Note
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Generate modeled activation maps¶
Start with the necessary imports¶
import os
import numpy as np
import matplotlib.pyplot as plt
from nilearn.plotting import plot_stat_map
import nimare
from nimare.tests.utils import get_test_data_path
Load Dataset¶
dset_file = os.path.join(get_test_data_path(), 'nidm_pain_dset.json')
dset = nimare.dataset.Dataset(dset_file)
MKDA kernel maps¶
kernel = nimare.meta.cbma.MKDAKernel(r=8)
mkda_r08 = kernel.transform(dset)
kernel = nimare.meta.cbma.MKDAKernel(r=9)
mkda_r09 = kernel.transform(dset)
kernel = nimare.meta.cbma.MKDAKernel(r=10)
mkda_r10 = kernel.transform(dset)
kernel = nimare.meta.cbma.MKDAKernel(r=11)
mkda_r11 = kernel.transform(dset)
fig, axes = plt.subplots(nrows=4, ncols=1, figsize=(10, 17.5))
plot_stat_map(mkda_r08[2], cut_coords=[-2, -10, -4],
title='r=8mm', vmax=2, axes=axes[0],
draw_cross=False)
plot_stat_map(mkda_r09[2], cut_coords=[-2, -10, -4],
title='r=9mm', vmax=2, axes=axes[1],
draw_cross=False)
plot_stat_map(mkda_r10[2], cut_coords=[-2, -10, -4],
title='r=10mm', vmax=2, axes=axes[2],
draw_cross=False)
plot_stat_map(mkda_r11[2], cut_coords=[-2, -10, -4],
title='r=11mm', vmax=2, axes=axes[3],
draw_cross=False)
fig.show()
Show different kernel types together¶
kernel = nimare.meta.cbma.MKDAKernel(r=10)
mkda_res = kernel.transform(dset)
kernel = nimare.meta.cbma.KDAKernel(r=10)
kda_res = kernel.transform(dset)
kernel = nimare.meta.cbma.ALEKernel(n=20)
ale_res = kernel.transform(dset)
max_conv = np.max(kda_res[2].get_data())
plot_stat_map(ale_res[2], cut_coords=[-2, -10, -4], title='ALE')
plot_stat_map(mkda_res[2], cut_coords=[-2, -10, -4], title='MKDA', vmax=max_conv)
plot_stat_map(kda_res[2], cut_coords=[-2, -10, -4], title='KDA', vmax=max_conv)
Total running time of the script: ( 0 minutes 0.000 seconds)