Hi, I'm following 2 ISC tutorials, the official Brainiak tutorial (https://brainiak.org/tutorials/10-isc/) and Nastase's tutorial (https://github.com/snastase/isc-tutorial). I notice these two tutorials use different ways to calculate ISC maps via isc(data, pairwise=False, summary_statistic=None, tolerate_nans=True).
In the former one, it calculates ISC maps for 2 groups separately via a for loop, then it uses np.vstack to concatenate ISC maps from both tasks for the permutation test.

# run ISC, loop over conditions 
isc_maps = {}
for task_name in all_task_names:
    isc_maps[task_name] = isc(bold[task_name], pairwise=False)
    print('Shape of %s condition:' % task_name, np.shape(isc_maps[task_name]))

# Concatenate ISCs from both tasks
isc_maps_all_tasks = np.vstack([isc_maps[task_name] for
                                task_name in all_task_names])

print('group_assignment: {}'.format(group_assignment))
print('isc_maps_all_tasks: {}' .format(np.shape(isc_maps_all_tasks)))

# permutation testing
n_permutations = 1000
summary_statistic='mean'

observed, p, distribution = permutation_isc(
    isc_maps_all_tasks, 
    pairwise=False,
    group_assignment=group_assignment, 
    summary_statistic=summary_statistic,
    n_permutations=n_permutations
)

In contrast, the data variable in the latter one contains 2 groups' data, and they only use isc(data, pairwise=False, summary_statistic=None, tolerate_nans=True) once to calculate ISC maps.

# Create data with noisy subset of subjects
noisy_data = np.dstack((np.dstack((
    simulated_timeseries(n_subjects // 2, n_TRs,
                         n_voxels=n_voxels, noise=1))),
                        np.dstack((
    simulated_timeseries(n_subjects // 2, n_TRs,
                         n_voxels=n_voxels, noise=5)))))

# Create group_assignment variable with group labels
group_assignment = [1]*10 + [2]*10
print(f"Group assignments: \n{group_assignment}")

# Compute ISCs and then run two-sample permutation test on ISCs
iscs = isc(noisy_data, pairwise=True, summary_statistic=None)
observed, p, distribution = permutation_isc(iscs,
                                            group_assignment=group_assignment,
                                            pairwise=True,
                                            summary_statistic='median',
                                            n_permutations=200)

I'm confused about this difference and wondering whether it makes different results when using permutation_isc becuase I think these 2 ways will create different numbers of isc maps with the pairwise approach. Does permutation_isc has different ways to calculate isc maps from different approach, thus these 2 way calculate ISC correctly?