alzheimer's disease research project

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ADRC Funded Projects

Current research scholars, past research scholars, current pilot projects, past pilot projects, past research projects, development projects.

Award Year 05/01/2024 - 04/30/2025

• Dr. Schneeberger-Pané employs state-of-the-art technologies in neuroscience combining unbiased whole mount imaging of circuits, activity, and vasculature; molecular profiling single-cell gene expression technologies, neuromodulation (optogenetics, and chemogenetics) to understand the fundamental principles in the brain governing homeostasis. He received an ADRC Scholar award to facilitate entire new studies focused on the brain vasculature as a substrate of obesity-mediated cognitive dysfunction and neurodegeneration.
• The goal of Dr. Favuzzi's project is to advance the understanding of the role of the innate immune system in regulating homeostatic functions in the brain including brain activity patterns and to elucidate mechanisms of altered states in the brain as a result of aberrant interactions between neurons and innate immune microglial cells.
• Dr. Snell's research addresses a major gap in knowledge regarding the cellular and electrophysiological mechanisms controlling cognitive functions of the cerebellum. The ultimate goal of her lab is to use cutting-edge techniques to study mechanisms underlying rare cerebellar disorders to pioneer discoveries that contribute to the understanding of basic cerebellar function and increase the quality of life of patients worldwide. In addition to advancing the cellular understanding of cerebellar cognitive functions, Dr. Snell plans to utilize resources and samples from the ADRC to develop a comprehensive proteomic study using novel proximity labeling techniques in the cerebellum in collaboration with other ADRC members.
• Dr. Nandy's project investigates the entorhinal cortex (ERC) and its vulnerability to tau pathology in Alzheimer’s disease (AD). Focusing on the ERC’s role in memory and spatial navigation, the study explores the molecular mechanisms, including calcium dysregulation and NMDAR-GluN2B receptor involvement. Using marmosets as a primate model, it aims to compare ERC functions with rodents and macaques to better understand AD progression and identify potential therapeutic targets.
• The main objective of Dr. Vives-Rodriguez's project is to characterize the progression of positive psychiatric symptoms and their association with cognitive deficits across the spectrum of Lewy body disease, including patients with PD who are cognitively unimpaired, PD-MCI, PDD, DLB, and age-matched healthy controls. The neuropsychological deep phenotyping, DNA, and bio sample collections will set the foundation for future phenotype-genotype and biomarker investigations across the Lewy body spectrum.
• Dysregulated alternative splicing has been identified as a major factor in Alzheimer’s disease and related dementias (ADRD). Loss of proteins like TDP-43 and mutations in MAPT disrupt critical neuronal functions. Dr. Wei's project aims to develop a new method called region-specific Perturb-seq, which will identify cellular regulators of these splicing issues. By focusing on specific mRNA regions, this method will improve the detection of low-abundance splicing events and allow for high-throughput analysis. The ultimate goal is to uncover new therapeutic targets to treat ADRD effectively

Award Year 5/01/2023 - 4/30/2024

• This project integrates multimodal neuroimaging, genetics, and clinical data to identify preclinical biomarkers for Alzheimer’s disease (AD). By combining advanced imaging techniques with genetic analysis and clinical assessments, Dr. Xu aims to detect early signs of AD before clinical symptoms appear. The goal is to improve early diagnosis and intervention strategies, enhancing our understanding of AD progression and enabling more effective treatments.
• Dr. Tavares Da Silva Pereira's project focuses on frontotemporal dementia (FTD) and its genetic overlap with ALS. It aims to identify early FTD phenotypes using iPSC-derived neurons with TDP43 and C9ORF72 mutations and cell painting assays combined with AI. The study will compare FTD and ALS phenotypes, explore stressor responses, and conduct a small-scale drug screen to find potential early interventions, utilizing advanced imaging and FDA-approved drug libraries. This approach aims to uncover the mechanisms behind FTD and ALS and identify druggable targets for early intervention.
• This project investigates the molecular changes in the hippocampus and amygdala of Alzheimer’s disease (AD) patients with recent depression episodes. Dr. Pathak aims to identify differential gene expression in these brain regions to understand the comorbidity of depression and AD. By analyzing 70 samples from 35 donors, the study will identify key molecular pathways and potential therapeutic targets. Techniques include RNA sequencing, differential gene expression analysis, and cross-tissue correlation to uncover insights into the transcriptomic regulation in AD and depression.
• Persons living with dementia (PLWD) visit emergency departments (ED) more frequently than those without cognitive impairments. The ED is a crucial but underutilized setting for engaging PLWD and their caregivers. Dr. Gettel's project aims to examine the assistance provided by caregivers to PLWD and those with undiagnosed cognitive impairments, to inform ED-based best practices and improve early detection of cognitive issues.
• This project aims to uncover the mechanisms behind Amyloid Precursor Protein processing by γ-secretase and Aβ production in Alzheimer’s disease. Utilizing techniques like lipid photoaffinity crosslinking, click chemistry, and LC-MS/MS lipidomic analysis, Dr. Kim's study will explore lipid-protein interactions and membrane homeostasis. Supported by experts in mass spectrometry, γ-secretase functions, and neurodegenerative disease models, this research seeks to provide new insights into Alzheimer’s disease progression.

Award Year 5/01/2022 - 4/30/2023

• Yifei Cai, PhD : "Molecular mechansms of axonal pathology in Alzheimer's disease human neurons"
• Sathish Ramakrishnan, PhD : "Mechanism of exocytosis protein in Alzheimer's disease"
• Dibyadeep Datta : "Interrogating the molecular mechanisms mediating the emergence of biomarker pT217-tau in AD"

Award Year 5/01/2021 - 4/30/2022

• Eyiyemisi Damisah, MD : "Investigating statistical learning in the Dementias"
• Hongying Shen, PhD : "Expanding Neurometabolic Biochemistry Underlying Alzheimer's disease"
• Takuya Toyonaga, MD, PhD : "Whole brain in vivo characterization of synaptic density in Alzheimer's disease model rat with different rearing environments"
• Le Zhang, PhD : "Sex-Specific Single Cell Expression Profiles and Genetic Risk in Alzheimer's disease"
• Yize Zhao, PhD : "Connectome coupling and genetic underpinning in the Supernormal"

Award Year 6/15/2020 - 4/30/2021

• Adam Mecca, MD, PhD : "Novel Biomarkers of Synaptic Health in Alzheimer’s disease"
• Carolyn Fredericks, MD : "Tau PET & Network Integrity in Atypical Alzheimer’s disease"
• Evelyn Lake, PhD : "A multimodal PET, MR, and fluorescence CA2 imaging study of a murine model of CTE"
• Marcello DiStasio, MD, PhD : "Uncovering Proinflammatory Signatures in Alzheimer's Disease Using Spatial Transcriptomics"
• Juan Young, MD : "Investigating a CD8 + T cell associated aging gene signature in Alzheimer's disease"
• Daniel Jane-Wit, MD, PhD, RPVI : "ZFYVE21 and Cerebral Amyloid Angiopathy in Alzheimer's disease Related Dementias"
• Dibyadeep Datta : "Investigating the role of pT217-tau in the pathogenesis of AD and relevance for biomarker development"

Previously awarded pilot projects funded by a NIA ADRC grant.

Award Year 4/1/2015 - 3/31/2016

• Janghoo Lim, PhD : "The role of Neuroinflammation in the pathogenesis of Alzheimer's disease with focus on nemo-Like Kinase"
• Ming-Kai Chen, MD, PhD : "Brain PET Imaging of Synaptic Density in Alzheimer's disease"

Award Year 4/1/2016 - 3/31/2017

• Becky Carlyle, PhD: "Profiling 3D Amyloid Patterns"

Award Year 4/1/2017 - 3/31/2018

• Marc Hammarlund, PhD : "A new C. elegans model for AB42 toxicity"

Award Year 4/1/2018 - 3/31/2019

• Carla Rothlin, PhD : "Mechanisms of reactive astrogliosis - a critical feature in Alzheimer's disease"
• Sreeganga Chandra, PhD : "Testing Synapse Protection Strategies for Alzheimer's disease"
• Anita Huttner, MD : "Modeling Sporadic Alzheimer's disease with Human IPSC-Derived Cerebral Organoids"

Award Year 4/1/2019 - 3/31/2020

• Kurt Zilm, PhD : "Structural characterization by NMR of amyloid-beta oligomers bounds in hydrogel phase to prion protein"
• Le Zhang, PhD : "Single nucleus and single-cell profiling of human brain and CSF in Alzheimer's disease"

Award Year 5/1/23 - 4/30/24

• Insoo Kang : “Investigating comprehensive genomic profile and heterogeneity of viral antigen-specific T cells in patients with Alzheimer's disease using single cell RNA sequencing”
• Jianbing Zhou : “Ribonucleoprotein-mediated genome editing therapy for Alzheimer's Disease”

Award Year 5/1/2022 - 4/30/2023

• Caroline Fredericks, MD : "Predictive modeling of genetic Alzheimer's risk and memory performance in healthy older adults"
• Chao Zheng, PhD : "Longitudinal ROCK2-PET imaging study in the TgF344-AD transgenic rat model of AD"
• Pallavi Gopal, MD, PhD : "TDP-43 transport and spatiotemporal regulation of RNA in Alzheimer's disease"
• David Matuskey, MD and Arman Fesharaki, MD, PhD, BMath : "Novel in vivo synaptic imaging in Behavioral Variant Frontotemporal Dementia (bvFTD)"

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Research on Alzheimer’s Disease and Related Dementias

Alzheimer’s disease and related dementias are a series of complex brain disorders that affect millions of Americans and many more people worldwide. These disorders have an enormous impact on individuals and their families, long-term care facilities, health care providers, health care systems and infrastructure, and the communities in which we all live. As the economic, social, and personal costs of these diseases climb, the research community is working to discover solutions that will improve the lives of those with dementia, their caregivers, and their communities.

The federal government’s Alzheimer’s and related dementias research strategy focuses on engaging a cross-disciplinary team of geneticists, epidemiologists, gerontologists, behavioral scientists, disease and structural biologists, pharmacologists, clinical researchers, and others to bring the greatest and most diverse expertise to the field. This includes training new generations of researchers and clinician-scientists and engaging in innovative partnerships with private industry, nonprofit groups, and more to foster collaboration and broaden access to research resources and data.

Critically, the government’s research strategy includes the search to find treatment and prevention strategies, as well as interventions, services, and supports to improve quality of life for those already living with these diseases and their families.

Who Funds Alzheimer’s and Related Dementias Research?

The National Institutes of Health (NIH) is made up of Institutes, Centers, and Offices that conduct and fund research into all aspects of human health. The National Institute on Aging (NIA) leads NIH’s efforts in clinical, behavioral, and social research in Alzheimer’s and related dementias through efforts aimed at finding ways to treat and ultimately prevent the disorder. NIA collaborates closely with the National Institute of Neurological Disorders and Stroke (NINDS), which manages a research portfolio targeting Alzheimer’s-related dementias. While some of this research takes place in NIH laboratories, the vast majority of NIH support is provided through a competitive grants process to institutions and small businesses across the country. Other federal agencies support a range of activities focused on public health and community programs.

Advances in Alzheimer's and Related Dementias Research

As the nation’s biomedical research agency, NIH supports research ranging from basic biology to drug development and from clinical studies to evaluating public health outcomes. Within the past several decades, researchers have made great strides toward better understanding what causes Alzheimer’s and related dementias and discovering approaches that may prevent, diagnose, and treat them. Some highlights of these efforts include:

• Drug discovery and drug repurposing. Thanks to the substantial investment in Alzheimer’s and related dementias research over the past decade, NIH has increased drug discovery significantly. Of the many compounds in NIH-supported drug development programs for Alzheimer’s and related dementias, 18 new dementia drug candidates have now matured through the pipeline, from discovery in the lab all the way through preclinical development, to reach the stage of human testing. NIA currently supports more than 60 clinical trials testing drug candidates that target many different aspects of the disease. Several of these drug candidates are intended to stop or slow the disease process rather than only treat symptoms. For example, some target amyloid plaques and tau tangles in new ways. Researchers are also exploring multiple ways to repurpose drugs for the potential treatment of dementia, including FDA-approved drugs used to treat epilepsy and diabetes.
• Early detection and diagnosis. Researchers have made significant progress in developing, testing, and validating biomarkers that detect signs of the disease process. For example, in addition to PET scans that detect abnormal beta-amyloid plaques and tau tangles in the brain, NIH-supported scientists have developed the first commercial blood test for Alzheimer’s. This test and others in development can not only help support diagnosis but also be used to screen volunteers for research studies. Other discoveries are leading to the development of potential biomarkers for other dementias. These include the detection of abnormal TDP-43 protein, found in frontotemporal dementias, and a cerebrospinal fluid test to help diagnose Lewy body dementia and Parkinson’s disease. Researchers are also studying behavioral and social indicators, including problems with paying bills and a combined decline in memory and walking speed, that may be early signs of these diseases. Other early markers are also under study.
• Risks factors, genetics, and disease pathways. NIH’s research investments to identify the biological mechanisms that lead to Alzheimer’s and related dementias are fundamental for the discovery of potential drugs that target them. There are many biological pathways that scientists can target with investigational drugs. For example, several recent studies have further revealed how components of the immune system, brain inflammation, vascular disease, and possibly viruses and bacteria — including the many tiny organisms that live in the digestive system, known as the gut microbiome — contribute to the development of these diseases. Scientists are also exploring genetic variations that may contribute to or prevent disease. Recent research has revealed that the genetic risk for Alzheimer’s differs between ethnic and racial groups, highlighting the need for more diversity in genetic research studies. Scientists are also discovering genetic variants that may help protect against Alzheimer’s. Other studies are identifying the genetic underpinnings of related dementias, including new gene variants linked to the development of Lewy body dementia.
• Population studies and precision medicine. By studying large, diverse groups of people, researchers are identifying which genes, behaviors, and lifestyle choices are linked with dementia. Population studies have shown that sedentary behavior, low socioeconomic status, low level of education, and living in a poor neighborhood may increase the risk of developing dementia. These observational discoveries, along with knowledge of genetic and other factors, can be used to advance the development of methods for diagnosis, prevention, and treatment at an individualized level.
• Health disparities and dementia risk. NIH-funded researchers are examining the biological, social, and environmental factors that contribute to the higher prevalence of dementia in Hispanic Americans and Black Americans compared with other White Americans. Since dementia is also underdiagnosed in these populations, researchers are studying approaches to improve diagnoses in underserved communities. NIH is also investing in strategies to increase diversity in research study participants.
• Lifestyle interventions. Researchers are investigating interventions around exercise, healthy eating, cognitive training, preventive health care, and management of chronic conditions, such as high blood pressure, that — if made early in life — may be able to prevent or delay disease symptoms. Emerging areas of study include interventions to enhance cognitive reserve — the mind’s ability to cope with the effects of aging — and interventions to potentially compensate for premature cognitive decline and dementia linked to adverse exposures in early life, such as abuse and malnutrition. NIA currently supports more than 150 trials testing behavioral and lifestyle interventions.
• Dementia care and caregiver support. NIH has significantly expanded research on how to improve dementia care and support for care partners. Researchers are investigating new dementia care models and strategies to equip family caregivers with tools and knowledge to manage the challenges of caring for a loved one with dementia. Studies are also underway to examine ways to improve quality of life for people with dementia and their caregivers. Other studies aim to understand the costs and challenges of dementia, including lost wages and paying for long-term care. NIA currently supports more than 200 studies on dementia care and caregiving.
• Infrastructure development. NIH is continually investing in research infrastructure to advance Alzheimer’s and related dementias research. Efforts in this area include launching a consortium for Alzheimer’s clinical trials, a collaboratory to test interventions to improve care of people with dementia in real-world settings, research efforts to validate cognitive tests in a primary care setting, and centralized data-sharing platforms and other technologies.

Challenges for the Alzheimer’s Research Community

Even with the progress that we’ve made, there’s still a lot of work to do before we can find treatment and prevention strategies for the millions of people affected by Alzheimer’s and related dementias. These devastating diseases are highly complex conditions caused by an interplay of genetic, lifestyle, and environmental factors. They usually develop gradually — changes in the brain take place over years and even decades, long before the first symptoms appear. This complexity presents challenges to the discovery and development of new drugs and other prevention and treatment approaches.

Researchers believe Alzheimer’s and related dementias will likely require multiple treatments customized to individuals. We also know that as the older population continues to grow — aging remains the most important risk factor for dementia — we will see increased numbers of people living with these diseases. That’s why thousands of researchers around the country are working on this issue.

Setting the Federal Research Agenda

NIH takes a collaborative, methodical approach to reviewing progress, identifying gaps, and setting the future agenda for research into Alzheimer’s and related dementias. NIH funding in this area is guided by gaps and opportunities identified in research summits , which alternate yearly to focus on Alzheimer’s, Alzheimer’s-related dementias, or dementia care and services. Smaller, focused workshops are held more frequently on specific aspects of this research.

NIH outlines its Alzheimer’s research efforts in the NIH AD/ADRD Research Implementation Milestones , a research framework detailing specific steps and success criteria toward achieving the goals of the National Plan to Address Alzheimer’s Disease . The milestones also showcase funding initiatives, accomplishments, and highlights of progress toward accomplishing the National Plan goals.

NIH’s research progress is highlighted in the annual Alzheimer’s and related dementias professional judgment budget , which is submitted to Congress each year.

What Is a Professional Judgment Budget?

Each year NIH submits a professional judgment budget that estimates the additional funding needed to advance NIH-supported research into the treatment and prevention of Alzheimer’s and related dementias. The report also summarizes progress and promising research opportunities. Only two other areas of biomedical research — cancer and HIV/AIDS — follow a similar process designed to accelerate research discovery. This approach is often referred to as a “bypass budget” because of its direct transmission to the President and then to Congress without modification through the traditional federal budget process.

Clinical Research Into Alzheimer’s and Related Dementias

No major advance in Alzheimer’s and related dementias treatment, prevention, or care will be possible without robust clinical research. Clinical research includes studies that involve people so scientists can learn more about disease progression, how behavior and lifestyle factors may affect health, and the safety and effectiveness of an intervention. Advances made through clinical research rely on the volunteers who participate in these types of studies. NIA is working on multiple initiatives to enhance recruitment and retention of diverse populations in clinical research. View some of those resources below.

NIA-funded clinical research includes both observational studies through which researchers gather important information, and clinical trials in which researchers test interventions to treat or prevent disease, improve care and caregiver support, and enhance quality of life for people living with dementia. NIA is currently funding more than 400 active clinical trials .

NIA also funds more than 30 Alzheimer’s Disease Research Centers across the country. Scientists at these centers conduct clinical research to improve diagnosis and care for people with dementia and their families, and to find a treatment or increase prevention.

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Find More Resources on Alzheimer’s Research

Explore the resources on this website and linked below to find more information from federal government agencies.

View professional judgment budgets for Alzheimer’s and related dementias from NIH, including yearly updates on research progress.

Browse this database to learn more about research implementation plans and progress toward the goal of treating or preventing Alzheimer’s and related dementias.

Search this repository of resources to support the recruitment and retention of participants into clinical trials and studies on Alzheimer’s disease and related dementias.

Learn about the data sharing policies, considerations, resources, and guidance available to support researchers in safely and efficiently sharing data from their studies.

Visit IADRP to search a database of categorized research across public and private sources.

Learn about NIA's efforts toward the National Plan and NIH annual summits that shape research priorities.

