group (CG) performed a standardized cognitive training. We used a seed region placed in the posterior cingulate cortex (PCC) for FC analysis, comparing scans acquired before and after the intervention. Effects were thresholded at a significance of p < 0.001 (uncorrected) and a minimal cluster size of 50 voxels.

RESULTS

The interaction of group by time showed a higher increase of PCC connectivity in IG compared to CG in the bilateral cerebellar cortex. CG revealed widespread, smaller clusters of higher FC increase compared with IG. Across all participants, an increase in quality of life was associated with connectivity increase over time in the bilateral precuneus.

CONCLUSIONS

CR showed an effect on the FC of the DMN in the IG. These effects need further study in larger samples to confirm if FC analysis may suit as a surrogate marker for the effect of cognitive interventions in AD.

Alzheimer disease brain atrophy subtypes are associated with cognition and rate of decline

OBJECTIVE

To test the hypothesis that cortical and hippocampal volumes, measured in vivo from volumetric MRI (vMRI) scans, could be used to identify variant subtypes of Alzheimer disease (AD) and to prospectively predict the rate of clinical decline.

METHODS

Amyloid-positive participants with AD from the Alzheimer's Disease Neuroimaging Initiative (ADNI) 1 and ADNI2 with baseline MRI scans (n = 229) and 2-year clinical follow-up (n = 100) were included. AD subtypes (hippocampal sparing [HpSp_MRI], limbic predominant [LP_MRI], typical AD [tAD_MRI]) were defined according to an algorithm analogous to one recently proposed for tau neuropathology. Relationships between baseline hippocampal volume to cortical volume ratio (HV:CTV) and clinical variables were examined by both continuous regression and categorical models.

RESULTS

When participants were divided categorically, the HpSp_MRI group showed significantly more AD-like hypometabolism on ¹⁸F-fluorodeoxyglucose-PET (p < 0.05) and poorer baseline executive function (p < 0.001). Other baseline clinical measures did not differ across the 3 groups. Participants with HpSp_MRI also showed faster subsequent clinical decline than participants with LP_MRI on the Alzheimer's Disease Assessment Scale, 13-Item Subscale (ADAS-Cog₁₃), Mini-Mental State Examination (MMSE), and Functional Assessment Questionnaire (all p < 0.05) and tAD_MRI on the MMSE and Clinical Dementia Rating Sum of Boxes (CDR-SB) (both p < 0.05). Finally, a larger HV:CTV was associated with poorer baseline executive function and a faster slope of decline in CDR-SB, MMSE, and ADAS-Cog₁₃ score (p < 0.05). These associations were driven mostly by the amount of cortical rather than hippocampal atrophy.

CONCLUSIONS

AD subtypes with phenotypes consistent with those observed with tau neuropathology can be identified in vivo with vMRI. An increased HV:CTV ratio was predictive of faster clinical decline in participants with AD who were clinically indistinguishable at baseline except for a greater dysexecutive presentation.

Reflections on the Future of Pharmaceutical Public-Private Partnerships: From Input to Impact

Public Private Partnerships (PPPs) are multiple stakeholder partnerships designed to improve research efficacy. We focus on PPPs in the biomedical/pharmaceutical field, which emerged as a logical result of the open innovation model. Originally, a typical PPP was based on an academic and an industrial pillar, with governmental or other third party funding as an incentive. Over time, other players joined in, often health foundations, patient organizations, and regulatory scientists. This review discusses reasons for initiating a PPP, focusing on precompetitive research. It looks at typical expectations and challenges when starting such an endeavor, the characteristics of PPPs, and approaches to assessing the success of the concept. Finally, four case studies are presented, of PPPs differing in size, geographical spread, and research focus.

Advancing Alzheimer's research: A review of big data promises

OBJECTIVE

To review the current state of science using big data to advance Alzheimer's disease (AD) research and practice. In particular, we analyzed the types of research foci addressed, corresponding methods employed and study findings reported using big data in AD.

METHOD

Systematic review was conducted for articles published in PubMed from January 1, 2010 through December 31, 2015. Keywords with AD and big data analytics were used for literature retrieval. Articles were reviewed and included if they met the eligibility criteria.

RESULTS

Thirty-eight articles were included in this review. They can be categorized into seven research foci: diagnosing AD or mild cognitive impairment (MCI) (n=10), predicting MCI to AD conversion (n=13), stratifying risks for AD (n=5), mining the literature for knowledge discovery (n=4), predicting AD progression (n=2), describing clinical care for persons with AD (n=3), and understanding the relationship between cognition and AD (n=3). The most commonly used datasets are AD Neuroimaging Initiative (ADNI) (n=16), electronic health records (EHR) (n=11), MEDLINE (n=3), and other research datasets (n=8). Logistic regression (n=9) and support vector machine (n=8) are the most used methods for data analysis.

CONCLUSION

Big data are increasingly used to address AD-related research questions. While existing research datasets are frequently used, other datasets such as EHR data provide a unique, yet under-utilized opportunity for advancing AD research.

