Hi,
Why is there a discrepancy between ‘activations’ and ‘images’ for the same study? Aren’t both supposed to be voxels in the MNI152 space
I am using the python API for neurosynth.
The code:
dataset = Dataset.load(opj(DATA_DIR, ‘neurosynth’, ‘dataset.pkl’))
activations = dataset.activations
img = dataset.get_image_data(ids=[9065511])
print img.sum()
#prints 1172
activations[activations[‘id’]==9065511].shape
#prints (10, 16)
Are activations thresholded?
The image data gives you a vector of voxels, where a value of 1 means the voxel falls within X mm (depending on the radius used at initialization) of a reported activation. The length is equal to the number of voxels within the gray matter mask.
The activation data is just a text table containing information about reported activations. The 16 columns are different variables (e.g., x, y, and z coordinates; detected space; etc.).
The reason the sum is different is that (a) in the image, the activations are convolved with spheres (so the sum will generally be much larger), and (b) it’s a binary map, so if activations overlap, the max value remains 1.
MANAGED BY INCF