Source code for tvb.analyzers.metric_proxy_metastability

# -*- coding: utf-8 -*-
# TheVirtualBrain-Scientific Package. This package holds all simulators, and
# analysers necessary to run brain-simulations. You can use it stand alone or
# in conjunction with TheVirtualBrain-Framework Package. See content of the
# documentation-folder for more details. See also
# (c) 2012-2023, Baycrest Centre for Geriatric Care ("Baycrest") and others
# This program is free software: you can redistribute it and/or modify it under the
# terms of the GNU General Public License as published by the Free Software Foundation,
# either version 3 of the License, or (at your option) any later version.
# This program is distributed in the hope that it will be useful, but WITHOUT ANY
# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
# PARTICULAR PURPOSE.  See the GNU General Public License for more details.
# You should have received a copy of the GNU General Public License along with this
# program.  If not, see <>.
# When using The Virtual Brain for scientific publications, please cite it as explained here:

Filler analyzer: Takes a TimeSeries object and returns two Floats.

These metrics are described and used in:

Hellyer et al. The Control of Global Brain Dynamics: Opposing Actions
of Frontoparietal Control and Default Mode Networks on Attention. 
The Journal of Neuroscience, January 8, 2014,  34(2):451– 461

Proxy of spatial coherence (V): 

Proxy metastability (M): the variability in spatial coherence of the signal
globally or locally (within a network) over time.

Proxy synchrony (S) : the reciprocal of mean spatial variance across time.

.. moduleauthor:: Paula Sanz Leon <paulala@tvb.invalid>


import numpy
from tvb.basic.logger.builder import get_logger

[docs]def remove_mean(x, axis): """ Remove mean from numpy array along axis """ # Example for demean(x, 2) with x.shape == 2,3,4,5 # m = x.mean(axis=2) collapses the 2'nd dimension making m and x incompatible # so we add it back m[:,:, np.newaxis, :] # Since the shape and axis are known only at runtime # Calculate the slicing dynamically return x - numpy.expand_dims(x.mean(axis=axis), axis)
r""" Subtract the mean time-series and compute. Input: TimeSeries DataType Output: Float, Float The two metrics given by this analyzers are a proxy for metastability and synchrony. The underlying dynamical model used in the article was the Kuramoto model. .. math:: V(t) &= \frac{1}{N} \sum_{i=1}^{N} |S_i(t) - <S(t)>| \\ M(t) &= \sqrt{E[V(t)^{2}]-(E[V(t)])^{2}} \\ S(t) &= \frac{1}{\bar{V(t)}} """ log = get_logger(__name__)
[docs]def compute_proxy_metastability_metric(params): """ # type: dict(TimeSeries, float, int) -> (float, float) Compute the zero centered variance of node variances for the time_series. Parameters ---------- params : a dictionary containing time_series : TimeSeries Input time series for which the metric will be computed. start_point : float Determines how many points of the TimeSeries will be discarded before computing the metric segment : int Divides the input time-series into discrete equally sized sequences and use the last segment to compute the metric. Only used when the start point is larger than the time-series length """ time_series = params['time_series'] start_point = params['start_point'] segment = params['segment'] shape = tpts = shape[0] if start_point != 0.0: start_tpt = start_point / time_series.sample_period log.debug("Will discard: %s time points" % start_tpt) else: start_tpt = 0 if start_tpt > tpts: log.warning("The time-series is shorter than the starting point") log.debug("Will divide the time-series into %d segments." % segment) # Lazy strategy start_tpt = int((segment - 1) * (tpts // segment)) start_tpt = int(start_tpt) time_series_diffs = remove_mean([start_tpt:, :], axis=2) v_data = abs(time_series_diffs).mean(axis=2) # handle state-variables & modes cat_tpts = v_data.shape[0] * shape[1] * shape[3] v_data = v_data.reshape((cat_tpts,), order="F") # std across time-points metastability = v_data.std(axis=0) synchrony = 1. / v_data.mean(axis=0) return {"Metastability": metastability, "Synchrony": synchrony}