def report_slm_oasis(): # pragma: no cover n_subjects = 5 # more subjects requires more memory oasis_dataset = nilearn.datasets.fetch_oasis_vbm(n_subjects=n_subjects) # Resample the images, since this mask has a different resolution mask_img = resample_to_img( nilearn.datasets.fetch_icbm152_brain_gm_mask(), oasis_dataset.gray_matter_maps[0], interpolation='nearest', ) design_matrix = _make_design_matrix_slm_oasis(oasis_dataset, n_subjects) second_level_model = SecondLevelModel(smoothing_fwhm=2.0, mask_img=mask_img) second_level_model.fit(oasis_dataset.gray_matter_maps, design_matrix=design_matrix) contrast = [[1, 0, 0], [0, 1, 0]] report = make_glm_report( model=second_level_model, contrasts=contrast, bg_img=nilearn.datasets.fetch_icbm152_2009()['t1'], height_control=None, ) output_filename = 'generated_report_slm_oasis.html' output_filepath = os.path.join(REPORTS_DIR, output_filename) report.save_as_html(output_filepath) report.get_iframe()
def report_flm_fiac(): # pragma: no cover data = datasets.func.fetch_fiac_first_level() fmri_img = [data['func1'], data['func2']] from nilearn.image import mean_img mean_img_ = mean_img(fmri_img[0]) design_files = [data['design_matrix1'], data['design_matrix2']] design_matrices = [pd.DataFrame(np.load(df)['X']) for df in design_files] fmri_glm = FirstLevelModel(mask_img=data['mask'], minimize_memory=True) fmri_glm = fmri_glm.fit(fmri_img, design_matrices=design_matrices) n_columns = design_matrices[0].shape[1] contrasts = { 'SStSSp_minus_DStDSp': _pad_vector([1, 0, 0, -1], n_columns), 'DStDSp_minus_SStSSp': _pad_vector([-1, 0, 0, 1], n_columns), 'DSt_minus_SSt': _pad_vector([-1, -1, 1, 1], n_columns), 'DSp_minus_SSp': _pad_vector([-1, 1, -1, 1], n_columns), 'DSt_minus_SSt_for_DSp': _pad_vector([0, -1, 0, 1], n_columns), 'DSp_minus_SSp_for_DSt': _pad_vector([0, 0, -1, 1], n_columns), 'Deactivation': _pad_vector([-1, -1, -1, -1, 4], n_columns), 'Effects_of_interest': np.eye(n_columns)[:5] } report = make_glm_report( fmri_glm, contrasts, bg_img=mean_img_, height_control='fdr', ) output_filename = 'generated_report_flm_fiac.html' output_filepath = os.path.join(REPORTS_DIR, output_filename) report.save_as_html(output_filepath) report.get_iframe()
def report_flm_bids_features(): # pragma: no cover data_dir = _fetch_bids_data() model, subject = _make_flm(data_dir) title = 'FLM Bids Features Stat maps' report = make_glm_report( model=model, contrasts='StopSuccess - Go', title=title, cluster_threshold=3, ) output_filename = 'generated_report_flm_bids_features.html' output_filepath = os.path.join(REPORTS_DIR, output_filename) report.save_as_html(output_filepath) report.get_iframe()
def report_flm_adhd_dmn(): # pragma: no cover t_r = 2. slice_time_ref = 0. n_scans = 176 pcc_coords = (0, -53, 26) adhd_dataset = nilearn.datasets.fetch_adhd(n_subjects=1) seed_masker = NiftiSpheresMasker([pcc_coords], radius=10, detrend=True, standardize=True, low_pass=0.1, high_pass=0.01, t_r=2., memory='nilearn_cache', memory_level=1, verbose=0) seed_time_series = seed_masker.fit_transform(adhd_dataset.func[0]) frametimes = np.linspace(0, (n_scans - 1) * t_r, n_scans) design_matrix = make_first_level_design_matrix(frametimes, hrf_model='spm', add_regs=seed_time_series, add_reg_names=["pcc_seed"]) dmn_contrast = np.array([1] + [0] * (design_matrix.shape[1] - 1)) contrasts = {'seed_based_glm': dmn_contrast} first_level_model = FirstLevelModel(t_r=t_r, slice_time_ref=slice_time_ref) first_level_model = first_level_model.fit(run_imgs=adhd_dataset.func[0], design_matrices=design_matrix) report = make_glm_report( first_level_model, contrasts=contrasts, title='ADHD DMN Report', cluster_threshold=15, height_control='bonferroni', min_distance=8., plot_type='glass', report_dims=(1200, 'a'), ) output_filename = 'generated_report_flm_adhd_dmn.html' output_filepath = os.path.join(REPORTS_DIR, output_filename) report.save_as_html(output_filepath) report.get_iframe()
