A massive problem like Alzheimer's disease (AD) — which affects nearly 50 million people worldwide — requires bold solutions. New funding expected to total $17.8 million, awarded to the Keck School of Medicine of USC's Mark and Mary Stevens Neuroimaging and Informatics Institute (INI) and its collaborators, is one key piece of that puzzle. The five-year National Institutes of Health (NIH)-funded effort, "Ultrascale Machine Learning to Empower Discovery in Alzheimer's Disease Biobanks," known as AI4AD, will develop state-of-the-art artificial intelligence (AI) methods and apply them to giant databases of genetic, imaging and cognitive data collected from AD patients. Forty co-investigators at 11 research centers will team up to leverage AI and machine learning to bolster precision diagnostics, prognosis and the development of new treatments for AD. "Our team of experts in computer science, genetics, neuroscience and imaging sciences will create algorithms that analyze data at a previously impossible scale," said Paul Thompson, PhD, associate director of the INI and project leader for the new grant. "Collectively, this will enable the discovery of new features in the genome that influence the biological processes involved in Alzheimer's disease." Predicting a diagnosis The project's first objective is to identify genetic and biological markers that predict an AD diagnosis — and to distinguish between several subtypes of the disease. To accomplish this, the research team will apply sophisticated AI and machine learning methods to a variety of data types, including tens of thousands of brain images and whole genome sequences. The investigators then will relate these findings to the clinical progression of AD, including in patients who have not yet developed dementia symptoms. The researchers will train AI methods on large databases of brain scans to identify patterns that can help detect the disease as it emerges in individual patients. "As we get older, each of us has a unique mix of brain changes that occur for decades before we develop any signs of Alzheimer's disease — changes in our blood vessels, the buildup of abnormal protein deposits and brain cell loss," said Thompson, who also directs INI's Imaging Genetics Center. "Our new AI methods will help us determine what changes are happening in each patient, as well as drivers of these processes in their DNA, that we can target with new drugs." The team is even creating a dedicated "Drug Repurposing Core" to identify ways to repurpose existing drugs to target newly identified segments of the genome, molecules or neurobiological processes involved in the disease. "We predict that combining AI with whole genome data and advanced brain scans will outperform methods used today to predict Alzheimer's disease progression," Thompson said. Advancing AI The AI4AD effort is part of the "Cognitive Systems Analysis of Alzheimer's Disease Genetic and Phenotypic Data" and "Harmonization of Alzheimer's Disease and Related Dementias (AD/ADRD) Genetic, Epidemiologic, and Clinical Data to Enhance Therapeutic Target Discovery" initiatives from the NIH's National Institute on Aging. These initiatives aim to create and develop advanced AI methods and apply them to extensive and harmonized rich genomic, imaging and cognitive data. Collectively, the goals of AI4AD leverage the promise of machine learning to contribute to precision diagnostics, prognostication, and targeted and novel treatments. Thompson and his USC team will collaborate with four co-principal investigators at the University of Pennsylvania, the University of Pittsburgh and the Indiana University School of Medicine. The researchers will also host regular training events at major AD neuroimaging and genetics conferences to help disseminate newly developed AI tools to investigators across the field. Research reported in this publication will be supported by the National Institute on Aging of the National Institutes of Health under Award Number U01AG068057. Also involved in the project are INI faculty members Neda Jahanshad and Lauren Salminen, as well as consortium manager Sophia Thomopoulos. — Zara Greenbaum
Researchers from across the country who collaborated on Alzheimer's Disease work may have found a way to detect the disease with a blood test before patients even exhibit symptoms. Alzheimer's is an incurable degenerative disease that impacts the brain causing confusion, memory loss and eventually leads to the loss of thinking skills that are key to simple task completion, according to the National Institute on Aging. Estimates put the Alzheimer's population in the United States around 5 million and suggest that the population will grow. The test actually does more than detect Alzheimer's, it can also distinguish between Alzheimer's, Parkinson's and regular control samples. This distinguishability is important because it means the test is sensitive to more than neurodegeneration which can be present with aging and other neurodegenerative diseases that might develop.
A new AI model, developed by IBM Research and Pfizer, has used short, non-invasive and standardized speech tests to help predict the eventual onset of Alzheimer's disease within healthy people with an accuracy of 0.7 and an AUC of 0.74 (area under the curve). These predictions were made against data samples from a group of healthy individuals who eventually did or did not develop the disease later in life, allowing researchers to verify the accuracy of the AI model's prediction. This is a significant increase over predictions based on clinical scales (59%), which is a prediction based on other available biomedical data from a patient, as well as random choice (50%). The model uses natural language processing to analyze one- to two-minute speech samples from a brief, clinically administered cognitive test. These short samples of language data were provided by the Framingham Heart Study, a long-running study tracking various aspects of health in more than 5,000 people and their families since 1948.
A handful of startups are employing artificial intelligence technologies and big data in an attempt to diagnose dementia, particularly Alzheimer's disease. The effort could lead to better interventions and even therapeutic drugs if it becomes possible to detect cognitive decline before it really starts. The benefits to society – not to mention market potential – for the early detection of dementia and Alzheimer's disease are huge. According to the World Health Organization, there were 47.5 million people worldwide with dementia in 2015, with 7.7 million new cases each year. The total number of people with dementia is projected to reach 75.6 million in 2030 and almost triple by 2050 to 135.5 million.
Nanoparticles could make a reliable blood test for Alzheimer's disease a reality; image credit: National Cancer Institute, Daniel Sone Using nanoparticles with different surface properties, researchers are able to detect subtle changes in the composition of proteins in the plasma years before the presentation of clinical symptoms of Alzheimer's disease, which include memory loss, confusion, and cognitive difficulties. Owing to the unique properties of nanoparticles, different proteins in biological fluids selectively stick onto their surface forming a protein corona, which was found to change during disease. Researchers from the United States and Italy identify these subtle changes in plasma protein patterns to distinguish plasma samples from healthy individuals and those diagnosed with Alzheimer's disease. "Protein corona composition is both influenced by specific health conditions as well as the chemical and physical properties of the nanoparticles themselves," says Dr. Claudia Corbo of the University of Milano-Bicocca and lead author of the study published in Advanced Healthcare Materials. "Binding of proteins to the surface of particles is very precise and dependent on the chemistry and shape of the particles and the chemistry and structure of the proteins," says senior author Professor Omid Farokhzad of Brigham and Women's Hospital and Harvard Medical School.