Complete up-to-date analysis is available here.

The Jupyter notebooks (under the notebooks directory) are used primarily for algorithm and method development and usually include runs for a single sample. After, the finalized algorithms are moved to the pipeline (to the R or python directories) and are used for all samples. While I try to rerun the notebooks for all changes, sometimes the notebooks may not be up-to-date. Please see the docs directory or the deployed website for the most recent results.

The data should be available under the raw directory in this repository root (create a symbolic link).

1. Install conda and mamba. Create the snakemake (env/snakemake.yml) conda environment (required to run the analysis) and the spontaneous-movement-mne (env/mne.yml) conda environment (required to generate the reports).

# In macOS M2
export CONDA_SUBDIR=osx-arm64

mamba env create -f env/snakemake.yml
mamba env create -f env/mne.yml

2. Activate the snakemake environment.

conda activate snakemake

3. Run snakemake:

snakemake --conda-frontend mamba --use-conda --rerun-triggers mtime params --cores 1 -p all

• correlation peak (when? / per cell)
• statistical test on 0 / max timepoint (mean per cell) (compare different groups, e.g. L2/3 vs L5, S1 vs M1)
• emg detection with 10th percentile compare with TKEO
• high pass filter (2 Hz) - average abs EMG on detected events

Use movements detected with 10th upper percentile method. Do not filter out any detected episodes for now.

• show that correlation is different from zero
• color by cortex in correlation plots
• detect EMG off
  • What is happening in W3? How to filter out the heartbeat?
  • Add counts of EMG on / off in the report (number of events, total time)
• AP detection (90 percentile?)
  • filter out false positives!
• vm report html
• compare movement on/off
  • number of episodes
  • episode length
  • mean vm
  • vm sd
  • AP
• correlation report html

To discuss

• Filtering had to be changed because heart beat was disturbing to the rest period detection. In some cases, it still is, and I am not sure how to get rid of it.
  • Few samples have very low number of rest episodes (e.g. W2 C3 S1 L23).
• Decided to use differential analysis for detection of action potentials (90th percentile and any average-like methods will likely introduce a lot of false positives).
  • AP detection: the hell happened in W4 C15 (S1 L5)?
• Why does the Vm continuously grow over time in some samples (e.g. W4 C11 S1 L5)? Is this some unwanted effect which should be corrected for?
• Combining S1 layers into a single group seems counter-productive since there are significant differences in the correlation patterns between the layers L2/3 and L5 of S1 (not seen in M1, though).

• Fix correlation report (max corr, non absolute for test)
• Use low-pass filtered data for movement / rest episode detection
  • Do any movement / rest episodes overlap?
• Fix statistics in the movement vs rest comparison report (need to correct for time)
• Vm methods (filtering AP detection)

To discuss

• Detection of movement / rest episodes was performed using the low-pass filtered EMG data. Included a check to make sure none of detected movement and rest episodes overlap.
• Fitted more complex models that account for episode length and episode onset time. Is this enough to account for the effect seen in continuous membrane potential increase? Or should I still cut and remove the data?

• kursinio planas
• pretty graph with all samples to show that EMG onset is good
• EMG event filtering (min time: 0.5 s, 400ms offset, 400ms onset)
  • Add figure 3 from the previous analysis
• frequency analysis (movement on / movement off): 2-100 Hz, >=0.5s
• compare conductance during on/off
• add method descriptions to reports
  • index (how to use the website)
  • correlation analysis
  • EMG (filtering movement detection)

To look into

• AP detection in some files is suspicious (e.g. S1 L23 W1 C8). Seems like some APs are detected twice?