コード例 #1
0
        # additional ``kwargs`` for more informative report
        paradigm=paradigm.__dict__,
        TR=tr,
        n_scans=n_scans,
        hfcut=hfcut,
        frametimes=frametimes,
        drift_model=drift_model,
        hrf_model=hrf_model,
        )

    ProgressReport().finish_dir(subject_session_output_dir)
    print "Statistic report written to %s\r\n" % stats_report_filename

# compute a population-level mask as the intersection of individual masks
print "Inter-session GLM"
grp_mask = nibabel.Nifti1Image(intersect_masks(mask_images).astype(np.int8),
                               nibabel.load(mask_images[0]).get_affine())
second_level_z_maps = {}
design_matrix = np.ones(len(first_level_effects_maps)
                        )[:, np.newaxis]  # only the intercept
for contrast_id in contrasts:
    print "\tcontrast id: %s" % contrast_id

    # effects maps will be the input to the second level GLM
    first_level_image = nibabel.concat_images(
        [x[contrast_id] for x in first_level_effects_maps])

    # fit 2nd level GLM for given contrast
    grp_model = FMRILinearModel(first_level_image, design_matrix, grp_mask)
    grp_model.fit(do_scaling=False, model='ols')
コード例 #2
0
def group_one_sample_t_test(masks, effects_maps, contrasts, output_dir,
                            start_time=base_reporter.pretty_time(),
                            **kwargs):
    """
    Runs a one-sample t-test procedure for group analysis. Here, we are
    for each experimental condition, only interested refuting the null
    hypothesis H0: "The average effect accross the subjects is zero!"

    Parameters
    ----------
    masks: list of strings or nibabel image objects
        subject masks, one per subject

    effects_maps: list of dicts of lists
        effects maps from subject-level GLM; each entry is a dictionary;
        each entry (indexed by condition id) of this dictionary is the
        filename (or correspinding nibabel image object) for the effects
        maps for that condition (aka contrast),for that subject

    contrasts: dictionary of array_likes
        contrasts vectors, indexed by condition id

    kwargs: dict_like
        parameters can be regular `nipy.labs.viz.plot_map` parameters
        (e.g slicer="y") or any parameter we want be reported (e.g
        fwhm=[5, 5, 5])

    """

    # make output directory
    if not os.path.exists(output_dir):
        os.makedirs(output_dir)

    assert len(masks) == len(effects_maps), (len(masks), len(effects_maps))

    # compute group mask
    group_mask = nibabel.Nifti1Image(
        intersect_masks(masks).astype(np.int8),
        (nibabel.load(masks[0]) if isinstance(
                masks[0], basestring) else masks[0]).get_affine())

    # construct design matrix (only one covariate, namely the "mean effect")
    design_matrix = np.ones(len(effects_maps)
                            )[:, np.newaxis]  # only the intercept

    group_level_z_maps = {}
    group_level_t_maps = {}
    for contrast_id in contrasts:
        print "\tcontrast id: %s" % contrast_id

        # effects maps will be the input to the second level GLM
        first_level_image = nibabel.concat_images(
            [x[contrast_id] for x in effects_maps])

        # fit 2nd level GLM for given contrast
        group_model = FMRILinearModel(first_level_image,
                                      design_matrix, group_mask)
        group_model.fit(do_scaling=False, model='ols')

        # specify and estimate the contrast
        contrast_val = np.array(([[1.]])
                                )  # the only possible contrast !
        z_map, t_map = group_model.contrast(contrast_val,
                                      con_id='one_sample %s' % contrast_id,
                                      output_z=True,
                                      output_stat=True)

        # save map
        for map_type, map_img in zip(["z", "t"], [z_map, t_map]):
            map_dir = os.path.join(output_dir, '%s_maps' % map_type)
            if not os.path.exists(map_dir):
                os.makedirs(map_dir)
            map_path = os.path.join(map_dir, 'group_level_%s.nii.gz' % (
                    contrast_id))
            print "\t\tWriting %s ..." % map_path
            nibabel.save(map_img, map_path)
            if map_type == "z":
                group_level_z_maps[contrast_id] = map_path
            elif map_type == "t":
                group_level_z_maps[contrast_id] = map_path

    # do stats report
    stats_report_filename = os.path.join(
        output_dir, "report_stats.html")
    generate_subject_stats_report(stats_report_filename, contrasts,
                                  group_level_z_maps, group_mask,
                                  start_time=start_time,
                                  **kwargs)

    print "\r\nStatistic report written to %s\r\n" % (
        stats_report_filename)

    return group_level_z_maps
コード例 #3
0
                                                                 -1)))
        if n_jobs > 1:
            subjects = Parallel(n_jobs=n_jobs, verbose=100)(
                delayed(run_suject_level1_glm)(subject_data, **kwargs)
                for subject_data in subjects)
        else:
            subjects = [
                run_suject_level1_glm(subject_data, **kwargs)
                for subject_data in subjects
            ]

