# 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, float_type=np.float64):

"""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.

float_type : either float16, float32, or float64,

Depends on the required precision

and available memory in the system.

All the arrays generated during the execution will be cast

to specified float type in order to save memory.

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]

n_perm = num_perm*int(return_p)

max_null = np.zeros(n_perm, dtype=float_type)

min_null = np.zeros(n_perm, dtype=float_type)

ISC = np.zeros((n_vox, n_subj), dtype=float_type)

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] = stats.pearsonr(group[v, :],

subj[v, :])[0]

if collapse_subj:

ISC = np.mean(ISC, axis=1)

for p in range(n_perm):

# Randomize phases of D to create next null dataset

D = phase_randomize(D, random_state)

# Loop across choice of leave-one-out subject

tmp_ISC = np.zeros((n_vox, n_subj), dtype=float_type)

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):

tmp_ISC[v, loo_subj] = stats.pearsonr(group[v, :],

subj[v, :])[0]

if collapse_subj:

tmp_ISC = np.mean(tmp_ISC, axis=1)

max_null[p] = np.max(tmp_ISC)

min_null[p] = np.min(tmp_ISC)

if return_p:

p = p_from_null(ISC, two_sided,

max_null_input=max_null,

min_null_input=min_null)

return ISC, p

else:

return ISC

def isfc(D, collapse_subj=True, return_p=False, num_perm=1000,

two_sided=False, random_state=0, float_type=np.float64):

"""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.

float_type : either float16, float32, or float64

Depends on the required precision

and available memory in the system.

All the arrays generated during the execution will be cast

to specified float type in order to save memory.

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]

n_perm = num_perm*int(return_p)

max_null = -np.ones(n_perm, dtype=float_type)

min_null = np.ones(n_perm, dtype=float_type)

ISFC = np.zeros((n_vox, n_vox, n_subj), dtype=float_type)

for loo_subj in range(D.shape[2]):

group = np.mean(D[:, :, np.arange(n_subj) != loo_subj], axis=2)

subj = D[:, :, loo_subj]

tmp_ISFC = compute_correlation(group, subj).astype(float_type)

# Symmetrize matrix

tmp_ISFC = (tmp_ISFC+tmp_ISFC.T)/2

ISFC[:, :, loo_subj] = tmp_ISFC

if collapse_subj:

ISFC = np.mean(ISFC, axis=2)

for p in range(n_perm):

# Randomize phases of D to create next null dataset

D = phase_randomize(D, random_state)

# Loop across choice of leave-one-out subject

ISFC_null = np.zeros((n_vox, n_vox), dtype=float_type)

for loo_subj in range(D.shape[2]):

group = np.mean(D[:, :, np.arange(n_subj) != loo_subj], axis=2)

subj = D[:, :, loo_subj]

tmp_ISFC = compute_correlation(group, subj).astype(float_type)

# Symmetrize matrix

tmp_ISFC = (tmp_ISFC+tmp_ISFC.T)/2

if not collapse_subj:

max_null[p] = max(np.max(tmp_ISFC), max_null[p])

min_null[p] = min(np.min(tmp_ISFC), min_null[p])

ISFC_null = ISFC_null + tmp_ISFC/n_subj

if collapse_subj:

max_null[p] = np.max(ISFC_null)

min_null[p] = np.min(ISFC_null)

if return_p:

p = p_from_null(ISFC, two_sided,

max_null_input=max_null,

min_null_input=min_null)

return ISFC, p

else:

return ISFC