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Tried to improved formatting in README.md (not yet fully done)
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‎README.md‎

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Baranger, D.A.A., Halchenko, Y.O., Satz, S., Ragozzino, R., Iyengar, S., Swartz, H.A., Manelis, A. Neuroimage: Clinical (2021).
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Aberrant levels of cortical myelin distinguish individuals with depressive disorders from healthy controls.
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#============= FOLDER STRUCTURE ============= #
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# FOLDER STRUCTURE
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MethodX_data/:
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data
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--mri_derivatives
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-----primary/ses-01 ** surface-level T1w/T2w ratio and cortical thickness images for subjects in primary analyses
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-----followup ** surface-level T1w/T2w ratio and cortical thickness images for the subject with follow-up data who converted
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-------ses-01 ** follow-up subject session 1
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-------ses-02 ** follow-up subject session 2
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--other_input ** clinical, demographic, and parcellated T1w/T2w ratio data
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outputs
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--cvs ** output of posthoc analyses varying the number of cross-validation folds
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----loocv_inneronly ** output of posthoc analyses varying the number of inner cross-validation folds
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--glmnet ** output of primary glmnet analysis
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--permutations ** output of permutation analyses
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--preprocessing ** output of preprocessing outlier detection
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--regressions ** output of regression analyses
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scripts
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--analyses ** scripts for primary analyses, including glmnet, permutations, and regressions
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--figures ** scripts to create figures in the paper
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--followup ** scripts for post hoc analyses
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--preprocessing ** scripts for parcellating mri derivative files and outlier detection
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#============= FOLDER CONTENTS ============= #
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MethodX_data/data/mri_derivatives/primary/ses-01:
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sub-******.L.midthickness.32k_fs_LR.surf.gii ** left cortical thickness file
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sub-******.R.midthickness.32k_fs_LR.surf.gii ** right cortical thickness file
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sub-******.SmoothedMyelinMap_BC.32k_fs_LR.dscalar.nii ** cortical myelin cifti file
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MethodX_data/data/other_input:
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converted_participant_parcels_bothsessions.xlsx ** Cortical myelin values for the follow-up subject who converted
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data_360parcels_Glasser32K.csv ** Cortical myelin for 360 Glasser parcels, output of scripts/preprocessing/parcellate.R
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data_clinical_and_parcels_all.csv ** Participant demographics, clinical variables, and cortical myelin values
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data_dictionary.csv ** Description of columns in data_clinical_and_parcels_all.csv
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ElasticNet_variables.csv ** All variables used for elastic net analyses
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glmnet_performance.csv ** Performance metrics for glmnet/LDA classifier
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MethodX_data/outputs/cvs:
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glmnet_leave-one-out_nested_1uniquepair_removed_HC_UD_[x]_outer_[y]_internalfolds_2021-07-02.txt ** output of follow up analyses, varying both the internal [y] and outer [x] cv folds
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MethodX_data/outputs/cvs/loocv_inneronly:
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glmnet_leave-one-out_nested_1uniquepair_removed_HC_UD_[i]_internalfolds_2021-07-02.txt ** output of follow up analyses, varying the number of inner cv folds [i] (retaining 2 pairs held-out)
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MethodX_data/outputs/glmnet:
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glmnet_variable_selection.csv ** frequency of variable selection in true and permutation analyses
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glmnet_with_age_sex_iq_leave-one-out_nested_1uniquepair_removed_HC_UD_2020-11-26.txt ** main results, output of scripts/analyses/glmnet_with_LDA_myelin_paper.R
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predict.followup.txt ** predicted class for followup participant who converted mid-study
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MethodX_data/outputs/permutations:
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split[i]_glmnet_permuted_labels_10times_leave-one-out_nested_with_age_sex_iq_1uniquepair_removed_HC_UD_2021-02-22 ** Outputs of permutation analyses (100 permutations per file)
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split[i]_glmnet_permuted_sets_10times_leave-one-out_nested_with_age_sex_iq_1uniquepair_removed_HC_UD_2021-02-22 ** Outputs of permutation analyses (100 permutations per file) - record of all permutation combinations
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glmnet_permuted_labels_100times_for_leave-one-out_nested_with_age_sex_iq_1uniquepair_removed_HC_UD_2021-02-22.txt ** Combined all label files
