Details of Grant 

EPSRC Reference:	EP/J005444/1
Title:	Advanced FMRI acquisition, reconstruction and signal processing for dynamic brain network imaging
Principal Investigator:	Miller, Dr KL
Other Investigators:	Blumensath, Dr T Smith, Professor SM
Researcher Co-Investigators:
Project Partners:
Department:	Clinical Neurosciences
Organisation:	University of Oxford
Scheme:	Standard Research
Starts:	01 February 2012	Ends:	07 August 2015	Value (£):	564,344
EPSRC Research Topic Classifications:	Medical Imaging
EPSRC Industrial Sector Classifications:	Healthcare
Related Grants:
Panel History:	Panel Date Panel Name Outcome 03 Nov 2011 Materials, Mechanical and Medical Engineering Announced
Summary on Grant Application Form
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.
Key Findings
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Organisation Website:	http://www.ox.ac.uk