View a list of all active NIA-funded clinical trials, including drug trials, intervention studies, and care and caregiver interventions.

Search for NIA-supported clinical research tools, datasets, samples, visualization tools, and more for Alzheimer’s and related dementias research.

Read the National Strategy for Recruitment and Participation in Alzheimer’s and Related Dementias Research and get resources to support study recruitment.

Read about the National Institute of Neurological Disorders and Stroke’s research into Alzheimer’s disease-related dementias.

Search NIH-funded research in Alzheimer’s and related dementias.

Other Articles in This Section

• Federal Response
• National Research Centers

Questions? Contact the ADEAR Center

The Alzheimer’s & related Dementias Education & Referral (ADEAR) Center is a service of the National Institute on Aging at the National Institutes of Health. Call 800-438-4380 or email [email protected] to talk with an information specialist.

Last updated: July 9, 2024

This content is provided by the National Institute on Aging (NIA), part of the National Institutes of Health. NIA scientists and other experts review this content to ensure it is accurate and up to date.

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Alzheimer's disease research.

Driving innovative research around the world to end Alzheimer’s , we identify high-risk, high-reward projects that have the most promise to change the trajectory of the disease.

Our 360° Approach to Research

Tangling with tau, battling amyloid beta, blood and the brain in dementia, immunity and inflammation, biology of apoe and lipids, new approaches.

BrightFocus takes a 360 ° approach to fund innovative scientific research worldwide to defeat Alzheimer’s, exploring the full range of scientific paths toward better treatments and ultimately a cure. Watch this video and learn more.

Genes are the “master blueprint” that instructs our cells to make unique proteins which in turn build, operate, and repair human tissue. Humans have an estimated 24,000 genes along our 23 matched pairs of chromosomes (46 in all), and “genomics” refers to the field that studies all of them at once.

A biological marker (biomarker) is a measurable substance in an organism whose presence is indicative of some phenomenon such as disease or infection. Biomarkers can help doctors and scientists diagnose diseases and health conditions, find health risks in a person, monitor responses to treatment, and see how a person's disease or health condition changes over time.

Tau is a small protein with a short name but a large reputation because of its association with multiple brain diseases, including Alzheimer’s disease (AD). The tau protein is predominantly found in brain cells (neurons).

There are many versions of amyloid protein in the human body, and most serve a useful role. One of the hallmarks of Alzheimer’s disease (AD) is the accumulation of amyloid plaques (abnormally configured proteins) between nerve cells (neurons) in the brain.

Scientists are interested in developing a screening tool for Alzheimer’s disease (AD) in blood. A simple blood draw is much less invasive than a spinal tap and may prove more cost effective. Developing blood biomarkers that accurately depict brain changes has proven challenging, as levels of AD hallmark proteins in the blood are low, but there are some very recent promising results observing tau and the ratio of Aβ42 and Aβ40.

One theory about Alzheimer’s disease (AD) is that it may be triggered, in part, by a breakdown in the brain’s immune system.

Alzheimer's disease (AD). Its primary function is to regulate a class of proteins involved in the metabolism of fats (lipids) in the body. However, APOE has several common variants (or "alleles") whose effect vary.

The human brain has an estimated 100 billion neurons. Extending from each of them is a long fiber, known as an “axon,” which can run several feet. Each axon forms a connection, known as a “synapse” with another neuron, creating a circuit over which brain signals travel. In Alzheimer’s disease (AD), individual neurons die and do not regenerate; while others have brains that are more are resilient and respond to meeting changing demands.

Years of innovative and dedicated research have paid off with the discovery of numerous factors contributing to Alzheimer’s disease (AD) pathology. With a disease as complex as this one, it’s very helpful to find multiple points where it may be possible to slow or halt its progress.

Research We Fund

BrightFocus drives innovative research worldwide on Alzheimer’s, macular degeneration, and glaucoma. Search our grant awards to learn more.

Insights and Breakthroughs

Well-designed research pays off. With further research, each of these discoveries may contribute to the development of new treatments and preventions.

Driving Innovation in Diagnosis and Treatment: Alzheimer’s Disease Research Roundup

Searching the Eye for Signs of an Inherited Rare Form of Alzheimer’s

New Alzheimer’s Drug Leqembi Granted Full FDA Approval

What Can We Learn From 100-Year-Olds Without Alzheimer’s?

FDA Approves First Treatment for Alzheimer’s-Associated Agitation

Researchers Develop First-of-its-Kind Artificial Intelligence Model That Could Detect Alzheimer’s Through Retinal Photographs

Researchers Identify a Genetic Factor in People of African Ancestry That May Lower Alzheimer’s Risk

Moderate Alcohol Use May Accelerate Alzheimer’s Disease

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Alzheimer's disease is the seventh leading cause of death in the United States. An irreversible degeneration of the brain that causes disruptions in memory, cognition, personality, and other functions, it eventually leads to death from complete brain failure.

Nearly 7 million Americans aged 65 and older are thought to have Alzheimer's disease. By 2050, that figure may increase to nearly 13 million.

Learn about Alzheimer's Disease

Alzheimer’s disease is the most common form of dementia, affecting more than six and a half million Americans aged 65 and older. In this section, you can find out more about Alzheimer’s and how you can manage care for yourself or a loved one.

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The Eye, A Window on the Brain

It is often said that “the eyes are the window to the soul,” and while that may or may not be true, the eye is certainly a window into many health conditions.

In fact, sometimes an eye doctor will be the first physician to diagnose a medical condition because the first signs may appear in the eye. Thus, having your eyes thoroughly examined is a lot more than just getting a prescription for new glasses or contact lenses.

Useful Resources

BrightFocus Foundation offers vetted resources to help you and your loved ones better understand, manage and live with an Alzheimer’s diagnosis.

• Healthy Living
• Understanding Alzheimer's
• Managing Alzheimer's
• Living with Alzheimer's

Expert Advice

Useful articles to help you understand and manage symptoms, treatment, and the latest discoveries in Alzheimer's Disease Research.

When Alzheimer’s Disease Begins with Vision Problems

What’s Next for Alzheimer’s Disease Treatments: A 2024 Forecast

Alzheimer’s Treatment Coverage: 6 Facts to Know About Patient Registries

Facts About Leqembi, a New Alzheimer’s Drug

Exploring a Connection Between ADHD and Alzheimer’s

"Is It Something I'm Taking?" Medications That Can Mimic Dementia

Biohacking Brain Health: Research Exploring Fasting and Diet Changes Shows Promise in Delaying Alzheimer's Disease, Improving Cognition

Navigating Neurodegenerative Diseases: What Causes Neurodegeneration and Can It Be Stopped?

Can a Multivitamin Prevent Alzheimer’s Disease?

Alzheimer’s Blood Tests: How Do They Work and Should You Request One?

Alzheimer’s Disease and COVID-19—What’s the Connection?

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Current research projects

Our research aims to understand the underlying causes of the condition, improve diagnosis and care, identifying ways to prevent dementia and searching for a cure. 

Below you can find out about the research projects that we are funding. Discover more about our researchers' work and how it will impact people affected by dementia.

In addition, learn about all our current:

• Fellowships
• PhD studentships
• Implementation and dissemination grants
• ADDF Drug Discovery grants
• International Research Partnerships 

Latest research projects 

Understanding the effects of the diabetes type 2 drug Metformin on models of Alzheimer’s disease

Lead Investigator: Dr Teresa Niccoli

Institution: University College London

Grant type: PhD Studentship

Awarded: 2020/21

Investigating how to clear toxic amyloid protein from the brain in Alzheimer’s disease

Lead Investigator: Professor K. Ravi Acharya

Institution: University of Bath

Mining a common food compound, Epicatechin, for a new Alzheimer’s disease treatment

Lead Investigator: Dr Robert Williams

Grant type: Project

Awarded: 2019/2020

Project grants 

Developing new ways to study and test treatments for small vessel disease in vascular dementia

Lead Investigator: Professor Karen Horsburgh

Institution: University of Edinburgh

Grant type: Project 

Awarded: 2016/2017

Understanding whether drugs for rheumatoid arthritis can reduce the risk of Alzheimer’s disease

Lead Investigator: Dr Bernadette McGuinness

Institution: Queen's Univeristy Belfast

Awarded: 2016/2017

Investigating a potential target for treatment of frontotemporal dementia

Lead Investigator: Dr Christopher Miller

Institution: Institute of Psychiatry, King's College London

Awarded: 2015/2016

Investigating heparins as a potential new drug for Alzheimer’s disease

Lead Investigator: Professor Jerry Turnbull

Institution: University of Liverpool

Testing the effect of the diabetes drug Liraglutide in Alzheimer's disease

Lead Investigator: Dr Paul Edison

Institution: Imperial College London

Awarded: 2014/2015

Fellowships 

Taking an innovative approach to designing potential treatments for Alzheimer’s disease

Lead Investigator: Dr Francesco Antonio Aprile 

Institution: University of Cambridge

Grant type: Senior Fellowship

Awarded: 2016/2017  

PhD studentships 

Can leptin, the anti-obesity hormone, protect brain cells?

Lead Investigator: Dr Jenni Harvey

Institution: University of Dundee Grant type: PhD studentship

Awarded: 2018/2019

Using an innovative approach to prevent the toxic build-up of amyloid in Alzheimer’s disease

Lead Investigator: Dr Jody Mason

Grant type: PhD studentship

Awarded: 2017/2018

Does targeting the immune system have the potential to treat Alzheimer’s disease?

Lead Investigator: Dr David Brough

Institution: University of Manchester

What factors affect the attitudes of young people to dementia?

Lead Investigator: Dr Nicolas Farina

Institution: Brighton and Sussex Medical School

Can we predict Alzheimer’s disease and its risk factors from the proteins found in the blood?

Lead Investigator: Dr Riccardo Marioni

Supporting people with dementia to continue their careers

Lead Investigator: Dr Louise Ritchie

Institution: University of West of Scotland

Supporting conversations about end-of-life care

Lead Investigator: Dr Nathan Davies

Redesigning care homes to improve the wellbeing of people with dementia

Lead Investigator: Professor Catherine Hennessy

Institution: University of Stirling

Improving exercise classes for people with dementia

Lead investigator: Dr Annabelle Long

Grant type: Fellowship

Awarded: 2020/21  

Interacting with children: What are the benefits for people with dementia?

Lead Investigator: Dr Suzanne Beeke

Institution: University College London Grant type: PhD Studentship Awarded: 2019/20  

Are smartphones a gamechanger for dementia research?

Lead Investigator: Dr Chris Hinds 

Institution: University of Oxford Grant type: Project  Awarded: 2019/20  

Can blood tests be used to diagnose delirium and improve diagnosis?

Lead Investigator: Dr Valerie Page

Institution: Watford General Hospital Grant type: Project Awarded: 2019/20  

Supporting person-centred care for people with dementia in hospitals

Lead Investigator: Dr Melanie Hanley

Institution: University of Hertfordshire Grant type: Project grant Awarded: 2019/20   

Understanding the genetics of depression in Alzheimer’s disease

Lead Investigator: Dr Lindsey Sinclair

Institution: University of Bristol Grant type: Junior Fellowship Awarded: 2019/20   

Supporting deaf carers of people with dementia

Lead Investigator: Dr Emma Ferguson-Coleman

Institution: University of Manchester   Grant type: Junior Fellowship Awarded: 2019/20  

Understanding the role of volunteering in the future of dementia care

Lead Investigator: Professor Heather Wilkinson

Institution: University of Edinburgh Grant type: PhD studentship Awarded: 2019/20 How can compassionate communities support end of life dementia care?

Lead Investigator: Dr Joseph Sawyer

Institution: University College London Grant type: Clinical Training Fellowship  Awarded: 2019/20  

Exploring dementia in the South Asian community Lead Investigator: Dr Naaheed Mukadam

Institution: University College London   Grant type: Senior Fellowship Awarded: 2019/20   

Care collaboration grants 

Exchanging knowledge with independent care homes in Exeter

Lead Investigator: Dr Iain Lang

Institution: University of Exeter  Grant type: Care Collaboration Grant  Awarded: 2019/20    

Can embedding a researcher in care homes help to improve care?

Lead Investigator: Professor Mo Ray 

Institution: University of Lincoln Grant type: Care Collaboration Grant  Awarded: 2019/20   

A new approach to collaborative research with care homes in Worcester

Lead Investigator: Professor Tracey Williamson

Institution: University of Worcester Grant type: Care Collaboration Grant  Awareded: 2019/20  

Understanding the benefits of community singing groups for people with dementia

Lead investigator: Professor Justine Schneider 

Awarded: 2018/2019  

Improving night-time care and reducing hypnotic drug use in care homes

Lead Investigator: Dr Anne Corbett

Institution: University of Exeter  Grant type: Project grant Awarded: 2019/2020 Optimising PET scanning to diagnose Alzheimer’s disease

Lead Investigator: Dr Paresh Malhotra 

Awarded: 2018/2019  

Understanding more about community based support for people affected by dementia

Lead investigator: Professor Dawn Brooker

Institution: University of Worcester

Awarded: 2017/2018  

Residential respite care: Experiences, access and outcomes Lead Investigator: Dr Kritika Samsi

Institution: King's College London

Awarded: 2018/2019 Understanding and improving end of life care for people with dementia and their carers

Lead Investigator: Professor Martin Knapp Institution: London School of Economics and Political Science Grant awarded:  2017/2018  

Detecting changes in the brain in frontotemporal dementias

Lead Investigator: Professor Jason Warren

Grant awarded: 2016/2017  

Improving diagnosis and support services for people with younger onset dementia

Lead Investigator: Dr Janet Carter

Grant awarded: 2015/2016

Identifying factors that lead to problems with everyday tasks for people with dementia

Lead Investigator: Dr Eneida Mioshi

Institution: University of East Anglia

Grant awarded: 2014/2015  

Implementation grants 

Supporting end of life decision-making in care homes

Lead Investigator: Professor Kevin Brazil

Institution: Queen’s University Belfast Grant type: Implementation Awarded: 2018/2019  

Beyond the Margins: Accessing support in the community Lead Investigator: Professor Charlotte Clarke Institution: University of Edinburgh

Grant type: Implementation

Awarded: 2018/2019   

Knowledge Exchange Fellowships 

A digital guide to support people with dementia to be part of the community

Lead Investigator: Dr Kieren Egan

Institution: University of Strathclyde Grant type: Knowledge exchange fellowship Awarded: 2019/20

How does your economic background affect the dementia care you access?

Lead Investigator: Clarissa Giebel

Grant type: Knowledge exchange fellowship

Awarded: 2019/20  

Clinical Training Fellowships & Partnerships

Providing research training to increase support and improve care for people with dementia and their carers

Lead Investigator:  Professor Linda Clare  Institution: University of Exeter

Grant type: Clinical Training Partnership

A mouth care programme for people with dementia and swallowing problems

Lead investigator: Dr Julie Pollock

Nottingham University  Hospitals NHS Trust

Grant type: Clinical Training Fellowship

Tackling critical issues in dementia diagnosis, care and treatment

Lead Investigator: Professor Dag Aarsland

Institution: King’s College London Grant type: Clinical Training Partnership  Awarded: 2019/20  

Using blood tests and psychological tests to predict familial Alzheimer’s disease Lead Investigator: Dr Antoinette O’Connor

Institution: Institute of Neurology, University College London

A mouth care programme for people with dementia and swallowing problems Lead Investigator: Julie Pollock Institution: Nottingham Healthcare NHS Trust

Helping people with dementia to better recover from delirium 

Lead Investigator: Dr Daniel Davis (Clinical Training  Partnership) 

Grant awarded: 2017/2018

Can keeping up routine activities help people with dementia to remain independent during a hospital stay?

Lead Investigator: Ms Lisa Patrick (Clinical Training Fellowship)

Institution: Nottingham University Hospitals NHS Trust

Grant awarded: 2017/2018  

Developing a technique to detect early signs of prion diseases

Lead Investigator: Dr Tze How Mok (Clinical Training Fellowship)

Institution: National Prion Clinic, University College London

Grant awarded: 2016/2017

Enabling people with dementia to access and understand assistive technology

Lead Investigator: Dr Lisa Newton (Clinical Training Fellowship)

Institution: Newcastle University

Understanding eating and drinking difficulties for people with dementia in care homes

Lead Investigator: Ms Lindsey Collins (Clinical Training Fellowship)

Grant awarded: 2014/2015  

Adapting cognitive behavioural therapy for people with dementia

Lead Investigator: Dr Joshua Stott (Clinical Training Fellowship)

Institution: University College London 

Grant awarded: 2014/2015

Understanding the role of delirium in dementia development

Lead Investigator: Dr Sarah Richardson (Clinical Training Fellowship)

Institution: Newcastle University

Junior Fellowships 

Can new brains scans improve diagnosis of dementia with Lewy bodies ? Lead investigator: Dr Elijah Mak

Institution: University of Cambridge 

Grant type: Junior Fellowship

Awarded:  2018/2019   

Developing a tool to maintain compassion

Lead investigator: Dr Nuriye Kupeli

Improving coordination in Posterior Cortical Atrophy and Alzheimer’s disease Lead Investigator: Dr Kier Yong  Institution: University College London

Improving diagnosis of Alzheimer’s disease by understanding changes seen in brain scans

Lead Investigator: Dr Kirsty Elizabeth McAleese (Junior Fellowship)

Reducing resistance to receiving personal care in advanced dementia 

Lead Investigator: Dr Tamara Backhouse (Junior Fellowship)

Helping people with dementia to make decisions about continence products

Lead Investigator: Dr Catherine Murphy (Junior Fellowship)

Institution: University of Southampton 

Creating a system to improve the treatment of additional medical conditions in people with dementia

Lead Investigator: Dr Joao Delgado (Junior Fellowship)

Institution: University of Exeter

What causes severe forgetting in Alzheimer’s disease?

Lead Investigator: Dr Michael Craig (Junior Fellowship)

Institution: Heriot-Watt University

Senior Fellowships 

Creating a resource to help current carers to cope with feelings of grief

Lead Investigator: Dr Kirsten Moore (Senior Fellowship)

Understanding the needs and experiences of people affected by dementia in rural areas

Lead Investigator: Dr Fiona Jayne Marshall (Senior Fellowship)

Institution: University of Nottingham

PhD Studentships 

Improving driving safety assessments for people with dementia

Lead Investigator: Dr Paul Donaghy 

Institution: Newcastle University Grant type: PhD Studentship Awarded: 2019/20  

Identifying brain features to diagnosis and monitor Alzheimer’s disease accurately

Lead Investigator: Dr Richard Killick Institution: Institute of Psychiatry, King’s College London Grant type: PhD studentship Awarded: 2019/2020  

Brain scans to spot signs of Alzheimer’s disease in the choroid plexus Lead Investigator: Dr Jack Wells

Supporting family carers to carry out person-centred care

Lead Investigator: Dr Gerard Riley

Institution: University of Birmingham 

Helping people in care homes with hearing problems to communicate

Lead Investigator: Dr Piers Dawes Institution: University of Manchester Grant awarded: 2017/2018  

Understanding how dementia services can meet the needs of Black African and Caribbean people

Lead Investigator: Professor Paul Higgs  Institution: University College London Grant awarded: 2017/2018  

Understanding the impact of visual impairment on life with dementia

Lead Investigator: Dr Claire Hutchinson

Institution: University of Leicester

Understanding how people from minority ethnic backgrounds can access better support 

Lead Investigator: Dr Jan Oyebode

Institution: University of Bradford

Keep talking for longer: speech therapy to help people living with dementia stay in the conversation

Lead Investigator: Dr Catherine Tattersall

Institution: University of Sheffield

Providing better care for care home residents with both dementia and cancer 

Lead Investigator: Dr Laura Ashley

Institution: Leeds Beckett University

Finding drugs to manage bladder and bowel control in people with Alzheimer's disease

Lead Investigator: Dr Jerome Swinny

Institution: University of Portsmouth

Global Brain Health Institute grants 

Investigating burdensome end of life care in people with advanced dementia

Lead investigator: Dr Elizabeth Dzeng 

Institution: King’s College London, University of California and France. Grant round awarded: 2018/19  

Identifying people with dementia who may benefit from palliative care

Lead Investigator: Corrina Grimes

Institution: Global Brain Health Institute, GP practices across Northern Ireland Grant round awarded: 2018/2019

Latest research projects

Understanding risk of dementia in professional rugby players

Lead Investigator: Professor Craig Ritchie

Does gum disease play a role in cognitive decline?

