neurodevelopmental functional network organization across scales

Steps are organized by units of analyses and construct of interest. You can also search by figure number to find specific analyses.

Step 1: Derive Group/Individual Parcels, Derive Parcel Properties, Extract FC

Step 2: Network-level: Age

Step 3: Network-level : Executive Function

Step 4: Network-level : Mediation

Step 5: Vertex-level : Age and Scale

Step 6: Vertex-level : Executive Function

Step 7: Age: Edge-level

Step 8: EF: Edge-level

Step 1: Derive Group/Individual Parcels, Derive Parcel Properties, Extract FC

1A) Derive group parcels:

scripts/derive_parcels/iterate_nmf_overscales.m - parent loop for group/single-subject parcellations over scales

scripts/derive_parcels/Step_2nd_SingleParcellation/Step_1st_CreatePrepData_AP.m - prepare data for pipeline

Step_2nd_ParcellationInitialize_AP.m - group consensus (takes > 6 weeks to run over all scales w/ cubic limitations)

Step_3rd_SelRobustInit_AP.m - prepare individ. data for parcels

Step_4th_IndividualParcel_AP.m - individualization

Step_5th_AtlasInformation_Extract_AP.m - minor reorganization of some output

Step_6th_GroupAtlas_Extract_AP.m - minor reorganization/converting of some output

Step_7th_NetworkNaming_Yeo_AP.m - find predominant yeo7/17 overlap and extent of overlap

1B) Derive Parcel Properties - Figure 1

scripts/derive_netstats/error_over_scales - calculates reconstruction error over scales for each subject. Iterates over script below

scripts/derive_netstats/calc_recon_error.m - analysis for figure S2B

scripts/derive_spatialprops/group_All_Ks_export2R.m - export for figure 1 alluvial

scripts/derive_parcels/Toolbox/PBP/PBP_final/PBP_GroCon.m* - figure 1 cortical maps

1C) Derive Parcel Properties - Figure 2

scripts/derive_parcels/Toolbox/PBP/PBP_final/PBP_SSCon.m* - for Figure 2A

scripts/derive_parcels/Step_2nd_SingleParcellation/Step_9th_1_Visualize_Workbench_AtlasVariability_AP.m - for MAD calculation

scripts/derive_parcels/Toolbox/PBP/PBP_final/PBP_MAD.m* - for Figure 2B

scripts/derive_spatialprops/SpinTest_MAD.m - Spin MAD maps for correlation null distributions

scripts/derive_spatialprops/calc_spinDistribs_MAD_PG.m - Calculate real and permuted MAD-PG1 correlations

scripts/derive_spatialprops/Boot_MADPGCorr_OverScales.R - Generate bootstrap indices for subject resampling (w/ replacement) in R, for use in matlab in next step

scripts/derive_spatialprops/Boot_MADPGCorr.m - Use R-generated Bootstrap indices to recalculate MAD at each scale iteratively, for 2C

scripts/derive_spatialprops/Vertex-level-MAD_PG.md - figures 2A, B, C

1D) Extract FC values from individual parcels and .mgh timeseries

scripts/derive_netstats/iterate_vert_fc.m - script to iterate qsubs over FC matrix derivations from subject time series and individualized parcels

scripts/derive_netstats/subj_vert_fc.m - the individual-level script ran on individual compute nodes compiled (c++) version exists to deal with paucity of stats toolbox licenses available, not reccomended for small runs unless licenses unavailable

scripts/derive_netstats/merge_ind_fc.m - merge individual derivations into cross-subject, cross-scale 3D matrices (matlab struct)

scripts/derive_netstats/fc_to_csv.m - likely the densest script in the entire project. Designed to take in fake data and spit out corresponding FC matrices + summary columns for sanity check.

scripts/derive_netstats/round_master_fcfeats.r - file master fc feats takes almost 10 minutes to load without rounding. no need for the many decimal places defaulted to by matlab.