plot_abs=False, display_mode='z', figure=plt.figure(figsize=(4, 4))) plt.show() ############################################################################### # We can get a latex table from a Pandas Dataframe for display and publication purposes from nilearn.reporting import get_clusters_table print(get_clusters_table(z_map, norm.isf(0.001), 10).to_latex()) ######################################################################### # Generating a report # ------------------- # Using the computed FirstLevelModel and contrast information, # we can quickly create a summary report. from nilearn.reporting import make_glm_report report = make_glm_report( model=model, contrasts='StopSuccess - Go', ) ######################################################################### # We have several ways to access the report: # report # This report can be viewed in a notebook # report.save_as_html('report.html') # report.open_in_browser()
plotting.plot_stat_map( z_map, threshold=threshold, colorbar=True, title='sex effect on grey matter density (FDR = .05)') ########################################################################### # Note that there does not seem to be any significant effect of sex on # grey matter density on that dataset. ########################################################################### # Generating a report # ------------------- # It can be useful to quickly generate a # portable, ready-to-view report with most of the pertinent information. # This is easy to do if you have a fitted model and the list of contrasts, # which we do here. from nilearn.reporting import make_glm_report icbm152_2009 = datasets.fetch_icbm152_2009() report = make_glm_report(model=second_level_model, contrasts=['age', 'sex'], bg_img=icbm152_2009['t1'], ) ######################################################################### # We have several ways to access the report: # report # This report can be viewed in a notebook # report.save_as_html('report.html') # report.open_in_browser()
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)
def generate_report(self, contrasts, title=None, bg_img="MNI152TEMPLATE", threshold=3.09, alpha=0.001, cluster_threshold=0, height_control='fpr', min_distance=8., plot_type='slice', display_mode=None, report_dims=(1600, 800)): """ Returns HTMLDocument object for a report which shows all important aspects of a fitted GLM. The object can be opened in a browser, displayed in a notebook, or saved to disk as a standalone HTML file. The GLM must be fitted and have the computed design matrix(ces). Parameters ---------- A fitted first or second level model object. contrasts: Dict[string, ndarray] or String or List[String] or ndarray or List[ndarray] Contrasts information for a first or second level model. Example: Dict of contrast names and coefficients, or list of contrast names or list of contrast coefficients or contrast name or contrast coefficient Each contrast name must be a string. Each contrast coefficient must be a list or numpy array of ints. Contrasts are passed to ``contrast_def`` for FirstLevelModel (:func:`nilearn.glm.first_level.FirstLevelModel.compute_contrast`) & second_level_contrast for SecondLevelModel (:func:`nilearn.glm.second_level.SecondLevelModel.compute_contrast`) title: String, optional If string, represents the web page's title and primary heading, model type is sub-heading. If None, page titles and headings are autogenerated using contrast names. bg_img: Niimg-like object Default is the MNI152 template See http://nilearn.github.io/manipulating_images/input_output.html The background image for mask and stat maps to be plotted on upon. To turn off background image, just pass "bg_img=None". threshold: float Default is 3.09 Cluster forming threshold in same scale as `stat_img` (either a t-scale or z-scale value). Used only if height_control is None. alpha: float Default is 0.001 Number controlling the thresholding (either a p-value or q-value). Its actual meaning depends on the height_control parameter. This function translates alpha to a z-scale threshold. cluster_threshold: int, optional Default is 0 Cluster size threshold, in voxels. height_control: string or None false positive control meaning of cluster forming threshold: 'fpr' (default) or 'fdr' or 'bonferroni' or None min_distance: `float` For display purposes only. Minimum distance between subpeaks in