• Subject list(S. Park)
• subjects list(J. Kim)

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Importing the Anaconda virtual environment package to run Jupyter notebook.

conda env create -f GP.yaml

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Behav_data

day1 (r00)

• extract.regressor.move-stop.py -s subject

day2 (r01-r03)

• extract.regressor.reward_per_1s.py -s subject
• extract.regressor.reward_per_trial.py -s subject -t time_shift
• extract.regressor.movement.py -s subject -t time_shift

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fMRI_data

preprocessing

• a1.convert_dcm.zsh -s subject -d day
• (test.create_proc.zsh is necessary!) a2.preproc.zsh -s subject -d day

making a group full mask

• 3dmask_tool.zsh -f fraction (default=0.7)

1st level analysis

1. GLM

• GLM.move-stop.zsh -s subject
• GLM.reward_per_1s.zsh -s subject
• (extract.lines.from_txt.py is necessary!) GLM.reward_per_trial.shifted.zsh -s subject -t time_shift (default=0s) -r run (default='all')
• GLM.movement.shifted.zsh -s subject -t time_shift (default=0s)
• extract.reward_per_trial.zsh -s subject -t time_shift (default=0s) -a analysis (default='GLM') -r run (default='all')

2. REML

• 3dREMLfit.reward_per_trial.shifted.zsh -s subject -t time_shift (default=0s)

group analysis (2nd level analysis)

1. GLM

• 3dttest++.move-stop.zsh
• 3dttest++.reward_per_1s.zsh
• 3dttest++.reward_per_trial.shifted.zsh -t time_shift (default=0s) -r run (default='all') -g group
• 3dttest++.movement.shifted.zsh -t time_shift (default=0s)

2. REML

• 3dMEMA.reward_per_trial.shifted.zsh - t time_shift (default=0s)