Anticorrelations between Active Brain Regions: An Agent-Based Model Simulation Study
Authored by Fabrizio Parente, Alfredo Colosimo
Date Published: 2018
DOI: 10.1155/2018/6815040
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Abstract
Anticorrelations among brain areas observed in fMRI acquisitions under
resting state are not endowed with a well-defined set of characters.
Some evidence points to a possible physiological role for them, and
simulation models showed that it is appropriate to explore such an
issue. A large-scale brain representation was considered, implementing
an agent-based brain-inspired model (ABBM) incorporating the SER
(susceptible-excited-refractory) cyclic mechanism of state change. The
experimental data used for validation included 30 selected functional
images of healthy controls from the 1000 Functional Connectomes Classic
collection. To study how different fractions of positive and negative
connectivities could modulate the model efficiency, the correlation
coefficient was systematically used to check the goodness-of-fit of
empirical data by simulations under different combinations of
parameters. The results show that a small fraction of positive
connectivity is necessary to match at best the empirical data.
Similarly, a goodness-of-fit improvement was observed upon addition of
negative links to an initial pattern of only-positive connections,
indicating a significant information intrinsic to negative links. As a
general conclusion, anticorrelations showed that it is crucial to
improve the performance of our simulation and, since these cannot be
assimilated to noise, should be always considered in order to refine any
brain functional model.
Tags
Complexity
Dynamics
Network
Organization
State functional connectivity
Noise correction
Global signal