Mining dynamic relationships from spatio-temporal datasets: an application to brain fMRI data

Univerty of Minnesota Ph.D. dissertation. May 2014. Major: Computer Science. Advisor: Vipin Kumar Co-advisor: Angus MacDonald III. 1 computer file (PDF); x, 108 pages, appendix A.Spatio-temporal datasets are being widely collected in several domains such as climate science, neuorscience, sociology, and transportation. These data sets offer tremendous opportunities to address the imminent problems facing our society such as climate change, dementia, traffic congestion, crime etc. One example of a spatio-temporal dataset that is the focus of this dissertation is Functional Magnetic Resonance Imaging (fMRI) data. fMRI captures the activity at all locations in the brain and at regular time intervals. Using this data one can investigate the processes in the brain that relate to human psychological functions such as cognition, decision making etc. or physiological functions such as sensory perception or motor skills. Above all, one can advance the diagnosis and treatment procedures for mental disorders.The focus of this thesis is to study dynamic relationships between brain regions using fMRI data. Existing work in neuroscience has predominantly treated the relationships among brain regions as stationary. There is growing evidence in this community that the relationships between brain regions are transient. In the time series data mining community transient relationships have been studied and are shown to be useful for various tasks such as clustering and classification of time series data. In this work we focused on discovering combinations of brain regions that exhibit high similarity in the activity time series in small intervals. We proposed an efficient approach that can discover all such combinations exhaustively. We demonstrated its effectiveness on synthetic and real world data sets.We applied our approach on fMRI data collected in different settings on different groups of people and studied the reliability and replicability of the combinations we discover. Reliability is the degree to which a combination that is discovered using fMRI scans from a population can be found again using a different set of scans on the same population. Replicability is the degree to which a combination discovered using scans from one set of subjects can be discovered again using scans from a different set of subjects. These two factors reflect the generality of the combinations we discover. Our results suggest that the combinations we discover are indeed reliable and replicable. This indicates the validity of the combinations and they suggest that the underlying neuronal principles drive these combinations. We also investigated the utility of the combinations in studying differences between healthy and schizophrenia subjects.Existing work in estimating transient relationships among time series typically uses sliding time windows of a fixed length that are shifted from one end to the other using a fixed step size. This approach does not directly identify the intervals in which a pair of time series exhibit similarity. We proposed another computational approach to discover the time intervals where a given pair of time series are highly similar. We showed that our approach is efficient using synthetic datasets. We demonstrated the effectiveness of our approach on a synthetic dataset. Using this approach we provided a characterization of the transient nature of a relationship between time series and showed its utility in identifying task related transient connectivity in fMRI data that is collected while a subject is resting and while involved in a task.In summary, the computational approaches proposed in this thesis advance the state-of-the-art in time series data mining. Whereas the extensive evaluations that are performed on multiple fMRI datasets demonstrate the validity of the findings and provide novel hypothesis that can be systematically studied to advance the state-of-the-art in neuroscience