Open Enouement opened 2 months ago
Hi, I'm not sure I understand. Which function are you using?
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()
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
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neuromaps
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