Lead Investigator: Dr Jing Kang

Institution: University of Leeds

Understanding the links between hearing problems and dementia Lead Investigator: Professor Jason Warren

Institution: University College London Grant type: Project Grant  Awarded 2019/20

PREVENT Dementia: Understanding changes in the brain through mid-life

Lead Investigator: Professor Craig W Ritchie

Institution: University of Edinburgh Grant type: Project Awarded: 2019/20  

Psychological interventions to tackle risk factors of dementia: Depression and anxiety

Lead Investigators: Professor Marcus Richards & Dr Joshua Stott

Institution: University College London Grant type:  Project 

Understanding the factors in mid-life that increase the risk of developing dementia

Institution: University of Edinburgh/Imperial College London

Grant type:  Project

Awarded: 2015/2016 (phase 2)

Investigating the links between anticholinergic drugs and benzodiazepines and risk of dementia

Lead investigator: Dr George Savva

Awarded: 2013/2014

Understanding whether anticholinergic drugs to treat bladder problems increases risk of dementia

Lead Investigator: Dr Kathryn Richardson 

Institution: University of East Anglia 

Heart-brain link: how heart disease increases risk of dementia Lead Investigator: Dr Sana Suri Institution: University of Oxford

Grant type:  Junior Fellowship

Understanding whether negative thinking influences dementia risk

Lead Investigator: Dr Natalie Marchant 

Grant type:  Senior Fellowship

PhD Studentships

Understanding the risk of developing dementia in UK military veterans

Lead Investigator: Professor Neil Greenberg

Institution: King’s College London Grant type: PhD studentship Awarded: 2019/20  

‘Use it or lose it’: What’s the truth for dementia? Lead Investigator: Dr Dorina Cadar

Awarded: 2019/2010 

Understanding links between diabetes, infections and dementia risk  

Lead Investigator: Dr Charlotte Warren-Gash

Institution: London School of Hygiene and Tropical Medicine

Grant type:  PhD studentship 

Awarded: 2017/2018

Getting a ‘Clu’ about the causes of Alzheimer’s disease

Lead Investigator: Professor Paul Morgan

Institution: Cardiff University

Understanding the genetics of the Brains for Dementia Research participants

Lead Investigator: Dr Keeley Brookes

Institution: Nottingham Trent University

Understanding the prevalence of LATE, a new form of dementia

Lead Investigator: Professor Carol Brayne

Institution: Cambridge Institute of Public Health

Exploring the waste disposal system in brain cells and what it means for Alzheimer’s disease and Frontotemporal dementia

Lead Investigator: Dr Gemma Lace

Institution: University of Salford

Grant type: PhD

Understanding the relationship between the internal structure of the brain and Alzheimer’s disease

Lead Investigator: Dr Juan Varela 

Institution: University of Cambridge   Grant type: PhD Awarded: 2019/20  

Exploring the relationship between blood pressure control and Alzheimer’s disease

Lead Investigator: Professor Mark Good 

Institution: University of Cardiff  Grant type: PhD  Awarded: 2019/20   

Using botox to investigate the spread of tau in Alzheimer’s disease   Lead Investigator:  Professor Giampietro Schiavo

Institution:  University College London Grant type:  PhD Studentship Awarded: 2019/20    

Understanding the early events that lead to brain cell death in Alzheimer’s disease

Lead Investigator: Dr Katrin Deinhardt

Institution: University of Southampton   Grant type: PhD studentship Awarded: 2019/20  

Understanding neuroinflammation in Down syndrome dementia

Lead Investigator: Dr Frances Wiseman

Institution: University College London Grant type: PhD Studentship Awarded: 2019/20

Predicting when mild cognitive impairment progresses to dementia in the clinic

Lead investigator: Professor Karl Herholz

Institution: University of Manchester Grant type: Project grant Awarded: 2019/20  

Exploring the role of the thalamus in early-stage Alzheimer’s disease

Lead Investigator:  Dr Tim Viney

Institution:  University of Oxford Grant type:  PhD Studentship Awarded: 2019/20  

Using the cell’s internal machinery to break down protein plaques in dementia 

Lead Investigator: Dr Edward Avezov

Institution: University of Cambridge  Grant type: PhD studentship Awarded: 2019/20

Investigating if peptide inhibitors can treat Alzheimer’s disease

Lead Investigator: Professor David Allsop

Institution: Lancaster University Grant type: Project Awarded: 2019/20

Spotting the early signs of inherited forms of frontotemporal dementia

Lead Investigator: Dr Martina Bocchetta

Institution: University College London Grant type: Junior Fellowship Awarded: 2019/20  

Project grants

BioResource: 100,000 people to support dementia research

Lead Investigator: Professor Patrick Chinnery

Institution: University of Cambridge Grant type: Project Awarded: 2019/2020  

Does failed waste removal explain tau’s role in dementia?

Lead Investigator: Professor Diane Hanger

Institution: King’s College London Grant type: Project Awarded: 2019/2020    

How are changes to blood flow to the brain linked to memory and thinking problems? Lead Investigator: Dr Alastair Webb Institution: University of Oxford

Could misplaced ribonuclear proteins play a role in Alzheimer’s? Lead Investigator: Professor John Hardy

Awarded: 2018/2019

Does the tau protein stop brain cells from sending inhibitory signals?

Lead Investigator: Dr Francesco Tamagnini

Institution: University of Reading

Understanding how cells in the brain called astrocytes respond to damage in the brain

Lead Investigator: Professor Stephen Wharton Institution: University of Sheffield Grant awarded: 2017/2018

Investigating who develops memory and thinking problems following a stroke and why

Lead Investigator: Professor Joanna Wardlaw

Investigating how fluid is drained from the brain in vascular dementia

Lead Investigator: Professor Roxana Carare 

Institution: University of Southampton

Can blocking the Dkk3 protein prevent the loss of connections between brain cells?

Lead Investigator: Professor Patricia Salinas

Understanding why dementia can occur after stroke

Lead Investigator: Professor Stephen Wharton

Investigating how changes to the immune system could be linked to development of Alzheimer’s disease  

Understanding how brain cells called astrocytes prevent toxic tau from causing nerve cell death  

Lead Investigator: Professor Maria Grazia Spillantini

Understanding whether chemical DNA tags affect the immune system in the development of Alzheimer’s disease

Lead Investigator: Dr Katie Lunnon

Understanding how small groups of proteins are toxic to brain cells

Lead Investigator: Professor Louise Serpell

Institution: University of Sussex

Investigating whether a particular type of amyloid protein is linked to Alzheimer’s disease development

Lead Investigator: Professor Johannes Attems

Improving how we study tau in the brain

Lead Investigator: Dr Diane Hanger

Institution: Institute of Psychiatry, King's College London

Understanding how the immune system contributes to Alzheimer's disease development

Lead Investigator: Professor Will Wood

Institution: University of Bristol

Grant awarded: 2015/2016

Investigating how connections between brain cells are lost in dementia

Lead Investigator: Dr Tara Spires-Jones

Building a resource to better understand the genetics of dementia

Lead Investigator: Professor Henry Houlden

Investigating the biological role of the Alzheimer's disease risk gene APOE

Lead Investigator: Professor Seth Love

Institution:   University of Bristol

Investigating whether changes to chemical DNA tags contribute to Alzheimer's disease

Delving into how changes to blood circulation contributes to dementia

Lead Investigator: Dr William Whiteley

Grant type: Clinical training partnership

What is the role of microglia in frontotemporal dementia? 

Lead Investigator: Dr Sarah Ryan

What can stem cells teach us about inflammation? Lead Investigator: Dr Charles Arber

Using artificial intelligence to understand the causes of Alzheimer’s disease Lead Investigator: Dr Ashwin Venkataraman   Institution: Imperial College London

Awarded:  2018/2019

Using ultra-high resolutions MRI scans to understand more about Dementia with Lewy bodies

Lead investigator: Dr Elizabeth McKiernan

Understanding how Posterior Cortical Atrophy affects the brain

Lead Investigator:  Dr Zeinab Abdi Institution: University College London Grant awarded: 2017/2018

Could ageing brain cells increase our risk of developing Alzheimer’s?

Lead investigator: Dr Nathaniel Woodling Institution: University College London Grant awarded: 2017/2018

Better understanding the role of a protein linked to frontotemporal dementia Lead Investigator: Dr Ryan West Institution: University of Manchester Grant awarded: 2016/2017

Understanding why certain parts of the brain are vulnerable to Alzheimer’s disease

Lead Investigator: Dr Lovesha Sivanantharajah Institution: Bangor University Grant awarded: 2016/2017

Understanding how Alzheimer’s disease affects different types of brain cells

Lead Investigator: Dr Lilach Soreq (Junior Fellowship)

Grant awarded: 2015/2016 Themes: Alzheimer's disease; cause; brain cells  

Exploring the link between major depressive disorder and Alzheimer’s disease

Lead Investigator: Dr Magdalena Sastre

Institution: Imperial College London   Grant type: PhD studentship Awarded: 2019/20  

Understanding the role of mRNA and tau in recognition memory Lead Investigator: Professor Elizabeth Warburton

Institution: University of Bristol Grant type: PhD Studentship Awarded: 2019/20  

Investigating cell waste removal genes in frontotemporal dementia

Lead Investigator: Professor John Hardy

Awarded: 2019/20

Understanding how to prevent the build-up of toxic tau in cell models Lead Investigator: Dr Will McEwan 

Institution: University of Cambridge Grant type: PhD Studentship Awarded: 2019/20  

Understanding the role of mitochondria in dementia with Lewy bodies Lead Investigator: Dr Ilse Pienaar

Institution: University of Sussex Grant type: PhD studentship

Saving synapses in Alzheimer's disease

Using virtual reality technology to detect the earliest stages of Alzheimer’s disease

Lead Investigator: Dr Dennis Chan Institution: University of Cambridge Grant awarded: 2017/2018  

Investigating a potential pathway in the development of frontotemporal dementia

Lead Investigator: Dr Kurt De Vos

Understanding the role of the C9orf72 gene in frontotemporal dementia

Lead Investigator: Dr Kevin Talbot

Institution: University of Oxford

Understanding how tiny changes in several genes could contribute towards risk of Alzheimer’s disease

Lead Investigator: Dr Jonathan Mill

Further understanding of the role of the immune system in blood flow to the brain

Understanding what causes the increased risk of Alzheimer’s disease in Down’s syndrome

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People affected by dementia interview Blood Biomarker Challenge researchers

The Blood Biomarker Challenge aims to revolutionise dementia diagnosis on the NHS. Anita Goundry and Tom Lawless interview two of its lead researchers.

Could finding changes common to different types of dementia lead to treatments?

Research into changes that are common to different types of dementia could point to similar ways of treating them.

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Department of Neurology

Knight Alzheimer Disease Research Center

• Memory & Aging Project

What is the Memory & Aging Project?

Since 1979, the Memory & Aging Project (MAP) at Washington University has studied cognitive functioning in persons as they age. Our efforts are designed to provide information on the aging process in healthy older persons and in those diagnosed as having a dementia of the Alzheimer type or another related disorder. The Memory and Aging Project has enrolled hundreds of volunteers for our studies and is at the forefront of a worldwide effort to uncover key causal factors in the development of Alzheimer disease. Our goal is the development of more effective treatments and an eventual cure or prevention of Alzheimer disease.

Who can volunteer?

• Individuals age 40 and older with or without memory loss
• In stable, general health
• No problems with memory or thinking OR have mild dementia
• Have a study partner (spouse, family member or friend) who will be interviewed yearly
• Willing and able to complete all study procedures
• We are especially seeking volunteers from underrepresented populations (e.g., Hispanic and African American).

What study procedures are a part of MAP?

Yearly interviews in our office with the study participant and study partner are performed to assess the participant’s memory and thinking. These last between 2-3 hours.

Blood sample is taken to test DNA for genetic causes of AD.

A lumbar puncture is used to collect cerebrospinal fluid (CSF) and is performed every 2-3 years. CSF contains proteins and other chemicals that are important for brain health and provides a unique “window” into understanding how Alzheimer disease develops and progresses.

Yearly psychometric testing of the study participant’s memory and thinking is preformed in our office. This testing takes between 2-3 hours.

Brain scans, including magnetic resonance imaging (MRI) and positron emission tomography (PET) are conducted every 2-3 years. Scans can last up to 2 hours, and you are welcome to take a break if needed.

Additional testing, such as a sleep study, for selected volunteers is also possible.

What is brain donation?

We now know more about Alzheimer disease than ever before. This is in large part due to the dedication of research volunteers and the gift of brain donation for autopsy. A brain autopsy confirms a diagnosis of Alzheimer disease (AD) and identifies clues about other diseases that may be present in brain tissue. Information from the autopsy helps researchers better understand Alzheimer Disease and find a cure to help future generations. Learn more about this voluntary contribution from our  Brain Donation Fact Sheet .

A Memory and Aging Project (MAP) team member can provide you with information about the study and answer any questions you may have. Use our online inquiry form or give us a call!

Call MAP at (314) 286-2683 for further information.

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• Open access
• Published: 17 October 2022

Generalizable deep learning model for early Alzheimer’s disease detection from structural MRIs

• Sheng Liu 1 ,
• Arjun V. Masurkar 2 , 3 ,
• Henry Rusinek 4 , 5 ,
• Jingyun Chen 2 , 4 ,
• Ben Zhang 4 ,
• Weicheng Zhu 1 ,
• Carlos Fernandez-Granda 1 , 6 &
• Narges Razavian 1 , 2 , 4 , 7  

Scientific Reports volume  12 , Article number:  17106 ( 2022 ) Cite this article

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• Alzheimer's disease
• Cognitive ageing
• Neuroscience

An Author Correction to this article was published on 02 October 2023

This article has been updated

Early diagnosis of Alzheimer’s disease plays a pivotal role in patient care and clinical trials. In this study, we have developed a new approach based on 3D deep convolutional neural networks to accurately differentiate mild Alzheimer’s disease dementia from mild cognitive impairment and cognitively normal individuals using structural MRIs. For comparison, we have built a reference model based on the volumes and thickness of previously reported brain regions that are known to be implicated in disease progression. We validate both models on an internal held-out cohort from The Alzheimer's Disease Neuroimaging Initiative (ADNI) and on an external independent cohort from The National Alzheimer's Coordinating Center (NACC). The deep-learning model is accurate, achieved an area-under-the-curve (AUC) of 85.12 when distinguishing between cognitive normal subjects and subjects with either MCI or mild Alzheimer’s dementia. In the more challenging task of detecting MCI, it achieves an AUC of 62.45. It is also significantly faster than the volume/thickness model in which the volumes and thickness need to be extracted beforehand. The model can also be used to forecast progression: subjects with mild cognitive impairment misclassified as having mild Alzheimer’s disease dementia by the model were faster to progress to dementia over time. An analysis of the features learned by the proposed model shows that it relies on a wide range of regions associated with Alzheimer's disease. These findings suggest that deep neural networks can automatically learn to identify imaging biomarkers that are predictive of Alzheimer's disease, and leverage them to achieve accurate early detection of the disease.

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Introduction.

Alzheimer’s disease is the leading cause of dementia, and the sixth leading cause of death in the United States 1 . Improving early detection of Alzheimer’s disease is a critical need for optimal intervention success, as well as for counseling patients and families, clinical trial enrollment, and determining which patients would benefit from future disease-modifying therapy 2 . Alzheimer’s disease related brain degeneration begins years before the clinical onset of symptoms. In recent years, the development of PET imaging techniques using tracers for amyloid and tau have improved our ability to detect Alzheimer’s disease at preclinical and prodromal stages, but they have a significant disadvantage of being expensive and requiring specialized tracers and equipment. Many studies have shown that structural MRI-based volume measurements, particularly of the hippocampus and medial temporal lobe, are somewhat predictive of Alzheimer’s disease progression 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 . While the availability and cost of MRI is beneficial, these early attempts to discriminate healthy aging from Alzheimer’s disease based on volumetry had significant limitations, including small sample size and reliance on semi-automated segmentation methods. This motivated the emergence of more sophisticated methods to analyze MRI data based on machine learning.

In the last decade, machine learning and fully automatic segmentation methods have achieved impressive results in multiple computer vision and image processing tasks. Early applications of machine learning to Alzheimer’s disease diagnosis from MRIs were based on discriminative features selected a priori 14 , 15 , 16 , 17 . These features include regional volumes and cortical thickness segmented from brain regions known to be involved/implicated with memory loss and accelerated neurodegeneration that accompany Alzheimer’s disease 17 , 18 , 19 . Newer machine learning methods based on deep convolutional neural networks (CNNs) make it possible to extract features directly from image data in a data-driven fashion 20 , 21 , 22 , 23 , 24 , 25 , 26 . These methods have been shown to outperform traditional techniques based on predefined features in most image processing and computer vision tasks 27 , 28 . In the biomedical field, CNN-based methods also have the potential to reveal new imaging biomarkers 29 , 30 . Multiple studies have addressed mild Alzheimer’s disease dementia detection from MRI via deep learning, with notable examples of 3D convolutional neural networks based on 3D AlexNet, 3D Resnet, patch based models, Siamese networks, auto-encoder based models, among others 31 , 32 , 33 . Based on systematic reviews and survey studies 34 , 35 , many of previous approaches had major limitations in their design or validation: Most of these studies focus on distinguishing Alzheimer’s disease dementia patients from normal controls. However, in order to develop effective and clinically relevant early detection methods, it is crucial to also differentiate prodromal Alzheimer’s disease, otherwise known as mild cognitive impairment (MCI), from both normal controls and patients with manifest Alzheimer’s disease dementia. Some recent studies have made inroads to this end 36 , 37 , 38 , but do not evaluate their results on large independent cohorts where there can be more variability in image acquisition and clinical diagnosis, more representative of real world scenarios. The goal of this work is to address these significant challenges.

We propose a deep-learning model based on a novel CNN architecture that is capable of distinguishing between persons who have normal cognition, MCI, and mild Alzheimer’s disease dementia. The proposed model is trained using a publicly available dataset from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Although a multisite study, ADNI sites follow a rigorous standard protocol and stringent quality control to minimize site differences and improve our ability to reliably detect neuroanatomical changes. To assess the performance of the proposed methodology when applied in more realistic conditions, we evaluated our approach on an entirely independent cohort of 1522 subjects from the National Alzheimer’s Coordinating Center (NACC). Since (until very recently) each NIH/NIA funded center contributing to the NACC database is free to employ different acquisition parameters, this enables us to validate our approach on imaging data acquired with variable and non standardized protocols.

