178 research outputs found
Self-organization of heterogeneous topology and symmetry breaking in networks with adaptive thresholds and rewiring
We study an evolutionary algorithm that locally adapts thresholds and wiring
in Random Threshold Networks, based on measurements of a dynamical order
parameter. A control parameter determines the probability of threshold
adaptations vs. link rewiring. For any , we find spontaneous symmetry
breaking into a new class of self-organized networks, characterized by a much
higher average connectivity than networks without threshold
adaptation (). While and evolved out-degree distributions
are independent from for , in-degree distributions become broader
when , approaching a power-law. In this limit, time scale separation
between threshold adaptions and rewiring also leads to strong correlations
between thresholds and in-degree. Finally, evidence is presented that networks
converge to self-organized criticality for large .Comment: 4 pages revtex, 6 figure
Breakdown of long-range temporal correlations in theta oscillations in patients with major depressive disorder
Neuroimaging has revealed robust large-scale patterns of high neuronal activity in the human brain in the classical eyes-closed wakeful rest condition, pointing to the presence of a baseline of sustained endogenous processing in the absence of stimulus-driven neuronal activity. This baseline state has been shown to differ in major depressive disorder. More recently, several studies have documented that despite having a complex temporal structure, baseline oscillatory activity is characterized by persistent autocorrelations for tens of seconds that are highly replicable within and across subjects. The functional significance of these long-range temporal correlations has remained unknown. We recorded neuromagnetic activity in patients with a major depressive disorder and in healthy control subjects during eyes-closed wakeful rest and quantified the long-range temporal correlations in the amplitude fluctuations of different frequency bands. We found that temporal correlations in the theta-frequency band (3-7 Hz) were almost absent in the 5-100 s time range in the patients but prominent in the control subjects. The magnitude of temporal correlations over the left temporocentral region predicted the severity of depression in the patients. These data indicate that long-range temporal correlations in theta oscillations are a salient characteristic of the healthy human brain and may have diagnostic potential in psychiatric disorders. We propose a link between the abnormal temporal structure of theta oscillations in the depressive patients and the systems-level impairments of limbic-cortical networks that have been identified in recent anatomical and functional studies of patients with major depressive disorder. Copyright © 2005 Society for Neuroscience
A power-law distribution of phase-locking intervals does not imply critical interaction
Neural synchronisation plays a critical role in information processing,
storage and transmission. Characterising the pattern of synchronisation is
therefore of great interest. It has recently been suggested that the brain
displays broadband criticality based on two measures of synchronisation - phase
locking intervals and global lability of synchronisation - showing power law
statistics at the critical threshold in a classical model of synchronisation.
In this paper, we provide evidence that, within the limits of the model
selection approach used to ascertain the presence of power law statistics, the
pooling of pairwise phase-locking intervals from a non-critically interacting
system can produce a distribution that is similarly assessed as being power
law. In contrast, the global lability of synchronisation measure is shown to
better discriminate critical from non critical interaction.Comment: (v3) Fixed error in Figure 1; (v2) Added references. Minor edits
throughout. Clarified relationship between theoretical critical coupling for
infinite size system and 'effective' critical coupling system for finite size
system. Improved presentation and discussion of results; results unchanged.
Revised Figure 1 to include error bars on r and N; results unchanged; (v1) 11
pages, 7 figure
Genetic contributions to long-range temporal correlations in ongoing oscillations
The amplitude fluctuations of ongoing oscillations in the electroencephalographic (EEG) signal of the human brain show autocorrelations that decay slowly and remain significant at time scales up to tens of seconds. We call these long-range temporal correlations (LRTC). Abnormal LRTC have been observed in several brain pathologies, but it has remained unknown whether genetic factors influence the temporal correlation structure of ongoing oscillations. We recorded the ongoing EEG during eyes-closed rest in 390 monozygotic and dizygotic twins and investigated the temporal structure of ongoing oscillations in the alpha- and beta-frequency bands using detrended fluctuation analysis (DFA). The strength of LRTC was more highly correlated in monozygotic than in dizygotic twins. Statistical analysis attributed up to ∼60% of the variance in DFA to genetic factors, indicating a high heritability for the temporal structure of amplitude fluctuations in EEG oscillations. Importantly, the DFA and EEG power were uncorrelated. LRTC in ongoing oscillations are robust, heritable, and independent of power, suggesting that LRTC and oscillation power are governed by distinct biophysical mechanisms and serve different functions in the brain. We propose that the DFA method is an important complement to classical spectral analysis in fundamental and clinical research on ongoing oscillations. Copyright © 2007 Society for Neuroscience
Evidence from human scalp electroencephalograms of global chaotic itinerancy
My objective of this study was to find evidence of chaotic itinerancy in human brains by means of noninvasive recording of the electroencephalogram (EEG) from the scalp of normal subjects. My premise was that chaotic itinerancy occurs in sequences of cortical states marked by state transitions that appear as temporal discontinuities in neural activity patterns. I based my study on unprecedented advances in spatial and temporal resolution of the phase of oscillations in scalp EEG. The spatial resolution was enhanced by use of a high-density curvilinear array of 64 electrodes, 189 mm in length, with 3 mm spacing. The temporal resolution was advanced to the limit provided by the digitizing step, here 5 ms, by use of the Hilbert transform. The numerical derivative of the analytic phase revealed plateaus in phase that lasted on the order of 0.1 s and repeated at rates in the theta (3-7 Hz) or alpha (7-12 Hz) ranges. The plateaus were bracketed by sudden jumps in phase that usually took place within 1 to 2 digitizing steps. The jumps were commonly synchronized in each cerebral hemisphere over distances of up to 189 mm, irrespective of the orientation of the array. The jumps were usually not synchronized across the midline separating the hemisphere or across the sulcus between the frontal and parietal lobes. I believe that the widespread synchrony of the jumps in analytic phase manifest a metastable cortical state in accord with the theory of self-organized criticality. The jumps appear to be subcritical bifurcations. They reflect the aperiodic evolution of brain states through sequences of attractors that on access support the experience of remembering. (C) 2003 American Institute of Physics
Does the 1/f frequency-scaling of brain signals reflect self-organized critical states?
Many complex systems display self-organized critical states characterized by
1/f frequency scaling of power spectra. Global variables such as the
electroencephalogram, scale as 1/f, which could be the sign of self-organized
critical states in neuronal activity. By analyzing simultaneous recordings of
global and neuronal activities, we confirm the 1/f scaling of global variables
for selected frequency bands, but show that neuronal activity is not consistent
with critical states. We propose a model of 1/f scaling which does not rely on
critical states, and which is testable experimentally.Comment: 3 figures, 6 page
Dissipation and spontaneous symmetry breaking in brain dynamics
We compare the predictions of the dissipative quantum model of brain with
neurophysiological data collected from electroencephalograms resulting from
high-density arrays fixed on the surfaces of primary sensory and limbic areas
of trained rabbits and cats. Functional brain imaging in relation to behavior
reveals the formation of coherent domains of synchronized neuronal oscillatory
activity and phase transitions predicted by the dissipative model.Comment: Restyled, slight changes in title and abstract, updated bibliography,
J. Phys. A: Math. Theor. Vol. 41 (2008) in prin
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