Theta dominates cross-frequency coupling in hippocampal-medial entorhinal circuit during awake-behavior in rats
Data files
Aug 31, 2022 version files 3.46 GB
-
r782LFP.mat
3.45 GB
-
r782Pos.mat
893.41 KB
-
README
595 B
Apr 07, 2025 version files 49.49 GB
-
ChannelInfo_HPCMEC.xlsx
9.85 KB
-
r1096_prep.mat
4.33 GB
-
r1096LFP.mat
3.91 GB
-
r1096Pos.mat
744.45 KB
-
r1224_EEG_Fragment_0946.fig
15.22 MB
-
r1224_prep.mat
1.20 GB
-
r1224LFP.mat
3.56 GB
-
r1224Pos.mat
817.25 KB
-
r1225_prep.mat
1.74 GB
-
r1225LFP.mat
3.56 GB
-
r1225Pos.mat
899.51 KB
-
r695_prep.mat
4.49 GB
-
r695LFP.mat
4.07 GB
-
r695Pos.mat
975.87 KB
-
r730_prep.mat
1.09 GB
-
r730LFP.mat
3.24 GB
-
r730Pos.mat
766.78 KB
-
r779_prep.mat
2.94 GB
-
r779LFP.mat
3.57 GB
-
r779Pos.mat
886.74 KB
-
r782_prep.mat
839.83 MB
-
r782LFP.mat
3.45 GB
-
r782Pos.mat
893.41 KB
-
r889_prep.mat
3.62 GB
-
r889LFP.mat
3.86 GB
-
r889Pos.mat
860.94 KB
-
README
2.05 KB
-
README.md
2.87 KB
Abstract
Hippocampal theta and gamma rhythms are hypothesized to play a role in the physiology of higher cognition. Prior research has reported that an offset in theta cycles between the entorhinal cortex, CA3, and CA1 regions promotes independence of population activity across the hippocampus. In line with this idea, it has recently been observed that CA1 pyramidal cells can establish and maintain coordinated place cell activity intrinsically, with minimal reliance on afferent input. Counter to these observations is the contemporary hypothesis that CA1 neuron activity is driven by a gamma oscillation arising from the medial entorhinal cortex (MEC) that relays information by providing precisely timed synchrony between MEC and CA1. Reinvestigating this in rats during appetitive track running, we found that theta is the dominant frequency of cross-frequency coupling between the MEC and hippocampus, with hippocampal gamma largely independent of entorhinal gamma.
Dataset DOI: 10.5061/dryad.jdfn2z3dj
Description of the data and file structure
Containing Matlab codes to generate Fig1. D and supplementary fig 5 in the manuscript.
Run “coherence_evomodel.m” to get Fig1. D
Run “cohvartest.m” to get supplementary fig 5
There are 8 animals [r695,r730,r779,r782,r889,r1096,r1224,r1225]
The LFP files are named as r#animalnumberLFP.mat
It’s a structure called EEG with EEG.Axis{1} as timestamps and EEG.Data being LFP recordings. In EEG.Data, rows are time stamps and columns are channels.
The position files are named as r#animalnumberPos.mat
It’s a structure called Pos with Pos.Axis{1} being timestamps. In Pos.Data, rows are timestamps and columns 1 & 2 are X and Y coordinates.
Channel information for each animal can be found in ChannelInfo_HPCMEC.xlsx, which include channels in HPC and MEC, as well as HPC layer information confirmed by ripple CSD analysis. In the table, rows are individual animals, columns are channels numbers in corresponding MEC and hippocampal layers.
The preprocessed data are named as r#animalnumber_prep.mat. It is a matlab structure names “EEG” with length equals to number of LFP segments. For each LFP segment, it has the length of around 1 second. EEG(i) is the ith LFP segments and it has following fields:
EEG(i).Data should be a 2048*64(or 128) matrix with row being time steps and columns being channels. Some animals have 64 channels and some have 128 channels. The mapping between channel number of brain stratum information can be found in ChannelInfo_HPCMEC.xlsx.
EEG(i).Axis{1} should be a 1*2048 vector which contains the time stamps.
EEG(i).Axis{2} should be a 1*64(or 128) vector which just contain channel numbers (should just be 1 to 64 or 1 to 128).
EEG(i).Name just tell you dimension 1 is time, and dimension 2 is channel number (not important).
EEG(i).Frequency is the sampling frequency, somehow we use 2000, should not cause big issue.
EEG(i).Period is the inverse of sampling frequency.
EEG(i).V should be a 2048*1 vector being the interpolated velocity of the animal at each LFP time stamp.
EEG(i).Vmean and EEG(i).Vstd gives the mean and STD value of velocity in this LFP segments.
We may exclude recording in sleep box from these preprocessed LFP.
r1224EEG_Fragment**0946.fig is a figure example of preprocessed LFP segments across all the channels from rat1224. It can be directly loaded into Matlab.
Version changes
03-Apr-2025: Added raw LFP and position recoding of 7 more animals, including the preprocessed LFP data for all the animals. Added ChannelInfo_HPCMEC.xlsx describing layer information across channels. Added one example plot of preprocessed LFP across channel for rat 1224.