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      Mixed Effect Modelling of Single Trial Variability in Ultra-High Field fMRI

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          Abstract

          Neuronal brain activity in response to repeated stimuli can be perceived using functional magnetic resonance imaging (fMRI). In this paper, we develop a statistical model for fMRI data that estimates both the associated haemodynamic response function and the within and between trial variability through maximum likelihood estimation. We discuss our results in the context of other model-driven approaches, extending models already popular in the literature, while removing the need for some of their assumptions. We consider an application to the motor cortex activity caused by a subject pressing a button and observe that the response changes significantly with task and through time.

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          A new look at the statistical model identification

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            Statistical parametric maps in functional imaging: A general linear approach

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              Probabilistic independent component analysis for functional magnetic resonance imaging.

              We present an integrated approach to probabilistic independent component analysis (ICA) for functional MRI (FMRI) data that allows for nonsquare mixing in the presence of Gaussian noise. In order to avoid overfitting, we employ objective estimation of the amount of Gaussian noise through Bayesian analysis of the true dimensionality of the data, i.e., the number of activation and non-Gaussian noise sources. This enables us to carry out probabilistic modeling and achieves an asymptotically unique decomposition of the data. It reduces problems of interpretation, as each final independent component is now much more likely to be due to only one physical or physiological process. We also describe other improvements to standard ICA, such as temporal prewhitening and variance normalization of timeseries, the latter being particularly useful in the context of dimensionality reduction when weak activation is present. We discuss the use of prior information about the spatiotemporal nature of the source processes, and an alternative-hypothesis testing approach for inference, using Gaussian mixture models. The performance of our approach is illustrated and evaluated on real and artificial FMRI data, and compared to the spatio-temporal accuracy of results obtained from classical ICA and GLM analyses.
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                1501.05763

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