BOLD Granger Causality Reflects Vascular Anatomy

Abstract

A number of studies have tried to exploit subtle phase differences in BOLD time series to resolve the order of sequential activation of brain regions, or more generally the ability of signal in one region to predict subsequent signal in another region. More recently, such lag-based measures have been applied to investigate directed functional connectivity, although this application has been controversial. We attempted to use large publicly available datasets (FCON 1000, ADHD 200, Human Connectome Project) to determine whether consistent spatial patterns of Granger Causality are observed in typical fMRI data. For BOLD datasets from 1,240 typically developing subjects ages 7–40, we measured Granger causality between time series for every pair of 7,266 spherical ROIs covering the gray matter and 264 seed ROIs at hubs of the brain’s functional network architecture. Granger causality estimates were strongly reproducible for connections in a test and replication sample (n=620 subjects for each group), as well as in data from a single subject scanned repeatedly, both during resting and passive video viewing. The same effect was even stronger in high temporal resolution fMRI data from the Human Connectome Project, and was observed independently in data collected during performance of 7 task paradigms. The spatial distribution of Granger causality reflected vascular anatomy with a progression from Granger causality sources, in Circle of Willis arterial inflow distributions, to sinks, near large venous vascular structures such as dural venous sinuses and at the periphery of the brain. Attempts to resolve BOLD phase differences with Granger causality should consider the possibility of reproducible vascular confounds, a problem that is independent of the known regional variability of the hemodynamic response.

Retrieving the Hemodynamic Response Function in resting state fMRI: methodology and applications

“…We observe opposite patterns of HRF shapes between the thalamus and occipital cortex under the two conditions, which is consistent with the correlation and anti-correlation between the alpha power spectrum and BOLD signal in thalamic and occipital cortex. It is worth to note how the variations in HRF are consistent with the differences in net arterial and venous flow, and the consequent effects on the estimation of Granger causality reported in (Webb, Ferguson et al 2013). This evidence confirms the importance of performing HRF deconvolution prior to estimating not only for lag-based directed connectivity (Wu, Liao et al 2013), but also for standard functional connectivity .…”

Retrieving the Hemodynamic Response Function in resting state fMRI: Methodology and application

“…Figure 2 reports the typical HRF parameters (Height, Time to Peak and Full Width at Half Maximum) for a pool of 32 healthy subjects, as described in [2]. It is worth to note how the variations in HRF are consistent with the differences in net arterial and venous flow, and the consequent effects on the estimation of Granger causality reported in [6]. This confirms the importance of performing HRF deconvolution prior to estimating lag-based directed connectivity.…”

Direction of information flow in large-scale resting-state networks is frequency-dependent

“…However, as yet, little is known about the directionality of these interactions in large-scale functional networks during the resting state. Estimating directionality from fMRI is challenging due to its limited temporal resolution and indirect relation to neuronal activity (15,16). In contrast, EEG studies in healthy controls have revealed a front-to-back pattern of directed connectivity, particularly in the alpha band (17)(18)(19)(20)(21)(22), consistent with modeling studies that have shown that such patterns may arise due to differences in the number of anatomical connections (the degree) of anterior and posterior regions (22,23).…”

“…From these time series, the first 20 artifact-free epochs, containing 4,096 samples (3.2768 s), were selected to obtain stable results (70). These time series were then filtered in classical EEG/MEG frequency bands [delta (0.5-4 Hz), theta (4-8 Hz), alpha1 (8-10 Hz), alpha2 (10-13 Hz), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and lower gamma (30-48 Hz)], using an offline discrete fast Fourier transform filter that does not distort the phases (BrainWave, version 0.9.150.6; home.kpn.nl/stam7883/brainwave.html). Subsequently, the instantaneous phase for each time series was computed by taking the argument of the analytic signal as computed using the Hilbert transform (see e.g., ref.…”