def fixed_effects_analysis(subject_dic, mask_img=None, mesh=False): """ Combine the AP and PA images """ from nibabel import load, save from nilearn.plotting import plot_stat_map session_ids = subject_dic['session_id'] task_ids = _session_id_to_task_id(session_ids) paradigms = np.unique(task_ids) if mask_img is None: mask_img = os.path.join(subject_dic['output_dir'], "mask.nii.gz") # Guessing paradigm from file name for paradigm in paradigms: # select the sessions relevant for the paradigm session_paradigm = [ session_id for (session_id, task_id) in zip(session_ids, task_ids) if task_id == paradigm ] # define the relevant contrasts contrasts = make_contrasts(paradigm).keys() # create write_dir if mesh is not False: if mesh == 'fsaverage5': write_dir = os.path.join(subject_dic['output_dir'], 'res_fsaverage5_%s_ffx' % paradigm) elif mesh == 'individual': write_dir = os.path.join(subject_dic['output_dir'], 'res_individual_%s_ffx' % paradigm) else: write_dir = os.path.join(subject_dic['output_dir'], 'res_fsaverage7_%s_ffx' % paradigm) dirs = [ os.path.join(write_dir, stat) for stat in ['effect_surf', 'variance_surf', 'stat_surf'] ] else: write_dir = os.path.join(subject_dic['output_dir'], 'res_stats_%s_ffx' % paradigm) dirs = [ os.path.join(write_dir, stat) for stat in ['effect_size_maps', 'effect_variance_maps', 'stat_maps'] ] for dir_ in dirs: if not os.path.exists(dir_): os.makedirs(dir_) print(write_dir) # iterate across contrasts for contrast in contrasts: print('fixed effects for contrast %s. ' % contrast) if mesh is not False: from nibabel.gifti import write for side in ['lh', 'rh']: effect_size_maps, effect_variance_maps, data_available =\ _load_summary_stats( subject_dic['output_dir'], np.unique(session_paradigm), contrast, data_available=True, side=side, mesh=mesh) if not data_available: raise ValueError('Missing texture stats files for ' 'fixed effects computations') ffx_effects, ffx_variance, ffx_stat = fixed_effects_surf( effect_size_maps, effect_variance_maps) write( ffx_effects, os.path.join( write_dir, 'effect_surf/%s_%s.gii' % (contrast, side))) write( ffx_variance, os.path.join( write_dir, 'variance_surf/%s_%s.gii' % (contrast, side))) write( ffx_stat, os.path.join(write_dir, 'stat_surf/%s_%s.gii' % (contrast, side))) else: effect_size_maps, effect_variance_maps, data_available =\ _load_summary_stats( subject_dic['output_dir'], session_paradigm, contrast, data_available=True) shape = load(effect_size_maps[0]).shape if len(shape) > 3: if shape[3] > 1: # F contrast, skipping continue ffx_effects, ffx_variance, ffx_stat = fixed_effects_img( effect_size_maps, effect_variance_maps, mask_img) save( ffx_effects, os.path.join(write_dir, 'effect_size_maps/%s.nii.gz' % contrast)) save( ffx_variance, os.path.join(write_dir, 'effect_variance_maps/%s.nii.gz' % contrast)) save(ffx_stat, os.path.join(write_dir, 'stat_maps/%s.nii.gz' % contrast)) plot_stat_map(ffx_stat, bg_img=subject_dic['anat'], display_mode='z', dim=0, cut_coords=7, title=contrast, threshold=3.0, output_file=os.path.join( write_dir, 'stat_maps/%s.png' % contrast))
def first_level(subject_dic, additional_regressors=None, compcorr=False, smooth=None, mesh=False, mask_img=None): """ Run the first-level analysis (GLM fitting + statistical maps) in a given subject Parameters ---------- subject_dic: dict, exhaustive description of an individual acquisition additional_regressors: dict or None, additional regressors provided as an already sampled design_matrix dictionary keys are session_ids compcorr: Bool, optional, whether confound estimation and removal should be done or not smooth: float or None, optional, how much the data should spatially smoothed during masking """ start_time = time.ctime() # experimental paradigm meta-params motion_names = ['tx', 'ty', 'tz', 'rx', 'ry', 'rz'] hrf_model = subject_dic['hrf_model'] high_pass = subject_dic['high_pass'] drift_model = subject_dic['drift_model'] tr = subject_dic['TR'] slice_time_ref = 1. if not mesh and (mask_img is None): mask_img = masking(subject_dic['func'], subject_dic['output_dir']) if additional_regressors is None: additional_regressors = dict([ (session_id, None) for session_id in subject_dic['session_id'] ]) for session_id, fmri_path, onset, motion_path in zip( subject_dic['session_id'], subject_dic['func'], subject_dic['onset'], subject_dic['realignment_parameters']): task_id = _session_id_to_task_id([session_id])[0] if mesh is not False: from nibabel.gifti import read n_scans = np.array( [darrays.data for darrays in read(fmri_path).darrays]).shape[0] else: n_scans = nib.load(fmri_path).shape[3] # motion parameters motion = np.loadtxt(motion_path) # define the time stamps for different images frametimes = np.linspace(slice_time_ref, (n_scans - 1 + slice_time_ref) * tr, n_scans) if task_id == 'audio': mask = np.array([1, 0, 1, 1, 0, 1, 1, 0, 1, 1]) n_cycles = 28 cycle_duration = 20 t_r = 2 cycle = np.arange(0, cycle_duration, t_r)[mask > 0] frametimes = np.tile(cycle, n_cycles) +\ np.repeat(np.arange(n_cycles) * cycle_duration, mask.sum()) frametimes = frametimes[:-2] # for some reason... if mesh is not False: compcorr = False # XXX Fixme if compcorr: confounds = high_variance_confounds(fmri_path, mask_img=mask_img) confounds = np.hstack((confounds, motion)) confound_names = ['conf_%d' % i for i in range(5)] + motion_names else: confounds = motion confound_names = motion_names if onset is None: warnings.warn('Onset file not provided. Trying to guess it') task = os.path.basename(fmri_path).split('task')[-1][4:] onset = os.path.join( os.path.split(os.path.dirname(fmri_path))[0], 'model001', 'onsets', 'task' + task + '_run001', 'task%s.csv' % task) if not os.path.exists(onset): warnings.warn('non-existant onset file. proceeding without it') paradigm = None else: paradigm = make_paradigm(onset, task_id) # handle manually supplied regressors add_reg_names = [] if additional_regressors[session_id] is None: add_regs = confounds else: df = read_csv(additional_regressors[session_id]) add_regs = [] for regressor in df: add_reg_names.append(regressor) add_regs.append(df[regressor]) add_regs = np.array(add_regs).T add_regs = np.hstack((add_regs, confounds)) add_reg_names += confound_names # create the design matrix design_matrix = make_first_level_design_matrix( frametimes, paradigm, hrf_model=hrf_model, drift_model=drift_model, high_pass=high_pass, add_regs=add_regs, add_reg_names=add_reg_names) _, dmtx, names = check_design_matrix(design_matrix) # create the relevant contrasts contrasts = make_contrasts(task_id, names) if mesh == 'fsaverage5': # this is low-resolution data subject_session_output_dir = os.path.join( subject_dic['output_dir'], 'res_fsaverage5_%s' % session_id) elif mesh == 'fsaverage7': subject_session_output_dir = os.path.join( subject_dic['output_dir'], 'res_fsaverage7_%s' % session_id) elif mesh == 'individual': subject_session_output_dir = os.path.join( subject_dic['output_dir'], 'res_individual_%s' % session_id) else: subject_session_output_dir = os.path.join( subject_dic['output_dir'], 'res_stats_%s' % session_id) if not os.path.exists(subject_session_output_dir): os.makedirs(subject_session_output_dir) np.savez(os.path.join(subject_session_output_dir, 'design_matrix.npz'), design_matrix=design_matrix) if mesh is not False: run_surface_glm(design_matrix, contrasts, fmri_path, subject_session_output_dir) else: z_maps, fmri_glm = run_glm(design_matrix, contrasts, fmri_path, mask_img, subject_dic, subject_session_output_dir, tr=tr, slice_time_ref=slice_time_ref, smoothing_fwhm=smooth) # do stats report anat_img = nib.load(subject_dic['anat']) stats_report_filename = os.path.join(subject_session_output_dir, 'report_stats.html') report = make_glm_report( fmri_glm, contrasts, threshold=3.0, bg_img=anat_img, cluster_threshold=15, title="GLM for subject %s" % session_id, ) report.save_as_html(stats_report_filename)