        # level 2
        stats_start_time = pretty_time()
        mask_images = [subject_data.mask for subject_data in subjects]
        group_mask = nibabel.Nifti1Image(
            intersect_masks(mask_images).astype(np.int8),
            nibabel.load(mask_images[0]).get_affine())
        nibabel.save(group_mask, os.path.join(task_output_dir, "mask.nii.gz"))

        print "... done.\r\n"
        print "Group GLM"
        contrasts = subjects[0].contrasts
        subjects_effects_maps = [
            subject_data.effects_maps for subject_data in subjects
        ]

        group_one_sample_t_test(
            mask_images,
            subjects_effects_maps,
            contrasts,
            task_output_dir,
コード例 #4
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            subjects = Parallel(
                n_jobs=n_jobs, verbose=100)(delayed(
                    run_suject_level1_glm)(
                        subject_data,
                        **kwargs) for subject_data in subjects)
        else:
            subjects = [run_suject_level1_glm(subject_data,
                                              **kwargs)
                        for subject_data in subjects]
        subjects = [subject for subject in subjects if subject]

        # level 2
        stats_start_time = pretty_time()
        mask_images = [subject_data.mask for subject_data in subjects]
        group_mask = nibabel.Nifti1Image(
            intersect_masks(mask_images).astype(np.int8),
            nibabel.load(mask_images[0]).get_affine())
        nibabel.save(group_mask, os.path.join(
                task_output_dir, "mask.nii.gz"))

        print "... done.\r\n"
        print "Group GLM"
        contrasts = subjects[0].contrasts
        subjects_effects_maps = [subject_data.effects_maps
                                 for subject_data in subjects]

        group_one_sample_t_test(
            mask_images,
            subjects_effects_maps,
            contrasts,
            task_output_dir,
コード例 #5
0
def group_one_sample_t_test(masks,
                            effects_maps,
                            contrasts,
                            output_dir,
                            start_time=base_reporter.pretty_time(),
                            **kwargs):
    """
    Runs a one-sample t-test procedure for group analysis. Here, we are
    for each experimental condition, only interested refuting the null
    hypothesis H0: "The average effect accross the subjects is zero!"

    Parameters
    ----------
    masks: list of strings or nibabel image objects
        subject masks, one per subject

    effects_maps: list of dicts of lists
        effects maps from subject-level GLM; each entry is a dictionary;
        each entry (indexed by condition id) of this dictionary is the
        filename (or correspinding nibabel image object) for the effects
        maps for that condition (aka contrast),for that subject

    contrasts: dictionary of array_likes
        contrasts vectors, indexed by condition id

    kwargs: dict_like
        parameters can be regular `nipy.labs.viz.plot_map` parameters
        (e.g slicer="y") or any parameter we want be reported (e.g
        fwhm=[5, 5, 5])

    """

    # make output directory
    if not os.path.exists(output_dir):
        os.makedirs(output_dir)

    assert len(masks) == len(effects_maps), (len(masks), len(effects_maps))

    # compute group mask
    group_mask = nibabel.Nifti1Image(
        intersect_masks(masks).astype(np.int8),
        (nibabel.load(masks[0])
         if isinstance(masks[0], basestring) else masks[0]).get_affine())

    # construct design matrix (only one covariate, namely the "mean effect")
    design_matrix = np.ones(
        len(effects_maps))[:, np.newaxis]  # only the intercept

    group_level_z_maps = {}
    group_level_t_maps = {}
    for contrast_id in contrasts:
        print "\tcontrast id: %s" % contrast_id

        # effects maps will be the input to the second level GLM
        first_level_image = nibabel.concat_images(
            [x[contrast_id] for x in effects_maps])

        # fit 2nd level GLM for given contrast
        group_model = FMRILinearModel(first_level_image, design_matrix,
                                      group_mask)
        group_model.fit(do_scaling=False, model='ols')

        # specify and estimate the contrast
        contrast_val = np.array(([[1.]]))  # the only possible contrast !
        z_map, t_map = group_model.contrast(contrast_val,
                                            con_id='one_sample %s' %
                                            contrast_id,
                                            output_z=True,
                                            output_stat=True)

        # save map
        for map_type, map_img in zip(["z", "t"], [z_map, t_map]):
            map_dir = os.path.join(output_dir, '%s_maps' % map_type)
            if not os.path.exists(map_dir):
                os.makedirs(map_dir)
            map_path = os.path.join(map_dir,
                                    'group_level_%s.nii.gz' % (contrast_id))
            print "\t\tWriting %s ..." % map_path
            nibabel.save(map_img, map_path)
            if map_type == "z":
                group_level_z_maps[contrast_id] = map_path
            elif map_type == "t":
                group_level_z_maps[contrast_id] = map_path

    # do stats report
    stats_report_filename = os.path.join(output_dir, "report_stats.html")
    generate_subject_stats_report(stats_report_filename,
                                  contrasts,
                                  group_level_z_maps,
                                  group_mask,
                                  start_time=start_time,
                                  **kwargs)

    print "\r\nStatistic report written to %s\r\n" % (stats_report_filename)

    return group_level_z_maps