Exemples de configuration

running:
  if_exists: "skip"   # "skip" | "overwrite" | "error"

# Paths
paths:
  simnibs_output: /Users/hippolyte.dreyfus/Desktop/hemianotACS/Data/derivatives/simnibs-config-healthyV2
  results_dir: /Users/hippolyte.dreyfus/Desktop/hemianotACS/Data/derivatives/results-V4
  mni_template: /Users/hippolyte.dreyfus/Documents/simnibs-modular/templates/MNI152_T1_1mm.nii.gz #TODO here should be one template directory and IO is responsable to find ellements within it
  mni_brain_mask: /Users/hippolyte.dreyfus/Documents/simnibs-modular/templates/MNI152_T1_1mm_brain_mask.nii.gz


# Configuration for hemianotACS e-field analysis pipeline
subjects: ["0001", "0002", "0004"] # "0005", "0007", "0008", "0009", "0011", "0012", "0014", "0015", "0016", "0018", "0019", "0020", "0021", "0023", "0024", "0025", "0026", "0027", "0028", "0029", "0030", "0031", "0032", "0033", "0034", "0035", "0036", "0037", "0038", "0039", "0040", "0041", "0042"]
stim_conditions: [fef, ips-left, ips-right] #TODO this should matched with the name of the simu or opti folder ??1!!
mode: [simulation, optimization]
space: native  # config values: 'mni' or 'native' ; output paths use suffixes like 'space-mni'/'space-native'

# Target generation
# method: sphere  → coords: [x_mni, y_mni, z_mni] (mm)
# method: atlas   → atlas: <name>  ('harvard-oxford', 'aal', 'destrieux')
#                   regions: <label>  (str or list of str)
target_generation: #TO MOVE INTO SIMNIBS INPUT !
  radius_mm: 5.0  # used for method: sphere
  rois:
    fef:
      method: sphere
      coords: [28, -8, 54]
    ips-left:
      method: sphere
      coords: [-25, -60, 52]
      folder_pattern: ips_left
    ips-right:
      method: sphere
      coords: [25, -60, 52]
      folder_pattern: ips_right #TO REMOVE WHEN FOLDER FROM SIMNIBS-MODULAR CORRECTLY GENERATED
    # atlas example:
    HA-fef:
      method: atlas
      atlas: harvard-oxford
      regions: "Precentral Gyrus"
    AAL-fef:
      method: atlas
      atlas: aal
      regions: "Frontal_Mid_2_R"



# Preprocessing parameters
preprocessing:
  smooth_fwhm: 2.0
  outlier_method: iqr
  portion: null

# Feature extraction parameters
feature_extraction:
  metrics: [mean, median, std, min, max]

# Analysis parameters
analysis:
  metric: mean
  subject_col: subject
  condition_col: condition
  clustering:
    method: mean              # colonne efield_ratio_<method> utilisée
    specificity_threshold: 1.5
    intensity_col: mean       # colonne numérique pour seuil haut/bas
  




####OLD
# ## All Outputs are:


# # Pour chaque sujet:
# - valeur efield dans la ROI - simu
# - valeur efield dans la ROI - opti
# # Valeur moyenne + variabilité
# # Amélioration moyenne + variabilité
# # Corrélation efield / réponse symptome du patient

# # ______________
# # check la robustesse de ses outputs étant donné les méthodes de calculs

running:
  if_exists: "overwrite"   # "skip" | "overwrite" | "error"

# Paths — structure séparée preps / simu / results
paths:
  simnibs_preps: /Users/hippolyte.dreyfus/Desktop/_stimSD/Data/derivatives/mri/simnibs-preps
  simnibs_simu:  /Users/hippolyte.dreyfus/Desktop/_stimSD/Data/derivatives/mri/simnibs-simu
  results_dir:   /Users/hippolyte.dreyfus/Desktop/_stimSD/Data/derivatives/mri/simnibs-analyze-V2
  mni_template:  /Users/hippolyte.dreyfus/Documents/simnibs-modular/templates/MNI152_T1_1mm.nii.gz
  mni_brain_mask: /Users/hippolyte.dreyfus/Documents/simnibs-modular/templates/MNI152_T1_1mm_brain_mask.nii.gz

subjects:
  - "0001"
  - "0005"
  - "0006"
  - "0007"
  - "0008"
  - "0010"
  - "0014"
  - "0016"
  - "0017"
  - "0018"
  - "0020"
  - "0021"
  - "0022"
  - "0030"
  - "0031"
  - "0032"

# Montage AF7 (anode) – FT8 (cathode) → cible fonctionnelle : DLPFC gauche
stim_conditions: [stimSD]
mode: [simulation]
space: mni

# ROI : sphère MNI DLPFC gauche (sous AF7)
target_generation:
  radius_mm: 10.0
  rois:
    stimSD:
      method: sphere
      coords: [-46, 36, 20]   # DLPFC gauche, MNI

# Preprocessing
preprocessing:
  smooth_fwhm: 2.0
  outlier_method: iqr
  portion: null

# Feature extraction
feature_extraction:
  metrics: [mean, median, std, min, max]

# Analysis
analysis:
  metric: mean
  subject_col: subject
  condition_col: condition
  clustering:
    method: mean
    specificity_threshold: 1.5
    intensity_col: mean