code: def report_flm_adhd_dmn():  # pragma: no cover # noqa
      t_r = 2.0
      slice_time_ref = 0.0
      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=2.0,
          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=8.0,
          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()
  
imports: import os
  import numpy
  import pandas
  import nilearn
  from nilearn import datasets
  from nilearn.glm.first_level import FirstLevelModel, first_level_from_bids
  from nilearn.glm.first_level.design_matrix import make_first_level_design_matrix
  from nilearn.glm.second_level import SecondLevelModel
  from nilearn.image import resample_to_img
  from nilearn.interfaces.fsl import get_design_from_fslmat
  from nilearn.maskers import NiftiSpheresMasker
  from nilearn.reporting import make_glm_report
label_desc: Use image node_id 1 for calling a global function with example usage: report_flm_adhd_dmn() without return types