title: Increasing fMRI sampling rate improves Granger causality estimates.
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.
authors: Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan; Department of Biomedical Engineering and Computational Science, Aalto University, Espoo, Finland.
authors: Fa-Hsuan
authors: FH
authors: Lin
authors: Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
authors: Jyrki
authors: J
authors: Ahveninen
authors: Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
authors: Tommi
authors: T
authors: Raij
authors: Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
authors: Thomas
authors: T
authors: Witzel
authors: Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan.
authors: Ying-Hua
authors: YH
authors: Chu
authors: Department of Biomedical Engineering and Computational Science, Aalto University, Espoo, Finland.
authors: Iiro P
authors: IP
authors: Jääskeläinen
authors: Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan; Department of Biomedical Engineering and Computational Science, Aalto University, Espoo, Finland.
authors: Kevin Wen-Kai
authors: KW
authors: Tsai
authors: Institute of Neuroscience, National Yang-Ming University, Taipei, Taiwan.
authors: Wen-Jui
authors: WJ
authors: Kuo
authors: Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
authors: John W
authors: JW
authors: Belliveau
mesh_terms: D000328:Adult; D001921:Brain / Q000502:physiology; D001931:Brain Mapping / Q000379:methods*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D013997:Time Factors; D055815:Young Adult