In [1]:

    

import neurosynth as ns
import glob, os


    

In [2]:

    

# load ROIsBzdok_DMN
atlas_nii = sorted(glob.glob('Bzdok_DMN/*.nii.gz'))
atlas_names = [roi.split(os.sep)[-1].split('.nii.gz')[0]  for roi in atlas_nii]


    

In [3]:

    

# load neurosynth dataset
dataset = ns.Dataset.load('data/dataset.pkl')


    

In [4]:

    

ids = dataset.get_studies(mask='./Bzdok_DMN/%s.nii.gz'%atlas_names[0], activation_threshold=0.1)
ma = ns.meta.MetaAnalysis(dataset, ids,  q=0.01, prior=0.5)


    

    




C:\Users\hw1012\AppData\Local\Continuum\Anaconda3\envs\analysis\lib\site-packages\neurosynth\analysis\meta.py:135: RuntimeWarning: invalid value encountered in divide
  pFgA = pAgF * pF / pA
C:\Users\hw1012\AppData\Local\Continuum\Anaconda3\envs\analysis\lib\site-packages\neurosynth\analysis\meta.py:140: RuntimeWarning: invalid value encountered in divide
  pFgA_prior = pAgF * prior / pA_prior

In [7]:

    

ma.images.keys()


    

    
Out[7]:





['pA_pF_emp_prior',
 'pFgA_pF=0.50',
 'consistency_z_FDR_0.01',
 'pFgA_pF=0.50_FDR_0.01',
 'specificity_z',
 'pFgA_emp_prior_FDR_0.01',
 'pA_pF=0.50',
 'consistency_z',
 'specificity_z_FDR_0.01',
 'pFgA_emp_prior',
 'pAgF']

In [12]:

    

# Seed-based coactivation meta-analysis
for a in atlas_names:
    ids = dataset.get_studies(mask='./Bzdok_DMN/%s.nii.gz'%a, activation_threshold=0.1)
    ma = ns.meta.MetaAnalysis(dataset, ids,  q=0.01, prior=0.5)
    ma.save_results(image_list=['specificity_z'], output_dir='./Neurosynth_meta/', prefix=a)