Heartbeat Induces a Cortical Theta-Synchronized Network in the Resting State
Bibliographic record
Abstract
In the resting state, heartbeats evoke cortical responses called heartbeat-evoked responses (HERs), which reflect cortical cardiac interoceptive processing. While previous studies have reported that the heartbeat evokes cortical responses at a regional level, whether the heartbeat induces synchronization between regions to form a network structure remains unknown. Using resting-state MEG data from 85 human subjects of both genders, we first showed that heartbeat increases the phase synchronization between cortical regions in the theta frequency but not in other frequency bands. This increase in synchronization between cortical regions formed a network structure called the heartbeat-induced network (HIN), which did not reflect artificial phase synchronization. In the HIN, the left inferior temporal gyrus and parahippocampal gyrus played a central role as hubs. Furthermore, the HIN was modularized, containing five subnetworks called modules. In particular, module 1 played a central role in between-module interactions in the HIN. Furthermore, synchronization within module 1 had a positive association with the mood of an individual. In this study, we show the existence of the HIN and its network properties, advancing the current understanding of cortical heartbeat processing and its relationship with mood, which was previously confined to region level.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".