Dear all,
I am doing unsmoothed first-level t-maps of each run (n=6) for further classification analysis.
The maps were correct between the contrasts. However, I tried but still don’t understand why the t-maps across each run look the same after visualization.
Thank you very much for your time and help in advance.
fmri_glm = fmri_glm.fit(bold, events, confounds_glm)
# Create contrasts
design_matrices = fmri_glm.design_matrices_
contrast_list = []
for design_matrix in design_matrices:
n_columns = design_matrix.shape[1]
def pad_vector(contrast_, n_columns):
return np.hstack((contrast_, np.zeros(n_columns - len(contrast_))))
contrasts = {'21-labial-BL': pad_vector([-1,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0], n_columns),
'22-dental-BL': pad_vector([-1,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,0,0], n_columns),
'25-bilabial-dental': pad_vector([0,0,1,1,1,1,0,0,0,0,0,0,-1,-1,-1,-1,0,0], n_columns),
'26-dental-bilabial': pad_vector([0,0,-1,-1,-1,-1,0,0,0,0,0,0,1,1,1,1,0,0], n_columns)}
contrast_list.append(contrasts)
### Create stats_map for each contrast and each run
for run_idx in range(len(design_matrices)):
for contrast_key in contrast_list[run_idx].keys():
run_contrasts = contrast_list[run_idx][contrast_key]
stats_type = 'stat'
stats_map = fmri_glm.compute_contrast(run_contrasts, output_type=stats_type)
res_name = f'sub-{sID}_task-phono_'
filename = res_name + f'desc-{contrast_key}-{run_idx + 1}_{stats_type}.nii.gz'
print(filename) ## filename is correct with each defined condition
stats_map.to_filename(os.path.join(outdir, filename))