Canada Spends Logo

Explore our Budget 2026 analysis

Grant: CIHR Fellowship

Direct Payment

$150,000.00

Integrating Personalized Brain Simulation, Machine Learning, and Multimodal Neuroimaging for Early Detection of Alzheimer's Disease: A New Data-Driven Methodological Framework

FY 2019-20

Summary
Maintaining a healthy brain through one's lifespan depends on the complex interaction between the brain, genetics, and lifestyle. With a rapidly aging population, the prevalence of neurodegenerative diseases like Alzheimer's Disease are significantly increasing, with a projected 107 million cases by 2050. This is now recognized as one of the major medical challenges facing the world in the 21st century. While treatment options are limited, early detection can allow prevention strategies to provide individuals with additional years of healthy living, significantly less burden on their loved ones, and a substantial reduction in healthcare costs. Current early detection methods mainly limited to inherited types of early-onset Alzheimer's Disease. Our goal is to greatly improve early detection of all types of Alzheimer's Disease. This project will, for the first time, integrate personalized brain simulation and the predictive power of artificial intelligence to improve our understanding of Alzheimer's Disease, to predict as early as possible who will develop the disease, and even to forecast what form it will take for each individual. Breakthroughs in personalized brain simulation, a type of mathematical brain modelling that is tuned to each individual, have already been used to show how and why the brain changes when inflicted with Alzheimer's Disease. We will use high-quality clinical databases that follow the development of thousands of individuals through the aging process in research that can lead to new clinical detection methods. We will use brain simulation to model the effects of Alzheimer's other dementias compared to healthy aging on each person's brain, and we will use artificial intelligence to discover changes in the brain that predict a diagnosis of Alzheimer's in later years. With this new data-driven approach, we aim to help add years of high quality of life for the millions of people who may be affected by Alzheimer's in the future, and their families.
Program
CIHR Fellowship
Location
Germany
Type
Grant
Source
CIHR Funding Decisions Database

As published

Id
412543
Name
Dhindsa Jaskiret
Country
Germany
Orgtype
Research Institution
Instname2
Neurosciences, Mental Health and Addiction
Theme2
Biomedical
Prcname2
Health Research Training A - Post-PhD (HTA)
Projecttitle
Integrating Personalized Brain Simulation, Machine Learning, and Multimodal Neuroimaging for Early Detection of Alzheimer's Disease: A New Data-Driven Methodological Framework
Programtype2
Fellowship Programs
Orgnameinp2
Direct Payment
Cihramount2
$150,000
Cihrequipment2
$0
Programname2
CIHR Fellowship
Keyworddelim
Artificial Intelligence; Brain Simulation; Data Science; Machine Learning; Multimodal Modelling; Neurodegeneration; Neuroimaging; Personalized Medicine; Precision Medicine; Topological Data Analysis
Abstract
Maintaining a healthy brain through one's lifespan depends on the complex interaction between the brain, genetics, and lifestyle. With a rapidly aging population, the prevalence of neurodegenerative diseases like Alzheimer's Disease are significantly increasing, with a projected 107 million cases by 2050. This is now recognized as one of the major medical challenges facing the world in the 21st century. While treatment options are limited, early detection can allow prevention strategies to provide individuals with additional years of healthy living, significantly less burden on their loved ones, and a substantial reduction in healthcare costs. Current early detection methods mainly limited to inherited types of early-onset Alzheimer's Disease. Our goal is to greatly improve early detection of all types of Alzheimer's Disease. This project will, for the first time, integrate personalized brain simulation and the predictive power of artificial intelligence to improve our understanding of Alzheimer's Disease, to predict as early as possible who will develop the disease, and even to forecast what form it will take for each individual. Breakthroughs in personalized brain simulation, a type of mathematical brain modelling that is tuned to each individual, have already been used to show how and why the brain changes when inflicted with Alzheimer's Disease. We will use high-quality clinical databases that follow the development of thousands of individuals through the aging process in research that can lead to new clinical detection methods. We will use brain simulation to model the effects of Alzheimer's other dementias compared to healthy aging on each person's brain, and we will use artificial intelligence to discover changes in the brain that predict a diagnosis of Alzheimer's in later years. With this new data-driven approach, we aim to help add years of high quality of life for the millions of people who may be affected by Alzheimer's in the future, and their families.
Competitiondate
201910
Approvedterm2
3 yrs 0 mth
Pinamesdelim
Dhindsa, Jaskiret
Approvedterm
3 ans 0 mois
Cihrcontribution2
~ $150,000~ $0~