A Combined fMRI and Heart Rate Variability Paradigm for Assessment of Central Autonomic Modulation

While the central autonomic network (CAN) has been adequately defined in animal models, data from the human have been lacking. In this study, we correlated cardiac-gated fMRI data with continuous-time heart rate variability (HRV) assessment in order to estimate central autonomic processing in response to a dynamic grip task. The electrocardiogram (ECG) was collected simultaneously with fMRI, and was analyzed with a new point process adaptive filter algorithm for evaluation of HRV indices reflecting time-varying dynamics of autonomic modulation. These were correlated with fMRI signal intensity using a general linear model and subsequent analysis of covariance. Our combined HRV-fMRI data analysis suggests that fMRI activity in several brain regions, including the hypothalamus, parabrachial nucleus, periaqueductal gray, amygdala, and posterior insula, demonstrated significant correlation with parasympathetic tone assessed by HRV high frequency (HF) power. This study demonstrates that inter-subject variability in brain response to an exercise task may help explain the natural variability in autonomic response, as assessed by HRV analysis