Skip to content

srini21/fmri-sim

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

61 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

fmri-sim

Generating a model with simulated fmri and dti data coupled with behavioral features and age , to be used for predicting disease symptoms in the brain.

The Samples are generated in a new directory called 'Samples_fMRI'

TODO : Generate DTI Samples

Writeup

Refer to fmri.pdf in writeup/fmri.pdf

Code


WARNING! Remember to cleanup before generating data to avoid mess.


CLEANUP (only if you have generated the data before)

Cleans up all the generated data.

USAGE sh cleanup.sh

main_script.py

USAGE: python main_script.py num_ppl num_samples noise_strength num_nodes topology sub_nodes behav_ft num_scans phenotypes

num_ppl : No. of persons for whom the data has to be simulated. num_samples : No. of samples of age for every person between the range 7 and 21. behav_ft : No. of behavioral features for every person. noise_strength: Amount of normal noise required to add to the behavioral data time series (use 1) num_nodes : No. of nodes in the brain under question. topology : star/ substar (use substar) sub_node : Size of sub cluster num_scans : No. of scans for every age. phenotypes : No. of observed variables.

generate_abF.py

Generates the age, b0, F for the number of individuals as reqd.

USAGE: python generate_abF.py num_ppl num_samples behav_ft

Current usage: num_ppl =10, num_samples=4, behav_ft=30

generate_b.py

Generates a time series of b based on age and F for every person for the age that has been sampled.

USAGE: python generate_b.py num_ppl behav_ft noise_strength

generate_graph.py

Generates an adjacency matrix 'adj_mat', nodes and edges in the local directory.

USAGE: python generate_graph.py num_nodes topology substar_count ( if single star, 300 star 0 else 300 substar 5)

generate_W.py

Generates Wd and Wf in the local directory.

USAGE: python generate_W.py num_nodes behav_ft

generate_samples.py

generates fMRI and DTI samples

USAGE: python generate_samples.py num_ppl num_nodes num_scans

generate_Z.py

generates Z= Thetaf*alpha where alpha is the predictive weight for all theta(a,b)

USAGE: python generate_Z


Proximal Gradient Descent


Sincere thanks to Eunho Yang and spams toolkit. Computes the weight from the input data using proximal_flat in spams.

Dependency : Python Sparse Modeling Toolkit - SPAMS

present in directory pgd.

USAGE : python pgd/pgd.py (from the curr dir)

generate_Z_est.py

same as generate_Z but generates estimated Zs using the recomputed weight.

w_results.py

USAGE : python w_results.py

performs an analysis of the computed weights.

About

Generating a model with simulated fmri and dti data coupled with behavioral features and age , to be used for predicting disease symptoms in the brain.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published