Globally conditioned Granger causality in brain-brain and brain-heart interactions: a combined heart rate variability/ultra-high-field (7 T) functional magnetic resonance imaging study

Abstract

The causal, directed interactions between brain regions at rest (brain-brain networks) and between resting-state brain activity and autonomic nervous system (ANS) outflow (brain-heart links) have not been completely elucidated. We collected 7 T resting-state functional magnetic resonance imaging (fMRI) data with simultaneous respiration and heartbeat recordings in nine healthy volunteers to investigate (i) the causal interactions between cortical and subcortical brain regions at rest and (ii) the causal interactions between resting-state brain activity and the ANS as quantified through a probabilistic, point-process-based heartbeat model which generates dynamical estimates for sympathetic and parasympathetic activity as well as sympathovagal balance. Given the high amount of information shared between brain-derived signals, we compared the results of traditional bivariate Granger causality (GC) with a globally conditioned approach which evaluated the additional influence of each brain region on the causal target while factoring out effects concomitantly mediated by other brain regions. The bivariate approach resulted in a large number of possibly spurious causal brain-brain links, while, using the globally conditioned approach, we demonstrated the existence of significant selective causal links between cortical/subcortical brain regions and sympathetic and parasympathetic modulation as well as sympathovagal balance. In particular, we demonstrated a causal role of the amygdala, hypothalamus, brainstem and, among others, medial, middle and superior frontal gyri, superior temporal pole, paracentral lobule and cerebellar regions in modulating the so-called central autonomic network (CAN). In summary, we show that, provided proper conditioning is employed to eliminate spurious causalities, ultra-high-field functional imaging coupled with physiological signal acquisition and GC analysis is able to quantify directed brain-brain and brain-heart interactions reflecting central modulation of ANS outflow.

Keywords: Granger causality; blood-oxygen-level dependent; brain–heart interactions; heart rate variability; ultra-high-field functional magnetic resonance imaging.

Figure 1.

Example of the fMRI BOLD ROI-wise signal (blue solid line) and HRV regressors HF (parasympathetic), LF (sympathetic+parasympathetic) and Bal (sympathovagal balance)(yellow, green and red solid lines, respectively) for one healthy volunteer. (Online version in colour.)

Figure 2.

Bivariate GC strength (see equation (3.5)) of within-brain interactions. All nine subjects are shown. In each matrix, rows depict causing variables, whereas columns depict caused variables. Brain regions are ordered according to Salvador et al. [79]. The colour scale shows connection strength. (Online version in colour.)

Figure 3.

Group-wise count of significant connections in bivariate GC. For each directed connection the group-wise count was computed as the number of subjects in whom that particular connection was statistically significant with p<0.01 (a) and p<0.05 (b). Rows label brain regions (GC sources) G-causing the brain regions labelled by columns (GC target). Brain regions are ordered according to Salvador et al. [79]. (Online version in colour.)

Figure 4.

GCGC strengths in within-brain interactions. All nine subjects are shown. In rows and columns, respectively, causing and caused brain regions are shown. Brain regions are ordered according to Salvador et al. [79]. The colour scale shows connection strength (see equation (3.5)). (Online version in colour.)

Figure 5.

Group-wise count of significant connections in GCGC. For each directed connection the number of subjects where that particular connection is statistically significant with p-value<0.01 (a) and p-value<0.05 (b) is shown. Rows label brain regions (GC sources) G-causing the brain regions labelled by columns (GC target). Brain regions are ordered according to Salvador et al. [79]. (Online version in colour.)

Figure 6.

Group-wise directed resting-state network graphs as inferred with bivariate GC (a) and GCGC (b). The matrices containing the number of subjects in which each connection was statistically significant (p<0.01), i.e. figures 3 and 5, respectively, were thresholded at six subjects. Colour scale, average (across subjects) GC strength. Refer to Salvador et al. [79] forlong label names. In order to project onto anatomical space, each node of the network was positioned on the (x,y) coordinates of the centre of mass of the corresponding ROI as defined by the automated anatomical labelling atlas [76]. (Online version in colour.)

Figure 7.

Group-wise directed resting state brain–brain (cut-off at p-value<0.01) and brain–heart network graphs as inferred using the GCGC approach (right). Within-brain networks are computed and presented as in figure 6a (see the electronic supplementary material for explicit brain region names; the order of the brain region follows a frontal–caudal direction, see Salvador et al. [79]) exceptfor the spatial distribution. In this figure, brain nodes are arranged according to a spring embedding (i.e. vertices are placed so that they minimize mechanical energy when each edge corresponds to a spring). Significant causal brain–heart connections have been added according to table 1. (Online version in colour.)