 Neurodegenerative diseases like Parkinson's are devastating illnesses that cause brain cells to die and the brain to atrophy. The hallmark symptom is movement disorders, but they're actually a whole host of others such as disturbances in smell and sleep and memory impairments. What we want to understand is how symptoms develop in individual subjects and patients. To address this problem, we harness the power of networks. Networks are mathematical objects that simply describe how a set of nodes is interconnected by a set of edges. The key idea here is that the function of an individual node and indeed the function of the whole network depends on the connection patterns. Now, we see examples of networks literally all around us when we look at how computers are wired together, when we look at human social networks, when we look at networks of roads and airline traffic and so on. In each of these systems, the same idea holds the function of an individual element depends on who it's connected with and how it's embedded in the network. Now, probably the most complex network of all is the human brain. The brain is made up of millions of cells called neurons that form intricate connection patterns with each other. These connections are used for two things. One, to send signals between cells and two, to transport materials that are needed for growth and repair. Now, we live in a particularly exciting time because we can now map, image and trace the connection patterns of various brains. We can do it in an organism as simple as the worm, but we can also do it in the mouse, for instance, and most excitingly for us, we can do this in the living human brain. What used to be done with painstaking manual dissection of post-mortem brains can now be done in living human beings. Using a technique called diffusion weighted magnetic resonance imaging, we can actually trace out the fibers that interconnect different parts of the brain to each other and we can assemble circuit diagrams of individual brains, which we call connectomes. These connectomes actually look a lot like traffic networks. You have a series of short range, more peripheral roads and then you get progressively longer roads akin to highways that interconnect the highly connected integrative centers of the brain or the so-called hubs. Being part of a network has many advantages but actually poses a lot of dangers as well. Pathology can easily spread from one element to other connected elements. An intuitive example that we're all familiar with is how epidemics spread on human social networks. The key idea for us is that neurodegenerative diseases are like epidemics that spread on brain networks. Specifically, what happens in Parkinson's, for instance, is that a normal protein called synuclein misfolds. When it comes into contact with other normal proteins, it causes them to misfold. These misfolded proteins can spread from brain cell to brain cell and when they accumulate in a brain cell, they cause the cell to die. As the misfolded proteins can too course through brain networks, they cause atrophy in specific parts of the brain and this manifests then as all of these symptoms such as sleep and smell disturbances, memory impairments and so on. So what we want to understand is how individual patients are vulnerable to each of these symptoms. The way that we address this problem is to take brain networks from individual patients and to try to predict the atrophy patterns that we observe in their brains. We do this by simulating the spread of disease like an epidemic that takes place on this brain network and we try to figure out exactly which parts of the brain are vulnerable to cell death and atrophy. We can now do this with a very high degree of precision and we can actually project the evolution of the disease. What you're about to see is a simulation where the disease starts out in one brain region and as it slowly encroaches on the hubs of the network, it accelerates and eventually encompasses the whole brain. What we're building is a virtual patient, a computer simulation where you can take information about brain connectivity, brain activity patterns, genetics to predict the course and the expression of the disease in an individual. You can also use this simulation platform to test the potential effects of novel therapeutics, things like drugs and stimulation protocols before they go for clinical trials. More generally, we know that other neuro-generative diseases also involve the spreading of misfolded proteins on brain networks such as ALS or Lou Gehrig's disease and Alzheimer's disease. So the technology that we're developing can actually be used much beyond just Parkinson's. We live in a very exciting time. We have technological advances that actually allow us to trace out the connection patterns of brain networks and we also have analytic advances that allow us to simulate how these networks function. As we gain access to more data with greater detail in depth in individual patients, we'll be able to build more powerful and sophisticated models to aid in prognosis and in developing new therapeutics. Thank you very much.