Big Data in traumatic brain injury; promise and challenges

Traumatic brain injury (TBI) is a spectrum disease of overwhelming complexity, the research of which generates enormous amounts of structured, semi-structured and unstructured data. This resulting big data has tremendous potential to be mined for valuable information regarding the "most complex disease of the most complex organ". Big data analyses require specialized big data analytics applications, machine learning and artificial intelligence platforms to reveal associations, trends, correlations and patterns not otherwise realized by current analytical approaches. The intersection of potential data sources between experimental TBI and clinical TBI research presents inherent challenges for setting parameters for the generation of common data elements and to mine existing legacy data that would allow highly translatable big data analyses. In order to successfully utilize big data analyses in TBI, we must be willing to accept the messiness of data, collect and store all data and give up causation for correlation. In this context, coupling the big data approach to established clinical and pre-clinical data sources will transform current practices for triage, diagnosis, treatment and prognosis into highly integrated evidence-based patient care.

Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials

INTRODUCTION

The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015.

METHODS

We used standard searches to find publications using ADNI data.

RESULTS

(1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β-amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion-like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by "classic" AD pathology but also by normal biomarkers, accelerated decline, and suspected non-Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers.

DISCUSSION

Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial design.

Overcoming translational barriers impeding development of Alzheimer's disease modifying therapies

It has now been ~ 30 years since the Alzheimer's disease (AD) research entered what may be termed the 'molecular era' that began with the identification of the amyloid β protein (Aβ) as the primary component of amyloid within senile plaques and cerebrovascular amyloid and the microtubule-associated protein tau as the primary component of neurofibrillary tangles in the AD brain. These pivotal discoveries and the subsequent genetic, pathological, and modeling studies supporting pivotal roles for tau and Aβ aggregation and accumulation have provided firm rationale for a new generation of AD therapies designed not to just provide symptomatic benefit, but as disease modifying agents that would slow or even reverse the disease course. Indeed, over the last 20 years numerous therapeutic strategies for disease modification have emerged, been preclinically validated, and advanced through various stages of clinical testing. Unfortunately, no therapy has yet to show significant clinical disease modification. In this review, I describe 10 translational barriers to successful disease modification, highlight current efforts addressing some of these barriers, and discuss how the field could focus future efforts to overcome barriers that are not major foci of current research efforts. Seminal discoveries made over the past 25 years have provided firm rationale for a new generation of Alzheimer's disease (AD) therapies designed as disease modifying agents that would slow or even reverse the disease course. Unfortunately, no therapy has yet to show significant clinical disease modification. In this review, I describe 10 translational barriers to successful AD disease modification, highlight current efforts addressing some of these barriers, and discuss how the field could focus future efforts to overcome these barriers. This article is part of the 60th Anniversary special issue.

Alzheimer disease research in the 21st century: past and current failures, new perspectives and funding priorities

Much of Alzheimer disease (AD) research has been traditionally based on the use of animals, which have been extensively applied in an effort to both improve our understanding of the pathophysiological mechanisms of the disease and to test novel therapeutic approaches. However, decades of such research have not effectively translated into substantial therapeutic success for human patients. Here we critically discuss these issues in order to determine how existing human-based methods can be applied to study AD pathology and develop novel therapeutics. These methods, which include patient-derived cells, computational analysis and models, together with large-scale epidemiological studies represent novel and exciting tools to enhance and forward AD research. In particular, these methods are helping advance AD research by contributing multifactorial and multidimensional perspectives, especially considering the crucial role played by lifestyle risk factors in the determination of AD risk. In addition to research techniques, we also consider related pitfalls and flaws in the current research funding system. Conversely, we identify encouraging new trends in research and government policy. In light of these new research directions, we provide recommendations regarding prioritization of research funding. The goal of this document is to stimulate scientific and public discussion on the need to explore new avenues in AD research, considering outcome and ethics as core principles to reliably judge traditional research efforts and eventually undertake new research strategies.

Alzheimer's Disease is an Important Risk Factor of Fractures: a Meta-analysis of Cohort Studies

The risk of fracture in individuals with Alzheimer's disease had not been fully quantified. A systematic review and meta-analysis of cohort studies was performed to estimate the impact of Alzheimer's disease on risk of fractures. Pubmed and Embase were searched for eligible cohort studies assessing the association between Alzheimer's disease and risk of fractures. The overall relative risks (RRs) with 95% CIs were calculated using a random-effects model to evaluate the association. Six cohort studies with a total of 137,986 participants were included into the meta-analysis. Meta-analysis of a total of six studies showed that Alzheimer's disease was significantly associated with two-fold increased risk of fractures (RR = 2.18, 95 % CI 1.64-2.90, P < 0.001; I ² = 91.4 %). Meta-regression analysis showed that type of fractures was a source of heterogeneity (P = 0.003). Meta-analysis of five studies on hip fracture showed that Alzheimer's disease was significantly associated with 2.5-fold increased risk of hip fracture (RR = 2.52, 95 % CI 2.26-2.81, P < 0.001; I ² = 25.2 %). There was no risk of publication bias observed in the funnel plot. There is strong evidence that Alzheimer's disease is a risk factor of hip fracture.