Beta-amyloid (β-amyloid) deposition is considered to be a necessary - but not sufficient - step on the path towards Alzheimer’s disease (AD). Depiction of brain β-amyloid in vivo can support an early diagnosis, and even provide an opportunity for consider secondary prevention of AD. Currently, however, the value of β-amyloid PET remains elusive, reason why this procedure is typically not reimbursed. Understanding the value of imaging of β-amyloid using positron emission tomography (PET) provides a unique opportunity to achieve 3 major goals: 1) improve the diagnostic workup of patients suspected of AD and their management; 2) stratify risk for AD and select homogenous groups for intervention; and 3) quantify treatment effects more accurately. AMYPAD will address the above objectives by studying the onset, dynamics, and clinical relevance of brain β-amyloid in the spectrum from normal aging to preclinical and prodromal AD. We will use β-amyloid-PET in an unprecedented large number of subjects (n=4000) from population studies, as well as memory clinics cohorts, with repeat imaging in up to 2000. In close collaboration with EPAD (www.ep-ad.org), the cohorts will be followed with to determine (surrogate) outcomes of cognitive decline and neurodegeneration. Lead from VU-VUmc, the Consortium brings together a word-class team of highly synergistic partners from across Europe constituting a highly experienced and efficient β-amyloid imaging network including the most active European PET sites, to ensure effective tracer delivery, access to patients and expertise to technical knowledge and disease modelling. AMYPAD will establish the true value of β-amyloid PET across the severity range of AD. Patients and clinicians will benefit from better and cost-effective diagnostic procedures and management, while academics and industry will benefit from better disease understanding, clear reimbursement strategy and more rationale and efficient clinical trial design.

The COVID pandemic can be seen as an experiment done with the entirety of humankind (as almost everybody has been or will get infected with SARS-CoV-2). It will therefore have the best coverage over the widest variation possible and is therefore ideally suited to study the effects of infections with SARS-CoV-2 on large quantities of heterogeneous individuals. In our project, we will focus on neurodegenerative diseases (NDDs), namely Alzheimer and Parkinsonism, as the NCDs under investigation. COMMUTE is a project characterized by the intelligent combination of two fundamentally different approaches: a hypotheses-free, data-driven approach is building on available big data and the application of cutting edge AI/ML technologies to answer the question, whether infection by SARS-CoV-2 causes effects that result in a higher risk for the development of NDDs at population-level. Complementary to that, a hypothesis-driven, knowledge-based approach leverages the substantial knowledge in the scientific community working on NDDs on the putative comorbidity mechanisms linking COVID and neurodegeneration. Both approaches are informing and supporting each other through an intensive crosstalk between computational and experimental biology methods. Understanding comorbidity between COVID and NDDs at causal level is the first goal of the COMMUTE project. The second goal is the translation of the actionable insights into personalized health applications. On the AI/ML side, the targeted outcome for translation into practice is a set of qualified biomarkers and predictive features that will be used for an AI-powered, model-generated recommender system that will allow for individualized risk assessment and personalized recommendations. On the side of the biomedical assay systems, COMMUTE will use cell-based assays based on clear pathophysiology mechanism understanding for drug-repurposing screenings in collaboration with REMEDI4ALL, the largest of the EU drug repurposing platforms.

Neurodegenerative diseases are one of the most important contributors to morbidity and mortality in the elderly. In Europe, over 14 million people are currently living with dementia, at a cost of over 400 billion EUR annually. Comorbidities with these conditions are frequent and a major obstacle to optimal diagnosis and management. Recent advances in diagnostic technologies and the advent of disease-modifying therapies (DMT) for Alzheimer’s disease (AD), the most common aetiology of dementia, heralds the beginning of precision medicine in this disease area. PROMINENT will develop a digital platform for precision medicine that will remove barriers that currently exists for leveraging these technological advancements in the routine care of patients with neurodegenerative disorders and co-morbidities. The platform gives clinicians access to prediction models leveraging multimodal diagnostic data automatically derived from multiple sources (imaging repositories, medical records, mobile devices), helping them choose optimal care pathways and improving diagnostic precision. It will provide personalized, relevant and meaningful information on diagnosis and prognosis in a format understandable by patients and care partners. Further, it will support the introduction of new health technologies such as DMT for AD, by ensuring adherence to appropriate use guidelines and facilitating the prospective collection of data on real-world usage, safety and effectiveness. The expected impact of the project is to increase diagnostic accuracy and optimized use of existing and new treatment options. It will empower patients and caregivers by engaging them in more person-centric health care decisions, leading to improved adherence and patient experience. Ultimately this is expected to lead to cost-effective care, improved health outcomes and quality of life.

This project aims at predicting early cognitive decline using a neurofeedback performance endophenotype based on realtime functional magnetic resonance imaging in a population at risk for Alzheimer’s Disease (AD). It has been proven that AD biomarkers change decades before the development of symptoms. In addition, there is a lack of robust hypersensitive methods capable of predicting impending cognitive decline in healthy subjects at risk. This constitutes a crucial obstacle for the implementation of AD prevention trials. To address this, we will identify and characterize a novel neurofeedback performance endophenotype in a subsample from a cohort of 2743 cognitively healthy volunteers at risk for AD. This will be based on state-of-the-art neuroimaging technology, in combination with a novel sensitive neuropsychological test that detects subtle alterations in episodic memory, which has been validated for our cohort. Then, we will evaluate the impact on our newly developed neurofeedback performance endophenotype of factors known to be related to AD such as various clinical and lifestyle variables, amyloid deposition, genetic background (including, but not restricted to APOE4) and most importantly cognitive reserve. A specific aim is to test the hypothesis that neurofeedback performance can become a reliable proxy to measure cognitive reserve. Finally, we will develop a novel statistical predictive model, featuring all the relevant genetic and clinical variables and will assess the capacity of neurofeedback performance for predicting impending cognitive decline during a two-year period in AD descendants.