Individual Autonomic Profiles Influence Brain–Heart Connectivity in Tonic Pain

Jana Luisa Aulenkamp,1– 3 Rasmus B Nedergaard,1 Imran Khan Niazi,4,5 Donghua Liao,1 Louise Kuhlmann,1 Fredy Rojas,4 Kamran Rasool,4 Anna Evans Phillips,6 Dhiraj Yadav,6 Enrique De-Madaria,7,8 Peter Hegyi,9– 11 Pramod Garg,12 Søren Schou Olesen,1 Soumya Jagannath,12 Rasmus Hagn-Meincke,1 Zoltán Hajnády,9,11 Christina Brock,1 Asbjørn M Drewes1 1Department of Gastroenterology and Hepatology, Centre for Pancreatic Diseases and Mech-Sense, Aalborg University Hospital, Aalborg, Denmark; 2Department of Anesthesiology and Intensive Care Medicine, University Hospital Essen, University Duisburg-Essen, Essen, Germany; 3Department of Neurology, Center for Translational Neuro- and Behavioral Sciences (C-TNBS), University Hospital Essen, University of Duisburg-Essen, Essen, Germany; 4Centre for Chiropractic Research, New Zealand College of Chiropractic, Auckland, 1060, New Zealand; 5Department of Health Science and Technology, Aalborg University, Aalborg, Denmark; 6Division of Gastroenterology, Hepatology & Nutrition, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA; 7Hospital General Universitario Dr. Balmis-ISABIAL, Alicante, Spain; 8Department of Clinical Medicine, Miguel Hernández University, Elche, Spain; 9Institute of Pancreatic Diseases, Semmelweis University, Budapest, Hungary; 10Centre for Translational Medicine, Semmelweis University, Budapest, Hungary; 11Institute for Translational Medicine, Medical School, University of Pecs, Pecs, Hungary; 12Department of Gastroenterology, All India Institute of Medical Science, New Delhi, IndiaCorrespondence: Jana Luisa Aulenkamp, Mech-Sense, Aalborg University Hospital, Department of Gastroenterology and Hepatology, Mølleparkvej 4, Aalborg, Denmark, Tel +49 201 723 – 84423, Fax +49 201/723-5949, Email [email protected]: Acute pain elicits distinct autonomic responses. Electroencephalography (EEG) and heart rate variability (HRV) provide insights into autonomic and cortical activity to pain, but they often fail to capture the integrated dynamics of brain–heart connectivity. This exploratory study used raw electrocardiogram (ECG) and EEG signals to investigate brain–heart coherence during resting and tonic experimental pain, aiming to detect direct electrical coupling patterns, and explored the influence of individual autonomic response in healthy participants.Methods: EEG, raw ECG data and HRV were collected from 33 healthy participants under two conditions: rest and a cold pressor test where subjects immersed their hand into ice water (tonic pain). HRV parameters were extracted to quantify autonomic dynamics in response to pain. Magnitude-squared coherence (MSC) quantified brain–heart connectivity across frequency bands (delta, theta, alpha, beta, gamma), and participants were stratified into subgroups based on changes in periodic repolarization dynamics (PRD), a marker of sympathetic modulation.Results: Brain–heart coherence remained stable across conditions, reflecting robust coupling, particularly in delta bands, for both conditions. On group-level HRV analysis revealed increased sympathetic response to pain, evidenced by decreased normal-to-normal interval (p < 0.001) and faster heart rates (p < 0.001). In an exploratory analysis, elevated MSC values (all p< 0.05) were seen in theta (Fp1, Cz), alpha-2 (T3, P4), and gamma (Fp1, Pz, T3, P4, O2) bands in the group where PRD decreased (n=16) compared to the group where it increased (n=17).Discussion: These findings highlight the stability of brain–heart coherence during resting and tonic pain in healthy individuals. However, individual autonomic profiles influenced coherence, with enhanced synchronization in the PRD-decreased group and reduced synchronization in the PRD-increased group. These preliminary findings, limited by the exploratory nature and sparse setup, require validation in studies with denser electrode arrays. Coherence analysis provides nuanced insights into brain–heart dynamics, advancing the understanding beyond single-system measures.Keywords: heart rate variability, electrocardiogram, electroencephalogram, interoception