Our approach achieves an area-under-the-curve (AUC) of 85.12 (95% CI: 84.98–85.26) when distinguishing between cognitive normal subjects and subjects with either MCI or mild Alzheimer’s dementia in the independent NACC cohort. For comparison, we have built a reference model based on the volumes and thickness of previously reported brain regions that are known to be implicated early in disease progression. These measures were obtained by the automated segmentation tool Freesurfer 39 . We demonstrate that our proposed deep-learning model is more accurate and orders-of-magnitude faster than the ROI-volume/thickness model. Our results suggest that CNN-based models hold significant promise as a tool for automatic early diagnosis of Alzheimer’s disease across multiple stages.

Study participants

The study is based on data from ADNI and NACC. The cohorts are described in Table 1 . ADNI is a longitudinal multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer’s disease 40 . NACC, established in 1999, is a large relational database of standardized clinical and neuropathological research data collected from Alzheimer’s disease centers across the USA 41 . Both datasets contain MRIs labeled with one of three possible diagnoses based on the cognitive status evaluated closest to the scanning time: cognitive normal (CN), mild cognitive impairment (MCI), or Alzheimer’s disease dementia. Labeling criteria are included/described in supplementary Table S1 .

We separated the ADNI subjects at random into three disjoint sets: a training set, with 1939 scans from 463 individuals, a validation set with 383 scans from 99 individuals, and a test cohort of 297 scans from 90 individuals. We built an additional independent test cohort based on NACC using the following inclusion criteria: individuals aged ≽ 55 years with MRIs within ± 6 months from the date of clinically-confirmed diagnosis of cognitively normal (CN), mild cognitive impairment (MCI), or mild Alzheimer’s disease dementia (AD). This resulted in a cohort of 1522 individuals (1281 CN, 322 MCI and 422 AD) and 2045 MRIs.

Table 1 reports the basic demographic and genetic characteristics of participants whose scans were used in this study. While cognitive groups in ADNI are well matched on age, in NACC cohort CN subjects were on the average ~ 5 years younger than the two impaired groups; they were 6–7 years younger than ADNI participants. There is a female predominance in NACC data, especially in CN and MCI, and a male predominance in ADNI, notable in the impaired stages. In the impaired population (MCI, AD), the prevalence of the AD genetic risk factor APOE4 is lower in NACC compared to ADNI. Considering these significant differences in cohort characteristics, using NACC as an external validation cohort allows us to assess the robustness of our method. In both ADNI and NACC, education seems to also be lower with progressive impairment stage, which may be indicative of lower structural reserve.

Identification of cognitive impairment status

Our deep-learning model is a 3D convolutional neural network (CNN) for multiclass classification, with an architecture that is specifically optimized for the task of distinguishing CN, MCI, and AD status based on MRIs (Fig.  1 b, see the “ Methods ” section for more details). We also designed a gradient-boosting model 42 based on 138 volumes and thickness of clinically-relevant brain ROIs (see supplementary Table S2 for the list) obtained by segmenting the MRIs using the Freesurfer software (v6.0, surfer.nmr.mgh.harvard.edu). Quality control was applied to the segmentations through sampling and visual inspection by a trained neuroimaging analyst (JC), in consultation with a clinical neurologist (AVM). Details of the quality control process are included in the “ Methods ” section.

Overview of the deep learning framework and performance for Alzheimer’s automatic diagnosis. ( a ) Deep learning framework used for automatic diagnosis. ( b ) Receiver operating characteristic (ROC) curves for classification of cognitively normal (CN), mild cognitive impairment (MCI) and Alzheimer’s disease (AD), computed on the ADNI held-out test set. ( c ) ROC curves for classification of cognitively normal (CN), mild cognitive impairment (MCI) and Alzheimer’s disease (AD) on the NACC test set. ( d ) Visualization using t-SNE projections of the features computed by the proposed deep-learning model. Each point represents a scan. Green, blue, red colors indicate predicted cognitive groups. CN and AD scans are clearly clustered. ( e ) Visualization using t-SNE projections of the 138 volumes and thickness in the ROI-volume/thickness model. Compared to ( d ) the separation between CN and AD scans is less marked. The t-SNE approach is described in details in the methods section.

In order to evaluate the diagnostic performance of the machine-learning models, we computed ROC curves for the task of distinguishing each class (CN, MCI, or AD) from the rest. Table 2 includes our results. On the ADNI held-out test set, the proposed deep-learning model achieved the following AUCs: 87.59 (95% CI: 87.13–88.05) for CN vs the rest, 62.59 (95% CI: 62.01–63.17) for MCI vs the rest, and 89.21 (95% CI: 88.88–89.54) for AD vs the rest.

The AUCs of the ROI-volume/thickness model were statistically significantly lower than those of the deep-learning model: 84.45 (95% CI: 84.19–84.71) for CN vs the rest, 56.95 (95% CI: 56.27–57.63) for MCI vs the rest, and 85.57 (95% CI: 85.16–85.98) versus the rest.

The deep-learning model achieved similar performance on the NACC external validation data compared to ADNI held-out test set, achieving AUCs of 85.12 (95% CI: 85.26–84.98) for CN vs the rest, 62.45 (95% CI: 62.82–62.08) for MCI vs the rest, and 89.21 (95% CI: 88.99–89.43) for AD vs the rest. The ROI-volume/thickness model suffered a more marked decrease in performance, achieving AUCs of 80.77 (95% CI: 80.55–80.99) for CN vs the rest, 57.88 (95% CI: 57.53–58.23) for MCI vs the rest, and 81.03 (95% CI: 80.84–81.21) for AD vs the rest. Note that in Fig.  1 b and c, the Micro-AUC is worse in the ADNI dataset than in the external dataset (NACC). This is because of the imbalance between classes. Micro AUC tends to be driven by the classes with more examples, which are the MCI class in ADNI, and the CN class in NACC. The superior micro-AUC of NACC is therefore due to the fact that the model performs better on the CN class than on the MCI class, which is more common in ADNI. In Supplementary Figure F1 we also report the precision-recall curve for the deep learning model on both the ADNI held-out test set and NACC external validation set. In Supplementary Tables S5 and S6 we provide confusion matrices that show the misclassification rate between different classes.

We analyze the features extracted by the deep-learning model using t-distributed stochastic neighbour embedding (t-SNE), which is a projection method suitable for data with high-dimensional features, such as those learned via deep learning 43 . Figure  1 (d) shows the two dimensional t-SNE projections of the deep learning based features corresponding to all subjects in the NACC dataset. Points corresponding to CN and AD subjects are well separated. Figure  1 (e) shows the t-SNE projections of the ROI-volume/thickness features. In this case the separation between AD and CN scans is less clear. In both cases, points corresponding to MCI scans are not clustered together. This visualization is consistent with our results, which suggest that the features extracted by the deep-learning model are more discriminative than the ROI-based features, and also that distinguishing individuals diagnosed as MCI is more challenging.

Our deep learning model is significantly faster than classification based on regions of interest. On average, for each MRI, our deep learning model requires 0.07 s (plus 7 min for NMI normalization as preprocessing), compared to 11.2 h required for extracting the regions of interest with Freesurfer (we calculate the average running time of the Freesurfer software on each MRI scan, details on the computational settings can be found in the “ Methods ” section).

Progression analysis

We investigated whether the deep-learning model and ROI-volume/thickness model learn features that may be predictive of cognitive decline of MCI subjects to AD. In the held-out test set of ADNI, we divided the subjects into two groups based on the classification results of the deep learning model from the baseline date defined as the time of initial diagnosis as MCI: group A if the model classified the first scan as AD (n = 18), and group B if it did not (n = 26). Figure  2 shows the proportion of subjects in each group who progressed to AD at different months past the baseline. Based on the deep learning model, 23.02% (95% CI: 21.43%–24.62%) of subjects in group A (blue line) progress to AD, compared to 8.81% (95% CI: 8.09%–9.53%) of subjects in group B (red line). For the ROI-volume/thickness model, 20.22% (95% CI: 18.49%–21.95%) of subjects in group A (blue line) progress to AD, compared to 11.11% (95% CI: 10.32%–11.89%) of subjects in group B (red line). The forecasting ability of the deep learning model is therefore significantly higher than that of the ROI-volume/thickness based model. Our results suggest that deep-learning models could be effective in forecasting the progression of Alzheimer’s disease.

Progression analysis for MCI subjects. ( a ) Progression analysis based on the deep learning model. ( b ) Progression analysis based on the ROI-volume/thickness model. The subjects in the ADNI test set are divided into two groups based on the classification results of the deep learning model from their first scan diagnosed as MCI: group A if the prediction is AD, and group B if it is not. The graph shows the fraction of subjects that progressed to AD at different months following the first scan diagnosed as MCI for both groups. Subjects in group A progress to AD at a significantly faster rate, suggesting that the features extracted by the deep-learning model may be predictive of the transition.

Sensitivity to group differences

Figure  3 shows the performance of the deep learning and the ROI-volume/thickness model across a range of sub-cohorts based on age, sex, education and APOE4 status. Supplementary Table S3 includes the AUC values and 95% confidence intervals. The deep learning model achieves statistically significantly better performance on both ADNI and NACC cohorts. One exception is the ApoE4-positive group within NACC, for which classification of CN vs the rest, and MCI vs the rest by deep learning were worse than the ROI-volume/thickness model. Differences in sex and age representation in NACC versus ADNI, as discussed above, could influence this result. However, deep learning outperformed the ROI-volume/thickness model in both males and females, in both cohorts. The CN cohort in the NACC dataset is on average younger than that in the ADNI dataset (Table 1 ). In order to control for the influence of age, we stratified the NACC cohort into two groups (above and below the median age of 70 years old). However, the AUCs of the deep learning model for classification of CN vs the rest were very similar in both groups (85.2 for the younger cohort, to 86.1 for the older cohort). Another possible explanation for the ApoE4 difference is that NACC has a more clinically heterogeneous population, including participants with early stages of other diseases for which ApoE4 can be a risk factor, such as Lewy body and vascular dementia, and which can be clinically indistinguishable from AD at CN and MCI stages. In both ADNI and NACC, low education (< 15 years) also identified a subgroup in which deep learning was outperformed by ROI-volume/thickness model (on distinguishing MCI from others). This subgroup’s clinical presentation, especially at prodromal stages, may be more directly related to brain volume changes according to the cognitive reserve hypothesis 44 .

Performance across different subgroups. Performance of the deep learning model (in blue) and of the ROI-volume/thickness model (in red) for different subpopulations of individuals, separated according to sex, education, and ApoE4 status.

Impact of dataset size

In order to evaluate the impact of the training dataset size, we trained the proposed deep-learning model and the ROI-volume/thickness model on datasets of varying sizes, by randomly subsampling the training dataset. As shown in Fig.  4 , the performance of the ROI-volume/thickness model improves when the training data increases from 50 to 70%, but remains essentially stagnant after further increases. In contrast, the performance of the deep learning model consistently improves as the size of the training set increases. This is also observed in recent works 45 .

Datasize Impact. Performance of the baseline ROI-volume/thickness model (left) and the proposed deep learning model (right) when trained on datasets with different sizes (obtained by randomly subsampling the training set). The performance of the ROI-volume/thickness model improves when the training data increases from 50 to 70%, but remains essentially stagnant after further increases. In contrast, the performance of the deep learning model consistently improves as the size of the training set increases. Given that the deep learning model is trained on a very small dataset compared to standard computer-vision tasks for natural images, this suggests that building larger training sets is a promising avenue to further improving performance.

Model interpretation

In order to visualize the features learned by the deep learning model, we computed saliency maps highlighting the regions of the input MRI scans that most influence the probability assigned by the model to each of the three classes (CN, MCI, or AD), as described in the “ Methods ” section. Figure  5 shows the saliency maps corresponding to each class, aggregated over all scans in the ADNI held-out test set. The figure also reports the relative importance of the top 30 ROIs, quantified by a normalized count of voxels with high gradient magnitudes (see “ Methods ” section for more details). In Supplementary Table S4 we report the full list, as well as a quantification of the importance of the ROIs for the baseline volume/thickness model.

( a – c ) Visualization of the aggregated importance of each voxel (in yellow) in the deep learning model when classifying subjects into CN/MCI/AD. For each subject, the importance map was computed using the gradient of the deep-learning model with respect to its input (Details in “ Methods ” section). The computed gradients are visualized over the MNI T1-weighted template. ( d – f ) Top 30 regions of interest, sorted by their normalized gradient count, which quantifies their importance (see “ Methods ” section), for each of the classes.

Combining deep learning and the ROI-volume/thickness models

We combined the deep learning and ROI-volume/thickness model model by treating the predictions of the deep learning model as new features, and fusing them with the volume/thickness features to train a new gradient boosting model. This model achieved the following AUCs on ADNI test set: 89.25 (95% CI: 88.82–89.63) for CN versus the rest, 70.04 (95% CI: 69.40–70.68) for MCI versus the rest, and 90.12 (95% CI: 89.75–90.49) for AD vs the rest. It achieved similar performance on the NACC external validation data: AUCs of 85.49 (95% CI: 85.06–85.92) for CN versus the rest, 65.85 (95% CI: 65.37–66.33) for MCI versus the rest, and 90.12 (95% CI: 89.86–90.38) for AD versus the rest.

Our analysis supports the feasibility of automated MRI-based prioritization of elderly patients for further neurocognitive screening using deep learning models. Recent literature 46 has consistently shown high prevalence of missed and delayed diagnosis of dementia in primary care. Major contributory factors include insufficient training, lack of resources, and limited time to comprehensively perform early dementia detection. We show that deep learning is a promising tool to perform automatic early detection of Alzheimer’s disease from MRI data. The proposed model is able to effectively identify CN and AD subjects based on MRI data, clearly outperforming the model based on more traditional features such as ROI volumes and thicknesses. While identifying MCI is more challenging, our method still demonstrates improved performance compared to traditional methods. Moreover, as demonstrated in Fig.  3 , MCI misclassification as mild Alzheimer’s dementia may prove to be clinically useful in identifying a higher risk MCI subgroup that progresses faster. For example, in practice, if subsequent functional assessment confirms MCI rather than dementia, these patients may need to be monitored more closely, counselled differently, and more quickly introduced to disease-modifying therapy that may be available in the future.

Our results suggest that deep convolutional neural networks automatically extract features associated with Alzheimer's disease. The analysis of feature importance for the ROI-volume/thickness method shows that the importance of the left hippocampus is an order of magnitude larger than any of the other ROIs, suggesting that this region may dominate the output of the model (see Supplementary Table S4 ). This discovery is consistent with existing literature where a strong association between AD and the volume of the left hippocampus has been reported 47 , 48 , 49 . In contrast, the deep-learning model exploits a much wider range of regions. This highlights the potential of such models to exploit features in imaging data, which are not restricted to traditional measures such as volume and thickness. Many regions previously implicated in distinguishing stage severity of Alzheimer’s disease are recognized as salient by the deep-learning model (see Results). The left and right entorhinal cortex and hippocampus, which are considered the most relevant ROIs to the early stages of Alzheimer’s disease progression by the Braak staging method 50 , appear within the 11 most salient regions. Other regions identified as salient that are in agreement with previous literature include the inferior lateral ventricles (left and right), parahippocampal gyrus (left and right), and white matter hypo-intensities 51 . When comparing to another study that used segmented volumes to distinguish Alzheimer’s disease dementia from controls 52 , the 4th ventricle was uniquely and highly relevant to the deep-learning model whereas certain gyri (fusiform, temporal, angular, supramarginal) were not identified as particularly salient.

Our results suggest several avenues for improving deep learning models for early detection of Alzheimer’s disease. First, the available datasets to train these models is quite limited compared to standard benchmarks for computer vision tasks, which have millions of examples 53 . We show that the number of training data has a strong effect on performance, so gathering larger training sets is likely to produce a significant boost in accuracy. Second, we have shown that combining features learned using deep learning with more traditional ROI-based features such as volume and thickness improves the performance. However, using segmentation-based features is very costly computationally (segmentation takes 11.2 h on average, compared to the 7.8 min needed to apply the deep-learning model). Designing deep-learning models trained to extract volumetric information automatically may improve performance, without incurring such a heavy computational cost.

In this work, we limit our analysis to brain structural MRIs, in order to develop imaging biomarkers for early detection of Alzheimer’s disease. Integrating information such as age or education, genetic data (e.g. single nucleotide polymorphisms), clinical test data from electronic health records, and cognitive performance tests results could provide a more holistic view of Alzheimer’s disease staging analysis. Building deep learning models capable of effectively combining such information with imaging data is an important direction for future research.

Materials and methods

The data used in this study consists of imaging and diagnosis data from Alzheimer’s Disease Neuroimaging Initiative (ADNI) and National Alzheimer’s Coordinating Center (NACC). Since all the analyses were performed on de-identified data which is publically available, IRB Review was not required. In addition, all methods were carried out in accordance with the approved guidelines.

The structural MRI scans (T1 MRIs) were downloaded from the ADNI portal (n = 2619, https://adni.loni.usc.edu/ ). As the diagnoses are done for each screen visit, we directly used the current diagnosis (DXCURREN column), in the ADNI’s diagnosis summary table for each scan.

We used the NACC dataset for external evaluation (n = 2025 MRI scans). The NACC initiative was established in 1999, and maintains a large relational database of standardized clinical and neuropathological research data collected from Alzheimer’s disease centres across the USA 54 . The scan-level diagnostic labels were obtained based on diagnosis within 6 months of the scanning time (closest visit). Scans which did not have any diagnostic information within 6 month of the scan were excluded. Volumetric data for the same cohort were compiled from Freesurfer outputs (with built-in commands asegstats2table and aparcstats2table). Both of these two datasets were from large-scale multicenter studies, in which subject inclusion criteria and/or image acquisition protocols can vary by study center, leading to the potential differences in the scan and diagnosis rating (See Supplementary Table S0 for comparison on image acquisition protocols of two cohorts).

Our analysis was restricted to patients over the age of 55 in both cohorts, and we only considered T1 MRIs (without contrast) for the study.

MRI Data preprocessing

All scans in both cohorts were preprocessed by applying bias correction and spatial normalization to the Montreal Neurological Institute (MNI) template using the Unified Segmentation procedure 55 as implemented in step A of the T1-volume pipeline in the Clinica software 56 . The preprocessed images consist of 121 × 145 × 121 voxels, with a voxel size of 1.5 × 1.5 × 1.5 mm 3 .

Deep learning model

A 3D CNN, composed of convolutional layers, instance normalization 57 , ReLUs and max-pooling layers, was designed to perform classification of Alzheimer’s disease and mild cognitive impairment and normal cognition cases. The architecture is described in more detail in Fig.  1 a(b). In a preliminary work we showed that the proposed architecture is superior to state-of-the-art CNNs for image classification 54 . The proposed architecture contains several design choices that are different from the standard convolutional neural networks for classification of natural images: (1) instance normalization, an alternative to batch normalization 58 , which is suitable for small batch sizes and is empirically observed to achieve better performance (See Supplementary Table S8 ) (2) small kernel and stride in the initial layer for preventing losing information in small regions; (3) wider network architecture with more filters and less layers for the diversity of the features and ease of training. These techniques all independently contribute to boosting performance.

As is standard in deep learning for image classification 59 , we performed data augmentation via Gaussian blurring with mean zero and standard deviation randomly chosen between 0 and 1.5, and via random cropping (using patches of size 96 × 96 × 96).