Step 2: Network-level: Age

2A) Network-level Generalized Additive Models - Figures 3 and 5

All within Network-level-age.md

B/w * Age - for figure 3B

Age Effect * Transmodality - for figure 3C

Age Effect derivative over Age (* Transmodality) - for figure 3D

Scale Effect on Age Effect (* Transmodality) - for figure 5C

2B) Cross-scale averaging & vertex-mapping - Figure 3

All vectors to be visualized (Predicted b.w. at ages 10 and 21, age effect derivatives at 10,16,21) are printed out from Network-level-age.md* above: to aggregate visualize these vectors into a single scale-wise matlab structure, use these matlab scripts:

scripts/viz/overlay_overscales_effects_onto_fsaverage_intercept.m - to overlay estimated b.w. at ages 10 and 21

scripts/viz/overlay_overscales_effects_onto_fsaverageDeriv.m - to overlay age effect derivatives at ages 10, 16 and 21

to plot scale-wise matlab structures, the scripts above will leverage the following script:

scripts/derive_parcels/Toolbox/PBP/PBP_final/PBP_effect_msOverlay_2View_R_lPFC.m*

Step 3: Network-level : Executive Function

3A) Network-level Generalized Additive Models - Figure 6

All within Network-level-ef.md

EF Effect * Transmodality - for figure 6C

Scale Effect on EF Effect (* Transmodality) - for figure 6D

Step 4: Network-level : Mediation

4A) Network-level Generalized Additive Models - Figure 7

All within Network-level-mediation.md

Mediation Weight * Transmodality - for figure 6C

Scale Effect on Mediation Weight (* Transmodality) - for figure 6D

Step 5: Vertex-level : Age and Scale

scripts/derive_GEE_stats/DemoData_to_Matlab.R - Prepare “forMLpc.csv” in R (for matlab)

scripts/derive_netstats/Win_Bw_Age_vertwise.m - save out cross-scale values for each subject for each vertex, bringing matrix dimensionality back down to 2

scp all vertex-level .csv files to pmacs

xbash module load R/3.6.3 - for consistent versioning of mgcv, doBy, geepack, reshape2. Should also take you to a bbl/linc compute node

loop over qsub_vertWise.sh - i.e:

for i in {1..17734}; do bsub ./qsub_vertWise.sh $i; echo $i; done

the command above iterates over scripts/vert_GEE_looper.r

scp all vertex-level GEE stats back out to cubic

scripts/derive_GEE_stats/aggregate_GEE_Effects.m - pull all vertex-level stats into one dataframe

scripts/derive_GEE_stats/FDR_GEEs_pt1.m - print out effects in R-friendly format for FDR correction. This is due to matlab limitation in available stat toolbox licenses. Requires pre-FDR EF to be run with no commenting out

scripts/derive_GEE_stats/FDR_GEEs_1point5.R FDR correct in R

scripts/derive_GEE_stats/FDR_GEEs_pt2 - re-aggregate FDR-corrected vertices - current output filepaths set to results/aggregated_data/*_GEE_FDRed_verts

scripts/derive_parcels/Toolbox/PBP/PBP_final/PBP_vertWiseEffect4View.m* - run with fdr-corrected vertices for final brainmaps

Step 6: Vertex-level : Executive Function

Files should all already be on pmacs from step 5.

xbash module load R/3.6.3 - for consistent versioning of mgcv, doBy, geepack, reshape2. Should also take you to a bbl/linc compute node

loop over qsub_vertWise_EF.sh - i.e:

for i in {1..17734}; do bsub ./qsub_vertWise_EF.sh $i; echo $i; done

the command above iterates over scripts/vert_GEE_looper_EF.r

scp all vertex-level GEE stats back out to cubic

scripts/derive_GEE_stats/aggregate_GEE_Effects_EF.m - pull all vertex-level stats into one dataframe

scripts/derive_GEE_stats/FDR_GEEs_pt1.m - print out effects in R-friendly format for FDR correction. This is due to matlab limitation in available stat toolbox licenses.

scripts/derive_GEE_stats/FDR_GEEs_1point5.R FDR correct in R

scripts/derive_GEE_stats/FDR_GEEs_pt2 - re-aggregate FDR-corrected vertices - current output filepaths set to results/aggregated_data/*_GEE_FDRed_verts

scripts/derive_parcels/Toolbox/PBP/PBP_final/PBP_vertWiseEffect4View.m* - run with fdr-corrected vertices for final brainmaps

