Data mining in large functional neuroimaging databases

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Functional MRI gives an indirect measure of brain activity with a good temporal and spatial resolution. This massive amount of information needs to be summarized to be interpretable. Cluster analysis can identify spatially distributed regions with a temporally correlated activity at rest, called resting-state networks (RSNs). The overarching goal of my research program is to capture how the connectivity between these RSNs changes in response to Alzheimer's disease. The short-term objectives of this proposal target the technical challenges raised by this long-term goal: (1) The number (and size) of RSNs is an arbitrary choice of the investigator, i.e. the brain can be decomposed into large distributed systems as well as into specialized brain regions. I want to determine which level of decomposition is the most optimal to detect the effects of Alzheimer's disease on brain connectivity. (2) Currently, RSNs are derived at the level of a group after the brains of all subjects have been aligned based on their morphology. The functional units of the brain can still present variations amongst individuals after this process. We will develop novel clustering techniques that will match this remaining functional variability across subjects. This operation is expected to reveal better the deleterious effects of Alzheimer's disease on brain connectivity, that could have been masked by the functional misregistration between subjects; (3) The generation of RSNs and associated connectivity measures is a long process involving many steps and decisions. We will systematically test the impact of various choices made in the preprocessing of fMRI datasets on the outcomes of Objectives 1 & 2, using both real and simulated datasets. I anticipate that the developments made in this research program will substantially increase the reliability and sensivity of outcome measures based on resting-state fMRI with important implications for both clinical and basic neuroscience.