Training and testing routines for the DL architectures were implemented on an NVIDIA CUDA parallel computing platform (accessing 2 Intel(R) Xeon(R) Gold 6230 CPU @ 2.10 GHz nodes on the IBM LSF cluster each with 2 NVIDIA Tesla V100 SXM2 32 GB GPUs) using GPU accelerated NVIDIA CUDA toolkit (cudatoolkit; https://developer.nvidia.com/cuda-toolkit ), CUDA Deep Neural Network (cudnn) and PyTorch 60 tensor libraries. The model was trained using stochastic gradient descent with momentum 0.9 (as implemented in the torch.optim package) to minimize a cross-entropy loss function. We used a batch size of 4 due to computational limitations. We used a learning rate of 0.01 with a total 60 epochs of training which were chosen by grid search based on validation set performance. During training, the model with the lowest validation loss was selected.

ROI-volume/thickness model

To build a model based on traditionally and commonly used ROI thickness and volumes, we first segmented each brain MRI using Freesurfer, and then computed volume and thickness from these ROIs (using the Freesurfer commands asegstats2table and aparcstats2table ). In order to get the volumetric data for each scan, we processed ADNI and NACC datasets with “recon-all” command at a high performance computer cluster. 16 parallel batch jobs were carried out together, each job was assigned with 320 RAM, 40 CPUs. The average processing time for each scan is about 12 h. 2730 scans of ADNI and 2999 of NACC were successfully processed.

For each brain MRI, a total of 138 MRI volume and thickness features (full list in Supplementary Table S2 ), were used as inputs to construct a Gradient Boosting (GB) classifier to predict Alzheimer's disease statuses. GB is a standard method to leverage pre-selected features as opposed to learning them from the data 61 . It constructs an ensemble of weak predictors by iteratively combining weaker base predictors in a greedy manner. We applied the implementation in the Python Sklearn package v0.24.1 sklearn.ensemble.GradientBoostingClassifier 62 . We set the learning rate to 0.1 (this value was selected based on validation performance). Other hyperparameters were set to their default values. After hyperparameter selection, we trained the model 5 times with different random seeds and reported average performances of these 5 models on the ADNI test set and the external NACC test dataset.

Quality control for freesurfer segmentation

Because of the large number of scans, we developed a two-stage approach for the quality control (QC) of a specific ROI. In the first stage, we located outlier cases within each cohort by fitting a Gaussian distribution to the volumes and centroids of all the segmented ROIs, using a cut-off of mean +/− 3 standard deviations. In the second stage, we conducted QC on the outlier and non-outlier cases separately. For the outliers, all cases were examined visually. For the non-outliers, 100 cases were randomly selected for visual examination. The visual examination was conducted by a trained neuroimaging researcher (JC), in consultation with a neurologist (AM) and a radiologist (HR). This two-stage approach was then repeated for two representative ROIs, namely hippocampus and entorhinal cortex, on each hemisphere, for each cohort. As a result, several segmentation errors were found in the outlier group of both ADNI and NACC cohorts (and excluded from follow-up machine-learning analyses), while no errors were found in the non-outlier group.

Performance metrics

We computed areas under the ROC curve (AUC), which are widely used for measuring the predictive accuracy of binary classification problems. This metric indicates the relationship between the true positive rate and false positive rate when the classification threshold varies. As AUC can only be computed for binary classification, we computed AUCs for all three binary problems of distinguishing between one of the categories and the rest. We also calculated two types of averages, micro- and macro-average denoted as Micro-AUC and Macro-AUC respectively. The micro average treats the entire set of data as an aggregated results, and consists of the sum of true positive rates divided by the sum of false positive rates. The macro average is computed by calculating the AUC for each of the binary cases, and then averaging the results.

t-SNE projection

t-SNE is a tool to visualize high-dimensional data. It converts similarities between data points to joint probabilities and minimizes the Kullback–Leibler divergence between the joint probabilities of the low-dimensional embedding and the high-dimensional data. We applied the implementation in the Python Sklearn package v0.24.1 sklearn.manifold.TSNE with default hyperparameters.

Interpretation of models in terms of ROIs

In order to analyze the features learned by the deep learning model, we computed saliency maps consisting of the magnitude of the gradient of the probability assigned by the model to each of the three classes (CN, MCI, or AD) with respect to its input 63 . Intuitively, changes in the voxel intensity of regions where this gradient is large have greater influence on the output of the model. As mentioned above, we segmented the MRI scans in our dataset to locate ROIs. To determine the relative importance of these regions for the deep-learning model, we calculated the total count of voxels where the gradient magnitude is above a certain threshold ( \({10}^{-3}\) , which is the magnitude observed in background regions where no brain tissue is present) within each ROI, and normalized it by the total number of voxels in the ROI. We excluded left-vessel, right-vessel, optic-chiasm, left-inf-lat-vent, and right-inf-lat-vent, due to their small size (less than 120 voxels).

For the ROI-volume/thickness models, we determined feature importance using a standard measure for gradient boosting methods 64 . This is obtained from the property feature_importances_ in the Python Sklearn package v0.24.1 sklearn.ensemble.GradientBoostingClassifier .

Statistical comparisons

To report statistical significance of descriptive statistics we employed 2-tailed, unpaired testing. We used python statsmodel v0.12.2 and scipy.stats v1.6.1. A p-value < 0.05 was reported as significant. To compute 95% confidence intervals, the bootstrapping method with 100 bootstrap iterations was used.

Reproducibility

The trained deep learning model, and corresponding code, notebooks, the IDs of subject-splits (training/validation/held-out) from publicly available ADNI, and the IDs of NACC participants included in our external validation study are all publicly available in our open-source repository: https://github.com/NYUMedML/CNN_design_for_AD .

The datasets used in this analysis are both de-identified and publicly available and therefore we did not need to get IRB approval for this study.

Data availability

Both cohorts used in our study are publicly available at no cost upon completion of the respective data-use agreements. We used all the T1 MRI scan and clinical data from ADNI (June 15th 2019 freeze), and NACC (Jan 5th 2019 freeze). The IDs of patients and scans used in our study and training, validation, test indicators are available in our github repository. Results of our Freesurfer segmentation (results of over 60,000 compute time on our HPC) for all of NACC and ADNI scans are also publicly available at no cost through NACC and ADNI websites, to anyone who has signed ADNI and NACC data-use agreements. Our trained model, training and validation scripts, and model predictions for each scan ID and patient ID, as well as Freesurfer segmentation volume and thickness features are also available as open-source on github.

Change history

02 october 2023.

A Correction to this paper has been published: https://doi.org/10.1038/s41598-023-43726-2

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Acknowledgements

SL was supported by Alzheimer’s Association grant AARG-NTF-21-848627, and received partial support from NSF DMS-2009752, NSF NRT-HDR-1922658, and the Leon Lowenstein Foundation. NR was partially supported by Leon Lowenstein Foundation and NIH/NIA P30AG066512. AVM was partially supported by NIH/NIA P30AG008051 and P30AG066512. HR was partially supported by NIH/NIA P30AG066512 and NIA/NIBIB U24EB028980. JC was partially supported by NIH/NIA P30AG066512 and NIH-NINDS 1RF1NS110041-01. The NACC database is funded by NIA/NIH Grant U01 AG016976. NACC data are contributed by the NIA-funded ADRCs: P30 AG019610 (PI Eric Reiman, MD), P30 AG013846 (PI Neil Kowall, MD), P50 AG008702 (PI Scott Small, MD), P50 AG025688 (PI Allan Levey, MD, PhD), P50 AG047266 (PI Todd Golde, MD, PhD), P30 AG010133 (PI Andrew Saykin, PsyD), P50 AG005146 (PI Marilyn Albert, PhD), P50 AG005134 (PI Bradley Hyman, MD, PhD), P50 AG016574 (PI Ronald Petersen, MD, PhD), P50 AG005138 (PI Mary Sano, PhD), P30 AG008051 (PI Thomas Wisniewski, MD), P30 AG013854 (PI Robert Vassar, PhD), P30 AG008017 (PI Jeffrey Kaye, MD), P30 AG010161 (PI David Bennett, MD), P50 AG047366 (PI Victor Henderson, MD, MS), P30 AG010129 (PI Charles DeCarli, MD), P50 AG016573 (PI Frank LaFerla, PhD), P50 AG005131 (PI James Brewer, MD, PhD), P50 AG023501 (PI Bruce Miller, MD), P30 AG035982 (PI Russell Swerdlow, MD), P30 AG028383 (PI Linda Van Eldik, PhD), P30 AG053760 (PI Henry Paulson, MD, PhD), P30 AG010124 (PI John Trojanowski, MD, PhD), P50 AG005133 (PI Oscar Lopez, MD), P50 AG005142 (PI Helena Chui, MD), P30 AG012300 (PI Roger Rosenberg, MD), P30 AG049638 (PI Suzanne Craft, PhD), P50 AG005136 (PI Thomas Grabowski, MD), P50 AG033514 (PI Sanjay Asthana, MD, FRCP), P50 AG005681 (PI John Morris, MD), P50 AG047270 (PI Stephen Strittmatter, MD, PhD). Data collection and sharing for the ADNI part of this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.;Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.;Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health ( www.fnih.org ). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database ( adni.loni.usc.edu ). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf .

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C.F.G. and N.R. co-led and supervised this study over all steps from design, development, and analysis. S.L. performed the majority of the data preprocessing, all of deep learning and baseline machine learning model development, training, validation and analysis. A.V.M. provided clinical supervision throughout the study from design to analysis. H.R. provided neuro-imaging supervision throughout the study from design to analysis. J.C. developed Freesurfer segmentation scripts, performed quality control analysis of the Freesurfer results, contributed to writing and analysis of the results overall, and provided neuro-imaging supervision during the study. B.Z. developed scalable Freesurfer segmentation scripts, performed all Freesurfer segmentation jobs in our institution’s high performance cluster. W.Z. performed pre-processing of all scans in the NACC dataset. All authors participated in the writing of the manuscript.

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Correspondence to Carlos Fernandez-Granda or Narges Razavian .

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AVM is on the Council of the Alzheimer's Association International Research Grant Program and is a Steering Committee Member of the Alzheimer's Disease Cooperative Study. The other authors declare that they have no competing interests.

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The original online version of this Article was revised: The original version of this Article contained an error in the Acknowledgements section: “SL was partially supported by NSF DMS-2009752, NSF NRT-HDR-1922658, and Leon Lowenstein Foundation.” now reads “SL was supported by Alzheimer’s Association grant AARG-NTF-21-848627, and received partial support from NSF DMS-2009752, NSF NRT-HDR-1922658, and the Leon Lowenstein Foundation.”

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Liu, S., Masurkar, A.V., Rusinek, H. et al. Generalizable deep learning model for early Alzheimer’s disease detection from structural MRIs. Sci Rep 12 , 17106 (2022). https://doi.org/10.1038/s41598-022-20674-x

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The Alzheimer's Disease Sequencing Project: Study design and sample selection

Late-onset Alzheimer disease (LOAD) is the leading cause of dementia worldwide, with substantial economic and public health implications. 1 LOAD is a neurodegenerative disease characterized by progressive dementia typically manifesting in the seventh to ninth decades. Neuropathological changes precede clinical symptoms by 10–20 years, resulting in clinically asymptomatic individuals carrying neuropathologic features of LOAD. 2 Much of the heritability of LOAD remains unexplained, despite LOAD having a high heritability (60%–80%) and despite the identification of the APOE locus, a major genetic determinant for LOAD. 3 Genetic analyses have identified more than 25 other variants associated with smaller individual effects on disease risk. 4

To identify novel genetic variation influencing AD risk and protection, the Alzheimer's Disease Sequencing Project (ADSP) was implemented as a collaborative effort of the National Institutes on Aging, the National Human Genome Research Institute, and the Alzheimer disease research community. Individual contributors include the Alzheimer's Disease Genetics Consortium, the Neurology Phenotype Working Group of the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium, and the Large Scale Sequencing and Analysis Centers at Baylor University, the Broad Institute, and Washington University.

Study design and sample selection were conducted to address issues of phenotypic heterogeneity and maximize statistical power. The study design includes 2 primary phases: a whole-genome sequencing (WGS) family-based study and a whole-exome sequencing (WES) case-control study. The WGS study was designed to target rarer variation through allelic segregation and linkage analyses in multiplex AD families. The WES case-control study was designed to target low-frequency coding variation in genes that contribute to AD risk or protection.

Approximately 1,400 multiplex LOAD families were reviewed for inclusion. Families were required to have multiple members with LOAD, genomic DNA, and available APOE genotypes. Families meeting initial criteria were assigned a priority rank based on number and age at onset of affected individuals, number of generations affected, and presence of APOE ε4 alleles. Priority was given to families heavily loaded for AD (≥4 affected members with DNA available) with minimal APOE ε 4 alleles. Cases met National Institute of Neurological Diseases–Alzheimer's NINCDS-ADRDA (National Institute of Neurological and Communicative Disorders and Stroke, and the Alzheimer's Disease and related Disorders Association; now, Alzheimer's Association) criteria for possible, probable, or definite AD. Controls were free of clinical AD on cognitive assessment. A detailed description of the family design is in Appendix 1 at Neurology.org/ng .

In total, we selected 582 individuals (498 affected and 84 unaffected) from 111 families for WGS to identify genomic regions associated with increased risk of LOAD. Selected individuals include 229 European ancestry and 353 Caribbean Hispanic (CH) individuals ( table ). The European ancestry families included 2 large Dutch families from the Erasmus Rucphen Family study. 5 Most of these families were recently analyzed for genetic linkage, an analysis that will be used in the analysis of the sequence data. 6 , 7 By design, no ε4/ε4 individuals were selected for sequencing, and we prioritized ε3/ε4 individuals with earlier disease onset. Twenty-seven percent of families had at least 1 case with autopsy confirmation.

Sample demographics for family and case-control studies

ADSP case-control design.

Over 30,000 samples were considered for inclusion in the case-control design. All cases met NINCDS-ADRDA criteria for possible, probable, or definite AD, had documented age at onset or age at death (for pathologically verified cases), and APOE genotyping. All controls were at least 60 years old and were free of dementia by direct, documented cognitive assessment. Three primary case-control selection strategies were evaluated, and ultimately, a design was chosen that targeted cases with minimal risk as predicted by known risk factors (age, sex, and APOE ) and targeted controls with the least probability of conversion to AD by age 85 years. The details and rationale of the case-control selection process and the evaluation of alternate study designs are described in detail in Appendix 2.

In total, we selected 5,096 cases and 4,965 controls under the chosen design ( table ). We selected 682 additional unrelated cases from additional multiplex families that had a strong family history for LOAD. Because some of these 682 cases arose from CH multiplex families, we included 171 cognitively normal CH control samples in the WES.

The sequencing of the nearly 600 whole genomes and 11,000 whole exomes has been completed; the data sets are currently available to the research community through qualified access (dbGaP study phs000572.v7.p4). This data set will be used to identify genetic factors influencing AD risk and protection and will be a critical resource for the LOAD research community.

This study has the approval of the institutional review boards of participating institutions, and informed consent was obtained from all patients.

Acknowledgments

Acknowledgment: The Alzheimer's Disease Sequencing Project (ADSP) comprises 2 Alzheimer's Disease (AD) genetics consortia and 3 National Human Genome Research Institute (NHGRI)-funded Large Scale Sequencing and Analysis Centers (LSAC). The 2 AD genetics consortia are the Alzheimer's Disease Genetics Consortium (ADGC) funded by the NIA (U01 AG032984), and the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) funded by the NIA (R01 AG033193), the National Heart, Lung, and Blood Institute (NHLBI), other NIH institutes, and other foreign governmental and nongovernmental organizations. The Discovery Phase analysis of sequence data is supported through UF1AG047133 (to G. Schellenberg, L.A. Farrer, M.A. Pericak-Vance, R. Mayeux, and J.L. Haines); U01AG049505 to S. Seshadri; U01AG049506 to E. Boerwinkle; U01AG049507 to E. Wijsman; and U01AG049508 to A. Goate. Data generation and harmonization in the Follow-up Phases is supported by U54AG052427 (to G. Schellenberg and Wang). The ADGC cohorts include Adult Changes in Thought (ACT), the Alzheimer's Disease Centers (ADC), the Chicago Health and Aging Project (CHAP), the Memory and Aging Project (MAP), Mayo Clinic (MAYO), Mayo Parkinson's Disease controls, the University of Miami, the Multi-Institutional Research in Alzheimer's Genetic Epidemiology Study (MIRAGE), the National Cell Repository for Alzheimer's Disease (NCRAD), the National Institute on Aging Late Onset Alzheimer's Disease Family Study (NIA-LOAD), the Religious Orders Study (ROS), the Texas Alzheimer's Research and Care Consortium (TARC), Vanderbilt University/Case Western Reserve University (VAN/CWRU), the Washington Heights-Inwood Columbia Aging Project (WHICAP) and the Washington University Sequencing Project (WUSP), the Columbia University Hispanic–Estudio Familiar de Influencia Genetica de Alzheimer (EFIGA), the University of Toronto (UT), and Genetic Differences (GD). The CHARGE cohorts with funding provided by 5RC2HL102419 and HL105756, include the following: the Atherosclerosis Risk in Communities (ARIC) Study which is conducted as a collaborative study supported by NHLBI contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C), the Austrian Stroke Prevention Study (ASPS), the Cardiovascular Health Study (CHS), the Erasmus Rucphen Family Study (ERF), the Framingham Heart Study (FHS), and the Rotterdam Study (RS). The 3 LSACs are the Human Genome Sequencing Center at the Baylor College of Medicine (U54 HG003273), the Broad Institute Genome Center (U54HG003067), and the Washington University Genome Institute (U54HG003079). Biological samples and associated phenotypic data used in primary data analyses were stored at Study Investigators institutions and at the National Cell Repository for Alzheimer's Disease (NCRAD, U24AG021886) at Indiana University funded by the NIA. Associated Phenotypic Data used in primary and secondary data analyses were provided by Study Investigators, the NIA-funded Alzheimer's Disease Centers (ADCs), and the National Alzheimer's Coordinating Center (NACC, U01AG016976) and the National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site (NIAGADS, U24AG041689) at the University of Pennsylvania, funded by the NIA and at the Database for Genotypes and Phenotypes (dbGaP) funded by the NIH. This research was supported in part by the Intramural Research Program of the NIH and the National Library of Medicine. Contributors to the Genetic Analysis Data included Study Investigators on projects that were individually funded by the NIA and other NIH institutes, and by private U.S. organizations, or foreign governmental or nongovernmental organizations.

Supplemental data at Neurology.org/ng

Author contributions: All authors contributed to the work presented in this article. Drafting: the primary manuscript was prepared by G.W.B., with significant contributions from S.S., E.B., G.S., M.A.P.-V., and J.C.B. All authors participated in the revision and editing of the manuscript. Concept and design: primary study concept and design was by G.W.B., with significant contributions from E.R.M., J.C.B., M.A.P.-V., J.L.H., R.M., S.S., E.B., G.S., L.A.F., A.G., C.M.v.D., A.C.N., and A.D. Analysis and interpretation: review of family data was performed by M.A.P.-V., R.M., E.B., S.S., C.M.v.D., and T.M.F. Primary statistical analyses were performed by G.W.B., with additions from J.C.B., A.C.N., E.R.M., S.-H.C., A.D., and S.S. All authors participated in the interpretation and discussion of results. Acquisition of data: sample data were contributed by C.M.v.D., A.D., T.M.F., L.A.F., A.G., J.L.H., M.A.P.-V., E.B., R.M., S.S., and G.S. Statistical analyses: statistical analyses were primarily conducted by G.W.B.; additional analyses conducted by J.C.B., A.C.N., E.R.M., S.-H.C., A.D., and S.S. (affiliations noted above, all academic). Study supervision and coordination: primary study supervision and coordination was by G.S., R.M., E.B., M.A.P.-V., J.L.H., S.S., A.G., L.A.F., and E.W.

