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# Copyright 2017 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Intersubject analyses (ISC/ISFC)
Functions for computing intersubject correlation (ISC) and intersubject
functional correlation (ISFC)
Paper references:
ISC: Hasson, U., Nir, Y., Levy, I., Fuhrmann, G. & Malach, R. Intersubject
synchronization of cortical activity during natural vision. Science 303,
1634–1640 (2004).
ISFC: Simony E, Honey CJ, Chen J, Lositsky O, Yeshurun Y, Wiesel A, Hasson U
(2016) Dynamic reconfiguration of the default mode network during narrative
comprehension. Nat Commun 7.
"""
# Authors: Christopher Baldassano and Mor Regev
# Princeton University, 2017
from brainiak.fcma.util import compute_correlation
import numpy as np
from scipy import stats
from .utils.utils import phase_randomize, p_from_null
def isc(D, collapse_subj=True, return_p=False, num_perm=1000,
two_sided=False, random_state=0):
"""Intersubject correlation
For each voxel, computes the correlation of each subject's timecourse with
the mean of all other subjects' timecourses. By default the result is
averaged across subjects, unless collapse_subj is set to False. A null
distribution can optionally be computed using phase randomization, to
compute a p value for each voxel.
Parameters
----------
D : voxel by time by subject ndarray
fMRI data for which to compute ISC
collapse_subj : bool, default:True
Whether to average across subjects before returning result
return_p : bool, default:False
Whether to use phase randomization to compute a p value for each voxel
num_perm : int, default:1000
Number of null samples to use for computing p values
two_sided : bool, default:False
Whether the p value should be one-sided (testing only for being
above the null) or two-sided (testing for both significantly positive
and significantly negative values)
random_state : RandomState or an int seed (0 by default)
A random number generator instance to define the state of the
random permutations generator.
Returns
-------
ISC : voxel ndarray (or voxel by subject ndarray, if collapse_subj=False)
pearson correlation for each voxel, across subjects
p : ndarray the same shape as ISC (if return_p = True)
p values for each ISC value under the null distribution
"""
n_vox = D.shape[0]
n_subj = D.shape[2]
if return_p:
n_perm = num_perm
else:
n_perm = 0
ISC = np.zeros((n_vox, n_subj, n_perm + 1))
for p in range(n_perm + 1):
# Loop across choice of leave-one-out subject
for loo_subj in range(n_subj):
group = np.mean(D[:, :, np.arange(n_subj) != loo_subj], axis=2)
subj = D[:, :, loo_subj]
for v in range(n_vox):
ISC[v, loo_subj, p] = stats.pearsonr(group[v, :],
subj[v, :])[0]
# Randomize phases of D to create next null dataset
D = phase_randomize(D, random_state)
if collapse_subj:
ISC = np.mean(ISC, axis=1)
if return_p:
p = p_from_null(ISC, two_sided)
return ISC[..., 0], p
else:
return ISC[..., 0]
def isfc(D, collapse_subj=True, return_p=False,
num_perm=1000, two_sided=False, random_state=0):
"""Intersubject functional correlation
Computes the correlation between the timecoure of each voxel in each
subject with the average of all other subjects' timecourses in *all*
voxels. By default the result is averaged across subjects, unless
collapse_subj is set to False. A null distribution can optionally be
computed using phase randomization, to compute a p value for each voxel-to-
voxel correlation.
Uses the high performance compute_correlation routine from fcma.util
Parameters
----------
D : voxel by time by subject ndarray
fMRI data for which to compute ISFC
collapse_subj : bool, default:True
Whether to average across subjects before returning result
return_p : bool, default:False
Whether to use phase randomization to compute a p value for each voxel
num_perm : int, default:1000
Number of null samples to use for computing p values
two_sided : bool, default:False
Whether the p value should be one-sided (testing only for being
above the null) or two-sided (testing for both significantly positive
and significantly negative values)
random_state : RandomState or an int seed (0 by default)
A random number generator instance to define the state of the
random permutations generator.
Returns
-------
ISFC : voxel by voxel ndarray
(or voxel by voxel by subject ndarray, if collapse_subj=False)
pearson correlation between all pairs of voxels, across subjects
p : ndarray the same shape as ISC (if return_p = True)
p values for each ISC value under the null distribution
"""
n_vox = D.shape[0]
n_subj = D.shape[2]
if return_p:
n_perm = num_perm
else:
n_perm = 0
ISFC = np.zeros((n_vox, n_vox, n_subj, n_perm + 1))
for p in range(n_perm + 1):
# Loop across choice of leave-one-out subject
for loo_subj in range(D.shape[2]):
group = np.mean(D[:, :, np.arange(n_subj) != loo_subj], axis=2)
subj = D[:, :, loo_subj]
ISFC[:, :, loo_subj, p] = compute_correlation(group, subj)
# Symmetrize matrix
ISFC[:, :, loo_subj, p] = (ISFC[:, :, loo_subj, p] +
ISFC[:, :, loo_subj, p].T) / 2
# Randomize phases of D to create next null dataset
D = phase_randomize(D, random_state)
if collapse_subj:
ISFC = np.mean(ISFC, axis=2)
if return_p:
p = p_from_null(ISFC, two_sided)
return ISFC[..., 0], p
else:
return ISFC[..., 0]