Decoding connectivity
It鈥檚 all math: How Professor Alan Evans is using cutting-edge statistics to unravel the complexity of brain disorders.
When Alan Evans was starting out in the 1970s, researchers didn鈥檛 ask the boss to foot their bar tab. But that鈥檚 exactly what some of the coders in his Montreal Neurological Institute lab recently proposed: a 9-to-5 Saturday hackathon, held in an Irish pub a few blocks from the 不良研究所 campus.
鈥淏leeding-edge coding technology and beer,鈥� Professor Evans says with a wry laugh. 鈥淲hat could possibly go wrong?鈥�
Still, he agreed to the idea. When he stopped in at the pub in the afternoon, he half-expected to find a bacchanal. Instead, fingers furiously tapped on laptops as the coders raced to meet their self-imposed deadline to solve some research problem. Come five o鈥檆lock, each team presented their work 鈥� and sometimes even working prototypes of apps 鈥� and talked about how to get their new ideas up and running in Evans鈥� lab come Monday morning.
鈥淚f I鈥檇 been a more hidebound old fool,鈥� Evans recalls, 鈥淚 would鈥檝e said 鈥楢bsolutely not鈥� to paying for those guys to hang out at the pub all Saturday. But I realized this was probably the most productive day鈥檚 work in my lab, ever. And they had a ball.鈥�
Mathematical whizzes and neuroscience
Openness to change, and a curiosity to explore new paths, may be the defining characteristics of Evans鈥� career. Today, he鈥檚 internationally recognized for his work in brain imaging; in July 2016, for example, he will be the guest of the U.S. White House Office of Science and Technology Policy, who have turned to his expertise to help them build an open-data neuroscience ecosystem. His 65-person lab brings together the types of researchers who, he says, 鈥渨ould never have talked to each other a few years ago.鈥� Some are scientists with domain-specific expertise in certain brain disorders. The others are hackathon-holding computer simulation and mathematics whizzes who 鈥渕ay not know one end of a brain from another鈥攁nd they don鈥檛 need to because, whether it鈥檚 neurodegeneration or aging or development or depression, it鈥檚 all the same mathematical problem.鈥� Together, they create wonders such as the most frequently used spatial reference system for cataloguing structural and function data for normal and diseased brains. Ultimately, their goal is to understand exactly how, and why, disorders occur鈥攇roundbreaking knowledge that will underpin improved diagnosis and, hopefully, interventions.
Evans may have recently co-led the 鈥淏ig Brain Project,鈥� an unprecedented map of the human brain with a near-cellular level of detail, but studying the brain wasn鈥檛 always his goal. The Wales-born Evans did his undergraduate work in mathematics and physics, then gradually started 鈥渕oving east鈥� into neuroscience. Next came a graduate degree in medical physics, then a PhD in biophysics. But it was a literal move, from the U.K. to his wife鈥檚 native Canada that proved career-changing.
Mapping the probability of brain activation
In 1979, Evans took a job with Atomic Energy of Canada to work with a prototype PET scanner that had been developed at 不良研究所. After Evans spent five years wearing out the highway between Ottawa and Montreal, the Montreal Neurological Institute鈥檚 then-director, William Feindel, said, 鈥淵ou really should just stay here.鈥� So he did.
Over time, Evans grew frustrated by the limitations of PET images. PET are great at showing metabolic processes 鈥� such as when dopamine receptors are activated 鈥� but 鈥渢he rest of the image is pretty fuzzy.鈥� MRI scans, on the other hand, give a sharp, detailed look at brain structure. Evans and his MNI were among the early adopters of 鈥渂rain mapping,鈥� or overlaying PET and MRI scans to get a multi-dimensional view of structure and function to create statistical probability maps of brain activation.
But, again, Evans grew restless. 鈥淐ould we create statistical probability maps of brain structure?鈥� he asked. 鈥淲hat about for disease?鈥� His curiosity led to the creation of a map that showed, for the first time ever, what multiple sclerosis looks like in the brain.
A cluster of factors come together to create brain disorders
Evans鈥� work is contributing to a new understanding of brain disorders. Whereas we had previously thought of disorders as 鈥渓iving鈥� in one part of the brain, which has been altered by one or two rogue genes, scientists are now realizing that disorders are the result of clusters of genes, and clusters of regions and connectivity. 鈥淚n my career,鈥� says Evans, 鈥淚鈥檝e watched as we go from single measurements of brain structure volume to much more sophisticated analysis of brain network analysis organization 鈥� and how those networks change during development or disorder.鈥�
鈥淲hat we鈥檙e doing is applying mathematics, physics and engineering principles to brain sciences,鈥� he continues, noting that many other sciences, such as epidemiology and genetics, are going through the same evolution. 鈥淲e鈥檙e increasingly turning neuroscience into a more quantitative discipline.鈥� Radiology, for example, is basically an expert looking at a single image, then weighing what they see against past diagnoses and experiences. 鈥淚f you want to do solid science that is not just descriptive and anecdotal, it requires you to understand the basics of statistics.鈥�
Merging brain structure, function, and genetic information creates what Evans calls 鈥渁 data tsunami鈥濃�攁n overwhelming amount of data that causes many people to throw up their hands in defeat. He, however, is excited. The new R&D collaboration between 不良研究所 and EMC, for one, promises to ramp up the already great advances in raw computing power. And the creation of the Ludmer Center for Neuroinformatics and Mental Health in 2014, Evans says, exactly embodies the kind of interdisciplinary work needed to make exponential leaps in our understanding of the basic machinery of cell function, and about how different regions communicate with each other at a systems level. The data tsunami can be surfed.
鈥淲e鈥檙e on the threshold of a new era, where information sciences are now being brought to bear to ask questions about the brain that, 20 years ago, we may not have even thought to ask.鈥�
鈥淎s my wife says, it鈥檚 taken 30 years for me to become an overnight success.鈥�
To read more about how the gift of new supercomputers from EMC Canada will aid in the creation of a neuroscience research hub: Creation of neuroscience research hub
