Principal Investigator: Dr Olivier Colliot
Department: ICM Institute for Brain and Spinal CordTags: 53185, ageing, Alzheimer's Disease, convolutional neural networks, MRI, neurology, prediction
Ageing-related disorders, and in particular neurological disorders, represent important challenges faced by our health systems. We need to advance early detection and diagnosis, intervention, treatment as well as the information we provide the patients and their families. We propose to use state of the art deep-learning approaches to improve detection and diagnosis of Alzheimer’s and Parkinson’s disease (AD & PD).
For AD & PD, deep-learning algorithms have not yet superseded other predictive approaches, which we attribute to insufficiently large training samples. Here, we would like to leverage the large-scale brain MRI collection from the UK Biobank to build a model of ageing and of those neurological disorders.
Despite having the highest computational cost at training, deep-learning algorithms may not require any MRI image processing, meaning they could be more easily implemented in the clinics, to assist diagnosis and evaluate response to treatment.
Project extension – May 2020
Brain pathologies associated with aging are a major public health issue. These diseases are currently diagnosed too late, due in particular to the difficulty of access to specialised medical centres. Artificial intelligence techniques are a promising way to improve diagnosis.
For the diagnosis of neurodegenerative diseases, deep learning techniques have not yet demonstrated their superiority over traditional machine learning techniques unlike what has been shown in many applications (breast cancer screening, chest radiographs, histology, etc). It can be assumed that this is because the datasets on which these techniques have been trained so far were too small (typically a few hundred patients).
The present project aims to develop a system to assist in the diagnosis of the various pathologies of the aging brain using deep learning techniques on very large databases. In addition, this study will allow a detailed evaluation of the influence of different parameters on the performance and a comparison with traditional statistical and machine learning approaches.
In addition, neurodegenerative diseases (Alzheimer in particularly) are progressive and take decades to become established. Prevention will be key to design efficient treatments by treating the cause of the disease. An additional goal from our team is to run a quantitative and systematic epidemiological study on several large cohorts to show which phenotypes can be considered as risk factors for dementia several years before the appearance of classical symptoms. This study will help to focus on the main risk factors and improve our understanding of the disease.
Project extension – June 2020
The study of brain pathologies associated with aging usually relies on case-control samples, with limited sample size. Population samples such as the UKB, suffer from a healthy bias, which limits studying clinical status. We propose to use polygenic risk scores, calculated from the largest GWAS to date, as proxy-phenotypes for the brain disorders. Such scores should be superior to self-reported family history, collected as part of the UKB. Further meta-analyses of the results obtained on the UKB and smaller clinical samples, should shed light on the brain regions associated with the disorders, but may also result in an improved risk prediction from brain MRI.
Last updated Jun 30, 2020