Study funding: Supported by the NIH, primarily the NIA, NHLBI, and NHGRI. Primary support includes the Alzheimer's Disease Genetics Consortium (ADGC) funded by the NIA (U01 {"type":"entrez-nucleotide","attrs":{"text":"AG032984","term_id":"16559857","term_text":"AG032984"}} AG032984 ), and the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) funded by the NIA (R01 AG033193), the Human Genome Sequencing Center at the Baylor College of Medicine (U54 HG003273), the Broad Institute Genome Center (U54HG003067), and the Washington University Genome Institute (U54HG003079). Additional funding of contributing sites is noted in the Acknowledgment section.

Disclosures: G.W. Beecham receives funding from the NIH and the Department of Defense. J.C. Bis reports no disclosures. E.R. Martin has served on the editorial board of Frontiers in Statistical Genetics and Methodology and holds US Patent No. 6697739 Test for Linkage and Association in General Pedigrees: The Pedigree Disequilibrium Test. S.-H. Choi reports no disclosures. A. DeStefano has received research support from the NIH. C.M. van Duijn and M. Fornage report no disclosures. S.B. Gabriel is an employee of a nonprofit entity and has been a consultant for WilmerHale Guidepoint Global. D.C. Koboldt receives a coinventor's share of license revenue for VarScan (a software tool for next-generation sequencing analysis), with licensing and disbursements handled by his former institution, Washington University in St. Louis. In the past 2 years, paying licensees included Bina Technologies, Janssen, Fera Science, Philips Electronics, and WuXi NextCODE. D.E. Larson has received research support from the NIH and St. Jude Children's Research Hospital. A.C. Naj has received speaker honoraria from Pfizer; has served on the editorial board of PLoS One ; and has received research support from the NIA, the BrightFocus Foundation, and Penn Institute on Aging. B.M. Psaty serves on the DSMB of a clinical trial for a device funded by the manufacturer (Zoll Lifecor) and on the Steering Committee for the Yale Open Data Access project funded by Johnson & Johnson; is a contributing writer for JAMA; and has received research support from an entity/entities listed in the Acknowledgment section. W. Salerno has been a consultant for Lasergen. W.S. Bush serves on the editorial boards of BMC BioData Mining and PLoS One ; and has received research support from the NIA. T.M. Foroud has served on the scientific advisory boards of the National Advisory Council on Alcohol Abuse and Alcoholism, the Washington University Alzheimer's Disease Research Center, and the NIA Genetics of Alzheimer's Disease Data Storage Site; has received travel funding from the Michael J. Fox Foundation for Parkinson's Research, the NIH, the University of Pittsburgh, and the University of Chicago; has received travel funding and speaker honoraria from the University of Texas at Austin; and has received research support from the NIH, the US Department of Defense, Columbia University, San Diego State University, the University of California, San Diego, the University of Massachusetts, the University of Pennsylvania, the State University of New York, and the Michael J. Fox Foundation for Parkinson's Research. E. Wijsman has served on the scientific advisory board of NIH NHLBI National Registry of Genetically Triggered Thoracic Aortic Aneurysms and Cardiovascular Conditions; has served on the editorial boards of BMC Proceedings and Bioinformatics ; and has received research support from the NIH and the Metropolitan Life Foundation Award for Medical Research. L.A. Farrer has served on the editorial boards of the American Journal of Alzheimer's Disease & Other Dementias and Clinical Genetics ; has 1 patent pending for the use of PLXNA4 as a drug target and biomarker for Alzheimer disease; has been a consultant for Novartis Pharmaceuticals, Gerson Lehrman, Guidepoint Global, and Finnegan & Associates, LLP; and has received research support from the NIH, the Fidelity Foundation, and the Thome Memorial Foundation. A. Goate has served on the scientific advisory board of Denali Therapeutics; has received travel funding from the Rainwater Foundation; has served on the editorial board of eLife ; holds patents for PSEN mutations in AD, Tau mutations in FTD, and TDP43 mutations in ALS\FTD; has been a consultant for Cognition Therapeutics and AbbVie; has received research support from F-Prime, the NIA, the Rainwater Charitable Foundation, and the JPB Foundation; and receives royalty payments from Taconic Industries for tau mutation patent. J.L. Haines has served on the editorial boards of Neurogenetics , Current Protocols in Human Genetics , and Human Molecular Genetics ; receives publishing royalties from John Wiley & Sons; and has received research support from the NIH. M.A. Pericak-Vance serves on the editorial boards of Genetic Epidemiology , Molecular Autism , and Advances in Genomics and Gene Expression ; her immediate family member Dr. Jeffery Vance has served on the editorial boards of Neurology Genetics and American Journal of Neurodegenerative Disease ; and she has received research support from the NIH and the JJ Vance Foundation. E. Boerwinkle has received a speaker honorarium from the American Society for Bone and Mineral Research; is a Scientific Officer at Codified Genomics, LLC; and has received research support from the NIH. R. Mayeux has received research support from the NIH. S. Seshadri serves on the editorial boards of Journal of Alzheimer's Disease , Stroke , and Neurology and has received research support from the NIA. G. Schellenberg has served on the scientific advisory boards of Alzheimer's Association, the Society of Progressive Supranuclear Palsy, the Alzheimer Research Consortium, the Peebler PSP Research Foundation, the United Kingdom Parkinson Disease Center, University College London, the Alzheimer's Disease Sequence Project, the Structural Variant Work Group, Mayo Clinic, Rochester, Udall Center, the University of Miami, and the Oxford Parkinson's Disease Centre; has received travel funding/speaker honoraria from the Alzheimer's Disease Center, CurePSP, the University of California, San Diego, Keystone Symposia, the University of California, the Institute for Memory Impairment and Neurological Disorders, Biomarkers in Neuropsychiatric Disorders (Toronto, Canada), the NIH, Novartis, the McKnight Brain Institute, the University of Florida, the NIA, the Keep Memory Alive Center (Cleveland Clinic), the Lou Ruvo Center for Brain Health, PSP/Lewy Body Disease Think-Tank, the American Association of Neuropathologists, the Fusion Conference, “What does the future hold?” (Tucson, AZ), “Progressive supranuclear palsy genetics—update” (La Jolla, CA), the Center for Public Health Genomics, Genome Sciences Seminar, the University of Virginia, Neurology Grand Rounds, and Columbia University; has served on the editorial boards of the Journal of Neural Transmission , Alzheimer's Research , the American Journal of Alzheimer's Disease and other Dementias , Neurodegenerative Diseases , Current Alzheimer Research , and Pathology and Laboratory Medicine International ; is a professor at the University of Pennsylvania; and has received research support from the NIA/NIH, CurePSP, and CBD Solutions. Go to Neurology.org/ng for full disclosure forms. The Article Processing Charge was funded by the authors.

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Alzheimer's Foundation of America

Our research projects.

AFA provides funding for research projects aimed at improving treatment and quality of life for the millions of people living with Alzheimer’s disease.

Click here to donate and support AFA

The following are some of the research projects AFA has supported:

Identifying At-Risk Individuals

Researchers at NYU Langone Hospital-Long Island are conducting an innovative study called “Platelet-Rich Plasma in the Study of Alzheimer’s Pathophysiology.” The team is studying platelet-rich plasma of individuals with and without Alzheimer’s disease, as well as interactions of the blood with neural progenitor cells. The study focuses on amyloid, an abnormal protein in the brains of people with Alzheimer’s, which some scientists believe to be part of what kills healthy brain cells.  The research has potential in both biomarker development-diagnosing who is at risk early on- and developing drug therapies to treat Alzheimer’s disease.

Exploring Role of Neuroimmune Interactions and Alzheimer’s Disease

A study by The Broad Institute of Harvard & MIT and One Mind is examining the role of the brain’s immune cells in the onset and progression of Alzheimer’s disease. Research is showing how, with increasing age and specific genetic influences, microglia (resident immune cells in the brain) respond to amyloid-beta peptides in a particular way that causes inflammation. In turn, researchers hypothesize that this triggers microglia to inappropriately remove synapses in the brain, resulting in dementia. The Broad Institute will leverage new single-cell RNA sequencing tools that allow deep characterization of individual microglia and immune cells. This could lead to new biological insight and inform the identification of biomarkers used for early detection and monitoring of progression and therapies.

Treating Hallucination and Aggressive Behaviors

Conducted by the Litwin-Zucker Research Center for the Study of Alzheimer’s Disease at the Feinstein Institute for Medical Research in New York, this study is exploring the causes of hallucination, agitation and aggression in relation to Alzheimer’s disease and how they can be better treated.  These types of behaviors are among the most troubling behaviors associated with Alzheimer’s disease and are often one of the main causes that lead to families moving their loved one living with the disease from their homes to a residential healthcare setting.

Improving Early Detection

The Haddasah Medical Organization in Israel is creating ways to detect Alzheimer’s disease earlier so that it can be treated more quickly and effectively. The research team, led by Dr. Shahar Arzy, is focusing on the brain’s orientation system to design new types of Alzheimer’s testing and a revolutionary diagnostic App which will enable doctors to diagnose and begin treating Alzheimer’s disease earlier, when brain tissue is healthier. Treatment at this stage can help slow the progression of Alzheimer’s disease and enable individuals living with it to have a higher and more meaningful quality of life.

AFA also awarded grant funding for Hadassah to purchase a semi-automated Quanterix system to use at the new Hadassah Center for Healthy Brain Aging. The system will be used to screen the aging population in order to identify “at risk” patients and assemble a clinical cohort, with the ultimate goal of improving early detection at the pre-symptomatic phase and developing personalized treatment plans.  

Uncovering APP’s Role in Alzheimer’s

The amyloid precursor protein (APP) gene family is essential for viability in mammals, but its function is unclear. Mutations in the genes for APP and in the enzymes that interact with APP have been found in familial Alzheimer’s disease (a form of Alzheimer’s disease which is linked to genes and affects at least two generations of a family), suggesting that disruption of APP can lead to Alzheimer’s disease. Researchers at the City College of New York (CCNY) are aiming to identify the role that APP plays in brain health and Alzheimer’s disease using the C. elegans  model system. This research can then be translated into discoveries in mammals that could potentially lead to the development of new medications to treat Alzheimer’s that do not interfere with APP function.

Minority Outreach Program

Emory University’s Alzheimer’s Disease Research Center (ADRC) is undertaking a comprehensive, grassroots outreach program to help African-Americans in the Atlanta-metropolitan area.  According to Emory ADRC, African-American seniors are two to three times more likely to develop Alzheimer’s disease as compared to Caucasians; part of the reason stems from a higher reluctance among African-Americans to see a physician about memory loss and other symptoms of Alzheimer’s, often stemming from experienced and perceived discrimination by medical providers. Emory’s grassroots outreach program is successfully working with leaders in the African-American community to connect African-Americans with free memory screenings and information about warning signs, ways to reduce their risk of Alzheimer’s and how to participate in research.

Developing More Effective Treatments for Memory Loss

Researchers at Stony Brook University are undertaking innovative research project that utilizes Positron Emission Technology (PET) imaging in an effort to further drug development. The most commonly used drugs to improve memory in neurodegenerative diseases such as Alzheimer’s target a set of neurons critical for memory called cholinergic neurons. Loss of cholinergic function is a hallmark of cognitive decline. But these medications, which target the cholinergic system and are known as cholinesterase inhibitors, have only a modest effect.  By gaining a better understanding of exactly how these neurons are damaged by Alzheimer’s, Stony Brook’s research team hopes to improve therapeutic strategies that can more effectively target and treat the damage and return these neurons to a normal state to help improve memory.

Read more about some of these and other AFA-funded research projects by clicking below (links to a pdf).

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NIA-Funded Active Alzheimer’s and Related Dementias Clinical Trials and Studies

The National Institute on Aging (NIA) is currently supporting 507 active clinical trials on Alzheimer’s disease and related dementias (AD/ADRD). These trials reflect diverse drug and mechanistic targets, as well as diversity in the stages of AD/ADRD they address. NIA’s active trials include: Clinical Drug Development – Phase I and II (57), Clinical Drug Development – Phase II/III and Phase III (15), Nondrug (159), Dementia Care and Caregiving (226), Understanding Disease Processes (24), Diagnostic Tools, Assessments, & Imaging Studies (19), and Treatments for Neuropsychiatric Symptoms (7). Please see the tables below for more details about these trials.*

Please note: The data in the graphics are from March 2024.

On this page:

• Section 1: Clinical Drug Development – Phase I and II (57 trials)
• Section 2: Clinical Drug Development – Phase II/III and Phase III (15 trials)
• Section 3: Nondrug (159 trials)
• Section 4: Dementia Care and Caregiver (226 trials)
• Section 5: Understanding Disease Processes (24 trials)
• Section 6: Diagnostic Tools, Assessments, & Imaging Studies (19 trials)
• Section 7: Treatments for Neuropsychiatric Symptoms (7 trials)

*The data in the tables below are from March 2024

Section 1: Clinical Drug Development – Phase I and II

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Section 2: Clinical Drug Development – Phase II/III and Phase III

Section 3: Nondrug

Section 4: Dementia Care and Caregiver

Section 5: Understanding Disease Processes

Section 6: Diagnostic Tools, Assessments, & Imaging Studies

Section 7: Treatments for Neuropsychiatric Symptoms

Subscribe to the NIA blog for weekly updates on funding policies and research priorities

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An official website of the National Institutes of Health

Increasing investment and innovation in dementia research is a key aim of ADI. Find out about the projects we are involved in.

One of the key strands of ADI’s mission is to increase investment and innovation in dementia research.

Facilitating research into treatments and hopefully a cure, plus research into improving care, underpins much of our advocacy work.

For over 10 years, ADI has tackled important dementia research issues through our  World Alzheimer Reports , that provide the most comprehensive data on dementia worldwide, including prevalence and economic impact.

ADI is actively involved in a number of research projects:

• WW-FINGERS : ADI is a partner in this project which builds on the successful experience of the FINnish GERiatric Intervention Study to prevent Cognitive Impairment and Disability. The FINGER model was the first randomized controlled trial that demonstrated how to benefit cognition using a multi-domain lifestyle intervention among older at-risk individuals. The results highlighted the value of addressing multiple dementia risk factors as a strategy to protect brain health, and promote overall health and functioning. WW-FINGERS aims to test, adapt, and optimize the FINGER model in different settings, across populations from a variety of geographical and cultural backgrounds.
• STRiDE project (Strengthening responses to dementia in developing countries): In partnership with the London School of Economics and Political Science (LSE), ADI joined forces on the STRiDE project, whose aim is to build research capacity, develop research evidence into what interventions work most effectively, and to better understand the impact and cost of dementia. The project works across seven countries – Brazil, India, Indonesia, Jamaica, Kenya, Mexico and South Africa – and 12 work packages. The four-year project aims to develop evidence to support development of national dementia plans and improve quality of life for people affected by dementia.
• COGNISANCE Project : An EU Joint Programme for Neurodegenerative Disease Research (JPND). A 3-year project working in 5 countries, led by Henry Brodaty in Australia, with partners in Australia, Canada, Netherlands, UK, and Poland. ADI is an external collaborator. The project’s focus is to co-design dementia diagnosis and post diagnostic care; develop a tool-kit to be disseminated internationally; develop a set of standards to guide the diagnostic and post-diagnostic process.
• CST International : ADI sits on the Advisory Board of this UCL (University College London) project focusing on work in Brazil, India, and Tanzania. The project will take place over three-years in four phases. The project will develop, test, refine and disseminate implementation strategies for people living with dementia – looking to increase quality of life and cognition, and to increase awareness and skills in the detection and management of dementia.
• DISTINCT project : ADI is an external collaborator on this project of nine key academic partners. The aim is to develop a multi-disciplinary, multi-professional education and training research framework for Europe aimed at improving technology and care for people with dementia and their carers. ADI offers expertise in areas of community-based practice and national policies through participating in training and education of the early stage researchers (ESRs).
• INDUCT project : Interdisciplinary network for dementia using current technology). A partnership across University of Nottingham, UCL, Maastricht University, University of Amsterdam, Karolinska Institutet, Vrije Universiteit Brussel, Charles University, and IDES in Spain, INDUCT aims to develop a multi-disciplinary, inter-sectorial educational research framework for Europe to improve technology and care for people with dementia, and to provide the evidence to show how technology can improve the lives of people with dementia. ADI sits on the project’s supervisory board an provides training a yearly summer school focusing on turning research into policy. ADI also provided internship opportunities for two early career researchers.
• The  10/66 Dementia Research Group  are researchers who are redressing the fact that, when the group was created, less than 10% of all population-based research into dementia had been directed towards the 66% of people with dementia who live in developing countries. The group looks at the numbers of people with dementia, care arrangements and support services in developing countries.

In addition to our project-specific work, ADI continues to support and promote research into treatments and ultimately a cure for dementia. Through our Medical and Scientific Advisory Panel (MSAP), we maintain our close connection to cutting-edge research on drug development and often represent the patient and carer voice on advisory panels for clinical trials.

ADI proposes that, nationally, 1% of the societal cost of dementia should be devoted to funding research in basic science, care improvements, prevention and risk reduction, drug development and public health. Without significant investments in these areas of dementia research we will be unable to venture into new frontiers.

More information about research can be found on the web sites of  Alzheimer’s Association (USA) ,  Alzheimer’s Society (UK) ,  Alzheimer Society of Canada , or the  association in your country .

Related content

Stride project website.

Project website for STRiDE (Strengthening responses to dementia in developing countries)

World Alzheimer Reports

The World Alzheimer Reports are a comprehensive source of global socioeconomic information on dementia. Each World Alzheimer Report is on a different topic, so the previous reports remain important sources of information on a range of topics of worldwide relevance.

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Blood tests for alzheimer's may be coming to your doctor's office. here's what to know.

Lauran Neergaard

Associated Press

WASHINGTON – New blood tests could help doctors diagnose Alzheimer’s disease faster and more accurately, researchers reported Sunday – but some appear to work far better than others.

It’s tricky to tell if memory problems are caused by Alzheimer’s. That requires confirming one of the disease’s hallmark signs — buildup of a sticky protein called beta-amyloid — with a hard-to-get brain scan or uncomfortable spinal tap. Many patients instead are diagnosed based on symptoms and cognitive exams.

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Labs have begun offering a variety of tests that can detect certain signs of Alzheimer's in blood. Scientists are excited by their potential but the tests aren't widely used yet because there's little data to guide doctors about which kind to order and when. The U.S. Food and Drug Administration hasn't formally approved any of them and there's little insurance coverage.

“What tests can we trust?” asked Dr. Suzanne Schindler, a neurologist at Washington University in St. Louis who’s part of a research project examining that. While some are very accurate, “other tests are not much better than a flip of a coin.”

Demand for earlier Alzheimer's diagnosis is increasing

More than 6 million people in the United States and millions more around the world have Alzheimer’s, the most common form of dementia. Its telltale “biomarkers” are brain-clogging amyloid plaques and abnormal tau protein that leads to neuron-killing tangles.

New drugs, Leqembi and Kisunla, can modestly slow worsening symptoms by removing gunky amyloid from the brain. But they only work in the earliest stages of Alzheimer’s and proving patients qualify in time can be difficult. Measuring amyloid in spinal fluid is invasive. A special PET scan to spot plaques is costly and getting an appointment can take months.

