intersubject¶

Intersubject correlation, functional connectivity, and phase synchrony.

Methods:

Methods¶

isc¶

isc(data, *, n_samples = 5000, summary = 'median', method = 'bootstrap', ci_percentile = 95, exclude_self_corr = True, tail = 2, metric = 'correlation', return_null = False, n_jobs = -1, random_state = None, progress_bar = False)

Compute pairwise intersubject correlation from observations by subjects array.

This function computes pairwise intersubject correlations (ISC) using the median as recommended by Chen et al., 2016). However, if the mean is preferred, we compute the mean correlation after performing the fisher r-to-z transformation and then convert back to correlations to minimize artificially inflating the correlation values.

There are currently three different methods to compute p-values. These include the classic methods for computing permuted time-series by either circle-shifting the data or phase-randomizing the data (see Lancaster et al., 2018). These methods create random surrogate data while preserving the temporal autocorrelation inherent to the signal. By default, we use the subject-wise bootstrap method from Chen et al., 2016. Instead of recomputing the pairwise ISC using circle_shift or phase_randomization methods, this approach uses the computationally more efficient method of bootstrapping the subjects and computing a new pairwise similarity matrix with randomly selected subjects with replacement. If the same subject is selected multiple times, we set the perfect correlation to a nan with (exclude_self_corr=True). We compute the p-values using the percentile method using the same method in Brainiak.

Chen, G., Shin, Y. W., Taylor, P. A., Glen, D. R., Reynolds, R. C., Israel, R. B., & Cox, R. W. (2016). Untangling the relatedness among correlations, part I: nonparametric approaches to inter-subject correlation analysis at the group level. NeuroImage, 142, 248-259.

Hall, P., & Wilson, S. R. (1991). Two guidelines for bootstrap hypothesis testing. Biometrics, 757-762.

Lancaster, G., Iatsenko, D., Pidde, A., Ticcinelli, V., & Stefanovska, A. (2018). Surrogate data for hypothesis testing of physical systems. Physics Reports, 748, 1-60.

This function is a wrapper around isc_permutation_test from the inference module, which provides optimized implementations with CPU-parallel and GPU acceleration support.

Parameters:

Returns:

isc_group¶

isc_group(group1, group2, *, n_samples = 5000, summary = 'median', method = 'permute', ci_percentile = 95, exclude_self_corr = True, return_null = False, tail = 2, metric = 'correlation', n_jobs = -1, random_state = None, progress_bar = False)

Compute difference in intersubject correlation between groups.

This function computes pairwise intersubject correlations (ISC) using the median as recommended by Chen et al., 2016). However, if the mean is preferred, we compute the mean correlation after performing the fisher r-to-z transformation and then convert back to correlations to minimize artificially inflating the correlation values.

There are currently two different methods to compute p-values. By default, we use the subject-wise permutation method recommended Chen et al., 2016. This method combines the two groups and computes pairwise similarity both within and between the groups. Then the group labels are permuted and the mean difference between the two groups are recomputed to generate a null distribution. The second method uses subject-wise bootstrapping, where a new pairwise similarity matrix with randomly selected subjects with replacement is created separately for each group and the ISC difference between these groups is used to generate a null distribution. If the same subject is selected multiple times, we set the perfect correlation to a nan with (exclude_self_corr=True). We compute the p-values using the percentile method (Hall & Wilson, 1991).

Chen, G., Shin, Y. W., Taylor, P. A., Glen, D. R., Reynolds, R. C., Israel, R. B., & Cox, R. W. (2016). Untangling the relatedness among correlations, part I: nonparametric approaches to inter-subject correlation analysis at the group level. NeuroImage, 142, 248-259.

Hall, P., & Wilson, S. R. (1991). Two guidelines for bootstrap hypothesis testing. Biometrics, 757-762.

This function is a thin wrapper around isc_group_permutation_test from the inference module (which provides optimized CPU parallelization and optional GPU acceleration), pinning the classic pairwise behavior and the n_samples vocabulary.

Parameters:

Returns:

isfc¶

isfc(data, method = 'average', n_jobs = -1)

Compute intersubject functional connectivity (ISFC) from a list of observation x feature matrices.

This function uses the leave one out approach to compute ISFC (Simony et al., 2016). For each subject, compute the cross-correlation between each voxel/roi with the average of the rest of the subjects data. In other words, compute the mean voxel/ROI response for all participants except the target subject. Then compute the correlation between each ROI within the target subject with the mean ROI response in the group average.

Simony, E., Honey, C. J., Chen, J., Lositsky, O., Yeshurun, Y., Wiesel, A., & Hasson, U. (2016). Dynamic reconfiguration of the default mode network during narrative comprehension. Nature communications, 7, 12141.

This function now uses the optimized implementation from the inference module, which provides efficient cross-correlation computation between matrix columns. CPU parallelization is available via joblib when n_jobs > 1 or n_jobs=-1. Each subject’s ISFC computation is independent and can be parallelized efficiently.

Parameters:

Returns:

isps¶

isps(data, *, sampling_freq = 0.5, low_cut = 0.04, high_cut = 0.07, order = 5, pairwise = False)

Compute dynamic intersubject phase synchrony (ISPS) from an observations-by-subjects array.

This function computes the instantaneous intersubject phase synchrony for a single voxel/roi timeseries. Requires multiple subjects. This method is largely based on that described by Glerean et al., 2012 and performs a hilbert transform on narrow bandpass filtered timeseries (butterworth) data to get the instantaneous phase angle. The function returns a dictionary containing the average phase angle, the average vector length, and parametric p-values computed using the rayleigh test using circular statistics (Fisher, 1993). If pairwise=True, then it will compute these on the pairwise phase angle differences, if pairwise=False, it will compute these on the actual phase angles. This is called inter-site phase coupling or inter-trial phase coupling respectively in the EEG literatures.

This function requires narrow band filtering your data. As a default we use the recommendations by (Glerean et al., 2012) of .04-.07Hz. This is similar to the “slow-4” band (0.025–0.067 Hz) described by (Zuo et al., 2010; Penttonen & Buzsáki, 2003), but excludes the .03 band, which has been demonstrated to contain aliased respiration signals (Birn, 2006).

Birn RM, Smith MA, Bandettini PA, Diamond JB. 2006. Separating respiratory-variation-related fluctuations from neuronal-activity- related fluctuations in fMRI. Neuroimage 31:1536–1548.

Buzsáki, G., & Draguhn, A. (2004). Neuronal oscillations in cortical networks. Science, 304(5679), 1926-1929.

Fisher, N. I. (1995). Statistical analysis of circular data. cambridge university press.

Glerean, E., Salmi, J., Lahnakoski, J. M., Jääskeläinen, I. P., & Sams, M. (2012). Functional magnetic resonance imaging phase synchronization as a measure of dynamic functional connectivity. Brain connectivity, 2(2), 91-101.

Parameters:

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