def macm_workflow(dataset_file,
mask_file,
output_dir=None,
prefix=None,
n_iters=10000,
v_thr=0.001,
n_cores=1):
"""Perform MACM with ALE algorithm."""
LGR.info("Loading coordinates...")
dset = Dataset(dataset_file)
sel_ids = dset.get_studies_by_mask(mask_file)
sel_dset = dset.slice(sel_ids)
# override sample size
n_subs_db = dset.coordinates.drop_duplicates("id")["n"].astype(
float).astype(int).sum()
n_subs_sel = sel_dset.coordinates.drop_duplicates("id")["n"].astype(
float).astype(int).sum()
LGR.info(f"{len(sel_ids)} studies selected out of {len(dset.ids)}.")
boilerplate = """
Meta-analytic connectivity modeling (MACM; Laird et al., 2009; Robinson et al.,
2009; Eickhoff et al., 2010) analysis was performed with the activation
likelihood estimation (ALE; Turkeltaub, Eden, Jones, & Zeffiro, 2002; Eickhoff,
Bzdok, Laird, Kurth, & Fox, 2012; Turkeltaub et al., 2012) meta-analysis
algorithm using NiMARE. The input dataset included {n_foci_db}
foci from {n_subs_db} participants across {n_exps_db} studies/experiments, from
which studies/experiments were selected for analysis if they had at least one
focus inside the target mask. The resulting sample included {n_foci_sel}
foci from {n_subs_sel} participants across {n_exps_sel} studies/experiments.
Modeled activation maps were generated for each study/experiment by convolving
each focus with a Gaussian kernel determined by the study/experiment's sample
size. For voxels with overlapping kernels, the maximum value was retained.
The modeled activation maps were rendered in MNI 152 space (Fonov et al., 2009;
Fonov et al., 2011) at 2x2x2mm resolution. A map of ALE values was then
computed for the sample as the union of modeled activation values across
studies/experiments. Voxelwise statistical significance was determined based on
an analytically derived null distribution using the method described in
Eickhoff, Bzdok, Laird, Kurth, & Fox (2012), prior to multiple comparisons
correction.
-> If the cluster-level FWE-corrected results were used, include the following:
A cluster-forming threshold of p < {unc} was used to perform cluster-level FWE
correction. {n_iters} iterations were performed to estimate a null distribution
of cluster sizes, in which the locations of coordinates were randomly drawn
from a gray matter template and the maximum cluster size was recorded after
applying an uncorrected cluster-forming threshold of p < {unc}. The negative
log-transformed p-value for each cluster in the thresholded map was determined
based on the cluster sizes.
-> If voxel-level FWE-corrected results were used, include the following:
Voxel-level FWE-correction was performed and results were thresholded at
p < {fwe}. {n_iters} iterations were performed to estimate a null
distribution of ALE values, in which the locations of coordinates were randomly
drawn from a gray matter template and the maximum ALE value was recorded.
References
----------
- Eickhoff, S. B., Bzdok, D., Laird, A. R., Kurth, F., & Fox, P. T. (2012).
Activation likelihood estimation meta-analysis revisited. NeuroImage,
59(3), 2349–2361.
- Eickhoff, S. B., Jbabdi, S., Caspers, S., Laird, A. R., Fox, P. T., Zilles,
K., & Behrens, T. E. (2010). Anatomical and functional connectivity of
cytoarchitectonic areas within the human parietal operculum. Journal of
Neuroscience, 30(18), 6409-6421.
- Fonov, V., Evans, A. C., Botteron, K., Almli, C. R., McKinstry, R. C.,
Collins, D. L., & Brain Development Cooperative Group. (2011).
Unbiased average age-appropriate atlases for pediatric studies.
Neuroimage, 54(1), 313-327.
- Fonov, V. S., Evans, A. C., McKinstry, R. C., Almli, C. R., & Collins, D. L.
(2009). Unbiased nonlinear average age-appropriate brain templates from birth
to adulthood. NeuroImage, (47), S102.
- Laird, A. R., Eickhoff, S. B., Li, K., Robin, D. A., Glahn, D. C., &
Fox, P. T. (2009). Investigating the functional heterogeneity of the default
mode network using coordinate-based meta-analytic modeling. The Journal of
Neuroscience: The Official Journal of the Society for Neuroscience, 29(46),
14496–14505.
- Robinson, J. L., Laird, A. R., Glahn, D. C., Lovallo, W. R., & Fox, P. T.
(2009). Metaanalytic connectivity modeling: Delineating the functional
connectivity of the human amygdala. Human Brain Mapping, 31(2), 173-184.
- Turkeltaub, P. E., Eden, G. F., Jones, K. M., & Zeffiro, T. A. (2002).
Meta-analysis of the functional neuroanatomy of single-word reading: method
and validation. NeuroImage, 16(3 Pt 1), 765–780.
- Turkeltaub, P. E., Eickhoff, S. B., Laird, A. R., Fox, M., Wiener, M.,
& Fox, P. (2012). Minimizing within-experiment and within-group effects in
Activation Likelihood Estimation meta-analyses. Human Brain Mapping,
33(1), 1–13.
"""
LGR.info("Performing meta-analysis...")
ale = ALE()
results = ale.fit(dset)
corr = FWECorrector(method="montecarlo",
n_iters=n_iters,
voxel_thresh=v_thr,
n_cores=n_cores)
cres = corr.transform(results)
boilerplate = boilerplate.format(
n_exps_db=len(dset.ids),
n_subs_db=n_subs_db,
n_foci_db=dset.coordinates.shape[0],
n_exps_sel=len(sel_dset.ids),
n_subs_sel=n_subs_sel,
n_foci_sel=sel_dset.coordinates.shape[0],
unc=v_thr,
n_iters=n_iters,
)
if output_dir is None:
output_dir = os.path.abspath(os.path.dirname(dataset_file))
else:
pathlib.Path(output_dir).mkdir(parents=True, exist_ok=True)
if prefix is None:
base = os.path.basename(dataset_file)
prefix, _ = os.path.splitext(base)
prefix += "_"
LGR.info("Saving output maps...")
cres.save_maps(output_dir=output_dir, prefix=prefix)
copyfile(dataset_file,
os.path.join(output_dir, prefix + "input_dataset.json"))
LGR.info("Workflow completed.")
LGR.info(boilerplate)
dset = Dataset(
os.path.join(get_resource_path(), "neurosynth_laird_studies.json"))
dset.annotations.head(5)
###############################################################################
# Create a region of interest
# -----------------------------------------------------------------------------
# First we'll make an ROI
arr = np.zeros(dset.masker.mask_img.shape, int)
arr[65:75, 50:60, 50:60] = 1
mask_img = nib.Nifti1Image(arr, dset.masker.mask_img.affine)
plot_roi(mask_img, draw_cross=False)
# Get studies with voxels in the mask
ids = dset.get_studies_by_mask(mask_img)
###############################################################################
#
# .. _brain-map-decoder-example:
#
# Decode an ROI image using the BrainMap method
# -----------------------------------------------------------------------------
# Run the decoder
decoder = discrete.BrainMapDecoder(correction=None)
decoder.fit(dset)
decoded_df = decoder.transform(ids=ids)
decoded_df.sort_values(by="probReverse", ascending=False).head()
###############################################################################