mm. Default is 8 mm. plot_type: String. ['slice' (default) or 'glass'] Specifies the type of plot to be drawn for the statistical maps. display_mode: string Default is 'z' if plot_type is 'slice'; ' ortho' if plot_type is 'glass'. Choose the direction of the cuts: 'x' - sagittal, 'y' - coronal, 'z' - axial, 'l' - sagittal left hemisphere only, 'r' - sagittal right hemisphere only, 'ortho' - three cuts are performed in orthogonal directions. Possible values are: 'ortho', 'x', 'y', 'z', 'xz', 'yx', 'yz', 'l', 'r', 'lr', 'lzr', 'lyr', 'lzry', 'lyrz'. report_dims: Sequence[int, int] Default is (1600, 800) pixels. Specifies width, height (in pixels) of report window within a notebook. Only applicable when inserting the report into a Jupyter notebook. Can be set after report creation using report.width, report.height. Returns ------- report_text: HTMLDocument Object Contains the HTML code for the GLM Report. """ from nilearn.reporting import make_glm_report return make_glm_report(self, contrasts, title=title, bg_img=bg_img, threshold=threshold, alpha=alpha, cluster_threshold=cluster_threshold, height_control=height_control, min_distance=min_distance, plot_type=plot_type, display_mode=display_mode, report_dims=report_dims)
plotting.plot_stat_map( z_map, bg_img=mean_img_, threshold=3.0, title='%s, fixed effects' % contrast_id) plotting.show() ######################################################################### # Not unexpectedly, the fixed effects version displays higher peaks than the # input sessions. Computing fixed effects enhances the signal-to-noise ratio of # the resulting brain maps. ######################################################################### # Generating a report # ------------------- # Since we have already computed the FirstLevelModel and # and have the contrast, we can quickly create a summary report. from nilearn.reporting import make_glm_report report = make_glm_report(fmri_glm, contrasts, bg_img=mean_img_, ) ######################################################################### # We have several ways to access the report: # report # This report can be viewed in a notebook # report.save_as_html('report.html') # report.open_in_browser()
for condition_ in conditions: z_maps.append(glm.compute_contrast(condition_)) condition_idx.append(condition_) session_idx.append(session) ######################################################################### # Generating a report # ------------------- # Since we have already computed the FirstLevelModel # and have the contrast, we can quickly create a summary report. from nilearn.image import mean_img from nilearn.reporting import make_glm_report mean_img_ = mean_img(func_filename) report = make_glm_report(glm, contrasts=conditions, bg_img=mean_img_, ) ############################################################################# # In a jupyter notebook, the report will be automatically inserted, as above. # We have several other ways to access the report: # report # This report can be viewed in a notebook # report.save_as_html('report.html') # report.open_in_browser() ############################################################################# # Transform the maps to an array of values # ---------------------------------------- from nilearn.input_data import NiftiMasker
marker_color='g', marker_size=300) display.savefig(filename) print("Save z-map in '{0}'.".format(filename)) ########################################################################### # Generating a report # ------------------- # It can be useful to quickly generate a # portable, ready-to-view report with most of the pertinent information. # This is easy to do if you have a fitted model and the list of contrasts, # which we do here. from nilearn.reporting import make_glm_report report = make_glm_report( first_level_model, contrasts=contrasts, title='ADHD DMN Report', cluster_threshold=15, min_distance=8., plot_type='glass', ) ######################################################################### # We have several ways to access the report: # report # This report can be viewed in a notebook # report.save_as_html('report.html') # report.open_in_browser()