Step 7: Age: Edge-level

7A) Edge-level Generalized Additive Models - Figure 4

All within Edge-level-age.md

Step 8: EF: Edge-level

8A and 8C) Pre and post-ridge - Figure 6

within Edge-level-EF.md

8B) Penalized regression portion

within penal_regresFC_AgeEFIndep.py (Python environment: source activate mv_preds)

* scripts not linked are intentionally hidden by .gitignore. Largely code written by others.

multiscale

neurodevelopmental functional net. organization across scales

neurodevelopmental functional network organization across scales

Step 1: Derive Group/Individual Parcels, Derive Parcel Properties, Extract FC

1A) Derive group parcels:

scripts/derive_parcels/iterate_nmf_overscales.m - parent loop for group/single-subject parcellations over scales

scripts/derive_parcels/Step_2nd_SingleParcellation/Step_1st_CreatePrepData_AP.m - prepare data for pipeline

Step_2nd_ParcellationInitialize_AP.m - group consensus (takes > 6 weeks to run over all scales w/ cubic limitations)

Step_3rd_SelRobustInit_AP.m - prepare individ. data for parcels

Step_4th_IndividualParcel_AP.m - individualization

Step_5th_AtlasInformation_Extract_AP.m - minor reorganization of some output

Step_6th_GroupAtlas_Extract_AP.m - minor reorganization/converting of some output

Step_7th_NetworkNaming_Yeo_AP.m - find predominant yeo7/17 overlap and extent of overlap

1B) Derive Parcel Properties - Figure 1

scripts/derive_netstats/error_over_scales - calculates reconstruction error over scales for each subject. Iterates over script below

scripts/derive_netstats/calc_recon_error.m - analysis for figure S2B

scripts/derive_spatialprops/group_All_Ks_export2R.m - export for figure 1 alluvial

scripts/derive_parcels/Toolbox/PBP/PBP_final/PBP_GroCon.m* - figure 1 cortical maps

1C) Derive Parcel Properties - Figure 2

scripts/derive_parcels/Toolbox/PBP/PBP_final/PBP_SSCon.m* - for Figure 2A

scripts/derive_parcels/Step_2nd_SingleParcellation/Step_9th_1_Visualize_Workbench_AtlasVariability_AP.m - for MAD calculation

scripts/derive_parcels/Toolbox/PBP/PBP_final/PBP_MAD.m* - for Figure 2B

scripts/derive_spatialprops/SpinTest_MAD.m - Spin MAD maps for correlation null distributions

scripts/derive_spatialprops/calc_spinDistribs_MAD_PG.m - Calculate real and permuted MAD-PG1 correlations

scripts/derive_spatialprops/Boot_MADPGCorr_OverScales.R - Generate bootstrap indices for subject resampling (w/ replacement) in R, for use in matlab in next step

scripts/derive_spatialprops/Boot_MADPGCorr.m - Use R-generated Bootstrap indices to recalculate MAD at each scale iteratively, for 2C

scripts/derive_spatialprops/Vertex-level-MAD_PG.md - figures 2A, B, C

1D) Extract FC values from individual parcels and .mgh timeseries

scripts/derive_netstats/iterate_vert_fc.m - script to iterate qsubs over FC matrix derivations from subject time series and individualized parcels

scripts/derive_netstats/subj_vert_fc.m - the individual-level script ran on individual compute nodes compiled (c++) version exists to deal with paucity of stats toolbox licenses available, not reccomended for small runs unless licenses unavailable

scripts/derive_netstats/merge_ind_fc.m - merge individual derivations into cross-subject, cross-scale 3D matrices (matlab struct)

scripts/derive_netstats/fc_to_csv.m - likely the densest script in the entire project. Designed to take in fake data and spit out corresponding FC matrices + summary columns for sanity check.

scripts/derive_netstats/round_master_fcfeats.r - file master fc feats takes almost 10 minutes to load without rounding. no need for the many decimal places defaulted to by matlab.

