Functional MRI and multivariate autoregressive models.
Connectivity refers to the relationships that exist between different regions of the brain. In the context of functional magnetic resonance imaging (fMRI), it implies a quantifiable relationship between hemodynamic signals from different regions. One aspect of this relationship is the existence of small timing differences in the signals in different regions. Delays of 100 ms or less may be measured with fMRI, and these may reflect important aspects of the manner in which brain circuits respond as well as the overall functional organization of the brain. The multivariate autoregressive time series model has features to recommend it for measuring these delays and is straightforward to apply to hemodynamic data. In this review, we describe the current usage of the multivariate autoregressive model for fMRI, discuss the issues that arise when it is applied to hemodynamic time series and consider several extensions. Connectivity measures like Granger causality that are based on the autoregressive model do not always reflect true neuronal connectivity; however, we conclude that careful experimental design could make this methodology quite useful in extending the information obtainable using fMRI.
Rogers
Baxter P
BP
Institute of Imaging Science, Vanderbilt University, Nashville, TN 37232-2310, USA. baxter.rogers@vanderbilt.edu
Katwal
Santosh B
SB
Morgan
Victoria L
VL
Asplund
Christopher L
CL
Gore
John C
JC
D000465:Algorithms; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D006439:Hemodynamics; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D015999:Multivariate Analysis; D012044:Regression Analysis; D013997:Time Factors
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