Advanced FMRI acquisition, reconstruction and signal processing for dynamic brain network imaging

How is the brain wired? Are all of our brains wired the same way? What happens when the brain's connections fail? For the first time, scientists are beginning to be able to ask these questions in living human subjects using non-invasive brain imaging methods like functional MRI (FMRI). FMRI measures brain activity; for example, it can show you what part of the brain responds when you look at a picture of a face. Interestingly, one of the most powerful ways to study brain connections is with FMRI of the brain "at rest", when the subject hasn't been asked to perform any specific task. In this state, all of the brain's networks can be observed to be "talking" amongst themselves simultaneously. If we observe this "random chatter" for long enough, we can determine which brain regions are connected to each other because they will tend to be active at the same time. These "resting state networks" have been shown to be the very same networks that are involved in active thought processes and mental tasks, and they are altered in neurological disease. They therefore offer a very powerful window into brain function and health.

Currently, our ability to study these brain networks is severely limited by the quality of FMRI data and sophistication of analysis to which scientists have access. Our project aims to improve both of these aspects of resting FMRI experiments. To do this, we have to address several tricky engineering challenges.

The first goal of our project is to improve the FMRI data quality. One major improvement that would enable us to study the brain's dynamics with greater accuracy is to acquire images faster. Conventional MRI acquires one data point per image pixel, and the more pixels an image has, the longer it takes to acquire. Since current techniques already operate at the "speed limit" of conventional MRI, we have to take a different approach to the problem. We will take advantage of the same mathematical principles that underlie image and video compression on the internet: sometimes images can be described using fewer information "bits" than the total number of pixels in the image. So images that are "compressible" can be assembled from far less data, enabling faster image acquisition. Our challenge is to find the best way to compress FMRI images given the properties of our resting networks. As part of this, we will take advantage of the state-of-the-art in MRI scanners, operating with 2-4 times stronger magnetic field than is currently common in hospitals.

The second goal of our project is to take advantage of this improved data to obtain better measures of brain connections. Currently, we can use resting FMRI to create images of areas that are connected to each other, but we don't know how these regions are connected. Just as one cannot plan a route from an atlas that shows cities but no roads, these brain maps cannot really tell us anything about how information flows in the brain. The first step, then, is to establish which brain regions have direct connections (roads) between them. In the brain, connections are often "one way", so the second step is to be able to detect the direction of connections. We will develop statistical tools for robustly finding these network properties, which will enable us to establish the general flow of information in the brain. However, many of the brain's most interesting properties come from the "dynamics" of these networks: connections may become stronger or weaker over time, or could shut down temporarily. For example, this is thought to be important in our ability to shift our attention between different tasks or thoughts. Our final goal, then, is to be able to detect the dynamics of both individual connections and interacting networks. For this, we will develop sophisticated methods for categorizing network states that may be changing over time.

These methods will enable neuroscientists to study the brain at an unprecedented level of detail.

Our proposal is expected to have major impact in a range of spheres outside of academia, including Society, Economy and Knowledge.

Impact on Society will largely be through improvements in healthcare and quality of life. The resting-state FMRI methods developed here have enormous potential as a tool for early detection of neurological disorders. For example, there is increasing evidence that resting brain fluctuations reflect the full range of the brain's functional capabilities. This raises the possibility of using resting FMRI to discover residual brain function that might respond to treatment in patients who cannot perform a task particularly well (e.g., Alzheimer's patients with memory deficits or stroke patients with motor impairment). In addition, our MRI acquisition methods could be used with a specific task in epilepsy or tumour pre-surgical planning. Resting-state FMRI also has tremendous potential as a marker of changes in the brain's connectivity, for use in population-wide studies. This would be particularly helpful to advance our understanding of diseases of connectivity such as autism, schizophrenia and epilepsy. There is already evidence that resting FMRI can detect some changes in connectivity long before onset of symptoms, for example in individuals with a genetic risk factor for Alzheimer's. There is already interest from the pharmaceutical industry in exploring the potential of resting FMRI in drug development, and we also envision uses in evaluating other forms of therapy (e.g., devices or rehabilitation regimens). The ability to detect the early changes has an enormous impact on our ability to identify the most promising therapies, improving health care and quality of life.

Impact on Economy is also tied to healthcare and quality of life. Improved diagnosis and treatment of disease can lead to tremendous financial savings in the healthcare sector where the economic burden of an aging population is an increasing problem. Any possible improvement in either patient diagnosis or population studies carries an economic benefit. There is also economic impact associated with the techniques themselves within the global market in healthcare. For example, the world-wide market for MRI scanners was $4.4 billion in 2010, with just three vendors dominating the market (GE, Siemens and Philips). Manufacture of the superconducting magnets that form the basis of these systems is a major industry in the UK. Similarly, drug trials run by pharmaceutical companies cost millions of pounds, and several UK companies have been founded to analyze data (including images) for these studies.

Impact on Knowledge will occur from our more general understanding of how the brain works. Resting FMRI measures the brain's intrinsic oscillations, the purpose and importance of which remain controversial and mysterious. Some scientists place them at the very heart of brain function (G. Buzsaki, "Rhythms of the Brain"). In the context of our project, these oscillations are known to reflect the brain's functional connections. Methods for mapping these connections in living humans are a recent technological advance that has precipitated a world-wide effort to provide a map of the brain's connections. Our group is part of a leading consortium, the US-based Human Connectome Project, which aims to provide the most comprehensive and rich in vivo map of the brain's connections. Our research will contribute to our understanding of the brain, helping to answer of the great remaining scientific questions: how does the brain work?