Dear Neurostars,
I’m trying to set up a firstlevel model with the hrf_model parameter set to spm + derivative + dispersion. When plotting the resulting zmaps the brain are empty and all values are zero. When I run the same firstlevelmodel with he hrf_model parameter set to just spm or spm + derivative this is not the case.
Does anyone know what may explain this finding?
for sub in subs[:1]:
print('---------- '+sub+' ----------')
sub_new = sub[0] + sub[-2:]
design_matrices=[]
fmri_img=[]
for run in [1]:
print(f'### run: {run} ###')
run_new = 'RUN'+str(run).zfill(2)
func_data_dir = opj(prep_dir, sub_new)
onset_dir = opj(base_dir,'data/bids/',sub,'func')
confound_dir = opj(base_dir,'data/bids/derivatives/fmriprep',sub,'func' )
func1_path = glob(opj(func_data_dir, f'w*{run_new}*topup.nii.gz'))[0]
func1_img = load_img(func1_path)
n_frames=func1_img.shape[3]
frame_times = np.arange(n_frames) * tr
# onest file
events = pd.read_csv(glob(opj(onset_dir,'*'+sub[-2:]+'*run-'+str(run).zfill(2)+'*_onsets_svo_BPdep_woBO.tsv'))[0], sep = '\t')
events['trial_type'] = events['trial_type'].replace('-', '', regex=True)
events.drop(['Unnamed: 0'],axis=1, inplace=True)
confounds = pd.read_table(glob(opj(confound_dir,sub+'*run-'+str(run)+'*confounds_timeseries.tsv'))[0])
confounds_sel = confounds[['rot_x','rot_y','rot_z',
'trans_x','trans_y','trans_z']].fillna(0)
# create design matrix
dm_ms = make_first_level_design_matrix(
frame_times,
events,
add_regs=confounds_sel,
add_reg_names=list(confounds_sel.columns),
hrf_model='spm + derivative + dispersion,
drift_model='polynomial',
drift_order=3,
)
plot_design_matrix(dm_ms)
plt.show()
design_matrices.append(dm_ms)
fmri_img.append(func1_img)
print('Length of img check' + str(len(fmri_img)))
print('Length of design_matrices check' + str(len(design_matrices)))
fmri_glm = FirstLevelModel(minimize_memory=True, standardize=False, smoothing_fwhm=5)#, signal_scaling=False)
fmri_glm = fmri_glm.fit(fmri_img, design_matrices=design_matrices) # might have to turn off signal scaling because data might be centered
trialwise_conditions = events['trial_type'].unique()
for condition in trialwise_conditions:
print(condition)
# V1
z_map = fmri_glm.compute_contrast(condition, output_type='z_score')
plot_glass_brain(z_map)
plt.show()```
type or paste code here