zhouAslNetwork

Pre: Simple outlier script: asl_outliers.R relMeanRMSMotion, negativeVoxels, normCoverage 2 SD: 129154 127417 110168 106880 114709 125554 126903 3SD: 110168 106880 114709 125554 126903

coregCrossCorr, coregJaccard, coregDice, coregCoverage 2 SD: 125554, 127305, 106880, 127417

ASL networks

1. Export xcp and R library paths

export XCPEDIR=/home/rciric/xcpAccelerator/xcpEngine
export R_LIBS_USER=$R_LIBS_USER:/data/jux/BBL/applications-from-joy/Rlibraries/3.2:/data/jux/BBL/applications-from-joy/Rlibraries
export R_LIBS=$R_LIBS:/data/jux/BBL/applications-from-joy/Rlibraries/3.2:/data/jux/BBL/applications-from-joy/Rlibraries

2. Run design_offlineRecon_9P.dsn through xcp pipeline with n48 offline (minus 3 outliers) and n18 online subjects. Check that mean perfusion and perfusion images exist in /cbf directory

${XCPEDIR}/xcpEngine -d /data/jux/BBL/projects/ASLnetwork/design_offlineRecon_9P.dsn -i /tmp/aslNets -c /data/jux/BBL/projects/ASLnetwork/n48_offlineRecon_aslCohort.csv -o /data/jux/BBL/projects/ASLnetwork -m c -t 2
${XCPEDIR}/xcpEngine -d /data/jux/BBL/projects/ASLnetwork/design_onlineRecon_9P.dsn -i /tmp/aslNets -c /data/jux/BBL/projects/ASLnetwork/n17_onlineRecon_aslCohort1.csv -o /data/jux/BBL/projects/ASLnetwork -m c -t 2

3. Collect all fcon connectivity matrix files and average them

ls */*/fcon/power264/*_network.txt > network_list.txt
awk '{a[FNR]+=$1;b[FNR]++;}END{for(i=1;i<=FNR;i++)print a[i]/b[i];}' `cat network_list.txt` > average_power264_network.txt

4. Generate 14x14 Power node network and compute within-between correlations _wbNetbyNet.csv is 14x14 adjacency matrix _wbNetWB.csv is within-between correlations

Rscript /home/rciric/xcpAccelerator/xcpEngine/utils/withinBetween.R -m /data/jux/BBL/projects/ASLnetwork/average_power264_network.txt -c /home/rciric/xcpAccelerator/xcpEngine/atlas/power264/power264CommunityAffiliation.1D -o /data/jux/BBL/projects/ASLnetwork/

rs-BOLD networks (documentation in progress)

1. Run design (uses an older version of xcp and R)

export XCPEDIR=/data/jux/BBL/applications-from-joy/xcpEngine
export RPATH=/share/apps/R/R-3.1.1/bin/R
${XCPEDIR}/xcpEngine -d /data/joy/BBL/projects/zhouCbfNetworks/zhouCbfNetworksScripts/n101_restbold_20180117_prelim.dsn -m c

Inspecting ASL, BOLD, FA, ODI, and ICVF (documentation in progress)

1. Generate lists of all subjects data in each modality and keep only subject ID (Bash commands not shown in code below)

BOLD

cd /data/joy/BBL/projects/zhouCbfNetworks/data/boldNetwork
ls */*/net/SchaeferPNC_200/*network.txt > procBold.txt

CBF

cd /data/joy/BBL/projects/zhouCbfNetworks/data/boldNetwork/cbfProc
ls */*/fcon/schaefer200/*network.txt > procCbf.txt

NODDI

cd /data/jux/BBL/projects/multishell_diffusion/processedData/multishellPipelineFall2017
ls */*/tractography/*_matrixts.csv > procNoddi.txt

2. Find common subjects among modalities (ceiling will be will be number of NODDI subjects). Relevant scripts:

cd /data/joy/BBL/projects/zhouCbfNetworks/zhouCbfNetworksScripts/findCommonSubjects.sh

3. Use selectSubjects.sh to write the subjects to include in analysis

/data/joy/BBL/projects/zhouCbfNetworks/zhouCbfNetworksScripts/selectSubjects.sh

Run within- and between- modules analysis across edges (documentation in progress)

1. Run within_between_coupling.m Matlab script

cd /data/joy/BBL/projects/zhouCbfNetworks/zhouCbfNetworksScripts/within_between_coupling.m

2. Visualize within-module coupling as violin plots via violin_plot.R

/data/joy/BBL/projects/zhouCbfNetworks/zhouCbfNetworksScripts/violin_plot.R