fmri_predict

predicting fmri activaties from connectome

##to work with git

1. git status: to check any changes in the repo
2. git add: to save the changes
3. git commit -a: to save new updates and commit
4. git push: to upload any local changes to github
5. git pull: to clone new changes in github to local computers
6. git log: to check the log information in the repo

##to create virual enviorment via conda

1. install miniconda: https://conda.io/miniconda.html , run "bash Miniconda3-latest-Linux-x86_64.sh" and "conda update conda" after downloading
2. create env: conda create -n tensorflow-py3.6 anaconda python=3.6
3. verify env is created: conda list
4. activate env: source activate tensorflow-py3.6
5. save packages info from another env2: pip3 freeze > requirements.txt
6. loading all requried packages: while read requirement; do conda install --yes $requirement || pip install $requirement; done < requirements.txt ; or simply use: pip install -r requirements.txt
7. install tensorflow for gpu: pip install --upgrade pip; pip3 install tensorflow-gpu; pip3 install --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.7.0-cp36-cp36m-linux_x86_64.whl for validation: ipython -> import tensorflow as tf; hello = tf.constant('Hello, TensorFlow!'); sess = tf.Session(); print(sess.run(hello)); or simply run: python -c "import tensorflow as tf; print(tf.GIT_VERSION, tf.VERSION)"
8. install tensorpack: pip3 install tensorpack; pip3 install --upgrade tensorpack
9. install notebook: conda install -c anaconda ipykernel ; conda install -c anaconda notebook; ipython kernel install --user --name tensorflow --display-name "Python3.6"; jupyter kernelspec list

##discussion with Pierre on Jan 29th

1. start with a simple model: predicting motor activation from functional connectivity using sparse linear regression model
2. using atlas: group atlas: MIST with two resolution (200/1000 regions) individual atlas
3. defining network structure: 7-functional networks (non-linear relationship could be learned through convolutional layers; thus no logical conflict)
4. for limited training samples: use sliding-windows to generate dynamic functional connectivity (duration:5min)

###Data

1. resting-state: 10 sessions under the folder: /data/cisl/raw_data/midnight/Rawdata/nii_data/preproc_fsl/sub01/rest using warped_F_sess*_res_ICA_filt_sm6.nii.gz for after ICA-AROMA, temporal filtering, spatial smoothing and registered
2. motor tasks: 10 sessions and 2 runs for each,under the folder: /data/cisl/raw_data/midnight/Rawdata/nii_data/preproc_fsl/sub01/motor preprocessed fmri: filtered_func_data_ICA.nii.gz brain activation map from contrasts: zstat1: foot movement zstat2: hand zstat3: tongue zstat4: foot_left zstat5: foot_right zstat6: hand_left zstat7: hand_right

###first practice: predicting task activation from RSFC using linear model

codes in linear_model folder

script: midnight_project_resting.ipynb

1. models: LinearRegression, RidgeRegression, Lasso, ElasticNetCV, LinearSVR for each region, the linear models are trained and the best model are chosen based on cross-validation

2. data: dynamic functional connectivity (window_size=10min), motor task (2 runs) for 10 sessions

3. atlas: we used MIST_ROI atlas (210 regions) to extract mean fMRI signal or activation for model training

4. regions: pre-select regions with moderate activity from the activation maps (z-score>1.9); after that, we trained the linear models for approximately 50 regions, independently

5. further considerations: a). combining multi-subject data and using multitask models during training b). statistical test on z-maps first and convert the activation map into binary maps. Thus, we could use classification models instead of regression, which might improve prediction accuracy