Significant feed-forward connectivity revealed by high frequency components of BOLD fMRI signals

Highlights

• Causal modulation in human brain was estimated up to 5 Hz using fMRI data.

• The BOLD signal at 3 Hz still gives consistent information flow estimates.

• BOLD signals carry inter-regional modulation info at frequency higher than 1 Hz.

Abstract

Granger causality analysis has been suggested as a method of estimating causal modulation without specifying the direction of information flow a priori. Using BOLD-contrast functional MRI (fMRI) data, such analysis has been typically implemented in the time domain. In this study, we used magnetic resonance inverse imaging, a method of fast fMRI enabled by massively parallel detection allowing up to 10 Hz sampling rate, to investigate the causal modulation at different frequencies up to 5 Hz. Using a visuomotor two-choice reaction-time task, both the spectral decomposition of Granger causality and isolated effective coherence revealed that the BOLD signal at frequency up to 3 Hz can still be used to estimate significant dominant directions of information flow consistent with results from the time-domain Granger causality analysis. We showed the specificity of estimated dominant directions of information flow at high frequencies by contrasting causality estimates using data collected during the visuomotor task and resting state. Our data suggest that hemodynamic responses carry physiological information related to inter-regional modulation at frequency higher than what has been commonly considered.

Introduction

Functional magnetic resonance imaging (fMRI) (Belliveau et al., 1991) using BOLD contrast (Kwong et al., 1992, Ogawa et al., 1992) has become an indispensable tool in non-invasive elucidation of brain areas subserving cognitive processes and behaviors. In addition to localizing individual functional areas, fMRI can also be used to reveal spatially distributed neuronal networks by studying either the temporal correlation (i.e., functional connectivity) or the causal modulation between brain areas (i.e., effective connectivity) activated by specific stimuli and tasks (for review, see (Friston, 2011b)).

The effective connectivity analysis of fMRI data has various implementations, including Structural Equation Modeling (SEM) (McArdle and McDonald, 1984), dynamic causal modeling (DCM) (Friston, 2011a, Friston et al., 2003, Penny et al., 2004), and Granger causality analysis (Granger, 1969). Different from SEM and DCM, where models of explicit directional influences among functional areas must be specified a priori, Granger causality analysis uses fMRI data to estimate the direction of information flow directly. The inference about the direction of information flow in Granger causality analysis is based on the effectiveness of the ‘prediction’ (Granger, 1969). Specifically, if one region is considered as the ‘source’, the prediction of the behavior at the ‘target’ region should be significantly improved when information about the ‘source’ is provided. In practice, most Granger causality analyses use fMRI time series and time-domain models in these prediction calculations. While the validity of Granger causality estimates using fMRI has been either supported (Abler et al., 2006, Deshpande et al., 2009, Eichler, 2005, Goebel et al., 2003, Kayser et al., 2009, Londei et al., 2006, Roebroeck et al., 2005, Sato et al., 2006) or questioned (David et al., 2008, Smith et al., 2012) because of regional difference in vasculature reactivity (Lee et al., 1995, Miezin et al., 2000), Granger causality analysis has been applied to many fMRI studies (for review, see (Stephan and Roebroeck, 2012)).

Note that Granger causality can be also formulated in the frequency domain (Brovelli et al., 2004, Geweke, 1982). Therefore the estimated causal modulations can be decomposed into different frequency ranges. When applied to fMRI, the sensitivity and specificity of such spectral Granger analysis is limited by the signal-to-noise ratio of fMRI BOLD signals at different frequencies. The spectral property of BOLD fMRI has been studied since the early days of fMRI (Weisskoff et al., 1993). Disturbances due to cardiac/respiratory fluctuations at characteristic frequencies (Beckmann et al., 2005, Birn et al., 2006) and low frequency drift (0–0.015 Hz) (Smith et al., 1999) have been reported. Interestingly, there is also empirical evidence suggesting that BOLD signal between 0.03 Hz and 0.06 Hz can be closely related to electrophysiological activity (Zuo et al., 2010) and gives greater small-world topology (Achard et al., 2006).

In this study, we use magnetic resonance inverse imaging (InI), a method of fast fMRI capable of sampling the whole-brain BOLD signal at 10 Hz with approximately 5 mm spatial resolution at cortex using a 32-channel head coil array at 3 T (Lin et al., 2006, Lin et al., 2008), to study the Granger causality spectrally up to 5 Hz. We specifically hypothesize that, at frequencies higher than 1 Hz, BOLD signals can still be used to provide estimates of directional information faithfully. Using a visuomotor two-choice reaction-time task, we first successfully identified clear feed-forward effective connectivity from visual to sensorimotor systems in our previous studies (Lin et al., 2013, Lin et al., 2014). Such feed-forward connectivity remains significant at frequencies up to 3 Hz. Our results suggest that the BOLD signal at frequencies higher than 2 Hz can still carry useful physiological information to disclose causal modulation.