A computational mechanism of cue-stimulus integration for pain in the brain
Data files
Aug 05, 2024 version files 978.09 MB
Abstract
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.
README: A Computational Mechanism of Cue-Stimulus Integration for Pain in the Brain
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 Pain Expectation and Pain Stimulus Integration.
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 usingneurAvg.FIR
as Y, andCueStimX
as XCueStimX
: [Intercept, CueInfo, StimInfo]. Normalized. Condition ordered as in variable "condName"parcelIndx
: spatial index of each networkbehvout
: 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 theneurAvg
.
Sharing/Access information
Links to other publicly accessible locations of the data:
Code/Software
Codes tested on Ubuntu 20.04, matlab R2021b.
Dependencies are...