Functional Magnetic Resonance Imaging of Brain States and Their Transitions

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Establishing accurate models of human brain region communication is critical for understanding the underlying functions responsible for cognitive processes and generating behaviour. My research program is dedicated to the development of signal processing and data analysis strategies for resting-state functional magnetic resonance imaging (fMRI) studies of inter-regional brain communication at the network level. fMRI tells us about brain activity through its sensitivity to the magnetic properties of blood, which are dependent on blood oxygenation levels. Intrinsic fluctuations of resting-state fMRI signals are highly synchronous between brain regions of a network; the degree of synchrony is termed "connectivity". Connectivity, however, varies as brain regions interact between networks, leading to transitions between temporary brain states associated with cognitive processes. Thus, is it important to understand the dynamic nature of connectivity. Existing connectivity data analysis approaches lack the ability to make statistical inferences regarding the presence of or change in a functional connection at any point in time. This greatly hinders our ability to accurately characterize the complexity of human brain activity and wastes the potential benefits of recently-developed sub-second fMRI. During this proposed project, my lab will develop signal processing and data analysis approaches to accurately model the spatial and temporal dynamics of the human brain's functional networks, with statistical confidence. In Aim 1, we will develop data processing and analysis techniques for resting-state fMRI that use a two-level hierarchical observation model and a Bayesian (conditional) approach to estimate model parameters. Using this approach we can compute the conditional mean and covariance, which in turn permits the computation of a t-statistic at any point in time. In Aim 2, we will develop techniques to generate surrogate data to accurately estimate null distribution data, which is necessary for accurately computing statistical significance. This is non-trivial and is lacking from current dynamic connectivity analysis approaches. We will then validate our approaches and test their reproducibility using simulated datasets and human data from online functional connectome databases. In Aim 3, we will use our approaches in clustering and machine learning algorithms, to define distinct but temporary stationary brain states and the transitions between them. These aims will generate novel technical advances to significantly advance our knowledge of the functional connections of the human brain, and they will provide the future ability to investigate more complex brain systems. Our research program will also provide an excellent environment in which to train high quality personnel in imaging technology development, an area of accelerating growth in Canadian research and industry.