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.

Tensor Analysis and Fusion of Multimodal Brain Images

“…Extensive simulations by Rodrigues and Andrade suggest that the optimal sampling frequency is around 100 ms [93]. This is also argued for experimentally by Lin et al, who employed a fast fMRI sequence of 100 ms [94]. It is this data that we have used in this paper.…”

“…This is fast fMRI data (sampled at 10 Hz) from one of the subjects reported in [94]. The subject recorded had to respond with the corresponding hand to right or left visual hemi-field stimuli.…”

The impact of hemodynamic variability and signal mixing on the identifiability of effective connectivity structures in BOLD fMRI

“…(2013), Barnett & Seth (2017)) and Lin et al. (2014), who conclude that GC estimation improves with upsampling. The design of our study is also different than the previous studies.…”

Optogenetic activation of nonhuman primate cortical and subcortical brain circuits highlights detection capabilities of MEG source imaging

“…Recent analysis techniques designed to increase fMRI sampling rate (e.g. dynamic functional magnetic resonance inverse imaging 41 ), have achieved a maximum sampling rate around 10 Hz with a 5 mm 3 voxel size 42,43 with the 10 Hz period corresponding to events lasting 100 ms. In contrast, arcuate cortex was stimulated with trials of 50 ms square wave inputs and beamforming revealed arcuate peaks at 75 ms, and with a voxel size of 750 µm 3 (Figure 2a), and 50 ms flashes of light elicited SAM peaks at 50 ms in occipital cortex ( Figure S1aviii).…”