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glmnet_permuted_sets_100times_for_leave-one-out_nested_with_age_sex_iq_1uniquepair_removed_HC_UD_2021-02-22.txt ** Combined all set files
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MethodX_data/outputs/preprocessing:
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outlier.results.csv ** results of parcel outlier detection
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MethodX_data/outputs/regressions:
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regression_dd_control_myelin.csv ** results of regression analyses between control/dd and myelin
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regression_demo_clin_myelin.csv ** results of regression analyses between clinical/demographic variables and myelin
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regression_performance_myelin.csv ** results of regression analyses between lda accuracy and myelin
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MethodX_data/scripts/analyses:
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glmnet_with_LDA_myelin_paper.R ** Nested cross-validation elastic net regression with LDA (primary analysis)
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permuted_glmnet_with_LDA_myelin_paper.R ** Permutation analyses
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process_glmnet_output.R ** Compute performance metrics of the primary analysis
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regression_lda_performance_and_clinical.R ** Regression analyses between model accuracy and clinical variables
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regression_myelin_and_clinical.R ** Regression analyses between cortical myelin and clinical variables
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regression_myelin_patients_vs_controls.R ** regression analyses comparing myelin values in patients and controls
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MethodX_data/scripts/figures:
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brain_plot.R ** Code for Figure 3
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performance_plots.R ** Code for Figure 2
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plot_antidepressants.R ** Code for Supplemental Figure 3
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MethodX_data/scripts/followup:
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glmnet_with_LDA_myelin_paper_cvs_outerloop.R ** repeating the glmnet analyses, varying both the number of inner and outer cv folds
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glmnet_with_LDA_myelin_paper_cvs.R ** repeating the glmnet analyses, varying the number of inner cv folds
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Predict_converted.R ** predict the group (control, DD) of the participant who converted
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process_cvs_parcels.R ** process the output of glmnet_with_LDA_myelin_paper_cvs.R & glmnet_with_LDA_myelin_paper_cvs_outerloop.R
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MethodX_data/scripts/preprocessing:
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outlier_regions.R ** Detects outlier parcels
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parcellate.R ** Parcellate mri derivative files to compute mean T1w/T2w ratio value for each parcel
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#============= Other needed files ============= #
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data/
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mri_derivatives/
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primary/ses-01 ** surface-level T1w/T2w ratio and cortical thickness images for subjects in primary analyses
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followup/ ** surface-level T1w/T2w ratio and cortical thickness images for the subject with follow-up data who converted
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ses-01/ ** follow-up subject session 1
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ses-02/ ** follow-up subject session 2
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other_input/ ** clinical, demographic, and parcellated T1w/T2w ratio data
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outputs/
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cvs/ ** output of posthoc analyses varying the number of cross-validation folds
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loocv_inneronly ** output of posthoc analyses varying the number of inner cross-validation folds
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glmnet/ ** output of primary glmnet analysis
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permutations/ ** output of permutation analyses
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preprocessing/ ** output of preprocessing outlier detection
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regressions/ ** output of regression analyses
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scripts/
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analyses/ ** scripts for primary analyses, including glmnet, permutations, and regressions
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figures/ ** scripts to create figures in the paper
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followup/ ** scripts for post hoc analyses
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preprocessing/ ** scripts for parcellating mri derivative files and outlier detection
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# FOLDER CONTENTS
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MethodX_data/data/mri_derivatives/primary/ses-01/sub-*/
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sub-*.L.midthickness.32k_fs_LR.surf.gii ** left cortical thickness file
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sub-*.R.midthickness.32k_fs_LR.surf.gii ** right cortical thickness file
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sub-*.SmoothedMyelinMap_BC.32k_fs_LR.dscalar.nii ** cortical myelin cifti file
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MethodX_data/data/other_input/
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converted_participant_parcels_bothsessions.xlsx ** Cortical myelin values for the follow-up subject who converted
