Computational models of blood flow circulation from the heart to the brain for a better understanding, treatment optimisation and prevention of neurodegenerative diseases
Computational models of blood flow circulation from the heart to the brain for a better understanding, treatment optimisation and prevention of neurodegenerative diseases
Computational models of blood flow circulation from the heart to the brain for a better understanding, treatment optimisation and prevention of neurodegenerative diseases
Computational models of blood flow circulation from the heart to the brain for a better understanding, treatment optimisation and prevention of neurodegenerative diseases
Alzheimer's disease is a progressive neurodegenerative disorder that affects memory, thinking, and behaviour. Alzheimer's disease is a significant burden on individuals, families, and society as a whole. In terms of individuals, Alzheimer's disease can significantly impair quality of life, making it difficult to perform everyday activities and eventually leading to the need for full-time care. The disease can also be emotionally and financially taxing on family members and caregivers who provide support and assistance.
On a larger scale, Alzheimer's disease is a significant public health issue. According to the World Health Organization, approximately 50 million people worldwide have dementia, and this number is projected to triple by 2050. In the United States, it is estimated that more than 6 million individuals are living with Alzheimer's disease, with a total cost of care exceeding $355 billion annually. This includes the cost of healthcare, long-term care, and lost wages and productivity for caregivers. The burden of Alzheimer's disease also extends beyond healthcare and economic costs. It can have significant social and emotional impacts, as individuals with the disease and their families may experience social isolation, stigma, and depression.
Overall, the burden of Alzheimer's disease is significant and growing, highlighting the need for effective prevention and treatment strategies, as well as support for individuals and families affected by the disease.
Moreover, there is a growing body of research examining the links between planetary wellbeing and Alzheimer's disease; there is a link between environmental factors such as air pollution, exposure to pesticides, and the risk of developing Alzheimer's disease. Addressing these environmental factors and promoting a cleaner and healthier environment could potentially reduce the incidence of Alzheimer's disease and its associated carbon footprint. Overall, while the carbon footprint directly related to Alzheimer's disease may be difficult to estimate, addressing environmental factors related to the disease and promoting a more sustainable and healthy living environment can have positive impacts on both public health and the environment.
This project offers a unique opportunity to bring together the researchers mentioned before, from different units at UPF and associated entities, strongly related with the DTIC, as well as to strengthen the collaboration with prestigious international institutions. The main goal of this MdM project would be to support research towards the development of realistic models of brain vasculature haemodynamics, based on advanced data from the FPM and CNIC (magnetic resonance and positron emission tomography images of 1000 cases), to better understand the relevance of blood flow abnormalities in neurodegenerative diseases. Unsupervised machine learning techniques (e.g., multiple kernel learning), will be used to combine patient data, environmental factors, and in-silico indices to determine the most relevant factors for neurodegeneration. Furthermore, the developed models will be implemented for communication with patients and subjects at risk of developing Alzheimer’s disease (e.g., Alpha study from FPM) to better explain its effects and promote a healthy lifestyle for prevention purposes.
Principal researchers
Óscar CámaraBart Bijnens
Researchers
Andy Luis OlivaresMireia Alenyà
Gemma Piella Fenoy
Miguel Ángel González Ballester
Inma Villanueva Baxarias
Gabriel Bernardino
Juan Domingo Gispert (Barcelonaβeta Brain Research Center)
Marco Lorenzi (INRIA)
Maxime Sermesant (INRIA)
Gonzalo Maso Talou (University of Auckland)