Increasing fMRI sampling rate improves Granger causality estimates

Fa Hsuan Lin, Jyrki Ahveninen, Tommi Raij, Thomas Witzel, Ying Hua Chu, Iiro Jääskeläinen, Kevin Wen Kai Tsai, Wen Jui Kuo, John W. Belliveau

    Research output: Contribution to journalArticleScientificpeer-review

    19 Citations (Scopus)
    182 Downloads (Pure)

    Abstract

    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.

    Original languageEnglish
    Article numbere100319
    Pages (from-to)1-13
    JournalPloS one
    Volume9
    Issue number6
    DOIs
    Publication statusPublished - 26 Jun 2014
    MoE publication typeA1 Journal article-refereed

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