Recent technological improvements in functional Magnetic Resonance Imaging (fMRI) are making it possible to study the brain as more than a collection of volume elements (voxels) but rather as a system of interacting components. Instead of considering individual regions, we can study functional networks. Instead of computing voxels' individual response curves, we can estimate their collective response to a stimulus. Instead of settling for responses averaged over brain regions, we can image fine spatial structure. Such a system-oriented approach requires advances in both imaging and statistical methodology. This project consists of two intertwined components. The first is performing fMRI experiments to address three questions about the representation of space in the human brain. The second is developing and validating three new statistical techniques that allow the system-level inferences needed to answer the neuroscientific questions. These techniques are motivated by and developed for the proposed experimental studies, but with minor adaptation, they will be broadly applicable to other neuroimaging studies.
In Aim 1, the project will develop methods for identifying and characterizing distributed functional networks. These methods will be used to study the cortical circuit that underlies visual remapping.
In Aim 2, the project wil develop methods for simultaneously estimating fMRI response fields. These methods will be used to test the interaction of visual and eye movement signals.
In Aim 3, the project will develop adaptive spatial smoothing techniques for high-resolution fMRI data. These tools will be used to test the fine-scale structure of eye position signals in visual cortex. The experimental protocols and theoretical principles developed in this project will increase understanding of the basic function of the human visual system. The statistical techniques developed in this project will give new ways to understand of functional systems with neuroimaging and will advance broadly applicable methods for making inferences about regions in spatio-temporal data.
The experimental protocols and theoretical principles that we develop for studying vision and remapping in healthy human subjects will be readily applicable to patient populations. A better understanding of visual remapping will lead to a better understanding of factors limiting peripheral vision, which are critical when central vision is compromised due to macular degeneration and related visual deficits. Basic knowledge of visual attention has implications for our understanding of several neuropsychological conditions, including unilateral neglect, schizophrenia and ADHD, and for informing the development of diagnostic tests. The statistical techniques developed in this project will be broadly applicable to other problems in neuroimaging and biostatistics that have direct implications for public health.
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|Yan, Xiaoran; Shalizi, Cosma; Jensen, Jacob E et al. (2014) Model selection for degree-corrected block models. J Stat Mech 2014:|
|Goerg, Georg M; Shalizi, Cosma Rohilla (2013) Mixed LICORS: A Nonparametric Algorithm for Predictive State Reconstruction. JMLR Workshop Conf Proc 31:289-297|
|Shalizi, Cosma Rohilla; Rinaldo, Alessandro (2013) CONSISTENCY UNDER SAMPLING OF EXPONENTIAL RANDOM GRAPH MODELS. Ann Stat 41:508-535|
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|Shalizi, Cosma Rohilla; Kontorovich, Aryeh (2013) Predictive PAC Learning and Process Decompositions. Adv Neural Inf Process Syst 26:|
|Shalizi, Cosma Rohilla (2012) Comment on ""Why and When 'Flawed' Social Network Analyses Still Yield Valid Tests of no Contagion"". Stat Politics Policy 3:5|
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