Even specialists can struggle to tell if Alzheimer’s or something else is to blame for a patient’s symptoms.

“I have patients not infrequently who I am convinced have Alzheimer’s disease and I do testing and it’s negative,” Schindler said.

New study suggests blood tests for Alzheimer’s can be simpler and faster

Blood tests so far have been used mostly in carefully controlled research settings. But a new study of about 1,200 patients in Sweden shows they also can work in the real-world bustle of doctors' offices — especially primary care doctors who see far more people with memory problems than specialists but have fewer tools to evaluate them.

In the study, patients who visited either a primary care doctor or a specialist for memory complaints got an initial diagnosis using traditional exams, gave blood for testing and were sent for a confirmatory spinal tap or brain scan.

Blood testing was far more accurate, Lund University researchers reported Sunday at the Alzheimer's Association International Conference in Philadelphia. The primary care doctors' initial diagnosis was 61% accurate and the specialists' 73% — but the blood test was 91% accurate, according to the findings, which also were published in the Journal of the American Medical Association.

Which blood tests for Alzheimer’s work best?

There’s almost “a wild West” in the variety being offered, said Dr. John Hsiao of the National Institute on Aging. They measure different biomarkers, in different ways.

Doctors and researchers should only use blood tests proven to have a greater than 90% accuracy rate, said Alzheimer’s Association chief science officer Maria Carrillo.

Today's tests most likely to meet that benchmark measure what’s called p-tau217, Carrillo and Hsiao agreed. Schindler helped lead an unusual direct comparison of several kinds of blood tests, funded by the Foundation for the National Institutes of Health, that came to the same conclusion.

That type of test measures a form of tau that correlates with how much plaque buildup someone has, Schindler explained. A high level signals a strong likelihood the person has Alzheimer’s while a low level indicates that’s probably not the cause of memory loss.

Several companies are developing p-tau217 tests including ALZpath Inc., Roche, Eli Lilly and C2N Diagnostics, which supplied the version used in the Swedish study.

Who should use blood tests for Alzheimer’s?

Only doctors can order them from labs. The Alzheimer’s Association is working on guidelines and several companies plan to seek FDA approval, which would clarify proper use.

For now, Carrillo said doctors should use blood testing only in people with memory problems, after checking the accuracy of the type they order.

Especially for primary care physicians, “it really has great potential to help them in sorting out who to give a reassuring message and who to send on to memory specialists,” said Dr. Sebastian Palmqvist of Lund University, who led the Swedish study with Lund’s Dr. Oskar Hansson.

The tests aren't yet for people who don't have symptoms but worry about Alzheimer's in the family — unless it's part of enrollment in research studies, Schindler stressed.

That's partly because amyloid buildup can begin two decades before the first sign of memory problems, and so far there are no preventive steps other than basic advice to eat healthy, exercise and get enough sleep. But there are studies underway testing possible therapies for people at high risk of Alzheimer's, and some include blood testing.

The Associated Press Health and Science Department receives support from the Howard Hughes Medical Institute’s Science and Educational Media Group. The AP is solely responsible for all content.

Copyright 2024 The Associated Press. All rights reserved. This material may not be published, broadcast, rewritten or redistributed without permission.

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Study across multiple brain regions discerns Alzheimer’s vulnerability and resilience factors

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An open-access MIT study published today in Nature provides new evidence for how specific cells and circuits become vulnerable in Alzheimer’s disease, and hones in on other factors that may help some people show resilience to cognitive decline, even amid clear signs of disease pathology. 

To highlight potential targets for interventions to sustain cognition and memory, the authors engaged in a novel comparison of gene expression across multiple brain regions in people with or without Alzheimer’s disease, and conducted lab experiments to test and validate their major findings.

Brain cells all have the same DNA but what makes them differ, both in their identity and their activity, are their patterns of how they express those genes. The new analysis measured gene expression differences in more than 1.3 million cells of more than 70 cell types in six brain regions from 48 tissue donors, 26 of whom died with an Alzheimer’s diagnosis and 22 of whom without. As such, the study provides a uniquely large, far-ranging, and yet detailed accounting of how brain cell activity differs amid Alzheimer’s disease by cell type, by brain region, by disease pathology, and by each person’s cognitive assessment while still alive.

“Specific brain regions are vulnerable in Alzheimer’s and there is an important need to understand how these regions or particular cell types are vulnerable,” says co-senior author Li-Huei Tsai , Picower Professor of Neuroscience and director of The Picower Institute for Learning and Memory and the Aging Brain Initiative at MIT. “And the brain is not just neurons. It’s many other cell types. How these cell types may respond differently, depending on where they are, is something fascinating we are only at the beginning of looking at.”

Co-senior author Manolis Kellis , professor of computer science and head of MIT’s Computational Biology Group, likens the technique used to measure gene expression comparisons, single-cell RNA profiling, to being a much more advanced “microscope” than the ones that first allowed Alois Alzheimer to characterize the disease’s pathology more than a century ago.

“Where Alzheimer saw amyloid protein plaques and phosphorylated tau tangles in his microscope, our single-cell ‘microscope’ tells us, cell by cell and gene by gene, about thousands of subtle yet important biological changes in response to pathology,” says Kellis. “Connecting this information with the cognitive state of patients reveals how cellular responses relate with cognitive loss or resilience, and can help propose new ways to treat cognitive loss. Pathology can precede cognitive symptoms by a decade or two before cognitive decline becomes diagnosed. If there’s not much we can do about the pathology at that stage, we can at least try to safeguard the cellular pathways that maintain cognitive function.”

Hansruedi Mathys, a former MIT postdoc in the Tsai Lab who is now an assistant professor at the University of Pittsburgh; Carles Boix PhD '22, a former graduate student in Kellis’s lab who is now a postdoc at Harvard Medical School; and Leyla Akay, a graduate student in Tsai’s lab, led the study analyzing the prefrontal cortex, entorhinal cortex, hippocampus, anterior thalamus, angular gyrus, and the midtemporal cortex. The brain samples came from the Religious Order Study and the Rush Memory and Aging Project at Rush University.

Neural vulnerability and Reelin

Some of the earliest signs of amyloid pathology and neuron loss in Alzheimer’s occur in memory-focused regions called the hippocampus and the entorhinal cortex. In those regions, and in other parts of the cerebral cortex, the researchers were able to pinpoint a potential reason why. One type of excitatory neuron in the hippocampus and four in the entorhinal cortex were significantly less abundant in people with Alzheimer’s than in people without. Individuals with depletion of those cells performed significantly worse on cognitive assessments. Moreover, many vulnerable neurons were interconnected in a common neuronal circuit. And just as importantly, several either directly expressed a protein called Reelin, or were directly affected by Reelin signaling. In all, therefore, the findings distinctly highlight especially vulnerable neurons, whose loss is associated with reduced cognition, that share a neuronal circuit and a molecular pathway.

Tsai notes that Reelin has become prominent in Alzheimer’s research because of a recent study of a man in Colombia. He had a rare mutation in the Reelin gene that caused the protein to be more active, and was able to stay cognitively healthy at an advanced age despite having a strong family predisposition to early-onset Alzheimer’s. The new study shows that loss of Reelin-producing neurons is associated with cognitive decline. Taken together, it might mean that the brain benefits from Reelin, but that neurons that produce it may be lost in at least some Alzheimer’s patients.

“We can think of Reelin as having maybe some kind of protective or beneficial effect,” Akay says. “But we don’t yet know what it does or how it could confer resilience.”

In further analysis the researchers also found that specifically vulnerable inhibitory neuron subtypes identified in a previously study from this group in the prefrontal cortex also were involved in Reelin signaling, further reinforcing the significance of the molecule and its signaling pathway.

To further check their results, the team directly examined the human brain tissue samples and the brains of two kinds of Alzheimer’s model mice. Sure enough, those experiments also showed a reduction in Reelin-positive neurons in the human and mouse entorhinal cortex.

Resilience associated with choline metabolism in astrocytes

To find factors that might preserve cognition, even amid pathology, the team examined which genes, in which cells, and in which regions, were most closely associated with cognitive resilience, which they defined as residual cognitive function, above the typical cognitive loss expected given the observed pathology.

Their analysis yielded a surprising and specific answer: across several brain regions, astrocytes that expressed genes associated with antioxidant activity and with choline metabolism and polyamine biosynthesis were significantly associated with sustained cognition, even amid high levels of tau and amyloid. The results reinforced previous research findings led by Tsai and Susan Lundqvist in which they showed that dietary supplement of choline helped astrocytes cope with the dysregulation of lipids caused by the most significant Alzheimer’s risk gene, the APOE4 variant. The antioxidant findings also pointed to a molecule that can be found as a dietary supplement, spermidine, which may have anti-inflammatory properties, although such an association would need further work to be established causally.

As before, the team went beyond the predictions from the single-cell RNA expression analysis to make direct observations in the brain tissue of samples. Those that came from cognitively resilient individuals indeed showed increased expression of several of the astrocyte-expressed genes predicted to be associated with cognitive resilience.

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New analysis method, open dataset

To analyze the mountains of single-cell data, the researchers developed a new robust methodology based on groups of coordinately-expressed genes (known as “gene modules”), thus exploiting the expression correlation patterns between functionally-related genes in the same module.

“In principle, the 1.3 million cells we surveyed could use their 20,000 genes in an astronomical number of different combinations,” explains Kellis. “In practice, however, we observe a much smaller subset of coordinated changes. Recognizing these coordinated patterns allow us to infer much more robust changes, because they are based on multiple genes in the same functionally-connected module.”

He offered this analogy: With many joints in their bodies, people could move in all kinds of crazy ways, but in practice they engage in many fewer coordinated movements like walking, running, or dancing. The new method enables scientists to identify such coordinated gene expression programs as a group.

While Kellis and Tsai’s labs already reported several noteworthy findings from the dataset, the researchers expect that many more possibly significant discoveries still wait to be found in the trove of data. To facilitate such discovery the team posted handy analytical and visualization tools along with the data on Kellis’s website .

“The dataset is so immensely rich. We focused on only a few aspects that are salient that we believe are very, very interesting, but by no means have we exhausted what can be learned with this dataset,” Kellis says. “We expect many more discoveries ahead, and we hope that young researchers (of all ages) will dive right in and surprise us with many more insights.”

Going forward, Kellis says, the researchers are studying the control circuitry associated with the differentially expressed genes, to understand the genetic variants, the regulators, and other driver factors that can be modulated to reverse disease circuitry across brain regions, cell types, and different stages of the disease.

Additional authors of the study include Ziting Xia, Jose Davila Velderrain, Ayesha P. Ng, Xueqiao Jiang, Ghada Abdelhady, Kyriaki Galani, Julio Mantero, Neil Band, Benjamin T. James, Sudhagar Babu, Fabiola Galiana-Melendez, Kate Louderback, Dmitry Prokopenko, Rudolph E. Tanzi, and David A. Bennett.

Support for the research came from the National Institutes of Health, The Picower Institute for Learning and Memory, The JPB Foundation, the Cure Alzheimer’s Fund, The Robert A. and Renee E. Belfer Family Foundation, Eduardo Eurnekian, and Joseph DiSabato.

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Prof. Li-Huei Tsai, director of the Picower Institute, speaks with NPR host Jon Hamilton about her work identifying a protein called reelin that appears to protect brain cells from Alzheimer's. “Tsai says she and her team are now using artificial intelligence to help find a drug that can replicate what reelin does naturally,” says Hamilton. 

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• Alzheimer's
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New analysis offers most comprehensive roadmap to date for more targeted Alzheimer's research, drug discovery

by Jackson Laboratory

From studying the human genome to analyzing the way proteins are encoded, or monitoring RNA expression, researchers are rapidly gaining a far richer understanding of the complex genetic and cellular mechanisms that underpin dementia. But there's a catch: While new technologies are revealing myriad avenues for Alzheimer's research, it's impossible to know in advance which research pathways will lead to effective treatments.

"We have countless potential targets, but we don't know which ones to aim at," said Greg Carter, the Bernard and Lusia Milch Endowed Chair at the Jackson Laboratory (JAX), who led the study. "Drug development is slow and costly, so to make use of these new insights, we need a way to prioritize them effectively."

Now, Carter and his colleagues at JAX—in collaboration with partners from Stanford University School of Medicine, Emory University, and Sage Bionetworks—are doing just that, offering the first comprehensive ranking of the relative role and significance of every gene and protein in the disease's development.

The research is reported in Alzheimer's & Dementia , in advance of the Alzheimer's Association International Conference on July 28, where the work will be presented.

"This is the most comprehensive study to date of Alzheimer's patients' brains," Carter said. "We're integrating research from multiple fields, including genetics and -omics, across the patient's lifespan, and at a much larger scale than has previously been possible."

The team used machine learning to draw together and overlay findings from more than two dozen large-scale genetic studies , along with multi-omic analyses of almost 2,900 brains, to identify thousands of potential targets for therapeutic interventions. The targets were then sorted into 19 separate "biodomains" reflecting biological mechanisms believed to contribute to Alzheimer's disease.

Carter and his colleagues didn't just want a long list of undifferentiated gene and protein targets. Instead, each target is associated with a specific therapeutic hypothesis—making it easier to understand how it works, and to identify candidates for experimental validation.

The team was also able to flag targets likely to play a role in the early stages of Alzheimer's, supporting the development of new diagnostic and therapeutic tools for pre-symptomatic interventions.

"This is incredibly important, but also very challenging: most of our data come from post-mortem brains, so our job was like trying to deduce where a forest fire began after everything has been incinerated," said Carter. "Our computer modeling effectively rewinds the progression of the disease to identify early markers that correspond to late-stage pathology."

That approach is already generating important insights, including new evidence that mitochondria—the powerhouse of the cell—could play a significant role in the early stages of Alzheimer's disease. The team found a number of promising targets in this biodomain, suggesting that mitochondrial function could be a very strong early indicator of Alzheimer's—and a key driver of the disease's progression.

The findings and their full dataset are being made publicly available through the Emory-Sage-Structural Genomics Consortium-JAX TREAT-AD Center, part of a consortium dedicated to de-risking Alzheimer's research, giving researchers and biotech innovators a foundational tool to support smarter and more targeted future research.

"We're taking an aggressively open approach," said Carter. "If any biotech or pharmaceutical company wants to pick this up and run with it, they can—and we hope they will."

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Targeted Alzheimer's research and drug discovery

Researchers at The Jackson Laboratory offer the first comprehensive ranking of the relative role and significance of every known gene and protein in the development of Alzheimer's Disease in advance of the Alzheimer's Association International Conference, July 28.

From studying the human genome, to analyzing the way proteins are encoded, or monitoring RNA expression, researchers are rapidly gaining a far richer understanding of the complex genetic and cellular mechanisms that underpin dementia. But there's a catch: While new technologies are revealing myriad avenues for Alzheimer's research, it's impossible to know in advance which research pathways will lead to effective treatments.

"We have countless potential targets, but we don't know which ones to aim at," said Greg Carter, the Bernard and Lusia Milch Endowed Chair at the Jackson Laboratory (JAX), who led the study. "Drug development is slow and costly, so to make use of these new insights, we need a way to prioritize them effectively."

Now, Carter and his colleagues at JAX -- in collaboration with partners from Stanford University School of Medicine, Emory University, and Sage Bionetworks -- are doing just that, offering the first comprehensive ranking of the relative role and significance of every gene and protein in the disease's development. The work is reported in the July issue of Alzheimer's & Dementia in advance of the Alzheimer's Association International Conference on July 28, where the work will be presented.

"This is the most comprehensive study to date of Alzheimer's patients' brains," Carter said. "We're integrating research from multiple fields, including genetics and -omics, across the patient's lifespan, and at a much larger scale than has previously been possible."

The team used machine learning to draw together and overlay findings from more than two dozen large-scale genetic studies, along with multi-omic analyses of almost 2,900 brains, to identify thousands of potential targets for therapeutic interventions. The targets were then sorted into 19 separate "biodomains" reflecting biological mechanisms believed to contribute to Alzheimer's disease.

Carter and his colleagues didn't just want a long list of undifferentiated gene and protein targets. Instead, each target is associated with a specific therapeutic hypothesis -- making it easier to understand how it works, and to identify candidates for experimental validation.

The team was also able to flag targets likely to play a role in the early stages of Alzheimer's, supporting the development of new diagnostic and therapeutic tools for pre-symptomatic interventions. "This is incredibly important, but also very challenging: most of our data come from post-mortem brains, so our job was like trying to deduce where a forest fire began after everything has been incinerated," said Carter. "Our computer modeling effectively rewinds the progression of the disease to identify early markers that correspond to late-stage pathology."

That approach is already generating important insights, including new evidence that mitochondria -- the powerhouse of the cell -- could play a significant role in the early stages of Alzheimer's disease. The team found a number of promising targets in this biodomain, suggesting that mitochondrial function could be a very strong early indicator of Alzheimer's -- and a key driver of the disease's progression.

The findings and their full dataset are being made publicly available through the Emory-Sage-Structural Genomics Consortium-JAX TREAT-AD Center, part of an NIH-funded consortium dedicated to de-risking Alzheimer's research, giving researchers and biotech innovators a foundational tool to support smarter and more targeted future research. "We're taking an aggressively open approach," said Carter. "If any biotech or pharmaceutical company wants to pick this up and run with it, they can -- and we hope they will."

• Alzheimer's Research
• Healthy Aging
• Diseases and Conditions
• Alzheimer's
• Huntington's Disease
• Alzheimer's disease
• Dementia with Lewy bodies
• Bioinformatics
• Gene therapy
• Huntington's disease
• Vector (biology)

Story Source:

Materials provided by Jackson Laboratory . Note: Content may be edited for style and length.

Journal Reference :

• Gregory A. Cary, Jesse C. Wiley, Jake Gockley, Stephen Keegan, Sai Sruthi Amirtha Ganesh, Laura Heath, Robert R. Butler, Lara M. Mangravite, Benjamin A. Logsdon, Frank M. Longo, Allan Levey, Anna K. Greenwood, Gregory W. Carter. Genetic and multi‐omic risk assessment of Alzheimer's disease implicates core associated biological domains . Alzheimer's & Dementia: Translational Research & Clinical Interventions , 2024; 10 (2) DOI: 10.1002/trc2.12461

Cite This Page :

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Potential new target for early treatment of Alzheimer's disease

A class of proteins that regulates cell repair and enhances cell growth-signaling systems could be a promising new target for the treatment of Alzheimer's and other neurodegenerative diseases, according to a new study led by researchers at Penn State. They found that disrupting necessary sugar modifications of these proteins promotes cell repair and reverses cellular abnormalities that occur in neurodegenerative diseases.

The study appeared today (July 2) in the journal iScience , and the researchers have a patent related to this work .

“Strategies to treat Alzheimer's disease to date have largely focused on pathological changes prominent in the late stages of the disease,” said Scott Selleck, professor of biochemistry and molecular biology in the Penn State Eberly College of Science and leader of the research team. “Although recently [U.S. Food and Drug Administration]-approved drugs have shown the ability to modestly slow the disease by targeting one of these changes, amyloid accumulation, drugs that affect the earliest cellular deficits might provide important tools to stop or reverse the disease process. We are interested in understanding the earliest cellular changes that are found not only in Alzheimer's, but shared across other neurodegenerative diseases, including Parkinson's and amyotrophic lateral sclerosis (ALS).”

