DOI: 10.5061/dryad.41ns1rnpj
title: A computational mechanism of cue-stimulus integration for pain in the brain

description: 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
 their integration underlying our pain perception. This dataset provides
 fMRI time series averaged across all subjects for different experimental
 conditions. It also includes voxel indices of network parcellations and
 behavioral data for each experimental condition.
description: # 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 FIR response. comprising whole voxel. * `neurAvg.CuetBeta`, `neurAvg.StimtBeta` : temporal encoding weights using `neurAvg.FIR` as Y, and `CueStimX` as X * `CueStimX` : [Intercept, CueInfo, StimInfo]. Normalized. Condition ordered as in variable "condName" * `parcelIndx`: spatial index of each network * `behvout`: mat size of #cond X #sub. pain reports. condition is ordered as in variable "condName" * `cmaps`: color maps for "condName" * `cmaps7`: color maps for 7 large-scale functional networks.  **template.nii** * Provides the brain template for variable "neurAvg" in the data_for_replication.mat. * Example usage * `obj = fmri_data(fullfile(basedir, 'data', 'template.nii'))`. This will results in same voxel size in data of the `neurAvg`. ## Sharing/Access information Links to other publicly accessible locations of the data: * [https://github.com/cocoanlab/PEPSI](https://github.com/cocoanlab/PEPSI) ## Code/Software Codes tested on Ubuntu 20.04, matlab R2021b. Dependencies are... * SPM12 * [https://github.com/cocoanlab/cocoanCORE.git](https://github.com/cocoanlab/cocoanCORE.git) * [https://github.com/canlab/CanlabCore.git](https://github.com/canlab/CanlabCore.git) * [https://github.com/didch1789/yanchogosu_toolbox.git](https://github.com/didch1789/yanchogosu_toolbox.git)