Source code for tvb.analyzers.metric_proxy_metastability
# -*- coding: utf-8 -*-
#
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# TheVirtualBrain-Scientific Package. This package holds all simulators, and
# analysers necessary to run brain-simulations. You can use it stand alone or
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"""
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__)