Roughly 6.9 million Americans over the age of 65 are estimated to be living with Alzheimer’s disease, according to the Alzheimer’s Association . Despite its widespread impact, there is no agreed upon biological cause or mechanism for the disease. Cell-signaling molecules called heparan sulfate–modified proteins have been implicated in the development of Alzheimer’s, but their specific role has remained unclear, Selleck said.  In this study, the research team first performed a series of analyses in human cell lines and mouse brain cells that express aspects of Alzheimer’s, showing that these proteins regulate cellular processes known to be affected in several neurodegenerative diseases.

Heparan sulfate–modified proteins are found both on the surface of animal cells and in the matrix between cells. This class of proteins are named for a sugar polymer that bears many sulfate groups, called heparan sulfate. Heparan sulfate chains are attached to specific proteins, and this modification allows these proteins to assemble signaling complexes that affect cell growth and influence how the cell interacts with its environment. These signaling pathways also regulate autophagy, a process of cell repair that clears out damaged or dysfunctional components in the cell.

“In the early stages of several neurodegenerative diseases, autophagy is compromised, which means cells have a reduced repair capacity,” Selleck said. “In this study, we determined that heparan sulfate-modified proteins suppress autophagy-dependent cell repair. What’s more, we show that by compromising the structure and function of the sugar modifications of these proteins, the levels of autophagy increase so cells can take care of damage.”

The researchers found that, in human and mouse cells, reducing the function of heparan sulfate-modified proteins also rescued other pathologies that arise early in neurodegenerative diseases, improving the function of mitochondria — which are responsible for energy production in the cell — and reducing build-up of lipids, or fatty compounds, inside cells.

The researchers then evaluated the role of heparan sulfate-modified proteins in an animal model of Alzheimer's, a fruit fly with deficits in a presenilin protein. Presenilin mutations cause early onset disease in humans and likewise in fruit flies; defective presenilin causes cell death and brain degeneration. In flies with deficits in presenilin, reducing the function of heparan sulfate chains suppressed the death of neurons and corrected other cell defects as well. These results are directly relevant to recent human genetics research, the researchers said.

“Individuals with mutations in a presenilin gene, PSEN1, develop Alzheimer's in their mid-40s. But if they also inherit a rare genetic change in a specific protein called APOE , the disease is delayed, sometimes by decades ,” Selleck said, explaining that APOE plays an important role in lipid transport and binds to heparan sulfate. “This change in APOE — which has been in the news lately — greatly reduces APOE binding to heparan sulfate. Our work builds on and extends these findings, directly implicating heparan sulfate in Alzheimer’s pathology involving both PSEN1 and APOE. Targeting the enzymes that make heparan sulfate could provide a means of blocking neurodegeneration in humans.”

Collectively, these results show that disrupting the structure of heparan sulfate modifications, blocks or reverses early cellular problems in these models of Alzheimer's.

“We save the animal from neuron cell loss, mitochondrial defects and rescue behavior deficits that serve as a measure of nervous system function,” Selleck said. “These findings suggest a promising target for future treatments that could rescue the earliest abnormalities that occur in many neurodegenerative diseases.”

 The researchers also explored how gene expression changed when they eliminated the capacity of human cells to make heparan sulfate chains. They found that expression levels of more than 50% of approximately 70 genes known to be associated with late-onset Alzheimer's disease were modulated, including APOE, suggesting a link between heparan sulfate-modified proteins and the more common and late onset forms of Alzheimer’s disease.

“There is a critical need to focus on cellular changes that occur at the earliest times in disease progression and develop treatments that block or reverse them,” Selleck said. “We demonstrate that reduced autophagy, mitochondrial defects and lipid build-up — all common changes in neurodegenerative disease — can be blocked by altering one class of proteins, those with heparan sulfate modifications. We think these molecules are promising targets for drug development.”

The researchers suspect that disrupting this pathway to promote cell repair systems could be important for a wide variety of other diseases where autophagy defects occur.

“The applications of manipulating this pathway may be broadly useful across a number of human medical conditions,” Selleck said.

The research team at Penn State also includes co-authors Nicholas Schultheis, doctoral student in the Biochemistry, Microbiology and Molecular Biology program; Alyssa Connell, research assistant; and Richard Mueller, Alexander Kapral, Robert Becker, Shalini Shah, Mackenzie O’Donnell, Matthew Roseman, Lindsey Swanson and Sophia DeGuara, all undergraduate students. Contributions were also made by researcher Weihua Wang and Associate Professor of Pharmacology Fei Yin at the University of Arizona and graduate student Tripti Saini and Assistant Professor of Biochemistry and Molecular Biology Ryan Weiss at the University of Georgia.

Funding from the National Institutes of Health and the Penn State Eberly College of Science supported this research.

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There's No Better Time to Support the Fight Against Alzheimer’s

Alzheimer's disease was first described in 1906. Since then, scientists have made remarkable strides in understanding how Alzheimer's affects the brain and learning how to make life better for affected individuals and families. Below are some important milestones in our progress, including the founding of the Alzheimer's Association in 1980, which has played a key role in advancing research and raising awareness of the disease. 

1906-1960: First discovery

1970-1979: modern research, 1980-1989: awareness and momentum, 1990-1999: treatments emerge, 2000-2009: progress and hope, 2010-2019: setting a national agenda, 2020-present: a new era of treatment, dr. alois alzheimer first describes "a peculiar disease".

German physician Alois Alzheimer, a pioneer in linking symptoms to microscopic brain changes, describes the haunting case of Auguste D., a patient who had profound memory loss, unfounded suspicions about her family, and other worsening psychological changes. In her brain at autopsy, he saw dramatic shrinkage and abnormal deposits in and around nerve cells. Dr. Alzheimer died in 1915, never suspecting that his encounter with Auguste D. would one day touch the lives of millions and ignite a massive international research effort. Scientists recognize Dr. Alzheimer not only for his groundbreaking characterization of a major disease but also as a role model. He set a new standard for understanding neurodegenerative disorders by establishing a close clinical relationship with his patients and using new scientific tools to determine how symptoms related to physical brain changes.

Alzheimer's disease named

Emil Kraepelin, a German psychiatrist who worked with Dr. Alzheimer, first names "Alzheimer's Disease" in the eighth edition of his book "Psychiatrie."

Invention of electron microscope allows further study of brain

In 1931, Germans Max Knoll and Ernst Ruska co-invent the electron microscope, which can magnify up to 1 million times. It is not until after WWII that the electron microscope becomes common in major research settings, enabling scientists to study brain cells in more detail.

Development of cognitive measurement scales

Researchers develop the first validated measurement scale for assessing cognitive and functional decline in older adults, paving the way to correlate the level of measured impairment with estimates of the number of brain lesions and the volume of damaged tissue.

Founding of National Institute on Aging

An act of Congress establishes the National Institute on Aging (NIA) as one of our National Institutes of Health (NIH). The NIA is our primary federal agency supporting Alzheimer's research.

Alzheimer's recognized as most common cause of dementia

Neurologist Robert Katzman identifies Alzheimer's disease as the most common cause of dementia and a major public health challenge in his editorial published in Archives of Neurology.

Alzheimer's Association founded

In 1979, Jerome H. Stone and representatives from several family support groups met with the National Institute on Aging to explore the value of a national, independent, nonprofit organization to complement and stimulate federal efforts on Alzheimer's disease. That meeting resulted in the 1980 formation of the Alzheimer's Association with Mr. Stone as founding president. Today, the Alzheimer’s Association is the leading voluntary health organization in Alzheimer’s care, support and research.

Declaration of National Alzheimer's Disease Month

Awareness of Alzheimer's disease increases, leading Congress to designate November 1983 as the first National Alzheimer's Disease Month.

Beta-amyloid identified

Researchers George Glenner and Cai'ne Wong report identification of "a novel cerebrovascular amyloid protein," known as beta-amyloid — the chief component of Alzheimer's brain plaques and a prime suspect in triggering nerve cell damage.

Nationwide infrastructure for Alzheimer's research established

The NIA begins funding its network of Alzheimer's Disease Centers at flagship medical institutions, establishing a nationwide infrastructure for research, diagnosis and treatment.

Tau protein identified

Researchers discover that tau protein is a key component of tangles — the second pathological hallmark of Alzheimer's disease and another prime suspect in nerve cell degeneration.

First Alzheimer's drug trial

The Alzheimer's Association assists the NIA and Warner-Lambert Pharmaceutical Company (now Pfizer) in launching and recruiting participants for clinical trials of tacrine, the first drug specifically targeting symptoms of Alzheimer's disease.

First deterministic Alzheimer's gene identified

Researchers identify the first gene associated with rare, inherited forms of Alzheimer's disease. This gene on chromosome 21 codes amyloid precursor protein (APP), the parent molecule from which beta-amyloid is formed. Chromosome 21 is also the chromosome of which those with Down syndrome have three copies rather than two. Many individuals with Down syndrome develop Alzheimer's disease, often as young as their 30s and 40s.

Federal clinical study consortium launched

The NIA established the Alzheimer's Disease Cooperative Study (ADCS), a nationwide medical network to facilitate clinical research and conduct federally funded clinical trials.

First Alzheimer's risk factor gene identified

Researchers identify APOE-e4, a form of the apolipoprotein-E (APOE) gene on chromosome 19, as the first gene that raises risk for Alzheimer's but does not determine that a person who has it will develop the disease.

First Alzheimer's drug approved by FDA

The Food and Drug Administration (FDA) approves tacrine (Cognex) as the first drug specifically targeting Alzheimer's memory and thinking symptoms. Four additional drugs are approved over the next 10 years.

President Reagan's diagnosis announced

Former U.S. President Ronald Reagan shares with the American people that he has been diagnosed with Alzheimer's disease. In an open letter to the American people about his decision to share his diagnosis, President Reagan wrote, "In opening our hearts, we hope this might promote greater awareness of this condition. Perhaps it will encourage a clearer understanding of the individuals and families who are affected by it."

First World Alzheimer's Day

The first World Alzheimer's Day (WAD) launches on September 21 by Alzheimer's Disease International, the umbrella organization of Alzheimer's associations.

First transgenic mouse model announced

Researchers announce the first transgenic mouse model that developed Alzheimer-like brain pathology. The mouse was developed by inserting one of the human APP genes linked to a rare, inherited form of Alzheimer's disease. The Alzheimer's Association first awarded a grant to develop a mouse model of a rare neurodegenerative disorder called Gerstmann-Sträussler-Scheinker syndrome in 1989, laying the technical foundation for Alzheimer's mouse models.

"Alzheimer's vaccine" successful in mice

The first in a series of reports is published showing that injecting transgenic "Alzheimer" mice with beta-amyloid prevents the animals from developing plaques and other Alzheimer-like brain changes.

National Alzheimer's Disease Genetics Study begins

The Alzheimer's Association partners with the National Institute on Aging to recruit participants for the National Alzheimer's Disease Genetics Study, a federal initiative to collect and bank blood samples from families with several members who developed Alzheimer's disease late in life in order to identify additional Alzheimer's risk genes.

First report on Pittsburgh Compound B (PIB)

Researchers at the Alzheimer's Association International Conference on Alzheimer's Disease (AAICAD) share their first report on an imaging agent called Pittsburgh Compound B (PIB), a major potential breakthrough in disease monitoring and early detection. PIB enters the brain through the bloodstream and attaches itself to beta-amyloid deposits, where it can be detected by positron emission tomography (PET). The Alzheimer's Association provided significant support to initiatives to develop PIB and conduct preclinical testing it in animal studies.

Neuroimaging Initiative (ADNI)

The Alzheimer's Association joins public and private donors as a major sponsor of the Alzheimer's Disease Neuroimaging Initiative (ADNI), a nationwide study to establish standards for obtaining and interpreting brain images. The ultimate goal of ADNI is to determine whether standardized images, possibly combined with laboratory and psychological tests, can identify high-risk individuals; provide early detection; and track and monitor treatment effects, especially in clinical trials of disease-modifying drugs. In 2006, the Association launched the European Alzheimer's Disease Neuroimaging Initiative (E-ADNI), to expand ADNI's scope by combining data from several European brain imaging initiatives with ADNI data. This effort has now grown into World Wide ADNI (WW-ADNI), a global network of flagship research sites united in a common effort to improve diagnosis and speed treatment development with standardized protocols and data shared internationally.

Alzheimer's & Dementia® journal launched

The Alzheimer's Association launches Alzheimer's & Dementia®: The Journal of the Alzheimer's Association to further support a global, interdisciplinary exchange within the Alzheimer's research community.

Healthy Brain Initiative launched

The Alzheimer’s Association and the U.S. Centers for Disease Control and Prevention launch the Healthy Brain Initiative with the publication of A National Public Health Road Map to Maintaining Cognitive Health. The Road Map advances 44 science-based actions emphasizing primary prevention of cognitive impairment. The goal of this initiative is to maintain or improve the cognitive performance of all adults.

International Society to Advance Alzheimer's Research and Treatment formed

To further the work of the global Alzheimer's research community, the Alzheimer's Association creates the International Society to Advance Alzheimer's Research and Treatment (ISTAART), the first and only professional society dedicated to Alzheimer's and dementia.

International Conference on Alzheimer's Disease becomes an annual event

With accelerating progress intensifying the need for global information exchange, the Alzheimer's Association International Conference on Alzheimer's Disease® (AAICAD®) becomes an annual event.

Effort to standardize biomarkers begins

The Alzheimer's Association announces funding of the Alzheimer's Association QC Program for CSF Biomarkers to help overcome variation among institutions in measuring potential biomarkers in cerebrospinal fluid (CSF).

Alzheimer's researchers unite to raise awareness and concern

Dozens of Alzheimer's researchers unite with the Alzheimer's Association for an "Alzheimer's Breakthrough Ride®," a 66-day bike relay across America to raise public and congressional awareness of the urgent need for more federal funding to support the search for effective Alzheimer's treatments.

Alzheimer's clinical trial database established

The Alzheimer's Association and its partners in the Coalition Against Major Diseases (CAMD) released a first-of-its kind database of 4,000 patients who participated in 11 pharmaceutical industry-sponsored clinical trials of Alzheimer's treatments. The combined data, accessible to any qualified researcher, will offer unprecedented power to understand the course of Alzheimer's.

Alzheimer's Association TrialMatch® launched

The Association launches TrialMatch®, a free, easy-to-use clinical studies matching service that connects individuals living with Alzheimer's disease, caregivers, and healthy volunteers with current research studies. Stakeholders unanimously identified building awareness of research studies and increasing enrollment as key strategies to accelerate treatment development.

An influential model of biomarker changes during Alzheimer’s disease progression is first published

A group of researchers publish a working model relating changes in Alzheimer’s biomarkers to disease stage and symptom severity. The model has become a focal point of research into Alzheimer’s biomarkers, and is revised periodically to account for new research.

President Obama signs National Alzheimer's Project Act (NAPA) into law

Groundbreaking legislation establishes our first-ever framework for a national strategic plan to address the Alzheimer's crisis and to coordinate our response on multiple fronts, including research, care and support.

New criteria and guidelines for Alzheimer's disease diagnosis

Three workgroups convened by the Alzheimer's Association and the National Institute on Aging issue updated criteria and guidelines for diagnosing Alzheimer's disease and propose a research agenda to define a new preclinical stage.

Annual assessment for cognitive impairment for all Medicare Beneficiaries implemented as part of Annual Wellness visits

The Centers for Medicare and Medicaid Services implement Annual Wellness Visits for all Medicare Beneficiaries under the Patient Protection and Affordable Care Act. A mandatory part of the Annual Wellness Visit is an assessment for detection of cognitive impairment.

First major clinical trial for prevention of Alzheimer’s disease is initiated

A multinational research consortium, the Dominantly Inherited Alzheimer Network, launches the first major clinical trial testing drug therapy to prevent the onset of Alzheimer’s disease symptoms in people who inherited an autosomal dominant mutation putting them at high risk for the disease.

International Genomics of Alzheimer’s Project (IGAP) researchers identify new genetic risk factors for Alzheimer’s disease

Hundreds of researchers from around the world collaborate to perform a meta-analysis of genome-wide association studies intended to identify genetic variations linked with an increased risk for Alzheimer’s disease. The collaboration revealed 20 genetic variations associated with increased risk, 11 of which had not been linked with Alzheimer’s before. Some of the newly identified genetic variations are thought to be specific to the immune system, adding to mounting evidence of a role for the immune system in Alzheimer’s disease.

Rates of death caused by Alzheimer’s disease found to be much higher than reported on death certificates

Researchers at Rush University find that the annual number of deaths attributable to Alzheimer’s disease in the U.S. among people at least 75 years old is about 500,000, much higher than the number reported on death certificates (>84,000).

Alzheimer's Accountability Act signed into law

The Alzheimer's Association led the fight for this revolutionary law that allows scientists at the NIH to submit an annual research budget directly to Congress.

Historic funding increase

Historic $400 million increase for federal Alzheimer’s disease research funding signed into law, bringing annual funding to $1.4 billion.

Dementia Care Practice Recommendations published

Alzheimer’s Association released Dementia Care Practice Recommendations aimed at helping professional care providers deliver optimal quality, person-centered care for those living with Alzheimer’s and other dementias.

International consortium established to improve care and psychosocial outcomes

The National Institutes of Health (NIH) awarded the Alzheimer’s Association $1.34 million over five years for an international research network, Leveraging an Interdisciplinary Consortium to Improve Care and Outcomes for Persons Living with Alzheimer’s and Dementia (LINC-AD), to improve care and psychosocial outcomes for individuals living with dementia.

Bill Gates helps fund Part the Cloud Research Grant Program

Bill Gates joined the Alzheimer’s Association’s Part the Cloud global research grant program with a $10 million award that will stimulate an additional $20 million in funding to the Alzheimer’s Association.

Alzheimer’s funding reaches all-time high

A $350 million increase for Alzheimer’s and dementia research funding at the National Institutes of Health (NIH) was signed into law, bringing the annual funding to $2.8 billion. An additional $10 million was also approved for the BOLD Infrastructure for Alzheimer’s Act.

Aducanumab approved for treatment of Alzheimer’s disease

Aducanumab (Aduhelm®) received accelerated approval as a treatment for Alzheimer's disease by the U.S. Food and Drug Administration (FDA). This is the first FDA-approved therapy to address the underlying biology of Alzheimer's disease. (Aducanumab will be discontinued on Nov. 1, 2024. Please connect with your provider on treatment options.)

Lecanemab receives traditional approval for treatment of Alzheimer's disease

Lecanemab (Leqembi®) received traditional approval as a treatment for early Alzheimer's from the FDA. It is the first traditionally approved treatment that addresses the underlying biology of Alzheimer's and changes the course of the disease in a meaningful way for people in the early stages.

CMS decides to cover PET imaging for Alzheimer's disease diagnosis

A policy change by the Centers for Medicare & Medicaid Services (CMS) expanded coverage of brain amyloid positron emission tomography (PET) imaging for the diagnosis of Alzheimer's disease, making a valuable tool more accessible across the country.

Alzheimer's Association announces milestone $100 million research investment

The Alzheimer's Association, the world's largest nonprofit funder of Alzheimer's and dementia research, invested a milestone $100 million in research initiatives in 2023, the largest single-year total since the organization's founding in 1980.

Donanemab receives traditional approval for treatment of Alzheimer's disease

Donanemab (Kisunla™) received traditional approval as a treatment for early Alzheimer's from the FDA. It is the second traditionally approved treatment that addresses the underlying biology of Alzheimer's and changes the course of the disease in a meaningful way for people in the early stages.

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