No visible boxplot with meta-analytical maps

[Enouement]

Description of issue

Hi,

I am currently trying to integrate neurompas into my meta-analysis. However, I have the problem that when I use the meta-analytical maps, it does not show me a boxplot in the graph, but it gives me the r and p-spin values. It does not give me an error message either. When i use an activation map from a task-based fMRI experiment, everything works fine. Is it possible that the binary values of the meta-analytical maps cannot be calculated correctly?

Thanks in advance

Code of Conduct

• [X] I agree to follow the neuromaps Code of Conduct

[justinehansen]

Hi, I'm not sure I understand. Which function are you using?

[Enouement]

Dear Mr. Hansen,

sry for the late reply concerning the functions. Was on holiday and
had no access to the analyses. Enclosed is the code we use with the
set-up and the first comparison with our meta-analytic map. It should
also include a screenshot with the result we got with the first
comparison as in the code.

Best regards

!pip install git+https://github.com/netneurolab/neuromaps

from neuromaps import transforms, nulls, datasets, plotting, images, resampling print(len(datasets.available_annotations()))

import neuromaps.images

from neuromaps.stats import compare_images

import matplotlib.pyplot as plt

from neuromaps.datasets import available_annotations

gene = datasets.fetch_annotation(source='abagen') ratio = datasets.fetch_annotation(source='hcps1200', desc='myelinmap') thick = datasets.fetch_annotation(source='hcps1200', desc='thickness') deve = datasets.fetch_annotation(source='hill2010', desc='devexp') evo = datasets.fetch_annotation(source='hill2010', desc='evoexp') inter = datasets.fetch_annotation(source='mueller2013', desc='intersubjvar') cbf = datasets.fetch_annotation(source='raichle', desc='cbf') cbv = datasets.fetch_annotation(source='raichle', desc='cbv') oxy = datasets.fetch_annotation(source='raichle', desc='cmr02') glc = datasets.fetch_annotation(source='raichle', desc='cmrglc') nih = datasets.fetch_annotation(source='reardon2018', desc='scalingnih') pnc = datasets.fetch_annotation(source='reardon2018', desc='scalingpnc')

cl1 = neuromaps.images.load_nifti('cl1_ALE.nii') cl2 = neuromaps.images.load_nifti('cl2_ALE.nii') cl3 = neuromaps.images.load_nifti('cl3_ALE.nii') cl4 = neuromaps.images.load_nifti('cl4_ALE.nii') cl5 = neuromaps.images.load_nifti('cl5_ALE.nii') cl6 = neuromaps.images.load_nifti('cl6_ALE.nii')

neuromaps.datasets.fetch_all_atlases

cl_fsaverage = transforms.mni152_to_fsaverage(cl1, '10k') print(cl_fsaverage) print(images.load_data(cl_fsaverage).shape)

cl_rotated = nulls.alexander_bloch(cl_fsaverage, 'fsaverage', '10k', n_perm=1000) print(cl_rotated.shape)

r, p, nulldist = compare_images(cl_fsaverage, gene, nulls=cl_rotated, return_nulls=True) print('rho =' + str(r) + ', pspin = ' + str(p)) print(nulldist.shape)

plt.ion() plt.figure() plt.scatter(1, r) plt.boxplot(nulldist) plt.show()