def _run_interface(self, runtime):
sxfmout = self.inputs.sxfmout
hemi = self.inputs.hemi
data = nb.load(sxfmout).get_data()
origdata = data.shape
affine = None
data.resize(data.shape[0]*data.shape[2],1,1,data.shape[3]) #in case of fsaverage, where nifti splits large dim
sourcemask = np.zeros(origdata, dtype=np.int)
targetmask = np.zeros(origdata, dtype=np.int)
#define which vertices are in the ROI
source_vertices = get_vertices(self.inputs.hemi, self.inputs.freesurferdir, self.inputs.fs, self.inputs.sourcelabels)
target_vertices = get_vertices(self.inputs.hemi, self.inputs.freesurferdir, self.inputs.fs, self.inputs.targetlabels)
#create masks for source and target vertices
sourcemask[source_vertices] = 1
targetmask[target_vertices] = 1
targetmask.resize(origdata)
targetmaskImg = nb.Nifti1Image(targetmask, affine)
nb.save(targetmaskImg, os.path.abspath('surfacemask.nii'))
## make new input data with source mask
sourcedata = np.where(sourcemask,data,0)
sourcedata.resize(origdata)
sourcedataImg = nb.Nifti1Image(sourcedata, affine)
nb.save(sourcedataImg, os.path.abspath('surfacedata.nii'))
return runtime
#'0115321', '0122844', '0132717', '0141860', '0153790', '0163508', '0171391', '0182376', '0194956'
]
exclude_subjects = ["0021001", "0172228"] # 0021001- strange morphometry, 0172228- no 1400
subjects = list(set(subjects) - set(exclude_subjects))
sessions = ["mx_645", "mx_1400", "std_2500"]
##ROI LABELS##
# Volume Data#
volume_sourcelabels = [
-1
] # -1 means No Volume #Example: [12114, 12113] #ctx_rh_G_front_inf-Triangul, ctx_rh_G_front_inf-Orbital
volume_targetlabels = [-1]
# Surface Data#
surface_sourcelabels = [] # empty set [] means all surface vertices
surface_targetlabels = [1, 5, 13, 14, 15, 16, 24, 31, 32, 39, 40, 53, 54, 55, 63, 64, 65, 71] # preFrontal Cortex
lhsource = get_vertices("lh", freesurferdir, surface_sourcelabels)
rhsource = get_vertices("rh", freesurferdir, surface_sourcelabels)
lhvertices = get_vertices("lh", freesurferdir, surface_targetlabels)
rhvertices = get_vertices("rh", freesurferdir, surface_targetlabels)
# Analysis Parameters#
hemispheres = ["lh", "rh"]
similarity_types = ["temp", "eta2", "spat"]
cluster_types = ["hiercluster", "kmeans", "spectral", "dbscan"]
n_clusters = [6] # [2,3,4,5,6,7,8,10,11,12,13,14,15,16,17,18,19,20,21,22]
epsilon = 0.03