Ventromedial prefrontal cortex and the regulation of physiological arousal

Abstract

Neuroimaging studies show a correlation between activity of the ventromedial prefrontal cortex (vmPFC) and skin conductance measurements. However, little is known whether this brain region plays a causal role in regulating physiological arousal. To address this question, we employed Granger causality analysis (GCA) to establish causality between cerebral blood oxygenation level-dependent and skin conductance signals in 24 healthy adults performing a cognitive task during functional magnetic resonance imaging. The results showed that activity of the vmPFC not only negatively correlated with skin conductance level (SCL) but also Granger caused SCL, thus establishing the direction of influence. Importantly, across participants, the strength of Granger causality was negatively correlated to phasic skin conductance responses elicited by external events during the behavioral task. In contrast, activity of the dorsal anterior cingulate cortex positively correlated with SCL but did not show a causal relationship in GCA. These new findings indicate that the vmPFC plays a causal role in regulating physiological arousal. Increased vmPFC activity leads to a decrease in skin conductance. The findings may also advance our understanding of dysfunctions of the vmPFC in mood and anxiety disorders that involve altered control of physiological arousal.

Keywords: Granger causality; arousal; skin conductance; ventromedial prefrontal cortex.

Fig. 1

Brain regions showed positive (top of A) and negative (top of B) correlations with the SCL across 24 subjects at voxel P < 0.0001 uncorrected and cluster P < 0.05 corrected for FWE of multiple comparisons. (Bottom of A and B) Data from a typical participant show that the time series of the ACC and vmPFC are each correlated and anti-correlated with SCL in a 10 min session.

Fig. 2

The strength of Granger causality (F-value) of the vmPFC in regulating skin conductance is negatively correlated with SCR elicited by go (P = 0.003, ρ = −0.58; Spearman regression), stop (P = 0.0004, ρ = −0.66), as well stop success (P = 0.0004, ρ = −0.67) and stop error (P = 0.0004, ρ = −0.66) trials. That is, the stronger the regulatory influence of vmPFC, the less the SCR is elicited. Assuming linearity between the F-value and SCR, Pearson regressions also showed significant correlation between the two variables: go (P = 0.01, r = −0.50); stop (P = 0.01, r = −0.50); stop success (P = 0.0098, r = −0.52) and stop error (P = 0.016, r = −0.49) trials.

Fig. 3

The anatomical relationship between the vmPFC identified in the current study and brain regions identified in earlier studies that negatively correlated with SCL or positively correlated with SCR (Table 1). Only regions with MNI coordinate z < 0 were included. (A) Our vmPFC (red) overlaps brain regions negatively correlated with SCL during biofeedback (Nagai et al., 2004) and resting state (Fan et al., 2012). Blue color represents an area that overlapped across all studies. (B) Our vmPFC (red) show little overlap (yellow) with brain regions (green, combined from all studies) positively correlated with SCR. Anatomical boundary of the brain regions identified from the other studies was manually drawn according to the figures, coordinates and number of voxels reported.