Dr. Alan Evans’ Research makes Discover Magazine’s “Top 100 Science Stories of 2016”

Dr. Alan Evans – who co-directs the Imaging, Database, and Information Technology platform of the Canadian Consortium on Neurodegeneration in Aging (CCNA) – has led a team in generating one of the top 100 stories of 2016, as featured in Discover magazine.


Discover magazine covers the latest news, theories, and developments in the world of science. Each year, they select the top 100 science stories worldwide, and this year they have featured Evans’ work on techniques developed to analyze data sets that are either so large or complex that traditional data processing applications are inadequate to deal with them.


Dr. Evans and his team examined more than 7,700 brain images from 1,171 people at various stages of Alzheimer’s disease (AD), who are enrolled in the Alzheimer’s Disease Neuroimaging Initiative. Their findings, published in Nature Communications (June, 2016), suggest that the earliest changes leading to AD are related to blood flow (i.e. vascular). This is significant, given that scientists have long maintained that the earliest changes leading to AD involve the build-up of a sticky protein (amyloid), which forms into plaques that eventually degrade neurons and impact brain function.


According to the CCNA’s Scientific Director, Dr. Howard Chertkow, “this is a well-deserved honour for Dr. Evans and his research team. There is no doubt that this paper has sparked interest and debate within the medical community. Although the accumulation of amyloid has long been viewed as the root cause of AD, therapies aimed at reducing the amount of it in the brain have not been effective at halting or even slowing the progression of the disease. Dr. Evans’ and his team’s work suggests that there are earlier events in the development of AD. These may prove to be important targets for effective treatment.”


Dr. Evans, in addition to directing the data management development team for the CCNA, will continue to build upon these findings using big data techniques and brain image analysis to characterize age-related neurodegeneration as part of the Broad and Deep Analyses in Neurodegeneration (BRAIN) initiative that is being undertaken by CCNA researchers, in collaboration with researchers affiliated with the Canadian Longitudinal Study on Aging. Through this partnership, researchers will closely examine how big data can contribute to understanding the causes and course of neurodegeneration and dementia.