REM in ADvsCA1depth

Broad Goal - Identify differences in hippocampal activity during REM sleep as a function of whether a rat carries Alzheimer's disease related transgenes.

Approach -  Implant rats with versus without the AD transgenes with silicon depth electrodes targeting the dorsal hippocampus and then collect 2 hour recordings during the 'light-phase' of the sleep cycle (when nocturnal rats should be sleeping) in a quiet isolated room. Using the recordings to analyze the activity during REM sleep. Analyses can start simple and become more elaborate as we go based on what we find.

Goal 1: Compare the frequency and duration of REM sleep epochs between TG and WT rats

Background

REM sleep is defined as moments of stillness when the hippocampal theta rhythm (8Hz) is two times larger than the hippocampal delta rhythm (2Hz)

Method: 

1. Locate relevant data

2. Synchronize behavioral videos with brain activity recordings (ephys)

3. Score the behavior using DeepLabCut (DLC)

4. Import synchronized DLC behavior data and ephys recordings

5. Find moments of stillness 

   1. Threshold velocity to find low-velocity epochs

6. Find moments of high theta power

   1. Threshold the ratio of theta to delta power to find these

7. Find moments of REM sleep 

   1. These are the epochs when the animal is still and the theta power is high

8. Compare frequency of REM epochs between WT and TG

   1. Compute the mean frequency per animal

   2. Compare the means across groups of animals with stats

9. Compare duration of REM epochs between WT and TG

   1. Compute the mean duration per animal

   2. Compare the means across groups of animals with stats

Skills:

Orange - Talk to the relevant people to find the data

Yellow - DLC: Use DLC on HPC to score videos (may involve building new CNN)

Green - Matlab: Update existing scripts to handle current data, may involve handing changes in data format / paths

Blue - Matlab: Using CMBHOME object, thresholding, saving results

Purple - Matlab: stats and aggregation. Will need to write your own using stats MATLAB tools

Goal 1.1 - Set up REM detection scripts using already imported data 

General method -

• Use Dylan's MS-Opto rat data already imported

• Load saved CMBHOME object

• Explore root.user_def

Goal 1.1.1 - Find high theta epochs

Method - 

• Load 1 channel of LFP

• Find theta power in root object

• plot theta power over full recording

• use 'OverThresholdDetect' to find epochs when theta power is 2 standard deviations larger than the mean power

• Bonus: find 'epochs' of high theta power, where epochs is a Nx2 matrix, with a row for each of the N detected epochs, each with the start index and the end index.

Goal 1.1.2 - Find low movement epochs

Method - 

• Using same data as already using...

• Find velocity in the root object

• plot velocity over full recording

• use 'UnderThresholdDetect' to find epochs where movement speed is less than 8 cm/sec, store this in root.user_def, run cmbSave(root)

• Plot this over the velocity to highlight epochs of stillness

• Bonus: find 'epochs' of stillness, where epochs is a Nx2 matrix

Goal 1.1.3 - Find theta-delta ratio

Method - 

• Double check delta is not already computed by CMBHOME. If it is, hurray! Use it.

• If not already computed by CMBHOME, compute the delta power over the full recording (use 'buttfilt.m' to find 1-4 Hz power)

• plot delta power over full recording

• compute ratio of theta and delta, plot over full recording

• use 'OverThresholdDetect' to find epochs when theta-delta ratio is 2 or larger, store this in root.user_def, run cmbSave(root)

• Bonus: find 'epochs' of high theta-delta power

Goal 1.1.3 - Putting it together

• Find where rat is still and there is high theta-delta ratio - our surrogate for REM sleep detection!

• Find 'epochs' of REM sleep. Store this in root.user_def, run cmbSave(root)