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date: 25 May 2022

Abstract and Keywords

A brief history of imaging neuroscience is presented followed by an introduction to data acquisition using positron emission tomography (PET)and functional magnetic resonance imaging (fMRI). Next, statistical parametric mapping is introduced in conjunction with random field theory as being fundamental to identifying sites of neural activation. The general linear model is discussed as being foundational for all imaging analyses. Finally, methods for studying functional and effective connectivity such as eigenimage analysis, partial least squares, multivariate autoregressive models, structural equation models, and dynamic causal models are reviewed in light of deterministic and stochastic analytic approaches.

Keywords: Functional magnetic resonance imaging (fMRI), positron emission tomography, SPM, Random Field Theory (RFT), Bayesian statistical inference (BSI), multivariate autoregressive models (MAR), multilevel models, structural equation modeling, activation likelih

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