Is there any way to pull unthresholded images from neurosynth?

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#1

Hello everyone! Pardon my inexperience with programming, I’m sure this is a simple task.

I’m trying to pull unthresholded images from the term-based meta-analysis data published on neurosynth, but I’m having difficulties because it is deprecated. I have been slowly learning other options such as NiMARE, but it seems like there are a lot of different packages to learn.

I was wondering if anyone could point me in the direction of a package/repository that would allow me to either access these unthresholded images, or to generate new ones using the same process.

Again, apologies for the simplicity of the question, I haven’t found a good answer anywhere. Thanks.

#2

I don’t believe the unthresholded maps are available in bulk anywhere, so I can see three possible solutions:

  1. Go to each term’s page on Neurosynth and download the maps by hand.
  2. Write a script to scrape the maps from the website. I wouldn’t recommend doing this, as the Neurosynth documentation explicitly warns that web scraping attempts will result in a permanent IP ban. See the Data section here for that warning.
  3. Use NiMARE. I’ve drafted some code that should work, though it’ll take a while.
import nimare

# Download the Neurosynth data and convert to a NiMARE Dataset
nimare.extract.fetch_neurosynth("data/", unpack=True)
ns_dset = nimare.io.convert_neurosynth_to_dataset(
    "data/database.txt",
    "data/features.txt",
)

# Initialize a meta-analysis
meta = nimare.meta.cbma.mkda.MKDAChi2()

# Get the full list of Neurosynth labels
# You may want to find a way to reduce this list,
# since it will include a bunch of "terms" that are not useful, like "001"
terms = ns_dset.get_labels()
all_ids = ns_dset.ids
for term in terms:
    # Remember to use a threshold of 0.001 to get associated studies,
    # since NiMARE's default is 0.5.
    term_ids = ns_dset.get_studies_by_label(term, label_threshold=0.001)
    notterm_ids = sorted(list(set(all_ids) - set(term_ids)))
    term_dset = ns_dset.slice(term_ids)
    notterm_dset = ns_dset.slice(notterm_ids)
    # Run the meta-analysis.
    # No multiple comparisons correction since you want the unthreshold maps.
    results = meta.fit(term_dset, notterm_dset)
    # Save the maps to files
    term_name = term.split("__")[1]
    results.save_maps(out_dir="meta-analyses/", prefix=term_name)
#3

Thanks for the response! That’s exactly what I was looking for.

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