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http://dx.doi.org/10.5351/KJAS.2016.29.6.1129

Statistical methods for modelling functional neuro-connectivity  

Kim, Sung-Ho (Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology)
Park, Chang-Hyun (Ewha Brain Institute, Ewha Womans University)
Publication Information
The Korean Journal of Applied Statistics / v.29, no.6, 2016 , pp. 1129-1145 More about this Journal
Abstract
Functional neuro-connectivity is one of the main issues in brain science in the sense that it is closely related to neurodynamics in the brain. In the paper, we choose fMRI as a main form of response data to brain activity due to its high resolution. We review methods for analyzing functional neuro-connectivity assuming that measurements are made on physiological responses to neuron activation. This means that we deal with a state-space and measurement model, where the state-space model is assumed to represent neurodynamics. Analysis methods and their interpretation should vary subject to what was measured. We included analysis results of real fMRI data by applying a high-dimensional autoregressive model, which indicated that different neurodynamics were required for solving different types of geometric problems.
Keywords
dynamic causal model; effective connectivity; functional connectivity; functional MRI; state space and measurement model; structural equation model; time series model;
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