Bayesian spatiotemporal modeling on complex-valued fMRI signals via kernel convolutions |
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Authors: | Cheng-Han Yu Raquel Prado Hernando Ombao Daniel Rowe |
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Institution: | 1. Department of Mathematical and Statistical Sciences, Marquette University, Milwaukee, Wisconsin, USA;2. Department of Statistics, University of California, Santa Cruz, California, USA;3. Statistics Program, King Abdullah University of Science and Technology University, Saudi Arabia |
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Abstract: | We propose a model-based approach that combines Bayesian variable selection tools, a novel spatial kernel convolution structure, and autoregressive processes for detecting a subject's brain activation at the voxel level in complex-valued functional magnetic resonance imaging (CV-fMRI) data. A computationally efficient Markov chain Monte Carlo algorithm for posterior inference is developed by taking advantage of the dimension reduction of the kernel-based structure. The proposed spatiotemporal model leads to more accurate posterior probability activation maps and less false positives than alternative spatial approaches based on Gaussian process models, and other complex-valued models that do not incorporate spatial and/or temporal structure. This is illustrated in the analysis of simulated data and human task-related CV-fMRI data. In addition, we show that complex-valued approaches dominate magnitude-only approaches and that the kernel structure in our proposed model considerably improves sensitivity rates when detecting activation at the voxel level. |
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Keywords: | autoregressive brain activation complex-valued time series functional magnetic resonance imaging Gaussian processes kernel convolution |
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