Synchrony analysis between blood pressure and sympathetic nerve signal inhibited by atrial receptor stimulation in Wistar rats

Functional brain networks formed using cross-sample entropy are scale free

Over the previous decade, there has been an explosion of interest in network science, in general, and its application to the human brain, in particular. Most brain network investigations to date have used linear correlations (LinCorr) between brain areas to construct and then interpret brain networks. In this study, we applied an entropy-based method to establish functional connectivity between brain areas. This method is sensitive to both nonlinear and linear associations. The LinCorr-based and entropy-based techniques were applied to resting-state functional magnetic resonance imaging data from 10 subjects, and the resulting networks were compared. The networks derived from the entropy-based method exhibited power-law degree distributions. Moreover, the entropy-based networks had a higher clustering coefficient and a shorter path length compared with that of the LinCorr-based networks. While the LinCorr-based networks were assortative, with nodes with similar degrees preferentially connected, the entropy-based networks were disassortative, with high-degree hubs directly connected to low-degree nodes. It is likely that the differences in clustering and assortativity are due to "mega-hubs" in the entropy-based networks. These mega-hubs connect to a large majority of the nodes in the network. This is the first work clearly demonstrating differences between functional brain networks using linear and nonlinear techniques. The key finding is that the nonlinear technique produced networks with scale-free degree distributions. There remains debate among the neuroscience community as to whether human brains are scale free. These data support the argument that at least some aspects of the human brain are perhaps scale free.

Decreased thalamo-cortical connectivity by alteration of neural information flow in theta oscillation in depression-model rats

Alterations in oscillatory brain activity are strongly correlated with cognitive performance in various physiological rhythms. The present study investigated whether the directionality of neural information flow (NIF) could be used to characterize the synaptic plasticity in thalamocortical (TC) pathway, and examined which frequency field oscillations were mostly related to the cognitive deficiency in depression. Two novel algorithms were employed to determine the coupling interaction between the LD thalamus and medial prefrontal cortex (mPFC) in five frequency bands, using the phase signals of local field potentials (LFP) in these two regions. The results showed that the power of neural activity in mPFC was increased in delta, theta and beta frequency bands in depression. However, the nonlinear characteristics of LFP activity were weakened in depression by means of sample entropy measurements. In the analysis of phase dynamics, the phase synchronization values were reduced in theta rhythm in stressed rats. Importantly, the coupling direction index d and the unidirectional influence from LD thalamus to mPFC were significantly reduced at the theta rhythm in rats in depression, and increased after memantine treatment, which were associated with the LTP alterations and cognitive impairment in our previous report. Moreover, the fact that the reduced entropy value was only found in mPFC might implicate postsynaptic effect involved in synaptic plasticity alteration in the depression model. The results suggest that the effects of depression on cognitive deficits are mediated via profound alterations in information flow in the TC pathway, and the directional index at theta rhythm could be used as a measurement of synaptic plasticity.

Long-range correlation of renal sympathetic nerve activity in both conscious and anesthetized rats

In this study we employed both detrended fluctuation analysis (DFA) and multiscale entropy (MSE) measurements to compare the long-range temporal correlation (LRTC) of multifibre renal sympathetic nerve activity (RSNA) between conscious and anesthetized Wistar rats. It was found that both methods showed the obvious LRTC properties in conscious state. Moreover, the scaling exponent of the RSNA in conscious rats was significantly higher than that in anesthetized rats. The results of MSE analysis showed that the entropy values, derived from the conscious group, increased on small time scales and then stabilized to a relatively constant value whereas the entropy measure, derived from anesthetized animals, almost monotonically decreased. This suggests that the fractal properties of underlying dynamics of the system have been reduced by anesthesia. The results demonstrate that apparently random fluctuations in multifibre RSNA are dictated by a complex deterministic process that imparts "long-term" memory to the dynamic system. However, this memory is significantly weakened by anesthesia.

Cross-sample entropy statistic as a measure of complexity and regularity of renal sympathetic nerve activity in the rat

In this study, we employed both power spectral analysis and cross-sample entropy measurement to assess the relationship between two time series, arterial blood pressure (ABP) and renal sympathetic nerve activity (RSNA), during a mild haemorrhage in anaesthetized Wistar rats. Removal of 1 ml of venous blood decreased BP (by 7.1 +/- 0.7 mmHg) and increased RSNA (by 25.9 +/- 2.4%). During these changes, the power in the RSNA signal at heart rate frequency was reduced but coherence between the spectra at heart rate frequency in RSNA and ABP remained unchanged. Cross-sample entropy was significantly increased (by 10%) by haemorrhage, revealing that there was greater asynchrony between ABP and the RSNA time series. Intrathecal administration of the glutamate receptor antagonist kynurenic acid (2 mm) almost halved (P < 0.01) the reflex increase in RSNA. Also during kynurenic acid block, haemorrhage failed to change total power, power at heart rate frequency, coherence at heart rate frequency, or the cross-sample entropy measurements. We conclude that the increase in asynchrony between ABP and RSNA during the reflex increase in RSNA was a consequence of an increase in synaptic input to the spinal renal neurones. The data show that the cross-sample entropy calculations can characterize the non-linearities of neural mechanisms underlying cardiovascular control and have a potential to reveal how some aspects of homeostatic regulation of kidney function is achieved by the autonomic nervous system.