Increasing fMRI sampling rate improves Granger causality estimates

Estimation of causal interactions between brain areas is necessary for elucidating large-scale functional brain networks underlying behavior and cognition. Granger causality analysis of time series data can quantitatively estimate directional information flow between brain regions. Here, we show tha...

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Bibliographic Details
Published in:PloS one 2014-06, Vol.9 (6), p.e100319-e100319
Main Authors: Lin, Fa-Hsuan, Ahveninen, Jyrki, Raij, Tommi, Witzel, Thomas, Chu, Ying-Hua, Jääskeläinen, Iiro P, Tsai, Kevin Wen-Kai, Kuo, Wen-Jui, Belliveau, John W
Format: Article
Language:English
Subjects:
Adult
Biology and Life Sciences
Biomedical engineering
Brain
Brain - physiology
Brain mapping
Brain Mapping - methods
Causality
Cognition
Cointegration analysis
Connectivity analysis
Data points
Economic models
Engineering
Engineering and Technology
Estimates
Female
Functional magnetic resonance imaging
Head
Hemodynamics
Humans
Image Processing, Computer-Assisted
Information flow
Magnetic resonance
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Male
Medical schools
Medicine and Health Sciences
Neural networks
Neuroimaging
Neurosciences
Oxygenation
Research and Analysis Methods
Sampling
Sensorimotor integration
Temporal resolution
Time Factors
Time series
Young Adult
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Summary:Estimation of causal interactions between brain areas is necessary for elucidating large-scale functional brain networks underlying behavior and cognition. Granger causality analysis of time series data can quantitatively estimate directional information flow between brain regions. Here, we show that such estimates are significantly improved when the temporal sampling rate of functional magnetic resonance imaging (fMRI) is increased 20-fold. Specifically, healthy volunteers performed a simple visuomotor task during blood oxygenation level dependent (BOLD) contrast based whole-head inverse imaging (InI). Granger causality analysis based on raw InI BOLD data sampled at 100-ms resolution detected the expected causal relations, whereas when the data were downsampled to the temporal resolution of 2 s typically used in echo-planar fMRI, the causality could not be detected. An additional control analysis, in which we SINC interpolated additional data points to the downsampled time series at 0.1-s intervals, confirmed that the improvements achieved with the real InI data were not explainable by the increased time-series length alone. We therefore conclude that the high-temporal resolution of InI improves the Granger causality connectivity analysis of the human brain.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0100319