A computational mechanism of cue-stimulus integration for pain in the brain

The brain integrates information from pain-predictive cues and noxious inputs to construct the pain experience. Although previous studies have identified neural encodings of individual pain components, how they are integrated remains elusive. Here, using a cue-induced pain task, we examined temporal fMRI activities within the state-space, where axes represent individual voxel activities. By analyzing the features of these activities at the large-scale network level, we demonstrated that overall brain networks preserve both cue and stimulus information in their respective subspaces within the state-space. However, only higher-order brain networks, including limbic and default mode networks, could reconstruct the pattern of participants’ reported pain by linear summation of subspace activities, providing evidence for the integration of cue and stimulus information. These results suggest a hierarchical organization of the brain for processing pain components and elucidate the mechanism for..., , , # A Computational Mechanism of Cue-Stimulus Integration for Pain in the Brain

[https://doi.org/10.5061/dryad.41ns1rnpj](https://doi.org/10.5061/dryad.41ns1rnpj)

The codes and data for the manuscript, \"A Computational Mechanism of Cue-Stimulus Integration for Pain in the Brain\"

PEPSI stands for **P**ain **E**xpectation and **P**ain **S**timulus **I**ntegration.

## Description of the data and file structure

### **codes**

**step1_CalcSubspacesEncodingperf.m**

* Implements the processes and visualizations depicted in Figs. 2A-B (Calculation of subspaces and encoding performances).

**step2_VisTraj.m**

* Implements the visualization of the trajectories in subspaces.

**step3_Traj2Behv.m**

* Implements the processes and visualizations depicted in Fig. 5 (Reconstructing behavioral patterns from the neural trajectories).

### **data**

**data_for_replication.mat**

* Contains all data necessary for implementing the steps in the codes folder.
* `neurAvg.FIR` : subject averaged F...