# The hidden route: an exploratory study on autonomic influences in early phases of information processing

## TL;DR

This study explores how heart rate variability and brain activity might be linked during early information processing, suggesting a synergistic regulatory mechanism.

## Contribution

The study proposes a novel link between heart rate variability and sensory gating as an indicator of early cognitive processing.

## Key findings

- LF HRV component was significantly associated with sensory gating indices S2 and S2/S1 ratio.
- Smoking habits and respiration frequency also showed significant correlations with sensory gating measures.
- Machine learning revealed that frontocentral EEG activity in theta and gamma frequencies influences LF HRV.

## Abstract

Adapting to an ever-evolving world and the constant changes taking place in one’s own body requires a great deal of regulatory effort in which the brain and periphery act in synergy. In this framework, heart rate variability (HRV) is thought to reflect autonomic regulatory adaptions to the environment. The hypothesis of this exploratory work is that the sensory gating (SG) evoked potential might represent an index of early phases of the cognitive counterpart. This study aimed to investigate the possible association between the two measures in young adults.

An ECG and a 32-channel EEG were recorded in 32 young adults (mean age 24.1 years, range 20–29) at rest and during an auditory SG paradigm. The peak amplitude for the first (S1) and second (S2) stimulus and the S2/S1 ratio of SG on central site (Cz) were calculated. HRV components in two frequency (low-LF and high-HF) domains and respiration frequency rate (EDR) estimation were calculated from ECG. Smoke habits were collected.

LF HRV component resulted associated with S2/S1 ratio and S2 (S2, rho=-0.498, p = 0.02; S2/S1, rho=-0.499, p = 0.02), while smoking with S2/S1 ratio (rho=-0.493, p = 0.02) and EDR only near significance with S2/S1. In the regression, LF, EDR, and smoke resulted in good predictors of the S2/S1 ratio (LF, Beta=-0.516, p < 0.001; EDR, Beta=-0.405, p = 0.002, smoke, Beta=-0.453, p < 0.001). Applying a machine learning approach showed that the LF HRV component was significantly influenced by frontocentral spectral EEG activity in theta and gamma frequencies.

Even if preliminary, these results suggest a filtering mechanism that operates throughout circuits strongly associated with those generating HRV to adapt to the outside world synergistically.

The online version contains supplementary material available at 10.1186/s40359-025-02561-y.