Data from: Progressively shifting patterns of co-modulation among premotor cortex neurons carry dynamically similar signals during action execution and observation
Data files
Jun 20, 2025 version files 102.77 GB
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DataFileSupportingFiles.zip
22.84 KB
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Figure5_FigureSupplement1_CumulativeSeparability.m
4.96 KB
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Figure8CD.zip
136.81 KB
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R_20220316_RGM_ExeObs_0213.nev
2.24 GB
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R_20220316_RGM_ExeObs_0213.ns5
100.54 GB
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README.md
3.79 KB
Abstract
Many neurons in the macaque premotor cortex show firing rate modulation whether the subject performs an action or observes another individual performing a similar action. Although such “mirror neurons” have been thought to have highly congruent discharge during execution and observation, many if not most actually show non-congruent activity. Studies of neuronal populations active during both execution and observation have shown that the most prevalent patterns of co-modulation—captured as neural trajectories—pass through subspaces which are shared in part, but in part are visited exclusively during either execution or observation. These studies focused on reaching movements for which low-dimensional neural trajectories exhibit comparatively simple dynamical motifs. But the neural dynamics of hand movements are more complex. We developed a novel approach to examine prevalent patterns of co-modulation during execution and observation of a task that involved reaching, grasping, and manipulation. Rather than following neural trajectories in subspaces that contain their entire time course, we identified time series of instantaneous subspaces, calculated principal angles among them, sampled trajectory segments at the times of selected behavioral events, and projected those segments into the time series of instantaneous subspaces. We found that instantaneous neural subspaces most often remained distinct during execution versus observation. Nevertheless, latent dynamics during execution and observation could be partially aligned with canonical correlation, indicating some similarity of the relationships among neural representations of different movements relative to one another during execution and observation. We also found that during action execution, mirror neurons showed consistent patterns of co-modulation both within and between sessions, but other non-mirror neurons that were modulated only during action execution and not during observation showed considerable variability of co-modulation.
Dataset DOI: 10.5061/dryad.cvdncjtfq
Description of the data and file structure
These source data files provide the processed data underlying Figure 5 - figure supplement 1 and Figure 8, panels C and D.
Files and variables
File: Figure5_FigureSupplement1_CumulativeSeparability.m
Description: This MATLAB script contains the processed source data for Figure 5 - figure supplement 1, which can be loaded to a cell array in MATLAB by running the script. Numbers are arranged in 4x4 arrays that correspond to the 4x4 color matrices of the figure. The values in Ex_self_sepa3d{9} and Obs_self_sepa3d{9} are from monkey T session 3, ‘T_20220603’, and are displayed as color matrices in A and B of ths figure. Averaging across all 9 sessions for Ex or Obs gives the values displayed as color matrices in C or D of the figure, respectively.
File: Figure8CD.zip
Description: The s#CC_for_plots_aug.mat files contain the canonical correlation coefficients which are loaded, extracted, and plotted by the MATLAB scripts Figure8C.m and Figure8D.m to create the respective panels of Figure 8. Files ‘s1…s4CC_for_plots_aug.mat’ hold the correlation coeffecients for the Instruction (s1), Go (s2), Move (s3), and Hold (s4) trajectory segments. The correlation coefficients for each type of comparison (e.g. MNEx1Ex2) are held in a separate array in which columns are CC1, CC2, and CC3 for each of the analyzed sessions (rows).
Code/software
MATLAB R2019b or later is needed to view and process the data.
File: Figure5_FigureSupplement1_CumulativeSeparability.m
Figure5FigureSupplement1_CumulativeSeparability.m
contains the processed source data for Figure 5 - figure supplement 1, which can be viewed with any text editor and can be loaded to a cell array in MATLAB by running the script.
Figure8CD.zip
contains several MATLAB files. The s#CC_for_plots_aug.mat files contain the canonical correlation coefficients which are loaded, extracted, and plotted by the MATLAB scripts Figure8C.m and Figure8D.m to create the respective panels of Figure 8.
Raw data: The raw data and the spike-sorted data used in this study each require ~1.5 Tb of storage space and therefore have not been shared on a publicly accessible server. Here, we provide the raw data files from one of the 9 recording sessions (monkey R, session 2) used in the study. Upon request, the remainder of these files are available from the lead contact, Marc H. Schieber (mschiebe@ur.rochester.edu). Files will be sent on an external hard drive. No application or project proposal is required.
The raw data are in *.NEV and *.NS5 file formats described in detail in the accompanying file, “LB-0023-7.00_NEV_File_Format.pdf.” (provided on Zenodo, Supplemental Information).
The *.NEV
file contains numerical behavioral event marker codes and their timestamps. The meaning of these numerical markers is given in the accompanying file, “Behavioral Event Marker Codes.xlsx.” In this file, the numerical markers are listed in the sequence they appear in each successful trial.
The *.NS5
file contains simultaneous broadband recordings from 512 channels, 64 contiguous channels from each of 8 cortical areas. The file “ChannelIndex.xlsx” gives the cortical area for each of the 512 channels. The present study used only channels 65-128 from the ventral premotor cortex (PMv) and 129 to 192 from the dorsal premotor cortex (PMd).
Access information
Other publicly accessible locations of the data:
- None