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add a notebook for comparing cbmr and cbma on neurosynth #835

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Closes # .

Changes proposed in this pull request:

  • Compare spatial convergence between pain studies and non-pain studies (in Neurosynth dataset) via CBMR and MKDA Chi-square analysis

@jdkent jdkent changed the title add a notebook for comparing cbmr and cbma on neuroth add a notebook for comparing cbmr and cbma on neurosynth Sep 15, 2023
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Codecov Report

All modified lines are covered by tests ✅

Comparison is base (d0b4c0f) 0.00% compared to head (63e6e4c) 89.07%.

Additional details and impacted files
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##           main     #835       +/-   ##
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+ Coverage      0   89.07%   +89.07%     
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  Files         0       48       +48     
  Lines         0     6113     +6113     
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+ Hits          0     5445     +5445     
- Misses        0      668      +668     

see 48 files with indirect coverage changes

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just some spelling errors to fix for now!

# of spatial intensity between pain studies and non-pain studies in Neurosynth.
# The null hypothesis assumes spatial intensity estimations of two groups are equal
# at voxel level, $H_0: \mu_{1j}=\mu_{2j}, j=1,\cdots,N$, where $N$ is number of
# voxels within brain mask, $j$ is the index of voxel. Areas with significant p-vaules
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# voxels within brain mask, $j$ is the index of voxel. Areas with significant p-vaules
# voxels within brain mask, $j$ is the index of voxel. Areas with significant p-values

# The null hypothesis assumes spatial intensity estimations of two groups are equal
# at voxel level, $H_0: \mu_{1j}=\mu_{2j}, j=1,\cdots,N$, where $N$ is number of
# voxels within brain mask, $j$ is the index of voxel. Areas with significant p-vaules
# (siginificant difference in spatial intensity estimation between two groups) are
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# (siginificant difference in spatial intensity estimation between two groups) are
# (significant difference in spatial intensity estimation between two groups) are

# voxels within brain mask, $j$ is the index of voxel. Areas with significant p-vaules
# (siginificant difference in spatial intensity estimation between two groups) are
# highlighted. We found that estimated activation level are significantly different
# in ... between pain group and non-pain group.
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# in ... between pain group and non-pain group.
# in ... between the pain group and non-pain group.

# Run MKDA on Neurosynth dataset
# -----------------------------------------------------------------------------
# For the purpose of justifying the validity of CBMR framework, we compare the estimated
# spatial covergence of activation regions between pain studies and non-pain fMRI studies
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# spatial covergence of activation regions between pain studies and non-pain fMRI studies
# spatial convergence of activation regions between pain studies and non-pain fMRI studies

)

###############################################################################
# This figure (displayed as z-statistics map) shows MKDA spatial covergence of
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# This figure (displayed as z-statistics map) shows MKDA spatial covergence of
# This figure (displayed as a z-statistic map) shows MKDA spatial convergence of

coordinate-based meta-analysis (CBMA) in NiMARE

CBMR is a generative framework to approximate smooth activation intensity function and investigate
the effect of study-level moderators (e.g., year of pubilication, sample size, subtype of stimuli).
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the effect of study-level moderators (e.g., year of pubilication, sample size, subtype of stimuli).
the effect of study-level moderators (e.g., year of publication, sample size, subtype of stimuli).

This tutorial is intended to provide an intuitive comparison of CBMA and MKDA results on
neurosynth dataset.

For more detailed introduction to CBMR implementation in NiMARE, see the `CBMR tutoral
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For more detailed introduction to CBMR implementation in NiMARE, see the `CBMR tutoral
For more detailed introduction to CBMR implementation in NiMARE, see the `CBMR tutorial


For more detailed introduction to CBMR implementation in NiMARE, see the `CBMR tutoral
<https://nimare.readthedocs.io/en/latest/auto_examples/02_meta-analyses/11_plot_cbmr.html>`_ and
`documatation <https://nimare.readthedocs.io/en/latest/generated/nimare.meta.cbmr.html>`_.
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`documatation <https://nimare.readthedocs.io/en/latest/generated/nimare.meta.cbmr.html>`_.
`documentation <https://nimare.readthedocs.io/en/latest/generated/nimare.meta.cbmr.html>`_.

###############################################################################
# Download the Neurosynth Dataset
# -----------------------------------------------------------------------------
# Neurosynth is a large-scale functional magnetic resonance imaing (fMRI) database.
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# Neurosynth is a large-scale functional magnetic resonance imaing (fMRI) database.
# Neurosynth is a large-scale functional magnetic resonance imaging (fMRI) database.

@yifan0330
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just some spelling errors to fix for now!

Sorry for the spelling errors! I've fixed them now

Comment on lines +61 to +62
neurosynth_dset = download_abstracts(neurosynth_dset, "example@example.edu")
neurosynth_dset.save(os.path.join(out_dir, "neurosynth_dataset_with_abstracts.pkl.gz"))
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think this bit can be removed, we are not using abstracts for this notebook

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Sorry for changing my mind about this, could you add a .ipynb notebook to misc-notebooks with the cells executed? Since this notebook is a bit intensive it would be nice to have a version of this where the code is already run and visible.

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