The central autonomic network (CAN) has been described in animal models but has been
difficult to elucidate in humans. Potential confounds include physiological noise
artifacts affecting brainstem neuroimaging data, and difficulty in deriving non-invasive
continuous assessments of autonomic modulation. We have developed and implemented
a new method which relates cardiac-gated fMRI timeseries with continuous-time heart
rate variability (HRV) to estimate central autonomic processing. As many autonomic
structures of interest are in brain regions strongly affected by cardiogenic pulsatility,
we chose to cardiac-gate our fMRI acquisition to increase sensitivity. Cardiac-gating
introduces T1-variability, which was corrected by transforming fMRI data to a fixed
TR using a previously published method [Guimaraes, A.R., Melcher, J.R., et al., 1998.
Imaging subcortical auditory activity in humans. Hum. Brain Mapp. 6(1), 33-41]. The
electrocardiogram was analyzed with a novel point process adaptive-filter algorithm
for computation of the high-frequency (HF) index, reflecting the time-varying dynamics
of efferent cardiovagal modulation. Central command of cardiovagal outflow was inferred
by using the resample HF timeseries as a regressor to the fMRI data. A grip task was
used to perturb the autonomic nervous system. Our combined HRV-fMRI approach demonstrated
HF correlation with fMRI activity in the hypothalamus, cerebellum, parabrachial nucleus/locus
ceruleus, periaqueductal gray, amygdala, hippocampus, thalamus, and dorsomedial/dorsolateral
prefrontal, posterior insular, and middle temporal cortices. While some regions consistent
with central cardiovagal control in animal models gave corroborative evidence for
our methodology, other mostly higher cortical or limbic-related brain regions may
be unique to humans. Our approach should be optimized and applied to study the human
brain correlates of autonomic modulation for various stimuli in both physiological
and pathological states.