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data_360parcels_Glasser32K.csv ** Cortical myelin for 360 Glasser parcels, output of scripts/preprocessing/parcellate.R
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data_clinical_and_parcels_all.csv ** Participant demographics, clinical variables, and cortical myelin values
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data_dictionary.csv ** Description of columns in data_clinical_and_parcels_all.csv
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ElasticNet_variables.csv ** All variables used for elastic net analyses
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glmnet_performance.csv ** Performance metrics for glmnet/LDA classifier
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MethodX_data/outputs/cvs/
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glmnet_leave-one-out_nested_1uniquepair_removed_HC_UD_[x]_outer_[y]_internalfolds_2021-07-02.txt
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** output of follow up analyses, varying both the internal [y] and outer [x] cv folds
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MethodX_data/outputs/cvs/loocv_inneronly/
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glmnet_leave-one-out_nested_1uniquepair_removed_HC_UD_[i]_internalfolds_2021-07-02.txt
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** output of follow up analyses, varying the number of inner cv folds [i] (retaining 2 pairs held-out)
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MethodX_data/outputs/glmnet/
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glmnet_variable_selection.csv ** frequency of variable selection in true and permutation analyses
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glmnet_with_age_sex_iq_leave-one-out_nested_1uniquepair_removed_HC_UD_2020-11-26.txt ** main results, output of scripts/analyses/glmnet_with_LDA_myelin_paper.R
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predict.followup.txt ** predicted class for followup participant who converted mid-study
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MethodX_data/outputs/permutations/
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split[i]_glmnet_permuted_labels_10times_leave-one-out_nested_with_age_sex_iq_1uniquepair_removed_HC_UD_2021-02-22 ** Outputs of permutation analyses (100 permutations per file)
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split[i]_glmnet_permuted_sets_10times_leave-one-out_nested_with_age_sex_iq_1uniquepair_removed_HC_UD_2021-02-22 ** Outputs of permutation analyses (100 permutations per file) - record of all permutation combinations
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glmnet_permuted_labels_100times_for_leave-one-out_nested_with_age_sex_iq_1uniquepair_removed_HC_UD_2021-02-22.txt ** Combined all label files
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glmnet_permuted_sets_100times_for_leave-one-out_nested_with_age_sex_iq_1uniquepair_removed_HC_UD_2021-02-22.txt ** Combined all set files
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MethodX_data/outputs/preprocessing/
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outlier.results.csv ** results of parcel outlier detection
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MethodX_data/outputs/regressions/
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regression_dd_control_myelin.csv ** results of regression analyses between control/dd and myelin
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regression_demo_clin_myelin.csv ** results of regression analyses between clinical/demographic variables and myelin
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regression_performance_myelin.csv ** results of regression analyses between lda accuracy and myelin
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MethodX_data/scripts/analyses/
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glmnet_with_LDA_myelin_paper.R ** Nested cross-validation elastic net regression with LDA (primary analysis)
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permuted_glmnet_with_LDA_myelin_paper.R ** Permutation analyses
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process_glmnet_output.R ** Compute performance metrics of the primary analysis
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regression_lda_performance_and_clinical.R ** Regression analyses between model accuracy and clinical variables
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regression_myelin_and_clinical.R ** Regression analyses between cortical myelin and clinical variables
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regression_myelin_patients_vs_controls.R ** regression analyses comparing myelin values in patients and controls
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MethodX_data/scripts/figures/
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brain_plot.R ** Code for Figure 3
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performance_plots.R ** Code for Figure 2
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plot_antidepressants.R ** Code for Supplemental Figure 3
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MethodX_data/scripts/followup/
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glmnet_with_LDA_myelin_paper_cvs_outerloop.R ** repeating the glmnet analyses, varying both the number of inner and outer cv folds
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glmnet_with_LDA_myelin_paper_cvs.R ** repeating the glmnet analyses, varying the number of inner cv folds
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Predict_converted.R ** predict the group (control, DD) of the participant who converted
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process_cvs_parcels.R ** process the output of glmnet_with_LDA_myelin_paper_cvs.R & glmnet_with_LDA_myelin_paper_cvs_outerloop.R
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MethodX_data/scripts/preprocessing/
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outlier_regions.R ** Detects outlier parcels
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parcellate.R ** Parcellate mri derivative files to compute mean T1w/T2w ratio value for each parcel
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# Other needed files
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https://balsa.wustl.edu/88mp ** Glasser 360 parcellation atlas
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