Decoding Brain-Heart Dynamics: Effective Connectivity Predictors of Heart Rate Variability

Autonomic dysregulation characterizes neuropsychiatric and somatic disorders, often reflecting disrupted brain-heart communication mediated by the Central Autonomic Network (CAN). The CAN integrates visceral inputs and cortical control to maintain autonomic balance. Heart rate variability (HRV) provides peripheral index of CAN regulation, yet the causal dynamics underlying HRV-brain interactions remain poorly understood. We investigated effective connectivity (EC) within a core (C-CAN), extended (E CAN) and non-canonical CAN (N-CAN) to characterize bidirectional brain-heart dynamics at rest. Resting-state fMRI and photoplethysmography were acquired from 232 adults (164 females; mean age = 47.8 ± 18.9 years). PPG-derived HRV metrics (time, frequency, entropy) were extracted and EC was estimated via regression dynamic causal modeling across 100 brain regions, including 42 C-CAN nodes. Predictive modeling used cross-validated ridge regression and bidirectional interactions were modeled using HRV as a driving input. The E-CAN EC model best predicted entropy metrics (ApEn: r = 0.22, SampEn: r = 0.21). The C-CAN model improved predictive performance (SampEn: r = 0.27, ApEn: r = 0.23). Non-CAN EC aligned with E-CAN EC predictions (SampEn: r = 0.17). Analyses revealed HRV-driven influences on distributed cortical and subcortical regions. Our findings show that EC predicts HRV through integrative brain networks beyond canonical CAN nodes. Entropy-based HRV measures emerged as sensitive indicators of central influence on heart dynamics, while bottom up cardio-autonomic signals causally influenced key brain regions supporting neurovisceral integration. Collectively, these results highlight that the complexity of causal brain-heart interactions, reflected in HRV dynamics, mirrors the ROI-to-ROI connectivity patterns across canonical and extended CAN parcellations.