269,702 research outputs found
Model of Brain Activation Predicts the Neural Collective Influence Map of the Brain
Efficient complex systems have a modular structure, but modularity does not
guarantee robustness, because efficiency also requires an ingenious interplay
of the interacting modular components. The human brain is the elemental
paradigm of an efficient robust modular system interconnected as a network of
networks (NoN). Understanding the emergence of robustness in such modular
architectures from the interconnections of its parts is a long-standing
challenge that has concerned many scientists. Current models of dependencies in
NoN inspired by the power grid express interactions among modules with fragile
couplings that amplify even small shocks, thus preventing functionality.
Therefore, we introduce a model of NoN to shape the pattern of brain
activations to form a modular environment that is robust. The model predicts
the map of neural collective influencers (NCIs) in the brain, through the
optimization of the influence of the minimal set of essential nodes responsible
for broadcasting information to the whole-brain NoN. Our results suggest new
intervention protocols to control brain activity by targeting influential
neural nodes predicted by network theory.Comment: 18 pages, 5 figure
Collective Correlations of Brodmann Areas fMRI Study with RMT-Denoising
We study collective behavior of Brodmann regions of human cerebral cortex
using functional Magnetic Resonance Imaging (fMRI) and Random Matrix Theory
(RMT). The raw fMRI data is mapped onto the cortex regions corresponding to the
Brodmann areas with the aid of the Talairach coordinates. Principal Component
Analysis (PCA) of the Pearson correlation matrix for 41 different Brodmann
regions is carried out to determine their collective activity in the idle state
and in the active state stimulated by tapping. The collective brain activity is
identified through the statistical analysis of the eigenvectors to the largest
eigenvalues of the Pearson correlation matrix. The leading eigenvectors have a
large participation ratio. This indicates that several Broadmann regions
collectively give rise to the brain activity associated with these
eigenvectors. We apply random matrix theory to interpret the underlying
multivariate data
Statistical Complexity and Nontrivial Collective Behavior in Electroencephalografic Signals
We calculate a measure of statistical complexity from the global dynamics of
electroencephalographic (EEG) signals from healthy subjects and epileptic
patients, and are able to stablish a criterion to characterize the collective
behavior in both groups of individuals. It is found that the collective
dynamics of EEG signals possess relative higher values of complexity for
healthy subjects in comparison to that for epileptic patients. To interpret
these results, we propose a model of a network of coupled chaotic maps where we
calculate the complexity as a function of a parameter and relate this measure
with the emergence of nontrivial collective behavior in the system. Our results
show that the presence of nontrivial collective behavior is associated to high
values of complexity; thus suggesting that similar dynamical collective process
may take place in the human brain. Our findings also suggest that epilepsy is a
degenerative illness related to the loss of complexity in the brain.Comment: 13 pages, 3 figure
Innovation in the collective brain
Innovation is often assumed to be the work of a talented few, whose products are passed on to the masses. Here, we argue that innovations are instead an emergent property of our species' cultural learning abilities, applied within our societies and social networks. Our societies and social networks act as collective brains. We outline how many human brains, which evolved primarily for the acquisition of culture, together beget a collective brain. Within these collective brains, the three main sources of innovation are serendipity, recombination and incremental improvement. We argue that rates of innovation are heavily influenced by (i) sociality, (ii) transmission fidelity, and (iii) cultural variance. We discuss some of the forces that affect these factors. These factors can also shape each other. For example, we provide preliminary evidence that transmission efficiency is affected by sociality—languages with more speakers are more efficient. We argue that collective brains can make each of their constituent cultural brains more innovative. This perspective sheds light on traits, such as IQ, that have been implicated in innovation. A collective brain perspective can help us understand otherwise puzzling findings in the IQ literature, including group differences, heritability differences and the dramatic increase in IQ test scores over time
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