Abstract
Abnormalities in the neural mechanisms of the stress response have the potential to serve as a transdiagnostic marker for stress-related disorders. This potential is rooted in the highly individual and dynamic stress response, which poses a challenge to classical experimentally informed models that focus on specific phases, conditions, or stimuli during a stress task. Here, we integrate individual heart rates (HR) as an immanent index of the stress-response and combine these with functional magnetic resonance imaging (fMRI) data. In this study, 83 healthy participants completed a multimodal psychosocial imaging stress task comprising three different task phases (PreStress,Stress,andPostStress), with each phase consisting of five 60 s blocks of active task interleaved with 40 s of rest, and simultaneous recordings of pulse plethysmography. Participants were asked to solve mental calculations and were exposed to negative social feedback during theStressphase. We estimated a general linear model (GLM) with individual heart rates averaged per active block as a single parametric modulator of the task regressor across all 15 active blocks irrespective of the task phase. Results revealed a negative correlation between HR and activation in the bilateral amygdala and anterior hippocampus as well as deactivation in the default mode network. Positive correlations with HR were detected in the bilateral insular cortex, bilateral angular gyrus, and parts of the inferior and superior parietal lobes. In summary, our findings emphasize the utility of integrating the commonly assessed cardiovascular stress response (here: HR) as an immediate index of the participant's stress status. We conclude that by such integration, brain regions involved in regulating the acute stress response, such as the anterior hippocampus and amygdala, are detected more sensitively by tracking the individual's "stress wave" rather than treating every experimental block uniformly. Our approach may serve as a complementary analysis to the task-regressor based model.