Step 2: Network-level: Age

2A) Network-level Generalized Additive Models - Figures 3 and 5

B/w * Age - for figure 3B

Age Effect * Transmodality - for figure 3C

Age Effect derivative over Age (* Transmodality) - for figure 3D

Scale Effect on Age Effect (* Transmodality) - for figure 5C

2B) Cross-scale averaging & vertex-mapping - Figure 3

*All vectors to be visualized (Predicted b.w. at ages 10 and 21, age effect derivatives at 10,16,21) are printed out from Network-level-age.md above: to aggregate visualize these vectors into a single scale-wise matlab structure, use these matlab scripts:

scripts/viz/overlay_overscales_effects_onto_fsaverage_intercept.m - to overlay estimated b.w. at ages 10 and 21

scripts/viz/overlay_overscales_effects_onto_fsaverageDeriv.m - to overlay age effect derivatives at ages 10, 16 and 21

to plot scale-wise matlab structures, the scripts above will leverage the following script:

scripts/derive_parcels/Toolbox/PBP/PBP_final/PBP_effect_msOverlay_2View_R_lPFC.m*

Step 3: Network-level : Executive Function

3A) Network-level Generalized Additive Models - Figure 6

EF Effect * Transmodality - for figure 6C

Scale Effect on EF Effect (* Transmodality) - for figure 6D

Step 4: Network-level : Mediation

4A) Network-level Generalized Additive Models - Figure 7

Mediation Weight * Transmodality - for figure 6C

Scale Effect on Mediation Weight (* Transmodality) - for figure 6D

Step 5: Vertex-level : Age and Scale

scripts/derive_GEE_stats/DemoData_to_Matlab.R - Prepare “forMLpc.csv” in R (for matlab)

scripts/derive_netstats/Win_Bw_Age_vertwise.m - save out cross-scale values for each subject for each vertex, bringing matrix dimensionality back down to 2

scp all vertex-level .csv files to pmacs

xbash module load R/3.6.3 - for consistent versioning of mgcv, doBy, geepack, reshape2. Should also take you to a bbl/linc compute node

loop over qsub_vertWise.sh - i.e:

the command above iterates over scripts/vert_GEE_looper.r

scp all vertex-level GEE stats back out to cubic

scripts/derive_GEE_stats/aggregate_GEE_Effects.m - pull all vertex-level stats into one dataframe

scripts/derive_GEE_stats/FDR_GEEs_pt1.m - print out effects in R-friendly format for FDR correction. This is due to matlab limitation in available stat toolbox licenses. Requires pre-FDR EF to be run with no commenting out

scripts/derive_GEE_stats/FDR_GEEs_1point5.R FDR correct in R

scripts/derive_GEE_stats/FDR_GEEs_pt2 - re-aggregate FDR-corrected vertices - current output filepaths set to results/aggregated_data/*_GEE_FDRed_verts

scripts/derive_parcels/Toolbox/PBP/PBP_final/PBP_vertWiseEffect4View.m* - run with fdr-corrected vertices for final brainmaps

Step 6: Vertex-level : Executive Function

Files should all already be on pmacs from step 5.

xbash module load R/3.6.3 - for consistent versioning of mgcv, doBy, geepack, reshape2. Should also take you to a bbl/linc compute node

loop over qsub_vertWise_EF.sh - i.e:

the command above iterates over scripts/vert_GEE_looper_EF.r

scp all vertex-level GEE stats back out to cubic

scripts/derive_GEE_stats/aggregate_GEE_Effects_EF.m - pull all vertex-level stats into one dataframe

scripts/derive_GEE_stats/FDR_GEEs_pt1.m - print out effects in R-friendly format for FDR correction. This is due to matlab limitation in available stat toolbox licenses.

scripts/derive_GEE_stats/FDR_GEEs_1point5.R FDR correct in R

scripts/derive_GEE_stats/FDR_GEEs_pt2 - re-aggregate FDR-corrected vertices - current output filepaths set to results/aggregated_data/*_GEE_FDRed_verts

scripts/derive_parcels/Toolbox/PBP/PBP_final/PBP_vertWiseEffect4View.m* - run with fdr-corrected vertices for final brainmaps

Step 7: Age: Edge-level

7A) Edge-level Generalized Additive Models - Figure 4

Step 8: EF: Edge-level

All vectors to be visualized (Predicted b.w. at ages 10 and 21, age effect derivatives at 10,16,21) are printed out from Network-level-age.md* above: to aggregate visualize these vectors into a single scale-wise matlab structure, use these matlab scripts: