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High-resolution electrophysiological mapping of effective connectivity of lateral prefrontal cortex.

Code ↔ Paper

9 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 9 matches
  1. [1] § Materials and methods › SEEG data ↔ compute_atlas_mats.py, lines 42–81 · score 0.70 · 0–100 ms, excluding LPFC, left LPFC, LPFC parcels, ipsilateral, lh
  2. [2] § Materials and methods › LPFC definition and parcellation ↔ compute_atlas_mats.py, lines 42–81 · score 0.69 · excluding LPFC, right LPFC, left LPFC, LPFC parcels, atlas, parcellations
  3. [3] § Materials and methods › SEEG data ↔ figures.py, lines 126–166 · score 0.67 · excluding LPFC, right hemisphere, Afferent connections, LPFC parcels, Efferent connections, ipsilateral
  4. [4] § Materials and methods › LPFC definition and parcellation ↔ figures.py, lines 70–124 · score 0.64 · Lausanne parcellation resolutions, LPFC definition, contacts, brain, stimulation, IFG
  5. [5] § Materials and methods › Statistical analysis ↔ tools/stats.py, lines 75–91 · score 0.62 · Agresti Coull, confidence intervals
  6. [6] § Materials and methods › LPFC definition and parcellation ↔ compute_atlas_mats.py, lines 83–125 · score 0.55 · Lausanne parcellation resolutions, coverage, definition, brain, stimulation, IFG
  7. [7] § Materials and methods › LPFC definition and parcellation ↔ compute_atlas/compute_atlas_brain_avg.py, lines 15–155 · score 0.54 · excluding LPFC, LPFC parcels, atlas, parcellations, Lausanne2008, definition
  8. [8] § Results › Connectivity from LPFC to brain functional networks ↔ Definitions.py, lines 308–384 · score 0.53 · fronto parietal, functional networks, limbic, rostral, posterior, frontal
  9. [9] § Materials and methods › SEEG data ↔ compute_atlas/compute_atlas_LPFC_Eff_Aff.py, lines 18–68 · score 0.51 · age, centres, pulses, intensity, aggregated, contact

Paper

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The authors' code

Python · 340 lines · 25 KB · no license · 3 matches

  1. #Statistic correction and shaping of atlas matrices.
  2. #New version of 'compute_and_plot' independent of plotting.
  3. import os
  4. from numpy.core.defchararray import startswith
  5. import matrices
  6. import numpy as np
  7. import Definitions
  8. from parcellation2.parcellation2 import Parcellation2
  10. # %% DEFINITIONS - X rows ; Y columns of the matrix
  11. L_s = Definitions.L_s
  12. res33 = Definitions.res33
  13. res125 = Definitions.res125
  14. res250 = Definitions.res250 # FN analysis
  15. res500 = Definitions.res500
  16. # Parcellation labels
  17. labels_33 = Definitions.labels_33
  18. labels_125 = Definitions.labels_125
  19. labels_250 = Definitions.labels_250
  20. labels_500 = Definitions.labels_500
  21. # For plotting
  22. labels_path = Definitions.labels_path
  23. labels_path_33 = Definitions.labels_path_33
  24. labels_path_125 = Definitions.labels_path_125
  25. labels_path_250 = Definitions.labels_path_250
  26. labels_path_500 = Definitions.labels_path_500
  28. # Selection of labels in use
  29. # Labels 33 corrected in Definitions. If problems with no labels found or only subcortical you might be using a version with 'ctx-lh-' ...
  30. labels_rec_33 = [name for name in labels_33 if name.startswith("lh.") or name.startswith("rh.") or name == 'Left-Hippocampus' or name == 'Left-Amygdala' or name == 'Right-Hippocampus' or name == 'Right-Amygdala']
  31. p = Parcellation2(Definitions.parcellation_path, res33)
  32. index_33 = [p.get_index_by_name(i) for i in labels_33]
  33. index_rec_33 = np.transpose([p.get_index_by_name(i) for i in labels_rec_33])
  34. # index all (other parcellations with specific selections or none)
  35. p = Parcellation2(Definitions.parcellation_path, res125)
  36. index_125 = [p.get_index_by_name(i) for i in labels_125]
  37. p = Parcellation2(Definitions.parcellation_path, res250)
  38. index_250 = [p.get_index_by_name(i) for i in labels_250]
  39. p = Parcellation2(Definitions.parcellation_path, res500)
  40. index_500 = [p.get_index_by_name(i) for i in labels_500]
  42. def compute_mats (base_path, output_folder, collapse_conf_name ,N_thresh, N_imp_thresh, gral_tw = [0,100], dir_tw = [0,50], ind_tw = [100,400], min_cb_dir = 20, max_cb_dir= 35, min_cb_ind = 150, max_cb_ind = 200, max_vb_gral = 80, max_avg = 0.175, min_avg = 0.1, max_cb_p = 0.25, max_p_gral = 0.5, max_cb_N = 10000, min_cb_N = 50) :
  43. #, max_ci = 0.1, n_imp = 3, min_n_suc = 50, min_n_fail = 50, min_n_feat = 50
  44. # %% AVERAGE CONNECTIVITY :
  45. # LPFC to AVG BRAIN (FIG 2B)
  46. # AVG LPFC- AVG BRAIN (not ploted)
  47. # Using square matrix of LPFC parcellation (X-X). All LPFC merged as a block toward all the rest of the ipsilateral brain (excluding LPFC).
  48. # Analysis to prove general efferent-afferent values of the ROI as one. AVG done in compute_atlas_brain_avg
  49. path_output_avg_all = os.path.join(output_folder, 'AVG_LPFC_AVG_all');os.makedirs(path_output_avg_all, exist_ok=True)
  51. # Left LPFC
  52. path_mat_avg_lpfc_avg_all_L_eff = os.path.join(base_path, 'lh_AVG_LPFC_AVG_all_eff', collapse_conf_name[0])
  53. path_mat_avg_lpfc_avg_all_L_aff = os.path.join(base_path, 'lh_AVG_LPFC_AVG_all_aff', collapse_conf_name[0])
  54. mat_all_avg_eff, N_all_avg_eff , I_all_avg_eff = matrices.atlas_mat(path_mat_avg_lpfc_avg_all_L_eff, [0,], [0,],path_output_avg_all, 'avg_lLPFC_brain_all_eff', flag_delays=False, N_thresh=N_thresh, N_imp_thresh=N_imp_thresh)
  55. mat_all_avg_aff , N_all_avg_aff, I_all_avg_aff = matrices.atlas_mat(path_mat_avg_lpfc_avg_all_L_aff, [0,], [0,],path_output_avg_all, 'avg_lLPFC_brain_all_aff', flag_delays=False, N_thresh=N_thresh, N_imp_thresh=N_imp_thresh)
  57. # Right LPFC
  58. path_mat_avg_lpfc_avg_all_R_eff = os.path.join(base_path, 'rh_AVG_LPFC_AVG_all_eff', collapse_conf_name[0])
  59. path_mat_avg_lpfc_avg_all_R_aff = os.path.join(base_path, 'rh_AVG_LPFC_AVG_all_aff', collapse_conf_name[0])
  60. mat_all_avg_eff, N_all_avg_eff , I_all_avg_eff = matrices.atlas_mat(path_mat_avg_lpfc_avg_all_R_eff, [0,], [0,],path_output_avg_all, 'avg_rLPFC_brain_all_eff', flag_delays=False, N_thresh=N_thresh, N_imp_thresh=N_imp_thresh)
  61. mat_all_avg_aff , N_all_avg_aff, I_all_avg_aff = matrices.atlas_mat(path_mat_avg_lpfc_avg_all_R_aff, [0,], [0,],path_output_avg_all, 'avg_rLPFC_brain_all_aff', flag_delays=False, N_thresh=N_thresh, N_imp_thresh=N_imp_thresh)
  63. # AVG LPFC parcels (Lausanne125) - BRAIN (FIG 2B)
  64. # ROI (LPFC) parcelled according to Lausanne2008-X towards the rest of the ipsilateral brain (excluding the ROI) merged as one.
  65. # Use mat xx, with x parcellatioin of roi (for the exclusion of the roi to be possible)
  66. path_output_avg = os.path.join(output_folder, 'AVG');os.makedirs(path_output_avg, exist_ok=True)
  68. # Left LPFC - all
  69. path_mat_avg_lpfc_avg_all_L_eff = os.path.join(base_path, 'lh_AVG_eff', collapse_conf_name[0])
  70. path_mat_avg_lpfc_avg_all_L_aff = os.path.join(base_path, 'lh_AVG_aff', collapse_conf_name[0])
  71. l_LPFC = [l for l in Definitions.roi_dlpfc_ifg_125 if l.startswith('lh')]
  73. mat_all_avg_eff, N_all_avg_eff , I_all_avg_eff = matrices.atlas_mat(path_mat_avg_lpfc_avg_all_L_eff, np.arange(0,len(l_LPFC)), [0,],path_output_avg, 'lLPFC_brain_all_eff', flag_delays=False, N_thresh=N_thresh, N_imp_thresh=N_imp_thresh)
  74. mat_all_avg_aff , N_all_avg_aff, I_all_avg_aff = matrices.atlas_mat(path_mat_avg_lpfc_avg_all_L_aff, [0,], np.arange(0,len(l_LPFC)), path_output_avg, 'lLPFC_brain_all_aff', flag_delays=False, N_thresh=N_thresh, N_imp_thresh=N_imp_thresh)
  76. # Right LPFC
  77. path_mat_avg_lpfc_avg_all_R_eff = os.path.join(base_path, 'rh_AVG_eff', collapse_conf_name[0])
  78. path_mat_avg_lpfc_avg_all_R_aff = os.path.join(base_path, 'rh_AVG_aff', collapse_conf_name[0])
  79. r_LPFC = [l for l in Definitions.roi_dlpfc_ifg_125 if l.startswith('rh')]
  80. mat_all_avg_eff, N_all_avg_eff , I_all_avg_eff = matrices.atlas_mat(path_mat_avg_lpfc_avg_all_R_eff, np.arange(0,len(r_LPFC)), [0,],path_output_avg, 'rLPFC_brain_all_eff', flag_delays=False, N_thresh=N_thresh, N_imp_thresh=N_imp_thresh)
  81. mat_all_avg_aff , N_all_avg_aff, I_all_avg_aff = matrices.atlas_mat(path_mat_avg_lpfc_avg_all_R_aff, [0,], np.arange(0,len(r_LPFC)), path_output_avg, 'rLPFC_brain_all_aff', flag_delays=False, N_thresh=N_thresh, N_imp_thresh=N_imp_thresh)
  83. #%% PARCELLATION RESOLUTIONS (FIG1D-E-F)
  84. # Corrected p for square matrices of Lausanne parcellations 33, 125, 500.
  85. # Stimulation on selected DLPFC equivalent parcels
  86. path_resolutions = os.path.join(output_folder, 'Resolutions');os.makedirs(path_resolutions, exist_ok=True)
  88. #Resolution 1
  89. path_33_33 = os.path.join(base_path, res33 + '_' + res33)
  90. path_mats_33_33 = os.path.join(path_33_33, collapse_conf_name[0])
  91. label_stim = "lh.rostralmiddlefrontal" #Lau33 stim parcel
  92. p_33, N_33, I_33 = matrices.atlas_mat(path_mats_33_33, labels_rec_33.index(label_stim) , index_rec_33 , path_resolutions,f'stim33to33_{gral_tw[0]}_{gral_tw[1]}_ms', flag_delays= False, N_thresh=N_thresh, N_imp_thresh=N_imp_thresh)
  93. p_stim_33, N_stim_33, I_stim_33= matrices.atlas_mat(path_mats_33_33, labels_rec_33.index(label_stim) , labels_rec_33.index(label_stim) , path_resolutions,f'stim33tostim33_{gral_tw[0]}_{gral_tw[1]}_ms', flag_delays= False, N_thresh=N_thresh, N_imp_thresh=N_imp_thresh)
  94. #Resolution 3
  95. path_125_125 = os.path.join(base_path, res125 + '_' + res125)
  96. path_mats_125_125 = os.path.join(path_125_125, collapse_conf_name[0])
  97. label_stim = "lh.rostralmiddlefrontal_1" #Lau125 stim parcel
  98. p_125, N_125, I_125= matrices.atlas_mat(path_mats_125_125, labels_125.index(label_stim) , index_125 , path_resolutions,f'stim125to125_{gral_tw[0]}_{gral_tw[1]}_ms', flag_delays= False, N_thresh=N_thresh, N_imp_thresh=N_imp_thresh)
  99. p_stim_125, N_stim_125, I_stim_125 = matrices.atlas_mat(path_mats_125_125, labels_125.index(label_stim) , labels_125.index(label_stim) , path_resolutions,f'stim125tostim125_{gral_tw[0]}_{gral_tw[1]}_ms', flag_delays= False, N_thresh=N_thresh, N_imp_thresh=N_imp_thresh)
  100. #Resolution 5
  101. path_500_500 = os.path.join(base_path, res500 + '_' + res500)
  102. path_mats_500_500 = os.path.join(path_500_500, collapse_conf_name[0])
  103. label_stim = "lh.rostralmiddlefrontal_22" #Lau500 stim parcel
  104. p_500, N_500, I_500= matrices.atlas_mat(path_mats_500_500, labels_500.index(label_stim) , index_500 , path_resolutions,f'stim500to500_{gral_tw[0]}_{gral_tw[1]}_ms', flag_delays= False, N_thresh=N_thresh, N_imp_thresh=N_imp_thresh)
  105. p_stim_500, N_stim_500, I_stim_500= matrices.atlas_mat(path_mats_500_500, labels_500.index(label_stim) , labels_500.index(label_stim) , path_resolutions,f'stim500tostim500_{gral_tw[0]}_{gral_tw[1]}_ms', flag_delays= False, N_thresh=N_thresh, N_imp_thresh=N_imp_thresh)
  107. #%% EFFERENT CONNECTIVITY (FIG 2E and FIG 2C - first row) - CONNECTIVITY FROM X to Y
  108. # Matrices of combined resolution xy. Fine resolution x over roi and bigger resolution y over all the brain.
  109. # From the raw original matrix we take the lines of roi stimulated parcels towards columns of interest (in our case all cortical parcels + Amygdala and Hyppocampus)
  110. # This is in the matrix of parcellations x to y
  111. # We use xx matrix for stimulations to the roi recorded by the roi.
  112. path_125_33 = os.path.join(base_path, res125 + '_' + res33)
  113. path_mats_125_33 = os.path.join(path_125_33, collapse_conf_name[0])
  114. path_125_125 = os.path.join(base_path, res125 + '_' + res125)
  115. path_mats_125_125 = os.path.join(path_125_125, collapse_conf_name[0])
  116. path_folder_output = os.path.join(output_folder, res125 + '_' + res33);os.makedirs(path_folder_output, exist_ok=True)
  118. #p_xy, N_xy, I_xy, D_med_xy, D_mean_xy, quant25_xy, quant75_xy = matrices.atlas_mat(path_mats_125_33,Definitions.index_dlpfc_ifg,index_rec_33, path_folder_output,'125to33_' + gral_tw,flag_delays=True, min_n_suc=N,min_n_fail=N, min_n_feat=N,min_n_impl=n_imp, max_ci=max_ci)
  119. #p_xy, N_xy, I_xy= matrices.atlas_mat(path_mats_125_33,Definitions.index_dlpfc_ifg,index_rec_33, path_folder_output,f'125to33_{gral_tw[0]}_{gral_tw[1]}_ms',flag_delays=False, N_thresh=N)
  120. p_xy, N_xy, I_xy, D_med_xy, D_mean_xy= matrices.atlas_mat(path_mats_125_33, Definitions.index_dlpfc_ifg, index_rec_33,path_folder_output, f'125to33_{gral_tw[0]}_{gral_tw[1]}_ms',flag_delays=True, N_thresh=N_thresh, N_imp_thresh=N_imp_thresh)
  122. not_nan_xy = np.sum(~np.isnan(p_xy));coverage_xy = not_nan_xy / p_xy.size
  123. print("Coverage eff xy: ", coverage_xy)
  124. coverage_file_path = os.path.join(path_folder_output, 'coverage_xy.txt')
  125. with open(coverage_file_path, 'w') as f: f.write(f"Coverage: {coverage_xy:.6f}\n")
  127. #p_xx, N_xx, I_xx, D_med_xx, D_mean_xx, quant25_xx, quant75_xx = matrices.atlas_mat(path_mats_125_125,Definitions.index_dlpfc_ifg,Definitions.index_dlpfc_ifg,path_folder_output,'125to125_' + gral_tw,flag_delays=True, min_n_suc=N,min_n_fail=N, min_n_feat=N, min_n_impl=n_imp, max_ci=max_ci)
  128. #p_xx, N_xx, I_xx = matrices.atlas_mat(path_mats_125_125,Definitions.index_dlpfc_ifg,Definitions.index_dlpfc_ifg,path_folder_output,f'125to125_{gral_tw[0]}_{gral_tw[1]}_ms', flag_delays=False, N_thresh=N)
  129. p_xx, N_xx, I_xx,D_med_xx, D_mean_xx, = matrices.atlas_mat(path_mats_125_125, Definitions.index_dlpfc_ifg, Definitions.index_dlpfc_ifg,path_folder_output, f'125to125_{gral_tw[0]}_{gral_tw[1]}_ms',flag_delays=True, N_thresh=N_thresh, N_imp_thresh=N_imp_thresh)
  130. not_nan_xx = np.sum(~np.isnan(p_xx));coverage_xx = not_nan_xx / p_xx.size
  131. print("Coverage eff xx: ", coverage_xx)
  132. coverage_file_path = os.path.join(path_folder_output, 'coverage_xx.txt')
  133. with open(coverage_file_path, 'w') as f: f.write(f"Coverage: {coverage_xx:.6f}\n")
  135. # %% AFFERENT CONNECTIVITY (FIG 2E and FIG 2C - second row) - CONNECTIVITY FROM Y to X
  136. # From the raw original matrix we take the lines of roi recorded parcels of stimulations done on columns of interest on the rest of the brain
  137. path_33_125 = os.path.join(base_path, res33 + '_' + res125)
  138. path_mats_33_125 = os.path.join(path_33_125, collapse_conf_name[0])
  139. # The square ones (xx) are repeted from eff, but this way its independent
  140. path_125_125 = os.path.join(base_path, res125 + '_' + res125)
  141. path_mats_125_125 = os.path.join(path_125_125, collapse_conf_name[0])
  142. path_folder_output_aff = os.path.join(output_folder, res33 + '_' + res125)
  143. os.makedirs(path_folder_output_aff, exist_ok=True)
  144. # probability
  145. #p_yx, N_yx, I_yx, D_med_yx, D_mean_yx, quant25_yx, quant75_yx = matrices.atlas_mat(path_mats_33_125, index_rec_33,Definitions.index_dlpfc_ifg,path_folder_output_aff,'33to125_' + gral_tw,flag_delays=True, min_n_suc=N, min_n_fail=N, min_n_feat=N,min_n_impl=n_imp, max_ci=max_ci)
  146. #p_yx, N_yx, I_yx= matrices.atlas_mat(path_mats_33_125, index_rec_33,Definitions.index_dlpfc_ifg,path_folder_output_aff,f'33to125_{gral_tw[0]}_{gral_tw[1]}_ms',flag_delays=False, N_thresh=N)
  147. p_yx, N_yx, I_yx, D_med_yx, D_mean_yx = matrices.atlas_mat(path_mats_33_125, index_rec_33, Definitions.index_dlpfc_ifg,path_folder_output_aff, f'33to125_{gral_tw[0]}_{gral_tw[1]}_ms',flag_delays=True, N_thresh=N_thresh, N_imp_thresh=N_imp_thresh)
  148. not_nan_yx = np.sum(~np.isnan(p_yx));coverage_yx = not_nan_yx / p_yx.size
  149. print("Coverage aff xy: ", coverage_yx)
  150. coverage_file_path = os.path.join(path_folder_output_aff, 'coverage_yx.txt')
  151. with open(coverage_file_path, 'w') as f: f.write(f"Coverage: {coverage_yx:.6f}\n")
  152. #p_xx, N_xx, I_xx, D_med_xx, D_mean_xx, quant25_yx, quant75_xy = matrices.atlas_mat(path_mats_125_125,Definitions.index_dlpfc_ifg,Definitions.index_dlpfc_ifg,path_folder_output_aff,'125to125_' + gral_tw,flag_delays=True, min_n_suc=N,min_n_fail=N, min_n_feat=N,min_n_impl=n_imp, max_ci=max_ci)
  153. #p_xx, N_xx, I_xx= matrices.atlas_mat(path_mats_125_125,Definitions.index_dlpfc_ifg,Definitions.index_dlpfc_ifg,path_folder_output_aff,f'125to125_{gral_tw[0]}_{gral_tw[1]}_ms',flag_delays=False, N_thresh=N)
  154. p_xx, N_xx, I_xx, D_med_xx, D_mean_xx= matrices.atlas_mat(path_mats_125_125, Definitions.index_dlpfc_ifg, Definitions.index_dlpfc_ifg,path_folder_output_aff, f'125to125_{gral_tw[0]}_{gral_tw[1]}_ms',flag_delays=True, N_thresh=N_thresh, N_imp_thresh=N_imp_thresh)
  156. not_nan_xx = np.sum(~np.isnan(p_xx));coverage_xx = not_nan_xx / p_xx.size
  157. coverage_file_path = os.path.join(path_folder_output_aff, 'coverage_xx.txt')
  158. with open(coverage_file_path, 'w') as f: f.write(f"Coverage: {coverage_xx:.6f}\n")
  159. print("Coverage aff xx: ", coverage_xx)
  161. # %% MERGED ROIS: DLPFC & IFG. NUMBER of RECORDINGS (FIG 3A) DIRECT (FIG 3B) & INDIRECT (FIG 3C) CONNECTIVITY (probability and delays)
  162. # DLPFC merged (using Lausanne x) towards the rest of the brain in Lausanne y
  163. # DIRECT
  164. path_output_dlpfc = os.path.join(output_folder, 'Mean_ROI_Eff')
  165. os.makedirs(path_output_dlpfc, exist_ok=True)
  166. output_paths_dlpfc = os.path.join(path_output_dlpfc, f'Direct_connectivity_{dir_tw[0]}_{dir_tw[1]}_ms')
  167. os.makedirs(output_paths_dlpfc, exist_ok=True)
  169. # - DIRECT MATRICES
  170. #all
  171. path_dir_roi_33 = os.path.join(base_path, 'DLPFC_IFG_to_Lausanne2008-33')
  172. stim_parcels_merged = np.loadtxt(os.path.join(path_dir_roi_33, 'stim_parcels_merged.txt'), dtype=str) #stim parcels is roi merged (created when compute atlas : lh.dlpfc, rh.dlpfc, lh.ifg, rh.ifg
  173. stim_parcels_merged = stim_parcels_merged.tolist()
  174. index_stims = [stim_parcels_merged.index(i) for i in stim_parcels_merged]
  175. rec_parcels = np.loadtxt(os.path.join(path_dir_roi_33, 'rec_parcels_Lausanne2008-33.txt'), dtype=str)
  176. rec_parcels = rec_parcels.tolist()
  177. index_recs = [rec_parcels.index(i) for i in labels_rec_33] #only labels I need, index from real atlas list. #TODO: maybe compute directly only labels wanted
  178. # Mats
  179. path_dir_roi_33 = os.path.join(base_path, 'DLPFC_IFG_to_Lausanne2008-33', 'Direct', collapse_conf_name[1])
  180. # stim_parcels_merged_fig_names = [f'{l}_to_33' for l in stim_parcels_merged]
  181. p_xy_ldlpfc, N_xy_ldlpfc, I_xy_ldlpfc, D_med_xy_ldlpfc, D_mean_xy_ldlpfc = matrices.atlas_mat(path_dir_roi_33, index_stims,index_recs,output_paths_dlpfc,'roi_to_33', flag_delays=True, N_thresh=N_thresh, N_imp_thresh=N_imp_thresh)
  182. #local
  183. path_dir_roi_roi = os.path.join(base_path, 'DLPFC_IFG_to_DLPFC_IFG')
  184. stim_parcels_merged = np.loadtxt(os.path.join(path_dir_roi_roi,'stim_parcels_merged.txt'), dtype=str) # stim parcels is roi merged (created when compute atlas : lh.dlpfc, rh.dlpfc, lh.ifg, rh.ifg
  185. stim_parcels_merged = stim_parcels_merged.tolist()
  186. index_stims = [stim_parcels_merged.index(i) for i in stim_parcels_merged]
  187. rec_parcels = np.loadtxt(os.path.join(path_dir_roi_roi, 'rec_parcels_merged.txt'), dtype=str)
  188. rec_parcels = rec_parcels.tolist()
  189. index_recs = [rec_parcels.index(i) for i in rec_parcels] # just to avoid mistakes, it should be = to stim_parcels_merged
  190. path_dir_roi_roi = os.path.join(base_path, 'DLPFC_IFG_to_DLPFC_IFG', 'Direct', collapse_conf_name[1])
  191. # stim_parcels_merged_fig_names = [f'{l}_to_{l}' for l in stim_parcels_merged]
  192. p_xx_ldlpfc, N_xx_ldlpfc, I_xx_ldlpfc, D_med_xx_ldlpfc, D_mean_xx_ldlpfc = matrices.atlas_mat(path_dir_roi_roi,index_stims, index_stims, output_paths_dlpfc, 'roi_to_roi',flag_delays=True, N_thresh=N_thresh, N_imp_thresh=N_imp_thresh)
  194. # INDIRECT
  195. output_paths_dlpfc = os.path.join(path_output_dlpfc, f'Indirect_connectivity_{ind_tw[0]}_{ind_tw[1]}_ms')
  196. os.makedirs(output_paths_dlpfc, exist_ok=True)
  197. # all
  198. path_ind_roi_33 = os.path.join(base_path, 'DLPFC_IFG_to_Lausanne2008-33')
  199. stim_parcels_merged = np.loadtxt(os.path.join(path_ind_roi_33, 'stim_parcels_merged.txt'),dtype=str) # stim parcels is roi merged (created when compute atlas : lh.dlpfc, rh.dlpfc, lh.ifg, rh.ifg
  200. stim_parcels_merged = stim_parcels_merged.tolist()
  201. index_stims = [stim_parcels_merged.index(i) for i in stim_parcels_merged]
  202. rec_parcels = np.loadtxt(os.path.join(path_ind_roi_33, 'rec_parcels_Lausanne2008-33.txt'), dtype=str)
  203. rec_parcels = rec_parcels.tolist()
  204. index_recs = [rec_parcels.index(i) for i in labels_rec_33] # only labels I need, index from real atlas list. #TODO: maybe compute directly only labels wanted
  205. # Mats
  206. path_ind_roi_33 = os.path.join(base_path, 'DLPFC_IFG_to_Lausanne2008-33', 'Indirect', collapse_conf_name[2])
  207. # stim_parcels_merged_fig_names = [f'{l}_to_33' for l in stim_parcels_merged]
  208. p_xy_ldlpfc, N_xy_ldlpfc, I_xy_ldlpfc, D_med_xy_ldlpfc, D_mean_xy_ldlpfc = matrices.atlas_mat(path_ind_roi_33, index_stims,index_recs,output_paths_dlpfc, 'roi_to_33', flag_delays=True, N_thresh=N_thresh, N_imp_thresh=N_imp_thresh)
  209. # local
  210. path_ind_roi_roi = os.path.join(base_path, 'DLPFC_IFG_to_DLPFC_IFG')
  211. stim_parcels_merged = np.loadtxt(os.path.join(path_ind_roi_roi, 'stim_parcels_merged.txt'),dtype=str) # stim parcels is roi merged (created when compute atlas : lh.dlpfc, rh.dlpfc, lh.ifg, rh.ifg
  212. stim_parcels_merged = stim_parcels_merged.tolist()
  213. index_stims = [stim_parcels_merged.index(i) for i in stim_parcels_merged]
  214. rec_parcels = np.loadtxt(os.path.join(path_ind_roi_roi, 'rec_parcels_merged.txt'), dtype=str)
  215. rec_parcels = rec_parcels.tolist()
  216. index_recs = [rec_parcels.index(i) for i in rec_parcels] # just to avoid mistakes, it should be = to stim_parcels_merged
  217. path_ind_roi_roi = os.path.join(base_path, 'DLPFC_IFG_to_DLPFC_IFG', 'Indirect', collapse_conf_name[2])
  218. p_xx_ldlpfc, N_xx_ldlpfc, I_xx_ldlpfc, D_med_xx_ldlpfc, D_mean_xx_ldlpfc = matrices.atlas_mat(path_ind_roi_roi, index_stims, index_stims,output_paths_dlpfc,'roi_to_roi',flag_delays=True, N_thresh=N_thresh, N_imp_thresh=N_imp_thresh)
  220. # %% ROI CONNECTIVITY TO FUNCTIONAL NETWORKS (FIG 4C)
  221. # Connectivity from roi (Lausanne x) to functional networks as defined by Yeo et al. We compute the functional networks as mergeds of Lausanne 250 parcels.
  222. path_folder_funct = os.path.join(output_folder, 'Functional_Networks')
  223. os.makedirs(path_folder_funct, exist_ok=True)
  225. path_fn_data = os.path.join(base_path, 'Functional_Networks')
  226. stim_parcels = np.loadtxt(os.path.join(path_fn_data, 'stim_parcels.txt'),dtype=str)
  227. stim_parcels = stim_parcels.tolist()
  229. rec_fn = np.loadtxt(os.path.join(path_fn_data, 'rec_parcels_merged.txt'), dtype=str)
  230. rec_fn = rec_fn.tolist()
  232. path_fn_data = os.path.join(path_fn_data, collapse_conf_name[0])
  233. #Left fn
  234. index_stims_L = [stim_parcels.index(i) for i in stim_parcels if i.startswith('lh')]
  235. index_rec_fn_L = [i for i, name in enumerate(rec_fn) if name.startswith('lh.')]
  236. #_{gral_tw[0]}_{gral_tw[1]}ms - I used to save the tw it s computed with. But always general for fn
  237. p_net_eff, N_net_eff, I_net_eff = matrices.atlas_mat(path_fn_data, index_stims_L,index_rec_fn_L,path_folder_funct, 'Eff_roi_L_nets' , flag_delays=False, N_thresh=N_thresh, N_imp_thresh=N_imp_thresh)
  239. #Right fn
  240. index_stims_R = [stim_parcels.index(i) for i in stim_parcels if i.startswith('rh')]
  241. index_rec_fn_R = [i for i, name in enumerate(rec_fn) if name.startswith('rh.')]
  242. p_net_eff, N_net_eff, I_net_eff = matrices.atlas_mat(path_fn_data, index_stims_R, index_rec_fn_R, path_folder_funct,'Eff_roi_R_nets', flag_delays=False, N_thresh=N_thresh, N_imp_thresh=N_imp_thresh)
  244. #Segments
  245. path_folder_funct_segments = os.path.join(output_folder, 'Functional_Networks', 'Segmented_roi')
  246. os.makedirs(path_folder_funct_segments, exist_ok=True)
  248. path_fn_data_segments = os.path.join(base_path, 'Functional_Networks', 'Segmented_roi')
  249. stim_parcels_merged = np.loadtxt(os.path.join(path_fn_data_segments, 'stim_parcels_merged.txt'),dtype=str)
  250. stim_parcels = stim_parcels_merged.tolist()
  252. rec_fn = np.loadtxt(os.path.join(path_fn_data_segments, 'rec_parcels_merged.txt'), dtype=str)
  253. rec_fn = rec_fn.tolist()
  254. path_fn_data_segments = os.path.join(path_fn_data_segments, collapse_conf_name[0])
  255. #left and right, save on mat per segment (adapt to plot bars)
  256. for s_id in range(0, len(stim_parcels)) :
  257. stim_segment_tmp = stim_parcels[s_id]
  258. suffix = stim_segment_tmp.split('_')[-1] if '_' in stim_segment_tmp else stim_segment_tmp.split('.')[-1]
  259. #intra hemi p
  260. if stim_segment_tmp.startswith('lh.') :
  261. index_rec_fn = [i for i, name in enumerate(rec_fn) if name.startswith('lh.')]
  262. mat_name = 'l_'
  263. else :
  264. index_rec_fn = [i for i, name in enumerate(rec_fn) if name.startswith('rh.')]
  265. mat_name = 'r_'
  266. #mat_name
  267. if suffix == 'ifg' : mat_name = mat_name + 'IFG_nets'
  268. else: mat_name = mat_name + suffix + '_DLPFC_nets'
  269. p_segment_net, N_segment_net, I_segment_net = matrices.atlas_mat(path_fn_data_segments, [s_id,],index_rec_fn,path_folder_funct_segments, mat_name, flag_delays=False, N_thresh=N_thresh, N_imp_thresh=N_imp_thresh)
  271. def save_params_txt(output_folder,gral_tw,dir_tw,ind_tw,collapse_conf_name, N_thresh, N_imp_thresh, min_cb_dir, max_cb_dir,min_cb_ind,max_cb_ind,max_cb_gral,min_avg,max_avg,max_cb_p,max_p_gral,min_cb_N, max_cb_N):
  272. os.makedirs(output_folder, exist_ok=True)
  273. params_text = f"""
  274. Time Windows:
  275. General: {gral_tw}
  276. Direct: {dir_tw}
  277. Indirect: {ind_tw}
  278. Collapse Config Names:
  279. General: {collapse_conf_name[0]}
  280. Direct: {collapse_conf_name[1]}
  281. Indirect: {collapse_conf_name[2]}
  282. N recordings mask: {N_thresh}
  283. N implantations mask: {N_imp_thresh}
  284. Delays cb (ms):
  285. Direct min: {min_cb_dir}
  286. Direct max: {max_cb_dir}
  287. Indirect min: {min_cb_ind}
  288. Indirect max: {max_cb_ind}
  289. General max: {max_cb_gral}
  290. P avg cb:
  291. Min avg: {min_avg}
  292. Max avg: {max_avg}
  293. P thresholds:
  294. Max p for connections: {max_cb_p}
  295. Max p general: {max_p_gral}
  296. N cb:
  297. Min cb N: {min_cb_N}
  298. Max cb N: {max_cb_N}
  299. """
  300. with open(os.path.join(output_folder, 'params.txt'), 'w', encoding='utf-8') as f: f.write(params_text)
  302. def main():
  303. Zth = str(5)
  304. #Data : atlas generation output folders (containing matrices)
  305. base_path = r'F:\FTRACT\Data_LPFC_FTRACT'
  306. output_folder = os.path.join(base_path, f'Results_Zth{Zth}')
  308. gral_tw = [0,100] #'0_100ms'
  309. dir_tw = [0,50] #'0_50ms' # time window of 'direct connections'
  310. ind_tw = [100, 400] #'100_400ms' # time window of 'indirect connections'
  311. #collapse_conf_name general, direct, indirect
  312. collapse_conf_name = [f'max_peak_delay_{gral_tw[1]}__zth{Zth}___min_peak_delay_{gral_tw[0]}__zth{Zth}',f'max_peak_delay_{dir_tw[1]}__zth{Zth}___min_peak_delay_{dir_tw[0]}__zth{Zth}',f'max_peak_delay_{ind_tw[1]}__zth{Zth}___min_peak_delay_{ind_tw[0]}__zth{Zth}'] # Name of folder that saved the collapse, features inside.
  313. N_thresh= 50
  314. N_imp_thresh= 3
  315. # max_ci = 0.1 #not in use
  317. # delays
  318. # thresh_d = 20
  319. min_cb_dir = 20
  320. max_cb_dir = 35
  321. min_cb_ind = 150
  322. max_cb_ind = 200
  323. max_cb_gral = 80 # min 10
  324. # p avg
  325. max_avg = 0.175
  326. min_avg = 0.1
  327. # p
  328. max_cb_p = 0.25
  329. max_p_gral = 0.5
  330. # N
  331. max_cb_N = 10000
  332. min_cb_N = 50
  334. compute_mats(base_path, output_folder, collapse_conf_name, N_thresh, N_imp_thresh, gral_tw, dir_tw, ind_tw, min_cb_dir, max_cb_dir, min_cb_ind, max_cb_ind, max_cb_gral, max_avg, min_avg, max_cb_p, max_p_gral, max_cb_N, min_cb_N)
  336. save_params_txt(output_folder, gral_tw, dir_tw, ind_tw, collapse_conf_name, N_thresh, N_imp_thresh, min_cb_dir, max_cb_dir, min_cb_ind,max_cb_ind, max_cb_gral, min_avg, max_avg, max_cb_p, max_p_gral, min_cb_N, max_cb_N)
  339. if __name__ == "__main__":
  340. main()

compute_atlas_mats.py at commit 0589077, no license · at the source

Overview

Authors: Sofia Avalos-Alais1, Maciej Jedynak1, Anthony Boyer1, Blandine Chanteloup-Forêt2, Cristiana Pinheiro1, Christopher C Cline3, Sara Parmigiani3, Yasser Alemán-Gómez4,5, Patric Hagmann4,5, Corey J Keller3,6,7, Olivier David1,8, the F-TRACT Consortium
ORCID iDs: Sofia Avalos-Alais, Maciej Jedynak, Anthony Boyer, Cristiana Pinheiro, Christopher C Cline, Sara Parmigiani, Yasser Alemán-Gómez, Patric Hagmann, Corey J Keller, Olivier David
  1. Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille 13005, France
  2. Université Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, Grenoble 38000, France
  3. Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
  4. Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne 1011, Switzerland
  5. Faculty of Biology and Medicine, University of Lausanne, Lausanne 1015, Switzerland
  6. Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA 94305, USA
  7. Veterans Affairs Palo Alto Healthcare System and Sierra Pacific Mental Illness, Research, Education and Clinical Center (MIRECC), Palo Alto, CA 94394, USA
  8. Department of Pediatric Neurosurgery, Fondation Lenval, Nice 06200, France
Institutions: Inserm (France); Aix-Marseille Université (France); Institut de Neurosciences des Systèmes (France); Grenoble Institute of Neurosciences (France); Université Grenoble Alpes (France); Stanford Medicine (United States); Stanford University (United States); Centre Hospitalier Universitaire Vaudois (Switzerland); University of Lausanne (Switzerland); VA Palo Alto Health Care System (United States); Mental Illness Research, Education and Clinical Centers (United States); Fondation Lenval (France)
Journal: Brain : a journal of neurology, volume 149, issue 3, pages 963-975
Dates: received 19 February 2025; accepted 8 August 2025; published online 9 September 2025; in print March 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1093/brain/awaf317 · PMID 40924875 · PMCID PMC13016832 · OpenAlex W4414085794
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: intracranial EEG (iEEG / ECoG / SEEG) (modality), human (organism), epilepsy (population), systems (subfield)
Methods: Spectral & time-frequency, Statistics, Preprocessing
Keywords: effective connectivity, brain stimulation, dorso-lateral prefrontal cortex, inferior frontal gyrus, stereoelectroencephalography (SEEG), cortico-cortical evoked potentials (CCEP)
MeSH: Brain Mapping*, Nerve Net*, Prefrontal Cortex*, Adolescent, Adult, Drug Resistant Epilepsy, Electrocorticography, Female, Humans, Male, Middle Aged, Neural Pathways, Young Adult (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: European Research Council; European Union's Seventh Framework Programme (616268F-TRACT); Horizon 2020 (785907, 945539); Agence Nationale de la Recherche (French National Research Agency) (ANR-21-NEUC-0005–01, ANR-22-CE17-0057-03, ANR-22-PESN-0012); Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Swiss National Science Foundation) (209470); NIH (R01MH129018)
Citations: cited by 15 papers (Europe PMC); 76 references in the paper
Notices: A comment on this paper has been published (41672731, from Europe PMC)

Abstract

The lateral prefrontal cortex (LPFC) serves as a critical hub for higher-order cognitive and executive functions in the human brain, coordinating brain networks whose disruption has been implicated in many neurological and psychiatric disorders. While transcranial brain stimulation treatments often target the LPFC, our current understanding of connectivity profiles guiding these interventions based on electrophysiology remains limited.

Here, we present a high-resolution probabilistic map of bidirectional effective connectivity between the LPFC and widespread cortical and subcortical regions. This map is derived from intracranial evoked potential analysis of 48 797 intracranial direct electrical stimulation runs across 759 implantations in 724 patients with refractory epilepsy (368 male, 354 female, two unspecified; mean age ± standard deviation: 24 ± 13.5 years). We mapped probabilistic connectivity between brain parcels with adaptive resolution—higher resolution in the LPFC in the hemisphere of interest and lower elsewhere—maintaining statistical power while achieving 95% average confidence interval of ∼0.03 for connectivity probability estimates. In addition, the significance threshold (P-value) for probabilistic connectivity was obtained from surrogate distributions.

Overall, we observed remarkable symmetry between afferent and efferent connectivity patterns of the LPFC, with a slight preference for efferent connections (mean slope = 0.92 ± 0.09, mean R2 = 0.93 ± 0.025). For example, connections between the inferior frontal gyrus (IFG) and anterior cingulate showed notable directional asymmetry. The IFG strongly projected to most brain networks compared to other LPFC regions, with the strongest connectivity to the ventral attention network (0.26 ± 0.01 compared to values between 0.15 and 0.21 in other LPFC regions). Posterior dorso-lateral prefrontal cortex (DLPFC) demonstrated stronger connectivity to brain networks compared to anterior DLPFC regions (e.g. 0.21 ± 0.01 versus 0.15 ± 0.01 for connectivity to ventral attention network), with the exception of the limbic cortex. All LPFC subregions strongly projected to the fronto-parietal (greater than 0.17) and ventral attention (greater than 0.15) networks, with moderate connections to the default network (between 0.1 and 0.15, with the maximum corresponding to superior DLPFC). Finally, latency analysis suggested that the left LPFC's influence on ipsilateral emotion-related regions is primarily polysynaptic, with particularly strong pathways from IFG to amygdala (0.16 ± 0.02) and hippocampus (0.12 ± 0.01).

Taken together, these comprehensive connectivity maps provide a new detailed electrophysiological foundation for understanding the functional anatomy of LPFC and guiding targeted brain stimulation protocols.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 9 matches between paragraphs and lines of code.

ins-amu/LPFC_F-TRACT

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 0589077da765e9d33383a338fe0b3e7afce89808, 23 February 2026
Languages: Python (28), C/C++ (10), Shell (1)
Size: 5,538 files, 39 scripts
Software Heritage: archived
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (25 files), Matplotlib (13 files), SciPy (3 files), MNE-Python (2 files), pandas (1 file), Pillow (1 file), statsmodels (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
40 files
At the source: github.com/ins-amu/LPFC_F-TRACT

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 39 scripts, each with its path and the digest of its content;
  • 9 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

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Data availability

The numerical data at the atlas level and the code are available at https://github.com/ins-amu/LPFC_F-TRACT.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 6 keywords, 13 MeSH terms, 6 funders, 73 references, 1 integrity notice.

Cite

This paper

Avalos-Alais, S., Jedynak, M., Boyer, A., Chanteloup-Forêt, B., Pinheiro, C., Cline, C. C., Parmigiani, S., Alemán-Gómez, Y., Hagmann, P., Keller, C. J., David, O., & the F-TRACT Consortium. (2026). High-resolution electrophysiological mapping of effective connectivity of lateral prefrontal cortex. Brain : a journal of neurology, 149(3), 963-975. https://doi.org/10.1093/brain/awaf317

BibTeX

@article{avalosalais2026high,
author = {Avalos-Alais, Sofia and Jedynak, Maciej and Boyer, Anthony and Chanteloup-Forêt, Blandine and Pinheiro, Cristiana and Cline, Christopher C and Parmigiani, Sara and Alemán-Gómez, Yasser and Hagmann, Patric and Keller, Corey J and David, Olivier and {the F-TRACT Consortium}},
title = {{High-resolution electrophysiological mapping of effective connectivity of lateral prefrontal cortex}},
journal = {Brain : a journal of neurology},
year = {2026},
month = mar,
volume = {149},
number = {3},
pages = {963--975},
publisher = {Oxford University Press},
issn = {0006-8950},
doi = {10.1093/brain/awaf317},
url = {https://doi.org/10.1093/brain/awaf317},
pmid = {40924875},
pmcid = {PMC13016832}
}

RIS

TY - JOUR
AU - Avalos-Alais, Sofia
AU - Jedynak, Maciej
AU - Boyer, Anthony
AU - Chanteloup-Forêt, Blandine
AU - Pinheiro, Cristiana
AU - Cline, Christopher C
AU - Parmigiani, Sara
AU - Alemán-Gómez, Yasser
AU - Hagmann, Patric
AU - Keller, Corey J
AU - David, Olivier
AU - the F-TRACT Consortium
TI - High-resolution electrophysiological mapping of effective connectivity of lateral prefrontal cortex
T2 - Brain : a journal of neurology
J2 - Brain
PY - 2026
DA - 2026/03/01
VL - 149
IS - 3
SP - 963
EP - 975
SN - 0006-8950
PB - Oxford University Press
DO - 10.1093/brain/awaf317
UR - https://doi.org/10.1093/brain/awaf317
LA - en
ER -

CSL-JSON

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"id": "10.1093/brain/awaf317",
"type": "article-journal",
"title": "High-resolution electrophysiological mapping of effective connectivity of lateral prefrontal cortex",
"container-title": "Brain : a journal of neurology",
"author": [
{
"family": "Avalos-Alais",
"given": "Sofia"
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"given": "Christopher C"
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{
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{
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"given": "Yasser"
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{
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"given": "Patric"
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"container-title-short": "Brain",
"volume": "149",
"issue": "3",
"page": "963-975",
"DOI": "10.1093/brain/awaf317",
"PMID": "40924875",
"PMCID": "PMC13016832",
"ISSN": "0006-8950",
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"date-parts": [
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1
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}

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[8] doi:10.3389/fncom.2026.1786996 [code]
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