NMF_neurodevelopment

analysis pipeline for non-negative matrix factorization on the Philadelphia Neurodevelopmental Cohort

install libraries

install nimfa, scipy, and numpy for python: pip install nimfa

place PNC data in the pnc_data directory, data available through NIH dbGaP

n780_censor.mat
n780_despike.mat
ts_36_ev.mat
age.mat
cognDataCollection.mat
coordinatesXYZ.mat
mvmt.mat
neuralSystem.xlsx
sex.mat

generate old and young subject groups in subject_indicies directory

we used the 100 oldest and 100 youngest subjects for our analyses

build association matrices using wavelet coherence

wavelet toolbox used for generated wavelet coherence: http://www.glaciology.net/wavelet-coherence
this toolbox is included in the matlab_functions directory
navigate to ts36_wavelets_highpass directory
run wavelet_coherence.m file in matlab
it may be useful to split this process into several batch processes
repeat these stets in the ts36_wavelets_censor directory

parameter grid search

in the parameter_sweep directory run python parameter_sweep.py
this saves the result in a .mat file in that directory
the grid search will take a few days to complete running

pre-nmf processing and running nmf

in the ts36_wavelets_highpass directory run python highpass_nmf.py
this will concatenate the subject matrices and regress out motion, then run nmf
you can also run python highpass_nmf_supplement.py in the same directory for additional processing pipelines
in the ts36_wavelets_censor directory run python censor_nmf.py

in nmf_master.py running each pipeline will generate figures
code to generate figures are located in the figures directory. BrainNetViewer was used to visualize the nodes